def main():
zernike_fn = zernike
parser = argparse.ArgumentParser()
parser.add_argument('--debug', nargs='?', default=None, const='debug', choices=['debug', 'info', 'warning', 'error', 'critical'])
parser.add_argument('vtk_file', nargs='?', default=None)
parser.add_argument('-o', '--order', type=int, default=3)
parser.add_argument('-p', '--profile', nargs='?', default=None, const='stdout')
parser.add_argument('-t', '--timecall', default=False, action='store_true')
parser.add_argument('-v', '--validate', default=False, action='store_true')
ns = parser.parse_args()
if ns.debug is not None:
logging.basicConfig(level=getattr(logging, ns.debug.upper()))
# if ns.profile is not None:
# filename = ns.profile
# if ns.profile == 'stdout':
# filename = None
# zernike_fn = profilehooks.profile(zernike_fn, immediate=False, filename=filename)
# if ns.timecall:
# zernike_fn = profilehooks.timecall(zernike_fn)
if ns.vtk_file is not None:
points, indices, lines, faces, depths, scalar_names, npoints, \
input_vtk = read_vtk(ns.vtk_file)
print('{0} {1}'.format(len(faces), len(points)))
X = zernike_fn(points, faces, order=ns.order, scale_input=True)
if ns.validate:
Y = zernike_fn(points, faces, order=ns.order, scale_input=True, pl_cls=MultiprocPipeline)
assert np.allclose(X, Y)
else:
example1()
def propagate_fundus_lines(surf_file, fundus_lines_file, thickness_file):
"""Propagate fundus lines to tile the surface.
Parameters
----------
surf_file: file containing the surface geometry in vtk format
fundus_lines_file: file containing scalars representing fundus lines
thickness_file: file containing cortical thickness scalar data
(for masking out the medial wall only)
Returns
-------
scalars indicating whether each vertex is part of the closed
fundus lines or not
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
points, indices, lines, faces, fundus_lines, scalar_names, num_points, \
input_vtk = read_vtk(surf_file, return_first=True, return_array=True)
fundus_lines, _ = read_scalars(fundus_lines_file)
fundus_line_indices = [i for i, x in enumerate(fundus_lines) if x > 0.5]
thickness, _ = read_scalars(thickness_file,
return_first=True, return_array=True)
return propagate_fundus_lines(
points, faces, fundus_line_indices, thickness)
def propagate_fundus_lines(surf_file, fundus_lines_file, thickness_file):
"""Propagate fundus lines to tile the surface.
Parameters
----------
surf_file: file containing the surface geometry in vtk format
fundus_lines_file: file containing scalars representing fundus lines
thickness_file: file containing cortical thickness scalar data
(for masking out the medial wall only)
Returns
-------
scalars indicating whether each vertex is part of the closed
fundus lines or not
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
faces, _, _, points, num_points, fundus_lines, _, _ = read_vtk(
surf_file, return_first=True, return_array=True)
fundus_lines, _ = read_scalars(fundus_lines_file)
fundus_line_indices = [i for i, x in enumerate(fundus_lines) if x > 0.5]
thickness, _ = read_scalars(thickness_file,
return_first=True, return_array=True)
return propagate_fundus_lines(
points, faces, fundus_line_indices, thickness)
def main():
zernike_fn = zernike_moments
parser = argparse.ArgumentParser()
parser.add_argument('--debug', nargs='?', default=None, const='debug', choices=['debug', 'info', 'warning', 'error', 'critical'])
parser.add_argument('vtk_file', nargs='?', default=None)
parser.add_argument('-o', '--order', type=int, default=3)
parser.add_argument('-p', '--profile', nargs='?', default=None, const='stdout')
parser.add_argument('-t', '--timecall', default=False, action='store_true')
parser.add_argument('-v', '--validate', default=False, action='store_true')
ns = parser.parse_args()
if ns.debug is not None:
logging.basicConfig(level=getattr(logging, ns.debug.upper()))
if ns.profile is not None:
filename = ns.profile
if ns.profile == 'stdout':
filename = None
zernike_fn = profilehooks.profile(zernike_fn, immediate=False, filename=filename)
if ns.timecall:
zernike_fn = profilehooks.timecall(zernike_fn)
if ns.vtk_file is not None:
points, indices, lines, faces, depths, scalar_names, npoints, \
input_vtk = read_vtk(ns.vtk_file)
print(len(faces), len(points))
X = zernike_fn(points, faces, order=ns.order, scale_input=True)
if ns.validate:
Y = zernike_fn(points, faces, order=ns.order, scale_input=True, pl_cls=MultiprocPipeline)
assert np.allclose(X, Y)
else:
example1()
def decimate_file(input_vtk,
reduction=0.5,
smooth_steps=100,
save_vtk=True,
output_vtk=''):
"""
Decimate vtk triangular mesh file with vtk.vtkDecimatePro.
Parameters
----------
input_vtk : string
input vtk file with triangular surface mesh
reduction : float
fraction of mesh faces to remove
do_smooth : Boolean
smooth after decimation?
save_vtk : Boolean
output decimated vtk file?
output_vtk : string
output decimated vtk file name
Returns
-------
output_vtk : string
output decimated vtk file
Examples
--------
>>> import os
>>> from mindboggle.guts.mesh import decimate_file
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_vtk = os.path.join(path, 'arno', 'labels', 'label22.vtk')
>>> #input_vtk='/drop/MB/data/arno/labels/lh.labels.DKT31.manual.vtk'
>>> save_vtk = True
>>> output_vtk = ''
>>> reduction = 0.5
>>> smooth_steps = 0
>>> decimate_file(input_vtk, reduction, smooth_steps, save_vtk, output_vtk)
>>> # View:
>>> plot_surfaces('decimated.vtk') # doctest: +SKIP
"""
from mindboggle.mio.vtks import read_vtk
from mindboggle.guts.mesh import decimate
if not save_vtk:
raise NotImplementedError()
# Read VTK surface mesh file:
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(input_vtk)
# Decimate vtk triangular mesh with vtk.vtkDecimatePro
points, faces, scalars, output_vtk = decimate(points, faces, reduction,
smooth_steps, scalars,
save_vtk, output_vtk)
return output_vtk
def spectrum_from_file(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization=None, area_file='', verbose=False):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
verbose : bool
print statements?
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6)
>>> print(np.array_str(np.array(spectrum[1::]),
... precision=5, suppress_small=True))
[ 0.00013 0.00027 0.00032 0.00047 0.00058]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas,
verbose)
return spectrum
def decimate_file(input_vtk, reduction=0.5, smooth_steps=100,
save_vtk=True, output_vtk=''):
"""
Decimate vtk triangular mesh file with vtk.vtkDecimatePro.
Parameters
----------
input_vtk : string
input vtk file with triangular surface mesh
reduction : float
fraction of mesh faces to remove
do_smooth : Boolean
smooth after decimation?
save_vtk : Boolean
output decimated vtk file?
output_vtk : string
output decimated vtk file name
Returns
-------
output_vtk : string
output decimated vtk file
Examples
--------
>>> import os
>>> from mindboggle.guts.mesh import decimate_file
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_vtk = os.path.join(path, 'arno', 'labels', 'label22.vtk')
>>> #input_vtk='/drop/MB/data/arno/labels/lh.labels.DKT31.manual.vtk'
>>> save_vtk = True
>>> output_vtk = ''
>>> reduction = 0.5
>>> smooth_steps = 0
>>> decimate_file(input_vtk, reduction, smooth_steps, save_vtk, output_vtk)
>>> # View:
>>> plot_surfaces('decimated.vtk') # doctest: +SKIP
"""
from mindboggle.mio.vtks import read_vtk
from mindboggle.guts.mesh import decimate
if not save_vtk:
raise NotImplementedError()
# Read VTK surface mesh file:
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(input_vtk)
# Decimate vtk triangular mesh with vtk.vtkDecimatePro
points, faces, scalars, output_vtk = decimate(points, faces, reduction,
smooth_steps, scalars,
save_vtk, output_vtk)
return output_vtk
def downsample_vtk(vtk_file, sample_rate):
"""Sample rate: number between 0 and 1."""
from mindboggle.mio.vtks import read_vtk, write_vtk
from mindboggle.guts.mesh import decimate_file
if (sample_rate < 0 or sample_rate > 1):
raise ValueError('0 <= sample_rate <= 1; you input %f' % sample_rate)
# Downsample
decimate_file(vtk_file, reduction=1 - sample_rate, output_vtk=vtk_file, save_vtk=True, smooth_steps=0)
# Hack to re-save in
vtk_data = read_vtk(vtk_file)
write_vtk(vtk_file, *vtk_data[:-2])
def spectrum_per_label(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization='area', area_file='',
largest_segment=True):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string (optional)
name of VTK file with surface area scalar values
largest_segment : Boolean
compute spectrum only for largest segment with a given label?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> spectrum_per_label(vtk_file, spectrum_size, exclude_labels, None,
>>> area_file, largest_segment)
([[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]],
[22])
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.guts.mesh import remove_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
faces, u1, u2, points, u4, labels, u5, u6 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i,x in enumerate(labels) if x == label]
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = remove_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size,
exclude_labels_inner,
normalization, areas)
else:
spectrum = fem_laplacian(pick_points, pick_faces,
spectrum_size, normalization)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def spectrum_per_label(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization='area', area_file='',
largest_segment=True, verbose=False):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string (optional)
name of VTK file with surface area scalar values
largest_segment : bool
compute spectrum only for largest segment with a given label?
verbose : bool
print statements?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> verbose = False
>>> spectrum_lists, label_list = spectrum_per_label(vtk_file,
... spectrum_size, exclude_labels, None, area_file, largest_segment,
... verbose)
>>> print(np.array_str(np.array(spectrum_lists[0][1::]),
... precision=5, suppress_small=True))
[ 0.00054 0.00244 0.00291 0.00456 0.00575]
>>> label_list[0:10]
[1029, 1005, 1011, 1021, 1008, 1025, 999, 1013, 1007, 1022]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.guts.mesh import keep_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
points, indices, lines, faces, labels, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i,x in enumerate(labels) if x == label]
if verbose:
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = keep_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size,
exclude_labels_inner,
normalization, areas, verbose)
else:
spectrum = fem_laplacian(pick_points, pick_faces, spectrum_size,
normalization, verbose)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def extract_sulci(
labels_file,
folds_or_file,
hemi,
min_boundary=1,
sulcus_names=[],
save_file=False,
output_file="",
background_value=-1,
verbose=False,
):
"""
Identify sulci from folds in a brain surface according to a labeling
protocol that includes a list of label pairs defining each sulcus.
Since folds are defined as deep, connected areas of a surface, and since
folds may be connected to each other in ways that differ across brains,
there usually does not exist a one-to-one mapping between folds of one
brain and those of another. To address the correspondence problem then,
we need to find just those portions of the folds that correspond across
brains. To accomplish this, Mindboggle segments folds into sulci, which
do have a one-to-one correspondence across non-pathological brains.
Mindboggle defines a sulcus as a folded portion of cortex whose opposing
banks are labeled with one or more sulcus label pairs in the DKT labeling
protocol, where each label pair is unique to one sulcus and represents
a boundary between two adjacent gyri, and each vertex has one gyrus label.
This function assigns vertices in a fold to a sulcus in one of two cases.
In the first case, vertices whose labels are in only one label pair in
the fold are assigned to the label pair’s sulcus if they are connected
through similarly labeled vertices to the boundary between the two labels.
In the second case, the segment_regions function propagates labels from
label borders to vertices whose labels are in multiple label pairs in the
fold.
Steps for each fold ::
1. Remove fold if it has fewer than two labels.
2. Remove fold if its labels do not contain a sulcus label pair.
3. Find vertices with labels that are in only one of the fold's
label boundary pairs. Assign the vertices the sulcus with the label
pair if they are connected to the label boundary for that pair.
4. If there are remaining vertices, segment into sets of vertices
connected to label boundaries, and assign a unique ID to each set.
Parameters
----------
labels_file : string
file name for surface mesh VTK containing labels for all vertices
folds_or_file : numpy array, list or string
fold number for each vertex / name of VTK file containing fold scalars
hemi : string
hemisphere abbreviation in {'lh', 'rh'} for sulcus labels
min_boundary : integer
minimum number of vertices for a sulcus label boundary segment
sulcus_names : list of strings
names of sulci
save_file : bool
save output VTK file?
output_file : string
name of output file in VTK format
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
sulci : list of integers
sulcus numbers for all vertices (-1 for non-sulcus vertices)
n_sulci : integers
number of sulci
sulci_file : string
output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> # Example 1: Extract sulcus from a fold with one sulcus label pair:
>>> import numpy as np
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> # Load labels, folds, neighbor lists, and sulcus names and label pairs
>>> labels_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> folds_or_file, name = read_scalars(folds_file, True, True)
>>> save_file = True
>>> output_file = 'extract_sulci_fold4_1sulcus.vtk'
>>> background_value = -1
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [4] #[4, 6]
... i0 = [i for i,x in enumerate(folds_or_file) if x not in fold_numbers]
... folds_or_file[i0] = background_value
>>> hemi = 'lh'
>>> min_boundary = 10
>>> sulcus_names = []
>>> verbose = False
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
... hemi, min_boundary, sulcus_names, save_file, output_file,
... background_value, verbose)
>>> n_sulci # 23 # (if not limit_folds)
1
>>> lens = [len([x for x in sulci if x==y])
... for y in np.unique(sulci) if y != -1]
>>> lens[0:10] # [6358, 3288, 7612, 5205, 4414, 6251, 3493, 2566, 4436, 739] # (if not limit_folds)
[1151]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> output = 'extract_sulci_fold4_1sulcus_no_background.vtk'
>>> rewrite_scalars(sulci_file, output, sulci,
... 'sulci', sulci) # doctest: +SKIP
>>> plot_surfaces(output) # doctest: +SKIP
Example 2: Extract sulcus from a fold with multiple sulcus label pairs:
>>> folds_or_file, name = read_scalars(folds_file, True, True)
>>> output_file = 'extract_sulci_fold7_2sulci.vtk'
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [7] #[4, 6]
... i0 = [i for i,x in enumerate(folds_or_file) if x not in fold_numbers]
... folds_or_file[i0] = background_value
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
... hemi, min_boundary, sulcus_names, save_file, output_file,
... background_value, verbose)
>>> n_sulci # 23 # (if not limit_folds)
2
>>> lens = [len([x for x in sulci if x==y])
... for y in np.unique(sulci) if y != -1]
>>> lens[0:10] # [6358, 3288, 7612, 5205, 4414, 6251, 3493, 2566, 4436, 739] # (if not limit_folds)
[369, 93]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> output = 'extract_sulci_fold7_2sulci_no_background.vtk'
>>> rewrite_scalars(sulci_file, output, sulci,
... 'sulci', sulci) # doctest: +SKIP
>>> plot_surfaces(output) # doctest: +SKIP
"""
import os
from time import time
import numpy as np
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import extract_borders, propagate, segment_regions
from mindboggle.mio.labels import DKTprotocol
# Load fold numbers if folds_or_file is a string:
if isinstance(folds_or_file, str):
folds, name = read_scalars(folds_or_file)
elif isinstance(folds_or_file, list):
folds = folds_or_file
elif isinstance(folds_or_file, np.ndarray):
folds = folds_or_file.tolist()
dkt = DKTprotocol()
if hemi == "lh":
pair_lists = dkt.left_sulcus_label_pair_lists
elif hemi == "rh":
pair_lists = dkt.right_sulcus_label_pair_lists
else:
raise IOError("Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors:
points, indices, lines, faces, labels, scalar_names, npoints, input_vtk = read_vtk(labels_file)
neighbor_lists = find_neighbors(faces, npoints)
# Array of sulcus IDs for fold vertices, initialized as -1.
# Since we do not touch gyral vertices and vertices whose labels
# are not in the label list, or vertices having only one label,
# their sulcus IDs will remain -1:
sulci = background_value * np.ones(npoints)
# ------------------------------------------------------------------------
# Loop through folds
# ------------------------------------------------------------------------
fold_numbers = [int(x) for x in np.unique(folds) if x != background_value]
n_folds = len(fold_numbers)
if verbose:
print("Extract sulci from {0} folds...".format(n_folds))
t0 = time()
for n_fold in fold_numbers:
fold_indices = [i for i, x in enumerate(folds) if x == n_fold]
len_fold = len(fold_indices)
# List the labels in this fold:
fold_labels = [labels[x] for x in fold_indices]
unique_fold_labels = [int(x) for x in np.unique(fold_labels) if x != background_value]
# --------------------------------------------------------------------
# NO MATCH -- fold has fewer than two labels
# --------------------------------------------------------------------
if verbose and len(unique_fold_labels) < 2:
# Ignore: sulci already initialized with -1 values:
if not unique_fold_labels:
print(" Fold {0} ({1} vertices): " "NO MATCH -- fold has no labels".format(n_fold, len_fold))
else:
print(
" Fold {0} ({1} vertices): "
"NO MATCH -- fold has only one label ({2})".format(n_fold, len_fold, unique_fold_labels[0])
)
# Ignore: sulci already initialized with -1 values
else:
# Find all label boundary pairs within the fold:
indices_fold_pairs, fold_pairs, unique_fold_pairs = extract_borders(
fold_indices, labels, neighbor_lists, ignore_values=[], return_label_pairs=True
)
# Find fold label pairs in the protocol (pairs are already sorted):
fold_pairs_in_protocol = [x for x in unique_fold_pairs if x in dkt.unique_sulcus_label_pairs]
if verbose and unique_fold_labels:
print(
" Fold {0} labels: {1} ({2} vertices)".format(
n_fold, ", ".join([str(x) for x in unique_fold_labels]), len_fold
)
)
# ----------------------------------------------------------------
# NO MATCH -- fold has no sulcus label pair
# ----------------------------------------------------------------
if verbose and not fold_pairs_in_protocol:
print(" Fold {0}: NO MATCH -- fold has no sulcus label pair".format(n_fold, len_fold))
# ----------------------------------------------------------------
# Possible matches
# ----------------------------------------------------------------
else:
if verbose:
print(
" Fold {0} label pairs in protocol: {1}".format(
n_fold, ", ".join([str(x) for x in fold_pairs_in_protocol])
)
)
# Labels in the protocol (includes repeats across label pairs):
labels_in_pairs = [x for lst in fold_pairs_in_protocol for x in lst]
# Labels that appear in one or more sulcus label boundary:
unique_labels = []
nonunique_labels = []
for label in np.unique(labels_in_pairs):
if len([x for x in labels_in_pairs if x == label]) == 1:
unique_labels.append(label)
else:
nonunique_labels.append(label)
# ------------------------------------------------------------
# Vertices whose labels are in only one sulcus label pair
# ------------------------------------------------------------
# Find vertices with a label that is in only one of the fold's
# label pairs (the other label in the pair can exist in other
# pairs). Assign the vertices the sulcus with the label pair
# if they are connected to the label boundary for that pair.
# ------------------------------------------------------------
if unique_labels:
for pair in fold_pairs_in_protocol:
# If one or both labels in label pair is/are unique:
unique_labels_in_pair = [x for x in pair if x in unique_labels]
n_unique = len(unique_labels_in_pair)
if n_unique:
ID = None
for i, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
if pair in pair_list:
ID = i
break
if ID:
# Seeds from label boundary vertices
# (fold_pairs and pair already sorted):
indices_pair = [x for i, x in enumerate(indices_fold_pairs) if fold_pairs[i] == pair]
# Vertices with unique label(s) in pair:
indices_unique_labels = [
fold_indices[i] for i, x in enumerate(fold_labels) if x in unique_labels_in_pair
]
# dkt.unique_sulcus_label_pairs]
# Propagate sulcus ID from seeds to vertices
# with "unique" labels (only exist in one
# label pair in a fold); propagation ensures
# that sulci consist of contiguous vertices
# for each label boundary:
sulci2 = segment_regions(
indices_unique_labels,
neighbor_lists,
min_region_size=1,
seed_lists=[indices_pair],
keep_seeding=False,
spread_within_labels=True,
labels=labels,
label_lists=[],
values=[],
max_steps="",
background_value=background_value,
verbose=False,
)
sulci[sulci2 != background_value] = ID
# Print statement:
if verbose:
if n_unique == 1:
ps1 = "One label"
else:
ps1 = "Both labels"
if len(sulcus_names):
ps2 = sulcus_names[ID]
else:
ps2 = ""
print(
" {0} unique to one fold pair: "
"{1} {2}".format(ps1, ps2, unique_labels_in_pair)
)
# ------------------------------------------------------------
# Vertex labels shared by multiple label pairs
# ------------------------------------------------------------
# Propagate labels from label borders to vertices with labels
# that are shared by multiple label pairs in the fold.
# ------------------------------------------------------------
if len(nonunique_labels):
# For each label shared by different label pairs:
for label in nonunique_labels:
# Print statement:
if verbose:
print(" Propagate sulcus borders with label {0}".format(int(label)))
# Construct seeds from label boundary vertices:
seeds = background_value * np.ones(npoints)
for ID, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
label_pairs = [x for x in pair_list if label in x]
for label_pair in label_pairs:
indices_pair = [
x
for i, x in enumerate(indices_fold_pairs)
if np.sort(fold_pairs[i]).tolist() == label_pair
]
if indices_pair:
# Do not include short boundary segments:
if min_boundary > 1:
indices_pair2 = []
seeds2 = segment_regions(
indices_pair,
neighbor_lists,
1,
[],
False,
False,
[],
[],
[],
"",
background_value,
verbose,
)
useeds2 = [x for x in np.unique(seeds2) if x != background_value]
for seed2 in useeds2:
iseed2 = [i for i, x in enumerate(seeds2) if x == seed2]
if len(iseed2) >= min_boundary:
indices_pair2.extend(iseed2)
elif verbose:
if len(iseed2) == 1:
print(
" Remove "
"assignment "
"of ID {0} from "
"1 vertex".format(seed2)
)
else:
print(
" Remove "
"assignment "
"of ID {0} from "
"{1} vertices".format(seed2, len(iseed2))
)
indices_pair = indices_pair2
# Assign sulcus IDs to seeds:
seeds[indices_pair] = ID
# Identify vertices with the label:
indices_label = [fold_indices[i] for i, x in enumerate(fold_labels) if x == label]
if len(indices_label):
# Propagate sulcus ID from seeds to vertices
# with a given shared label:
seg_vs_prop = False
if seg_vs_prop:
indices_seeds = []
for seed in [x for x in np.unique(seeds) if x != background_value]:
indices_seeds.append([i for i, x in enumerate(seeds) if x == seed])
sulci2 = segment_regions(
indices_label,
neighbor_lists,
50,
indices_seeds,
False,
True,
labels,
[],
[],
"",
background_value,
verbose,
)
else:
label_array = background_value * np.ones(npoints)
label_array[indices_label] = 1
sulci2 = propagate(
points,
faces,
label_array,
seeds,
sulci,
max_iters=10000,
tol=0.001,
sigma=5,
background_value=background_value,
verbose=verbose,
)
sulci[sulci2 != background_value] = sulci2[sulci2 != background_value]
sulcus_numbers = [int(x) for x in np.unique(sulci) if x != background_value]
n_sulci = len(sulcus_numbers)
# ------------------------------------------------------------------------
# Print statements
# ------------------------------------------------------------------------
if verbose:
if n_sulci == 1:
sulcus_str = "sulcus"
else:
sulcus_str = "sulci"
if n_folds == 1:
folds_str = "fold"
else:
folds_str = "folds"
print("Extracted {0} {1} from {2} {3} ({4:.1f}s):".format(n_sulci, sulcus_str, n_folds, folds_str, time() - t0))
if sulcus_names:
for sulcus_number in sulcus_numbers:
print(" {0}: {1}".format(sulcus_number, sulcus_names[sulcus_number]))
elif sulcus_numbers:
print(" " + ", ".join([str(x) for x in sulcus_numbers]))
unresolved = [i for i in range(len(pair_lists)) if i not in sulcus_numbers]
if len(unresolved) == 1:
print("The following sulcus is unaccounted for:")
else:
print("The following {0} sulci are unaccounted for:".format(len(unresolved)))
if sulcus_names:
for sulcus_number in unresolved:
print(" {0}: {1}".format(sulcus_number, sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in unresolved]))
# ------------------------------------------------------------------------
# Return sulci, number of sulci, and file name
# ------------------------------------------------------------------------
sulci = [int(x) for x in sulci]
sulci_file = os.path.join(os.getcwd(), "sulci.vtk")
rewrite_scalars(labels_file, sulci_file, sulci, "sulci", [], background_value)
if not os.path.exists(sulci_file):
raise IOError(sulci_file + " not found")
return sulci, n_sulci, sulci_file
def write_vertex_measures(output_table, labels_or_file, sulci=[], fundi=[],
affine_transform_files=[], inverse_booleans=[],
transform_format='itk',
area_file='', mean_curvature_file='', travel_depth_file='',
geodesic_depth_file='', freesurfer_thickness_file='',
freesurfer_curvature_file='', freesurfer_sulc_file=''):
"""
Make a table of shape values per vertex.
Note ::
This function is tailored for Mindboggle outputs.
Parameters
----------
output_table : string
output file (full path)
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
sulci : list of integers
indices to sulci, one per vertex, with -1 indicating no sulcus
fundi : list of integers
indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_files : list of strings
affine transform files to standard space
inverse_booleans : list of of zeros and ones
for each transform, 1 to take the inverse, else 0
transform_format : string
format for transform file
Ex: 'txt' for text, 'itk' for ITK, and 'mat' for Matlab format
area_file : string
name of VTK file with surface area scalar values
mean_curvature_file : string
name of VTK file with mean curvature scalar values
travel_depth_file : string
name of VTK file with travel depth scalar values
geodesic_depth_file : string
name of VTK file with geodesic depth scalar values
freesurfer_thickness_file : string
name of VTK file with FreeSurfer thickness scalar values
freesurfer_curvature_file : string
name of VTK file with FreeSurfer curvature (curv) scalar values
freesurfer_sulc_file : string
name of VTK file with FreeSurfer convexity (sulc) scalar values
Returns
-------
output_table : table file name for vertex shape values
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.tables import write_vertex_measures
>>> #
>>> output_table = ''#vertex_shapes.csv'
>>> path = os.environ['MINDBOGGLE_DATA']
>>> labels_or_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> sulci_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> fundi_file = os.path.join(path, 'arno', 'features', 'fundi.vtk')
>>> sulci, name = read_scalars(sulci_file)
>>> fundi, name = read_scalars(fundi_file)
>>> affine_transform_files = [os.path.join(path, 'arno', 'mri',
>>> 't1weighted_brain.MNI152Affine.txt')]
>>> inverse_booleans = [1]
>>> transform_format = 'itk'
>>> swap_xy = True
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> mean_curvature_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> travel_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> geodesic_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.geodesic_depth.vtk')
>>> freesurfer_thickness_file = ''
>>> freesurfer_curvature_file = ''
>>> freesurfer_sulc_file = ''
>>> #
>>> write_vertex_measures(output_table, labels_or_file, sulci, fundi,
>>> affine_transform_files, inverse_booleans, transform_format, area_file,
>>> mean_curvature_file, travel_depth_file, geodesic_depth_file,
>>> freesurfer_thickness_file, freesurfer_curvature_file, freesurfer_sulc_file)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_scalars, read_vtk, \
apply_affine_transforms
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = [int(x) for x in labels_or_file]
elif isinstance(labels_or_file, list):
labels = labels_or_file
elif isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file)
if isinstance(sulci, np.ndarray):
sulci = [int(x) for x in sulci]
if isinstance(fundi, np.ndarray):
fundi = [int(x) for x in fundi]
if not labels and not sulci and not fundi:
import sys
sys.exit('No feature data to tabulate in write_vertex_measures().')
# Feature names and corresponding feature lists:
feature_names = ['label ID', 'sulcus ID', 'fundus ID']
feature_lists = [labels, sulci, fundi]
# Shape names corresponding to shape files below:
shape_names = ['area', 'travel depth', 'geodesic depth',
'mean curvature', 'freesurfer curvature',
'freesurfer thickness', 'freesurfer convexity (sulc)']
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [area_file, travel_depth_file, geodesic_depth_file,
mean_curvature_file, freesurfer_curvature_file,
freesurfer_thickness_file, freesurfer_sulc_file]
# Append columns of per-vertex scalar values:
columns = []
column_names = []
for ifeature, values in enumerate(feature_lists):
if values:
columns.append(values)
column_names.append(feature_names[ifeature])
first_pass = True
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
# Append x,y,z position per vertex to columns:
points, indices, lines, faces, scalars, scalar_names, \
npoints, input_vtk = read_vtk(shape_file)
xyz_positions = np.asarray(points)
for ixyz, xyz in enumerate(['x','y','z']):
column_names.append('position: {0}'.format(xyz))
columns.append(xyz_positions[:, ixyz].tolist())
first_pass = False
# Append standard space x,y,z position to columns:
if affine_transform_files and transform_format:
affine_points, \
foo1 = apply_affine_transforms(affine_transform_files,
inverse_booleans, transform_format,
points, vtk_file_stem='')
xyz_std_positions = affine_points
for ixyz, xyz in enumerate(['x','y','z']):
column_names.append('position in standard space:'
' {0}'.format(xyz))
columns.append(xyz_std_positions[:, ixyz].tolist())
else:
scalars, name = read_scalars(shape_file)
if len(scalars):
columns.append(scalars)
column_names.append(shape_names[ishape])
# Prepend with column of indices and write table
if not output_table:
output_table = os.path.join(os.getcwd(), 'vertices.csv')
df = pd.DataFrame(np.transpose(columns), columns = column_names)
df.to_csv(output_table, index=False)
if not os.path.exists(output_table):
raise(IOError(output_table + " not found"))
return output_table
def evaluate_deep_features(features_file, labels_file, sulci_file='', hemi='',
excludeIDs=[-1], output_vtk_name='', verbose=True):
"""
Evaluate deep surface features by computing the minimum distance from each
label border vertex to all of the feature vertices in the same sulcus,
and from each feature vertex to all of the label border vertices in the
same sulcus. The label borders run along the deepest parts of sulci
and correspond to fundi in the DKT cortical labeling protocol.
Parameters
----------
features_file : string
VTK surface file with feature numbers for vertex scalars
labels_file : string
VTK surface file with label numbers for vertex scalars
sulci_file : string
VTK surface file with sulcus numbers for vertex scalars
excludeIDs : list of integers
feature/sulcus/label IDs to exclude (background set to -1)
output_vtk_name : Boolean
if not empty, output a VTK file beginning with output_vtk_name that
contains a surface with mean distances as scalars
verbose : Boolean
print mean distances to standard output?
Returns
-------
feature_to_border_mean_distances : numpy array [number of features x 1]
mean distance from each feature to sulcus label border
feature_to_border_sd_distances : numpy array [number of features x 1]
standard deviations of feature-to-border distances
feature_to_border_distances_vtk : string
VTK surface file containing feature-to-border distances
border_to_feature_mean_distances : numpy array [number of features x 1]
mean distances from each sulcus label border to feature
border_to_feature_sd_distances : numpy array [number of features x 1]
standard deviations of border-to-feature distances
border_to_feature_distances_vtk : string
VTK surface file containing border-to-feature distances
"""
import os
import sys
import numpy as np
from mindboggle.mio.vtks import read_vtk, read_scalars, write_vtk
from mindboggle.guts.mesh import find_neighbors, remove_faces
from mindboggle.guts.segment import extract_borders
from mindboggle.guts.compute import source_to_target_distances
from mindboggle.mio.labels import DKTprotocol
dkt = DKTprotocol()
#-------------------------------------------------------------------------
# Load labels, features, and sulci:
#-------------------------------------------------------------------------
faces, lines, indices, points, npoints, labels, scalar_names, \
input_vtk = read_vtk(labels_file, True, True)
features, name = read_scalars(features_file, True, True)
if sulci_file:
sulci, name = read_scalars(sulci_file, True, True)
# List of indices to sulcus vertices:
sulcus_indices = [i for i,x in enumerate(sulci) if x != -1]
segmentIDs = sulci
sulcus_faces = remove_faces(faces, sulcus_indices)
else:
sulcus_indices = range(len(labels))
segmentIDs = []
sulcus_faces = faces
#-------------------------------------------------------------------------
# Prepare neighbors, label pairs, border IDs, and outputs:
#-------------------------------------------------------------------------
# Calculate neighbor lists for all points:
print('Find neighbors for all vertices...')
neighbor_lists = find_neighbors(faces, npoints)
# Find label border points in any of the sulci:
print('Find label border points in any of the sulci...')
border_indices, border_label_tuples, unique_border_label_tuples = \
extract_borders(sulcus_indices, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
if not len(border_indices):
sys.exit('There are no label border points!')
# Initialize an array of label border IDs
# (label border vertices that define sulci in the labeling protocol):
print('Build an array of label border IDs...')
label_borders = -1 * np.ones(npoints)
if hemi == 'lh':
nsulcus_lists = len(dkt.left_sulcus_label_pair_lists)
else:
nsulcus_lists = len(dkt.right_sulcus_label_pair_lists)
feature_to_border_mean_distances = -1 * np.ones(nsulcus_lists)
feature_to_border_sd_distances = -1 * np.ones(nsulcus_lists)
border_to_feature_mean_distances = -1 * np.ones(nsulcus_lists)
border_to_feature_sd_distances = -1 * np.ones(nsulcus_lists)
feature_to_border_distances_vtk = ''
border_to_feature_distances_vtk = ''
#-------------------------------------------------------------------------
# Loop through sulci:
#-------------------------------------------------------------------------
# For each list of sorted label pairs (corresponding to a sulcus):
for isulcus, label_pairs in enumerate(dkt.sulcus_label_pair_lists):
# Keep the border points with label pair labels:
label_pair_border_indices = [x for i,x in enumerate(border_indices)
if np.unique(border_label_tuples[i]).tolist()
in label_pairs]
# Store the points as sulcus IDs in the border IDs array:
if label_pair_border_indices:
label_borders[label_pair_border_indices] = isulcus
if len(np.unique(label_borders)) > 1:
#---------------------------------------------------------------------
# Construct a feature-to-border distance matrix and VTK file:
#---------------------------------------------------------------------
# Construct a distance matrix:
print('Construct a feature-to-border distance matrix...')
sourceIDs = features
targetIDs = label_borders
distances, distance_matrix = source_to_target_distances(
sourceIDs, targetIDs, points, segmentIDs, excludeIDs)
# Compute mean distances for each feature:
nfeatures = min(np.shape(distance_matrix)[1], nsulcus_lists)
for ifeature in range(nfeatures):
feature_distances = [x for x in distance_matrix[:, ifeature]
if x != -1]
feature_to_border_mean_distances[ifeature] = \
np.mean(feature_distances)
feature_to_border_sd_distances[ifeature] = \
np.std(feature_distances)
if verbose:
print('Feature-to-border mean distances:')
print(feature_to_border_mean_distances)
print('Feature-to-border standard deviations of distances:')
print(feature_to_border_sd_distances)
# Write resulting feature-label border distances to VTK file:
if output_vtk_name:
feature_to_border_distances_vtk = os.path.join(os.getcwd(),
output_vtk_name + '_feature_to_border_mean_distances.vtk')
print('Write feature-to-border distances to {0}...'.
format(feature_to_border_distances_vtk))
write_vtk(feature_to_border_distances_vtk, points,
[], [], sulcus_faces, [distances],
['feature-to-border_distances'], 'float')
#---------------------------------------------------------------------
# Construct a border-to-feature distance matrix and VTK file:
#---------------------------------------------------------------------
# Construct a distance matrix:
print('Construct a border-to-feature distance matrix...')
sourceIDs = label_borders
targetIDs = features
distances, distance_matrix = source_to_target_distances(
sourceIDs, targetIDs, points, segmentIDs, excludeIDs)
# Compute mean distances for each feature:
nfeatures = min(np.shape(distance_matrix)[1], nsulcus_lists)
for ifeature in range(nfeatures):
border_distances = [x for x in distance_matrix[:, ifeature]
if x != -1]
border_to_feature_mean_distances[ifeature] = \
np.mean(border_distances)
border_to_feature_sd_distances[ifeature] = \
np.std(border_distances)
if verbose:
print('border-to-feature mean distances:')
print(border_to_feature_mean_distances)
print('border-to-feature standard deviations of distances:')
print(border_to_feature_sd_distances)
# Write resulting feature-label border distances to VTK file:
if output_vtk_name:
border_to_feature_distances_vtk = os.path.join(os.getcwd(),
output_vtk_name + '_border_to_feature_mean_distances.vtk')
print('Write border-to-feature distances to {0}...'.
format(border_to_feature_distances_vtk))
write_vtk(border_to_feature_distances_vtk, points,
[], [], sulcus_faces, [distances],
['border-to-feature_distances'], 'float')
#-------------------------------------------------------------------------
# Return outputs:
#-------------------------------------------------------------------------
return feature_to_border_mean_distances, feature_to_border_sd_distances,\
feature_to_border_distances_vtk,\
border_to_feature_mean_distances, border_to_feature_sd_distances,\
border_to_feature_distances_vtk
def write_shape_stats(labels_or_file=[],
sulci=[],
fundi=[],
affine_transform_files=[],
inverse_booleans=[],
transform_format='itk',
area_file='',
normalize_by_area=False,
mean_curvature_file='',
travel_depth_file='',
geodesic_depth_file='',
freesurfer_thickness_file='',
freesurfer_curvature_file='',
freesurfer_sulc_file='',
labels_spectra=[],
labels_spectra_IDs=[],
sulci_spectra=[],
sulci_spectra_IDs=[],
labels_zernike=[],
labels_zernike_IDs=[],
sulci_zernike=[],
sulci_zernike_IDs=[],
exclude_labels=[-1],
verbose=False):
"""
Make tables of shape statistics per label, sulcus, and/or fundus.
There can be thousands of vertices in a single feature such as a gyrus,
sulcus, or fundus, and for per-vertex shape measures, it makes sense to
characterize their collective shape as a distribution of shape values.
Mindboggle's stats_per_label function generates tables of summary
statistical measures for these distributions, and includes the shape
measures computed on cortical features as well.
Note ::
This function is tailored for Mindboggle outputs.
Parameters
----------
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
sulci : list of integers
indices to sulci, one per vertex, with -1 indicating no sulcus
fundi : list of integers
indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_files : list of strings
affine transform files to standard space
inverse_booleans : list of of zeros and ones
for each transform, 1 to take the inverse, else 0
transform_format : string
format for transform file
Ex: 'txt' for text, 'itk' for ITK, and 'mat' for Matlab format
area_file : string
name of VTK file with surface area scalar values
normalize_by_area : bool
normalize all shape measures by area of label/feature? (UNTESTED)
mean_curvature_file : string
name of VTK file with mean curvature scalar values
travel_depth_file : string
name of VTK file with travel depth scalar values
geodesic_depth_file : string
name of VTK file with geodesic depth scalar values
freesurfer_thickness_file : string
name of VTK file with FreeSurfer thickness scalar values
freesurfer_curvature_file : string
name of VTK file with FreeSurfer curvature (curv) scalar values
freesurfer_sulc_file : string
name of VTK file with FreeSurfer convexity (sulc) scalar values
labels_spectra : list of lists of floats
Laplace-Beltrami spectra for each labeled region
labels_spectra_IDs : list of integers
unique labels for labels_spectra
sulci_spectra : list of lists of floats
Laplace-Beltrami spectra for each sulcus
sulci_spectra_IDs : list of integers
unique sulcus IDs for sulci_spectra
labels_zernike : list of lists of floats
Zernike moments for each labeled region
labels_zernike_IDs : list of integers
unique labels for labels_zernike
sulci_zernike : list of lists of floats
Zernike moments for each sulcus
sulci_zernike_IDs : list of integers
unique sulcus IDs for sulci_zernike
exclude_labels : list of lists of integers
indices to be excluded (in addition to -1)
verbose : bool
print statements?
Returns
-------
label_table : string
output table filename for label shapes
sulcus_table : string
output table filename for sulcus shapes
fundus_table : string
output table filename for fundus shapes
Examples
--------
>>> from mindboggle.mio.tables import write_shape_stats
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> label_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> sulci_file = fetch_data(urls['left_sulci'], '', '.vtk')
>>> fundi_file = fetch_data(urls['left_fundus_per_sulcus'], '', '.vtk')
>>> mean_curvature_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> travel_depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> geodesic_depth_file = fetch_data(urls['left_geodesic_depth'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> freesurfer_thickness_file = ''
>>> freesurfer_curvature_file = ''
>>> freesurfer_sulc_file = ''
>>> sulci, name = read_scalars(sulci_file)
>>> fundi, name = read_scalars(fundi_file)
>>> affine_transform_files = []
>>> inverse_booleans = []
>>> transform_format = 'itk'
>>> normalize_by_area = False
>>> labels, name = read_scalars(label_file)
>>> labels_spectra = []
>>> labels_spectra_IDs = []
>>> sulci_spectra = []
>>> sulci_spectra_IDs = []
>>> labels_zernike = []
>>> labels_zernike_IDs = []
>>> sulci_zernike = []
>>> sulci_zernike_IDs = []
>>> exclude_labels = [-1]
>>> verbose = False
>>> label_table, sulcus_table, fundus_table = write_shape_stats(label_file,
... sulci, fundi, affine_transform_files, inverse_booleans,
... transform_format, area_file, normalize_by_area,
... mean_curvature_file, travel_depth_file, geodesic_depth_file,
... freesurfer_thickness_file, freesurfer_curvature_file,
... freesurfer_sulc_file, labels_spectra, labels_spectra_IDs,
... sulci_spectra, sulci_spectra_IDs, labels_zernike,
... labels_zernike_IDs, sulci_zernike, sulci_zernike_IDs,
... exclude_labels, verbose)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.guts.compute import stats_per_label
from mindboggle.guts.compute import means_per_label
from mindboggle.guts.compute import sum_per_label
from mindboggle.mio.vtks import read_scalars, read_vtk
from mindboggle.mio.vtks import apply_affine_transforms
from mindboggle.mio.labels import DKTprotocol
dkt = DKTprotocol()
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = [int(x) for x in labels_or_file]
elif isinstance(labels_or_file, list):
labels = labels_or_file
elif isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file)
if isinstance(sulci, np.ndarray):
sulci = [int(x) for x in sulci]
if isinstance(fundi, np.ndarray):
fundi = [int(x) for x in fundi]
if not labels and not sulci and not fundi:
raise IOError('No feature data to tabulate in write_shape_stats().')
spectrum_start = 1 # Store all columns of spectral components (0),
# or start from higher frequency components (>=1)
# ------------------------------------------------------------------------
# Feature lists, shape names, and shape files:
# ------------------------------------------------------------------------
# Feature lists:
feature_lists = [labels, sulci, fundi]
feature_names = ['label', 'sulcus', 'fundus']
spectra_lists = [labels_spectra, sulci_spectra]
spectra_ID_lists = [labels_spectra_IDs, sulci_spectra_IDs]
zernike_lists = [labels_zernike, sulci_zernike]
zernike_ID_lists = [labels_zernike_IDs, sulci_zernike_IDs]
table_names = [
'label_shapes.csv', 'sulcus_shapes.csv', 'fundus_shapes.csv'
]
# Shape names corresponding to shape files below:
shape_names = [
'area', 'travel depth', 'geodesic depth', 'mean curvature',
'freesurfer curvature', 'freesurfer thickness',
'freesurfer convexity (sulc)'
]
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [
area_file, travel_depth_file, geodesic_depth_file, mean_curvature_file,
freesurfer_curvature_file, freesurfer_thickness_file,
freesurfer_sulc_file
]
shape_arrays = []
first_pass = True
area_array = []
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
points, indices, lines, faces, scalars_array, scalar_names, \
npoints, input_vtk = read_vtk(shape_file, True, True)
points = np.array(points)
first_pass = False
if affine_transform_files and transform_format:
affine_points, \
foo1 = apply_affine_transforms(affine_transform_files,
inverse_booleans, transform_format,
points, vtk_file_stem='')
else:
scalars_array, name = read_scalars(shape_file, True, True)
if scalars_array.size:
shape_arrays.append(scalars_array)
# Store area array:
if ishape == 0:
area_array = scalars_array.copy()
if normalize_by_area:
use_area = area_array
else:
use_area = []
# Initialize table file names:
label_table = ''
sulcus_table = ''
fundus_table = ''
# Loop through features / tables:
for itable, feature_list in enumerate(feature_lists):
column_names = []
# ----------------------------------------------------------------
# Label names:
# ----------------------------------------------------------------
label_title = 'name'
if itable == 0:
label_numbers = dkt.cerebrum_cortex_DKT31_numbers
label_names = dkt.cerebrum_cortex_DKT31_names
elif itable in [1, 2]:
label_numbers = dkt.sulcus_numbers
label_names = dkt.sulcus_names
else:
label_numbers = []
label_names = []
include_labels = label_numbers
nlabels = len(label_numbers)
# --------------------------------------------------------------------
# For each feature, construct a table of average shape values:
# --------------------------------------------------------------------
if feature_list:
feature_name = feature_names[itable]
columns = []
# ----------------------------------------------------------------
# Loop through shape measures:
# ----------------------------------------------------------------
column_names.extend(column_names[:])
for ishape, shape_array in enumerate(shape_arrays):
shape = shape_names[ishape]
if verbose:
print(' Compute statistics on {0} {1}...'.format(
feature_name, shape))
# ------------------------------------------------------------
# Append feature areas to columns:
# ------------------------------------------------------------
if ishape == 0 and np.size(area_array):
sums, label_list = sum_per_label(shape_array, feature_list,
include_labels,
exclude_labels)
column_names.append(shape)
columns.append(sums)
# ------------------------------------------------------------
# Append feature shape statistics to columns:
# ------------------------------------------------------------
else:
medians, mads, means, sdevs, skews, kurts, \
lower_quarts, upper_quarts, \
label_list = stats_per_label(shape_array, feature_list,
include_labels, exclude_labels,
area_array, precision=1)
column_names.append(shape + ': median')
column_names.append(shape + ': MAD')
column_names.append(shape + ': mean')
column_names.append(shape + ': SD')
column_names.append(shape + ': skew')
column_names.append(shape + ': kurtosis')
column_names.append(shape + ': 25%')
column_names.append(shape + ': 75%')
columns.append(medians)
columns.append(mads)
columns.append(means)
columns.append(sdevs)
columns.append(skews)
columns.append(kurts)
columns.append(lower_quarts)
columns.append(upper_quarts)
# ----------------------------------------------------------------
# Mean positions in the original space:
# ----------------------------------------------------------------
# Compute mean position per feature:
positions, sdevs, label_list, foo = means_per_label(
points, feature_list, include_labels, exclude_labels, use_area)
# Append mean x,y,z position per feature to columns:
xyz_positions = np.asarray(positions)
for ixyz, xyz in enumerate(['x', 'y', 'z']):
column_names.append('mean position: {0}'.format(xyz))
columns.append(xyz_positions[:, ixyz].tolist())
# ----------------------------------------------------------------
# Mean positions in standard space:
# ----------------------------------------------------------------
if affine_transform_files and transform_format:
# Compute standard space mean position per feature:
standard_positions, sdevs, label_list, \
foo = means_per_label(affine_points,
feature_list, include_labels, exclude_labels, use_area)
# Append standard space x,y,z position per feature to columns:
xyz_std_positions = np.asarray(standard_positions)
for ixyz, xyz in enumerate(['x', 'y', 'z']):
column_names.append('mean position in standard space:'
' {0}'.format(xyz))
columns.append(xyz_std_positions[:, ixyz].tolist())
# ----------------------------------------------------------------
# Laplace-Beltrami spectra:
# ----------------------------------------------------------------
if itable in [0, 1]:
spectra = spectra_lists[itable]
if spectra:
spectra_IDs = spectra_ID_lists[itable]
# Construct a matrix of spectra:
len_spectrum = len(spectra[0])
spectrum_matrix = np.zeros((nlabels, len_spectrum))
for ilabel, label in enumerate(include_labels):
if label in spectra_IDs:
spectrum = spectra[spectra_IDs.index(label)]
spectrum_matrix[ilabel, 0:len_spectrum] = spectrum
# Append spectral shape name and values to columns:
for ispec in range(spectrum_start, len_spectrum):
columns.append(spectrum_matrix[:, ispec].tolist())
column_names.append('Laplace-Beltrami spectrum:'
' component {0}'.format(ispec + 1))
# ----------------------------------------------------------------
# Zernike moments:
# ----------------------------------------------------------------
if itable in [0, 1]:
zernike = zernike_lists[itable]
if zernike:
zernike_IDs = zernike_ID_lists[itable]
# Construct a matrix of Zernike moments:
len_moments = len(zernike[0])
moments_matrix = np.zeros((nlabels, len_moments))
for ilabel, label in enumerate(include_labels):
if label in zernike_IDs:
moments = zernike[zernike_IDs.index(label)]
moments_matrix[ilabel, 0:len_moments] = moments
# Append Zernike shape name and values to columns:
for imoment in range(0, len_moments):
columns.append(moments_matrix[:, imoment].tolist())
column_names.append(
'Zernike moments: component {0}'.format(imoment +
1))
# ----------------------------------------------------------------
# Write labels/IDs and values to table:
# ----------------------------------------------------------------
# Write labels/IDs to table:
output_table = os.path.join(os.getcwd(), table_names[itable])
if columns:
df1 = pd.DataFrame({'ID': label_numbers})
df2 = pd.DataFrame(np.transpose(columns), columns=column_names)
df = pd.concat([df1, df2], axis=1)
if label_names:
df0 = pd.DataFrame({'name': label_names})
df = pd.concat([df0, df], axis=1)
df.to_csv(output_table, index=False, encoding='utf-8')
if not os.path.exists(output_table):
raise IOError(output_table + " not found")
# ----------------------------------------------------------------
# Return correct table file name:
# ----------------------------------------------------------------
if itable == 0:
label_table = output_table
elif itable == 1:
sulcus_table = output_table
elif itable == 2:
fundus_table = output_table
return label_table, sulcus_table, fundus_table
def spectrum_per_label(vtk_file,
spectrum_size=10,
exclude_labels=[-1],
normalization='areaindex',
area_file='',
largest_segment=True,
verbose=False):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues
if None, no normalization is used
if "area", use area of the 2D structure as in Reuter et al. 2006
if "index", divide eigenvalue by index to account for linear trend
if "areaindex", do both (default)
area_file : string (optional)
name of VTK file with surface area scalar values
largest_segment : bool
compute spectrum only for largest segment with a given label?
verbose : bool
print statements?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> verbose = False
>>> spectrum_lists, label_list = spectrum_per_label(vtk_file,
... spectrum_size, exclude_labels, None, area_file, largest_segment,
... verbose)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum_lists[0]]
[0.0, 0.00054, 0.00244, 0.00291, 0.00456, 0.00575]
>>> label_list[0:10]
[1029, 1005, 1011, 1021, 1008, 1025, 999, 1013, 1007, 1022]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.guts.mesh import keep_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
points, indices, lines, faces, labels, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[
ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels
]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i, x in enumerate(labels) if x == label]
if verbose:
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = keep_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size, exclude_labels_inner,
normalization, areas, verbose)
else:
spectrum = fem_laplacian(pick_points, pick_faces, spectrum_size,
normalization, verbose)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def realign_boundaries_to_fundus_lines(surf_file,
init_label_file,
fundus_lines_file,
thickness_file,
out_label_file=None):
"""
Fix label boundaries to fundus lines.
Parameters
----------
surf_file : file containing the surface geometry in vtk format
init_label_file : file containing scalars that represent the
initial guess at labels
fundus_lines_file : file containing scalars representing fundus lines.
thickness_file: file containing cortical thickness scalar data
(for masking out the medial wall only)
out_label_file : if specified, the realigned labels will be writen to
this file
Returns
-------
numpy array representing the realigned label for each surface vertex.
"""
import numpy as np
from mindboggle.guts.segment import extract_borders
import mindboggle.guts.graph as go
from mindboggle.mio.vtks import read_vtk, read_scalars, write_vtk
from mindboggle.guts.mesh import find_neighbors
import propagate_fundus_lines
## read files
points, indices, lines, faces, scalars, scalar_names, num_points, \
input_vtk = read_vtk(surf_file, return_first=True, return_array=True)
indices = range(num_points)
init_labels, _ = read_scalars(init_label_file,
return_first=True,
return_array=True)
fundus_lines, _ = read_scalars(fundus_lines_file,
return_first=True,
return_array=True)
thickness, _ = read_scalars(thickness_file,
return_first=True,
return_array=True)
# remove labels from vertices with zero thickness (get around
# DKT40 annotations having the label '3' for all the Corpus
# Callosum vertices).
cc_inds = [x for x in indices if thickness[x] < 0.001]
init_labels[cc_inds] = 0
## setup seeds from initial label boundaries
neighbor_lists = find_neighbors(faces, num_points)
# extract all vertices that are on a boundary between labels
boundary_indices, label_pairs, _ = extract_borders(indices,
init_labels,
neighbor_lists,
return_label_pairs=True)
# split boundary vertices into segments with common boundary pairs.
boundary_segments = {}
for boundary_index, label_pair in zip(boundary_indices, label_pairs):
key = ((label_pair[0],
label_pair[1]) if label_pair[0] < label_pair[1] else
(label_pair[1], label_pair[0]))
if key not in boundary_segments:
boundary_segments[key] = []
boundary_segments[key].append(boundary_index)
boundary_matrix, boundary_matrix_keys = _build_boundary_matrix(
boundary_segments, num_points)
# build the affinity matrix
affinity_matrix = go.weight_graph(np.array(points),
indices,
np.array(faces),
sigma=10,
add_to_graph=False)
## propagate boundaries to fundus line vertices
learned_matrix = _propagate_labels(affinity_matrix, boundary_matrix,
boundary_indices, 100, 1)
# assign labels to fundus line vertices based on highest probability
new_boundaries = -1 * np.ones(init_labels.shape)
fundus_line_indices = [i for i, x in enumerate(fundus_lines) if x > 0.5]
# tile the surface into connected components delimited by fundus lines
closed_fundus_lines, _, _ = propagate_fundus_lines.propagate_fundus_lines(
points, faces, fundus_line_indices, thickness)
closed_fundus_line_indices = np.where(closed_fundus_lines > 0)[0]
# split surface into connected components
connected_component_faces = _remove_boundary_faces(
points, faces, closed_fundus_line_indices)
# label components based on most probable label assignment
new_labels = _label_components(connected_component_faces, num_points,
boundary_indices, learned_matrix,
boundary_matrix_keys)
# propagate new labels to fill holes
label_matrix, label_map = _build_label_matrix(new_labels)
new_learned_matrix = _propagate_labels(
affinity_matrix, label_matrix,
[i for i in range(num_points) if new_labels[i] >= 0], 100, 1)
# assign most probable labels
for idx in [i for i in range(num_points) if new_labels[i] == -1]:
max_idx = np.argmax(new_learned_matrix[idx])
new_labels[idx] = label_map[max_idx]
# save
if out_label_file is not None:
write_vtk(out_label_file,
points,
faces=faces,
scalars=[int(x) for x in new_labels],
scalar_type='int')
return new_labels
def zernike_moments_per_label(vtk_file, order=10, exclude_labels=[-1],
scale_input=True,
decimate_fraction=0, decimate_smooth=25):
"""
Compute the Zernike moments per labeled region in a file.
Optionally decimate the input mesh.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
order : integer
number of moments to compute
exclude_labels : list of integers
labels to be excluded
scale_input : Boolean
translate and scale each object so it is bounded by a unit sphere?
(this is the expected input to zernike_moments())
decimate_fraction : float
fraction of mesh faces to remove for decimation (1 for no decimation)
decimate_smooth : integer
number of smoothing steps for decimation
Returns
-------
descriptors_lists : list of lists of floats
Zernike descriptors per label
label_list : list of integers
list of unique labels for which moments are computed
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Twins-2-1 left postcentral (22) pial surface:
>>> import os
>>> from mindboggle.shapes.zernike.zernike import zernike_moments_per_label
>>> path = os.path.join(os.environ['HOME'], 'mindboggled', 'OASIS-TRT-20-1')
>>> vtk_file = os.path.join(path, 'labels', 'left_surface', 'relabeled_classifier.vtk')
>>> order = 3
>>> exclude_labels = [-1, 0]
>>> scale_input = True
>>> zernike_moments_per_label(vtk_file, order, exclude_labels, scale_input)
([[0.00528486237819844,
0.009571754617699853,
0.0033489494903015944,
0.00875603468268444,
0.0015879536633349918,
0.0008080165707033097]],
[22])
([[0.0018758013185778298,
0.001757973693050823,
0.002352403177686726,
0.0032281044369938286,
0.002215900343702539,
0.0019646380916703856]],
[14.0])
Arthur Mikhno's result:
1.0e+07 *
0.0000
0.0179
0.0008
4.2547
0.0534
4.4043
"""
import numpy as np
from mindboggle.mio.vtks import read_vtk
from mindboggle.guts.mesh import remove_faces
from mindboggle.shapes.zernike.zernike import zernike_moments
min_points_faces = 4
#-------------------------------------------------------------------------
# Read VTK surface mesh file:
#-------------------------------------------------------------------------
faces, u1,u2, points, u3, labels, u4,u5 = read_vtk(vtk_file)
#-------------------------------------------------------------------------
# Loop through labeled regions:
#-------------------------------------------------------------------------
ulabels = [x for x in np.unique(labels) if x not in exclude_labels]
label_list = []
descriptors_lists = []
for label in ulabels:
#if label == 1022: # 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
#---------------------------------------------------------------------
# Determine the indices per label:
#---------------------------------------------------------------------
Ilabel = [i for i,x in enumerate(labels) if x == label]
print(' {0} vertices for label {1}'.format(len(Ilabel), label))
if len(Ilabel) > min_points_faces:
#-----------------------------------------------------------------
# Remove background faces:
#-----------------------------------------------------------------
pick_faces = remove_faces(faces, Ilabel)
if len(pick_faces) > min_points_faces:
#-------------------------------------------------------------
# Compute Zernike moments for the label:
#-------------------------------------------------------------
descriptors = zernike_moments(points, pick_faces,
order, scale_input,
decimate_fraction,
decimate_smooth)
#-------------------------------------------------------------
# Append to a list of lists of spectra:
#-------------------------------------------------------------
descriptors_lists.append(descriptors)
label_list.append(label)
return descriptors_lists, label_list
def extract_fundi(folds,
curv_file,
depth_file,
min_separation=10,
erode_ratio=0.1,
erode_min_size=1,
save_file=False,
verbose=False):
"""
Extract fundi from folds.
A fundus is a branching curve that runs along the deepest and most
highly curved portions of a fold.
Steps ::
1. Find fundus endpoints (outer anchors) with find_outer_anchors().
2. Include inner anchor points.
3. Connect anchor points using connect_points_erosion();
inner anchors are removed if they result in endpoints.
Parameters
----------
folds : numpy array or list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : bool
save output VTK file?
verbose : bool
print statements?
Returns
-------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
n_fundi_in_folds : integer
number of fundi
fundus_per_fold_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> single_fold = True
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> curv_file = fetch_data(urls['left_mean_curvature'])
>>> depth_file = fetch_data(urls['left_travel_depth'])
>>> folds_file = fetch_data(urls['left_folds'])
>>> folds, name = read_scalars(folds_file, True, True)
>>> if single_fold:
... fold_number = 2 #11
... folds[folds != fold_number] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> verbose = False
>>> o1, o2, fundus_per_fold_file = extract_fundi(folds, curv_file,
... depth_file, min_separation, erode_ratio, erode_min_size,
... save_file, verbose)
>>> if single_fold:
... lens = [len([x for x in o1 if x == 2])]
... else:
... lens = [len([x for x in o1 if x == y]) for y in range(o2)]
>>> lens[0:10]
[115]
View result (skip test):
>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces(fundus_per_fold_file) # doctest: +SKIP
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.compute import median_abs_dev
from mindboggle.guts.paths import find_max_values
from mindboggle.guts.mesh import find_neighbors_from_file
from mindboggle.guts.mesh import find_complete_faces
from mindboggle.guts.paths import find_outer_anchors
from mindboggle.guts.paths import connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
points, indices, lines, faces, curvs, scalar_names, npoints, \
input_vtk = read_vtk(curv_file, True, True)
depths, name = read_scalars(depth_file, True, True)
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
#-------------------------------------------------------------------------
# Loop through folds:
#-------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != -1]
if verbose:
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".format(
len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i, x in enumerate(folds) if x == fold_ID]
if indices_fold:
if verbose:
print(' Fold {0}:'.format(int(fold_ID)))
#-----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints:
#-----------------------------------------------------------------
verbose = False
outer_anchors, tracks = find_outer_anchors(indices_fold,
neighbor_lists, values,
depths, min_separation,
verbose)
#-----------------------------------------------------------------
# Find inner anchor points:
#-----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation,
thr)
#-----------------------------------------------------------------
# Connect anchor points to create skeleton:
#-----------------------------------------------------------------
B = -1 * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B,
neighbor_lists,
outer_anchors,
inner_anchors,
values,
erode_ratio,
erode_min_size,
save_steps=[],
save_vtk='',
verbose=False)
if skeleton:
skeletons.extend(skeleton)
#-----------------------------------------------------------------
# Remove fundus vertices if they complete triangle faces:
#-----------------------------------------------------------------
Iremove = find_complete_faces(skeletons, faces)
if Iremove:
skeletons = list(frozenset(skeletons).difference(Iremove))
indices_skel = [x for x in skeletons if folds[x] != -1]
fundus_per_fold = -1 * np.ones(npoints)
fundus_per_fold[indices_skel] = folds[indices_skel]
n_fundi_in_folds = len([x for x in np.unique(fundus_per_fold) if x != -1])
if n_fundi_in_folds == 1:
sdum = 'fold fundus'
else:
sdum = 'fold fundi'
if verbose:
print(' ...Extracted {0} {1}; {2} total ({3:.2f} seconds)'.format(
n_fundi_in_folds, sdum, n_fundi_in_folds,
time() - t1))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
if n_fundi_in_folds > 0:
fundus_per_fold = [int(x) for x in fundus_per_fold]
if save_file:
fundus_per_fold_file = os.path.join(os.getcwd(),
'fundus_per_fold.vtk')
rewrite_scalars(curv_file, fundus_per_fold_file, fundus_per_fold,
'fundi', folds)
if not os.path.exists(fundus_per_fold_file):
raise IOError(fundus_per_fold_file + " not found")
else:
fundus_per_fold_file = None
return fundus_per_fold, n_fundi_in_folds, fundus_per_fold_file
def spectrum_from_file(vtk_file,
spectrum_size=10,
exclude_labels=[-1],
normalization=None,
area_file='',
verbose=False):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
verbose : bool
print statements?
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6)
>>> print(np.array_str(np.array(spectrum[1::]),
... precision=5, suppress_small=True))
[ 0.00013 0.00027 0.00032 0.00047 0.00058]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas,
verbose)
return spectrum
def plot_mask_surface(vtk_file, mask_file='', nonmask_value=-1,
masked_output='', remove_nonmask=False,
program='vtkviewer',
use_colormap=False, colormap_file=''):
"""
Use vtkviewer or mayavi2 to visualize VTK surface mesh data.
If a mask_file is provided, a temporary masked file is saved,
and it is this file that is viewed.
If using vtkviewer, can optionally provide colormap file
or set $COLORMAP environment variable.
Parameters
----------
vtk_file : string
name of VTK surface mesh file
mask_file : string
name of VTK surface mesh file to mask vtk_file vertices
nonmask_value : integer
nonmask (usually background) value
masked_output : string
temporary masked output file name
remove_nonmask : Boolean
remove vertices that are not in mask? (otherwise assign nonmask_value)
program : string {'vtkviewer', 'mayavi2'}
program to visualize VTK file
use_colormap : Boolean
use Paraview-style XML colormap file set by $COLORMAP env variable?
colormap_file : string
use colormap in given file if use_colormap==True? if empty and
use_colormap==True, use file set by $COLORMAP environment variable
Examples
--------
>>> import os
>>> from mindboggle.mio.plots import plot_mask_surface
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> mask_file = os.path.join(path, 'test_one_label.vtk')
>>> nonmask_value = 0 #-1
>>> masked_output = ''
>>> remove_nonmask = True
>>> program = 'vtkviewer'
>>> use_colormap = True
>>> colormap_file = '' #'/software/surface_cpp_tools/colormap.xml'
>>> plot_mask_surface(vtk_file, mask_file, nonmask_value, masked_output, remove_nonmask, program, use_colormap, colormap_file)
"""
import os
import numpy as np
from mindboggle.guts.mesh import remove_faces, reindex_faces_points
from mindboggle.guts.utilities import execute
from mindboggle.mio.plots import plot_surfaces
from mindboggle.mio.vtks import read_scalars, rewrite_scalars, \
read_vtk, write_vtk
#-------------------------------------------------------------------------
# Filter mesh with non-background values from a second (same-size) mesh:
#-------------------------------------------------------------------------
if mask_file:
mask, name = read_scalars(mask_file, True, True)
if not masked_output:
masked_output = os.path.join(os.getcwd(), 'temp.vtk')
file_to_plot = masked_output
#---------------------------------------------------------------------
# Remove nonmask-valued vertices:
#---------------------------------------------------------------------
if remove_nonmask:
#-----------------------------------------------------------------
# Load VTK files:
#-----------------------------------------------------------------
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file, True, True)
#-----------------------------------------------------------------
# Find mask indices, remove nonmask faces, and reindex:
#-----------------------------------------------------------------
Imask = [i for i,x in enumerate(mask) if x != nonmask_value]
mask_faces = remove_faces(faces, Imask)
mask_faces, points, \
original_indices = reindex_faces_points(mask_faces, points)
#-----------------------------------------------------------------
# Write VTK file with scalar values:
#-----------------------------------------------------------------
if np.ndim(scalars) == 1:
scalar_type = type(scalars[0]).__name__
elif np.ndim(scalars) == 2:
scalar_type = type(scalars[0][0]).__name__
else:
print("Undefined scalar type!")
write_vtk(file_to_plot, points, [], [], mask_faces,
scalars[original_indices].tolist(), scalar_names,
scalar_type=scalar_type)
else:
scalars, name = read_scalars(vtk_file, True, True)
scalars[mask == nonmask_value] = nonmask_value
rewrite_scalars(vtk_file, file_to_plot, scalars)
else:
file_to_plot = vtk_file
#-------------------------------------------------------------------------
# Display with vtkviewer.py:
#-------------------------------------------------------------------------
if program == 'vtkviewer':
plot_surfaces(file_to_plot, use_colormap=use_colormap,
colormap_file=colormap_file)
#-------------------------------------------------------------------------
# Display with mayavi2:
#-------------------------------------------------------------------------
elif program == 'mayavi2':
cmd = ["mayavi2", "-d", file_to_plot, "-m", "Surface", "&"]
execute(cmd, 'os')
def relabel_surface(vtk_file,
hemi='',
old_labels=[],
new_labels=[],
erase_remaining=True,
erase_labels=[],
erase_value=-1,
output_file=''):
"""
Relabel surface in a VTK file.
Parameters
----------
vtk_file : string
input labeled VTK file
hemi : string
hemisphere ('lh' or 'rh' or '')
if set, add 1000 to left and 2000 to right hemisphere labels;
old_labels : list of integers
old labels (empty list if labels drawn from vtk scalars);
may be used in conjunction with hemi
new_labels : list of integers
new labels (empty list if labels drawn from vtk scalars);
may be used in conjunction with hemi
erase_remaining : Boolean
set all values not in old_labels to erase_value?
erase_labels : list of integers
values to erase (set to erase_value)
erase_value : integer
set vertices with labels in erase_labels to this value
output_file : string
new vtk file name
Returns
-------
output_file : string
new vtk file name
Examples
--------
>>> import os
>>> from mindboggle.guts.relabel import relabel_surface
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> hemi = 'lh'
>>> old_labels = [1003,1009,1030]
>>> new_labels = [3,9,30]
>>> erase_remaining = False
>>> erase_labels = [0]
>>> erase_value = -1
>>> output_file = ''
>>> #
>>> relabel_surface(vtk_file, hemi, old_labels, new_labels, erase_remaining, erase_labels, erase_value, output_file)
>>> # View
>>> plot_surfaces('relabeled_FreeSurfer_cortex_labels.vtk')
>>> #plot_surfaces('relabeled_rh.labels.DKT31.manual.vtk')
"""
import os
import numpy as np
from mindboggle.mio.vtks import read_vtk, write_vtk
# Load labeled vtk surfaces:
faces, lines, indices, points, npoints, scalars, \
name, input_vtk = read_vtk(vtk_file, return_first=True, return_array=True)
new_scalars = scalars[:]
# Raise an error if inputs set incorrectly:
if (new_labels and not old_labels) or \
(hemi and hemi not in ['lh','rh']) or \
(new_labels and len(old_labels) != len(new_labels)) or \
(erase_remaining and not old_labels):
raise IOError("Please check inputs for relabel_surface().")
# Loop through unique labels in scalars:
ulabels = np.unique(scalars)
for label in ulabels:
I = np.where(scalars == label)[0]
# If label in erase_labels list, replace with erase_value:
if label in erase_labels:
new_scalars[I] = erase_value
# If label in old_labels list, replace with corresponding new label,
# and if hemi set, add 1000 or 2000 to the new label:
elif label in old_labels and (len(old_labels) == len(new_labels)):
new_label = new_labels[old_labels.index(label)]
if hemi == 'lh':
new_scalars[I] = 1000 + new_label
elif hemi == 'rh':
new_scalars[I] = 2000 + new_label
else:
new_scalars[I] = new_label
# If labels not set then optionally add hemi value:
elif hemi and not new_labels:
if hemi == 'lh':
new_scalars[I] = 1000 + label
elif hemi == 'rh':
new_scalars[I] = 2000 + label
# If label unaccounted for and erase_remaining, set to erase_value:
elif erase_remaining:
new_scalars[I] = erase_value
# Ensure that the new scalars are integer values:
new_scalars = [int(x) for x in new_scalars]
# Write output VTK file:
if not output_file:
output_file = os.path.join(os.getcwd(),
'relabeled_' + os.path.basename(vtk_file))
write_vtk(output_file,
points,
indices,
lines,
faces, [new_scalars], ['Labels'],
scalar_type='int')
if not os.path.exists(output_file):
s = "relabel_surface() did not create " + output_file + "."
raise (IOError(s))
return output_file
def extract_folds(depth_file, min_vertices=10000, min_fold_size=50,
do_fill_holes=False, min_hole_depth=0.001,
save_file=False):
"""
Use depth to extract folds from a triangular surface mesh.
Steps ::
1. Compute histogram of depth measures.
2. Define a depth threshold and find the deepest vertices.
3. Segment deep vertices as an initial set of folds.
4. Remove small folds.
5. Find and fill holes in the folds (optional).
6. Renumber folds.
Step 2 ::
To extract an initial set of deep vertices from the surface mesh,
we anticipate that there will be a rapidly decreasing distribution
of low depth values (on the outer surface) with a long tail
of higher depth values (in the folds), so we smooth the histogram's
bin values, convolve to compute slopes, and find the depth value
for the first bin with slope = 0. This is our threshold.
Step 5 ::
The folds could have holes in areas shallower than the depth threshold.
Calling fill_holes() could accidentally include very shallow areas
(in an annulus-shaped fold, for example), so we include the argument
exclude_range to check for any values from zero to min_hole_depth;
holes are not filled if they contains values within this range.
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
min_fold_size : integer
minimum fold size (number of vertices)
do_fill_holes : Boolean
fill holes in the folds?
min_hole_depth : float
largest non-zero depth value that will stop a hole from being filled
save_file : Boolean
save output VTK file?
Returns
-------
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_folds : int
number of folds
depth_threshold : float
threshold defining the minimum depth for vertices to be in a fold
bins : list of integers
histogram bins: each is the number of vertices within a range of depth values
bin_edges : list of floats
histogram bin edge values defining the bin ranges of depth values
folds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> import os
>>> import numpy as np
>>> import pylab
>>> from scipy.ndimage.filters import gaussian_filter1d
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.guts.mesh import find_neighbors_from_file
>>> from mindboggle.mio.plots import plot_surfaces
>>> from mindboggle.features.folds import extract_folds
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = 'travel_depth.vtk' #os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> neighbor_lists = find_neighbors_from_file(depth_file)
>>> min_vertices = 10000
>>> min_fold_size = 50
>>> do_fill_holes = False #True
>>> min_hole_depth = 0.001
>>> save_file = True
>>> #
>>> folds, n_folds, thr, bins, bin_edges, folds_file = extract_folds(depth_file,
>>> min_vertices, min_fold_size, do_fill_holes, min_hole_depth, save_file)
>>> #
>>> # View folds:
>>> plot_surfaces('folds.vtk')
>>> # Plot histogram and depth threshold:
>>> depths, name = read_scalars(depth_file)
>>> nbins = np.round(len(depths) / 100.0)
>>> a,b,c = pylab.hist(depths, bins=nbins)
>>> pylab.plot(thr*np.ones((100,1)), np.linspace(0, max(bins), 100), 'r.')
>>> pylab.show()
>>> # Plot smoothed histogram:
>>> bins_smooth = gaussian_filter1d(bins.tolist(), 5)
>>> pylab.plot(range(len(bins)), bins, '.', range(len(bins)), bins_smooth,'-')
>>> pylab.show()
"""
import os
import sys
import numpy as np
from time import time
from scipy.ndimage.filters import gaussian_filter1d
from mindboggle.mio.vtks import rewrite_scalars, read_vtk
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.morph import fill_holes
from mindboggle.guts.segment import segment
print("Extract folds in surface mesh")
t0 = time()
#-------------------------------------------------------------------------
# Load depth values for all vertices
#-------------------------------------------------------------------------
faces, lines, indices, points, npoints, depths, name, input_vtk = read_vtk(depth_file,
return_first=True, return_array=True)
#-------------------------------------------------------------------------
# Find neighbors for each vertex
#-------------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
#-------------------------------------------------------------------------
# Compute histogram of depth measures
#-------------------------------------------------------------------------
if npoints > min_vertices:
nbins = np.round(npoints / 100.0)
else:
sys.err(" Expecting at least {0} vertices to create depth histogram".
format(min_vertices))
bins, bin_edges = np.histogram(depths, bins=nbins)
#-------------------------------------------------------------------------
# Anticipating that there will be a rapidly decreasing distribution
# of low depth values (on the outer surface) with a long tail of higher
# depth values (in the folds), smooth the bin values (Gaussian), convolve
# to compute slopes, and find the depth for the first bin with slope = 0.
#-------------------------------------------------------------------------
bins_smooth = gaussian_filter1d(bins.tolist(), 5)
window = [-1, 0, 1]
bin_slopes = np.convolve(bins_smooth, window, mode='same') / (len(window) - 1)
ibins0 = np.where(bin_slopes == 0)[0]
if ibins0.shape:
depth_threshold = bin_edges[ibins0[0]]
else:
depth_threshold = np.median(depths)
#-------------------------------------------------------------------------
# Find the deepest vertices
#-------------------------------------------------------------------------
indices_deep = [i for i,x in enumerate(depths) if x >= depth_threshold]
if indices_deep:
#---------------------------------------------------------------------
# Segment deep vertices as an initial set of folds
#---------------------------------------------------------------------
print(" Segment vertices deeper than {0:.2f} as folds".format(depth_threshold))
t1 = time()
folds = segment(indices_deep, neighbor_lists)
# Slightly slower alternative -- fill boundaries:
#regions = -1 * np.ones(len(points))
#regions[indices_deep] = 1
#folds = segment_by_filling_borders(regions, neighbor_lists)
print(' ...Segmented folds ({0:.2f} seconds)'.format(time() - t1))
#---------------------------------------------------------------------
# Remove small folds
#---------------------------------------------------------------------
if min_fold_size > 1:
print(' Remove folds smaller than {0}'.format(min_fold_size))
unique_folds = [x for x in np.unique(folds) if x != -1]
for nfold in unique_folds:
indices_fold = [i for i,x in enumerate(folds) if x == nfold]
if len(indices_fold) < min_fold_size:
folds[indices_fold] = -1
#---------------------------------------------------------------------
# Find and fill holes in the folds
# Note: Surfaces surrounded by folds can be mistaken for holes,
# so exclude_range includes outer surface values close to zero.
#---------------------------------------------------------------------
if do_fill_holes:
print(" Find and fill holes in the folds")
folds = fill_holes(folds, neighbor_lists, values=depths,
exclude_range=[0, min_hole_depth])
#---------------------------------------------------------------------
# Renumber folds so they are sequential
#---------------------------------------------------------------------
renumber_folds = -1 * np.ones(len(folds))
fold_numbers = [int(x) for x in np.unique(folds) if x != -1]
for i_fold, n_fold in enumerate(fold_numbers):
fold = [i for i,x in enumerate(folds) if x == n_fold]
renumber_folds[fold] = i_fold
folds = renumber_folds
n_folds = i_fold + 1
# Print statement
print(' ...Extracted {0} folds ({1:.2f} seconds)'.
format(n_folds, time() - t0))
else:
print(' No deep vertices')
folds = [int(x) for x in folds]
#-------------------------------------------------------------------------
# Return folds, number of folds, file name
#-------------------------------------------------------------------------
if save_file:
folds_file = os.path.join(os.getcwd(), 'folds.vtk')
rewrite_scalars(depth_file, folds_file, folds, 'folds', folds)
if not os.path.exists(folds_file):
raise(IOError(folds_file + " not found"))
else:
folds_file = None
return folds, n_folds, depth_threshold, bins, bin_edges, folds_file
def zernike_moments_per_label(vtk_file,
order=10,
exclude_labels=[-1],
scale_input=True,
decimate_fraction=0,
decimate_smooth=25,
verbose=False):
"""
Compute the Zernike moments per labeled region in a file.
Optionally decimate the input mesh.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
order : integer
number of moments to compute
exclude_labels : list of integers
labels to be excluded
scale_input : bool
translate and scale each object so it is bounded by a unit sphere?
(this is the expected input to zernike_moments())
decimate_fraction : float
fraction of mesh faces to remove for decimation (1 for no decimation)
decimate_smooth : integer
number of smoothing steps for decimation
verbose : bool
print statements?
Returns
-------
descriptors_lists : list of lists of floats
Zernike descriptors per label
label_list : list of integers
list of unique labels for which moments are computed
Examples
--------
>>> # Zernike moments per label of a FreeSurfer-labeled left cortex.
>>> # Uncomment "if label==22:" below to run example
>>> # for left postcentral (22) pial surface:
>>> import numpy as np
>>> from mindboggle.shapes.zernike.zernike import zernike_moments_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> order = 3
>>> exclude_labels = [-1]
>>> scale_input = True
>>> verbose = False
>>> descriptors_lists, label_list = zernike_moments_per_label(vtk_file,
... order, exclude_labels, scale_input, verbose)
>>> label_list[0:10]
[999, 1001, 1002, 1003, 1005, 1006, 1007, 1008, 1009, 1010]
>>> print(np.array_str(np.array(descriptors_lists[0]),
... precision=5, suppress_small=True))
[ 0.00587 0.01143 0.0031 0.00881 0.00107 0.00041]
>>> print(np.array_str(np.array(descriptors_lists[1]),
... precision=5, suppress_small=True))
[ 0.00004 0.00009 0.00003 0.00009 0.00002 0.00001]
>>> print(np.array_str(np.array(descriptors_lists[2]),
... precision=5, suppress_small=True))
[ 0.00144 0.00232 0.00128 0.00304 0.00084 0.00051]
>>> print(np.array_str(np.array(descriptors_lists[3]),
... precision=5, suppress_small=True))
[ 0.00393 0.006 0.00371 0.00852 0.00251 0.00153]
>>> print(np.array_str(np.array(descriptors_lists[4]),
... precision=5, suppress_small=True))
[ 0.00043 0.0003 0.00095 0.00051 0.00115 0.00116]
"""
import numpy as np
from mindboggle.mio.vtks import read_vtk
from mindboggle.guts.mesh import keep_faces
from mindboggle.shapes.zernike.zernike import zernike_moments
min_points_faces = 4
# ------------------------------------------------------------------------
# Read VTK surface mesh file:
# ------------------------------------------------------------------------
points, indices, lines, faces, labels, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# ------------------------------------------------------------------------
# Loop through labeled regions:
# ------------------------------------------------------------------------
ulabels = [x for x in np.unique(labels) if x not in exclude_labels]
label_list = []
descriptors_lists = []
for label in ulabels:
#if label == 1022: # 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# --------------------------------------------------------------------
# Determine the indices per label:
# --------------------------------------------------------------------
Ilabel = [i for i, x in enumerate(labels) if x == label]
if verbose:
print(' {0} vertices for label {1}'.format(len(Ilabel), label))
if len(Ilabel) > min_points_faces:
# ----------------------------------------------------------------
# Remove background faces:
# ----------------------------------------------------------------
pick_faces = keep_faces(faces, Ilabel)
if len(pick_faces) > min_points_faces:
# ------------------------------------------------------------
# Compute Zernike moments for the label:
# ------------------------------------------------------------
descriptors = zernike_moments(points, pick_faces, order,
scale_input, decimate_fraction,
decimate_smooth, verbose)
# ------------------------------------------------------------
# Append to a list of lists of spectra:
# ------------------------------------------------------------
descriptors_lists.append(descriptors)
label_list.append(label)
return descriptors_lists, label_list
def extract_sulci(labels_file, folds_or_file, hemi, min_boundary=1,
sulcus_names=[]):
"""
Identify sulci from folds in a brain surface according to a labeling
protocol that includes a list of label pairs defining each sulcus.
A fold is a group of connected, deep vertices.
Steps for each fold ::
1. Remove fold if it has fewer than two labels.
2. Remove fold if its labels do not contain a sulcus label pair.
3. Find vertices with labels that are in only one of the fold's
label boundary pairs. Assign the vertices the sulcus with the label
pair if they are connected to the label boundary for that pair.
4. If there are remaining vertices, segment into sets of vertices
connected to label boundaries, and assign a unique ID to each set.
Parameters
----------
labels_file : string
file name for surface mesh VTK containing labels for all vertices
folds_or_file : list or string
fold number for each vertex / name of VTK file containing fold scalars
hemi : string
hemisphere abbreviation in {'lh', 'rh'} for sulcus labels
min_boundary : integer
minimum number of vertices for a sulcus label boundary segment
sulcus_names : list of strings
names of sulci
Returns
-------
sulci : list of integers
sulcus numbers for all vertices (-1 for non-sulcus vertices)
n_sulci : integers
number of sulci
sulci_file : string
output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> # Load labels, folds, neighbor lists, and sulcus names and label pairs
>>> labels_file = os.path.join(path, 'arno', 'labels', 'relabeled_lh.DKTatlas40.gcs.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds_or_file, name = read_scalars(folds_file)
>>> hemi = 'lh'
>>> min_boundary = 10
>>> sulcus_names = []
>>> #
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file, hemi, min_boundary, sulcus_names)
>>> # View:
>>> plot_surfaces('sulci.vtk')
"""
import os
from time import time
import numpy as np
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import extract_borders, propagate, segment
from mindboggle.mio.labels import DKTprotocol
# Load fold numbers if folds_or_file is a string:
if isinstance(folds_or_file, str):
folds, name = read_scalars(folds_or_file)
elif isinstance(folds_or_file, list):
folds = folds_or_file
dkt = DKTprotocol()
if hemi == 'lh':
pair_lists = dkt.left_sulcus_label_pair_lists
elif hemi == 'rh':
pair_lists = dkt.right_sulcus_label_pair_lists
else:
print("Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors:
faces, o1, o2, points, npoints, labels, o3, o4 = read_vtk(labels_file)
neighbor_lists = find_neighbors(faces, npoints)
# Array of sulcus IDs for fold vertices, initialized as -1.
# Since we do not touch gyral vertices and vertices whose labels
# are not in the label list, or vertices having only one label,
# their sulcus IDs will remain -1:
sulci = -1 * np.ones(npoints)
#-------------------------------------------------------------------------
# Loop through folds
#-------------------------------------------------------------------------
fold_numbers = [int(x) for x in np.unique(folds) if x != -1]
n_folds = len(fold_numbers)
print("Extract sulci from {0} folds...".format(n_folds))
t0 = time()
for n_fold in fold_numbers:
fold = [i for i,x in enumerate(folds) if x == n_fold]
len_fold = len(fold)
# List the labels in this fold:
fold_labels = [labels[x] for x in fold]
unique_fold_labels = [int(x) for x in np.unique(fold_labels)
if x != -1]
#---------------------------------------------------------------------
# NO MATCH -- fold has fewer than two labels
#---------------------------------------------------------------------
if len(unique_fold_labels) < 2:
# Ignore: sulci already initialized with -1 values:
if not unique_fold_labels:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has no labels".
format(n_fold, len_fold))
else:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has only one label ({2})".
format(n_fold, len_fold, unique_fold_labels[0]))
# Ignore: sulci already initialized with -1 values
else:
# Find all label boundary pairs within the fold:
indices_fold_pairs, fold_pairs, unique_fold_pairs = \
extract_borders(fold, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
# Find fold label pairs in the protocol (pairs are already sorted):
fold_pairs_in_protocol = [x for x in unique_fold_pairs
if x in dkt.unique_sulcus_label_pairs]
if unique_fold_labels:
print(" Fold {0} labels: {1} ({2} vertices)".format(n_fold,
', '.join([str(x) for x in unique_fold_labels]),
len_fold))
#-----------------------------------------------------------------
# NO MATCH -- fold has no sulcus label pair
#-----------------------------------------------------------------
if not fold_pairs_in_protocol:
print(" Fold {0}: NO MATCH -- fold has no sulcus label pair".
format(n_fold, len_fold))
#-----------------------------------------------------------------
# Possible matches
#-----------------------------------------------------------------
else:
print(" Fold {0} label pairs in protocol: {1}".format(n_fold,
', '.join([str(x) for x in fold_pairs_in_protocol])))
# Labels in the protocol (includes repeats across label pairs):
labels_in_pairs = [x for lst in fold_pairs_in_protocol
for x in lst]
# Labels that appear in one or more sulcus label boundary:
unique_labels = []
nonunique_labels = []
for label in np.unique(labels_in_pairs):
if len([x for x in labels_in_pairs if x == label]) == 1:
unique_labels.append(label)
else:
nonunique_labels.append(label)
#-------------------------------------------------------------
# Vertices whose labels are in only one sulcus label pair
#-------------------------------------------------------------
# Find vertices with a label that is in only one of the fold's
# label pairs (the other label in the pair can exist in other
# pairs). Assign the vertices the sulcus with the label pair
# if they are connected to the label boundary for that pair.
#-------------------------------------------------------------
if unique_labels:
for pair in fold_pairs_in_protocol:
# If one or both labels in label pair is/are unique:
unique_labels_in_pair = [x for x in pair
if x in unique_labels]
n_unique = len(unique_labels_in_pair)
if n_unique:
ID = None
for i, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
if pair in pair_list:
ID = i
break
if ID:
# Seeds from label boundary vertices
# (fold_pairs and pair already sorted):
indices_pair = [x for i,x
in enumerate(indices_fold_pairs)
if fold_pairs[i] == pair]
# Vertices with unique label(s) in pair:
indices_unique_labels = [fold[i]
for i,x in enumerate(fold_labels)
if x in dkt.unique_sulcus_label_pairs]
# Propagate from seeds to labels in label pair:
sulci2 = segment(indices_unique_labels,
neighbor_lists,
min_region_size=1,
seed_lists=[indices_pair],
keep_seeding=False,
spread_within_labels=True,
labels=labels)
sulci[sulci2 != -1] = ID
# Print statement:
if n_unique == 1:
ps1 = '1 label'
else:
ps1 = 'Both labels'
if len(sulcus_names):
ps2 = sulcus_names[ID]
else:
ps2 = ''
print(" {0} unique to one fold pair: "
"{1} {2}".
format(ps1, ps2, unique_labels_in_pair))
#-------------------------------------------------------------
# Vertex labels shared by multiple label pairs
#-------------------------------------------------------------
# Propagate labels from label borders to vertices with labels
# that are shared by multiple label pairs in the fold.
#-------------------------------------------------------------
if len(nonunique_labels):
# For each label shared by different label pairs:
for label in nonunique_labels:
# Print statement:
print(" Propagate sulcus borders with label {0}".
format(int(label)))
# Construct seeds from label boundary vertices:
seeds = -1 * np.ones(len(points))
for ID, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
label_pairs = [x for x in pair_list if label in x]
for label_pair in label_pairs:
indices_pair = [x for i,x
in enumerate(indices_fold_pairs)
if np.sort(fold_pairs[i]).
tolist() == label_pair]
if indices_pair:
# Do not include short boundary segments:
if min_boundary > 1:
indices_pair2 = []
seeds2 = segment(indices_pair,
neighbor_lists)
useeds2 = [x for x in
np.unique(seeds2)
if x != -1]
for seed2 in useeds2:
iseed2 = [i for i,x
in enumerate(seeds2)
if x == seed2]
if len(iseed2) >= min_boundary:
indices_pair2.extend(iseed2)
else:
if len(iseed2) == 1:
print(" Remove "
"assignment "
"of ID {0} from "
"1 vertex".
format(seed2))
else:
print(" Remove "
"assignment "
"of ID {0} from "
"{1} vertices".
format(seed2,
len(iseed2)))
indices_pair = indices_pair2
# Assign sulcus IDs to seeds:
seeds[indices_pair] = ID
# Identify vertices with the label:
label_array = -1 * np.ones(len(points))
indices_label = [fold[i] for i,x
in enumerate(fold_labels)
if x == label]
if len(indices_label):
label_array[indices_label] = 1
# Propagate from seeds to vertices with label:
#indices_seeds = []
#for seed in range(int(max(seeds))+1):
# indices_seeds.append([i for i,x
# in enumerate(seeds)
# if x == seed])
#sulci2 = segment(indices_label, neighbor_lists,
# 50, indices_seeds, False, True,
# labels)
sulci2 = propagate(points, faces,
label_array, seeds, sulci,
max_iters=10000,
tol=0.001, sigma=5)
sulci[sulci2 != -1] = sulci2[sulci2 != -1]
#-------------------------------------------------------------------------
# Print out assigned sulci
#-------------------------------------------------------------------------
sulcus_numbers = [int(x) for x in np.unique(sulci) if x != -1]
# if not np.isnan(x)]
n_sulci = len(sulcus_numbers)
print("Extracted {0} sulci from {1} folds ({2:.1f}s):".
format(n_sulci, n_folds, time()-t0))
if sulcus_names:
for sulcus_number in sulcus_numbers:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
elif sulcus_numbers:
print(" " + ", ".join([str(x) for x in sulcus_numbers]))
#-------------------------------------------------------------------------
# Print out unresolved sulci
#-------------------------------------------------------------------------
unresolved = [i for i in range(len(pair_lists))
if i not in sulcus_numbers]
if len(unresolved) == 1:
print("The following sulcus is unaccounted for:")
else:
print("The following {0} sulci are unaccounted for:".
format(len(unresolved)))
if sulcus_names:
for sulcus_number in unresolved:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in unresolved]))
#-------------------------------------------------------------------------
# Return sulci, number of sulci, and file name
#-------------------------------------------------------------------------
sulci = [int(x) for x in sulci]
sulci_file = os.path.join(os.getcwd(), 'sulci.vtk')
rewrite_scalars(labels_file, sulci_file, sulci, 'sulci', sulci)
if not os.path.exists(sulci_file):
raise(IOError(sulci_file + " not found"))
return sulci, n_sulci, sulci_file
def find_depth_threshold(depth_file, min_vertices=10000, verbose=False):
"""
Find depth threshold to extract folds from a triangular surface mesh.
Steps ::
1. Compute histogram of depth measures.
2. Define a depth threshold and find the deepest vertices.
To extract an initial set of deep vertices from the surface mesh,
we anticipate that there will be a rapidly decreasing distribution
of low depth values (on the outer surface) with a long tail
of higher depth values (in the folds), so we smooth the histogram's
bin values, convolve to compute slopes, and find the depth value
for the first bin with slope = 0. This is our threshold.
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
min_vertices : integer
minimum number of vertices
verbose : bool
print statements?
Returns
-------
depth_threshold : float
threshold defining the minimum depth for vertices to be in a fold
bins : list of integers
histogram bins: each is the number of vertices within a range of depth values
bin_edges : list of floats
histogram bin edge values defining the bin ranges of depth values
Examples
--------
>>> import numpy as np
>>> from mindboggle.features.folds import find_depth_threshold
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> min_vertices = 10000
>>> verbose = False
>>> depth_threshold, bins, bin_edges = find_depth_threshold(depth_file,
... min_vertices, verbose)
>>> np.float("{0:.{1}f}".format(depth_threshold, 5))
2.36089
View threshold histogram plots (skip test):
>>> def vis():
... import numpy as np
... import pylab
... from scipy.ndimage.filters import gaussian_filter1d
... from mindboggle.mio.vtks import read_scalars
... # Plot histogram and depth threshold:
... depths, name = read_scalars(depth_file)
... nbins = np.round(len(depths) / 100.0)
... a,b,c = pylab.hist(depths, bins=nbins)
... pylab.plot(depth_threshold * np.ones((100,1)),
... np.linspace(0, max(bins), 100), 'r.')
... pylab.title('Histogram of depth values with threshold')
... pylab.xlabel('Depth')
... pylab.ylabel('Number of vertices')
... pylab.show()
... # Plot smoothed histogram:
... bins_smooth = gaussian_filter1d(bins.tolist(), 5)
... pylab.plot(list(range(len(bins))), bins, '.',
... list(range(len(bins))), bins_smooth,'-')
... pylab.title('Smoothed histogram of depth values')
... pylab.show()
>>> vis() # doctest: +SKIP
"""
import numpy as np
from scipy.ndimage.filters import gaussian_filter1d
from mindboggle.mio.vtks import read_vtk
# ------------------------------------------------------------------------
# Load depth values for all vertices:
# ------------------------------------------------------------------------
points, indices, lines, faces, depths, scalar_names, npoints, \
input_vtk = read_vtk(depth_file, return_first=True, return_array=True)
# ------------------------------------------------------------------------
# Compute histogram of depth measures:
# ------------------------------------------------------------------------
if npoints > min_vertices:
nbins = np.int(np.round(npoints / 100.0))
else:
raise IOError(" Expecting at least {0} vertices to create "
"depth histogram".format(min_vertices))
bins, bin_edges = np.histogram(depths, bins=nbins)
# ------------------------------------------------------------------------
# Anticipating that there will be a rapidly decreasing distribution
# of low depth values (on the outer surface) with a long tail of higher
# depth values (in the folds), smooth the bin values (Gaussian), convolve
# to compute slopes, and find the depth for the first bin with slope = 0.
# ------------------------------------------------------------------------
bins_smooth = gaussian_filter1d(bins.tolist(), 5)
window = [-1, 0, 1]
bin_slopes = np.convolve(bins_smooth, window, mode='same') / \
(len(window) - 1)
ibins0 = np.where(bin_slopes == 0)[0]
if ibins0.shape:
depth_threshold = bin_edges[ibins0[0]]
else:
depth_threshold = np.median(depths)
# Print statement:
if verbose:
print(' Depth threshold: {0}'.format(depth_threshold))
return depth_threshold, bins, bin_edges
def plot_mask_surface(vtk_file,
mask_file='',
nonmask_value=-1,
masked_output='',
remove_nonmask=False,
program='vtkviewer',
use_colormap=False,
colormap_file='',
background_value=-1):
"""
Use vtkviewer or mayavi2 to visualize VTK surface mesh data.
If a mask_file is provided, a temporary masked file is saved,
and it is this file that is viewed.
If using vtkviewer, optionally provide colormap file
or set $COLORMAP environment variable.
Parameters
----------
vtk_file : string
name of VTK surface mesh file
mask_file : string
name of VTK surface mesh file to mask vtk_file vertices
nonmask_value : integer
nonmask (usually background) value
masked_output : string
temporary masked output file name
remove_nonmask : bool
remove vertices that are not in mask? (otherwise assign nonmask_value)
program : string {'vtkviewer', 'mayavi2'}
program to visualize VTK file
use_colormap : bool
use Paraview-style XML colormap file set by $COLORMAP env variable?
colormap_file : string
use colormap in given file if use_colormap==True? if empty and
use_colormap==True, use file set by $COLORMAP environment variable
background_value : integer or float
background value
Examples
--------
>>> import os
>>> from mindboggle.mio.plots import plot_mask_surface
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['freesurfer_labels'], '', '.vtk')
>>> os.rename(vtk_file, vtk_file + '.nii.gz')
>>> vtk_file = vtk_file + '.nii.gz'
>>> mask_file = ''
>>> nonmask_value = 0 #-1
>>> masked_output = ''
>>> remove_nonmask = True
>>> program = 'vtkviewer'
>>> use_colormap = True
>>> colormap_file = ''
>>> background_value = -1
>>> plot_mask_surface(vtk_file, mask_file, nonmask_value, masked_output,
... remove_nonmask, program, use_colormap, colormap_file,
... background_value) # doctest: +SKIP
"""
import os
import numpy as np
from mindboggle.guts.mesh import keep_faces, reindex_faces_points
from mindboggle.guts.utilities import execute
from mindboggle.mio.plots import plot_surfaces
from mindboggle.mio.vtks import read_scalars, rewrite_scalars, \
read_vtk, write_vtk
# ------------------------------------------------------------------------
# Filter mesh with non-background values from a second (same-size) mesh:
# ------------------------------------------------------------------------
if mask_file:
mask, name = read_scalars(mask_file, True, True)
if not masked_output:
masked_output = os.path.join(os.getcwd(), 'temp.vtk')
file_to_plot = masked_output
# --------------------------------------------------------------------
# Remove nonmask-valued vertices:
# --------------------------------------------------------------------
if remove_nonmask:
# ----------------------------------------------------------------
# Load VTK files:
# ----------------------------------------------------------------
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file, True, True)
# ----------------------------------------------------------------
# Find mask indices, remove nonmask faces, and reindex:
# ----------------------------------------------------------------
Imask = [i for i, x in enumerate(mask) if x != nonmask_value]
mask_faces = keep_faces(faces, Imask)
mask_faces, points, \
original_indices = reindex_faces_points(mask_faces, points)
# ----------------------------------------------------------------
# Write VTK file with scalar values:
# ----------------------------------------------------------------
if np.ndim(scalars) == 1:
scalar_type = type(scalars[0]).__name__
elif np.ndim(scalars) == 2:
scalar_type = type(scalars[0][0]).__name__
else:
print("Undefined scalar type!")
write_vtk(file_to_plot,
points, [], [],
mask_faces,
scalars[original_indices].tolist(),
scalar_names,
scalar_type=scalar_type)
else:
scalars, name = read_scalars(vtk_file, True, True)
scalars[mask == nonmask_value] = nonmask_value
rewrite_scalars(vtk_file, file_to_plot, scalars, ['scalars'], [],
background_value)
else:
file_to_plot = vtk_file
# ------------------------------------------------------------------------
# Display with vtkviewer.py:
# ------------------------------------------------------------------------
if program == 'vtkviewer':
plot_surfaces(file_to_plot,
use_colormap=use_colormap,
colormap_file=colormap_file)
# ------------------------------------------------------------------------
# Display with mayavi2:
# ------------------------------------------------------------------------
elif program == 'mayavi2':
cmd = ["mayavi2", "-d", file_to_plot, "-m", "Surface", "&"]
execute(cmd, 'os')
def write_face_vertex_averages(input_file, output_table='', area_file=''):
"""
Make table of average vertex values per face
(divided by face area if area_file provided).
Parameters
----------
input_file : string
name of VTK file with scalars to average
area_file : string
name of VTK file with surface area scalar values
output_table : string
output table filename
Returns
-------
output_table : string
output table filename
Examples
--------
>>> from mindboggle.mio.tables import write_face_vertex_averages
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> output_table = ''
>>> output_table = write_face_vertex_averages(input_file, output_table,
... area_file)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_vtk, read_scalars
points, indices, lines, faces, scalars, scalar_names, \
npoints, input_vtk = read_vtk(input_file, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
# --------------------------------------------------------------------
# For each face, average vertex values:
# --------------------------------------------------------------------
columns = []
for face in faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
# ----------------------------------------------------------------
# Write to table:
# ----------------------------------------------------------------
if not output_table:
output_table = os.path.join(os.getcwd(), 'average_face_values.csv')
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False, encoding='utf-8')
if not os.path.exists(output_table):
raise IOError(output_table + " not found")
return output_table
def extract_sulci(labels_file,
folds_or_file,
hemi,
min_boundary=1,
sulcus_names=[],
save_file=False,
output_file='',
background_value=-1,
verbose=False):
"""
Identify sulci from folds in a brain surface according to a labeling
protocol that includes a list of label pairs defining each sulcus.
Since folds are defined as deep, connected areas of a surface, and since
folds may be connected to each other in ways that differ across brains,
there usually does not exist a one-to-one mapping between folds of one
brain and those of another. To address the correspondence problem then,
we need to find just those portions of the folds that correspond across
brains. To accomplish this, Mindboggle segments folds into sulci, which
do have a one-to-one correspondence across non-pathological brains.
Mindboggle defines a sulcus as a folded portion of cortex whose opposing
banks are labeled with one or more sulcus label pairs in the DKT labeling
protocol, where each label pair is unique to one sulcus and represents
a boundary between two adjacent gyri, and each vertex has one gyrus label.
This function assigns vertices in a fold to a sulcus in one of two cases.
In the first case, vertices whose labels are in only one label pair in
the fold are assigned to the label pair’s sulcus if they are connected
through similarly labeled vertices to the boundary between the two labels.
In the second case, the segment_regions function propagates labels from
label borders to vertices whose labels are in multiple label pairs in the
fold.
Steps for each fold ::
1. Remove fold if it has fewer than two labels.
2. Remove fold if its labels do not contain a sulcus label pair.
3. Find vertices with labels that are in only one of the fold's
label boundary pairs. Assign the vertices the sulcus with the label
pair if they are connected to the label boundary for that pair.
4. If there are remaining vertices, segment into sets of vertices
connected to label boundaries, and assign a unique ID to each set.
Parameters
----------
labels_file : string
file name for surface mesh VTK containing labels for all vertices
folds_or_file : numpy array, list or string
fold number for each vertex / name of VTK file containing fold scalars
hemi : string
hemisphere abbreviation in {'lh', 'rh'} for sulcus labels
min_boundary : integer
minimum number of vertices for a sulcus label boundary segment
sulcus_names : list of strings
names of sulci
save_file : bool
save output VTK file?
output_file : string
name of output file in VTK format
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
sulci : list of integers
sulcus numbers for all vertices (-1 for non-sulcus vertices)
n_sulci : integers
number of sulci
sulci_file : string
output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> # Example 1: Extract sulcus from a fold with one sulcus label pair:
>>> import numpy as np
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> # Load labels, folds, neighbor lists, and sulcus names and label pairs
>>> labels_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> folds_or_file, name = read_scalars(folds_file, True, True)
>>> save_file = True
>>> output_file = 'extract_sulci_fold4_1sulcus.vtk'
>>> background_value = -1
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [4] #[4, 6]
... i0 = [i for i,x in enumerate(folds_or_file) if x not in fold_numbers]
... folds_or_file[i0] = background_value
>>> hemi = 'lh'
>>> min_boundary = 10
>>> sulcus_names = []
>>> verbose = False
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
... hemi, min_boundary, sulcus_names, save_file, output_file,
... background_value, verbose)
>>> n_sulci # 23 # (if not limit_folds)
1
>>> lens = [len([x for x in sulci if x==y])
... for y in np.unique(sulci) if y != -1]
>>> lens[0:10] # [6358, 3288, 7612, 5205, 4414, 6251, 3493, 2566, 4436, 739] # (if not limit_folds)
[1151]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> output = 'extract_sulci_fold4_1sulcus_no_background.vtk'
>>> rewrite_scalars(sulci_file, output, sulci,
... 'sulci', sulci) # doctest: +SKIP
>>> plot_surfaces(output) # doctest: +SKIP
Example 2: Extract sulcus from a fold with multiple sulcus label pairs:
>>> folds_or_file, name = read_scalars(folds_file, True, True)
>>> output_file = 'extract_sulci_fold7_2sulci.vtk'
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [7] #[4, 6]
... i0 = [i for i,x in enumerate(folds_or_file) if x not in fold_numbers]
... folds_or_file[i0] = background_value
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
... hemi, min_boundary, sulcus_names, save_file, output_file,
... background_value, verbose)
>>> n_sulci # 23 # (if not limit_folds)
2
>>> lens = [len([x for x in sulci if x==y])
... for y in np.unique(sulci) if y != -1]
>>> lens[0:10] # [6358, 3288, 7612, 5205, 4414, 6251, 3493, 2566, 4436, 739] # (if not limit_folds)
[369, 93]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> output = 'extract_sulci_fold7_2sulci_no_background.vtk'
>>> rewrite_scalars(sulci_file, output, sulci,
... 'sulci', sulci) # doctest: +SKIP
>>> plot_surfaces(output) # doctest: +SKIP
"""
import os
from time import time
import numpy as np
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import extract_borders, propagate, segment_regions
from mindboggle.mio.labels import DKTprotocol
# Load fold numbers if folds_or_file is a string:
if isinstance(folds_or_file, str):
folds, name = read_scalars(folds_or_file)
elif isinstance(folds_or_file, list):
folds = folds_or_file
elif isinstance(folds_or_file, np.ndarray):
folds = folds_or_file.tolist()
dkt = DKTprotocol()
if hemi == 'lh':
pair_lists = dkt.left_sulcus_label_pair_lists
elif hemi == 'rh':
pair_lists = dkt.right_sulcus_label_pair_lists
else:
raise IOError(
"Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors:
points, indices, lines, faces, labels, scalar_names, npoints, \
input_vtk = read_vtk(labels_file)
neighbor_lists = find_neighbors(faces, npoints)
# Array of sulcus IDs for fold vertices, initialized as -1.
# Since we do not touch gyral vertices and vertices whose labels
# are not in the label list, or vertices having only one label,
# their sulcus IDs will remain -1:
sulci = background_value * np.ones(npoints)
# ------------------------------------------------------------------------
# Loop through folds
# ------------------------------------------------------------------------
fold_numbers = [int(x) for x in np.unique(folds) if x != background_value]
n_folds = len(fold_numbers)
if verbose:
print("Extract sulci from {0} folds...".format(n_folds))
t0 = time()
for n_fold in fold_numbers:
fold_indices = [i for i, x in enumerate(folds) if x == n_fold]
len_fold = len(fold_indices)
# List the labels in this fold:
fold_labels = [labels[x] for x in fold_indices]
unique_fold_labels = [
int(x) for x in np.unique(fold_labels) if x != background_value
]
# --------------------------------------------------------------------
# NO MATCH -- fold has fewer than two labels
# --------------------------------------------------------------------
if verbose and len(unique_fold_labels) < 2:
# Ignore: sulci already initialized with -1 values:
if not unique_fold_labels:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has no labels".format(
n_fold, len_fold))
else:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has only one label ({2})".format(
n_fold, len_fold, unique_fold_labels[0]))
# Ignore: sulci already initialized with -1 values
else:
# Find all label boundary pairs within the fold:
indices_fold_pairs, fold_pairs, unique_fold_pairs = \
extract_borders(fold_indices, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
# Find fold label pairs in the protocol (pairs are already sorted):
fold_pairs_in_protocol = [
x for x in unique_fold_pairs
if x in dkt.unique_sulcus_label_pairs
]
if verbose and unique_fold_labels:
print(" Fold {0} labels: {1} ({2} vertices)".format(
n_fold, ', '.join([str(x) for x in unique_fold_labels]),
len_fold))
# ----------------------------------------------------------------
# NO MATCH -- fold has no sulcus label pair
# ----------------------------------------------------------------
if verbose and not fold_pairs_in_protocol:
print(" Fold {0}: NO MATCH -- fold has no sulcus label pair".
format(n_fold, len_fold))
# ----------------------------------------------------------------
# Possible matches
# ----------------------------------------------------------------
else:
if verbose:
print(" Fold {0} label pairs in protocol: {1}".format(
n_fold,
', '.join([str(x) for x in fold_pairs_in_protocol])))
# Labels in the protocol (includes repeats across label pairs):
labels_in_pairs = [
x for lst in fold_pairs_in_protocol for x in lst
]
# Labels that appear in one or more sulcus label boundary:
unique_labels = []
nonunique_labels = []
for label in np.unique(labels_in_pairs):
if len([x for x in labels_in_pairs if x == label]) == 1:
unique_labels.append(label)
else:
nonunique_labels.append(label)
# ------------------------------------------------------------
# Vertices whose labels are in only one sulcus label pair
# ------------------------------------------------------------
# Find vertices with a label that is in only one of the fold's
# label pairs (the other label in the pair can exist in other
# pairs). Assign the vertices the sulcus with the label pair
# if they are connected to the label boundary for that pair.
# ------------------------------------------------------------
if unique_labels:
for pair in fold_pairs_in_protocol:
# If one or both labels in label pair is/are unique:
unique_labels_in_pair = [
x for x in pair if x in unique_labels
]
n_unique = len(unique_labels_in_pair)
if n_unique:
ID = None
for i, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
if pair in pair_list:
ID = i
break
if ID:
# Seeds from label boundary vertices
# (fold_pairs and pair already sorted):
indices_pair = [
x for i, x in enumerate(indices_fold_pairs)
if fold_pairs[i] == pair
]
# Vertices with unique label(s) in pair:
indices_unique_labels = [
fold_indices[i]
for i, x in enumerate(fold_labels)
if x in unique_labels_in_pair
]
#dkt.unique_sulcus_label_pairs]
# Propagate sulcus ID from seeds to vertices
# with "unique" labels (only exist in one
# label pair in a fold); propagation ensures
# that sulci consist of contiguous vertices
# for each label boundary:
sulci2 = segment_regions(
indices_unique_labels,
neighbor_lists,
min_region_size=1,
seed_lists=[indices_pair],
keep_seeding=False,
spread_within_labels=True,
labels=labels,
label_lists=[],
values=[],
max_steps='',
background_value=background_value,
verbose=False)
sulci[sulci2 != background_value] = ID
# Print statement:
if verbose:
if n_unique == 1:
ps1 = 'One label'
else:
ps1 = 'Both labels'
if len(sulcus_names):
ps2 = sulcus_names[ID]
else:
ps2 = ''
print(" {0} unique to one fold pair: "
"{1} {2}".format(
ps1, ps2, unique_labels_in_pair))
# ------------------------------------------------------------
# Vertex labels shared by multiple label pairs
# ------------------------------------------------------------
# Propagate labels from label borders to vertices with labels
# that are shared by multiple label pairs in the fold.
# ------------------------------------------------------------
if len(nonunique_labels):
# For each label shared by different label pairs:
for label in nonunique_labels:
# Print statement:
if verbose:
print(
" Propagate sulcus borders with label {0}".
format(int(label)))
# Construct seeds from label boundary vertices:
seeds = background_value * np.ones(npoints)
for ID, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
label_pairs = [x for x in pair_list if label in x]
for label_pair in label_pairs:
indices_pair = [
x for i, x in enumerate(indices_fold_pairs)
if np.sort(fold_pairs[i]).tolist() ==
label_pair
]
if indices_pair:
# Do not include short boundary segments:
if min_boundary > 1:
indices_pair2 = []
seeds2 = segment_regions(
indices_pair, neighbor_lists, 1,
[], False, False, [], [], [], '',
background_value, verbose)
useeds2 = [
x for x in np.unique(seeds2)
if x != background_value
]
for seed2 in useeds2:
iseed2 = [
i for i, x in enumerate(seeds2)
if x == seed2
]
if len(iseed2) >= min_boundary:
indices_pair2.extend(iseed2)
elif verbose:
if len(iseed2) == 1:
print(" Remove "
"assignment "
"of ID {0} from "
"1 vertex".format(
seed2))
else:
print(
" Remove "
"assignment "
"of ID {0} from "
"{1} vertices".format(
seed2,
len(iseed2)))
indices_pair = indices_pair2
# Assign sulcus IDs to seeds:
seeds[indices_pair] = ID
# Identify vertices with the label:
indices_label = [
fold_indices[i] for i, x in enumerate(fold_labels)
if x == label
]
if len(indices_label):
# Propagate sulcus ID from seeds to vertices
# with a given shared label:
seg_vs_prop = False
if seg_vs_prop:
indices_seeds = []
for seed in [
x for x in np.unique(seeds)
if x != background_value
]:
indices_seeds.append([
i for i, x in enumerate(seeds)
if x == seed
])
sulci2 = segment_regions(
indices_label, neighbor_lists, 50,
indices_seeds, False, True, labels, [], [],
'', background_value, verbose)
else:
label_array = background_value * \
np.ones(npoints)
label_array[indices_label] = 1
sulci2 = propagate(
points,
faces,
label_array,
seeds,
sulci,
max_iters=10000,
tol=0.001,
sigma=5,
background_value=background_value,
verbose=verbose)
sulci[sulci2 != background_value] = \
sulci2[sulci2 != background_value]
sulcus_numbers = [
int(x) for x in np.unique(sulci) if x != background_value
]
n_sulci = len(sulcus_numbers)
# ------------------------------------------------------------------------
# Print statements
# ------------------------------------------------------------------------
if verbose:
if n_sulci == 1:
sulcus_str = 'sulcus'
else:
sulcus_str = 'sulci'
if n_folds == 1:
folds_str = 'fold'
else:
folds_str = 'folds'
print("Extracted {0} {1} from {2} {3} ({4:.1f}s):".format(
n_sulci, sulcus_str, n_folds, folds_str,
time() - t0))
if sulcus_names:
for sulcus_number in sulcus_numbers:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
elif sulcus_numbers:
print(" " + ", ".join([str(x) for x in sulcus_numbers]))
unresolved = [
i for i in range(len(pair_lists)) if i not in sulcus_numbers
]
if len(unresolved) == 1:
print("The following sulcus is unaccounted for:")
else:
print("The following {0} sulci are unaccounted for:".format(
len(unresolved)))
if sulcus_names:
for sulcus_number in unresolved:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in unresolved]))
# ------------------------------------------------------------------------
# Return sulci, number of sulci, and file name
# ------------------------------------------------------------------------
sulci = [int(x) for x in sulci]
sulci_file = os.path.join(os.getcwd(), 'sulci.vtk')
rewrite_scalars(labels_file, sulci_file, sulci, 'sulci', [],
background_value)
if not os.path.exists(sulci_file):
raise IOError(sulci_file + " not found")
return sulci, n_sulci, sulci_file
def extract_subfolds(depth_file,
folds,
min_size=10,
depth_factor=0.25,
depth_ratio=0.1,
tolerance=0.01,
save_file=False):
"""
Use depth to segment folds into subfolds in a triangular surface mesh.
Note ::
The function extract_sulci() performs about the same whether folds
or subfolds are used as input. The latter leads to some loss of
small subfolds and possibly holes for small subfolds in the middle
of other subfolds.
Note about the watershed() function:
The watershed() function performs individual seed growing from deep seeds,
repeats segmentation from the resulting seeds until each seed's segment
touches a boundary. The function segment() fills in the rest. Finally
segments are joined if their seeds are too close to each other.
Despite these precautions, the order of seed selection in segment() could
possibly influence the resulting borders between adjoining segments.
[The propagate() function is slower and insensitive to depth,
but is not biased by seed order.]
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
min_size : integer
minimum number of vertices for a subfold
depth_factor : float
watershed() depth_factor:
factor to determine whether to merge two neighboring watershed catchment
basins -- they are merged if the Euclidean distance between their basin
seeds is less than this fraction of the maximum Euclidean distance
between points having minimum and maximum depths
depth_ratio : float
watershed() depth_ratio:
the minimum fraction of depth for a neighboring shallower
watershed catchment basin (otherwise merged with the deeper basin)
tolerance : float
watershed() tolerance:
tolerance for detecting differences in depth between vertices
save_file : Boolean
save output VTK file?
Returns
-------
subfolds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_subfolds : int
number of subfolds
subfolds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.guts.mesh import find_neighbors_from_file
>>> from mindboggle.features.folds import extract_subfolds
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> min_size = 10
>>> depth_factor = 0.5
>>> depth_ratio = 0.1
>>> tolerance = 0.01
>>> #
>>> subfolds, n_subfolds, subfolds_file = extract_subfolds(depth_file,
>>> folds, min_size, depth_factor, depth_ratio, tolerance, True)
>>> #
>>> # View:
>>> rewrite_scalars(depth_file, 'subfolds.vtk', subfolds, 'subfolds', subfolds)
>>> plot_surfaces('subfolds.vtk')
"""
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import rewrite_scalars, read_vtk
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import segment, propagate, watershed
print("Segment folds into subfolds")
t0 = time()
#-------------------------------------------------------------------------
# Load depth values for all vertices
#-------------------------------------------------------------------------
points, indices, lines, faces, depths, scalar_names, npoints, \
input_vtk = read_vtk(depth_file, return_first=True, return_array=True)
#-------------------------------------------------------------------------
# Find neighbors for each vertex
#-------------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
#-------------------------------------------------------------------------
# Segment folds into "watershed basins"
#-------------------------------------------------------------------------
indices_folds = [i for i, x in enumerate(folds) if x != -1]
subfolds, seed_indices = watershed(depths,
points,
indices_folds,
neighbor_lists,
min_size,
depth_factor=0.25,
depth_ratio=0.1,
tolerance=0.01,
regrow=True)
# Print statement
n_subfolds = len([x for x in np.unique(subfolds) if x != -1])
print(' ...Extracted {0} subfolds ({1:.2f} seconds)'.format(
n_subfolds,
time() - t0))
#-------------------------------------------------------------------------
# Return subfolds, number of subfolds, file name
#-------------------------------------------------------------------------
if save_file:
subfolds_file = os.path.join(os.getcwd(), 'subfolds.vtk')
rewrite_scalars(depth_file, subfolds_file, subfolds, 'subfolds',
subfolds)
if not os.path.exists(subfolds_file):
raise (IOError(subfolds_file + " not found"))
else:
subfolds_file = None
return subfolds, n_subfolds, subfolds_file
def write_face_vertex_averages(input_file, output_table='', area_file=''):
"""
Make table of average vertex values per face
(divided by face area if area_file provided).
Parameters
----------
input_file : string
name of VTK file with scalars to average
area_file : string
name of VTK file with surface area scalar values
output_table : string
output table filename
Returns
-------
output_table : string
output table filename
Examples
--------
>>> from mindboggle.mio.tables import write_face_vertex_averages
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_file = fetch_data(urls['left_travel_depth'])
>>> area_file = fetch_data(urls['left_area'])
>>> output_table = ''
>>> output_table = write_face_vertex_averages(input_file, output_table,
... area_file)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_vtk, read_scalars
points, indices, lines, faces, scalars, scalar_names, \
npoints, input_vtk = read_vtk(input_file, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
#---------------------------------------------------------------------
# For each face, average vertex values:
#---------------------------------------------------------------------
columns = []
for face in faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
#-----------------------------------------------------------------
# Write to table:
#-----------------------------------------------------------------
if not output_table:
output_table = os.path.join(os.getcwd(), 'average_face_values.csv')
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False)
if not os.path.exists(output_table):
raise IOError(output_table + " not found")
return output_table
def write_average_face_values_per_label(input_indices_vtk,
input_values_vtk='', area_file='',
output_stem='', exclude_values=[-1], background_value=-1):
"""
Write out a separate VTK file for each integer
in (the first) scalar list of an input VTK file.
Optionally write the values drawn from a second VTK file.
Parameters
----------
input_indices_vtk : string
path of the input VTK file that contains indices as scalars
input_values_vtk : string
path of the input VTK file that contains values as scalars
output_stem : string
path and stem of the output VTK file
exclude_values : list or array
values to exclude
background_value : integer or float
background value in output VTK files
scalar_name : string
name of a lookup table of scalars values
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import write_average_face_values_per_label
>>> path = '/homedir/mindboggled'
>>> input_indices_vtk = os.path.join(path, 'Twins-2-1', 'labels', 'left_cortical_surface', 'freesurfer_cortex_labels.vtk')
>>> input_values_vtk = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'freesurfer_thickness.vtk')
>>> area_file = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'area.vtk')
>>> output_stem = 'labels_thickness'
>>> exclude_values = [-1]
>>> background_value = -1
>>> #
>>> write_average_face_values_per_label(input_indices_vtk,
>>> input_values_vtk, area_file, output_stem, exclude_values, background_value)
>>> #
>>> # View:
>>> #example_vtk = os.path.join(os.getcwd(), output_stem + '0.vtk')
>>> #from mindboggle.mio.plots import plot_surfaces
>>> #plot_surfaces(example_vtk)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_scalars, read_vtk, write_vtk
from mindboggle.guts.mesh import remove_faces
# Load VTK file:
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(input_indices_vtk, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
print("Explode the scalar list in {0}".
format(os.path.basename(input_indices_vtk)))
if input_values_vtk != input_indices_vtk:
values, name = read_scalars(input_values_vtk, True, True)
print("Explode the scalar list of values in {0} "
"with the scalar list of indices in {1}".
format(os.path.basename(input_values_vtk),
os.path.basename(input_indices_vtk)))
else:
values = np.copy(scalars)
# Loop through unique (non-excluded) scalar values:
unique_scalars = [int(x) for x in np.unique(scalars)
if x not in exclude_values]
for scalar in unique_scalars:
keep_indices = [x for sublst in faces for x in sublst]
new_faces = remove_faces(faces, keep_indices)
# Create array and indices for scalar value:
select_scalars = np.copy(scalars)
select_scalars[scalars != scalar] = background_value
scalar_indices = [i for i,x in enumerate(select_scalars) if x==scalar]
print(" Scalar {0}: {1} vertices".format(scalar, len(scalar_indices)))
#---------------------------------------------------------------------
# For each face, average vertex values:
#---------------------------------------------------------------------
output_table = os.path.join(os.getcwd(),
output_stem+str(scalar)+'.csv')
columns = []
for face in new_faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
#-----------------------------------------------------------------
# Write to table:
#-----------------------------------------------------------------
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False)
# Write VTK file with scalar value:
#output_vtk = os.path.join(os.getcwd(), output_stem + str(scalar) + '.vtk')
#write_vtk(output_vtk, points, indices, lines, new_faces,
# [select_values.tolist()], [output_scalar_name])
if not os.path.exists(output_table):
raise(IOError(output_table + " not found"))
def write_average_face_values_per_label(input_indices_vtk,
input_values_vtk='', area_file='', output_stem='',
exclude_values=[-1], background_value=-1, verbose=False):
"""
Write out a separate VTK file for each integer
in (the first) scalar list of an input VTK file.
Optionally write the values drawn from a second VTK file.
Parameters
----------
input_indices_vtk : string
path of the input VTK file that contains indices as scalars
input_values_vtk : string
path of the input VTK file that contains values as scalars
output_stem : string
path and stem of the output VTK file
exclude_values : list or array
values to exclude
background_value : integer or float
background value in output VTK files
scalar_name : string
name of a lookup table of scalars values
verbose : bool
print statements?
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import write_average_face_values_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_indices_vtk = fetch_data(urls['left_freesurfer_labels'])
>>> input_values_vtk = fetch_data(urls['left_mean_curvature'])
>>> area_file = fetch_data(urls['left_area'])
>>> output_stem = 'labels_thickness'
>>> exclude_values = [-1]
>>> background_value = -1
>>> verbose = False
>>> write_average_face_values_per_label(input_indices_vtk,
... input_values_vtk, area_file, output_stem, exclude_values,
... background_value, verbose)
View vtk file (skip test):
>>> from mindboggle.mio.plots import plot_surfaces
>>> example_vtk = os.path.join(os.getcwd(), output_stem + '0.vtk')
>>> plot_surfaces(example_vtk) # doctest: +SKIP
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_scalars, read_vtk, write_vtk
from mindboggle.guts.mesh import keep_faces
# Load VTK file:
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(input_indices_vtk, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
if verbose:
print("Explode the scalar list in {0}".
format(os.path.basename(input_indices_vtk)))
if input_values_vtk != input_indices_vtk:
if verbose:
print("Explode the scalar list of values in {0} "
"with the scalar list of indices in {1}".
format(os.path.basename(input_values_vtk),
os.path.basename(input_indices_vtk)))
# Loop through unique (non-excluded) scalar values:
unique_scalars = [int(x) for x in np.unique(scalars)
if x not in exclude_values]
for scalar in unique_scalars:
keep_indices = [x for sublst in faces for x in sublst]
new_faces = keep_faces(faces, keep_indices)
# Create array and indices for scalar value:
select_scalars = np.copy(scalars)
select_scalars[scalars != scalar] = background_value
scalar_indices = [i for i,x in enumerate(select_scalars) if x==scalar]
if verbose:
print(" Scalar {0}: {1} vertices".format(scalar,
len(scalar_indices)))
#---------------------------------------------------------------------
# For each face, average vertex values:
#---------------------------------------------------------------------
output_table = os.path.join(os.getcwd(),
output_stem+str(scalar)+'.csv')
columns = []
for face in new_faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
#-----------------------------------------------------------------
# Write to table:
#-----------------------------------------------------------------
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False)
if not os.path.exists(output_table):
raise IOError(output_table + " not found")
def spectrum_from_file(vtk_file,
spectrum_size=10,
exclude_labels=[-1],
normalization=None,
area_file=''):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> spectrum_from_file(vtk_file, spectrum_size=6)
[4.829758648026223e-18,
0.00012841730024671977,
0.0002715181572272744,
0.00032051508471594173,
0.000470162807048644,
0.0005768904023010327]
>>> # Spectrum for Twins-2-1 left postcentral pial surface (22)
>>> # (after running explode_scalars() with reindex=True):
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'label22.vtk')
>>> spectrum_from_file(vtk_file, spectrum_size=6)
[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]
>>> # Loop thru all MB 101 brains
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> for hemidir in os.listdir(header):
>>> print hemidir
>>> sulci_file = os.path.join(header, hemidir, "sulci.vtk")
>>> spectrum = spectrum_from_file(sulci_file)
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
faces, u1, u2, points, u4, u5, u6, u7 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas)
return spectrum
def zernike_moments_per_label(vtk_file, order=10, exclude_labels=[-1],
scale_input=True, decimate_fraction=0,
decimate_smooth=25, verbose=False):
"""
Compute the Zernike moments per labeled region in a file.
Optionally decimate the input mesh.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
order : integer
number of moments to compute
exclude_labels : list of integers
labels to be excluded
scale_input : bool
translate and scale each object so it is bounded by a unit sphere?
(this is the expected input to zernike_moments())
decimate_fraction : float
fraction of mesh faces to remove for decimation (1 for no decimation)
decimate_smooth : integer
number of smoothing steps for decimation
verbose : bool
print statements?
Returns
-------
descriptors_lists : list of lists of floats
Zernike descriptors per label
label_list : list of integers
list of unique labels for which moments are computed
Examples
--------
>>> # Zernike moments per label of a FreeSurfer-labeled left cortex.
>>> # Uncomment "if label==22:" below to run example
>>> # for left postcentral (22) pial surface:
>>> import numpy as np
>>> from mindboggle.shapes.zernike.zernike import zernike_moments_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'])
>>> order = 3
>>> exclude_labels = [-1]
>>> scale_input = True
>>> verbose = False
>>> descriptors_lists, label_list = zernike_moments_per_label(vtk_file,
... order, exclude_labels, scale_input, verbose)
>>> label_list[0:10]
[999, 1001, 1002, 1003, 1005, 1006, 1007, 1008, 1009, 1010]
>>> print(np.array_str(np.array(descriptors_lists[0]),
... precision=5, suppress_small=True))
[ 0.00587 0.01143 0.0031 0.00881 0.00107 0.00041]
>>> print(np.array_str(np.array(descriptors_lists[1]),
... precision=5, suppress_small=True))
[ 0.00004 0.00009 0.00003 0.00009 0.00002 0.00001]
>>> print(np.array_str(np.array(descriptors_lists[2]),
... precision=5, suppress_small=True))
[ 0.00144 0.00232 0.00128 0.00304 0.00084 0.00051]
>>> print(np.array_str(np.array(descriptors_lists[3]),
... precision=5, suppress_small=True))
[ 0.00393 0.006 0.00371 0.00852 0.00251 0.00153]
>>> print(np.array_str(np.array(descriptors_lists[4]),
... precision=5, suppress_small=True))
[ 0.00043 0.0003 0.00095 0.00051 0.00115 0.00116]
"""
import numpy as np
from mindboggle.mio.vtks import read_vtk
from mindboggle.guts.mesh import keep_faces
from mindboggle.shapes.zernike.zernike import zernike_moments
min_points_faces = 4
#-------------------------------------------------------------------------
# Read VTK surface mesh file:
#-------------------------------------------------------------------------
points, indices, lines, faces, labels, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
#-------------------------------------------------------------------------
# Loop through labeled regions:
#-------------------------------------------------------------------------
ulabels = [x for x in np.unique(labels) if x not in exclude_labels]
label_list = []
descriptors_lists = []
for label in ulabels:
#if label == 1022: # 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
#---------------------------------------------------------------------
# Determine the indices per label:
#---------------------------------------------------------------------
Ilabel = [i for i,x in enumerate(labels) if x == label]
if verbose:
print(' {0} vertices for label {1}'.format(len(Ilabel), label))
if len(Ilabel) > min_points_faces:
#-----------------------------------------------------------------
# Remove background faces:
#-----------------------------------------------------------------
pick_faces = keep_faces(faces, Ilabel)
if len(pick_faces) > min_points_faces:
#-------------------------------------------------------------
# Compute Zernike moments for the label:
#-------------------------------------------------------------
descriptors = zernike_moments(points, pick_faces,
order, scale_input,
decimate_fraction,
decimate_smooth, verbose)
#-------------------------------------------------------------
# Append to a list of lists of spectra:
#-------------------------------------------------------------
descriptors_lists.append(descriptors)
label_list.append(label)
return descriptors_lists, label_list
def spectrum_from_file(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization=None, area_file=''):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> spectrum_from_file(vtk_file, spectrum_size=6)
[4.829758648026223e-18,
0.00012841730024671977,
0.0002715181572272744,
0.00032051508471594173,
0.000470162807048644,
0.0005768904023010327]
>>> # Spectrum for Twins-2-1 left postcentral pial surface (22)
>>> # (after running explode_scalars() with reindex=True):
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'label22.vtk')
>>> spectrum_from_file(vtk_file, spectrum_size=6)
[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]
>>> # Loop thru all MB 101 brains
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> for hemidir in os.listdir(header):
>>> print hemidir
>>> sulci_file = os.path.join(header, hemidir, "sulci.vtk")
>>> spectrum = spectrum_from_file(sulci_file)
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
faces, u1, u2, points, u4, u5, u6, u7 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas)
return spectrum
def label_adjacency_matrix(label_file, ignore_values=[-1, 999], add_value=0,
save_table=True, output_format='csv',
verbose=True):
"""
Extract surface or volume label boundaries, find unique label pairs,
and write adjacency matrix (useful for constructing a colormap).
Each row of the (upper triangular) adjacency matrix corresponds to an
index to a unique label, where each column has a 1 if the label indexed
by that column is adjacent to the label indexed by the row.
Parameters
----------
label_file : string
path to VTK surface file or nibabel-readable volume file with labels
ignore_values : list of integers
labels to ignore
add_value : integer
value to add to labels
matrix : pandas dataframe
adjacency matrix
save_table : Boolean
output table file?
output_format : string
format of adjacency table file name (currently only 'csv')
verbose : Boolean
print to stdout?
Returns
-------
labels : list
label numbers
matrix : pandas DataFrame
adjacency matrix
output_table : string
adjacency table file name
Examples
--------
>>> from mindboggle.mio.colors import label_adjacency_matrix
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> ignore_values = [-1, 0]
>>> add_value = 0
>>> save_table = False
>>> output_format = 'csv'
>>> verbose = False
>>> label_file = fetch_data(urls['left_manual_labels'], '', '.vtk')
>>> labels, matrix, output_table = label_adjacency_matrix(label_file,
... ignore_values, add_value, save_table, output_format, verbose)
>>> matrix.lookup([20,21,22,23,24,25,26,27,28,29],
... [35,35,35,35,35,35,35,35,35,35])
array([ 0., 1., 0., 0., 0., 0., 0., 1., 1., 1.])
>>> label_file = fetch_data(urls['freesurfer_labels'], '', '.nii.gz')
>>> labels, matrix, output_table = label_adjacency_matrix(label_file,
... ignore_values, add_value, save_table, output_format, verbose)
>>> matrix.lookup([4,5,7,8,10,11,12,13,14,15], [4,4,4,4,4,4,4,4,4,4])
array([ 1., 1., 0., 0., 0., 1., 0., 0., 1., 0.])
"""
import numpy as np
import pandas as pd
from nibabel import load
from scipy import ndimage
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import extract_borders
from mindboggle.mio.vtks import read_vtk
# Use Mindboggle's extract_borders() function for surface VTK files:
if label_file.endswith('.vtk'):
f1,f2,f3, faces, labels, f4, npoints, f5 = read_vtk(label_file,
True, True)
neighbor_lists = find_neighbors(faces, npoints)
return_label_pairs = True
indices_borders, label_pairs, f1 = extract_borders(list(range(npoints)),
labels, neighbor_lists, ignore_values, return_label_pairs)
output_table = 'adjacent_surface_labels.' + output_format
# Use scipy to dilate volume files to find neighboring labels:
elif label_file.endswith('.nii.gz'):
L = load(label_file).get_data()
unique_volume_labels = np.unique(L)
label_pairs = []
for label in unique_volume_labels:
if label not in ignore_values:
B = L * np.logical_xor(ndimage.binary_dilation(L==int(label)),
(L==int(label)))
neighbor_labels = np.unique(np.ravel(B))
for neigh in neighbor_labels:
if neigh > 0 and neigh in unique_volume_labels:
# and neigh%2==(int(label)%2):
label_pairs.append([int(label), int(neigh)])
output_table = 'adjacent_volume_labels.' + output_format
else:
raise IOError("Use appropriate input file type.")
# Find unique pairs (or first two of each list):
pairs = []
for pair in label_pairs:
new_pair = [int(pair[0]) + add_value,
int(pair[1]) + add_value]
if new_pair not in pairs:
pairs.append(new_pair)
# Write adjacency matrix:
unique_labels = np.unique(pairs)
nlabels = np.size(unique_labels)
matrix = np.zeros((nlabels, nlabels))
for pair in pairs:
index1 = [i for i, x in enumerate(unique_labels) if x == pair[0]]
index2 = [i for i, x in enumerate(unique_labels) if x == pair[1]]
matrix[index1, index2] = 1
df1 = pd.DataFrame({'ID': unique_labels}, index=None)
df2 = pd.DataFrame(matrix, index=None)
df2.columns = unique_labels
matrix = pd.concat([df1, df2], axis=1)
if save_table:
if output_format == 'csv':
matrix.to_csv(output_table, index=False)
if verbose:
print("Adjacency matrix saved to {0}".format(output_table))
else:
raise IOError("Set appropriate output file format.")
else:
output_table = None
labels = list(unique_labels)
return labels, matrix, output_table
def extract_fundi(folds,
curv_file,
depth_file,
min_separation=10,
erode_ratio=0.1,
erode_min_size=1,
save_file=False,
output_file='',
background_value=-1,
verbose=False):
"""
Extract fundi from folds.
A fundus is a branching curve that runs along the deepest and most highly
curved portions of a fold. This function extracts one fundus from each
fold by finding the deepest vertices inside the fold, finding endpoints
along the edge of the fold, and connecting the former to the latter with
tracks that run along deep and curved paths (through vertices with high
values of travel depth multiplied by curvature), and a final filtration
step.
The deepest vertices are those with values at least two median
absolute deviations above the median (non-zero) value, with the higher
value chosen if two of the vertices are within (a default of) 10 edges
from each other (to reduce the number of possible fundus paths as well
as computation time).
To find the endpoints, the find_outer_endpoints function propagates
multiple tracks from seed vertices at median depth in the fold through
concentric rings toward the fold’s edge, selecting maximal values within
each ring, and terminating at candidate endpoints. The final endpoints
are those candidates at the end of tracks that have a high median value,
with the higher value chosen if two candidate endpoints are within
(a default of) 10 edges from each other (otherwise, the resulting fundi
can have spurious branching at the fold’s edge).
The connect_points_erosion function connects the deepest fold vertices
to the endpoints with a skeleton of 1-vertex-thick curves by erosion.
It erodes by iteratively removing simple topological points and endpoints
in order of lowest to highest values, where a simple topological point
is a vertex that when added to or removed from an object on a surface
mesh (such as a fundus curve) does not alter the object's topology.
Steps ::
1. Find fundus endpoints (outer anchors) with find_outer_endpoints().
2. Include inner anchor points.
3. Connect anchor points using connect_points_erosion();
inner anchors are removed if they result in endpoints.
Note ::
Follow this with segment_by_region() to segment fundi by sulci.
Parameters
----------
folds : numpy array or list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : bool
save output VTK file?
output_file : string
output VTK file
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
n_fundi_in_folds : integer
number of fundi
fundus_per_fold_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> import numpy as np
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> curv_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> folds, name = read_scalars(folds_file, True, True)
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [4] #[4, 6]
... i0 = [i for i,x in enumerate(folds) if x not in fold_numbers]
... folds[i0] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> output_file = 'extract_fundi_fold4.vtk'
>>> background_value = -1
>>> verbose = False
>>> o1, o2, fundus_per_fold_file = extract_fundi(folds, curv_file,
... depth_file, min_separation, erode_ratio, erode_min_size,
... save_file, output_file, background_value, verbose)
>>> lens = [len([x for x in o1 if x == y])
... for y in np.unique(o1) if y != background_value]
>>> lens[0:10] # [66, 2914, 100, 363, 73, 331, 59, 30, 1, 14] # (if not limit_folds)
[73]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> rewrite_scalars(fundus_per_fold_file,
... 'extract_fundi_fold4_no_background.vtk', o1,
... 'fundus_per_fold', folds) # doctest: +SKIP
>>> plot_surfaces('extract_fundi_fold4_no_background.vtk') # doctest: +SKIP
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.compute import median_abs_dev
from mindboggle.guts.paths import find_max_values
from mindboggle.guts.mesh import find_neighbors_from_file
#from mindboggle.guts.mesh import find_complete_faces
from mindboggle.guts.paths import find_outer_endpoints
from mindboggle.guts.paths import connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
if os.path.isfile(curv_file):
points, indices, lines, faces, curvs, scalar_names, npoints, \
input_vtk = read_vtk(curv_file, True, True)
else:
raise IOError("{0} doesn't exist!".format(curv_file))
if os.path.isfile(curv_file):
depths, name = read_scalars(depth_file, True, True)
else:
raise IOError("{0} doesn't exist!".format(depth_file))
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
# ------------------------------------------------------------------------
# Loop through folds:
# ------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != background_value]
if verbose:
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".format(
len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i, x in enumerate(folds) if x == fold_ID]
if indices_fold:
if verbose:
print(' Fold {0}:'.format(int(fold_ID)))
# ----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints:
# ----------------------------------------------------------------
outer_anchors, tracks = find_outer_endpoints(
indices_fold, neighbor_lists, values, depths, min_separation,
background_value, verbose)
# ----------------------------------------------------------------
# Find inner anchor points:
# ----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation,
thr)
# ----------------------------------------------------------------
# Connect anchor points to create skeleton:
# ----------------------------------------------------------------
B = background_value * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B, neighbor_lists, outer_anchors,
inner_anchors, values,
erode_ratio, erode_min_size, [],
'', background_value, verbose)
if skeleton:
skeletons.extend(skeleton)
## ---------------------------------------------------------------
## Remove fundus vertices if they make complete triangle faces:
## ---------------------------------------------------------------
#Iremove = find_complete_faces(skeletons, faces)
#if Iremove:
# skeletons = list(frozenset(skeletons).difference(Iremove))
indices_skel = [x for x in skeletons if folds[x] != background_value]
fundus_per_fold = background_value * np.ones(npoints)
fundus_per_fold[indices_skel] = folds[indices_skel]
n_fundi_in_folds = len(
[x for x in np.unique(fundus_per_fold) if x != background_value])
if n_fundi_in_folds == 1:
sdum = 'fold fundus'
else:
sdum = 'fold fundi'
if verbose:
print(' ...Extracted {0} {1}; {2} total ({3:.2f} seconds)'.format(
n_fundi_in_folds, sdum, n_fundi_in_folds,
time() - t1))
# ------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
# ------------------------------------------------------------------------
fundus_per_fold_file = None
if n_fundi_in_folds > 0:
fundus_per_fold = [int(x) for x in fundus_per_fold]
if save_file:
if output_file:
fundus_per_fold_file = output_file
else:
fundus_per_fold_file = os.path.join(os.getcwd(),
'fundus_per_fold.vtk')
rewrite_scalars(curv_file, fundus_per_fold_file, fundus_per_fold,
'fundi', [], background_value)
if not os.path.exists(fundus_per_fold_file):
raise IOError(fundus_per_fold_file + " not found")
return fundus_per_fold, n_fundi_in_folds, fundus_per_fold_file
def extract_fundi(folds, curv_file, depth_file, min_separation=10,
erode_ratio=0.1, erode_min_size=1, save_file=False,
output_file='', background_value=-1, verbose=False):
"""
Extract fundi from folds.
A fundus is a branching curve that runs along the deepest and most highly
curved portions of a fold. This function extracts one fundus from each
fold by finding the deepest vertices inside the fold, finding endpoints
along the edge of the fold, and connecting the former to the latter with
tracks that run along deep and curved paths (through vertices with high
values of travel depth multiplied by curvature), and a final filtration
step.
The deepest vertices are those with values at least two median
absolute deviations above the median (non-zero) value, with the higher
value chosen if two of the vertices are within (a default of) 10 edges
from each other (to reduce the number of possible fundus paths as well
as computation time).
To find the endpoints, the find_outer_endpoints function propagates
multiple tracks from seed vertices at median depth in the fold through
concentric rings toward the fold’s edge, selecting maximal values within
each ring, and terminating at candidate endpoints. The final endpoints
are those candidates at the end of tracks that have a high median value,
with the higher value chosen if two candidate endpoints are within
(a default of) 10 edges from each other (otherwise, the resulting fundi
can have spurious branching at the fold’s edge).
The connect_points_erosion function connects the deepest fold vertices
to the endpoints with a skeleton of 1-vertex-thick curves by erosion.
It erodes by iteratively removing simple topological points and endpoints
in order of lowest to highest values, where a simple topological point
is a vertex that when added to or removed from an object on a surface
mesh (such as a fundus curve) does not alter the object's topology.
Steps ::
1. Find fundus endpoints (outer anchors) with find_outer_endpoints().
2. Include inner anchor points.
3. Connect anchor points using connect_points_erosion();
inner anchors are removed if they result in endpoints.
Note ::
Follow this with segment_by_region() to segment fundi by sulci.
Parameters
----------
folds : numpy array or list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : bool
save output VTK file?
output_file : string
output VTK file
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
n_fundi_in_folds : integer
number of fundi
fundus_per_fold_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> import numpy as np
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> curv_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> folds, name = read_scalars(folds_file, True, True)
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [4] #[4, 6]
... i0 = [i for i,x in enumerate(folds) if x not in fold_numbers]
... folds[i0] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> output_file = 'extract_fundi_fold4.vtk'
>>> background_value = -1
>>> verbose = False
>>> o1, o2, fundus_per_fold_file = extract_fundi(folds, curv_file,
... depth_file, min_separation, erode_ratio, erode_min_size,
... save_file, output_file, background_value, verbose)
>>> lens = [len([x for x in o1 if x == y])
... for y in np.unique(o1) if y != background_value]
>>> lens[0:10] # [66, 2914, 100, 363, 73, 331, 59, 30, 1, 14] # (if not limit_folds)
[73]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> rewrite_scalars(fundus_per_fold_file,
... 'extract_fundi_fold4_no_background.vtk', o1,
... 'fundus_per_fold', folds) # doctest: +SKIP
>>> plot_surfaces('extract_fundi_fold4_no_background.vtk') # doctest: +SKIP
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.compute import median_abs_dev
from mindboggle.guts.paths import find_max_values
from mindboggle.guts.mesh import find_neighbors_from_file
from mindboggle.guts.mesh import find_complete_faces
from mindboggle.guts.paths import find_outer_endpoints
from mindboggle.guts.paths import connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
if os.path.isfile(curv_file):
points, indices, lines, faces, curvs, scalar_names, npoints, \
input_vtk = read_vtk(curv_file, True, True)
else:
raise IOError("{0} doesn't exist!".format(curv_file))
if os.path.isfile(curv_file):
depths, name = read_scalars(depth_file, True, True)
else:
raise IOError("{0} doesn't exist!".format(depth_file))
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
# ------------------------------------------------------------------------
# Loop through folds:
# ------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != background_value]
if verbose:
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".
format(len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i,x in enumerate(folds) if x == fold_ID]
if indices_fold:
if verbose:
print(' Fold {0}:'.format(int(fold_ID)))
# ----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints:
# ----------------------------------------------------------------
outer_anchors, tracks = find_outer_endpoints(indices_fold,
neighbor_lists, values, depths, min_separation,
background_value, verbose)
# ----------------------------------------------------------------
# Find inner anchor points:
# ----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation,
thr)
# ----------------------------------------------------------------
# Connect anchor points to create skeleton:
# ----------------------------------------------------------------
B = background_value * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B, neighbor_lists,
outer_anchors, inner_anchors, values, erode_ratio,
erode_min_size, [], '', background_value, verbose)
if skeleton:
skeletons.extend(skeleton)
## ---------------------------------------------------------------
## Remove fundus vertices if they make complete triangle faces:
## ---------------------------------------------------------------
#Iremove = find_complete_faces(skeletons, faces)
#if Iremove:
# skeletons = list(frozenset(skeletons).difference(Iremove))
indices_skel = [x for x in skeletons if folds[x] != background_value]
fundus_per_fold = background_value * np.ones(npoints)
fundus_per_fold[indices_skel] = folds[indices_skel]
n_fundi_in_folds = len([x for x in np.unique(fundus_per_fold)
if x != background_value])
if n_fundi_in_folds == 1:
sdum = 'fold fundus'
else:
sdum = 'fold fundi'
if verbose:
print(' ...Extracted {0} {1}; {2} total ({3:.2f} seconds)'.
format(n_fundi_in_folds, sdum, n_fundi_in_folds, time() - t1))
# ------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
# ------------------------------------------------------------------------
fundus_per_fold_file = None
if n_fundi_in_folds > 0:
fundus_per_fold = [int(x) for x in fundus_per_fold]
if save_file:
if output_file:
fundus_per_fold_file = output_file
else:
fundus_per_fold_file = os.path.join(os.getcwd(),
'fundus_per_fold.vtk')
rewrite_scalars(curv_file, fundus_per_fold_file, fundus_per_fold,
'fundi', [], background_value)
if not os.path.exists(fundus_per_fold_file):
raise IOError(fundus_per_fold_file + " not found")
return fundus_per_fold, n_fundi_in_folds, fundus_per_fold_file
def extract_subfolds(depth_file, folds, min_size=10, depth_factor=0.25,
depth_ratio=0.1, tolerance=0.01, save_file=False):
"""
Use depth to segment folds into subfolds in a triangular surface mesh.
Note ::
The function extract_sulci() performs about the same whether folds
or subfolds are used as input. The latter leads to some loss of
small subfolds and possibly holes for small subfolds in the middle
of other subfolds.
Note about the watershed() function:
The watershed() function performs individual seed growing from deep seeds,
repeats segmentation from the resulting seeds until each seed's segment
touches a boundary. The function segment() fills in the rest. Finally
segments are joined if their seeds are too close to each other.
Despite these precautions, the order of seed selection in segment() could
possibly influence the resulting borders between adjoining segments.
[The propagate() function is slower and insensitive to depth,
but is not biased by seed order.]
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
min_size : integer
minimum number of vertices for a subfold
depth_factor : float
watershed() depth_factor:
factor to determine whether to merge two neighboring watershed catchment
basins -- they are merged if the Euclidean distance between their basin
seeds is less than this fraction of the maximum Euclidean distance
between points having minimum and maximum depths
depth_ratio : float
watershed() depth_ratio:
the minimum fraction of depth for a neighboring shallower
watershed catchment basin (otherwise merged with the deeper basin)
tolerance : float
watershed() tolerance:
tolerance for detecting differences in depth between vertices
save_file : Boolean
save output VTK file?
Returns
-------
subfolds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_subfolds : int
number of subfolds
subfolds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.guts.mesh import find_neighbors_from_file
>>> from mindboggle.features.folds import extract_subfolds
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> min_size = 10
>>> depth_factor = 0.5
>>> depth_ratio = 0.1
>>> tolerance = 0.01
>>> #
>>> subfolds, n_subfolds, subfolds_file = extract_subfolds(depth_file,
>>> folds, min_size, depth_factor, depth_ratio, tolerance, True)
>>> #
>>> # View:
>>> rewrite_scalars(depth_file, 'subfolds.vtk', subfolds, 'subfolds', subfolds)
>>> plot_surfaces('subfolds.vtk')
"""
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import rewrite_scalars, read_vtk
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import segment, propagate, watershed
print("Segment folds into subfolds")
t0 = time()
#-------------------------------------------------------------------------
# Load depth values for all vertices
#-------------------------------------------------------------------------
faces, lines, indices, points, npoints, depths, \
name, input_vtk = read_vtk(depth_file, return_first=True,
return_array=True)
#-------------------------------------------------------------------------
# Find neighbors for each vertex
#-------------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
#-------------------------------------------------------------------------
# Segment folds into "watershed basins"
#-------------------------------------------------------------------------
indices_folds = [i for i,x in enumerate(folds) if x != -1]
subfolds, seed_indices = watershed(depths, points, indices_folds,
neighbor_lists, min_size, depth_factor=0.25,
depth_ratio=0.1, tolerance=0.01, regrow=True)
# Print statement
n_subfolds = len([x for x in np.unique(subfolds) if x != -1])
print(' ...Extracted {0} subfolds ({1:.2f} seconds)'.
format(n_subfolds, time() - t0))
#-------------------------------------------------------------------------
# Return subfolds, number of subfolds, file name
#-------------------------------------------------------------------------
if save_file:
subfolds_file = os.path.join(os.getcwd(), 'subfolds.vtk')
rewrite_scalars(depth_file, subfolds_file,
subfolds, 'subfolds', subfolds)
if not os.path.exists(subfolds_file):
raise(IOError(subfolds_file + " not found"))
else:
subfolds_file = None
return subfolds, n_subfolds, subfolds_file
def evaluate_deep_features(features_file,
labels_file,
sulci_file='',
hemi='',
excludeIDs=[-1],
output_vtk_name='',
verbose=True):
"""
Evaluate deep surface features by computing the minimum distance from each
label border vertex to all of the feature vertices in the same sulcus,
and from each feature vertex to all of the label border vertices in the
same sulcus. The label borders run along the deepest parts of sulci
and correspond to fundi in the DKT cortical labeling protocol.
Parameters
----------
features_file : string
VTK surface file with feature numbers for vertex scalars
labels_file : string
VTK surface file with label numbers for vertex scalars
sulci_file : string
VTK surface file with sulcus numbers for vertex scalars
excludeIDs : list of integers
feature/sulcus/label IDs to exclude (background set to -1)
output_vtk_name : Boolean
if not empty, output a VTK file beginning with output_vtk_name that
contains a surface with mean distances as scalars
verbose : Boolean
print mean distances to standard output?
Returns
-------
feature_to_border_mean_distances : numpy array [number of features x 1]
mean distance from each feature to sulcus label border
feature_to_border_sd_distances : numpy array [number of features x 1]
standard deviations of feature-to-border distances
feature_to_border_distances_vtk : string
VTK surface file containing feature-to-border distances
border_to_feature_mean_distances : numpy array [number of features x 1]
mean distances from each sulcus label border to feature
border_to_feature_sd_distances : numpy array [number of features x 1]
standard deviations of border-to-feature distances
border_to_feature_distances_vtk : string
VTK surface file containing border-to-feature distances
"""
import os
import sys
import numpy as np
from mindboggle.mio.vtks import read_vtk, read_scalars, write_vtk
from mindboggle.guts.mesh import find_neighbors, remove_faces
from mindboggle.guts.segment import extract_borders
from mindboggle.guts.compute import source_to_target_distances
from mindboggle.mio.labels import DKTprotocol
dkt = DKTprotocol()
#-------------------------------------------------------------------------
# Load labels, features, and sulci:
#-------------------------------------------------------------------------
faces, lines, indices, points, npoints, labels, scalar_names, \
input_vtk = read_vtk(labels_file, True, True)
features, name = read_scalars(features_file, True, True)
if sulci_file:
sulci, name = read_scalars(sulci_file, True, True)
# List of indices to sulcus vertices:
sulcus_indices = [i for i, x in enumerate(sulci) if x != -1]
segmentIDs = sulci
sulcus_faces = remove_faces(faces, sulcus_indices)
else:
sulcus_indices = range(len(labels))
segmentIDs = []
sulcus_faces = faces
#-------------------------------------------------------------------------
# Prepare neighbors, label pairs, border IDs, and outputs:
#-------------------------------------------------------------------------
# Calculate neighbor lists for all points:
print('Find neighbors for all vertices...')
neighbor_lists = find_neighbors(faces, npoints)
# Find label border points in any of the sulci:
print('Find label border points in any of the sulci...')
border_indices, border_label_tuples, unique_border_label_tuples = \
extract_borders(sulcus_indices, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
if not len(border_indices):
sys.exit('There are no label border points!')
# Initialize an array of label border IDs
# (label border vertices that define sulci in the labeling protocol):
print('Build an array of label border IDs...')
label_borders = -1 * np.ones(npoints)
if hemi == 'lh':
nsulcus_lists = len(dkt.left_sulcus_label_pair_lists)
else:
nsulcus_lists = len(dkt.right_sulcus_label_pair_lists)
feature_to_border_mean_distances = -1 * np.ones(nsulcus_lists)
feature_to_border_sd_distances = -1 * np.ones(nsulcus_lists)
border_to_feature_mean_distances = -1 * np.ones(nsulcus_lists)
border_to_feature_sd_distances = -1 * np.ones(nsulcus_lists)
feature_to_border_distances_vtk = ''
border_to_feature_distances_vtk = ''
#-------------------------------------------------------------------------
# Loop through sulci:
#-------------------------------------------------------------------------
# For each list of sorted label pairs (corresponding to a sulcus):
for isulcus, label_pairs in enumerate(dkt.sulcus_label_pair_lists):
# Keep the border points with label pair labels:
label_pair_border_indices = [
x for i, x in enumerate(border_indices)
if np.unique(border_label_tuples[i]).tolist() in label_pairs
]
# Store the points as sulcus IDs in the border IDs array:
if label_pair_border_indices:
label_borders[label_pair_border_indices] = isulcus
if len(np.unique(label_borders)) > 1:
#---------------------------------------------------------------------
# Construct a feature-to-border distance matrix and VTK file:
#---------------------------------------------------------------------
# Construct a distance matrix:
print('Construct a feature-to-border distance matrix...')
sourceIDs = features
targetIDs = label_borders
distances, distance_matrix = source_to_target_distances(
sourceIDs, targetIDs, points, segmentIDs, excludeIDs)
# Compute mean distances for each feature:
nfeatures = min(np.shape(distance_matrix)[1], nsulcus_lists)
for ifeature in range(nfeatures):
feature_distances = [
x for x in distance_matrix[:, ifeature] if x != -1
]
feature_to_border_mean_distances[ifeature] = \
np.mean(feature_distances)
feature_to_border_sd_distances[ifeature] = \
np.std(feature_distances)
if verbose:
print('Feature-to-border mean distances:')
print(feature_to_border_mean_distances)
print('Feature-to-border standard deviations of distances:')
print(feature_to_border_sd_distances)
# Write resulting feature-label border distances to VTK file:
if output_vtk_name:
feature_to_border_distances_vtk = os.path.join(
os.getcwd(),
output_vtk_name + '_feature_to_border_mean_distances.vtk')
print('Write feature-to-border distances to {0}...'.format(
feature_to_border_distances_vtk))
write_vtk(feature_to_border_distances_vtk, points, [], [],
sulcus_faces, [distances],
['feature-to-border_distances'], 'float')
#---------------------------------------------------------------------
# Construct a border-to-feature distance matrix and VTK file:
#---------------------------------------------------------------------
# Construct a distance matrix:
print('Construct a border-to-feature distance matrix...')
sourceIDs = label_borders
targetIDs = features
distances, distance_matrix = source_to_target_distances(
sourceIDs, targetIDs, points, segmentIDs, excludeIDs)
# Compute mean distances for each feature:
nfeatures = min(np.shape(distance_matrix)[1], nsulcus_lists)
for ifeature in range(nfeatures):
border_distances = [
x for x in distance_matrix[:, ifeature] if x != -1
]
border_to_feature_mean_distances[ifeature] = \
np.mean(border_distances)
border_to_feature_sd_distances[ifeature] = \
np.std(border_distances)
if verbose:
print('border-to-feature mean distances:')
print(border_to_feature_mean_distances)
print('border-to-feature standard deviations of distances:')
print(border_to_feature_sd_distances)
# Write resulting feature-label border distances to VTK file:
if output_vtk_name:
border_to_feature_distances_vtk = os.path.join(
os.getcwd(),
output_vtk_name + '_border_to_feature_mean_distances.vtk')
print('Write border-to-feature distances to {0}...'.format(
border_to_feature_distances_vtk))
write_vtk(border_to_feature_distances_vtk, points, [], [],
sulcus_faces, [distances],
['border-to-feature_distances'], 'float')
#-------------------------------------------------------------------------
# Return outputs:
#-------------------------------------------------------------------------
return feature_to_border_mean_distances, feature_to_border_sd_distances,\
feature_to_border_distances_vtk,\
border_to_feature_mean_distances, border_to_feature_sd_distances,\
border_to_feature_distances_vtk
def spectrum_from_file(vtk_file,
spectrum_size=10,
exclude_labels=[-1],
normalization="areaindex",
area_file='',
verbose=False):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues
if None, no normalization is used
if "area", use area of the 2D structure as in Reuter et al. 2006
if "index", divide eigenvalue by index to account for linear trend
if "areaindex", do both (default)
area_file : string
name of VTK file with surface area scalar values
verbose : bool
print statements?
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6,
... exclude_labels=[-1], normalization=None, area_file="", verbose=False)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum[1::]]
[0.00013, 0.00027, 0.00032, 0.00047, 0.00058]
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6,
... exclude_labels=[-1], normalization="areaindex", area_file="",
... verbose=False)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum[1::]]
[14.12801, 14.93573, 11.75397, 12.93141, 12.69348]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas,
verbose)
return spectrum
def write_vertex_measures(output_table,
labels_or_file,
sulci=[],
fundi=[],
affine_transform_files=[],
inverse_booleans=[],
transform_format='itk',
area_file='',
mean_curvature_file='',
travel_depth_file='',
geodesic_depth_file='',
freesurfer_thickness_file='',
freesurfer_curvature_file='',
freesurfer_sulc_file=''):
"""
Make a table of shape values per vertex.
Note ::
This function is tailored for Mindboggle outputs.
Parameters
----------
output_table : string
output file (full path)
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
sulci : list of integers
indices to sulci, one per vertex, with -1 indicating no sulcus
fundi : list of integers
indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_files : list of strings
affine transform files to standard space
inverse_booleans : list of of zeros and ones
for each transform, 1 to take the inverse, else 0
transform_format : string
format for transform file
Ex: 'txt' for text, 'itk' for ITK, and 'mat' for Matlab format
area_file : string
name of VTK file with surface area scalar values
mean_curvature_file : string
name of VTK file with mean curvature scalar values
travel_depth_file : string
name of VTK file with travel depth scalar values
geodesic_depth_file : string
name of VTK file with geodesic depth scalar values
freesurfer_thickness_file : string
name of VTK file with FreeSurfer thickness scalar values
freesurfer_curvature_file : string
name of VTK file with FreeSurfer curvature (curv) scalar values
freesurfer_sulc_file : string
name of VTK file with FreeSurfer convexity (sulc) scalar values
Returns
-------
output_table : table file name for vertex shape values
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.tables import write_vertex_measures
>>> #
>>> output_table = ''#vertex_shapes.csv'
>>> path = os.environ['MINDBOGGLE_DATA']
>>> labels_or_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> sulci_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> fundi_file = os.path.join(path, 'arno', 'features', 'fundi.vtk')
>>> sulci, name = read_scalars(sulci_file)
>>> fundi, name = read_scalars(fundi_file)
>>> affine_transform_files = [os.path.join(path, 'arno', 'mri',
>>> 't1weighted_brain.MNI152Affine.txt')]
>>> inverse_booleans = [1]
>>> transform_format = 'itk'
>>> swap_xy = True
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> mean_curvature_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> travel_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> geodesic_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.geodesic_depth.vtk')
>>> freesurfer_thickness_file = ''
>>> freesurfer_curvature_file = ''
>>> freesurfer_sulc_file = ''
>>> #
>>> write_vertex_measures(output_table, labels_or_file, sulci, fundi,
>>> affine_transform_files, inverse_booleans, transform_format, area_file,
>>> mean_curvature_file, travel_depth_file, geodesic_depth_file,
>>> freesurfer_thickness_file, freesurfer_curvature_file, freesurfer_sulc_file)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_scalars, read_vtk, \
apply_affine_transforms
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = [int(x) for x in labels_or_file]
elif isinstance(labels_or_file, list):
labels = labels_or_file
elif isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file)
if isinstance(sulci, np.ndarray):
sulci = [int(x) for x in sulci]
if isinstance(fundi, np.ndarray):
fundi = [int(x) for x in fundi]
if not labels and not sulci and not fundi:
import sys
sys.exit('No feature data to tabulate in write_vertex_measures().')
# Feature names and corresponding feature lists:
feature_names = ['label ID', 'sulcus ID', 'fundus ID']
feature_lists = [labels, sulci, fundi]
# Shape names corresponding to shape files below:
shape_names = [
'area', 'travel depth', 'geodesic depth', 'mean curvature',
'freesurfer curvature', 'freesurfer thickness',
'freesurfer convexity (sulc)'
]
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [
area_file, travel_depth_file, geodesic_depth_file, mean_curvature_file,
freesurfer_curvature_file, freesurfer_thickness_file,
freesurfer_sulc_file
]
# Append columns of per-vertex scalar values:
columns = []
column_names = []
for ifeature, values in enumerate(feature_lists):
if values:
columns.append(values)
column_names.append(feature_names[ifeature])
first_pass = True
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
# Append x,y,z position per vertex to columns:
u1, u2, u3, points, u4, scalars, u5, u6 = read_vtk(shape_file)
xyz_positions = np.asarray(points)
for ixyz, xyz in enumerate(['x', 'y', 'z']):
column_names.append('position: {0}'.format(xyz))
columns.append(xyz_positions[:, ixyz].tolist())
first_pass = False
# Append standard space x,y,z position to columns:
if affine_transform_files and transform_format:
affine_points, \
foo1 = apply_affine_transforms(affine_transform_files,
inverse_booleans, transform_format,
points, vtk_file_stem='')
xyz_std_positions = affine_points
for ixyz, xyz in enumerate(['x', 'y', 'z']):
column_names.append('position in standard space:'
' {0}'.format(xyz))
columns.append(xyz_std_positions[:, ixyz].tolist())
else:
scalars, name = read_scalars(shape_file)
if len(scalars):
columns.append(scalars)
column_names.append(shape_names[ishape])
# Prepend with column of indices and write table
if not output_table:
output_table = os.path.join(os.getcwd(), 'vertices.csv')
df = pd.DataFrame(np.transpose(columns), columns=column_names)
df.to_csv(output_table, index=False)
if not os.path.exists(output_table):
raise (IOError(output_table + " not found"))
return output_table
def zernike_moments_per_label(vtk_file,
order=10,
exclude_labels=[-1],
scale_input=True,
decimate_fraction=0,
decimate_smooth=25):
"""
Compute the Zernike moments per labeled region in a file.
Optionally decimate the input mesh.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
order : integer
number of moments to compute
exclude_labels : list of integers
labels to be excluded
scale_input : Boolean
translate and scale each object so it is bounded by a unit sphere?
(this is the expected input to zernike_moments())
decimate_fraction : float
fraction of mesh faces to remove for decimation (1 for no decimation)
decimate_smooth : integer
number of smoothing steps for decimation
Returns
-------
descriptors_lists : list of lists of floats
Zernike descriptors per label
label_list : list of integers
list of unique labels for which moments are computed
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Twins-2-1 left postcentral (22) pial surface:
>>> import os
>>> from mindboggle.shapes.zernike.zernike import zernike_moments_per_label
>>> path = os.path.join(os.environ['HOME'], 'mindboggled', 'OASIS-TRT-20-1')
>>> vtk_file = os.path.join(path, 'labels', 'left_surface', 'relabeled_classifier.vtk')
>>> order = 3
>>> exclude_labels = [-1, 0]
>>> scale_input = True
>>> zernike_moments_per_label(vtk_file, order, exclude_labels, scale_input)
([[0.00528486237819844,
0.009571754617699853,
0.0033489494903015944,
0.00875603468268444,
0.0015879536633349918,
0.0008080165707033097]],
[22])
([[0.0018758013185778298,
0.001757973693050823,
0.002352403177686726,
0.0032281044369938286,
0.002215900343702539,
0.0019646380916703856]],
[14.0])
Arthur Mikhno's result:
1.0e+07 *
0.0000
0.0179
0.0008
4.2547
0.0534
4.4043
"""
import numpy as np
from mindboggle.mio.vtks import read_vtk
from mindboggle.guts.mesh import remove_faces
from mindboggle.shapes.zernike.zernike import zernike_moments
min_points_faces = 4
#-------------------------------------------------------------------------
# Read VTK surface mesh file:
#-------------------------------------------------------------------------
faces, u1, u2, points, u3, labels, u4, u5 = read_vtk(vtk_file)
#-------------------------------------------------------------------------
# Loop through labeled regions:
#-------------------------------------------------------------------------
ulabels = [x for x in np.unique(labels) if x not in exclude_labels]
label_list = []
descriptors_lists = []
for label in ulabels:
#if label == 1022: # 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
#---------------------------------------------------------------------
# Determine the indices per label:
#---------------------------------------------------------------------
Ilabel = [i for i, x in enumerate(labels) if x == label]
print(' {0} vertices for label {1}'.format(len(Ilabel), label))
if len(Ilabel) > min_points_faces:
#-----------------------------------------------------------------
# Remove background faces:
#-----------------------------------------------------------------
pick_faces = remove_faces(faces, Ilabel)
if len(pick_faces) > min_points_faces:
#-------------------------------------------------------------
# Compute Zernike moments for the label:
#-------------------------------------------------------------
descriptors = zernike_moments(points, pick_faces, order,
scale_input, decimate_fraction,
decimate_smooth)
#-------------------------------------------------------------
# Append to a list of lists of spectra:
#-------------------------------------------------------------
descriptors_lists.append(descriptors)
label_list.append(label)
return descriptors_lists, label_list
def write_face_vertex_averages(input_file, output_table='', area_file=''):
"""
Make table of average vertex values per face
(divided by face area if area_file provided).
Parameters
----------
input_file : string
name of VTK file with scalars to average
area_file : string
name of VTK file with surface area scalar values
output_table : string
output table filename
Returns
-------
output_table : string
output table filename
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import write_face_vertex_averages
>>> path = '/homedir/mindboggled'
>>> input_file = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'freesurfer_thickness.vtk')
>>> area_file = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'area.vtk')
>>> output_table = ''
>>> #
>>> write_face_vertex_averages(input_file, output_table, area_file)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_vtk, read_scalars
faces, lines, indices, points, npoints, scalars, name, \
input_vtk = read_vtk(input_file, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
#---------------------------------------------------------------------
# For each face, average vertex values:
#---------------------------------------------------------------------
columns = []
for face in faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
#-----------------------------------------------------------------
# Write to table:
#-----------------------------------------------------------------
if not output_table:
output_table = os.path.join(os.getcwd(), 'average_face_values.csv')
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False)
if not os.path.exists(output_table):
raise (IOError(output_table + " not found"))
return output_table
def extract_folds(depth_file, depth_threshold=2, min_fold_size=50,
save_file=False, output_file='', background_value=-1,
verbose=False):
"""
Use depth threshold to extract folds from a triangular surface mesh.
A fold is a group of connected, deep vertices. To extract folds,
a depth threshold is used to segment deep vertices of the surface mesh.
We have observed in the histograms of travel depth measures of cortical
surfaces that there is a rapidly decreasing distribution of low depth
values (corresponding to the outer surface, or gyral crowns) with a
long tail of higher depth values (corresponding to the folds).
The find_depth_threshold function therefore computes a histogram of
travel depth measures, smooths the histogram's bin values, convolves
to compute slopes, and finds the depth value for the first bin with
zero slope. The extract_folds function uses this depth value, segments
deep vertices, and removes extremely small folds (empirically set at 50
vertices or fewer out of a total mesh size of over 100,000 vertices).
Steps ::
1. Segment deep vertices as an initial set of folds.
2. Remove small folds.
3. Renumber folds.
Note ::
Removed option: Find and fill holes in the folds:
Folds could have holes in areas shallower than the depth threshold.
Calling fill_holes() could accidentally include very shallow areas
(in an annulus-shaped fold, for example).
However, we could include the argument exclude_range to check for
any values from zero to min_hole_depth; holes would not be filled
if they were to contain values within this range.
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
depth_threshold : float
threshold defining the minimum depth for vertices to be in a fold
min_fold_size : integer
minimum fold size (number of vertices)
save_file : bool
save output VTK file?
output_file : string
name of output file in VTK format
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_folds : int
number of folds
folds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> from mindboggle.features.folds import extract_folds
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> depth_threshold = 2.36089
>>> min_fold_size = 50
>>> save_file = True
>>> output_file = 'extract_folds.vtk'
>>> background_value = -1
>>> verbose = False
>>> folds, n_folds, folds_file = extract_folds(depth_file,
... depth_threshold, min_fold_size, save_file, output_file,
... background_value, verbose)
>>> n_folds
33
>>> lens = [len([x for x in folds if x == y]) for y in range(n_folds)]
>>> lens[0:10]
[726, 67241, 2750, 5799, 1151, 6360, 1001, 505, 228, 198]
View folds (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> plot_surfaces('extract_folds.vtk') # doctest: +SKIP
View folds without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> rewrite_scalars(depth_file, 'extract_folds_no_background.vtk', folds,
... 'just_folds', folds, -1) # doctest: +SKIP
>>> plot_surfaces('extract_folds_no_background.vtk') # doctest: +SKIP
"""
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import rewrite_scalars, read_vtk
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import segment_regions
if verbose:
print("Extract folds in surface mesh")
t0 = time()
# ------------------------------------------------------------------------
# Load depth values for all vertices
# ------------------------------------------------------------------------
points, indices, lines, faces, depths, scalar_names, npoints, \
input_vtk = read_vtk(depth_file, return_first=True, return_array=True)
# ------------------------------------------------------------------------
# Find the deepest vertices
# ------------------------------------------------------------------------
indices_deep = [i for i,x in enumerate(depths) if x >= depth_threshold]
if indices_deep:
# --------------------------------------------------------------------
# Find neighbors for each vertex
# --------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
# --------------------------------------------------------------------
# Segment deep vertices as an initial set of folds
# --------------------------------------------------------------------
if verbose:
print(" Segment vertices deeper than {0:.2f} as folds".format(depth_threshold))
t1 = time()
folds = segment_regions(indices_deep, neighbor_lists, 1, [], False,
False, [], [], [], '', background_value, False)
if verbose:
print(' ...Segmented folds ({0:.2f} seconds)'.format(time() - t1))
# --------------------------------------------------------------------
# Remove small folds
# --------------------------------------------------------------------
if min_fold_size > 1:
if verbose:
print(' Remove folds smaller than {0}'.format(min_fold_size))
unique_folds = [x for x in np.unique(folds)
if x != background_value]
for nfold in unique_folds:
indices_fold = [i for i,x in enumerate(folds) if x == nfold]
if len(indices_fold) < min_fold_size:
folds[indices_fold] = background_value
# --------------------------------------------------------------------
# Find and fill holes in the folds
# Note: Surfaces surrounded by folds can be mistaken for holes,
# so exclude_range includes outer surface values close to zero.
# --------------------------------------------------------------------
# folds = fill_holes(folds, neighbor_lists, values=depths,
# exclude_range=[0, min_hole_depth])
# --------------------------------------------------------------------
# Renumber folds so they are sequential.
# NOTE: All vertices are included (-1 for non-fold vertices).
# --------------------------------------------------------------------
renumber_folds = background_value * np.ones(npoints)
fold_numbers = [x for x in np.unique(folds) if x != background_value]
for i_fold, n_fold in enumerate(fold_numbers):
fold_indices = [i for i,x in enumerate(folds) if x == n_fold]
renumber_folds[fold_indices] = i_fold
folds = renumber_folds
folds = [int(x) for x in folds]
n_folds = i_fold + 1
# Print statement
if verbose:
print(' ...Extracted {0} folds ({1:.2f} seconds)'.
format(n_folds, time() - t0))
else:
if verbose:
print(' No deep vertices')
# ------------------------------------------------------------------------
# Return folds, number of folds, file name
# ------------------------------------------------------------------------
if save_file:
if output_file:
folds_file = output_file
else:
folds_file = os.path.join(os.getcwd(), 'folds.vtk')
rewrite_scalars(depth_file, folds_file, folds, 'folds', [],
background_value)
if not os.path.exists(folds_file):
raise IOError(folds_file + " not found")
else:
folds_file = None
return folds, n_folds, folds_file
def write_average_face_values_per_label(input_indices_vtk,
input_values_vtk='',
area_file='',
output_stem='',
exclude_values=[-1],
background_value=-1):
"""
Write out a separate VTK file for each integer
in (the first) scalar list of an input VTK file.
Optionally write the values drawn from a second VTK file.
Parameters
----------
input_indices_vtk : string
path of the input VTK file that contains indices as scalars
input_values_vtk : string
path of the input VTK file that contains values as scalars
output_stem : string
path and stem of the output VTK file
exclude_values : list or array
values to exclude
background_value : integer or float
background value in output VTK files
scalar_name : string
name of a lookup table of scalars values
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import write_average_face_values_per_label
>>> path = '/homedir/mindboggled'
>>> input_indices_vtk = os.path.join(path, 'Twins-2-1', 'labels', 'left_cortical_surface', 'freesurfer_cortex_labels.vtk')
>>> input_values_vtk = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'freesurfer_thickness.vtk')
>>> area_file = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'area.vtk')
>>> output_stem = 'labels_thickness'
>>> exclude_values = [-1]
>>> background_value = -1
>>> #
>>> write_average_face_values_per_label(input_indices_vtk,
>>> input_values_vtk, area_file, output_stem, exclude_values, background_value)
>>> #
>>> # View:
>>> #example_vtk = os.path.join(os.getcwd(), output_stem + '0.vtk')
>>> #from mindboggle.mio.plots import plot_surfaces
>>> #plot_surfaces(example_vtk)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_scalars, read_vtk, write_vtk
from mindboggle.guts.mesh import remove_faces
# Load VTK file:
faces, lines, indices, points, npoints, scalars, scalar_names, \
foo1 = read_vtk(input_indices_vtk, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
print("Explode the scalar list in {0}".format(
os.path.basename(input_indices_vtk)))
if input_values_vtk != input_indices_vtk:
values, name = read_scalars(input_values_vtk, True, True)
print("Explode the scalar list of values in {0} "
"with the scalar list of indices in {1}".format(
os.path.basename(input_values_vtk),
os.path.basename(input_indices_vtk)))
else:
values = np.copy(scalars)
# Loop through unique (non-excluded) scalar values:
unique_scalars = [
int(x) for x in np.unique(scalars) if x not in exclude_values
]
for scalar in unique_scalars:
keep_indices = [x for sublst in faces for x in sublst]
new_faces = remove_faces(faces, keep_indices)
# Create array and indices for scalar value:
select_scalars = np.copy(scalars)
select_scalars[scalars != scalar] = background_value
scalar_indices = [
i for i, x in enumerate(select_scalars) if x == scalar
]
print(" Scalar {0}: {1} vertices".format(scalar, len(scalar_indices)))
#---------------------------------------------------------------------
# For each face, average vertex values:
#---------------------------------------------------------------------
output_table = os.path.join(os.getcwd(),
output_stem + str(scalar) + '.csv')
columns = []
for face in new_faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
#-----------------------------------------------------------------
# Write to table:
#-----------------------------------------------------------------
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False)
# Write VTK file with scalar value:
#output_vtk = os.path.join(os.getcwd(), output_stem + str(scalar) + '.vtk')
#write_vtk(output_vtk, points, indices, lines, new_faces,
# [select_values.tolist()], [output_scalar_name])
if not os.path.exists(output_table):
raise (IOError(output_table + " not found"))
def write_vertex_measures(output_table,
labels_or_file,
sulci=[],
fundi=[],
affine_transform_files=[],
inverse_booleans=[],
transform_format='itk',
area_file='',
mean_curvature_file='',
travel_depth_file='',
geodesic_depth_file='',
freesurfer_thickness_file='',
freesurfer_curvature_file='',
freesurfer_sulc_file=''):
"""
Make a table of shape values per vertex.
Note ::
This function is tailored for Mindboggle outputs.
Parameters
----------
output_table : string
output file (full path)
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
sulci : list of integers
indices to sulci, one per vertex, with -1 indicating no sulcus
fundi : list of integers
indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_files : list of strings
affine transform files to standard space
inverse_booleans : list of of zeros and ones
for each transform, 1 to take the inverse, else 0
transform_format : string
format for transform file
Ex: 'txt' for text, 'itk' for ITK, and 'mat' for Matlab format
area_file : string
name of VTK file with surface area scalar values
mean_curvature_file : string
name of VTK file with mean curvature scalar values
travel_depth_file : string
name of VTK file with travel depth scalar values
geodesic_depth_file : string
name of VTK file with geodesic depth scalar values
freesurfer_thickness_file : string
name of VTK file with FreeSurfer thickness scalar values
freesurfer_curvature_file : string
name of VTK file with FreeSurfer curvature (curv) scalar values
freesurfer_sulc_file : string
name of VTK file with FreeSurfer convexity (sulc) scalar values
Returns
-------
output_table : table file name for vertex shape values
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.tables import write_vertex_measures
>>> output_table = '' #vertex_shapes.csv'
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> labels_or_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> sulci_file = fetch_data(urls['left_sulci'], '', '.vtk')
>>> fundi_file = fetch_data(urls['left_fundus_per_sulcus'], '', '.vtk')
>>> mean_curvature_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> travel_depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> geodesic_depth_file = fetch_data(urls['left_geodesic_depth'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> freesurfer_thickness_file = fetch_data(urls['left_freesurfer_thickness'], '', '.vtk')
>>> freesurfer_curvature_file = fetch_data(urls['left_freesurfer_curvature'], '', '.vtk')
>>> freesurfer_sulc_file = fetch_data(urls['left_freesurfer_sulc'], '', '.vtk')
>>> sulci, name = read_scalars(sulci_file)
>>> if fundi_file:
... fundi, name = read_scalars(fundi_file)
... else:
... fundi = []
>>> affine_transform_file = fetch_data(urls['affine_mni_transform'], '', '.txt')
>>> inverse_booleans = [1]
>>> transform_format = 'itk'
>>> swap_xy = True
>>> affine_rename = affine_transform_file + '.txt'
>>> os.rename(affine_transform_file, affine_rename)
>>> os.rename(labels_or_file, labels_or_file + '.vtk')
>>> os.rename(area_file, area_file + '.vtk')
>>> os.rename(mean_curvature_file, mean_curvature_file + '.vtk')
>>> os.rename(travel_depth_file, travel_depth_file + '.vtk')
>>> os.rename(geodesic_depth_file, geodesic_depth_file + '.vtk')
>>> os.rename(freesurfer_thickness_file, freesurfer_thickness_file + '.vtk')
>>> os.rename(freesurfer_curvature_file, freesurfer_curvature_file + '.vtk')
>>> os.rename(freesurfer_sulc_file, freesurfer_sulc_file + '.vtk')
>>> labels_or_file = labels_or_file + '.vtk'
>>> area_file = area_file + '.vtk'
>>> mean_curvature_file = mean_curvature_file + '.vtk'
>>> travel_depth_file = travel_depth_file + '.vtk'
>>> geodesic_depth_file = geodesic_depth_file + '.vtk'
>>> freesurfer_thickness_file = freesurfer_thickness_file + '.vtk'
>>> freesurfer_curvature_file = freesurfer_curvature_file + '.vtk'
>>> freesurfer_sulc_file = freesurfer_sulc_file + '.vtk'
>>> affine_transform_files = [] # [affine_rename] # requires ANTs to test
>>> output_table = write_vertex_measures(output_table, labels_or_file,
... sulci, fundi, affine_transform_files, inverse_booleans,
... transform_format, area_file, mean_curvature_file,
... travel_depth_file, geodesic_depth_file, freesurfer_thickness_file,
... freesurfer_curvature_file, freesurfer_sulc_file)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_scalars, read_vtk, \
apply_affine_transforms
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = [int(x) for x in labels_or_file]
elif isinstance(labels_or_file, list):
labels = labels_or_file
elif isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file)
if isinstance(sulci, np.ndarray):
sulci = [int(x) for x in sulci]
if isinstance(fundi, np.ndarray):
fundi = [int(x) for x in fundi]
if not labels and not sulci and not fundi:
raise IOError(
'No feature data to tabulate in write_vertex_measures().')
# Feature names and corresponding feature lists:
feature_names = ['label ID', 'sulcus ID', 'fundus ID']
feature_lists = [labels, sulci, fundi]
# Shape names corresponding to shape files below:
shape_names = [
'area', 'travel depth', 'geodesic depth', 'mean curvature',
'freesurfer curvature', 'freesurfer thickness',
'freesurfer convexity (sulc)'
]
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [
area_file, travel_depth_file, geodesic_depth_file, mean_curvature_file,
freesurfer_curvature_file, freesurfer_thickness_file,
freesurfer_sulc_file
]
# Append columns of per-vertex scalar values:
columns = []
column_names = []
for ifeature, values in enumerate(feature_lists):
if values:
columns.append(values)
column_names.append(feature_names[ifeature])
first_pass = True
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
# Append x,y,z position per vertex to columns:
points, indices, lines, faces, scalars, scalar_names, \
npoints, input_vtk = read_vtk(shape_file)
xyz_positions = np.asarray(points)
for ixyz, xyz in enumerate(['x', 'y', 'z']):
column_names.append('position: {0}'.format(xyz))
columns.append(xyz_positions[:, ixyz].tolist())
first_pass = False
# Append standard space x,y,z position to columns:
if affine_transform_files and transform_format:
affine_points, \
foo1 = apply_affine_transforms(affine_transform_files,
inverse_booleans, transform_format,
points, vtk_file_stem='')
xyz_std_positions = affine_points
for ixyz, xyz in enumerate(['x', 'y', 'z']):
column_names.append('position in standard space:'
' {0}'.format(xyz))
columns.append(xyz_std_positions[:, ixyz].tolist())
else:
scalars, name = read_scalars(shape_file)
if len(scalars):
columns.append(scalars)
column_names.append(shape_names[ishape])
# Prepend with column of indices and write table
if not output_table:
output_table = os.path.join(os.getcwd(), 'vertices.csv')
df = pd.DataFrame(np.transpose(columns), columns=column_names)
df.to_csv(output_table, index=False, encoding='utf-8')
if not os.path.exists(output_table):
raise IOError(output_table + " not found")
return output_table
def spectrum_from_file(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization="areaindex", area_file='', verbose=False):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues
if None, no normalization is used
if "area", use area of the 2D structure as in Reuter et al. 2006
if "index", divide eigenvalue by index to account for linear trend
if "areaindex", do both (default)
area_file : string
name of VTK file with surface area scalar values
verbose : bool
print statements?
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6,
... exclude_labels=[-1], normalization=None, area_file="", verbose=False)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum[1::]]
[0.00013, 0.00027, 0.00032, 0.00047, 0.00058]
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6,
... exclude_labels=[-1], normalization="areaindex", area_file="",
... verbose=False)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum[1::]]
[14.12801, 14.93573, 11.75397, 12.93141, 12.69348]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas,
verbose)
return spectrum
def write_average_face_values_per_label(input_indices_vtk,
input_values_vtk='',
area_file='',
output_stem='',
exclude_values=[-1],
background_value=-1,
verbose=False):
"""
Write out a separate csv table file for each integer
in (the first) scalar list of an input VTK file.
Optionally write the values drawn from a second VTK file.
Parameters
----------
input_indices_vtk : string
path of the input VTK file that contains indices as scalars
input_values_vtk : string
path of the input VTK file that contains values as scalars
output_stem : string
path and stem of the output VTK file
exclude_values : list or array
values to exclude
background_value : integer or float
background value in output VTK files
scalar_name : string
name of a lookup table of scalars values
verbose : bool
print statements?
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import write_average_face_values_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_indices_vtk = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> input_values_vtk = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> output_stem = 'labels_thickness'
>>> exclude_values = [-1]
>>> background_value = -1
>>> verbose = False
>>> write_average_face_values_per_label(input_indices_vtk,
... input_values_vtk, area_file, output_stem, exclude_values,
... background_value, verbose)
View vtk file (skip test):
>>> from mindboggle.mio.plots import plot_surfaces
>>> example_vtk = os.path.join(os.getcwd(), output_stem + '0.vtk')
>>> plot_surfaces(example_vtk) # doctest: +SKIP
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_scalars, read_vtk, write_vtk
from mindboggle.guts.mesh import keep_faces
# Load VTK file:
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(input_indices_vtk, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
if verbose:
print("Explode the scalar list in {0}".format(
os.path.basename(input_indices_vtk)))
if input_values_vtk != input_indices_vtk:
if verbose:
print("Explode the scalar list of values in {0} "
"with the scalar list of indices in {1}".format(
os.path.basename(input_values_vtk),
os.path.basename(input_indices_vtk)))
# Loop through unique (non-excluded) scalar values:
unique_scalars = [
int(x) for x in np.unique(scalars) if x not in exclude_values
]
for scalar in unique_scalars:
keep_indices = [x for sublst in faces for x in sublst]
new_faces = keep_faces(faces, keep_indices)
# Create array and indices for scalar value:
select_scalars = np.copy(scalars)
select_scalars[scalars != scalar] = background_value
scalar_indices = [
i for i, x in enumerate(select_scalars) if x == scalar
]
if verbose:
print(" Scalar {0}: {1} vertices".format(scalar,
len(scalar_indices)))
# --------------------------------------------------------------------
# For each face, average vertex values:
# --------------------------------------------------------------------
output_table = os.path.join(os.getcwd(),
output_stem + str(scalar) + '.csv')
columns = []
for face in new_faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
# ----------------------------------------------------------------
# Write to table:
# ----------------------------------------------------------------
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False, encoding='utf-8')
if not os.path.exists(output_table):
raise IOError(output_table + " not found")
def extract_fundi(folds, curv_file, depth_file, min_separation=10,
erode_ratio=0.1, erode_min_size=1, save_file=False):
"""
Extract fundi from folds.
A fundus is a branching curve that runs along the deepest and most
highly curved portions of a fold.
Steps ::
1. Find fundus endpoints (outer anchors) with find_outer_anchors().
2. Include inner anchor points.
3. Connect anchor points using connect_points_erosion();
inner anchors are removed if they result in endpoints.
Parameters
----------
folds : numpy array or list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : Boolean
save output VTK file?
Returns
-------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
n_fundi_in_folds : integer
number of fundi
fundus_per_fold_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> single_fold = True
>>> import os
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> curv_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'travel_depth_rescaled.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file, True, True)
>>> if single_fold:
>>> fold_number = 2 #11
>>> folds[folds != fold_number] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> o1, o2, fundus_per_fold_file = extract_fundi(folds, curv_file,
... depth_file, min_separation, erode_ratio, erode_min_size, save_file)
>>> #
>>> # View:
>>> plot_surfaces(fundi_file)
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.compute import median_abs_dev
from mindboggle.guts.paths import find_max_values
from mindboggle.guts.mesh import find_neighbors_from_file, find_complete_faces
from mindboggle.guts.paths import find_outer_anchors, connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
points, indices, lines, faces, curvs, scalar_names, npoints, \
input_vtk = read_vtk(curv_file, True, True)
depths, name = read_scalars(depth_file, True, True)
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
#-------------------------------------------------------------------------
# Loop through folds:
#-------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != -1]
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".
format(len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i,x in enumerate(folds) if x == fold_ID]
if indices_fold:
print(' Fold {0}:'.format(int(fold_ID)))
#-----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints:
#-----------------------------------------------------------------
outer_anchors, tracks = find_outer_anchors(indices_fold,
neighbor_lists, values, depths, min_separation)
#-----------------------------------------------------------------
# Find inner anchor points:
#-----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation, thr)
#-----------------------------------------------------------------
# Connect anchor points to create skeleton:
#-----------------------------------------------------------------
B = -1 * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B, neighbor_lists,
outer_anchors, inner_anchors, values,
erode_ratio, erode_min_size, save_steps=[], save_vtk='')
if skeleton:
skeletons.extend(skeleton)
#-----------------------------------------------------------------
# Remove fundus vertices if they complete triangle faces:
#-----------------------------------------------------------------
Iremove = find_complete_faces(skeletons, faces)
if Iremove:
skeletons = list(frozenset(skeletons).difference(Iremove))
indices_skel = [x for x in skeletons if folds[x] != -1]
fundus_per_fold = -1 * np.ones(npoints)
fundus_per_fold[indices_skel] = folds[indices_skel]
n_fundi_in_folds = len([x for x in np.unique(fundus_per_fold)
if x != -1])
if n_fundi_in_folds == 1:
sdum = 'fold fundus'
else:
sdum = 'fold fundi'
print(' ...Extracted {0} {1}; {2} total ({3:.2f} seconds)'.
format(n_fundi_in_folds, sdum, n_fundi_in_folds, time() - t1))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
if n_fundi_in_folds > 0:
fundus_per_fold = [int(x) for x in fundus_per_fold]
if save_file:
fundus_per_fold_file = os.path.join(os.getcwd(),
'fundus_per_fold.vtk')
rewrite_scalars(curv_file, fundus_per_fold_file, fundus_per_fold,
'fundi', folds)
if not os.path.exists(fundus_per_fold_file):
raise(IOError(fundus_per_fold_file + " not found"))
else:
fundus_per_fold_file = None
return fundus_per_fold, n_fundi_in_folds, fundus_per_fold_file
def extract_folds(depth_file,
min_vertices=10000,
min_fold_size=50,
do_fill_holes=False,
min_hole_depth=0.001,
save_file=False):
"""
Use depth to extract folds from a triangular surface mesh.
Steps ::
1. Compute histogram of depth measures.
2. Define a depth threshold and find the deepest vertices.
3. Segment deep vertices as an initial set of folds.
4. Remove small folds.
5. Find and fill holes in the folds (optional).
6. Renumber folds.
Step 2 ::
To extract an initial set of deep vertices from the surface mesh,
we anticipate that there will be a rapidly decreasing distribution
of low depth values (on the outer surface) with a long tail
of higher depth values (in the folds), so we smooth the histogram's
bin values, convolve to compute slopes, and find the depth value
for the first bin with slope = 0. This is our threshold.
Step 5 ::
The folds could have holes in areas shallower than the depth threshold.
Calling fill_holes() could accidentally include very shallow areas
(in an annulus-shaped fold, for example), so we include the argument
exclude_range to check for any values from zero to min_hole_depth;
holes are not filled if they contains values within this range.
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
min_fold_size : integer
minimum fold size (number of vertices)
do_fill_holes : Boolean
fill holes in the folds?
min_hole_depth : float
largest non-zero depth value that will stop a hole from being filled
save_file : Boolean
save output VTK file?
Returns
-------
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_folds : int
number of folds
depth_threshold : float
threshold defining the minimum depth for vertices to be in a fold
bins : list of integers
histogram bins: each is the number of vertices within a range of depth values
bin_edges : list of floats
histogram bin edge values defining the bin ranges of depth values
folds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> import os
>>> import numpy as np
>>> import pylab
>>> from scipy.ndimage.filters import gaussian_filter1d
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.guts.mesh import find_neighbors_from_file
>>> from mindboggle.mio.plots import plot_surfaces
>>> from mindboggle.features.folds import extract_folds
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = 'travel_depth.vtk' #os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> neighbor_lists = find_neighbors_from_file(depth_file)
>>> min_vertices = 10000
>>> min_fold_size = 50
>>> do_fill_holes = False #True
>>> min_hole_depth = 0.001
>>> save_file = True
>>> #
>>> folds, n_folds, thr, bins, bin_edges, folds_file = extract_folds(depth_file,
>>> min_vertices, min_fold_size, do_fill_holes, min_hole_depth, save_file)
>>> #
>>> # View folds:
>>> plot_surfaces('folds.vtk')
>>> # Plot histogram and depth threshold:
>>> depths, name = read_scalars(depth_file)
>>> nbins = np.round(len(depths) / 100.0)
>>> a,b,c = pylab.hist(depths, bins=nbins)
>>> pylab.plot(thr*np.ones((100,1)), np.linspace(0, max(bins), 100), 'r.')
>>> pylab.show()
>>> # Plot smoothed histogram:
>>> bins_smooth = gaussian_filter1d(bins.tolist(), 5)
>>> pylab.plot(range(len(bins)), bins, '.', range(len(bins)), bins_smooth,'-')
>>> pylab.show()
"""
import os
import sys
import numpy as np
from time import time
from scipy.ndimage.filters import gaussian_filter1d
from mindboggle.mio.vtks import rewrite_scalars, read_vtk
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.morph import fill_holes
from mindboggle.guts.segment import segment
print("Extract folds in surface mesh")
t0 = time()
#-------------------------------------------------------------------------
# Load depth values for all vertices
#-------------------------------------------------------------------------
points, indices, lines, faces, depths, scalar_names, npoints, \
input_vtk = read_vtk(depth_file, return_first=True, return_array=True)
#-------------------------------------------------------------------------
# Find neighbors for each vertex
#-------------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
#-------------------------------------------------------------------------
# Compute histogram of depth measures
#-------------------------------------------------------------------------
if npoints > min_vertices:
nbins = np.round(npoints / 100.0)
else:
sys.err(" Expecting at least {0} vertices to create depth histogram".
format(min_vertices))
bins, bin_edges = np.histogram(depths, bins=nbins)
#-------------------------------------------------------------------------
# Anticipating that there will be a rapidly decreasing distribution
# of low depth values (on the outer surface) with a long tail of higher
# depth values (in the folds), smooth the bin values (Gaussian), convolve
# to compute slopes, and find the depth for the first bin with slope = 0.
#-------------------------------------------------------------------------
bins_smooth = gaussian_filter1d(bins.tolist(), 5)
window = [-1, 0, 1]
bin_slopes = np.convolve(bins_smooth, window,
mode='same') / (len(window) - 1)
ibins0 = np.where(bin_slopes == 0)[0]
if ibins0.shape:
depth_threshold = bin_edges[ibins0[0]]
else:
depth_threshold = np.median(depths)
#-------------------------------------------------------------------------
# Find the deepest vertices
#-------------------------------------------------------------------------
indices_deep = [i for i, x in enumerate(depths) if x >= depth_threshold]
if indices_deep:
#---------------------------------------------------------------------
# Segment deep vertices as an initial set of folds
#---------------------------------------------------------------------
print(" Segment vertices deeper than {0:.2f} as folds".format(
depth_threshold))
t1 = time()
folds = segment(indices_deep, neighbor_lists)
# Slightly slower alternative -- fill boundaries:
#regions = -1 * np.ones(len(points))
#regions[indices_deep] = 1
#folds = segment_by_filling_borders(regions, neighbor_lists)
print(' ...Segmented folds ({0:.2f} seconds)'.format(time() - t1))
#---------------------------------------------------------------------
# Remove small folds
#---------------------------------------------------------------------
if min_fold_size > 1:
print(' Remove folds smaller than {0}'.format(min_fold_size))
unique_folds = [x for x in np.unique(folds) if x != -1]
for nfold in unique_folds:
indices_fold = [i for i, x in enumerate(folds) if x == nfold]
if len(indices_fold) < min_fold_size:
folds[indices_fold] = -1
#---------------------------------------------------------------------
# Find and fill holes in the folds
# Note: Surfaces surrounded by folds can be mistaken for holes,
# so exclude_range includes outer surface values close to zero.
#---------------------------------------------------------------------
if do_fill_holes:
print(" Find and fill holes in the folds")
folds = fill_holes(folds,
neighbor_lists,
values=depths,
exclude_range=[0, min_hole_depth])
#---------------------------------------------------------------------
# Renumber folds so they are sequential
#---------------------------------------------------------------------
renumber_folds = -1 * np.ones(len(folds))
fold_numbers = [int(x) for x in np.unique(folds) if x != -1]
for i_fold, n_fold in enumerate(fold_numbers):
fold = [i for i, x in enumerate(folds) if x == n_fold]
renumber_folds[fold] = i_fold
folds = renumber_folds
n_folds = i_fold + 1
# Print statement
print(' ...Extracted {0} folds ({1:.2f} seconds)'.format(
n_folds,
time() - t0))
else:
print(' No deep vertices')
folds = [int(x) for x in folds]
#-------------------------------------------------------------------------
# Return folds, number of folds, file name
#-------------------------------------------------------------------------
if save_file:
folds_file = os.path.join(os.getcwd(), 'folds.vtk')
rewrite_scalars(depth_file, folds_file, folds, 'folds', folds)
if not os.path.exists(folds_file):
raise (IOError(folds_file + " not found"))
else:
folds_file = None
return folds, n_folds, depth_threshold, bins, bin_edges, folds_file
def find_depth_threshold(depth_file, min_vertices=10000, verbose=False):
"""
Find depth threshold to extract folds from a triangular surface mesh.
Steps ::
1. Compute histogram of depth measures.
2. Define a depth threshold and find the deepest vertices.
To extract an initial set of deep vertices from the surface mesh,
we anticipate that there will be a rapidly decreasing distribution
of low depth values (on the outer surface) with a long tail
of higher depth values (in the folds), so we smooth the histogram's
bin values, convolve to compute slopes, and find the depth value
for the first bin with slope = 0. This is our threshold.
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
min_vertices : integer
minimum number of vertices
verbose : bool
print statements?
Returns
-------
depth_threshold : float
threshold defining the minimum depth for vertices to be in a fold
bins : list of integers
histogram bins: each is the number of vertices within a range of depth values
bin_edges : list of floats
histogram bin edge values defining the bin ranges of depth values
Examples
--------
>>> import numpy as np
>>> from mindboggle.features.folds import find_depth_threshold
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> min_vertices = 10000
>>> verbose = False
>>> depth_threshold, bins, bin_edges = find_depth_threshold(depth_file,
... min_vertices, verbose)
>>> np.float(np.array_str(np.array([depth_threshold]),
... precision=5).strip('[').strip(']').strip())
2.36089
View threshold histogram plots (skip test):
>>> def vis():
... import numpy as np
... import pylab
... from scipy.ndimage.filters import gaussian_filter1d
... from mindboggle.mio.vtks import read_scalars
... # Plot histogram and depth threshold:
... depths, name = read_scalars(depth_file)
... nbins = np.round(len(depths) / 100.0)
... a,b,c = pylab.hist(depths, bins=nbins)
... pylab.plot(depth_threshold * np.ones((100,1)),
... np.linspace(0, max(bins), 100), 'r.')
... pylab.title('Histogram of depth values with threshold')
... pylab.xlabel('Depth')
... pylab.ylabel('Number of vertices')
... pylab.show()
... # Plot smoothed histogram:
... bins_smooth = gaussian_filter1d(bins.tolist(), 5)
... pylab.plot(list(range(len(bins))), bins, '.',
... list(range(len(bins))), bins_smooth,'-')
... pylab.title('Smoothed histogram of depth values')
... pylab.show()
>>> vis() # doctest: +SKIP
"""
import numpy as np
from scipy.ndimage.filters import gaussian_filter1d
from mindboggle.mio.vtks import read_vtk
# ------------------------------------------------------------------------
# Load depth values for all vertices:
# ------------------------------------------------------------------------
points, indices, lines, faces, depths, scalar_names, npoints, \
input_vtk = read_vtk(depth_file, return_first=True, return_array=True)
# ------------------------------------------------------------------------
# Compute histogram of depth measures:
# ------------------------------------------------------------------------
if npoints > min_vertices:
nbins = np.round(npoints / 100.0)
else:
raise IOError(" Expecting at least {0} vertices to create "
"depth histogram".format(min_vertices))
bins, bin_edges = np.histogram(depths, bins=nbins)
# ------------------------------------------------------------------------
# Anticipating that there will be a rapidly decreasing distribution
# of low depth values (on the outer surface) with a long tail of higher
# depth values (in the folds), smooth the bin values (Gaussian), convolve
# to compute slopes, and find the depth for the first bin with slope = 0.
# ------------------------------------------------------------------------
bins_smooth = gaussian_filter1d(bins.tolist(), 5)
window = [-1, 0, 1]
bin_slopes = np.convolve(bins_smooth, window, mode='same') / \
(len(window) - 1)
ibins0 = np.where(bin_slopes == 0)[0]
if ibins0.shape:
depth_threshold = bin_edges[ibins0[0]]
else:
depth_threshold = np.median(depths)
# Print statement:
if verbose:
print(' Depth threshold: {0}'.format(depth_threshold))
return depth_threshold, bins, bin_edges
def write_shape_stats(labels_or_file=[], sulci=[], fundi=[],
affine_transform_files=[], inverse_booleans=[], transform_format='itk',
area_file='', normalize_by_area=False, mean_curvature_file='',
travel_depth_file='', geodesic_depth_file='',
freesurfer_thickness_file='', freesurfer_curvature_file='',
freesurfer_sulc_file='',
labels_spectra=[], labels_spectra_IDs=[],
sulci_spectra=[], sulci_spectra_IDs=[],
labels_zernike=[], labels_zernike_IDs=[],
sulci_zernike=[], sulci_zernike_IDs=[],
exclude_labels=[-1]):
"""
Make tables of shape statistics per label, sulcus, and/or fundus.
Note ::
This function is tailored for Mindboggle outputs.
Parameters
----------
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
sulci : list of integers
indices to sulci, one per vertex, with -1 indicating no sulcus
fundi : list of integers
indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_files : list of strings
affine transform files to standard space
inverse_booleans : list of of zeros and ones
for each transform, 1 to take the inverse, else 0
transform_format : string
format for transform file
Ex: 'txt' for text, 'itk' for ITK, and 'mat' for Matlab format
area_file : string
name of VTK file with surface area scalar values
normalize_by_area : Boolean
normalize all shape measures by area of label/feature? (UNTESTED)
mean_curvature_file : string
name of VTK file with mean curvature scalar values
travel_depth_file : string
name of VTK file with travel depth scalar values
geodesic_depth_file : string
name of VTK file with geodesic depth scalar values
freesurfer_thickness_file : string
name of VTK file with FreeSurfer thickness scalar values
freesurfer_curvature_file : string
name of VTK file with FreeSurfer curvature (curv) scalar values
freesurfer_sulc_file : string
name of VTK file with FreeSurfer convexity (sulc) scalar values
labels_spectra : list of lists of floats
Laplace-Beltrami spectra for each labeled region
labels_spectra_IDs : list of integers
unique labels for labels_spectra
sulci_spectra : list of lists of floats
Laplace-Beltrami spectra for each sulcus
sulci_spectra_IDs : list of integers
unique sulcus IDs for sulci_spectra
labels_zernike : list of lists of floats
Zernike moments for each labeled region
labels_zernike_IDs : list of integers
unique labels for labels_zernike
sulci_zernike : list of lists of floats
Zernike moments for each sulcus
sulci_zernike_IDs : list of integers
unique sulcus IDs for sulci_zernike
exclude_labels : list of lists of integers
indices to be excluded (in addition to -1)
Returns
-------
label_table : string
output table filename for label shapes
sulcus_table : string
output table filename for sulcus shapes
fundus_table : string
output table filename for fundus shapes
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.tables import write_shape_stats
>>> path = '/homedir/mindboggled/Twins-2-1'
>>> labels_or_file = os.path.join(path, 'labels', 'left_cortical_surface', 'freesurfer_cortex_labels.vtk')
>>> sulci_file = os.path.join(path, 'features', 'left_cortical_surface', 'sulci.vtk')
>>> fundi_file = os.path.join(path, 'features', 'left_cortical_surface', 'fundus_per_sulcus.vtk')
>>> sulci, name = read_scalars(sulci_file)
>>> fundi, name = read_scalars(fundi_file)
>>> affine_transform_files = [] #os.path.join(path, 'arno', 'mri', 't1weighted_brain.MNI152Affine.txt')
>>> inverse_booleans = []
>>> #transform_format = 'mat'
>>> transform_format = 'itk'
>>> area_file = os.path.join(path, 'shapes', 'left_cortical_surface', 'area.vtk')
>>> normalize_by_area = False
>>> mean_curvature_file = os.path.join(path, 'shapes', 'left_cortical_surface', 'mean_curvature.vtk')
>>> travel_depth_file = os.path.join(path, 'shapes', 'left_cortical_surface', 'travel_depth.vtk')
>>> geodesic_depth_file = os.path.join(path, 'shapes', 'left_cortical_surface', 'geodesic_depth.vtk')
>>> freesurfer_thickness_file = ''
>>> freesurfer_curvature_file = ''
>>> freesurfer_sulc_file = ''
>>> #
>>> labels, name = read_scalars(labels_or_file)
>>> labels_spectra = []
>>> labels_spectra_IDs = []
>>> sulci_spectra = []
>>> sulci_spectra_IDs = []
>>> labels_zernike = []
>>> labels_zernike_IDs = []
>>> sulci_zernike = []
>>> sulci_zernike_IDs = []
>>> exclude_labels = [-1]
>>> #
>>> write_shape_stats(labels_or_file, sulci, fundi,
>>> affine_transform_files, inverse_booleans, transform_format,
>>> area_file, normalize_by_area,
>>> mean_curvature_file, travel_depth_file, geodesic_depth_file,
>>> freesurfer_thickness_file, freesurfer_curvature_file,
>>> freesurfer_sulc_file,
>>> labels_spectra, labels_spectra_IDs,
>>> sulci_spectra, sulci_spectra_IDs,
>>> labels_zernike, labels_zernike_IDs,
>>> sulci_zernike, sulci_zernike_IDs,
>>> exclude_labels)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.guts.compute import means_per_label, stats_per_label, \
sum_per_label
from mindboggle.mio.vtks import read_scalars, read_vtk, \
apply_affine_transforms
from mindboggle.mio.labels import DKTprotocol
dkt = DKTprotocol()
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = [int(x) for x in labels_or_file]
elif isinstance(labels_or_file, list):
labels = labels_or_file
elif isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file)
if isinstance(sulci, np.ndarray):
sulci = [int(x) for x in sulci]
if isinstance(fundi, np.ndarray):
fundi = [int(x) for x in fundi]
if not labels and not sulci and not fundi:
import sys
sys.exit('No feature data to tabulate in write_shape_stats().')
spectrum_start = 1 # Store all columns of spectral components (0),
# or start from higher frequency components (>=1)
#-------------------------------------------------------------------------
# Feature lists, shape names, and shape files:
#-------------------------------------------------------------------------
# Feature lists:
feature_lists = [labels, sulci, fundi]
feature_names = ['label', 'sulcus', 'fundus']
spectra_lists = [labels_spectra, sulci_spectra]
spectra_ID_lists = [labels_spectra_IDs, sulci_spectra_IDs]
zernike_lists = [labels_zernike, sulci_zernike]
zernike_ID_lists = [labels_zernike_IDs, sulci_zernike_IDs]
table_names = ['label_shapes.csv', 'sulcus_shapes.csv',
'fundus_shapes.csv']
# Shape names corresponding to shape files below:
shape_names = ['area', 'travel depth', 'geodesic depth',
'mean curvature', 'freesurfer curvature',
'freesurfer thickness', 'freesurfer convexity (sulc)']
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [area_file, travel_depth_file, geodesic_depth_file,
mean_curvature_file, freesurfer_curvature_file,
freesurfer_thickness_file, freesurfer_sulc_file]
shape_arrays = []
first_pass = True
area_array = []
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
points, indices, lines, faces, scalars_array, scalar_names, \
npoints, input_vtk = read_vtk(shape_file, True, True)
points = np.array(points)
first_pass = False
if affine_transform_files and transform_format:
affine_points, \
foo1 = apply_affine_transforms(affine_transform_files,
inverse_booleans, transform_format,
points, vtk_file_stem='')
else:
scalars_array, name = read_scalars(shape_file, True, True)
if scalars_array.size:
shape_arrays.append(scalars_array)
# Store area array:
if ishape == 0:
area_array = scalars_array.copy()
if normalize_by_area:
use_area = area_array
else:
use_area = []
# Initialize table file names:
label_table = ''
sulcus_table = ''
fundus_table = ''
# Loop through features / tables:
for itable, feature_list in enumerate(feature_lists):
column_names = []
#-----------------------------------------------------------------
# Label names:
#-----------------------------------------------------------------
label_title = 'name'
if itable == 0:
label_numbers = dkt.cerebrum_cortex_DKT31_numbers
label_names = dkt.cerebrum_cortex_DKT31_names
elif itable in [1, 2]:
label_numbers = dkt.sulcus_numbers
label_names = dkt.sulcus_names
else:
label_numbers = []
label_names = []
include_labels = label_numbers
nlabels = len(label_numbers)
#---------------------------------------------------------------------
# For each feature, construct a table of average shape values:
#---------------------------------------------------------------------
if feature_list:
feature_name = feature_names[itable]
columns = []
#-----------------------------------------------------------------
# Loop through shape measures:
#-----------------------------------------------------------------
column_names.extend(column_names[:])
for ishape, shape_array in enumerate(shape_arrays):
shape = shape_names[ishape]
print(' Compute statistics on {0} {1}...'.
format(feature_name, shape))
#-------------------------------------------------------------
# Append feature areas to columns:
#-------------------------------------------------------------
if ishape == 0 and np.size(area_array):
sums, label_list = sum_per_label(shape_array,
feature_list, include_labels, exclude_labels)
column_names.append(shape)
columns.append(sums)
#-------------------------------------------------------------
# Append feature shape statistics to columns:
#-------------------------------------------------------------
else:
medians, mads, means, sdevs, skews, kurts, \
lower_quarts, upper_quarts, \
label_list = stats_per_label(shape_array, feature_list,
include_labels, exclude_labels,
area_array, precision=1)
column_names.append(shape + ': median')
column_names.append(shape + ': MAD')
column_names.append(shape + ': mean')
column_names.append(shape + ': SD')
column_names.append(shape + ': skew')
column_names.append(shape + ': kurtosis')
column_names.append(shape + ': 25%')
column_names.append(shape + ': 75%')
columns.append(medians)
columns.append(mads)
columns.append(means)
columns.append(sdevs)
columns.append(skews)
columns.append(kurts)
columns.append(lower_quarts)
columns.append(upper_quarts)
#-----------------------------------------------------------------
# Mean positions in the original space:
#-----------------------------------------------------------------
# Compute mean position per feature:
positions, sdevs, label_list, foo = means_per_label(points,
feature_list, include_labels, exclude_labels, use_area)
# Append mean x,y,z position per feature to columns:
xyz_positions = np.asarray(positions)
for ixyz, xyz in enumerate(['x','y','z']):
column_names.append('mean position: {0}'.format(xyz))
columns.append(xyz_positions[:, ixyz].tolist())
#-----------------------------------------------------------------
# Mean positions in standard space:
#-----------------------------------------------------------------
if affine_transform_files and transform_format:
# Compute standard space mean position per feature:
standard_positions, sdevs, label_list, \
foo = means_per_label(affine_points,
feature_list, include_labels, exclude_labels, use_area)
# Append standard space x,y,z position per feature to columns:
xyz_std_positions = np.asarray(standard_positions)
for ixyz, xyz in enumerate(['x','y','z']):
column_names.append('mean position in standard space:'
' {0}'.format(xyz))
columns.append(xyz_std_positions[:, ixyz].tolist())
#-----------------------------------------------------------------
# Laplace-Beltrami spectra:
#-----------------------------------------------------------------
if itable in [0, 1]:
spectra = spectra_lists[itable]
if spectra:
spectra_IDs = spectra_ID_lists[itable]
# Construct a matrix of spectra:
len_spectrum = len(spectra[0])
spectrum_matrix = np.zeros((nlabels, len_spectrum))
for ilabel, label in enumerate(include_labels):
if label in spectra_IDs:
spectrum = spectra[spectra_IDs.index(label)]
spectrum_matrix[ilabel, 0:len_spectrum] = spectrum
# Append spectral shape name and values to columns:
for ispec in range(spectrum_start, len_spectrum):
columns.append(spectrum_matrix[:, ispec].tolist())
column_names.append('Laplace-Beltrami spectrum:'
' component {0}'.format(ispec+1))
#-----------------------------------------------------------------
# Zernike moments:
#-----------------------------------------------------------------
if itable in [0, 1]:
zernike = zernike_lists[itable]
if zernike:
zernike_IDs = zernike_ID_lists[itable]
# Construct a matrix of Zernike moments:
len_moments = len(zernike[0])
moments_matrix = np.zeros((nlabels, len_moments))
for ilabel, label in enumerate(include_labels):
if label in zernike_IDs:
moments = zernike[zernike_IDs.index(label)]
moments_matrix[ilabel, 0:len_moments] = moments
# Append Zernike shape name and values to columns:
for imoment in range(0, len_moments):
columns.append(moments_matrix[:, imoment].tolist())
column_names.append('Zernike moments: component {0}'.
format(imoment+1))
#-----------------------------------------------------------------
# Write labels/IDs and values to table:
#-----------------------------------------------------------------
# Write labels/IDs to table:
output_table = os.path.join(os.getcwd(), table_names[itable])
if columns:
df1 = pd.DataFrame({'ID': label_numbers})
df2 = pd.DataFrame(np.transpose(columns),
columns = column_names)
df = pd.concat([df1, df2], axis=1)
if label_names:
df0 = pd.DataFrame({'name': label_names})
df = pd.concat([df0, df], axis=1)
df.to_csv(output_table, index=False)
if not os.path.exists(output_table):
raise(IOError(output_table + " not found"))
#-----------------------------------------------------------------
# Return correct table file name:
#-----------------------------------------------------------------
if itable == 0:
label_table = output_table
elif itable == 1:
sulcus_table = output_table
elif itable == 2:
fundus_table = output_table
return label_table, sulcus_table, fundus_table
def extract_folds(depth_file,
depth_threshold=2,
min_fold_size=50,
save_file=False,
output_file='',
background_value=-1,
verbose=False):
"""
Use depth threshold to extract folds from a triangular surface mesh.
A fold is a group of connected, deep vertices. To extract folds,
a depth threshold is used to segment deep vertices of the surface mesh.
We have observed in the histograms of travel depth measures of cortical
surfaces that there is a rapidly decreasing distribution of low depth
values (corresponding to the outer surface, or gyral crowns) with a
long tail of higher depth values (corresponding to the folds).
The find_depth_threshold function therefore computes a histogram of
travel depth measures, smooths the histogram's bin values, convolves
to compute slopes, and finds the depth value for the first bin with
zero slope. The extract_folds function uses this depth value, segments
deep vertices, and removes extremely small folds (empirically set at 50
vertices or fewer out of a total mesh size of over 100,000 vertices).
Steps ::
1. Segment deep vertices as an initial set of folds.
2. Remove small folds.
3. Renumber folds.
Note ::
Removed option: Find and fill holes in the folds:
Folds could have holes in areas shallower than the depth threshold.
Calling fill_holes() could accidentally include very shallow areas
(in an annulus-shaped fold, for example).
However, we could include the argument exclude_range to check for
any values from zero to min_hole_depth; holes would not be filled
if they were to contain values within this range.
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
depth_threshold : float
threshold defining the minimum depth for vertices to be in a fold
min_fold_size : integer
minimum fold size (number of vertices)
save_file : bool
save output VTK file?
output_file : string
name of output file in VTK format
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_folds : int
number of folds
folds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> from mindboggle.features.folds import extract_folds
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> depth_threshold = 2.36089
>>> min_fold_size = 50
>>> save_file = True
>>> output_file = 'extract_folds.vtk'
>>> background_value = -1
>>> verbose = False
>>> folds, n_folds, folds_file = extract_folds(depth_file,
... depth_threshold, min_fold_size, save_file, output_file,
... background_value, verbose)
>>> n_folds
33
>>> lens = [len([x for x in folds if x == y]) for y in range(n_folds)]
>>> lens[0:10]
[726, 67241, 2750, 5799, 1151, 6360, 1001, 505, 228, 198]
View folds (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> plot_surfaces('extract_folds.vtk') # doctest: +SKIP
View folds without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> rewrite_scalars(depth_file, 'extract_folds_no_background.vtk', folds,
... 'just_folds', folds, -1) # doctest: +SKIP
>>> plot_surfaces('extract_folds_no_background.vtk') # doctest: +SKIP
"""
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import rewrite_scalars, read_vtk
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import segment_regions
if verbose:
print("Extract folds in surface mesh")
t0 = time()
# ------------------------------------------------------------------------
# Load depth values for all vertices
# ------------------------------------------------------------------------
points, indices, lines, faces, depths, scalar_names, npoints, \
input_vtk = read_vtk(depth_file, return_first=True, return_array=True)
# ------------------------------------------------------------------------
# Find the deepest vertices
# ------------------------------------------------------------------------
indices_deep = [i for i, x in enumerate(depths) if x >= depth_threshold]
if indices_deep:
# --------------------------------------------------------------------
# Find neighbors for each vertex
# --------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
# --------------------------------------------------------------------
# Segment deep vertices as an initial set of folds
# --------------------------------------------------------------------
if verbose:
print(" Segment vertices deeper than {0:.2f} as folds".format(
depth_threshold))
t1 = time()
folds = segment_regions(indices_deep, neighbor_lists, 1, [], False,
False, [], [], [], '', background_value, False)
if verbose:
print(' ...Segmented folds ({0:.2f} seconds)'.format(time() - t1))
# --------------------------------------------------------------------
# Remove small folds
# --------------------------------------------------------------------
if min_fold_size > 1:
if verbose:
print(' Remove folds smaller than {0}'.format(min_fold_size))
unique_folds = [
x for x in np.unique(folds) if x != background_value
]
for nfold in unique_folds:
indices_fold = [i for i, x in enumerate(folds) if x == nfold]
if len(indices_fold) < min_fold_size:
folds[indices_fold] = background_value
# --------------------------------------------------------------------
# Find and fill holes in the folds
# Note: Surfaces surrounded by folds can be mistaken for holes,
# so exclude_range includes outer surface values close to zero.
# --------------------------------------------------------------------
# folds = fill_holes(folds, neighbor_lists, values=depths,
# exclude_range=[0, min_hole_depth])
# --------------------------------------------------------------------
# Renumber folds so they are sequential.
# NOTE: All vertices are included (-1 for non-fold vertices).
# --------------------------------------------------------------------
renumber_folds = background_value * np.ones(npoints)
fold_numbers = [x for x in np.unique(folds) if x != background_value]
for i_fold, n_fold in enumerate(fold_numbers):
fold_indices = [i for i, x in enumerate(folds) if x == n_fold]
renumber_folds[fold_indices] = i_fold
folds = renumber_folds
folds = [int(x) for x in folds]
n_folds = i_fold + 1
# Print statement
if verbose:
print(' ...Extracted {0} folds ({1:.2f} seconds)'.format(
n_folds,
time() - t0))
else:
if verbose:
print(' No deep vertices')
# ------------------------------------------------------------------------
# Return folds, number of folds, file name
# ------------------------------------------------------------------------
if save_file:
if output_file:
folds_file = output_file
else:
folds_file = os.path.join(os.getcwd(), 'folds.vtk')
rewrite_scalars(depth_file, folds_file, folds, 'folds', [],
background_value)
if not os.path.exists(folds_file):
raise IOError(folds_file + " not found")
else:
folds_file = None
return folds, n_folds, folds_file
def write_face_vertex_averages(input_file, output_table='', area_file=''):
"""
Make table of average vertex values per face
(divided by face area if area_file provided).
Parameters
----------
input_file : string
name of VTK file with scalars to average
area_file : string
name of VTK file with surface area scalar values
output_table : string
output table filename
Returns
-------
output_table : string
output table filename
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import write_face_vertex_averages
>>> path = '/homedir/mindboggled'
>>> input_file = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'freesurfer_thickness.vtk')
>>> area_file = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'area.vtk')
>>> output_table = ''
>>> #
>>> write_face_vertex_averages(input_file, output_table, area_file)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_vtk, read_scalars
points, indices, lines, faces, scalars, scalar_names, \
npoints, input_vtk = read_vtk(input_file, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
#---------------------------------------------------------------------
# For each face, average vertex values:
#---------------------------------------------------------------------
columns = []
for face in faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
#-----------------------------------------------------------------
# Write to table:
#-----------------------------------------------------------------
if not output_table:
output_table = os.path.join(os.getcwd(), 'average_face_values.csv')
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False)
if not os.path.exists(output_table):
raise(IOError(output_table + " not found"))
return output_table
def spectrum_per_label(vtk_file,
spectrum_size=10,
exclude_labels=[-1],
normalization='area',
area_file='',
largest_segment=True):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string (optional)
name of VTK file with surface area scalar values
largest_segment : Boolean
compute spectrum only for largest segment with a given label?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> spectrum_per_label(vtk_file, spectrum_size, exclude_labels, None,
>>> area_file, largest_segment)
([[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]],
[22])
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.guts.mesh import remove_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
faces, u1, u2, points, u4, labels, u5, u6 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[
ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels
]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i, x in enumerate(labels) if x == label]
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = remove_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size, exclude_labels_inner,
normalization, areas)
else:
spectrum = fem_laplacian(pick_points, pick_faces, spectrum_size,
normalization)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def relabel_surface(vtk_file, hemi='', old_labels=[], new_labels=[],
erase_remaining=True, erase_labels=[], erase_value=-1,
output_file=''):
"""
Relabel surface in a VTK file.
Parameters
----------
vtk_file : string
input labeled VTK file
hemi : string
hemisphere ('lh' or 'rh' or '')
if set, add 1000 to left and 2000 to right hemisphere labels;
old_labels : list of integers
old labels (empty list if labels drawn from vtk scalars);
may be used in conjunction with hemi
new_labels : list of integers
new labels (empty list if labels drawn from vtk scalars);
may be used in conjunction with hemi
erase_remaining : bool
set all values not in old_labels to erase_value?
erase_labels : list of integers
values to erase (set to erase_value)
erase_value : integer
set vertices with labels in erase_labels to this value
output_file : string
new vtk file name
Returns
-------
output_file : string
new vtk file name
Examples
--------
>>> import numpy as np
>>> from mindboggle.guts.relabel import relabel_surface
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'])
>>> hemi = 'lh'
>>> old_labels = [1003,1009,1030]
>>> new_labels = [0,500,1000]
>>> erase_remaining = True
>>> erase_labels = [0]
>>> erase_value = -1
>>> output_file = ''
>>> output_file = relabel_surface(vtk_file, hemi, old_labels, new_labels,
... erase_remaining, erase_labels, erase_value, output_file)
>>> labels, name = read_scalars(output_file, True, True)
>>> np.unique(labels)
array([ -1, 1000, 1500, 2000])
View relabeled surface file (skip test):
>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces(output_file) # doctest: +SKIP
"""
import os
import numpy as np
from mindboggle.mio.vtks import read_vtk, write_vtk
# Load labeled vtk surfaces:
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file, return_first=True, return_array=True)
new_scalars = scalars[:]
# Raise an error if inputs set incorrectly:
if (new_labels and not old_labels) or \
(hemi and hemi not in ['lh','rh']) or \
(new_labels and len(old_labels) != len(new_labels)) or \
(erase_remaining and not old_labels):
raise IOError("Please check inputs for relabel_surface().")
# Loop through unique labels in scalars:
ulabels = np.unique(scalars)
for label in ulabels:
I = np.where(scalars == label)[0]
# If label in erase_labels list, replace with erase_value:
if label in erase_labels:
new_scalars[I] = erase_value
# If label in old_labels list, replace with corresponding new label,
# and if hemi set, add 1000 or 2000 to the new label:
elif label in old_labels and (len(old_labels) == len(new_labels)):
new_label = new_labels[old_labels.index(label)]
if hemi == 'lh':
new_scalars[I] = 1000 + new_label
elif hemi == 'rh':
new_scalars[I] = 2000 + new_label
else:
new_scalars[I] = new_label
# If labels not set then optionally add hemi value:
elif hemi and not new_labels:
if hemi == 'lh':
new_scalars[I] = 1000 + label
elif hemi == 'rh':
new_scalars[I] = 2000 + label
# If label unaccounted for and erase_remaining, set to erase_value:
elif erase_remaining:
new_scalars[I] = erase_value
# Ensure that the new scalars are integer values:
new_scalars = [int(x) for x in new_scalars]
# Write output VTK file:
if not output_file:
output_file = os.path.join(os.getcwd(),
'relabeled_' + os.path.basename(vtk_file))
write_vtk(output_file, points, indices, lines, faces,
[new_scalars], ['Labels'], scalar_type='int')
if not os.path.exists(output_file):
raise IOError("relabel_surface() did not create " + output_file + ".")
return output_file
def realign_boundaries_to_fundus_lines(
surf_file, init_label_file, fundus_lines_file, thickness_file,
out_label_file=None):
"""
Fix label boundaries to fundus lines.
Parameters
----------
surf_file : file containing the surface geometry in vtk format
init_label_file : file containing scalars that represent the
initial guess at labels
fundus_lines_file : file containing scalars representing fundus lines.
thickness_file: file containing cortical thickness scalar data
(for masking out the medial wall only)
out_label_file : if specified, the realigned labels will be writen to
this file
Returns
-------
numpy array representing the realigned label for each surface vertex.
"""
import numpy as np
from mindboggle.guts.segment import extract_borders
import mindboggle.guts.graph as go
from mindboggle.mio.vtks import read_vtk, read_scalars, write_vtk
from mindboggle.guts.mesh import find_neighbors
import propagate_fundus_lines
## read files
faces, _, indices, points, num_points, _, _, _ = read_vtk(
surf_file, return_first=True, return_array=True)
indices = range(num_points)
init_labels, _ = read_scalars(init_label_file,
return_first=True, return_array=True)
fundus_lines, _ = read_scalars(fundus_lines_file,
return_first=True, return_array=True)
thickness, _ = read_scalars(thickness_file,
return_first=True, return_array=True)
# remove labels from vertices with zero thickness (get around
# DKT40 annotations having the label '3' for all the Corpus
# Callosum vertices).
cc_inds = [x for x in indices if thickness[x] < 0.001]
init_labels[cc_inds] = 0
## setup seeds from initial label boundaries
neighbor_lists = find_neighbors(faces, num_points)
# extract all vertices that are on a boundary between labels
boundary_indices, label_pairs, _ = extract_borders(
indices, init_labels, neighbor_lists,
return_label_pairs=True)
# split boundary vertices into segments with common boundary pairs.
boundary_segments = {}
for boundary_index, label_pair in zip(boundary_indices, label_pairs):
key = ((label_pair[0], label_pair[1]) if label_pair[0] < label_pair[1]
else (label_pair[1], label_pair[0]))
if key not in boundary_segments:
boundary_segments[key] = []
boundary_segments[key].append(boundary_index)
boundary_matrix, boundary_matrix_keys = _build_boundary_matrix(
boundary_segments, num_points)
# build the affinity matrix
affinity_matrix = go.weight_graph(
np.array(points), indices, np.array(faces), sigma=10, add_to_graph=False)
## propagate boundaries to fundus line vertices
learned_matrix = _propagate_labels(
affinity_matrix, boundary_matrix, boundary_indices, 100, 1)
# assign labels to fundus line vertices based on highest probability
new_boundaries = -1 * np.ones(init_labels.shape)
fundus_line_indices = [i for i, x in enumerate(fundus_lines) if x > 0.5]
# tile the surface into connected components delimited by fundus lines
closed_fundus_lines, _, _ = propagate_fundus_lines.propagate_fundus_lines(
points, faces, fundus_line_indices, thickness)
closed_fundus_line_indices = np.where(closed_fundus_lines > 0)[0]
# split surface into connected components
connected_component_faces = _remove_boundary_faces(
points, faces, closed_fundus_line_indices)
# label components based on most probable label assignment
new_labels = _label_components(
connected_component_faces, num_points, boundary_indices, learned_matrix,
boundary_matrix_keys)
# propagate new labels to fill holes
label_matrix, label_map = _build_label_matrix(new_labels)
new_learned_matrix = _propagate_labels(
affinity_matrix, label_matrix,
[i for i in range(num_points) if new_labels[i] >= 0], 100, 1)
# assign most probable labels
for idx in [i for i in range(num_points) if new_labels[i] == -1]:
max_idx = np.argmax(new_learned_matrix[idx])
new_labels[idx] = label_map[max_idx]
# save
if out_label_file is not None:
write_vtk(out_label_file, points, faces=faces,
scalars=[int(x) for x in new_labels], scalar_type='int')
return new_labels
