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.utils.io_vtk 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 decimate_file(input_vtk,
reduction=0.5,
smooth_steps=100,
save_vtk=False,
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.utils.mesh import decimate_file
>>> from mindboggle.utils.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.utils.io_vtk import read_vtk
from mindboggle.utils.mesh import decimate
# Read VTK surface mesh file:
faces, u1, u2, points, u4, scalars, u5, u6 = 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 main() :
parser = ArgumentParser()
parser.add_argument('vtk_file')
ns = parser.parse_args()
faces, lines, indices, points, npoints, scalars, scalar_names, input_vtk = read_vtk(ns.vtk_file)
points = [[0,0,0], [1,0,0], [0,0,1], [0,1,1], [1,0,1], [0,1,0], [1,1,1], [1,1,0]]
faces = [[0,2,4], [0,1,4], [2,3,4], [3,4,5], [3,5,6], [0,1,7]]
zernike_moments( array(points), array(faces) )
def decimate_file(input_vtk, reduction=0.5, smooth_steps=100,
save_vtk=False, 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.utils.mesh import decimate_file
>>> from mindboggle.utils.plots import plot_vtk
>>> 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_vtk('decimated.vtk') # doctest: +SKIP
"""
from mindboggle.utils.io_vtk import read_vtk
from mindboggle.utils.mesh import decimate
# Read VTK surface mesh file:
faces, u1, u2, points, u4, scalars, u5, u6 = 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 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:
faces, lines, indices, points, n_points, depths, name, 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, 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?
output_vtk : string
output decimated vtk file
Returns
-------
output_vtk : string
output decimated vtk file
Examples
--------
>>> import os
>>> from mindboggle.utils.mesh import decimate_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_vtk = os.path.join(path, 'arno', 'labels', 'label22.vtk')
>>> output_vtk = 'decimated_file.vtk'
>>> reduction = 0.9
>>> smooth_steps = 0
>>> decimate_file(input_vtk, reduction=reduction,
>>> smooth_steps=smooth_steps, output_vtk=output_vtk)
>>> # View:
>>> os.system('mayavi2 -d ' + output_vtk + ' -m Surface &')
"""
import os
import vtk
from mindboggle.utils.io_vtk import read_vtk
# Read VTK surface mesh file:
faces, u1, u2, points, u4, labels, u5, u6 = read_vtk(input_vtk)
# vtk points:
vtk_points = vtk.vtkPoints()
[vtk_points.InsertPoint(i, x[0], x[1], x[2]) for i,x in enumerate(points)]
# vtk faces:
vtk_faces = vtk.vtkCellArray()
for face in faces:
vtk_face = vtk.vtkPolygon()
vtk_face.GetPointIds().SetNumberOfIds(3)
vtk_face.GetPointIds().SetId(0, face[0])
vtk_face.GetPointIds().SetId(1, face[1])
vtk_face.GetPointIds().SetId(2, face[2])
vtk_faces.InsertNextCell(vtk_face)
# vtkPolyData:
polydata = vtk.vtkPolyData()
polydata.SetPoints(vtk_points)
polydata.SetPolys(vtk_faces)
# We want to preserve topology (not let any cracks form).
# This may limit the total reduction possible.
decimate = vtk.vtkDecimatePro()
decimate.SetInput(polydata)
decimate.SetTargetReduction(reduction)
decimate.PreserveTopologyOn()
# Export output:
if not output_vtk:
output_vtk = os.path.join(os.getcwd(), 'decimated_file.vtk')
exporter = vtk.vtkPolyDataWriter()
if smooth_steps > 0:
smoother = vtk.vtkSmoothPolyDataFilter()
smoother.SetInputConnection(decimate.GetOutputPort())
smoother.SetNumberOfIterations(smooth_steps)
exporter.SetInput(smoother.GetOutput())
else:
exporter.SetInput(decimate.GetOutput())
exporter.SetFileName(output_vtk)
exporter.Write()
return output_vtk
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.utils.segment import extract_borders
import mindboggle.utils.graph as go
from mindboggle.utils.io_vtk import read_vtk, read_scalars, write_vtk
from mindboggle.utils.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
def rewrite_scalars(input_vtk, output_vtk, new_scalars,
new_scalar_names=['scalars'], filter_scalars=[]):
"""
Load VTK format file and save a subset of scalars into a new file.
Parameters
----------
input_vtk : string
input VTK file name
output_vtk : string
output VTK file name
new_scalars : list of lists of floats (or single list or array of floats)
each list (lookup table) contains new values to assign to the vertices
new_scalar_names : string or list of strings
each element is the new name for a lookup table
filter_scalars : list or numpy array (optional)
scalar values used to filter faces (values > -1 retained)
Returns
-------
output_vtk : string
output VTK file name
Examples
--------
>>> # Write vtk file with curvature values on sulci
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
>>> path = os.environ['MINDBOGGLE_DATA']
>>> curv_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> curvs, name = read_scalars(curv_file, True,True)
>>> sulci_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> sulci, name = read_scalars(sulci_file)
>>> #
>>> rewrite_scalars(curv_file, 'rewrite_scalars.vtk',
>>> [curvs, sulci], ['curvs', 'sulci'], sulci)
>>> #
>>> # View:
>>> from mindboggle.utils.plots import plot_vtk
>>> plot_vtk('rewrite_scalars.vtk')
"""
import os
import numpy as np
from mindboggle.utils.mesh import remove_faces
from mindboggle.utils.io_vtk import write_header, write_points, \
write_vertices, write_faces, write_scalars, read_vtk, \
scalars_checker
# Convert numpy arrays to lists
if isinstance(new_scalars, np.ndarray):
new_scalars = new_scalars.tolist()
if isinstance(filter_scalars, np.ndarray):
filter_scalars = filter_scalars.tolist()
# Output VTK file to current working directory
output_vtk = os.path.join(os.getcwd(), output_vtk)
# Load VTK file
faces, lines, indices, points, npoints, scalars, name, input_vtk = read_vtk(input_vtk)
# Find indices to nonzero values
indices = range(npoints)
if filter_scalars:
indices_filter = [i for i,x in enumerate(filter_scalars) if x > -1]
indices_remove = [i for i,x in enumerate(filter_scalars) if x == -1]
# Remove surface mesh faces whose three vertices are not all in indices
faces = remove_faces(faces, indices_filter)
# Write VTK file
Fp = open(output_vtk,'w')
write_header(Fp)
write_points(Fp, points)
if indices:
write_vertices(Fp, indices)
if faces:
write_faces(Fp, faces)
if new_scalars:
new_scalars, new_scalar_names = scalars_checker(new_scalars, new_scalar_names)
for i, new_scalar_list in enumerate(new_scalars):
if filter_scalars:
for iremove in indices_remove:
new_scalar_list[iremove] = -1
if i == 0:
new_scalar_name = new_scalar_names[0]
write_scalars(Fp, new_scalar_list, new_scalar_name)
else:
if len(new_scalar_names) < i + 1:
new_scalar_name = new_scalar_names[0]
else:
new_scalar_name = new_scalar_names[i]
write_scalars(Fp, new_scalar_list, new_scalar_name, begin_scalars=False)
else:
print('Error: new_scalars is empty')
exit()
Fp.close()
return output_vtk
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.utils.io_vtk 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 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.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.utils.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.utils.io_vtk import read_scalars, read_vtk, rewrite_scalars
from mindboggle.utils.mesh import find_neighbors
from mindboggle.utils.segment import extract_borders, propagate, segment
from mindboggle.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 write_face_vertex_averages(input_file, area_file="", delimiter=","):
"""
Make table of average vertex values per face.
Parameters
----------
input_file : string
name of VTK file with scalars to average
area_file : string
name of VTK file with surface area scalar values
delimiter : string
delimiter between columns, such as ','
Returns
-------
output_table : string
output table filename
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.utils.io_table import write_face_vertex_averages
>>> path = os.environ['MINDBOGGLE_DATA']
>>> #input_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> #input_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> input_file = os.path.join(path, 'arno', 'shapes', 'lh.thickness.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> delimiter = ','
>>> #
>>> write_face_vertex_averages(input_file, area_file, delimiter)
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_vtk, read_scalars
from mindboggle.utils.io_table import write_columns
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:
# ---------------------------------------------------------------------
output_table = os.path.join(os.getcwd(), "average_face_values.csv")
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:
# -----------------------------------------------------------------
write_columns(columns, "", output_table, delimiter, quote=False)
return output_table
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.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.utils.mesh import find_neighbors_from_file
>>> from mindboggle.features.folds import extract_subfolds
>>> from mindboggle.utils.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.utils.io_vtk import rewrite_scalars, read_vtk
from mindboggle.utils.mesh import find_neighbors
from mindboggle.utils.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 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.labels.relabel import relabel_surface
>>> from mindboggle.utils.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 = True
>>> 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')
"""
import os
import numpy as np
from mindboggle.utils.io_vtk 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 \
(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 spectrum_from_file(vtk_file, n_eigenvalues=6, 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
n_eigenvalues : 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 n_eigenvalues 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, n_eigenvalues=6)
Load "Labels" scalars from lh.labels.DKT25.manual.vtk
Linear FEM Laplace-Beltrami spectrum:
[4.829758648026221e-18,
0.00012841730024672036,
0.00027151815722727465,
0.00032051508471594065,
0.0004701628070486447,
0.0005768904023010338]
>>> # Spectrum for label 22 (postcentral) (after running explode_scalars()):
>>> 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, n_eigenvalues=6)
Load "scalars" scalars from label22.vtk
Linear FEM Laplace-Beltrami spectrum:
[6.3469513010430304e-18,
0.0005178862383467466,
0.0017434911095630806,
0.003667561767487689,
0.005429017880363778,
0.006309346984678918]
>>> # 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.utils.io_vtk 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, n_eigenvalues,
exclude_labels, normalization, areas)
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.utils.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/mindboggle_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.utils.mesh import remove_faces, reindex_faces_points
from mindboggle.utils.utils import execute
from mindboggle.utils.plots import plot_surfaces
from mindboggle.utils.io_vtk 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:
#-----------------------------------------------------------------
faces, lines, indices, points, npoints, scalars, scalar_names, \
o1 = 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 spectrum_per_label(vtk_file, n_eigenvalues=3, exclude_labels=[-1],
normalization='area', area_file=''):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
n_eigenvalues : 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_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
--------
>>> # Spectrum for label 22 (postcentral) in Twins-2-1:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import laplacian_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> n_eigenvalues = 6
>>> exclude_labels = [0] #[-1]
>>> laplacian_per_label(vtk_file, n_eigenvalues, exclude_labels,
>>> normalization=None, area_file=area_file)
Load "Labels" scalars from lh.labels.DKT25.manual.vtk
Load "scalars" scalars from lh.pial.area.vtk
7819 vertices for label 22
Reduced 290134 to 15230 triangular faces
Linear FEM Laplace-Beltrami spectrum:
([[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]],
[22])
"""
from mindboggle.utils.io_vtk import read_vtk, read_scalars
from mindboggle.utils.mesh import remove_faces
from mindboggle.shapes.laplace_beltrami import 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:
# Determine the indices per label:
label_indices = [i for i,x in enumerate(labels) if x == label]
print('{0} vertices for label {1}'.format(len(label_indices), label))
# Remove background faces:
select_faces = remove_faces(faces, label_indices)
# Compute Laplace-Beltrami spectrum for the label:
spectrum = spectrum_of_largest(points, select_faces, n_eigenvalues,
exclude_labels, normalization, areas)
# 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):
"""
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
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.utils.io_vtk import read_vtk, read_scalars
from mindboggle.utils.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=[],
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
output_file : string
new vtk file name
Returns
-------
output_file : string
new vtk file name
Examples
--------
>>> import os
>>> from mindboggle.labels.relabel import relabel_surface
>>> from mindboggle.utils.plots import plot_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> hemi = 'lh'
>>> old_labels = []
>>> new_labels = []
>>> output_file = ''
>>> #
>>> relabel_surface(vtk_file, hemi, old_labels, new_labels, output_file)
>>> # View
>>> plot_vtk('relabeled_lh.labels.DKT25.manual.vtk')
"""
import os
import numpy as np
from mindboggle.utils.io_vtk 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)
# Add a hemisphere value to each unique label drawn from scalars:
if hemi and not old_labels and not new_labels:
ulabels = np.unique(scalars)
for label in ulabels:
I = np.where(scalars == int(label))[0]
if hemi == 'lh':
scalars[I] = 1000 + int(label)
elif hemi == 'rh':
scalars[I] = 2000 + int(label)
# OR replace each old label with a corresponding new label
# (hemisphere setting optionally adds 1000 or 2000 to the new label):
else:
for ilabel, new_label in enumerate(new_labels):
I = np.where(scalars == int(old_labels[ilabel]))[0]
if hemi == 'lh':
scalars[I] = 1000 + int(new_label)
elif hemi == 'rh':
scalars[I] = 2000 + int(new_label)
else:
scalars[I] = int(new_label)
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,
[scalars.tolist()], ['Labels'])
if not os.path.exists(output_file):
raise(IOError(output_file + " not found"))
return output_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.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.utils.mesh import find_neighbors_from_file
>>> from mindboggle.features.folds import extract_subfolds
>>> from mindboggle.utils.plots import plot_vtk
>>> 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_vtk('subfolds.vtk')
"""
import os
import numpy as np
from time import time
from mindboggle.utils.io_vtk import rewrite_scalars, read_vtk
from mindboggle.utils.mesh import find_neighbors
from mindboggle.utils.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 realign_boundaries_to_fundus_lines(
surf_file, init_label_file, fundus_lines_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.
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 os
import numpy as np
from mindboggle.labels.labels import extract_borders
import mindboggle.utils.graph as go
from mindboggle.utils.io_vtk import read_vtk, read_scalars, write_vtk
# import mindboggle.utils.kernels as kernels
from mindboggle.utils.mesh import find_neighbors
# from mindboggle.labels.protocol import dkt_protocol
#
# protocol = 'DKT25'
# sulcus_names, sulcus_label_pair_lists, unique_sulcus_label_pairs, \
# label_names, label_numbers, cortex_names, cortex_numbers, \
# noncortex_names, noncortex_numbers = dkt_protocol(protocol)
## 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)
## 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, 1000, 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]
# TODO: this currently only works for fundus lines that tile the
# surface into connected components (which is fine when you want
# to test this method on fundus lines generated from manual
# labeling). However, to work on real data, fundus lines will
# need to be connected together using shortest paths.
# split surface into connected components
connected_component_faces = _remove_boundary_faces(
points, faces, 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=new_labels.tolist())
return new_labels
def extract_fundi(folds, sulci, 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 sulcus 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().
4. Segment fundi by sulcus definitions.
Possible postprocessing step: smooth with smooth_skeleton().
Parameters
----------
folds : 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
sulci : list of integers
sulcus number for each vertex
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
-------
fundi : list of integers
fundus numbers for all vertices (-1 for non-fundus vertices)
n_fundi : integer
number of fundi
fundi_file : string (if save_file)
name of 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.utils.io_vtk import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.utils.plots import plot_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> sulci_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> sulci, name = read_scalars(sulci_file, True, True)
>>> 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
>>> fundi, n_fundi, fundi_file = extract_fundi(folds, sulci, curv_file,
>>> depth_file, min_separation, erode_ratio, erode_min_size, save_file)
>>> #
>>> # View:
>>> plot_vtk(fundi_file)
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.utils.io_vtk import read_scalars, read_vtk, rewrite_scalars
from mindboggle.utils.compute import median_abs_dev
from mindboggle.utils.paths import find_max_values
from mindboggle.utils.mesh import find_neighbors_from_file
from mindboggle.utils.paths import find_outer_anchors, connect_points_erosion
# Load values, threshold, and neighbors:
u1,u2,u3, points, npoints, curvs, u4,u5 = 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 endpoints 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)
#-------------------------------------------------------------------------
# Create fundi by segmenting skeletons with overlapping sulcus labels:
#-------------------------------------------------------------------------
fundi = -1 * np.ones(npoints)
indices = [x for x in skeletons if sulci[x] != -1]
fundi[indices] = sulci[indices]
n_fundi = len([x for x in np.unique(fundi) if x != -1])
if n_fundi == 1:
sdum = 'fundus'
else:
sdum = 'fundi'
print(' ...Extracted {0} {1} ({2:.2f} seconds)'.
format(n_fundi, sdum, time() - t1))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
fundi = fundi.tolist()
if save_file:
fundi_file = os.path.join(os.getcwd(), 'fundi.vtk')
rewrite_scalars(curv_file, fundi_file, fundi, 'fundi', folds)
else:
fundi_file = None
return fundi, n_fundi, fundi_file
def extract_borders_2nd_surface(labels_file, mask_file="", values_file=""):
"""
Extract borders (between labels) on a surface.
Options: Mask out values; extract border values on a second surface.
Parameters
----------
labels_file : string
file name for surface mesh with labels
mask_file : string
file name for surface mesh with mask (>-1) values
values_file : string
file name for surface mesh with values to extract along borders
Returns
-------
border_file : string
file name for surface mesh with label borders (-1 background values)
border_values : numpy array
values for all vertices (-1 for vertices not along label borders)
Examples
--------
>>> # Extract depth values along label borders in sulci (mask):
>>> import os
>>> from mindboggle.labels.labels import extract_borders_2nd_surface
>>> from mindboggle.utils.plots import plot_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> labels_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> mask_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> values_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> #
>>> border_file, border_values = extract_borders_2nd_surface(labels_file, mask_file, values_file)
>>> #
>>> plot_vtk(border_file)
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, read_vtk, rewrite_scalars
from mindboggle.utils.mesh import find_neighbors
from mindboggle.labels.labels import extract_borders
# Load labeled surface file
faces, foo1, foo2, foo3, npoints, labels, foo4, foo5 = read_vtk(labels_file, return_first=True, return_array=True)
# Detect borders
neighbor_lists = find_neighbors(faces, npoints)
indices_borders, foo1, foo2 = extract_borders(range(npoints), labels, neighbor_lists)
# Filter values with label borders
border_values = -1 * np.ones(npoints)
if values_file:
values, name = read_scalars(values_file, return_first=True, return_array=True)
border_values[indices_borders] = values[indices_borders]
else:
border_values[indices_borders] = 1
# Mask values (for mask >-1)
if mask_file:
mask_values, name = read_scalars(mask_file)
else:
mask_values = []
# Write out label boundary vtk file
border_file = os.path.join(os.getcwd(), "borders_" + os.path.basename(labels_file))
rewrite_scalars(labels_file, border_file, border_values, "label_borders_in_mask", mask_values)
if not os.path.exists(border_file):
raise (IOError(border_file + " not found"))
return border_file, border_values
def write_vertex_measures(
table_file,
labels_or_file,
sulci=[],
fundi=[],
affine_transform_file="",
transform_format="itk",
area_file="",
mean_curvature_file="",
travel_depth_file="",
geodesic_depth_file="",
convexity_file="",
thickness_file="",
delimiter=",",
):
"""
Make a table of shape values per vertex.
Parameters
----------
table_file : output filename (without 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_file : string
affine transform file to standard space
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
convexity_file : string
name of VTK file with convexity scalar values
thickness_file : string
name of VTK file with thickness scalar values
delimiter : string
delimiter between columns, such as ','
Returns
-------
shape_table : table file name for vertex shape values
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.tables.all_shapes import write_vertex_measures
>>> #
>>> table_file = '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_file = os.path.join(path, 'arno', 'mri',
>>> 't1weighted_brain.MNI152Affine.txt')
>>> transform_format = 'itk'
>>> 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')
>>> convexity_file = ''
>>> thickness_file = ''
>>> delimiter = ','
>>> #
>>> write_vertex_measures(table_file, labels_or_file, sulci, fundi,
>>> affine_transform_file, transform_format, area_file,
>>> mean_curvature_file, travel_depth_file, geodesic_depth_file,
>>> convexity_file, thickness_file, delimiter)
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, read_vtk, apply_affine_transform
from mindboggle.utils.io_table import write_columns
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = labels_or_file.tolist()
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 = sulci.tolist()
if isinstance(fundi, np.ndarray):
fundi = fundi.tolist()
# Feature names and corresponding feature lists:
feature_names = ["label", "sulcus", "fundus"]
feature_lists = [labels, sulci, fundi]
# Shape names corresponding to shape files below:
shape_names = ["area", "mean curvature", "travel depth", "geodesic depth", "convexity", "thickness"]
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [
area_file,
mean_curvature_file,
travel_depth_file,
geodesic_depth_file,
convexity_file,
thickness_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:
u1, u2, u3, points, u4, scalars, u5, u6 = read_vtk(shape_file)
columns.append(points)
column_names.append("coordinates")
first_pass = False
if affine_transform_file:
affine_points, foo1 = apply_affine_transform(affine_transform_file, points, transform_format)
columns.append(affine_points)
column_names.append("coordinates in standard space")
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
shapes_table = os.path.join(os.getcwd(), table_file)
write_columns(range(len(columns[0])), "index", shapes_table, delimiter)
write_columns(columns, column_names, shapes_table, delimiter, quote=True, input_table=shapes_table)
return shapes_table
def close_surface_pair_from_files(patch_surface1, whole_surface2,
background_value=-1, output_vtk=''):
"""
Close a surface patch by connecting its border vertices with
corresponding vertices in a second surface file.
Assumes no lines or indices when reading VTK files in.
Note ::
The first VTK file contains scalar values different than background
for a surface patch. The second VTK file contains the (entire)
surface whose corresponding vertices are shifted in position.
For pial vs. gray-white matter, the two surfaces are not parallel,
so connecting the vertices leads to intersecting faces.
Parameters
----------
patch_surface1 : string
vtk file with surface patch of non-background scalar values
whole_surface2 : string
second vtk file with 1-to-1 vertex correspondence with patch_surface1
(whole surface so as to derive vertex neighbor lists)
background_value : integer
scalar value for background vertices
output_vtk : string
output vtk file name with closed surface patch
Returns
-------
output_vtk : string
output vtk file name with closed surface patch
Examples
--------
>>> import os
>>> from mindboggle.utils.morph import close_surface_pair_from_files
>>> from mindboggle.utils.plots import plot_surfaces
>>> from mindboggle.utils.io_vtk import read_scalars, read_vtk, read_points, write_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> patch_surface1 = 'fold.pial.vtk'
>>> whole_surface2 = 'fold.white.vtk'
>>> # Select a single fold:
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> points = read_points(folds_file)
>>> folds, name = read_scalars(folds_file, True, True)
>>> fold_number = 11
>>> folds[folds != fold_number] = -1
>>> white_surface = os.path.join(path, 'arno', 'freesurfer', 'lh.white.vtk')
>>> faces, u1, u2, points2, N, u3, u4, u5 = read_vtk(white_surface)
>>> write_vtk(patch_surface1, points, [], [], faces, folds, name)
>>> write_vtk(whole_surface2, points2, [], [], faces, folds, name)
>>> background_value = -1
>>> output_vtk = ''
>>> close_surface_pair_from_files(patch_surface1, whole_surface2, background_value, output_vtk)
>>> # View:
>>> plot_surfaces('closed.vtk') # doctest: +SKIP
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_vtk, write_vtk
from mindboggle.utils.morph import close_surface_pair
# Read VTK surface mesh files:
u1, u2, u3, points1, N, scalars, name, u4 = read_vtk(patch_surface1,
True, True)
faces, u1, u2, points2, N, u3, u4, u5 = read_vtk(whole_surface2,
True, True)
# Close surface:
closed_faces, closed_points, closed_scalars = close_surface_pair(faces,
points1, points2, scalars, background_value)
# Write output file:
if not output_vtk:
output_vtk = os.path.join(os.getcwd(), 'closed.vtk')
# closed_scalars is a list
if np.ndim(closed_scalars) == 1:
scalar_type = type(closed_scalars[0]).__name__
elif np.ndim(closed_scalars) == 2:
scalar_type = type(closed_scalars[0][0]).__name__
else:
print("Undefined scalar type!")
write_vtk(output_vtk, closed_points, [], [], closed_faces, closed_scalars,
name, scalar_type=scalar_type)
return output_vtk
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 (>-1)
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.utils.io_table import write_average_face_values_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_indices_vtk = os.path.join(path, 'allen', 'labels', 'lh.DKTatlas100.gcs.vtk')
>>> input_values_vtk = os.path.join(path, 'allen', 'shapes', 'lh.thickness.vtk')
>>> area_file = os.path.join(path, 'allen', 'shapes', 'lh.pial.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.utils.plots import plot_vtk
>>> #plot_vtk(example_vtk)
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, read_vtk, write_vtk
from mindboggle.utils.io_table import write_columns
from mindboggle.utils.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:
# -----------------------------------------------------------------
write_columns(columns, "", output_table, delimiter=",", quote=False)
def extract_sulci(labels_file, folds_or_file, hemi, sulcus_label_pair_lists,
unique_sulcus_label_pairs, 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 segment.
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 or name of VTK file containing folds scalars
hemi : string
hemisphere ('lh' or 'rh')
sulcus_label_pair_lists : list of two lists of multiple lists of integer pairs
list containing left and right lists, each with multiple lists of
integer pairs corresponding to label boundaries / sulcus / fundus
unique_sulcus_label_pairs : list of unique pairs of integers
unique label pairs
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
name of output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.labels.protocol import dkt_protocol
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.utils.plots import plot_vtk
>>> 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)
>>> protocol = 'DKT31'
>>> hemi = 'lh'
>>> sulcus_names, sulcus_label_pair_lists, unique_sulcus_label_pairs,
... label_names, label_numbers, cortex_names, cortex_numbers,
... noncortex_names, noncortex_numbers = dkt_protocol(protocol)
>>> min_boundary = 10
>>> #
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
>>> hemi, sulcus_label_pair_lists, unique_sulcus_label_pairs,
>>> min_boundary, sulcus_names)
>>> # View:
>>> plot_vtk('sulci.vtk')
"""
import os
from time import time
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, read_vtk, rewrite_scalars
from mindboggle.utils.mesh import find_neighbors
from mindboggle.labels.labels import extract_borders
from mindboggle.utils.segment import propagate, segment
# 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
if hemi == 'lh':
sulcus_label_pair_lists = sulcus_label_pair_lists[0]
elif hemi == 'rh':
sulcus_label_pair_lists = sulcus_label_pair_lists[1]
else:
print("Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors
faces, foo1, foo2, points, npoints, labels, foo3, foo4 = 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 (greater than zero)
fold_labels = [labels[x] for x in fold]
unique_fold_labels = [int(x) for x in np.unique(fold_labels) if x > 0]
#---------------------------------------------------------------------
# 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 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 than one 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 len(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 = [i for i,x in enumerate(sulcus_label_pair_lists)
if pair in x][0]
# Construct 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]
# Identify vertices with unique label(s) in pair
indices_unique_labels = [fold[i]
for i,x in enumerate(fold_labels)
if x in 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 label borders with label {0}".
format(int(label)))
# Construct seeds from label boundary vertices
seeds = -1 * np.ones(len(points))
for ID, label_pair_list in enumerate(sulcus_label_pair_lists):
label_pairs = [x for x in label_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)
for seed2 in range(int(max(seeds2))+1):
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]
n_sulci = len(sulcus_numbers)
print("Extracted {0} sulci from {1} folds ({2:.1f}s):".
format(n_sulci, n_folds, time()-t0))
if len(sulcus_names):
for sulcus_number in sulcus_numbers:
print(" {0}: {1}".format(sulcus_number, sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in sulcus_numbers]))
#-------------------------------------------------------------------------
# Print out unresolved sulci
#-------------------------------------------------------------------------
unresolved = [i for i in range(len(sulcus_label_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 len(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_file = os.path.join(os.getcwd(), 'sulci.vtk')
rewrite_scalars(labels_file, sulci_file, sulci, 'sulci', sulci)
sulci.tolist()
return sulci, n_sulci, sulci_file
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.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> order = 3
>>> exclude_labels = [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.utils.io_vtk import read_vtk
from mindboggle.utils.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 == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
if label == 14:
#---------------------------------------------------------------------
# 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 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.utils.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/mindboggle_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.utils.mesh import remove_faces, reindex_faces_points
from mindboggle.utils.utils import execute
from mindboggle.utils.plots import plot_surfaces
from mindboggle.utils.io_vtk 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:
#-----------------------------------------------------------------
faces, lines, indices, points, npoints, scalars, scalar_names, \
o1 = 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 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
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.utils.io_vtk import read_vtk, read_scalars
from mindboggle.utils.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 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.utils.io_vtk import read_vtk, read_scalars, write_vtk
from mindboggle.utils.mesh import find_neighbors, remove_faces
from mindboggle.utils.segment import extract_borders
from mindboggle.utils.compute import source_to_target_distances
from mindboggle.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 getFeatures(InputFiles, Type, Options):
'''Loads input files of different types and extraction types, and pass them to functions that really does fundi/pits/sulci extraction
Parameters
===========
Type: string
If Types == 'FreeSurfer,
the VTK files should at least be a curvature/convexity and a surface file.
If Type == 'vtk',
The user needs to specify using which map to threshold the surface
and using which map to extract pits and fundi.
mapThreshold: list
a list of per-vertex values that will be used to threshold the cortical surface to get sulcal basins, e.g., curvature/convexity
mapExtract: list
a list of per-vertex values that will be used to extract fundi/pits, e.g., curvature/convexity
mapFeature: lists of lists of floats
lists of list of per-vertex values that will be used as structure feature vectors, e.g., thickness
PrefixBasin: string
the prefix for all outputs that are only related to basin extraction,
e.g., connected components, basins and gyri.
PrefixExtract: string
the prefix for all outputs that are the result of extraction,
e.g., pits and fundi.
Maps: dictionary
Keys are scalar names, e.g., depth and meancurv. Values are float-value lists, representing maps.
Each map is of size 1 by #vertices
Notes
======
12/23/2011: We are now rewriting the interface from getFundi to libbasin.getBaisn, fundiFromPits and fundiFromSkel
Now variables passed into them are data rather than file names
'''
if Type == 'FreeSurfer':
print "\t FreeSurfer mode\n"
[SurfFile, ThickFile, CurvFile, ConvFile,\
FundiVTK, PitsVTK, SulciVTK, Use, SulciThld]\
= InputFiles
Clouchoux = False
Maps = {}
if ThickFile != "":
Maps["thickness"] = freesurfer.read_curvature(ThickFile)
if CurvFile != "":
Maps["meancurv"] = freesurfer.read_curvature(CurvFile)
if ConvFile != "":
Maps["conv"] = freesurfer.read_curvature(ConvFile)
if Use == 'conv':
Extract_Sulci_on_Map = "conv"
elif Use == 'curv':
Extract_Sulci_on_Map = "meancurv"
else:
print "[ERROR] Unrecognized map to use:", Use
exit()
Mesh = freesurfer.read_surface(SurfFile)
Extract_Fundi_on_Map = "conv"
elif Type == 'vtk':
print "\t Joachim's VTK mode\n"
[DepthVTK, ConvexityFile, ThickFile, MeanCurvVTK, GaussCurvVTK, FundiVTK, PitsVTK, SulciVTK, SulciThld, Clouchoux] = InputFiles
Maps = {}
print " Loading depth map"
Faces, Lines, Vertexes, Points, nPoints, Depth, name, input_vtk = io_vtk.read_vtk(DepthVTK)
Maps['depth'] = Depth
if MeanCurvVTK != "":
print " Loading mean curvature map"
Faces, Lines, Vertexes, Points, nPoints, Maps['meancurv'], name, input_vtk = io_vtk.read_vtk(MeanCurvVTK)
if GaussCurvVTK != "":
print " Loading Gaussian curvature map"
Faces, Lines, Vertexes, Points, nPoints, Maps['gausscurv'], name, input_vtk = io_vtk.read_vtk(GaussCurvVTK)
if ThickFile != '':
Maps['thickness'] = freesurfer.read_curvature(ThickFile)
if ConvexityFile != '':
Maps['sulc'] = freesurfer.read_curvature(ConvexityFile)
Mesh = [Vertexes, Faces]
Extract_Sulci_on_Map = 'depth' # This will become an option for users later.
Extract_Fundi_on_Map = 'depth' # This will become an option for users later.
## common parts for both FreeSurfer and vtk type
if Clouchoux:
libbasin.getBasin_and_Pits(Maps, Mesh, SulciVTK, PitsVTK, SulciThld = SulciThld, PitsThld =0, Quick=False, Clouchoux=True, SulciMap =Extract_Sulci_on_Map) # extract depth map from sulci and pits from mean and Gaussian curvatures
else:
libbasin.getBasin_and_Pits(Maps, Mesh, SulciVTK, PitsVTK, SulciThld = SulciThld, PitsThld =0, Quick=False, Clouchoux=False, SulciMap =Extract_Sulci_on_Map) # by default, extract sulci and pits from depth map
Pits=io_vtk.read_vertices(PitsVTK)
fundiFromPits(Pits, Maps, Mesh, FundiVTK, SulciThld, Extract_Sulci_on_Map, Extract_Fundi_on_Map)
def apply_affine_transform(transform_file, vtk_or_points,
transform_format='itk', vtk_file_stem='affine_'):
"""
Transform coordinates using an affine matrix.
Parameters
----------
transform file : string
name of affine transform file
vtk_or_points : string or list of lists of three integers
name of VTK file containing point coordinate data, or the data
(if vtk file, assumes scalars are a list of floats or integers)
transform_format : string
format for transform file (currently 'itk');
complications arise with other formats, such as
'txt' for text, or 'mat' for Matlab format, since
software-specific assignment of parameters such as
the origin need to be taken into account
vtk_file_stem : string
save transformed coordinates in a vtk file with this file append
(empty string if vtk_or_points is points)
Returns
-------
affine_points : list of lists of floats
transformed coordinates
output_file : string or None (if not vtk_file_stem or vtk_or_points is points)
name of VTK file containing transformed point data
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import apply_affine_transform
>>> from mindboggle.utils.plots import plot_surfaces
>>> #path = os.environ['MINDBOGGLE_DATA']
>>> #transform_file = os.path.join(path, 'arno', 'mri',
>>> # 't1weighted_brain.MNI152Affine.txt')
>>> transform_file = '/Users/arno/mindboggle_working/OASIS-TRT-20-1/Mindboggle/Compose_affine_transform/affine.txt'
>>> vtk_or_points = '/Users/arno/mindboggle_working/OASIS-TRT-20-1/Mindboggle/_hemi_lh/Surface_to_vtk/lh.pial.vtk'
>>> transform_format = 'itk'
>>> vtk_file_stem = True
>>> affine_points, output_file = apply_affine_transform(transform_file, vtk_or_points, transform_format, vtk_file_stem)
>>> # View
>>> plot_surfaces('affine_lh.pial.vtk')
"""
import os
import numpy as np
from scipy.io import loadmat
from mindboggle.utils.io_vtk import read_vtk, write_vtk, read_itk_transform
from mindboggle.utils.ants import antsApplyTransformsToPoints
# Read affine transform file:
if transform_format == 'itk':
#pass
transform = read_itk_transform(transform_file)
elif transform_format == 'txt':
transform = np.loadtxt(transform_file)
elif transform_format == 'mat':
transform = loadmat(transform_file)
else:
import sys
sys.exit('Transform file format not understood.')
# Read VTK file:
if isinstance(vtk_or_points, str):
faces, lines, indices, points, npoints, scalars, name, \
foo1 = read_vtk(vtk_or_points)
points = np.array(points)
elif isinstance(vtk_or_points, list):
points = np.array(vtk_or_points)
vtk_file_stem = ''
elif isinstance(vtk_or_points, np.ndarray):
points = vtk_or_points.copy()
vtk_file_stem = ''
# Transform points:
if transform_format == 'itk':
affine_points = antsApplyTransformsToPoints(points,
[transform_file], [0])
#points = np.concatenate((points,
# np.ones((np.shape(points)[0],1))), axis=1)
#affine_points = np.transpose(np.dot(transform,
# np.transpose(points)))[:,0:3]
#affine_points = [x.tolist() for x in affine_points]
else:
points = np.concatenate((points,
np.ones((np.shape(points)[0],1))), axis=1)
affine_points = np.transpose(np.dot(transform,
np.transpose(points)))[:,0:3]
affine_points = [x.tolist() for x in affine_points]
# Write transformed VTK file:
if vtk_file_stem and isinstance(vtk_or_points, str):
output_file = os.path.join(os.getcwd(), vtk_file_stem +
os.path.basename(vtk_or_points))
if np.size(scalars):
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!")
else:
scalars = []
scalar_type = 'int'
write_vtk(output_file, affine_points, indices, lines, faces,
scalars, name, scalar_type)
else:
output_file = None
return affine_points, output_file
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.utils.io_vtk import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.utils.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.utils.io_vtk import read_scalars, read_vtk, rewrite_scalars
from mindboggle.utils.compute import median_abs_dev
from mindboggle.utils.paths import find_max_values
from mindboggle.utils.mesh import find_neighbors_from_file, find_complete_faces
from mindboggle.utils.paths import find_outer_anchors, connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
faces, u1, u2, points, npoints, curvs, u3, u4 = 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 = [x for x in skeletons if folds[x] != -1]
fundus_per_fold = -1 * np.ones(npoints)
fundus_per_fold[indices] = folds[indices]
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 transform_to_volume(vtk_file, volume_file, output_volume=''):
"""
Transform vtk coordinates to voxel index coordinates in a target
volume by using the header transformation.
This function assumes that the nibabel-readable volume has LPI orientation.
Parameters
----------
vtk_file : string
name of VTK file containing point coordinate data
volume_file : string
name of target nibabel-readable image volume file
output_volume : string
name of output nibabel-readable image volume file
Returns
-------
output_volume : string
name of nifti file containing transformed point data
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import transform_to_volume
>>> from mindboggle.utils.plots import plot_volumes
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> volume_file = os.path.join(path, 'arno', 'mri', 't1weighted_brain.nii.gz')
>>> output_volume = ''
>>> #
>>> transform_to_volume(vtk_file, volume_file, output_volume)
>>> # View
>>> plot_volumes(['affine_lh.pial.mean_curvature.vtk.nii.gz', volume_file])
"""
import os
import numpy as np
import nibabel as nb
from mindboggle.utils.io_vtk import read_vtk
# Read vtk file:
u1, u2, u3, xyz, npoints, scalars, u4, u5 = read_vtk(vtk_file)
# Read target image volume header information:
img = nb.load(volume_file)
hdr = img.get_header()
dims = img.get_shape()
ndims = len(dims)
affine = img.get_affine()
inv_transform = np.linalg.inv(affine)
# Transform vtk coordinates:
xyz = np.array(xyz)
xyz = np.concatenate((xyz, np.ones((npoints,1))), axis=1)
voxels = np.transpose(np.dot(inv_transform, np.transpose(xyz)))[:,0:ndims]
voxels = np.reshape([int(np.round(x)) for lst in voxels for x in lst],
(-1,ndims))
# Write vtk scalar values to voxels:
data = np.zeros(dims)
for ivoxel, ijk in enumerate(voxels):
data[ijk[0], ijk[1], ijk[2]] = scalars[ivoxel]
# Write output image volume:
if not output_volume:
output_volume = os.path.join(os.getcwd(),
os.path.basename(vtk_file) +
'_to_volume.nii.gz')
img = nb.Nifti1Image(data, affine, header=hdr)
img.to_filename(output_volume)
if not os.path.exists(output_volume):
raise(IOError(output_volume + " not found"))
return output_volume
adjacency_matrix, W, Path, Degree, TreeNbr = M.mst_prim(adjacency_matrix,
[Root], [], M.degree, M.tree_nbr)
return W, Path, Degree, TreeNbr
# Example use of the minimum spanning tree algorithm
if __name__ == "__main__" :
import os
import networkx as nx
from mindboggle.utils.io_vtk import read_vtk, rewrite_scalars
from mindboggle.utils.mesh import find_neighbors, remove_faces
from mindboggle.utils.mesh import min_span_tree
from mindboggle.utils.plots import plot_vtk
data_path = os.environ['MINDBOGGLE_DATA']
sulci_file = os.path.join(data_path, 'arno', 'features', 'sulci.vtk')
faces, lines, indices, points, npoints, sulci, name, input_vtk = read_vtk(sulci_file)
sulcus_ID = 1
sulcus_indices = [i for i,x in enumerate(sulci) if x == sulcus_ID]
sulcus_faces = remove_faces(faces, sulcus_indices)
sulcus_neighbor_lists = find_neighbors(sulcus_faces, len(points))
G=nx.Graph()
G.add_nodes_from(sulcus_indices)
for i, sulcus_neighbor_list in enumerate(sulcus_neighbor_lists):
G.add_edges_from([[i,x] for x in sulcus_neighbor_list])
adjacency_matrix = nx.adjacency_matrix(G, nodelist=None, weight='weight')
indices_to_connect = [0, len(sulcus_indices)-1]
adjacency_matrix2, W, Path, Degree, TreeNbr = min_span_tree(adjacency_matrix,
indices_to_connect)
# Write results to vtk file and view:
MST = np.zeros(len(points))
def rewrite_scalars(input_vtk, output_vtk, new_scalars,
new_scalar_names=['scalars'], filter_scalars=[],
background_value=-1):
"""
Load VTK format file and save a subset of scalars into a new file.
Parameters
----------
input_vtk : string
input VTK file name
output_vtk : string
output VTK file name
new_scalars : list of lists of floats (or single list or array of floats)
each list (lookup table) contains new values to assign to the vertices
new_scalar_names : string or list of strings
each element is the new name for a lookup table
filter_scalars : list or numpy array (optional)
scalar values used to filter faces (foreground values retained)
background_value : integer
background value
Returns
-------
output_vtk : string
output VTK file name
Examples
--------
>>> # Write vtk file with curvature values on sulci
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_vtk = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> sulci_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> output_vtk = 'rewrite_scalars.vtk'
>>> curvs, name = read_scalars(input_vtk, True,True)
>>> sulci, name = read_scalars(sulci_file)
>>> new_scalars = [curvs, sulci]
>>> new_scalar_names = ['curvs', 'sulci']
>>> filter_scalars = sulci
>>> background_value = -1
>>> #
>>> rewrite_scalars(input_vtk, output_vtk, new_scalars, new_scalar_names, filter_scalars, background_value)
>>> #
>>> # View:
>>> from mindboggle.utils.plots import plot_surfaces
>>> plot_surfaces('rewrite_scalars.vtk')
"""
import os
import numpy as np
from mindboggle.utils.mesh import remove_faces
from mindboggle.utils.io_vtk import write_header, write_points, \
write_vertices, write_faces, write_scalars, read_vtk, scalars_checker
# Convert numpy arrays to lists
if isinstance(new_scalars, np.ndarray):
new_scalars = new_scalars.tolist()
if isinstance(filter_scalars, np.ndarray):
filter_scalars = filter_scalars.tolist()
# Output VTK file to current working directory
output_vtk = os.path.join(os.getcwd(), output_vtk)
# Load VTK file
faces, lines, indices, points, npoints, scalars, name, \
input_vtk = read_vtk(input_vtk)
# Find indices to foreground values
if filter_scalars:
indices_keep = [i for i,x in enumerate(filter_scalars)
if x != background_value]
indices_remove = [i for i,x in enumerate(filter_scalars)
if x == background_value]
# Remove surface faces whose three vertices are not all in indices
faces = remove_faces(faces, indices_keep)
# Write VTK file
Fp = open(output_vtk,'w')
write_header(Fp)
write_points(Fp, points)
if indices:
write_vertices(Fp, indices)
if faces:
write_faces(Fp, faces)
if new_scalars:
new_scalars, new_scalar_names = scalars_checker(new_scalars,
new_scalar_names)
# scalars_checker() returns a list of lists for scalars:
for i, new_scalar_list in enumerate(new_scalars):
if filter_scalars:
for iremove in indices_remove:
new_scalar_list[iremove] = background_value
if np.ndim(new_scalar_list) == 1:
scalar_type = type(new_scalar_list[0]).__name__
elif np.ndim(new_scalar_list) == 2:
scalar_type = type(new_scalar_list[0][0]).__name__
else:
print("Undefined scalar type!")
if i == 0:
new_scalar_name = new_scalar_names[0]
write_scalars(Fp, new_scalar_list, new_scalar_name,
begin_scalars=True,
scalar_type=scalar_type)
else:
if len(new_scalar_names) < i + 1:
new_scalar_name = new_scalar_names[0]
else:
new_scalar_name = new_scalar_names[i]
write_scalars(Fp, new_scalar_list, new_scalar_name,
begin_scalars=False,
scalar_type=scalar_type)
else:
print('Error: new_scalars is empty')
exit()
Fp.close()
if not os.path.exists(output_vtk):
raise(IOError(output_vtk + " not found"))
return output_vtk
exporter.SetFileName(output_vtk)
exporter.Write()
return output_vtk
if __name__ == "__main__":
import os
from mindboggle.utils.io_vtk import read_vtk, write_vtk
from mindboggle.utils.mesh import decimate
path = os.environ['MINDBOGGLE_DATA']
input_vtk = os.path.join(path, 'arno', 'labels', 'label22.vtk')
reduction = 0.5
smooth_steps = 100
faces, lines, indices, points, npoints, labels, o1, o2 = read_vtk(input_vtk)
import vtk
# vtk points:
vtk_points = vtk.vtkPoints()
[vtk_points.InsertPoint(i, x[0], x[1], x[2]) for i,x in enumerate(points)]
# vtk faces:
vtk_faces = vtk.vtkCellArray()
for face in faces:
vtk_face = vtk.vtkPolygon()
vtk_face.GetPointIds().SetNumberOfIds(3)
vtk_face.GetPointIds().SetId(0, face[0])
vtk_face.GetPointIds().SetId(1, face[1])
vtk_face.GetPointIds().SetId(2, face[2])
def explode_scalars(input_indices_vtk, input_values_vtk='', output_stem='',
exclude_values=[-1], background_value=-1,
output_scalar_name='scalars',
remove_background_faces=True, reindex=True):
"""
Write out a separate VTK file for each integer (not in exclude_values)
in (the first) scalar list of an input VTK file.
Optionally write the values drawn from a second VTK file,
remove background values, and reindex indices.
Parameters
----------
input_indices_vtk : string
path of the input VTK file that contains indices as scalars
(assumes that the scalars are a list of floats or integers)
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
remove_background_faces : Boolean
remove all faces whose three vertices are not all a given index?
reindex : Boolean
reindex all indices in faces?
Examples
--------
>>> # Example 1: explode sulci with thickness values
>>> import os
>>> from mindboggle.utils.io_vtk import explode_scalars
>>> from mindboggle.utils.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_indices_vtk = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> input_values_vtk = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> output_stem = 'sulci_depth'
>>> #
>>> explode_scalars(input_indices_vtk, input_values_vtk, output_stem)
>>> #
>>> # View:
>>> example_vtk = os.path.join(os.getcwd(), output_stem + '0.vtk')
>>> plot_surfaces(example_vtk)
>>> #
>>> # Example 2: explode labels
>>> import os
>>> from mindboggle.utils.io_vtk import explode_scalars
>>> from mindboggle.utils.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_values_vtk = os.path.join(path, 'arno', 'labels',
>>> 'lh.labels.DKT25.manual.vtk')
>>> input_indices_vtk = input_values_vtk
>>> output_stem = 'label'
>>> exclude_values = [-1]
>>> background_value = -1,
>>> output_scalar_name = 'scalars'
>>> remove_background_faces = True
>>> reindex = True
>>> #
>>> explode_scalars(input_indices_vtk, input_values_vtk, output_stem,
>>> exclude_values, background_value,
>>> output_scalar_name, remove_background_faces, reindex)
>>> # View:
>>> example_vtk = os.path.join(os.getcwd(), output_stem + '2.vtk')
>>> plot_surfaces(example_vtk)
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, read_vtk, write_vtk
from mindboggle.utils.mesh import reindex_faces_points, remove_faces
# Load VTK file:
faces, lines, indices, points, npoints, scalars, scalar_names, \
foo1 = read_vtk(input_indices_vtk, 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 = np.unique(scalars)
if all(unique_scalars==np.round(unique_scalars)):
unique_scalars = [int(x) for x in unique_scalars
if x not in exclude_values]
else:
unique_scalars = [x for x in unique_scalars
if x not in exclude_values]
for scalar in unique_scalars:
# Remove background (keep only faces with the scalar):
if remove_background_faces:
scalar_indices = [i for i,x in enumerate(scalars) if x == scalar]
scalar_faces = remove_faces(faces, scalar_indices)
else:
scalar_faces = faces
# Reindex:
if reindex:
scalar_faces, select_points, \
o1 = reindex_faces_points(scalar_faces, points)
else:
select_points = points
# Create array and indices for scalar value:
if reindex:
len_indices = len(select_points)
select_values = scalar * np.ones(len_indices)
else:
select_values = np.copy(values)
select_values[scalars != scalar] = background_value
len_indices = len([i for i,x in enumerate(select_values)
if x != background_value])
print(" Scalar {0}: {1} vertices".format(scalar, len_indices))
# Write VTK file with scalar values (list of values):
if np.ndim(select_values) == 1:
scalar_type = type(select_values[0]).__name__
elif np.ndim(select_values) == 2:
scalar_type = type(select_values[0][0]).__name__
else:
print("Undefined scalar type!")
output_vtk = os.path.join(os.getcwd(),
output_stem + str(scalar) + '.vtk')
write_vtk(output_vtk, select_points, indices, lines, scalar_faces,
select_values.tolist(), output_scalar_name,
scalar_type=scalar_type)
def extract_folds(depth_file, min_fold_size=50, tiny_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.
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 call fill_holes() with
the argument exclude_range set close to zero to retain these areas.
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)
tiny_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.utils.io_vtk import read_scalars
>>> from mindboggle.utils.mesh import find_neighbors_from_file
>>> from mindboggle.utils.plots import plot_vtk
>>> from mindboggle.features.folds import extract_folds
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> neighbor_lists = find_neighbors_from_file(depth_file)
>>> min_fold_size = 50
>>> tiny_depth = 0.001
>>> save_file = True
>>> #
>>> folds, n_folds, thr, bins, bin_edges, folds_file = extract_folds(depth_file,
>>> min_fold_size, tiny_depth, save_file)
>>> #
>>> # View folds:
>>> plot_vtk('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.utils.io_vtk import rewrite_scalars, read_vtk
from mindboggle.utils.mesh import find_neighbors
from mindboggle.utils.morph import fill_holes
from mindboggle.utils.segment import segment
do_fill_holes = True
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
#-------------------------------------------------------------------------
min_vertices = 10000
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, tiny_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')
#-------------------------------------------------------------------------
# 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.tolist(), n_folds, depth_threshold, bins, bin_edges, folds_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.utils.segment import extract_borders
import mindboggle.utils.graph as go
from mindboggle.utils.io_vtk import read_vtk, read_scalars, write_vtk
from mindboggle.utils.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
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.utils.io_vtk import read_vtk
from mindboggle.utils.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 close_surface_pair_from_files(patch_surface1,
whole_surface2,
background_value=-1,
output_vtk=''):
"""
Close a surface patch by connecting its border vertices with
corresponding vertices in a second surface file.
Assumes no lines or indices when reading VTK files in.
Note ::
The first VTK file contains scalar values different than background
for a surface patch. The second VTK file contains the (entire)
surface whose corresponding vertices are shifted in position.
For pial vs. gray-white matter, the two surfaces are not parallel,
so connecting the vertices leads to intersecting faces.
Parameters
----------
patch_surface1 : string
vtk file with surface patch of non-background scalar values
whole_surface2 : string
second vtk file with 1-to-1 vertex correspondence with patch_surface1
(whole surface so as to derive vertex neighbor lists)
background_value : integer
scalar value for background vertices
output_vtk : string
output vtk file name with closed surface patch
Returns
-------
output_vtk : string
output vtk file name with closed surface patch
Examples
--------
>>> import os
>>> from mindboggle.utils.morph import close_surface_pair_from_files
>>> from mindboggle.utils.plots import plot_surfaces
>>> from mindboggle.utils.io_vtk import read_scalars, read_vtk, read_points, write_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> patch_surface1 = 'fold.pial.vtk'
>>> whole_surface2 = 'fold.white.vtk'
>>> # Select a single fold:
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> points = read_points(folds_file)
>>> folds, name = read_scalars(folds_file, True, True)
>>> fold_number = 11
>>> folds[folds != fold_number] = -1
>>> white_surface = os.path.join(path, 'arno', 'freesurfer', 'lh.white.vtk')
>>> faces, u1, u2, points2, N, u3, u4, u5 = read_vtk(white_surface)
>>> write_vtk(patch_surface1, points, [], [], faces, folds, name)
>>> write_vtk(whole_surface2, points2, [], [], faces, folds, name)
>>> background_value = -1
>>> output_vtk = ''
>>> close_surface_pair_from_files(patch_surface1, whole_surface2, background_value, output_vtk)
>>> # View:
>>> plot_surfaces('closed.vtk') # doctest: +SKIP
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_vtk, write_vtk
from mindboggle.utils.morph import close_surface_pair
# Read VTK surface mesh files:
u1, u2, u3, points1, N, scalars, name, u4 = read_vtk(
patch_surface1, True, True)
faces, u1, u2, points2, N, u3, u4, u5 = read_vtk(whole_surface2, True,
True)
# Close surface:
closed_faces, closed_points, closed_scalars = close_surface_pair(
faces, points1, points2, scalars, background_value)
# Write output file:
if not output_vtk:
output_vtk = os.path.join(os.getcwd(), 'closed.vtk')
# closed_scalars is a list
if np.ndim(closed_scalars) == 1:
scalar_type = type(closed_scalars[0]).__name__
elif np.ndim(closed_scalars) == 2:
scalar_type = type(closed_scalars[0][0]).__name__
else:
print("Undefined scalar type!")
write_vtk(output_vtk,
closed_points, [], [],
closed_faces,
closed_scalars,
name,
scalar_type=scalar_type)
return output_vtk
def write_shape_stats(
labels_or_file,
sulci=[],
fundi=[],
affine_transform_file="",
transform_format="itk",
area_file="",
mean_curvature_file="",
travel_depth_file="",
geodesic_depth_file="",
convexity_file="",
thickness_file="",
labels_spectra=[],
labels_spectra_IDs=[],
sulci_spectra=[],
sulci_spectra_IDs=[],
exclude_labels=[-1],
delimiter=",",
):
"""
Make tables of shape statistics per label, fundus, and/or sulcus.
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_file : string
affine transform file to standard space
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
convexity_file : string
name of VTK file with convexity scalar values
thickness_file : string
name of VTK file with thickness scalar values
labels_spectra : list of lists of floats
Laplace-Beltrami spectra for labeled regions
labels_spectra_IDs : list of integers
unique ID numbers (labels) for labels_spectra
sulci_spectra : list of lists of floats
Laplace-Beltrami spectra for sulci
sulci_spectra_IDs : list of integers
unique ID numbers (labels) for sulci_spectra
exclude_labels : list of lists of integers
indices to be excluded (in addition to -1)
delimiter : string
delimiter between columns, such as ','
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.utils.io_vtk import read_scalars
>>> from mindboggle.utils.io_table import write_shape_stats
>>> 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_file = os.path.join(path, 'arno', 'mri',
>>> # 'affine_to_template.mat')
>>> 't1weighted_brain.MNI152Affine.txt')
>>> #transform_format = 'mat'
>>> transform_format = 'itk'
>>> 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')
>>> convexity_file = ''
>>> thickness_file = ''
>>> delimiter = ','
>>> #
>>> import numpy as np
>>> labels, name = read_scalars(labels_or_file)
>>> labels_spectra = [[1,2,3] for x in labels]
>>> labels_spectra_IDs = np.unique(labels).tolist()
>>> sulci_spectra = [[1,2,3] for x in sulci]
>>> sulci_spectra_IDs = np.unique(sulci).tolist()
>>> exclude_labels = [-1]
>>> #
>>> write_shape_stats(labels_or_file, sulci, fundi,
>>> affine_transform_file, transform_format, area_file,
>>> mean_curvature_file, travel_depth_file, geodesic_depth_file,
>>> convexity_file, thickness_file, labels_spectra,
>>> labels_spectra_IDs, sulci_spectra,
>>> sulci_spectra_IDs, exclude_labels, delimiter)
"""
import os
import numpy as np
from mindboggle.shapes.measure import means_per_label, stats_per_label, sum_per_label
from mindboggle.utils.io_vtk import read_scalars, read_vtk, apply_affine_transform
from mindboggle.utils.io_table import write_columns
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = labels_or_file.tolist()
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 = sulci.tolist()
if isinstance(fundi, np.ndarray):
fundi = fundi.tolist()
# -------------------------------------------------------------------------
# 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]
spectra_names = ["label spectrum", "sulcus spectrum"]
table_names = ["label_shapes.csv", "sulcus_shapes.csv", "fundus_shapes.csv"]
# Shape names corresponding to shape files below:
shape_names = ["area", "mean curvature", "travel depth", "geodesic depth", "convexity", "thickness"]
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [
area_file,
mean_curvature_file,
travel_depth_file,
geodesic_depth_file,
convexity_file,
thickness_file,
]
shape_arrays = []
column_names = []
first_pass = True
area_array = []
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
faces, lines, indices, points, npoints, scalars_array, name, input_vtk = read_vtk(
shape_file, True, True
)
points = np.array(points)
first_pass = False
if affine_transform_file:
affine_points, foo1 = apply_affine_transform(
affine_transform_file, points, transform_format, save_file=False
)
affine_points = np.array(affine_points)
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()
# Initialize table file names:
sulcus_table = None
fundus_table = None
# Loop through features / tables:
for itable, feature_list in enumerate(feature_lists):
table_column_names = []
# ---------------------------------------------------------------------
# For each feature, construct a table of average shape values:
# ---------------------------------------------------------------------
table_file = os.path.join(os.getcwd(), table_names[itable])
if feature_list:
feature_name = feature_names[itable]
columns = []
# -----------------------------------------------------------------
# Mean positions in the original space:
# -----------------------------------------------------------------
# Compute mean position per feature:
positions, sdevs, label_list, foo = means_per_label(points, feature_list, exclude_labels, area_array)
# Append mean position per feature to columns:
table_column_names.append("mean position")
columns.append(positions)
# -----------------------------------------------------------------
# Mean positions in standard space:
# -----------------------------------------------------------------
if affine_transform_file:
# Compute standard space mean position per feature:
standard_positions, sdevs, label_list, foo = means_per_label(
affine_points, feature_list, exclude_labels, area_array
)
# Append standard space mean position per feature to columns:
table_column_names.append("mean position in standard space")
columns.append(standard_positions)
# -----------------------------------------------------------------
# Loop through shape measures:
# -----------------------------------------------------------------
table_column_names.extend(column_names[:])
for ishape, shape_array in enumerate(shape_arrays):
shape_name = shape_names[ishape]
print(" Compute statistics on {0} {1}".format(feature_name, shape_name))
# Append shape names and values per feature to columns:
pr = feature_name + ": " + shape_name + ": "
if np.size(area_array):
po = " (weighted)"
else:
po = ""
# -------------------------------------------------------------
# Append total feature areas to columns:
# -------------------------------------------------------------
if ishape == 0 and np.size(area_array):
sums, label_list = sum_per_label(shape_array, feature_list, exclude_labels)
table_column_names.append(pr + "total")
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, exclude_labels, area_array, precision=1
)
table_column_names.append(pr + "median" + po)
table_column_names.append(pr + "median absolute deviation" + po)
table_column_names.append(pr + "mean" + po)
table_column_names.append(pr + "standard deviation" + po)
table_column_names.append(pr + "skew" + po)
table_column_names.append(pr + "kurtosis" + po)
table_column_names.append(pr + "lower quartile" + po)
table_column_names.append(pr + "upper quartile" + po)
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)
# -----------------------------------------------------------------
# Laplace-Beltrami spectra:
# -----------------------------------------------------------------
if itable in [0, 1]:
spectra = spectra_lists[itable]
spectra_name = spectra_names[itable]
spectra_IDs = spectra_ID_lists[itable]
# Order spectra into a list:
spectrum_list = []
for label in label_list:
if label in spectra_IDs:
spectrum = spectra[spectra_IDs.index(label)]
spectrum_list.append(spectrum)
else:
spectrum_list.append("")
# Append spectral shape name and values to relevant columns:
columns.append(spectrum_list)
table_column_names.append(spectra_name)
# -----------------------------------------------------------------
# Write labels/IDs and values to table:
# -----------------------------------------------------------------
# Write labels/IDs to table:
write_columns(label_list, feature_name, table_file, delimiter)
# Append columns of shape values to table:
if columns:
write_columns(columns, table_column_names, table_file, delimiter, quote=True, input_table=table_file)
else:
# Write something to table:
write_columns([], "", table_file, delimiter)
# ---------------------------------------------------------------------
# Return correct table file name:
# ---------------------------------------------------------------------
if itable == 0:
label_table = table_file
elif itable == 1:
sulcus_table = table_file
elif itable == 2:
fundus_table = table_file
return label_table, sulcus_table, fundus_table
def extract_folds(depth_file,
min_fold_size=50,
tiny_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.
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 call fill_holes() with
the argument exclude_range set close to zero to retain these areas.
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)
tiny_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.utils.io_vtk import read_scalars
>>> from mindboggle.utils.mesh import find_neighbors_from_file
>>> from mindboggle.utils.plots import plot_surfaces
>>> from mindboggle.features.folds import extract_folds
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> neighbor_lists = find_neighbors_from_file(depth_file)
>>> min_fold_size = 50
>>> tiny_depth = 0.001
>>> save_file = True
>>> #
>>> folds, n_folds, thr, bins, bin_edges, folds_file = extract_folds(depth_file,
>>> min_fold_size, tiny_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.utils.io_vtk import rewrite_scalars, read_vtk
from mindboggle.utils.mesh import find_neighbors
from mindboggle.utils.morph import fill_holes
from mindboggle.utils.segment import segment
do_fill_holes = True
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
#-------------------------------------------------------------------------
min_vertices = 10000
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, tiny_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
