def compute_likelihood(trained_file, depth_file, curvature_file, folds,
save_file=False, background_value=-1):
"""
Compute likelihoods based on input values, folds, estimated parameters.
Compute likelihood values for a given VTK surface mesh file, after
training on distributions of depth and curvature values from multiple
files.
Parameters
----------
trained_file : pickle compressed file
depth_border, curv_border, depth_nonborder, and curv_nonborder are
dictionaries containing lists of floats
(estimates of depth or curvature means, sigmas, and weights
trained on fold vertices either on or off sulcus label borders)
depth_file : string
VTK surface mesh file with depth values in [0,1] for all vertices
curvature_file : string
VTK surface mesh file with curvature values in [-1,1] for all vertices
folds : list of integers
fold number for all vertices (-1 for non-fold vertices)
save_file : bool
save output VTK file?
background_value : integer or float
background value
Returns
-------
likelihoods : list of floats
likelihood values for all vertices (0 for non-fold vertices)
likelihoods_file : string (if save_file)
name of output VTK file with likelihood scalars
(-1 for non-fold vertices)
Notes
-----
The depth_curv_border_nonborder_parameters.pkl file needs to be updated.
Examples
--------
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.shapes.likelihood import compute_likelihood
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> curvature_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> trained_file = fetch_data(urls[
... 'depth_curv_border_nonborder_parameters'], '', '.pkl') # doctest: +SKIP
>>> folds, name = read_scalars(folds_file)
>>> save_file = True
>>> background_value = -1
>>> likelihoods, likelihoods_file = compute_likelihood(trained_file,
... depth_file, curvature_file, folds, save_file, background_value) # doctest: +SKIP
View result (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> plot_surfaces('likelihoods.vtk', folds_file) # doctest: +SKIP
"""
import os
import numpy as np
from math import pi
import pickle
from io import open
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
# Initialize variables:
tiny = 0.000000001
L = np.zeros(len(folds))
probs_border = np.zeros(len(folds))
probs_nonborder = np.zeros(len(folds))
# Load estimated depth and curvature distribution parameters:
depth_border, curv_border, depth_nonborder, \
curv_nonborder = pickle.load(open(trained_file, "rb"))
# Load depths, curvatures:
depths, name = read_scalars(depth_file, True, True)
curvatures, name = read_scalars(curvature_file, True, True)
# Prep for below:
n = 2
twopiexp = (2*pi)**(n/2)
border_sigmas = depth_border['sigmas'] * curv_border['sigmas']
nonborder_sigmas = depth_nonborder['sigmas'] * curv_nonborder['sigmas']
norm_border = 1 / (twopiexp * border_sigmas + tiny)
norm_nonborder = 1 / (twopiexp * nonborder_sigmas + tiny)
I = [i for i,x in enumerate(folds) if x != background_value]
N = depth_border['sigmas'].shape[0]
for j in range(N):
# Border:
expB = depth_border['weights'][j] * \
((depths[I]-depth_border['means'][j])**2) / \
depth_border['sigmas'][j]**2
expB += curv_border['weights'][j] * \
((curvatures[I]-curv_border['means'][j])**2) / \
curv_border['sigmas'][j]**2
expB = -expB / 2
probs_border[I] = probs_border[I] + norm_border[j] * np.exp(expB)
# Non-border:
expNB = depth_nonborder['weights'][j] * \
((depths[I]-depth_nonborder['means'][j])**2) / \
depth_nonborder['sigmas'][j]**2
expNB += curv_nonborder['weights'][j] * \
((curvatures[I]-curv_nonborder['means'][j])**2) / \
curv_nonborder['sigmas'][j]**2
expNB = -expNB / 2
probs_nonborder[I] = probs_nonborder[I] + \
norm_nonborder[j] * np.exp(expNB)
likelihoods = probs_border / (probs_nonborder + probs_border + tiny)
likelihoods = likelihoods.tolist()
# ------------------------------------------------------------------------
# Return likelihoods and output file name
# ------------------------------------------------------------------------
if save_file:
likelihoods_file = os.path.join(os.getcwd(), 'likelihoods.vtk')
rewrite_scalars(depth_file, likelihoods_file, likelihoods,
'likelihoods', likelihoods, background_value)
if not os.path.exists(likelihoods_file):
raise IOError(likelihoods_file + " not found")
else:
likelihoods_file = None
return likelihoods, likelihoods_file
def extract_folds(depth_file,
min_vertices=10000,
min_fold_size=50,
do_fill_holes=False,
min_hole_depth=0.001,
save_file=False):
"""
Use depth to extract folds from a triangular surface mesh.
Steps ::
1. Compute histogram of depth measures.
2. Define a depth threshold and find the deepest vertices.
3. Segment deep vertices as an initial set of folds.
4. Remove small folds.
5. Find and fill holes in the folds (optional).
6. Renumber folds.
Step 2 ::
To extract an initial set of deep vertices from the surface mesh,
we anticipate that there will be a rapidly decreasing distribution
of low depth values (on the outer surface) with a long tail
of higher depth values (in the folds), so we smooth the histogram's
bin values, convolve to compute slopes, and find the depth value
for the first bin with slope = 0. This is our threshold.
Step 5 ::
The folds could have holes in areas shallower than the depth threshold.
Calling fill_holes() could accidentally include very shallow areas
(in an annulus-shaped fold, for example), so we include the argument
exclude_range to check for any values from zero to min_hole_depth;
holes are not filled if they contains values within this range.
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
min_fold_size : integer
minimum fold size (number of vertices)
do_fill_holes : Boolean
fill holes in the folds?
min_hole_depth : float
largest non-zero depth value that will stop a hole from being filled
save_file : Boolean
save output VTK file?
Returns
-------
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_folds : int
number of folds
depth_threshold : float
threshold defining the minimum depth for vertices to be in a fold
bins : list of integers
histogram bins: each is the number of vertices within a range of depth values
bin_edges : list of floats
histogram bin edge values defining the bin ranges of depth values
folds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> import os
>>> import numpy as np
>>> import pylab
>>> from scipy.ndimage.filters import gaussian_filter1d
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.guts.mesh import find_neighbors_from_file
>>> from mindboggle.mio.plots import plot_surfaces
>>> from mindboggle.features.folds import extract_folds
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = 'travel_depth.vtk' #os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> neighbor_lists = find_neighbors_from_file(depth_file)
>>> min_vertices = 10000
>>> min_fold_size = 50
>>> do_fill_holes = False #True
>>> min_hole_depth = 0.001
>>> save_file = True
>>> #
>>> folds, n_folds, thr, bins, bin_edges, folds_file = extract_folds(depth_file,
>>> min_vertices, min_fold_size, do_fill_holes, min_hole_depth, save_file)
>>> #
>>> # View folds:
>>> plot_surfaces('folds.vtk')
>>> # Plot histogram and depth threshold:
>>> depths, name = read_scalars(depth_file)
>>> nbins = np.round(len(depths) / 100.0)
>>> a,b,c = pylab.hist(depths, bins=nbins)
>>> pylab.plot(thr*np.ones((100,1)), np.linspace(0, max(bins), 100), 'r.')
>>> pylab.show()
>>> # Plot smoothed histogram:
>>> bins_smooth = gaussian_filter1d(bins.tolist(), 5)
>>> pylab.plot(range(len(bins)), bins, '.', range(len(bins)), bins_smooth,'-')
>>> pylab.show()
"""
import os
import sys
import numpy as np
from time import time
from scipy.ndimage.filters import gaussian_filter1d
from mindboggle.mio.vtks import rewrite_scalars, read_vtk
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.morph import fill_holes
from mindboggle.guts.segment import segment
print("Extract folds in surface mesh")
t0 = time()
#-------------------------------------------------------------------------
# Load depth values for all vertices
#-------------------------------------------------------------------------
points, indices, lines, faces, depths, scalar_names, npoints, \
input_vtk = read_vtk(depth_file, return_first=True, return_array=True)
#-------------------------------------------------------------------------
# Find neighbors for each vertex
#-------------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
#-------------------------------------------------------------------------
# Compute histogram of depth measures
#-------------------------------------------------------------------------
if npoints > min_vertices:
nbins = np.round(npoints / 100.0)
else:
sys.err(" Expecting at least {0} vertices to create depth histogram".
format(min_vertices))
bins, bin_edges = np.histogram(depths, bins=nbins)
#-------------------------------------------------------------------------
# Anticipating that there will be a rapidly decreasing distribution
# of low depth values (on the outer surface) with a long tail of higher
# depth values (in the folds), smooth the bin values (Gaussian), convolve
# to compute slopes, and find the depth for the first bin with slope = 0.
#-------------------------------------------------------------------------
bins_smooth = gaussian_filter1d(bins.tolist(), 5)
window = [-1, 0, 1]
bin_slopes = np.convolve(bins_smooth, window,
mode='same') / (len(window) - 1)
ibins0 = np.where(bin_slopes == 0)[0]
if ibins0.shape:
depth_threshold = bin_edges[ibins0[0]]
else:
depth_threshold = np.median(depths)
#-------------------------------------------------------------------------
# Find the deepest vertices
#-------------------------------------------------------------------------
indices_deep = [i for i, x in enumerate(depths) if x >= depth_threshold]
if indices_deep:
#---------------------------------------------------------------------
# Segment deep vertices as an initial set of folds
#---------------------------------------------------------------------
print(" Segment vertices deeper than {0:.2f} as folds".format(
depth_threshold))
t1 = time()
folds = segment(indices_deep, neighbor_lists)
# Slightly slower alternative -- fill boundaries:
#regions = -1 * np.ones(len(points))
#regions[indices_deep] = 1
#folds = segment_by_filling_borders(regions, neighbor_lists)
print(' ...Segmented folds ({0:.2f} seconds)'.format(time() - t1))
#---------------------------------------------------------------------
# Remove small folds
#---------------------------------------------------------------------
if min_fold_size > 1:
print(' Remove folds smaller than {0}'.format(min_fold_size))
unique_folds = [x for x in np.unique(folds) if x != -1]
for nfold in unique_folds:
indices_fold = [i for i, x in enumerate(folds) if x == nfold]
if len(indices_fold) < min_fold_size:
folds[indices_fold] = -1
#---------------------------------------------------------------------
# Find and fill holes in the folds
# Note: Surfaces surrounded by folds can be mistaken for holes,
# so exclude_range includes outer surface values close to zero.
#---------------------------------------------------------------------
if do_fill_holes:
print(" Find and fill holes in the folds")
folds = fill_holes(folds,
neighbor_lists,
values=depths,
exclude_range=[0, min_hole_depth])
#---------------------------------------------------------------------
# Renumber folds so they are sequential
#---------------------------------------------------------------------
renumber_folds = -1 * np.ones(len(folds))
fold_numbers = [int(x) for x in np.unique(folds) if x != -1]
for i_fold, n_fold in enumerate(fold_numbers):
fold = [i for i, x in enumerate(folds) if x == n_fold]
renumber_folds[fold] = i_fold
folds = renumber_folds
n_folds = i_fold + 1
# Print statement
print(' ...Extracted {0} folds ({1:.2f} seconds)'.format(
n_folds,
time() - t0))
else:
print(' No deep vertices')
folds = [int(x) for x in folds]
#-------------------------------------------------------------------------
# Return folds, number of folds, file name
#-------------------------------------------------------------------------
if save_file:
folds_file = os.path.join(os.getcwd(), 'folds.vtk')
rewrite_scalars(depth_file, folds_file, folds, 'folds', folds)
if not os.path.exists(folds_file):
raise (IOError(folds_file + " not found"))
else:
folds_file = None
return folds, n_folds, depth_threshold, bins, bin_edges, folds_file
def rescale_by_label(input_vtk,
labels_or_file,
save_file=False,
output_filestring='rescaled_scalars'):
"""
Rescale scalars for each label (such as depth values within each fold).
Default is to normalize the scalar values of a VTK file by
a percentile value in each vertex's surface mesh for each label.
Parameters
----------
input_vtk : string
name of VTK file with a scalar value for each vertex
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
save_file : Boolean
save output VTK file?
output_filestring : string (if save_file)
name of output file
Returns
-------
rescaled_scalars : list of floats
scalar values rescaled for each label, for label numbers not equal to -1
rescaled_scalars_file : string (if save_file)
name of output VTK file with rescaled scalar values for each label
Examples
--------
>>> # Rescale depths by neighborhood within each label:
>>> import os
>>> from mindboggle.guts.mesh import rescale_by_label
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_vtk = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> labels_or_file = os.path.join(path, 'arno', 'features', 'subfolds.vtk')
>>> save_file = True
>>> output_filestring = 'rescaled_scalars'
>>> #
>>> rescaled_scalars, rescaled_scalars_file = rescale_by_label(input_vtk,
>>> labels_or_file, save_file, output_filestring)
>>> #
>>> # View rescaled scalar values per fold:
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> #
>>> rewrite_scalars(rescaled_scalars_file, rescaled_scalars_file,
>>> rescaled_scalars, 'rescaled_depths', folds)
>>> plot_surfaces(rescaled_scalars_file)
"""
import os
import numpy as np
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
# Load scalars and vertex neighbor lists:
scalars, name = read_scalars(input_vtk, True, True)
print(" Rescaling scalar values within each label...")
# Load label numbers:
if isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file, True, True)
elif isinstance(labels_or_file, list):
labels = labels_or_file
unique_labels = np.unique(labels)
unique_labels = [x for x in unique_labels if x >= 0]
# Loop through labels:
for label in unique_labels:
#print(" Rescaling scalar values within label {0} of {1} labels...".format(
# int(label), len(unique_labels)))
indices = [i for i, x in enumerate(labels) if x == label]
if indices:
# Rescale by the maximum label scalar value:
scalars[indices] = scalars[indices] / np.max(scalars[indices])
rescaled_scalars = scalars.tolist()
#-------------------------------------------------------------------------
# Return rescaled scalars and file name
#-------------------------------------------------------------------------
if save_file:
rescaled_scalars_file = os.path.join(os.getcwd(),
output_filestring + '.vtk')
rewrite_scalars(input_vtk, rescaled_scalars_file, rescaled_scalars,
'rescaled_scalars', labels)
if not os.path.exists(rescaled_scalars_file):
raise (IOError(rescaled_scalars_file + " not found"))
else:
rescaled_scalars_file = None
return rescaled_scalars, rescaled_scalars_file
def extract_folds(depth_file, min_vertices=10000, min_fold_size=50,
do_fill_holes=False, min_hole_depth=0.001,
save_file=False):
"""
Use depth to extract folds from a triangular surface mesh.
Steps ::
1. Compute histogram of depth measures.
2. Define a depth threshold and find the deepest vertices.
3. Segment deep vertices as an initial set of folds.
4. Remove small folds.
5. Find and fill holes in the folds (optional).
6. Renumber folds.
Step 2 ::
To extract an initial set of deep vertices from the surface mesh,
we anticipate that there will be a rapidly decreasing distribution
of low depth values (on the outer surface) with a long tail
of higher depth values (in the folds), so we smooth the histogram's
bin values, convolve to compute slopes, and find the depth value
for the first bin with slope = 0. This is our threshold.
Step 5 ::
The folds could have holes in areas shallower than the depth threshold.
Calling fill_holes() could accidentally include very shallow areas
(in an annulus-shaped fold, for example), so we include the argument
exclude_range to check for any values from zero to min_hole_depth;
holes are not filled if they contains values within this range.
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
min_fold_size : integer
minimum fold size (number of vertices)
do_fill_holes : Boolean
fill holes in the folds?
min_hole_depth : float
largest non-zero depth value that will stop a hole from being filled
save_file : Boolean
save output VTK file?
Returns
-------
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_folds : int
number of folds
depth_threshold : float
threshold defining the minimum depth for vertices to be in a fold
bins : list of integers
histogram bins: each is the number of vertices within a range of depth values
bin_edges : list of floats
histogram bin edge values defining the bin ranges of depth values
folds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> import os
>>> import numpy as np
>>> import pylab
>>> from scipy.ndimage.filters import gaussian_filter1d
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.guts.mesh import find_neighbors_from_file
>>> from mindboggle.mio.plots import plot_surfaces
>>> from mindboggle.features.folds import extract_folds
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = 'travel_depth.vtk' #os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> neighbor_lists = find_neighbors_from_file(depth_file)
>>> min_vertices = 10000
>>> min_fold_size = 50
>>> do_fill_holes = False #True
>>> min_hole_depth = 0.001
>>> save_file = True
>>> #
>>> folds, n_folds, thr, bins, bin_edges, folds_file = extract_folds(depth_file,
>>> min_vertices, min_fold_size, do_fill_holes, min_hole_depth, save_file)
>>> #
>>> # View folds:
>>> plot_surfaces('folds.vtk')
>>> # Plot histogram and depth threshold:
>>> depths, name = read_scalars(depth_file)
>>> nbins = np.round(len(depths) / 100.0)
>>> a,b,c = pylab.hist(depths, bins=nbins)
>>> pylab.plot(thr*np.ones((100,1)), np.linspace(0, max(bins), 100), 'r.')
>>> pylab.show()
>>> # Plot smoothed histogram:
>>> bins_smooth = gaussian_filter1d(bins.tolist(), 5)
>>> pylab.plot(range(len(bins)), bins, '.', range(len(bins)), bins_smooth,'-')
>>> pylab.show()
"""
import os
import sys
import numpy as np
from time import time
from scipy.ndimage.filters import gaussian_filter1d
from mindboggle.mio.vtks import rewrite_scalars, read_vtk
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.morph import fill_holes
from mindboggle.guts.segment import segment
print("Extract folds in surface mesh")
t0 = time()
#-------------------------------------------------------------------------
# Load depth values for all vertices
#-------------------------------------------------------------------------
faces, lines, indices, points, npoints, depths, name, input_vtk = read_vtk(depth_file,
return_first=True, return_array=True)
#-------------------------------------------------------------------------
# Find neighbors for each vertex
#-------------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
#-------------------------------------------------------------------------
# Compute histogram of depth measures
#-------------------------------------------------------------------------
if npoints > min_vertices:
nbins = np.round(npoints / 100.0)
else:
sys.err(" Expecting at least {0} vertices to create depth histogram".
format(min_vertices))
bins, bin_edges = np.histogram(depths, bins=nbins)
#-------------------------------------------------------------------------
# Anticipating that there will be a rapidly decreasing distribution
# of low depth values (on the outer surface) with a long tail of higher
# depth values (in the folds), smooth the bin values (Gaussian), convolve
# to compute slopes, and find the depth for the first bin with slope = 0.
#-------------------------------------------------------------------------
bins_smooth = gaussian_filter1d(bins.tolist(), 5)
window = [-1, 0, 1]
bin_slopes = np.convolve(bins_smooth, window, mode='same') / (len(window) - 1)
ibins0 = np.where(bin_slopes == 0)[0]
if ibins0.shape:
depth_threshold = bin_edges[ibins0[0]]
else:
depth_threshold = np.median(depths)
#-------------------------------------------------------------------------
# Find the deepest vertices
#-------------------------------------------------------------------------
indices_deep = [i for i,x in enumerate(depths) if x >= depth_threshold]
if indices_deep:
#---------------------------------------------------------------------
# Segment deep vertices as an initial set of folds
#---------------------------------------------------------------------
print(" Segment vertices deeper than {0:.2f} as folds".format(depth_threshold))
t1 = time()
folds = segment(indices_deep, neighbor_lists)
# Slightly slower alternative -- fill boundaries:
#regions = -1 * np.ones(len(points))
#regions[indices_deep] = 1
#folds = segment_by_filling_borders(regions, neighbor_lists)
print(' ...Segmented folds ({0:.2f} seconds)'.format(time() - t1))
#---------------------------------------------------------------------
# Remove small folds
#---------------------------------------------------------------------
if min_fold_size > 1:
print(' Remove folds smaller than {0}'.format(min_fold_size))
unique_folds = [x for x in np.unique(folds) if x != -1]
for nfold in unique_folds:
indices_fold = [i for i,x in enumerate(folds) if x == nfold]
if len(indices_fold) < min_fold_size:
folds[indices_fold] = -1
#---------------------------------------------------------------------
# Find and fill holes in the folds
# Note: Surfaces surrounded by folds can be mistaken for holes,
# so exclude_range includes outer surface values close to zero.
#---------------------------------------------------------------------
if do_fill_holes:
print(" Find and fill holes in the folds")
folds = fill_holes(folds, neighbor_lists, values=depths,
exclude_range=[0, min_hole_depth])
#---------------------------------------------------------------------
# Renumber folds so they are sequential
#---------------------------------------------------------------------
renumber_folds = -1 * np.ones(len(folds))
fold_numbers = [int(x) for x in np.unique(folds) if x != -1]
for i_fold, n_fold in enumerate(fold_numbers):
fold = [i for i,x in enumerate(folds) if x == n_fold]
renumber_folds[fold] = i_fold
folds = renumber_folds
n_folds = i_fold + 1
# Print statement
print(' ...Extracted {0} folds ({1:.2f} seconds)'.
format(n_folds, time() - t0))
else:
print(' No deep vertices')
folds = [int(x) for x in folds]
#-------------------------------------------------------------------------
# Return folds, number of folds, file name
#-------------------------------------------------------------------------
if save_file:
folds_file = os.path.join(os.getcwd(), 'folds.vtk')
rewrite_scalars(depth_file, folds_file, folds, 'folds', folds)
if not os.path.exists(folds_file):
raise(IOError(folds_file + " not found"))
else:
folds_file = None
return folds, n_folds, depth_threshold, bins, bin_edges, folds_file
def plot_mask_surface(vtk_file,
mask_file='',
nonmask_value=-1,
masked_output='',
remove_nonmask=False,
program='vtkviewer',
use_colormap=False,
colormap_file='',
background_value=-1):
"""
Use vtkviewer or mayavi2 to visualize VTK surface mesh data.
If a mask_file is provided, a temporary masked file is saved,
and it is this file that is viewed.
If using vtkviewer, optionally provide colormap file
or set $COLORMAP environment variable.
Parameters
----------
vtk_file : string
name of VTK surface mesh file
mask_file : string
name of VTK surface mesh file to mask vtk_file vertices
nonmask_value : integer
nonmask (usually background) value
masked_output : string
temporary masked output file name
remove_nonmask : bool
remove vertices that are not in mask? (otherwise assign nonmask_value)
program : string {'vtkviewer', 'mayavi2'}
program to visualize VTK file
use_colormap : bool
use Paraview-style XML colormap file set by $COLORMAP env variable?
colormap_file : string
use colormap in given file if use_colormap==True? if empty and
use_colormap==True, use file set by $COLORMAP environment variable
background_value : integer or float
background value
Examples
--------
>>> import os
>>> from mindboggle.mio.plots import plot_mask_surface
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['freesurfer_labels'], '', '.vtk')
>>> os.rename(vtk_file, vtk_file + '.nii.gz')
>>> vtk_file = vtk_file + '.nii.gz'
>>> mask_file = ''
>>> nonmask_value = 0 #-1
>>> masked_output = ''
>>> remove_nonmask = True
>>> program = 'vtkviewer'
>>> use_colormap = True
>>> colormap_file = ''
>>> background_value = -1
>>> plot_mask_surface(vtk_file, mask_file, nonmask_value, masked_output,
... remove_nonmask, program, use_colormap, colormap_file,
... background_value) # doctest: +SKIP
"""
import os
import numpy as np
from mindboggle.guts.mesh import keep_faces, reindex_faces_points
from mindboggle.guts.utilities import execute
from mindboggle.mio.plots import plot_surfaces
from mindboggle.mio.vtks import read_scalars, rewrite_scalars, \
read_vtk, write_vtk
# ------------------------------------------------------------------------
# Filter mesh with non-background values from a second (same-size) mesh:
# ------------------------------------------------------------------------
if mask_file:
mask, name = read_scalars(mask_file, True, True)
if not masked_output:
masked_output = os.path.join(os.getcwd(), 'temp.vtk')
file_to_plot = masked_output
# --------------------------------------------------------------------
# Remove nonmask-valued vertices:
# --------------------------------------------------------------------
if remove_nonmask:
# ----------------------------------------------------------------
# Load VTK files:
# ----------------------------------------------------------------
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file, True, True)
# ----------------------------------------------------------------
# Find mask indices, remove nonmask faces, and reindex:
# ----------------------------------------------------------------
Imask = [i for i, x in enumerate(mask) if x != nonmask_value]
mask_faces = keep_faces(faces, Imask)
mask_faces, points, \
original_indices = reindex_faces_points(mask_faces, points)
# ----------------------------------------------------------------
# Write VTK file with scalar values:
# ----------------------------------------------------------------
if np.ndim(scalars) == 1:
scalar_type = type(scalars[0]).__name__
elif np.ndim(scalars) == 2:
scalar_type = type(scalars[0][0]).__name__
else:
print("Undefined scalar type!")
write_vtk(file_to_plot,
points, [], [],
mask_faces,
scalars[original_indices].tolist(),
scalar_names,
scalar_type=scalar_type)
else:
scalars, name = read_scalars(vtk_file, True, True)
scalars[mask == nonmask_value] = nonmask_value
rewrite_scalars(vtk_file, file_to_plot, scalars, ['scalars'], [],
background_value)
else:
file_to_plot = vtk_file
# ------------------------------------------------------------------------
# Display with vtkviewer.py:
# ------------------------------------------------------------------------
if program == 'vtkviewer':
plot_surfaces(file_to_plot,
use_colormap=use_colormap,
colormap_file=colormap_file)
# ------------------------------------------------------------------------
# Display with mayavi2:
# ------------------------------------------------------------------------
elif program == 'mayavi2':
cmd = ["mayavi2", "-d", file_to_plot, "-m", "Surface", "&"]
execute(cmd, 'os')
def extract_fundi(folds, curv_file, depth_file, min_separation=10,
erode_ratio=0.1, erode_min_size=1, save_file=False,
output_file='', background_value=-1, verbose=False):
"""
Extract fundi from folds.
A fundus is a branching curve that runs along the deepest and most highly
curved portions of a fold. This function extracts one fundus from each
fold by finding the deepest vertices inside the fold, finding endpoints
along the edge of the fold, and connecting the former to the latter with
tracks that run along deep and curved paths (through vertices with high
values of travel depth multiplied by curvature), and a final filtration
step.
The deepest vertices are those with values at least two median
absolute deviations above the median (non-zero) value, with the higher
value chosen if two of the vertices are within (a default of) 10 edges
from each other (to reduce the number of possible fundus paths as well
as computation time).
To find the endpoints, the find_outer_endpoints function propagates
multiple tracks from seed vertices at median depth in the fold through
concentric rings toward the fold’s edge, selecting maximal values within
each ring, and terminating at candidate endpoints. The final endpoints
are those candidates at the end of tracks that have a high median value,
with the higher value chosen if two candidate endpoints are within
(a default of) 10 edges from each other (otherwise, the resulting fundi
can have spurious branching at the fold’s edge).
The connect_points_erosion function connects the deepest fold vertices
to the endpoints with a skeleton of 1-vertex-thick curves by erosion.
It erodes by iteratively removing simple topological points and endpoints
in order of lowest to highest values, where a simple topological point
is a vertex that when added to or removed from an object on a surface
mesh (such as a fundus curve) does not alter the object's topology.
Steps ::
1. Find fundus endpoints (outer anchors) with find_outer_endpoints().
2. Include inner anchor points.
3. Connect anchor points using connect_points_erosion();
inner anchors are removed if they result in endpoints.
Note ::
Follow this with segment_by_region() to segment fundi by sulci.
Parameters
----------
folds : numpy array or list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : bool
save output VTK file?
output_file : string
output VTK file
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
n_fundi_in_folds : integer
number of fundi
fundus_per_fold_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> import numpy as np
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> curv_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> folds, name = read_scalars(folds_file, True, True)
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [4] #[4, 6]
... i0 = [i for i,x in enumerate(folds) if x not in fold_numbers]
... folds[i0] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> output_file = 'extract_fundi_fold4.vtk'
>>> background_value = -1
>>> verbose = False
>>> o1, o2, fundus_per_fold_file = extract_fundi(folds, curv_file,
... depth_file, min_separation, erode_ratio, erode_min_size,
... save_file, output_file, background_value, verbose)
>>> lens = [len([x for x in o1 if x == y])
... for y in np.unique(o1) if y != background_value]
>>> lens[0:10] # [66, 2914, 100, 363, 73, 331, 59, 30, 1, 14] # (if not limit_folds)
[73]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> rewrite_scalars(fundus_per_fold_file,
... 'extract_fundi_fold4_no_background.vtk', o1,
... 'fundus_per_fold', folds) # doctest: +SKIP
>>> plot_surfaces('extract_fundi_fold4_no_background.vtk') # doctest: +SKIP
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.compute import median_abs_dev
from mindboggle.guts.paths import find_max_values
from mindboggle.guts.mesh import find_neighbors_from_file
from mindboggle.guts.mesh import find_complete_faces
from mindboggle.guts.paths import find_outer_endpoints
from mindboggle.guts.paths import connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
if os.path.isfile(curv_file):
points, indices, lines, faces, curvs, scalar_names, npoints, \
input_vtk = read_vtk(curv_file, True, True)
else:
raise IOError("{0} doesn't exist!".format(curv_file))
if os.path.isfile(curv_file):
depths, name = read_scalars(depth_file, True, True)
else:
raise IOError("{0} doesn't exist!".format(depth_file))
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
# ------------------------------------------------------------------------
# Loop through folds:
# ------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != background_value]
if verbose:
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".
format(len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i,x in enumerate(folds) if x == fold_ID]
if indices_fold:
if verbose:
print(' Fold {0}:'.format(int(fold_ID)))
# ----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints:
# ----------------------------------------------------------------
outer_anchors, tracks = find_outer_endpoints(indices_fold,
neighbor_lists, values, depths, min_separation,
background_value, verbose)
# ----------------------------------------------------------------
# Find inner anchor points:
# ----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation,
thr)
# ----------------------------------------------------------------
# Connect anchor points to create skeleton:
# ----------------------------------------------------------------
B = background_value * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B, neighbor_lists,
outer_anchors, inner_anchors, values, erode_ratio,
erode_min_size, [], '', background_value, verbose)
if skeleton:
skeletons.extend(skeleton)
## ---------------------------------------------------------------
## Remove fundus vertices if they make complete triangle faces:
## ---------------------------------------------------------------
#Iremove = find_complete_faces(skeletons, faces)
#if Iremove:
# skeletons = list(frozenset(skeletons).difference(Iremove))
indices_skel = [x for x in skeletons if folds[x] != background_value]
fundus_per_fold = background_value * np.ones(npoints)
fundus_per_fold[indices_skel] = folds[indices_skel]
n_fundi_in_folds = len([x for x in np.unique(fundus_per_fold)
if x != background_value])
if n_fundi_in_folds == 1:
sdum = 'fold fundus'
else:
sdum = 'fold fundi'
if verbose:
print(' ...Extracted {0} {1}; {2} total ({3:.2f} seconds)'.
format(n_fundi_in_folds, sdum, n_fundi_in_folds, time() - t1))
# ------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
# ------------------------------------------------------------------------
fundus_per_fold_file = None
if n_fundi_in_folds > 0:
fundus_per_fold = [int(x) for x in fundus_per_fold]
if save_file:
if output_file:
fundus_per_fold_file = output_file
else:
fundus_per_fold_file = os.path.join(os.getcwd(),
'fundus_per_fold.vtk')
rewrite_scalars(curv_file, fundus_per_fold_file, fundus_per_fold,
'fundi', [], background_value)
if not os.path.exists(fundus_per_fold_file):
raise IOError(fundus_per_fold_file + " not found")
return fundus_per_fold, n_fundi_in_folds, fundus_per_fold_file
def extract_fundi(folds, curv_file, depth_file, min_separation=10,
erode_ratio=0.1, erode_min_size=1, save_file=False):
"""
Extract fundi from folds.
A fundus is a branching curve that runs along the deepest and most
highly curved portions of a fold.
Steps ::
1. Find fundus endpoints (outer anchors) with find_outer_anchors().
2. Include inner anchor points.
3. Connect anchor points using connect_points_erosion();
inner anchors are removed if they result in endpoints.
Parameters
----------
folds : numpy array or list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : Boolean
save output VTK file?
Returns
-------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
n_fundi_in_folds : integer
number of fundi
fundus_per_fold_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> single_fold = True
>>> import os
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> curv_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'travel_depth_rescaled.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file, True, True)
>>> if single_fold:
>>> fold_number = 2 #11
>>> folds[folds != fold_number] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> o1, o2, fundus_per_fold_file = extract_fundi(folds, curv_file,
... depth_file, min_separation, erode_ratio, erode_min_size, save_file)
>>> #
>>> # View:
>>> plot_surfaces(fundi_file)
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.compute import median_abs_dev
from mindboggle.guts.paths import find_max_values
from mindboggle.guts.mesh import find_neighbors_from_file, find_complete_faces
from mindboggle.guts.paths import find_outer_anchors, connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
points, indices, lines, faces, curvs, scalar_names, npoints, \
input_vtk = read_vtk(curv_file, True, True)
depths, name = read_scalars(depth_file, True, True)
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
#-------------------------------------------------------------------------
# Loop through folds:
#-------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != -1]
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".
format(len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i,x in enumerate(folds) if x == fold_ID]
if indices_fold:
print(' Fold {0}:'.format(int(fold_ID)))
#-----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints:
#-----------------------------------------------------------------
outer_anchors, tracks = find_outer_anchors(indices_fold,
neighbor_lists, values, depths, min_separation)
#-----------------------------------------------------------------
# Find inner anchor points:
#-----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation, thr)
#-----------------------------------------------------------------
# Connect anchor points to create skeleton:
#-----------------------------------------------------------------
B = -1 * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B, neighbor_lists,
outer_anchors, inner_anchors, values,
erode_ratio, erode_min_size, save_steps=[], save_vtk='')
if skeleton:
skeletons.extend(skeleton)
#-----------------------------------------------------------------
# Remove fundus vertices if they complete triangle faces:
#-----------------------------------------------------------------
Iremove = find_complete_faces(skeletons, faces)
if Iremove:
skeletons = list(frozenset(skeletons).difference(Iremove))
indices_skel = [x for x in skeletons if folds[x] != -1]
fundus_per_fold = -1 * np.ones(npoints)
fundus_per_fold[indices_skel] = folds[indices_skel]
n_fundi_in_folds = len([x for x in np.unique(fundus_per_fold)
if x != -1])
if n_fundi_in_folds == 1:
sdum = 'fold fundus'
else:
sdum = 'fold fundi'
print(' ...Extracted {0} {1}; {2} total ({3:.2f} seconds)'.
format(n_fundi_in_folds, sdum, n_fundi_in_folds, time() - t1))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
if n_fundi_in_folds > 0:
fundus_per_fold = [int(x) for x in fundus_per_fold]
if save_file:
fundus_per_fold_file = os.path.join(os.getcwd(),
'fundus_per_fold.vtk')
rewrite_scalars(curv_file, fundus_per_fold_file, fundus_per_fold,
'fundi', folds)
if not os.path.exists(fundus_per_fold_file):
raise(IOError(fundus_per_fold_file + " not found"))
else:
fundus_per_fold_file = None
return fundus_per_fold, n_fundi_in_folds, fundus_per_fold_file
def compute_likelihood(trained_file,
depth_file,
curvature_file,
folds,
save_file=False,
background_value=-1):
"""
Compute likelihoods based on input values, folds, estimated parameters.
Compute likelihood values for a given VTK surface mesh file, after
training on distributions of depth and curvature values from multiple
files.
Parameters
----------
trained_file : pickle compressed file
depth_border, curv_border, depth_nonborder, and curv_nonborder are
dictionaries containing lists of floats
(estimates of depth or curvature means, sigmas, and weights
trained on fold vertices either on or off sulcus label borders)
depth_file : string
VTK surface mesh file with depth values in [0,1] for all vertices
curvature_file : string
VTK surface mesh file with curvature values in [-1,1] for all vertices
folds : list of integers
fold number for all vertices (-1 for non-fold vertices)
save_file : bool
save output VTK file?
background_value : integer or float
background value
Returns
-------
likelihoods : list of floats
likelihood values for all vertices (0 for non-fold vertices)
likelihoods_file : string (if save_file)
name of output VTK file with likelihood scalars
(-1 for non-fold vertices)
Notes
-----
The depth_curv_border_nonborder_parameters.pkl file needs to be updated.
Examples
--------
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.shapes.likelihood import compute_likelihood
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> curvature_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> trained_file = fetch_data(urls[
... 'depth_curv_border_nonborder_parameters'], '', '.pkl') # doctest: +SKIP
>>> folds, name = read_scalars(folds_file)
>>> save_file = True
>>> background_value = -1
>>> likelihoods, likelihoods_file = compute_likelihood(trained_file,
... depth_file, curvature_file, folds, save_file, background_value) # doctest: +SKIP
View result (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> plot_surfaces('likelihoods.vtk', folds_file) # doctest: +SKIP
"""
import os
import numpy as np
from math import pi
import pickle
from io import open
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
# Initialize variables:
tiny = 0.000000001
L = np.zeros(len(folds))
probs_border = np.zeros(len(folds))
probs_nonborder = np.zeros(len(folds))
# Load estimated depth and curvature distribution parameters:
depth_border, curv_border, depth_nonborder, \
curv_nonborder = pickle.load(open(trained_file, "rb"))
# Load depths, curvatures:
depths, name = read_scalars(depth_file, True, True)
curvatures, name = read_scalars(curvature_file, True, True)
# Prep for below:
n = 2
twopiexp = (2 * pi)**(n / 2)
border_sigmas = depth_border['sigmas'] * curv_border['sigmas']
nonborder_sigmas = depth_nonborder['sigmas'] * curv_nonborder['sigmas']
norm_border = 1 / (twopiexp * border_sigmas + tiny)
norm_nonborder = 1 / (twopiexp * nonborder_sigmas + tiny)
I = [i for i, x in enumerate(folds) if x != background_value]
N = depth_border['sigmas'].shape[0]
for j in range(N):
# Border:
expB = depth_border['weights'][j] * \
((depths[I]-depth_border['means'][j])**2) / \
depth_border['sigmas'][j]**2
expB += curv_border['weights'][j] * \
((curvatures[I]-curv_border['means'][j])**2) / \
curv_border['sigmas'][j]**2
expB = -expB / 2
probs_border[I] = probs_border[I] + norm_border[j] * np.exp(expB)
# Non-border:
expNB = depth_nonborder['weights'][j] * \
((depths[I]-depth_nonborder['means'][j])**2) / \
depth_nonborder['sigmas'][j]**2
expNB += curv_nonborder['weights'][j] * \
((curvatures[I]-curv_nonborder['means'][j])**2) / \
curv_nonborder['sigmas'][j]**2
expNB = -expNB / 2
probs_nonborder[I] = probs_nonborder[I] + \
norm_nonborder[j] * np.exp(expNB)
likelihoods = probs_border / (probs_nonborder + probs_border + tiny)
likelihoods = likelihoods.tolist()
# ------------------------------------------------------------------------
# Return likelihoods and output file name
# ------------------------------------------------------------------------
if save_file:
likelihoods_file = os.path.join(os.getcwd(), 'likelihoods.vtk')
rewrite_scalars(depth_file, likelihoods_file, likelihoods,
'likelihoods', likelihoods, background_value)
if not os.path.exists(likelihoods_file):
raise IOError(likelihoods_file + " not found")
else:
likelihoods_file = None
return likelihoods, likelihoods_file
def rescale_by_neighborhood(input_vtk, indices=[], nedges=10, p=99,
set_max_to_1=True, save_file=False, output_filestring='rescaled_scalars',
background_value=-1):
"""
Rescale the scalar values of a VTK file by a percentile value
in each vertex's surface mesh neighborhood.
Parameters
----------
input_vtk : string
name of VTK file with a scalar value for each vertex
indices : list of integers (optional)
indices of scalars to normalize
nedges : integer
number or edges from vertex, defining the size of its neighborhood
p : float in range of [0,100]
percentile used to normalize each scalar
set_max_to_1 : Boolean
set all rescaled values greater than 1 to 1.0?
save_file : Boolean
save output VTK file?
output_filestring : string (if save_file)
name of output file
background_value : integer
background value
Returns
-------
rescaled_scalars : list of floats
rescaled scalar values
rescaled_scalars_file : string (if save_file)
name of output VTK file with rescaled scalar values
Examples
--------
>>> import os
>>> from mindboggle.guts.mesh import rescale_by_neighborhood
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_vtk = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> indices = []
>>> nedges = 10
>>> p = 99
>>> set_max_to_1 = True
>>> save_file = True
>>> output_filestring = 'rescaled_scalars'
>>> background_value = -1
>>> #
>>> rescaled_scalars, rescaled_scalars_file = rescale_by_neighborhood(input_vtk,
>>> indices, nedges, p, set_max_to_1, save_file, output_filestring, background_value)
>>> #
>>> # View rescaled scalar values per fold:
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> #
>>> rewrite_scalars(rescaled_scalars_file, rescaled_scalars_file,
>>> rescaled_scalars, 'rescaled_depths', folds)
>>> plot_surfaces(rescaled_scalars_file)
"""
import os
import numpy as np
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors_from_file, find_neighborhood
# Load scalars and vertex neighbor lists:
scalars, name = read_scalars(input_vtk, True, True)
if not indices:
indices = [i for i,x in enumerate(scalars) if x != background_value]
print(" Rescaling {0} scalar values by neighborhood...".format(len(indices)))
neighbor_lists = find_neighbors_from_file(input_vtk)
# Loop through vertices:
rescaled_scalars = scalars.copy()
for index in indices:
# Determine the scalars in the vertex's neighborhood:
neighborhood = find_neighborhood(neighbor_lists, [index], nedges)
# Compute a high neighborhood percentile to normalize vertex's value:
normalization_factor = np.percentile(scalars[neighborhood], p)
rescaled_scalar = scalars[index] / normalization_factor
rescaled_scalars[index] = rescaled_scalar
# Make any rescaled value greater than 1 equal to 1:
if set_max_to_1:
rescaled_scalars[[x for x in indices if rescaled_scalars[x] > 1.0]] = 1
rescaled_scalars = rescaled_scalars.tolist()
#-------------------------------------------------------------------------
# Return rescaled scalars and file name
#-------------------------------------------------------------------------
if save_file:
rescaled_scalars_file = os.path.join(os.getcwd(), output_filestring + '.vtk')
rewrite_scalars(input_vtk, rescaled_scalars_file,
rescaled_scalars, 'rescaled_scalars')
if not os.path.exists(rescaled_scalars_file):
raise(IOError(rescaled_scalars_file + " not found"))
else:
rescaled_scalars_file = None
return rescaled_scalars, rescaled_scalars_file
def extract_sulci(labels_file, folds_or_file, hemi, min_boundary=1,
sulcus_names=[], verbose=False):
"""
Identify sulci from folds in a brain surface according to a labeling
protocol that includes a list of label pairs defining each sulcus.
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
verbose : bool
print statements?
Returns
-------
sulci : list of integers
sulcus numbers for all vertices (-1 for non-sulcus vertices)
n_sulci : integers
number of sulci
sulci_file : string
output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> # Load labels, folds, neighbor lists, and sulcus names and label pairs
>>> labels_file = fetch_data(urls['left_freesurfer_labels'])
>>> folds_file = fetch_data(urls['left_folds'])
>>> folds_or_file, name = read_scalars(folds_file)
>>> hemi = 'lh'
>>> min_boundary = 10
>>> sulcus_names = []
>>> verbose = False
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
... hemi, min_boundary, sulcus_names, verbose)
>>> n_sulci
23
>>> lens = [len([x for x in sulci if x == y]) for y in range(n_sulci)]
>>> lens[0:10]
[0, 6573, 3366, 6689, 5358, 4049, 6379, 3551, 2632, 4225]
>>> lens[10::]
[754, 3724, 2197, 5823, 1808, 5122, 513, 2153, 1445, 418, 0, 3556, 1221]
View result (skip test):
>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces('sulci.vtk') # doctest: +SKIP
"""
import os
from time import time
import numpy as np
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import extract_borders, propagate, segment
from mindboggle.mio.labels import DKTprotocol
# Load fold numbers if folds_or_file is a string:
if isinstance(folds_or_file, str):
folds, name = read_scalars(folds_or_file)
elif isinstance(folds_or_file, list):
folds = folds_or_file
dkt = DKTprotocol()
if hemi == 'lh':
pair_lists = dkt.left_sulcus_label_pair_lists
elif hemi == 'rh':
pair_lists = dkt.right_sulcus_label_pair_lists
else:
raise IOError("Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors:
points, indices, lines, faces, labels, scalar_names, npoints, \
input_vtk = read_vtk(labels_file)
neighbor_lists = find_neighbors(faces, npoints)
# Array of sulcus IDs for fold vertices, initialized as -1.
# Since we do not touch gyral vertices and vertices whose labels
# are not in the label list, or vertices having only one label,
# their sulcus IDs will remain -1:
sulci = -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)
if verbose:
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 verbose and len(unique_fold_labels) < 2:
# Ignore: sulci already initialized with -1 values:
if not unique_fold_labels:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has no labels".
format(n_fold, len_fold))
else:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has only one label ({2})".
format(n_fold, len_fold, unique_fold_labels[0]))
# Ignore: sulci already initialized with -1 values
else:
# Find all label boundary pairs within the fold:
indices_fold_pairs, fold_pairs, unique_fold_pairs = \
extract_borders(fold, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
# Find fold label pairs in the protocol (pairs are already sorted):
fold_pairs_in_protocol = [x for x in unique_fold_pairs
if x in dkt.unique_sulcus_label_pairs]
if verbose and unique_fold_labels:
print(" Fold {0} labels: {1} ({2} vertices)".format(n_fold,
', '.join([str(x) for x in unique_fold_labels]),
len_fold))
#-----------------------------------------------------------------
# NO MATCH -- fold has no sulcus label pair
#-----------------------------------------------------------------
if verbose and not fold_pairs_in_protocol:
print(" Fold {0}: NO MATCH -- fold has no sulcus label pair".
format(n_fold, len_fold))
#-----------------------------------------------------------------
# Possible matches
#-----------------------------------------------------------------
else:
if verbose:
print(" Fold {0} label pairs in protocol: {1}".format(n_fold,
', '.join([str(x) for x in fold_pairs_in_protocol])))
# Labels in the protocol (includes repeats across label pairs):
labels_in_pairs = [x for lst in fold_pairs_in_protocol
for x in lst]
# Labels that appear in one or more sulcus label boundary:
unique_labels = []
nonunique_labels = []
for label in np.unique(labels_in_pairs):
if len([x for x in labels_in_pairs if x == label]) == 1:
unique_labels.append(label)
else:
nonunique_labels.append(label)
#-------------------------------------------------------------
# Vertices whose labels are in only one sulcus label pair
#-------------------------------------------------------------
# Find vertices with a label that is in only one of the fold's
# label pairs (the other label in the pair can exist in other
# pairs). Assign the vertices the sulcus with the label pair
# if they are connected to the label boundary for that pair.
#-------------------------------------------------------------
if unique_labels:
for pair in fold_pairs_in_protocol:
# If one or both labels in label pair is/are unique:
unique_labels_in_pair = [x for x in pair
if x in unique_labels]
n_unique = len(unique_labels_in_pair)
if n_unique:
ID = None
for i, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
if pair in pair_list:
ID = i
break
if ID:
# Seeds from label boundary vertices
# (fold_pairs and pair already sorted):
indices_pair = [x for i,x
in enumerate(indices_fold_pairs)
if fold_pairs[i] == pair]
# Vertices with unique label(s) in pair:
indices_unique_labels = [fold[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 verbose:
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:
if verbose:
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)
elif verbose:
if len(iseed2) == 1:
print(" Remove "
"assignment "
"of ID {0} from "
"1 vertex".
format(seed2))
else:
print(" Remove "
"assignment "
"of ID {0} from "
"{1} vertices".
format(seed2,
len(iseed2)))
indices_pair = indices_pair2
# Assign sulcus IDs to seeds:
seeds[indices_pair] = ID
# Identify vertices with the label:
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]
sulcus_numbers = [int(x) for x in np.unique(sulci) if x != -1]
# if not np.isnan(x)]
n_sulci = len(sulcus_numbers)
#-------------------------------------------------------------------------
# Print statements
#-------------------------------------------------------------------------
if verbose:
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]))
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 rescale_by_label(input_vtk, labels_or_file, save_file=False,
output_filestring='rescaled_scalars'):
"""
Rescale scalars for each label (such as depth values within each fold).
Default is to normalize the scalar values of a VTK file by
a percentile value in each vertex's surface mesh for each label.
Parameters
----------
input_vtk : string
name of VTK file with a scalar value for each vertex
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
save_file : Boolean
save output VTK file?
output_filestring : string (if save_file)
name of output file
Returns
-------
rescaled_scalars : list of floats
scalar values rescaled for each label, for label numbers not equal to -1
rescaled_scalars_file : string (if save_file)
name of output VTK file with rescaled scalar values for each label
Examples
--------
>>> # Rescale depths by neighborhood within each label:
>>> import os
>>> from mindboggle.guts.mesh import rescale_by_label
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_vtk = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> labels_or_file = os.path.join(path, 'arno', 'features', 'subfolds.vtk')
>>> save_file = True
>>> output_filestring = 'rescaled_scalars'
>>> #
>>> rescaled_scalars, rescaled_scalars_file = rescale_by_label(input_vtk,
>>> labels_or_file, save_file, output_filestring)
>>> #
>>> # View rescaled scalar values per fold:
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> #
>>> rewrite_scalars(rescaled_scalars_file, rescaled_scalars_file,
>>> rescaled_scalars, 'rescaled_depths', folds)
>>> plot_surfaces(rescaled_scalars_file)
"""
import os
import numpy as np
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
# Load scalars and vertex neighbor lists:
scalars, name = read_scalars(input_vtk, True, True)
print(" Rescaling scalar values within each label...")
# Load label numbers:
if isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file, True, True)
elif isinstance(labels_or_file, list):
labels = labels_or_file
unique_labels = np.unique(labels)
unique_labels = [x for x in unique_labels if x >= 0]
# Loop through labels:
for label in unique_labels:
#print(" Rescaling scalar values within label {0} of {1} labels...".format(
# int(label), len(unique_labels)))
indices = [i for i,x in enumerate(labels) if x == label]
if indices:
# Rescale by the maximum label scalar value:
scalars[indices] = scalars[indices] / np.max(scalars[indices])
rescaled_scalars = scalars.tolist()
#-------------------------------------------------------------------------
# Return rescaled scalars and file name
#-------------------------------------------------------------------------
if save_file:
rescaled_scalars_file = os.path.join(os.getcwd(), output_filestring + '.vtk')
rewrite_scalars(input_vtk, rescaled_scalars_file,
rescaled_scalars, 'rescaled_scalars', labels)
if not os.path.exists(rescaled_scalars_file):
raise(IOError(rescaled_scalars_file + " not found"))
else:
rescaled_scalars_file = None
return rescaled_scalars, rescaled_scalars_file
def segment_fundi(fundus_per_fold, sulci=[], vtk_file='', save_file=False,
verbose=False):
"""
Segment fundi by sulcus definitions.
Parameters
----------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
sulci : numpy array or list of integers
sulcus number for each vertex, used to filter and label fundi
vtk_file : string (if save_file)
VTK file with sulcus number for each vertex
save_file : bool
save output VTK file?
verbose : bool
print statements?
Returns
-------
fundus_per_sulcus : list of integers
fundus numbers for all vertices, labeled by sulcus
(-1 for non-fundus vertices)
n_fundi : integer
number of fundi
fundus_per_sulcus_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more sulci:
>>> import numpy as np
>>> single_fold = True
>>> from mindboggle.features.fundi import segment_fundi
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> curv_file = fetch_data(urls['left_mean_curvature'])
>>> depth_file = fetch_data(urls['left_travel_depth'])
>>> folds_file = fetch_data(urls['left_folds'])
>>> vtk_file = fetch_data(urls['left_sulci'])
>>> sulci, name = read_scalars(vtk_file, True, True)
>>> folds, name = read_scalars(folds_file, True, True)
>>> if single_fold:
... fold_number = 2 #11
... folds[folds != fold_number] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> verbose = False
>>> fundus_per_fold, o1, o2 = extract_fundi(folds,
... curv_file, depth_file, min_separation, erode_ratio,
... erode_min_size, save_file, verbose)
>>> o1, o2, fundus_per_sulcus_file = segment_fundi(fundus_per_fold,
... sulci, vtk_file, save_file, verbose)
>>> segment_numbers = [x for x in np.unique(o1) if x != -1]
>>> lens = []
>>> if single_fold:
... for segment_number in segment_numbers:
... lens.append(len([x for x in o1 if x == segment_number]))
>>> lens
[14, 13, 88]
View result (skip test):
>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces(fundus_per_sulcus_file) # doctest: +SKIP
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from mindboggle.mio.vtks import rewrite_scalars
if isinstance(sulci, list):
sulci = np.array(sulci)
#-------------------------------------------------------------------------
# Create fundi by segmenting fold fundi with overlapping sulcus labels:
#-------------------------------------------------------------------------
indices = [i for i,x in enumerate(fundus_per_fold) if x != -1]
if indices and np.size(sulci):
fundus_per_sulcus = -1 * np.ones(len(sulci))
fundus_per_sulcus[indices] = sulci[indices]
n_fundi = len([x for x in np.unique(fundus_per_sulcus) if x != -1])
else:
fundus_per_sulcus = []
n_fundi = 0
if n_fundi == 1:
sdum = 'sulcus fundus'
else:
sdum = 'sulcus fundi'
if verbose:
print(' Segmented {0} {1}'.format(n_fundi, sdum))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
fundus_per_sulcus_file = None
if n_fundi > 0:
fundus_per_sulcus = [int(x) for x in fundus_per_sulcus]
if save_file and os.path.exists(vtk_file):
fundus_per_sulcus_file = os.path.join(os.getcwd(),
'fundus_per_sulcus.vtk')
# Do not filter faces/points by scalars when saving file:
rewrite_scalars(vtk_file, fundus_per_sulcus_file,
fundus_per_sulcus, 'fundus_per_sulcus')
if not os.path.exists(fundus_per_sulcus_file):
raise IOError(fundus_per_sulcus_file + " not found")
return fundus_per_sulcus, n_fundi, fundus_per_sulcus_file
def compute_likelihood(trained_file,
depth_file,
curvature_file,
folds,
save_file=False):
"""
Compute likelihoods based on input values, folds, and estimated parameters.
Compute likelihood values for a given VTK surface mesh file, after training
on distributions of depth and curvature values from multiple files.
Parameters
----------
trained_file : pickle compressed file
contains the following dictionaries containing lists of floats
(estimates of depth or curvature means, sigmas, and weights
trained on fold vertices either on or off sulcus label borders)
depth_border, curv_border, depth_nonborder, curv_nonborder
depth_file : string
VTK surface mesh file with depth values in [0,1] for all vertices
curvature_file : string
VTK surface mesh file with curvature values in [-1,1] for all vertices
folds : list of integers
fold number for all vertices (-1 for non-fold vertices)
save_file : Boolean
save output VTK file?
Returns
-------
likelihoods : list of floats
likelihood values for all vertices (0 for non-fold vertices)
likelihoods_file : string (if save_file)
name of output VTK file with likelihood scalars
(-1 for non-fold vertices)
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.shapes.likelihood import compute_likelihood
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> trained_file = os.path.join(path, 'atlases', 'depth_curv_border_nonborder_parameters.pkl')
>>> #depth_file = os.path.join(path, 'arno', 'shapes', 'travel_depth_rescaled.vtk')
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> curvature_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> save_file = True
>>> #
>>> compute_likelihood(trained_file, depth_file, curvature_file, folds, save_file)
>>> # View:
>>> plot_surfaces('likelihoods.vtk', folds_file)
"""
import os
import numpy as np
from math import pi
import cPickle as pickle
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
# Initialize variables:
tiny = 0.000000001
L = np.zeros(len(folds))
probs_border = np.zeros(len(folds))
probs_nonborder = np.zeros(len(folds))
# Load estimated depth and curvature distribution parameters:
depth_border, curv_border, depth_nonborder, curv_nonborder = pickle.load(
open(trained_file, "r"))
# Load depths, curvatures:
depths, name = read_scalars(depth_file, True, True)
curvatures, name = read_scalars(curvature_file, True, True)
# Prep for below:
n = 2
twopiexp = (2 * pi)**(n / 2)
border_sigmas = depth_border['sigmas'] * curv_border['sigmas']
nonborder_sigmas = depth_nonborder['sigmas'] * curv_nonborder['sigmas']
norm_border = 1 / (twopiexp * border_sigmas + tiny)
norm_nonborder = 1 / (twopiexp * nonborder_sigmas + tiny)
I = [i for i, x in enumerate(folds) if x != -1]
N = depth_border['sigmas'].shape[0]
for j in range(N):
# Border:
expB = depth_border['weights'][j] * \
((depths[I]-depth_border['means'][j])**2) / \
depth_border['sigmas'][j]**2
expB += curv_border['weights'][j] * \
((curvatures[I]-curv_border['means'][j])**2) / \
curv_border['sigmas'][j]**2
expB = -expB / 2
probs_border[I] = probs_border[I] + norm_border[j] * np.exp(expB)
# Non-border:
expNB = depth_nonborder['weights'][j] * \
((depths[I]-depth_nonborder['means'][j])**2) / \
depth_nonborder['sigmas'][j]**2
expNB += curv_nonborder['weights'][j] * \
((curvatures[I]-curv_nonborder['means'][j])**2) / \
curv_nonborder['sigmas'][j]**2
expNB = -expNB / 2
probs_nonborder[
I] = probs_nonborder[I] + norm_nonborder[j] * np.exp(expNB)
likelihoods = probs_border / (probs_nonborder + probs_border + tiny)
likelihoods = likelihoods.tolist()
#-------------------------------------------------------------------------
# Return likelihoods and output file name
#-------------------------------------------------------------------------
if save_file:
likelihoods_file = os.path.join(os.getcwd(), 'likelihoods.vtk')
rewrite_scalars(depth_file, likelihoods_file, likelihoods,
'likelihoods', likelihoods)
if not os.path.exists(likelihoods_file):
raise (IOError(likelihoods_file + " not found"))
else:
likelihoods_file = None
return likelihoods, likelihoods_file
def rescale_by_label(input_vtk,
labels_or_file,
save_file=False,
output_filestring='rescaled_scalars',
verbose=False):
"""
Rescale scalars for each label (such as depth values within each fold).
Default is to normalize the scalar values of a VTK file by
a percentile value in each vertex's surface mesh for each label.
Parameters
----------
input_vtk : string
name of VTK file with a scalar value for each vertex
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
save_file : bool
save output VTK file?
output_filestring : string (if save_file)
name of output file
verbose : bool
print statements?
Returns
-------
rescaled_scalars : list of floats
scalar values rescaled for each label, for label numbers not equal to -1
rescaled_scalars_file : string (if save_file)
name of output VTK file with rescaled scalar values for each label
Examples
--------
>>> # Rescale depths by neighborhood within each label:
>>> import numpy as np
>>> from mindboggle.guts.mesh import rescale_by_label
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.plots import plot_surfaces
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_vtk = fetch_data(urls['left_travel_depth'])
>>> labels_or_file = fetch_data(urls['left_folds'])
>>> save_file = True
>>> output_filestring = 'rescaled_scalars'
>>> verbose = False
>>> rescaled, rescaled_label_file = rescale_by_label(input_vtk,
... labels_or_file, save_file, output_filestring, verbose)
>>> scalars1, name = read_scalars(input_vtk)
>>> print('{0:0.5f}, {1:0.5f}'.format(max(scalars1), max(rescaled)))
34.95560, 1.00000
>>> print('{0:0.5f}, {1:0.5f}'.format(np.mean(scalars1), np.mean(rescaled)))
7.43822, 0.28389
View rescaled scalar values on surface (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> plot_surfaces(rescaled_label_file) # doctest: +SKIP
"""
import os
import numpy as np
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
# Load scalars and vertex neighbor lists:
scalars, name = read_scalars(input_vtk, True, True)
if verbose:
print(" Rescaling scalar values within each label...")
# Load label numbers:
if isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file, True, True)
elif isinstance(labels_or_file, list):
labels = labels_or_file
unique_labels = np.unique(labels)
# Loop through labels:
for label in unique_labels:
if verbose:
print(
" Rescaling values within label {0} of {1} labels...".format(
int(label), len(unique_labels)))
indices = [i for i, x in enumerate(labels) if x == label]
if indices:
# Rescale by the maximum label scalar value:
scalars[indices] = scalars[indices] / np.max(scalars[indices])
#print(max(scalars), max(scalars[indices]))
rescaled_scalars = scalars.tolist()
#-------------------------------------------------------------------------
# Return rescaled scalars and file name
#-------------------------------------------------------------------------
if save_file:
rescaled_scalars_file = os.path.join(os.getcwd(),
output_filestring + '.vtk')
rewrite_scalars(input_vtk, rescaled_scalars_file, rescaled_scalars,
'rescaled_scalars', labels)
if not os.path.exists(rescaled_scalars_file):
raise IOError(rescaled_scalars_file + " not found")
else:
rescaled_scalars_file = None
return rescaled_scalars, rescaled_scalars_file
def segment_fundi(fundus_per_fold, sulci=[], vtk_file='', save_file=False):
"""
Segment fundi by sulcus definitions.
Parameters
----------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
sulci : numpy array or list of integers
sulcus number for each vertex, used to filter and label fundi
vtk_file : string (if save_file)
VTK file with sulcus number for each vertex
save_file : Boolean
save output VTK file?
Returns
-------
fundus_per_sulcus : list of integers
fundus numbers for all vertices, labeled by sulcus
(-1 for non-fundus vertices)
n_fundi : integer
number of fundi
fundus_per_sulcus_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more sulci:
>>> single_fold = True
>>> import os
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.features.fundi import extract_fundi, segment_fundi
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> sulci, name = read_scalars(vtk_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
>>> fundus_per_fold, o1, o2 = extract_fundi(folds, curv_file, depth_file, min_separation, erode_ratio, erode_min_size, save_file)
>>> o1, o2, fundus_per_sulcus_file = segment_fundi(fundus_per_fold, sulci, vtk_file, save_file)
>>> #
>>> # View:
>>> plot_surfaces(fundus_per_sulcus_file)
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from mindboggle.mio.vtks import rewrite_scalars
if isinstance(sulci, list):
sulci = np.array(sulci)
#-------------------------------------------------------------------------
# Create fundi by segmenting fold fundi with overlapping sulcus labels:
#-------------------------------------------------------------------------
indices = [i for i,x in enumerate(fundus_per_fold) if x != -1]
if indices and np.size(sulci):
fundus_per_sulcus = -1 * np.ones(len(sulci))
fundus_per_sulcus[indices] = sulci[indices]
n_fundi = len([x for x in np.unique(fundus_per_sulcus) if x != -1])
else:
fundus_per_sulcus = []
n_fundi = 0
if n_fundi == 1:
sdum = 'sulcus fundus'
else:
sdum = 'sulcus fundi'
print(' Segmented {0} {1}'.format(n_fundi, sdum))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
fundus_per_sulcus_file = None
if n_fundi > 0:
fundus_per_sulcus = [int(x) for x in fundus_per_sulcus]
if save_file and os.path.exists(vtk_file):
fundus_per_sulcus_file = os.path.join(os.getcwd(),
'fundus_per_sulcus.vtk')
rewrite_scalars(vtk_file, fundus_per_sulcus_file,
fundus_per_sulcus, 'fundus_per_sulcus',
fundus_per_sulcus)
if not os.path.exists(fundus_per_sulcus_file):
raise(IOError(fundus_per_sulcus_file + " not found"))
return fundus_per_sulcus, n_fundi, fundus_per_sulcus_file
def segment_fundi(fundus_per_fold,
sulci=[],
vtk_file='',
save_file=False,
verbose=False):
"""
Segment fundi by sulcus definitions.
Parameters
----------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
sulci : numpy array or list of integers
sulcus number for each vertex, used to filter and label fundi
vtk_file : string (if save_file)
VTK file with sulcus number for each vertex
save_file : bool
save output VTK file?
verbose : bool
print statements?
Returns
-------
fundus_per_sulcus : list of integers
fundus numbers for all vertices, labeled by sulcus
(-1 for non-fundus vertices)
n_fundi : integer
number of fundi
fundus_per_sulcus_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more sulci:
>>> import numpy as np
>>> single_fold = True
>>> from mindboggle.features.fundi import segment_fundi
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> curv_file = fetch_data(urls['left_mean_curvature'])
>>> depth_file = fetch_data(urls['left_travel_depth'])
>>> folds_file = fetch_data(urls['left_folds'])
>>> vtk_file = fetch_data(urls['left_sulci'])
>>> sulci, name = read_scalars(vtk_file, True, True)
>>> folds, name = read_scalars(folds_file, True, True)
>>> if single_fold:
... fold_number = 2 #11
... folds[folds != fold_number] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> verbose = False
>>> fundus_per_fold, o1, o2 = extract_fundi(folds,
... curv_file, depth_file, min_separation, erode_ratio,
... erode_min_size, save_file, verbose)
>>> o1, o2, fundus_per_sulcus_file = segment_fundi(fundus_per_fold,
... sulci, vtk_file, save_file, verbose)
>>> segment_numbers = [x for x in np.unique(o1) if x != -1]
>>> lens = []
>>> if single_fold:
... for segment_number in segment_numbers:
... lens.append(len([x for x in o1 if x == segment_number]))
>>> lens
[14, 13, 88]
View result (skip test):
>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces(fundus_per_sulcus_file) # doctest: +SKIP
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from mindboggle.mio.vtks import rewrite_scalars
if isinstance(sulci, list):
sulci = np.array(sulci)
#-------------------------------------------------------------------------
# Create fundi by segmenting fold fundi with overlapping sulcus labels:
#-------------------------------------------------------------------------
indices = [i for i, x in enumerate(fundus_per_fold) if x != -1]
if indices and np.size(sulci):
fundus_per_sulcus = -1 * np.ones(len(sulci))
fundus_per_sulcus[indices] = sulci[indices]
n_fundi = len([x for x in np.unique(fundus_per_sulcus) if x != -1])
else:
fundus_per_sulcus = []
n_fundi = 0
if n_fundi == 1:
sdum = 'sulcus fundus'
else:
sdum = 'sulcus fundi'
if verbose:
print(' Segmented {0} {1}'.format(n_fundi, sdum))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
fundus_per_sulcus_file = None
if n_fundi > 0:
fundus_per_sulcus = [int(x) for x in fundus_per_sulcus]
if save_file and os.path.exists(vtk_file):
fundus_per_sulcus_file = os.path.join(os.getcwd(),
'fundus_per_sulcus.vtk')
# Do not filter faces/points by scalars when saving file:
rewrite_scalars(vtk_file, fundus_per_sulcus_file,
fundus_per_sulcus, 'fundus_per_sulcus')
if not os.path.exists(fundus_per_sulcus_file):
raise IOError(fundus_per_sulcus_file + " not found")
return fundus_per_sulcus, n_fundi, fundus_per_sulcus_file
def extract_fundi(folds,
curv_file,
depth_file,
min_separation=10,
erode_ratio=0.1,
erode_min_size=1,
save_file=False,
output_file='',
background_value=-1,
verbose=False):
"""
Extract fundi from folds.
A fundus is a branching curve that runs along the deepest and most highly
curved portions of a fold. This function extracts one fundus from each
fold by finding the deepest vertices inside the fold, finding endpoints
along the edge of the fold, and connecting the former to the latter with
tracks that run along deep and curved paths (through vertices with high
values of travel depth multiplied by curvature), and a final filtration
step.
The deepest vertices are those with values at least two median
absolute deviations above the median (non-zero) value, with the higher
value chosen if two of the vertices are within (a default of) 10 edges
from each other (to reduce the number of possible fundus paths as well
as computation time).
To find the endpoints, the find_outer_endpoints function propagates
multiple tracks from seed vertices at median depth in the fold through
concentric rings toward the fold’s edge, selecting maximal values within
each ring, and terminating at candidate endpoints. The final endpoints
are those candidates at the end of tracks that have a high median value,
with the higher value chosen if two candidate endpoints are within
(a default of) 10 edges from each other (otherwise, the resulting fundi
can have spurious branching at the fold’s edge).
The connect_points_erosion function connects the deepest fold vertices
to the endpoints with a skeleton of 1-vertex-thick curves by erosion.
It erodes by iteratively removing simple topological points and endpoints
in order of lowest to highest values, where a simple topological point
is a vertex that when added to or removed from an object on a surface
mesh (such as a fundus curve) does not alter the object's topology.
Steps ::
1. Find fundus endpoints (outer anchors) with find_outer_endpoints().
2. Include inner anchor points.
3. Connect anchor points using connect_points_erosion();
inner anchors are removed if they result in endpoints.
Note ::
Follow this with segment_by_region() to segment fundi by sulci.
Parameters
----------
folds : numpy array or list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : bool
save output VTK file?
output_file : string
output VTK file
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
n_fundi_in_folds : integer
number of fundi
fundus_per_fold_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> import numpy as np
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> curv_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> folds, name = read_scalars(folds_file, True, True)
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [4] #[4, 6]
... i0 = [i for i,x in enumerate(folds) if x not in fold_numbers]
... folds[i0] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> output_file = 'extract_fundi_fold4.vtk'
>>> background_value = -1
>>> verbose = False
>>> o1, o2, fundus_per_fold_file = extract_fundi(folds, curv_file,
... depth_file, min_separation, erode_ratio, erode_min_size,
... save_file, output_file, background_value, verbose)
>>> lens = [len([x for x in o1 if x == y])
... for y in np.unique(o1) if y != background_value]
>>> lens[0:10] # [66, 2914, 100, 363, 73, 331, 59, 30, 1, 14] # (if not limit_folds)
[73]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> rewrite_scalars(fundus_per_fold_file,
... 'extract_fundi_fold4_no_background.vtk', o1,
... 'fundus_per_fold', folds) # doctest: +SKIP
>>> plot_surfaces('extract_fundi_fold4_no_background.vtk') # doctest: +SKIP
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.compute import median_abs_dev
from mindboggle.guts.paths import find_max_values
from mindboggle.guts.mesh import find_neighbors_from_file
#from mindboggle.guts.mesh import find_complete_faces
from mindboggle.guts.paths import find_outer_endpoints
from mindboggle.guts.paths import connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
if os.path.isfile(curv_file):
points, indices, lines, faces, curvs, scalar_names, npoints, \
input_vtk = read_vtk(curv_file, True, True)
else:
raise IOError("{0} doesn't exist!".format(curv_file))
if os.path.isfile(curv_file):
depths, name = read_scalars(depth_file, True, True)
else:
raise IOError("{0} doesn't exist!".format(depth_file))
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
# ------------------------------------------------------------------------
# Loop through folds:
# ------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != background_value]
if verbose:
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".format(
len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i, x in enumerate(folds) if x == fold_ID]
if indices_fold:
if verbose:
print(' Fold {0}:'.format(int(fold_ID)))
# ----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints:
# ----------------------------------------------------------------
outer_anchors, tracks = find_outer_endpoints(
indices_fold, neighbor_lists, values, depths, min_separation,
background_value, verbose)
# ----------------------------------------------------------------
# Find inner anchor points:
# ----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation,
thr)
# ----------------------------------------------------------------
# Connect anchor points to create skeleton:
# ----------------------------------------------------------------
B = background_value * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B, neighbor_lists, outer_anchors,
inner_anchors, values,
erode_ratio, erode_min_size, [],
'', background_value, verbose)
if skeleton:
skeletons.extend(skeleton)
## ---------------------------------------------------------------
## Remove fundus vertices if they make complete triangle faces:
## ---------------------------------------------------------------
#Iremove = find_complete_faces(skeletons, faces)
#if Iremove:
# skeletons = list(frozenset(skeletons).difference(Iremove))
indices_skel = [x for x in skeletons if folds[x] != background_value]
fundus_per_fold = background_value * np.ones(npoints)
fundus_per_fold[indices_skel] = folds[indices_skel]
n_fundi_in_folds = len(
[x for x in np.unique(fundus_per_fold) if x != background_value])
if n_fundi_in_folds == 1:
sdum = 'fold fundus'
else:
sdum = 'fold fundi'
if verbose:
print(' ...Extracted {0} {1}; {2} total ({3:.2f} seconds)'.format(
n_fundi_in_folds, sdum, n_fundi_in_folds,
time() - t1))
# ------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
# ------------------------------------------------------------------------
fundus_per_fold_file = None
if n_fundi_in_folds > 0:
fundus_per_fold = [int(x) for x in fundus_per_fold]
if save_file:
if output_file:
fundus_per_fold_file = output_file
else:
fundus_per_fold_file = os.path.join(os.getcwd(),
'fundus_per_fold.vtk')
rewrite_scalars(curv_file, fundus_per_fold_file, fundus_per_fold,
'fundi', [], background_value)
if not os.path.exists(fundus_per_fold_file):
raise IOError(fundus_per_fold_file + " not found")
return fundus_per_fold, n_fundi_in_folds, fundus_per_fold_file
def extract_sulci(labels_file, folds_or_file, hemi, min_boundary=1,
sulcus_names=[]):
"""
Identify sulci from folds in a brain surface according to a labeling
protocol that includes a list of label pairs defining each sulcus.
A fold is a group of connected, deep vertices.
Steps for each fold ::
1. Remove fold if it has fewer than two labels.
2. Remove fold if its labels do not contain a sulcus label pair.
3. Find vertices with labels that are in only one of the fold's
label boundary pairs. Assign the vertices the sulcus with the label
pair if they are connected to the label boundary for that pair.
4. If there are remaining vertices, segment into sets of vertices
connected to label boundaries, and assign a unique ID to each set.
Parameters
----------
labels_file : string
file name for surface mesh VTK containing labels for all vertices
folds_or_file : list or string
fold number for each vertex / name of VTK file containing fold scalars
hemi : string
hemisphere abbreviation in {'lh', 'rh'} for sulcus labels
min_boundary : integer
minimum number of vertices for a sulcus label boundary segment
sulcus_names : list of strings
names of sulci
Returns
-------
sulci : list of integers
sulcus numbers for all vertices (-1 for non-sulcus vertices)
n_sulci : integers
number of sulci
sulci_file : string
output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> # Load labels, folds, neighbor lists, and sulcus names and label pairs
>>> labels_file = os.path.join(path, 'arno', 'labels', 'relabeled_lh.DKTatlas40.gcs.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds_or_file, name = read_scalars(folds_file)
>>> hemi = 'lh'
>>> min_boundary = 10
>>> sulcus_names = []
>>> #
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file, hemi, min_boundary, sulcus_names)
>>> # View:
>>> plot_surfaces('sulci.vtk')
"""
import os
from time import time
import numpy as np
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import extract_borders, propagate, segment
from mindboggle.mio.labels import DKTprotocol
# Load fold numbers if folds_or_file is a string:
if isinstance(folds_or_file, str):
folds, name = read_scalars(folds_or_file)
elif isinstance(folds_or_file, list):
folds = folds_or_file
dkt = DKTprotocol()
if hemi == 'lh':
pair_lists = dkt.left_sulcus_label_pair_lists
elif hemi == 'rh':
pair_lists = dkt.right_sulcus_label_pair_lists
else:
print("Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors:
faces, o1, o2, points, npoints, labels, o3, o4 = read_vtk(labels_file)
neighbor_lists = find_neighbors(faces, npoints)
# Array of sulcus IDs for fold vertices, initialized as -1.
# Since we do not touch gyral vertices and vertices whose labels
# are not in the label list, or vertices having only one label,
# their sulcus IDs will remain -1:
sulci = -1 * np.ones(npoints)
#-------------------------------------------------------------------------
# Loop through folds
#-------------------------------------------------------------------------
fold_numbers = [int(x) for x in np.unique(folds) if x != -1]
n_folds = len(fold_numbers)
print("Extract sulci from {0} folds...".format(n_folds))
t0 = time()
for n_fold in fold_numbers:
fold = [i for i,x in enumerate(folds) if x == n_fold]
len_fold = len(fold)
# List the labels in this fold:
fold_labels = [labels[x] for x in fold]
unique_fold_labels = [int(x) for x in np.unique(fold_labels)
if x != -1]
#---------------------------------------------------------------------
# NO MATCH -- fold has fewer than two labels
#---------------------------------------------------------------------
if len(unique_fold_labels) < 2:
# Ignore: sulci already initialized with -1 values:
if not unique_fold_labels:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has no labels".
format(n_fold, len_fold))
else:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has only one label ({2})".
format(n_fold, len_fold, unique_fold_labels[0]))
# Ignore: sulci already initialized with -1 values
else:
# Find all label boundary pairs within the fold:
indices_fold_pairs, fold_pairs, unique_fold_pairs = \
extract_borders(fold, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
# Find fold label pairs in the protocol (pairs are already sorted):
fold_pairs_in_protocol = [x for x in unique_fold_pairs
if x in dkt.unique_sulcus_label_pairs]
if unique_fold_labels:
print(" Fold {0} labels: {1} ({2} vertices)".format(n_fold,
', '.join([str(x) for x in unique_fold_labels]),
len_fold))
#-----------------------------------------------------------------
# NO MATCH -- fold has no sulcus label pair
#-----------------------------------------------------------------
if not fold_pairs_in_protocol:
print(" Fold {0}: NO MATCH -- fold has no sulcus label pair".
format(n_fold, len_fold))
#-----------------------------------------------------------------
# Possible matches
#-----------------------------------------------------------------
else:
print(" Fold {0} label pairs in protocol: {1}".format(n_fold,
', '.join([str(x) for x in fold_pairs_in_protocol])))
# Labels in the protocol (includes repeats across label pairs):
labels_in_pairs = [x for lst in fold_pairs_in_protocol
for x in lst]
# Labels that appear in one or more sulcus label boundary:
unique_labels = []
nonunique_labels = []
for label in np.unique(labels_in_pairs):
if len([x for x in labels_in_pairs if x == label]) == 1:
unique_labels.append(label)
else:
nonunique_labels.append(label)
#-------------------------------------------------------------
# Vertices whose labels are in only one sulcus label pair
#-------------------------------------------------------------
# Find vertices with a label that is in only one of the fold's
# label pairs (the other label in the pair can exist in other
# pairs). Assign the vertices the sulcus with the label pair
# if they are connected to the label boundary for that pair.
#-------------------------------------------------------------
if unique_labels:
for pair in fold_pairs_in_protocol:
# If one or both labels in label pair is/are unique:
unique_labels_in_pair = [x for x in pair
if x in unique_labels]
n_unique = len(unique_labels_in_pair)
if n_unique:
ID = None
for i, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
if pair in pair_list:
ID = i
break
if ID:
# Seeds from label boundary vertices
# (fold_pairs and pair already sorted):
indices_pair = [x for i,x
in enumerate(indices_fold_pairs)
if fold_pairs[i] == pair]
# Vertices with unique label(s) in pair:
indices_unique_labels = [fold[i]
for i,x in enumerate(fold_labels)
if x in dkt.unique_sulcus_label_pairs]
# Propagate from seeds to labels in label pair:
sulci2 = segment(indices_unique_labels,
neighbor_lists,
min_region_size=1,
seed_lists=[indices_pair],
keep_seeding=False,
spread_within_labels=True,
labels=labels)
sulci[sulci2 != -1] = ID
# Print statement:
if n_unique == 1:
ps1 = '1 label'
else:
ps1 = 'Both labels'
if len(sulcus_names):
ps2 = sulcus_names[ID]
else:
ps2 = ''
print(" {0} unique to one fold pair: "
"{1} {2}".
format(ps1, ps2, unique_labels_in_pair))
#-------------------------------------------------------------
# Vertex labels shared by multiple label pairs
#-------------------------------------------------------------
# Propagate labels from label borders to vertices with labels
# that are shared by multiple label pairs in the fold.
#-------------------------------------------------------------
if len(nonunique_labels):
# For each label shared by different label pairs:
for label in nonunique_labels:
# Print statement:
print(" Propagate sulcus borders with label {0}".
format(int(label)))
# Construct seeds from label boundary vertices:
seeds = -1 * np.ones(len(points))
for ID, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
label_pairs = [x for x in pair_list if label in x]
for label_pair in label_pairs:
indices_pair = [x for i,x
in enumerate(indices_fold_pairs)
if np.sort(fold_pairs[i]).
tolist() == label_pair]
if indices_pair:
# Do not include short boundary segments:
if min_boundary > 1:
indices_pair2 = []
seeds2 = segment(indices_pair,
neighbor_lists)
useeds2 = [x for x in
np.unique(seeds2)
if x != -1]
for seed2 in useeds2:
iseed2 = [i for i,x
in enumerate(seeds2)
if x == seed2]
if len(iseed2) >= min_boundary:
indices_pair2.extend(iseed2)
else:
if len(iseed2) == 1:
print(" Remove "
"assignment "
"of ID {0} from "
"1 vertex".
format(seed2))
else:
print(" Remove "
"assignment "
"of ID {0} from "
"{1} vertices".
format(seed2,
len(iseed2)))
indices_pair = indices_pair2
# Assign sulcus IDs to seeds:
seeds[indices_pair] = ID
# Identify vertices with the label:
label_array = -1 * np.ones(len(points))
indices_label = [fold[i] for i,x
in enumerate(fold_labels)
if x == label]
if len(indices_label):
label_array[indices_label] = 1
# Propagate from seeds to vertices with label:
#indices_seeds = []
#for seed in range(int(max(seeds))+1):
# indices_seeds.append([i for i,x
# in enumerate(seeds)
# if x == seed])
#sulci2 = segment(indices_label, neighbor_lists,
# 50, indices_seeds, False, True,
# labels)
sulci2 = propagate(points, faces,
label_array, seeds, sulci,
max_iters=10000,
tol=0.001, sigma=5)
sulci[sulci2 != -1] = sulci2[sulci2 != -1]
#-------------------------------------------------------------------------
# Print out assigned sulci
#-------------------------------------------------------------------------
sulcus_numbers = [int(x) for x in np.unique(sulci) if x != -1]
# if not np.isnan(x)]
n_sulci = len(sulcus_numbers)
print("Extracted {0} sulci from {1} folds ({2:.1f}s):".
format(n_sulci, n_folds, time()-t0))
if sulcus_names:
for sulcus_number in sulcus_numbers:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
elif sulcus_numbers:
print(" " + ", ".join([str(x) for x in sulcus_numbers]))
#-------------------------------------------------------------------------
# Print out unresolved sulci
#-------------------------------------------------------------------------
unresolved = [i for i in range(len(pair_lists))
if i not in sulcus_numbers]
if len(unresolved) == 1:
print("The following sulcus is unaccounted for:")
else:
print("The following {0} sulci are unaccounted for:".
format(len(unresolved)))
if sulcus_names:
for sulcus_number in unresolved:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in unresolved]))
#-------------------------------------------------------------------------
# Return sulci, number of sulci, and file name
#-------------------------------------------------------------------------
sulci = [int(x) for x in sulci]
sulci_file = os.path.join(os.getcwd(), 'sulci.vtk')
rewrite_scalars(labels_file, sulci_file, sulci, 'sulci', sulci)
if not os.path.exists(sulci_file):
raise(IOError(sulci_file + " not found"))
return sulci, n_sulci, sulci_file
def segment_fundi(fundus_per_fold, sulci=[], vtk_file='', save_file=False):
"""
Segment fundi by sulcus definitions.
Parameters
----------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
sulci : numpy array or list of integers
sulcus number for each vertex, used to filter and label fundi
vtk_file : string (if save_file)
VTK file with sulcus number for each vertex
save_file : Boolean
save output VTK file?
Returns
-------
fundus_per_sulcus : list of integers
fundus numbers for all vertices, labeled by sulcus
(-1 for non-fundus vertices)
n_fundi : integer
number of fundi
fundus_per_sulcus_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more sulci:
>>> single_fold = True
>>> import os
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.features.fundi import extract_fundi, segment_fundi
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> sulci, name = read_scalars(vtk_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
>>> fundus_per_fold, o1, o2 = extract_fundi(folds, curv_file, depth_file, min_separation, erode_ratio, erode_min_size, save_file)
>>> o1, o2, fundus_per_sulcus_file = segment_fundi(fundus_per_fold, sulci, vtk_file, save_file)
>>> #
>>> # View:
>>> plot_surfaces(fundus_per_sulcus_file)
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from mindboggle.mio.vtks import rewrite_scalars
if isinstance(sulci, list):
sulci = np.array(sulci)
#-------------------------------------------------------------------------
# Create fundi by segmenting fold fundi with overlapping sulcus labels:
#-------------------------------------------------------------------------
indices = [i for i, x in enumerate(fundus_per_fold) if x != -1]
if indices and np.size(sulci):
fundus_per_sulcus = -1 * np.ones(len(sulci))
fundus_per_sulcus[indices] = sulci[indices]
n_fundi = len([x for x in np.unique(fundus_per_sulcus) if x != -1])
else:
fundus_per_sulcus = []
n_fundi = 0
if n_fundi == 1:
sdum = 'sulcus fundus'
else:
sdum = 'sulcus fundi'
print(' Segmented {0} {1}'.format(n_fundi, sdum))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
fundus_per_sulcus_file = None
if n_fundi > 0:
fundus_per_sulcus = [int(x) for x in fundus_per_sulcus]
if save_file and os.path.exists(vtk_file):
fundus_per_sulcus_file = os.path.join(os.getcwd(),
'fundus_per_sulcus.vtk')
rewrite_scalars(vtk_file, fundus_per_sulcus_file,
fundus_per_sulcus, 'fundus_per_sulcus',
fundus_per_sulcus)
if not os.path.exists(fundus_per_sulcus_file):
raise (IOError(fundus_per_sulcus_file + " not found"))
return fundus_per_sulcus, n_fundi, fundus_per_sulcus_file
def extract_sulci(labels_file,
folds_or_file,
hemi,
min_boundary=1,
sulcus_names=[],
save_file=False,
output_file='',
background_value=-1,
verbose=False):
"""
Identify sulci from folds in a brain surface according to a labeling
protocol that includes a list of label pairs defining each sulcus.
Since folds are defined as deep, connected areas of a surface, and since
folds may be connected to each other in ways that differ across brains,
there usually does not exist a one-to-one mapping between folds of one
brain and those of another. To address the correspondence problem then,
we need to find just those portions of the folds that correspond across
brains. To accomplish this, Mindboggle segments folds into sulci, which
do have a one-to-one correspondence across non-pathological brains.
Mindboggle defines a sulcus as a folded portion of cortex whose opposing
banks are labeled with one or more sulcus label pairs in the DKT labeling
protocol, where each label pair is unique to one sulcus and represents
a boundary between two adjacent gyri, and each vertex has one gyrus label.
This function assigns vertices in a fold to a sulcus in one of two cases.
In the first case, vertices whose labels are in only one label pair in
the fold are assigned to the label pair’s sulcus if they are connected
through similarly labeled vertices to the boundary between the two labels.
In the second case, the segment_regions function propagates labels from
label borders to vertices whose labels are in multiple label pairs in the
fold.
Steps for each fold ::
1. Remove fold if it has fewer than two labels.
2. Remove fold if its labels do not contain a sulcus label pair.
3. Find vertices with labels that are in only one of the fold's
label boundary pairs. Assign the vertices the sulcus with the label
pair if they are connected to the label boundary for that pair.
4. If there are remaining vertices, segment into sets of vertices
connected to label boundaries, and assign a unique ID to each set.
Parameters
----------
labels_file : string
file name for surface mesh VTK containing labels for all vertices
folds_or_file : numpy array, list or string
fold number for each vertex / name of VTK file containing fold scalars
hemi : string
hemisphere abbreviation in {'lh', 'rh'} for sulcus labels
min_boundary : integer
minimum number of vertices for a sulcus label boundary segment
sulcus_names : list of strings
names of sulci
save_file : bool
save output VTK file?
output_file : string
name of output file in VTK format
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
sulci : list of integers
sulcus numbers for all vertices (-1 for non-sulcus vertices)
n_sulci : integers
number of sulci
sulci_file : string
output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> # Example 1: Extract sulcus from a fold with one sulcus label pair:
>>> import numpy as np
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> # Load labels, folds, neighbor lists, and sulcus names and label pairs
>>> labels_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> folds_or_file, name = read_scalars(folds_file, True, True)
>>> save_file = True
>>> output_file = 'extract_sulci_fold4_1sulcus.vtk'
>>> background_value = -1
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [4] #[4, 6]
... i0 = [i for i,x in enumerate(folds_or_file) if x not in fold_numbers]
... folds_or_file[i0] = background_value
>>> hemi = 'lh'
>>> min_boundary = 10
>>> sulcus_names = []
>>> verbose = False
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
... hemi, min_boundary, sulcus_names, save_file, output_file,
... background_value, verbose)
>>> n_sulci # 23 # (if not limit_folds)
1
>>> lens = [len([x for x in sulci if x==y])
... for y in np.unique(sulci) if y != -1]
>>> lens[0:10] # [6358, 3288, 7612, 5205, 4414, 6251, 3493, 2566, 4436, 739] # (if not limit_folds)
[1151]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> output = 'extract_sulci_fold4_1sulcus_no_background.vtk'
>>> rewrite_scalars(sulci_file, output, sulci,
... 'sulci', sulci) # doctest: +SKIP
>>> plot_surfaces(output) # doctest: +SKIP
Example 2: Extract sulcus from a fold with multiple sulcus label pairs:
>>> folds_or_file, name = read_scalars(folds_file, True, True)
>>> output_file = 'extract_sulci_fold7_2sulci.vtk'
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [7] #[4, 6]
... i0 = [i for i,x in enumerate(folds_or_file) if x not in fold_numbers]
... folds_or_file[i0] = background_value
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
... hemi, min_boundary, sulcus_names, save_file, output_file,
... background_value, verbose)
>>> n_sulci # 23 # (if not limit_folds)
2
>>> lens = [len([x for x in sulci if x==y])
... for y in np.unique(sulci) if y != -1]
>>> lens[0:10] # [6358, 3288, 7612, 5205, 4414, 6251, 3493, 2566, 4436, 739] # (if not limit_folds)
[369, 93]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> output = 'extract_sulci_fold7_2sulci_no_background.vtk'
>>> rewrite_scalars(sulci_file, output, sulci,
... 'sulci', sulci) # doctest: +SKIP
>>> plot_surfaces(output) # doctest: +SKIP
"""
import os
from time import time
import numpy as np
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import extract_borders, propagate, segment_regions
from mindboggle.mio.labels import DKTprotocol
# Load fold numbers if folds_or_file is a string:
if isinstance(folds_or_file, str):
folds, name = read_scalars(folds_or_file)
elif isinstance(folds_or_file, list):
folds = folds_or_file
elif isinstance(folds_or_file, np.ndarray):
folds = folds_or_file.tolist()
dkt = DKTprotocol()
if hemi == 'lh':
pair_lists = dkt.left_sulcus_label_pair_lists
elif hemi == 'rh':
pair_lists = dkt.right_sulcus_label_pair_lists
else:
raise IOError(
"Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors:
points, indices, lines, faces, labels, scalar_names, npoints, \
input_vtk = read_vtk(labels_file)
neighbor_lists = find_neighbors(faces, npoints)
# Array of sulcus IDs for fold vertices, initialized as -1.
# Since we do not touch gyral vertices and vertices whose labels
# are not in the label list, or vertices having only one label,
# their sulcus IDs will remain -1:
sulci = background_value * np.ones(npoints)
# ------------------------------------------------------------------------
# Loop through folds
# ------------------------------------------------------------------------
fold_numbers = [int(x) for x in np.unique(folds) if x != background_value]
n_folds = len(fold_numbers)
if verbose:
print("Extract sulci from {0} folds...".format(n_folds))
t0 = time()
for n_fold in fold_numbers:
fold_indices = [i for i, x in enumerate(folds) if x == n_fold]
len_fold = len(fold_indices)
# List the labels in this fold:
fold_labels = [labels[x] for x in fold_indices]
unique_fold_labels = [
int(x) for x in np.unique(fold_labels) if x != background_value
]
# --------------------------------------------------------------------
# NO MATCH -- fold has fewer than two labels
# --------------------------------------------------------------------
if verbose and len(unique_fold_labels) < 2:
# Ignore: sulci already initialized with -1 values:
if not unique_fold_labels:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has no labels".format(
n_fold, len_fold))
else:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has only one label ({2})".format(
n_fold, len_fold, unique_fold_labels[0]))
# Ignore: sulci already initialized with -1 values
else:
# Find all label boundary pairs within the fold:
indices_fold_pairs, fold_pairs, unique_fold_pairs = \
extract_borders(fold_indices, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
# Find fold label pairs in the protocol (pairs are already sorted):
fold_pairs_in_protocol = [
x for x in unique_fold_pairs
if x in dkt.unique_sulcus_label_pairs
]
if verbose and unique_fold_labels:
print(" Fold {0} labels: {1} ({2} vertices)".format(
n_fold, ', '.join([str(x) for x in unique_fold_labels]),
len_fold))
# ----------------------------------------------------------------
# NO MATCH -- fold has no sulcus label pair
# ----------------------------------------------------------------
if verbose and not fold_pairs_in_protocol:
print(" Fold {0}: NO MATCH -- fold has no sulcus label pair".
format(n_fold, len_fold))
# ----------------------------------------------------------------
# Possible matches
# ----------------------------------------------------------------
else:
if verbose:
print(" Fold {0} label pairs in protocol: {1}".format(
n_fold,
', '.join([str(x) for x in fold_pairs_in_protocol])))
# Labels in the protocol (includes repeats across label pairs):
labels_in_pairs = [
x for lst in fold_pairs_in_protocol for x in lst
]
# Labels that appear in one or more sulcus label boundary:
unique_labels = []
nonunique_labels = []
for label in np.unique(labels_in_pairs):
if len([x for x in labels_in_pairs if x == label]) == 1:
unique_labels.append(label)
else:
nonunique_labels.append(label)
# ------------------------------------------------------------
# Vertices whose labels are in only one sulcus label pair
# ------------------------------------------------------------
# Find vertices with a label that is in only one of the fold's
# label pairs (the other label in the pair can exist in other
# pairs). Assign the vertices the sulcus with the label pair
# if they are connected to the label boundary for that pair.
# ------------------------------------------------------------
if unique_labels:
for pair in fold_pairs_in_protocol:
# If one or both labels in label pair is/are unique:
unique_labels_in_pair = [
x for x in pair if x in unique_labels
]
n_unique = len(unique_labels_in_pair)
if n_unique:
ID = None
for i, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
if pair in pair_list:
ID = i
break
if ID:
# Seeds from label boundary vertices
# (fold_pairs and pair already sorted):
indices_pair = [
x for i, x in enumerate(indices_fold_pairs)
if fold_pairs[i] == pair
]
# Vertices with unique label(s) in pair:
indices_unique_labels = [
fold_indices[i]
for i, x in enumerate(fold_labels)
if x in unique_labels_in_pair
]
#dkt.unique_sulcus_label_pairs]
# Propagate sulcus ID from seeds to vertices
# with "unique" labels (only exist in one
# label pair in a fold); propagation ensures
# that sulci consist of contiguous vertices
# for each label boundary:
sulci2 = segment_regions(
indices_unique_labels,
neighbor_lists,
min_region_size=1,
seed_lists=[indices_pair],
keep_seeding=False,
spread_within_labels=True,
labels=labels,
label_lists=[],
values=[],
max_steps='',
background_value=background_value,
verbose=False)
sulci[sulci2 != background_value] = ID
# Print statement:
if verbose:
if n_unique == 1:
ps1 = 'One label'
else:
ps1 = 'Both labels'
if len(sulcus_names):
ps2 = sulcus_names[ID]
else:
ps2 = ''
print(" {0} unique to one fold pair: "
"{1} {2}".format(
ps1, ps2, unique_labels_in_pair))
# ------------------------------------------------------------
# Vertex labels shared by multiple label pairs
# ------------------------------------------------------------
# Propagate labels from label borders to vertices with labels
# that are shared by multiple label pairs in the fold.
# ------------------------------------------------------------
if len(nonunique_labels):
# For each label shared by different label pairs:
for label in nonunique_labels:
# Print statement:
if verbose:
print(
" Propagate sulcus borders with label {0}".
format(int(label)))
# Construct seeds from label boundary vertices:
seeds = background_value * np.ones(npoints)
for ID, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
label_pairs = [x for x in pair_list if label in x]
for label_pair in label_pairs:
indices_pair = [
x for i, x in enumerate(indices_fold_pairs)
if np.sort(fold_pairs[i]).tolist() ==
label_pair
]
if indices_pair:
# Do not include short boundary segments:
if min_boundary > 1:
indices_pair2 = []
seeds2 = segment_regions(
indices_pair, neighbor_lists, 1,
[], False, False, [], [], [], '',
background_value, verbose)
useeds2 = [
x for x in np.unique(seeds2)
if x != background_value
]
for seed2 in useeds2:
iseed2 = [
i for i, x in enumerate(seeds2)
if x == seed2
]
if len(iseed2) >= min_boundary:
indices_pair2.extend(iseed2)
elif verbose:
if len(iseed2) == 1:
print(" Remove "
"assignment "
"of ID {0} from "
"1 vertex".format(
seed2))
else:
print(
" Remove "
"assignment "
"of ID {0} from "
"{1} vertices".format(
seed2,
len(iseed2)))
indices_pair = indices_pair2
# Assign sulcus IDs to seeds:
seeds[indices_pair] = ID
# Identify vertices with the label:
indices_label = [
fold_indices[i] for i, x in enumerate(fold_labels)
if x == label
]
if len(indices_label):
# Propagate sulcus ID from seeds to vertices
# with a given shared label:
seg_vs_prop = False
if seg_vs_prop:
indices_seeds = []
for seed in [
x for x in np.unique(seeds)
if x != background_value
]:
indices_seeds.append([
i for i, x in enumerate(seeds)
if x == seed
])
sulci2 = segment_regions(
indices_label, neighbor_lists, 50,
indices_seeds, False, True, labels, [], [],
'', background_value, verbose)
else:
label_array = background_value * \
np.ones(npoints)
label_array[indices_label] = 1
sulci2 = propagate(
points,
faces,
label_array,
seeds,
sulci,
max_iters=10000,
tol=0.001,
sigma=5,
background_value=background_value,
verbose=verbose)
sulci[sulci2 != background_value] = \
sulci2[sulci2 != background_value]
sulcus_numbers = [
int(x) for x in np.unique(sulci) if x != background_value
]
n_sulci = len(sulcus_numbers)
# ------------------------------------------------------------------------
# Print statements
# ------------------------------------------------------------------------
if verbose:
if n_sulci == 1:
sulcus_str = 'sulcus'
else:
sulcus_str = 'sulci'
if n_folds == 1:
folds_str = 'fold'
else:
folds_str = 'folds'
print("Extracted {0} {1} from {2} {3} ({4:.1f}s):".format(
n_sulci, sulcus_str, n_folds, folds_str,
time() - t0))
if sulcus_names:
for sulcus_number in sulcus_numbers:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
elif sulcus_numbers:
print(" " + ", ".join([str(x) for x in sulcus_numbers]))
unresolved = [
i for i in range(len(pair_lists)) if i not in sulcus_numbers
]
if len(unresolved) == 1:
print("The following sulcus is unaccounted for:")
else:
print("The following {0} sulci are unaccounted for:".format(
len(unresolved)))
if sulcus_names:
for sulcus_number in unresolved:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in unresolved]))
# ------------------------------------------------------------------------
# Return sulci, number of sulci, and file name
# ------------------------------------------------------------------------
sulci = [int(x) for x in sulci]
sulci_file = os.path.join(os.getcwd(), 'sulci.vtk')
rewrite_scalars(labels_file, sulci_file, sulci, 'sulci', [],
background_value)
if not os.path.exists(sulci_file):
raise IOError(sulci_file + " not found")
return sulci, n_sulci, sulci_file
# [[x1, y1, z1], [x2, y2, z2], ...]
points = np.array(read_points(mean_curv_file))
xyz = points.T # transposed: [[x1, x2, ...], [y1, y2, ...], [z1, z2, ...]]
mean_curv, _ = read_scalars(
mean_curv_file, return_first=True, return_array=True)
print('number of points: {}'.format(mean_curv.size))
if method == 0 or method == 1:
gauss_curv, _ = read_scalars(
gauss_curv_file, return_first=True, return_array=True)
if method == 0:
max_curv, _ = read_scalars(
max_curv_file, return_first=True, return_array=True)
min_curv, _ = read_scalars(
min_curv_file, return_first=True, return_array=True)
curvedness = np.sqrt((max_curv ** 2 + min_curv ** 2) / 2)
rewrite_scalars(surface_file, curvedness_file, curvedness, "curvedness")
# Write the curvatures to a CSV file
df = pd.DataFrame()
df['x'] = xyz[0]
df['y'] = xyz[1]
df['z'] = xyz[2]
df['mean_curvature'] = mean_curv
if method == 0 or method == 1:
df['gauss_curvature'] = gauss_curv
if method == 0:
df['kappa1'] = max_curv
df['kappa2'] = min_curv
df['curvedness'] = curvedness
df.to_csv(curvatures_file, sep=';')
def extract_folds(depth_file,
depth_threshold=2,
min_fold_size=50,
save_file=False,
output_file='',
background_value=-1,
verbose=False):
"""
Use depth threshold to extract folds from a triangular surface mesh.
A fold is a group of connected, deep vertices. To extract folds,
a depth threshold is used to segment deep vertices of the surface mesh.
We have observed in the histograms of travel depth measures of cortical
surfaces that there is a rapidly decreasing distribution of low depth
values (corresponding to the outer surface, or gyral crowns) with a
long tail of higher depth values (corresponding to the folds).
The find_depth_threshold function therefore computes a histogram of
travel depth measures, smooths the histogram's bin values, convolves
to compute slopes, and finds the depth value for the first bin with
zero slope. The extract_folds function uses this depth value, segments
deep vertices, and removes extremely small folds (empirically set at 50
vertices or fewer out of a total mesh size of over 100,000 vertices).
Steps ::
1. Segment deep vertices as an initial set of folds.
2. Remove small folds.
3. Renumber folds.
Note ::
Removed option: Find and fill holes in the folds:
Folds could have holes in areas shallower than the depth threshold.
Calling fill_holes() could accidentally include very shallow areas
(in an annulus-shaped fold, for example).
However, we could include the argument exclude_range to check for
any values from zero to min_hole_depth; holes would not be filled
if they were to contain values within this range.
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
depth_threshold : float
threshold defining the minimum depth for vertices to be in a fold
min_fold_size : integer
minimum fold size (number of vertices)
save_file : bool
save output VTK file?
output_file : string
name of output file in VTK format
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_folds : int
number of folds
folds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> from mindboggle.features.folds import extract_folds
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> depth_threshold = 2.36089
>>> min_fold_size = 50
>>> save_file = True
>>> output_file = 'extract_folds.vtk'
>>> background_value = -1
>>> verbose = False
>>> folds, n_folds, folds_file = extract_folds(depth_file,
... depth_threshold, min_fold_size, save_file, output_file,
... background_value, verbose)
>>> n_folds
33
>>> lens = [len([x for x in folds if x == y]) for y in range(n_folds)]
>>> lens[0:10]
[726, 67241, 2750, 5799, 1151, 6360, 1001, 505, 228, 198]
View folds (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> plot_surfaces('extract_folds.vtk') # doctest: +SKIP
View folds without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> rewrite_scalars(depth_file, 'extract_folds_no_background.vtk', folds,
... 'just_folds', folds, -1) # doctest: +SKIP
>>> plot_surfaces('extract_folds_no_background.vtk') # doctest: +SKIP
"""
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import rewrite_scalars, read_vtk
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import segment_regions
if verbose:
print("Extract folds in surface mesh")
t0 = time()
# ------------------------------------------------------------------------
# Load depth values for all vertices
# ------------------------------------------------------------------------
points, indices, lines, faces, depths, scalar_names, npoints, \
input_vtk = read_vtk(depth_file, return_first=True, return_array=True)
# ------------------------------------------------------------------------
# Find the deepest vertices
# ------------------------------------------------------------------------
indices_deep = [i for i, x in enumerate(depths) if x >= depth_threshold]
if indices_deep:
# --------------------------------------------------------------------
# Find neighbors for each vertex
# --------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
# --------------------------------------------------------------------
# Segment deep vertices as an initial set of folds
# --------------------------------------------------------------------
if verbose:
print(" Segment vertices deeper than {0:.2f} as folds".format(
depth_threshold))
t1 = time()
folds = segment_regions(indices_deep, neighbor_lists, 1, [], False,
False, [], [], [], '', background_value, False)
if verbose:
print(' ...Segmented folds ({0:.2f} seconds)'.format(time() - t1))
# --------------------------------------------------------------------
# Remove small folds
# --------------------------------------------------------------------
if min_fold_size > 1:
if verbose:
print(' Remove folds smaller than {0}'.format(min_fold_size))
unique_folds = [
x for x in np.unique(folds) if x != background_value
]
for nfold in unique_folds:
indices_fold = [i for i, x in enumerate(folds) if x == nfold]
if len(indices_fold) < min_fold_size:
folds[indices_fold] = background_value
# --------------------------------------------------------------------
# Find and fill holes in the folds
# Note: Surfaces surrounded by folds can be mistaken for holes,
# so exclude_range includes outer surface values close to zero.
# --------------------------------------------------------------------
# folds = fill_holes(folds, neighbor_lists, values=depths,
# exclude_range=[0, min_hole_depth])
# --------------------------------------------------------------------
# Renumber folds so they are sequential.
# NOTE: All vertices are included (-1 for non-fold vertices).
# --------------------------------------------------------------------
renumber_folds = background_value * np.ones(npoints)
fold_numbers = [x for x in np.unique(folds) if x != background_value]
for i_fold, n_fold in enumerate(fold_numbers):
fold_indices = [i for i, x in enumerate(folds) if x == n_fold]
renumber_folds[fold_indices] = i_fold
folds = renumber_folds
folds = [int(x) for x in folds]
n_folds = i_fold + 1
# Print statement
if verbose:
print(' ...Extracted {0} folds ({1:.2f} seconds)'.format(
n_folds,
time() - t0))
else:
if verbose:
print(' No deep vertices')
# ------------------------------------------------------------------------
# Return folds, number of folds, file name
# ------------------------------------------------------------------------
if save_file:
if output_file:
folds_file = output_file
else:
folds_file = os.path.join(os.getcwd(), 'folds.vtk')
rewrite_scalars(depth_file, folds_file, folds, 'folds', [],
background_value)
if not os.path.exists(folds_file):
raise IOError(folds_file + " not found")
else:
folds_file = None
return folds, n_folds, folds_file
def extract_sulci(
labels_file,
folds_or_file,
hemi,
min_boundary=1,
sulcus_names=[],
save_file=False,
output_file="",
background_value=-1,
verbose=False,
):
"""
Identify sulci from folds in a brain surface according to a labeling
protocol that includes a list of label pairs defining each sulcus.
Since folds are defined as deep, connected areas of a surface, and since
folds may be connected to each other in ways that differ across brains,
there usually does not exist a one-to-one mapping between folds of one
brain and those of another. To address the correspondence problem then,
we need to find just those portions of the folds that correspond across
brains. To accomplish this, Mindboggle segments folds into sulci, which
do have a one-to-one correspondence across non-pathological brains.
Mindboggle defines a sulcus as a folded portion of cortex whose opposing
banks are labeled with one or more sulcus label pairs in the DKT labeling
protocol, where each label pair is unique to one sulcus and represents
a boundary between two adjacent gyri, and each vertex has one gyrus label.
This function assigns vertices in a fold to a sulcus in one of two cases.
In the first case, vertices whose labels are in only one label pair in
the fold are assigned to the label pair’s sulcus if they are connected
through similarly labeled vertices to the boundary between the two labels.
In the second case, the segment_regions function propagates labels from
label borders to vertices whose labels are in multiple label pairs in the
fold.
Steps for each fold ::
1. Remove fold if it has fewer than two labels.
2. Remove fold if its labels do not contain a sulcus label pair.
3. Find vertices with labels that are in only one of the fold's
label boundary pairs. Assign the vertices the sulcus with the label
pair if they are connected to the label boundary for that pair.
4. If there are remaining vertices, segment into sets of vertices
connected to label boundaries, and assign a unique ID to each set.
Parameters
----------
labels_file : string
file name for surface mesh VTK containing labels for all vertices
folds_or_file : numpy array, list or string
fold number for each vertex / name of VTK file containing fold scalars
hemi : string
hemisphere abbreviation in {'lh', 'rh'} for sulcus labels
min_boundary : integer
minimum number of vertices for a sulcus label boundary segment
sulcus_names : list of strings
names of sulci
save_file : bool
save output VTK file?
output_file : string
name of output file in VTK format
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
sulci : list of integers
sulcus numbers for all vertices (-1 for non-sulcus vertices)
n_sulci : integers
number of sulci
sulci_file : string
output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> # Example 1: Extract sulcus from a fold with one sulcus label pair:
>>> import numpy as np
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> # Load labels, folds, neighbor lists, and sulcus names and label pairs
>>> labels_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> folds_or_file, name = read_scalars(folds_file, True, True)
>>> save_file = True
>>> output_file = 'extract_sulci_fold4_1sulcus.vtk'
>>> background_value = -1
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [4] #[4, 6]
... i0 = [i for i,x in enumerate(folds_or_file) if x not in fold_numbers]
... folds_or_file[i0] = background_value
>>> hemi = 'lh'
>>> min_boundary = 10
>>> sulcus_names = []
>>> verbose = False
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
... hemi, min_boundary, sulcus_names, save_file, output_file,
... background_value, verbose)
>>> n_sulci # 23 # (if not limit_folds)
1
>>> lens = [len([x for x in sulci if x==y])
... for y in np.unique(sulci) if y != -1]
>>> lens[0:10] # [6358, 3288, 7612, 5205, 4414, 6251, 3493, 2566, 4436, 739] # (if not limit_folds)
[1151]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> output = 'extract_sulci_fold4_1sulcus_no_background.vtk'
>>> rewrite_scalars(sulci_file, output, sulci,
... 'sulci', sulci) # doctest: +SKIP
>>> plot_surfaces(output) # doctest: +SKIP
Example 2: Extract sulcus from a fold with multiple sulcus label pairs:
>>> folds_or_file, name = read_scalars(folds_file, True, True)
>>> output_file = 'extract_sulci_fold7_2sulci.vtk'
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [7] #[4, 6]
... i0 = [i for i,x in enumerate(folds_or_file) if x not in fold_numbers]
... folds_or_file[i0] = background_value
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
... hemi, min_boundary, sulcus_names, save_file, output_file,
... background_value, verbose)
>>> n_sulci # 23 # (if not limit_folds)
2
>>> lens = [len([x for x in sulci if x==y])
... for y in np.unique(sulci) if y != -1]
>>> lens[0:10] # [6358, 3288, 7612, 5205, 4414, 6251, 3493, 2566, 4436, 739] # (if not limit_folds)
[369, 93]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> output = 'extract_sulci_fold7_2sulci_no_background.vtk'
>>> rewrite_scalars(sulci_file, output, sulci,
... 'sulci', sulci) # doctest: +SKIP
>>> plot_surfaces(output) # doctest: +SKIP
"""
import os
from time import time
import numpy as np
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import extract_borders, propagate, segment_regions
from mindboggle.mio.labels import DKTprotocol
# Load fold numbers if folds_or_file is a string:
if isinstance(folds_or_file, str):
folds, name = read_scalars(folds_or_file)
elif isinstance(folds_or_file, list):
folds = folds_or_file
elif isinstance(folds_or_file, np.ndarray):
folds = folds_or_file.tolist()
dkt = DKTprotocol()
if hemi == "lh":
pair_lists = dkt.left_sulcus_label_pair_lists
elif hemi == "rh":
pair_lists = dkt.right_sulcus_label_pair_lists
else:
raise IOError("Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors:
points, indices, lines, faces, labels, scalar_names, npoints, input_vtk = read_vtk(labels_file)
neighbor_lists = find_neighbors(faces, npoints)
# Array of sulcus IDs for fold vertices, initialized as -1.
# Since we do not touch gyral vertices and vertices whose labels
# are not in the label list, or vertices having only one label,
# their sulcus IDs will remain -1:
sulci = background_value * np.ones(npoints)
# ------------------------------------------------------------------------
# Loop through folds
# ------------------------------------------------------------------------
fold_numbers = [int(x) for x in np.unique(folds) if x != background_value]
n_folds = len(fold_numbers)
if verbose:
print("Extract sulci from {0} folds...".format(n_folds))
t0 = time()
for n_fold in fold_numbers:
fold_indices = [i for i, x in enumerate(folds) if x == n_fold]
len_fold = len(fold_indices)
# List the labels in this fold:
fold_labels = [labels[x] for x in fold_indices]
unique_fold_labels = [int(x) for x in np.unique(fold_labels) if x != background_value]
# --------------------------------------------------------------------
# NO MATCH -- fold has fewer than two labels
# --------------------------------------------------------------------
if verbose and len(unique_fold_labels) < 2:
# Ignore: sulci already initialized with -1 values:
if not unique_fold_labels:
print(" Fold {0} ({1} vertices): " "NO MATCH -- fold has no labels".format(n_fold, len_fold))
else:
print(
" Fold {0} ({1} vertices): "
"NO MATCH -- fold has only one label ({2})".format(n_fold, len_fold, unique_fold_labels[0])
)
# Ignore: sulci already initialized with -1 values
else:
# Find all label boundary pairs within the fold:
indices_fold_pairs, fold_pairs, unique_fold_pairs = extract_borders(
fold_indices, labels, neighbor_lists, ignore_values=[], return_label_pairs=True
)
# Find fold label pairs in the protocol (pairs are already sorted):
fold_pairs_in_protocol = [x for x in unique_fold_pairs if x in dkt.unique_sulcus_label_pairs]
if verbose and unique_fold_labels:
print(
" Fold {0} labels: {1} ({2} vertices)".format(
n_fold, ", ".join([str(x) for x in unique_fold_labels]), len_fold
)
)
# ----------------------------------------------------------------
# NO MATCH -- fold has no sulcus label pair
# ----------------------------------------------------------------
if verbose and not fold_pairs_in_protocol:
print(" Fold {0}: NO MATCH -- fold has no sulcus label pair".format(n_fold, len_fold))
# ----------------------------------------------------------------
# Possible matches
# ----------------------------------------------------------------
else:
if verbose:
print(
" Fold {0} label pairs in protocol: {1}".format(
n_fold, ", ".join([str(x) for x in fold_pairs_in_protocol])
)
)
# Labels in the protocol (includes repeats across label pairs):
labels_in_pairs = [x for lst in fold_pairs_in_protocol for x in lst]
# Labels that appear in one or more sulcus label boundary:
unique_labels = []
nonunique_labels = []
for label in np.unique(labels_in_pairs):
if len([x for x in labels_in_pairs if x == label]) == 1:
unique_labels.append(label)
else:
nonunique_labels.append(label)
# ------------------------------------------------------------
# Vertices whose labels are in only one sulcus label pair
# ------------------------------------------------------------
# Find vertices with a label that is in only one of the fold's
# label pairs (the other label in the pair can exist in other
# pairs). Assign the vertices the sulcus with the label pair
# if they are connected to the label boundary for that pair.
# ------------------------------------------------------------
if unique_labels:
for pair in fold_pairs_in_protocol:
# If one or both labels in label pair is/are unique:
unique_labels_in_pair = [x for x in pair if x in unique_labels]
n_unique = len(unique_labels_in_pair)
if n_unique:
ID = None
for i, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
if pair in pair_list:
ID = i
break
if ID:
# Seeds from label boundary vertices
# (fold_pairs and pair already sorted):
indices_pair = [x for i, x in enumerate(indices_fold_pairs) if fold_pairs[i] == pair]
# Vertices with unique label(s) in pair:
indices_unique_labels = [
fold_indices[i] for i, x in enumerate(fold_labels) if x in unique_labels_in_pair
]
# dkt.unique_sulcus_label_pairs]
# Propagate sulcus ID from seeds to vertices
# with "unique" labels (only exist in one
# label pair in a fold); propagation ensures
# that sulci consist of contiguous vertices
# for each label boundary:
sulci2 = segment_regions(
indices_unique_labels,
neighbor_lists,
min_region_size=1,
seed_lists=[indices_pair],
keep_seeding=False,
spread_within_labels=True,
labels=labels,
label_lists=[],
values=[],
max_steps="",
background_value=background_value,
verbose=False,
)
sulci[sulci2 != background_value] = ID
# Print statement:
if verbose:
if n_unique == 1:
ps1 = "One label"
else:
ps1 = "Both labels"
if len(sulcus_names):
ps2 = sulcus_names[ID]
else:
ps2 = ""
print(
" {0} unique to one fold pair: "
"{1} {2}".format(ps1, ps2, unique_labels_in_pair)
)
# ------------------------------------------------------------
# Vertex labels shared by multiple label pairs
# ------------------------------------------------------------
# Propagate labels from label borders to vertices with labels
# that are shared by multiple label pairs in the fold.
# ------------------------------------------------------------
if len(nonunique_labels):
# For each label shared by different label pairs:
for label in nonunique_labels:
# Print statement:
if verbose:
print(" Propagate sulcus borders with label {0}".format(int(label)))
# Construct seeds from label boundary vertices:
seeds = background_value * np.ones(npoints)
for ID, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
label_pairs = [x for x in pair_list if label in x]
for label_pair in label_pairs:
indices_pair = [
x
for i, x in enumerate(indices_fold_pairs)
if np.sort(fold_pairs[i]).tolist() == label_pair
]
if indices_pair:
# Do not include short boundary segments:
if min_boundary > 1:
indices_pair2 = []
seeds2 = segment_regions(
indices_pair,
neighbor_lists,
1,
[],
False,
False,
[],
[],
[],
"",
background_value,
verbose,
)
useeds2 = [x for x in np.unique(seeds2) if x != background_value]
for seed2 in useeds2:
iseed2 = [i for i, x in enumerate(seeds2) if x == seed2]
if len(iseed2) >= min_boundary:
indices_pair2.extend(iseed2)
elif verbose:
if len(iseed2) == 1:
print(
" Remove "
"assignment "
"of ID {0} from "
"1 vertex".format(seed2)
)
else:
print(
" Remove "
"assignment "
"of ID {0} from "
"{1} vertices".format(seed2, len(iseed2))
)
indices_pair = indices_pair2
# Assign sulcus IDs to seeds:
seeds[indices_pair] = ID
# Identify vertices with the label:
indices_label = [fold_indices[i] for i, x in enumerate(fold_labels) if x == label]
if len(indices_label):
# Propagate sulcus ID from seeds to vertices
# with a given shared label:
seg_vs_prop = False
if seg_vs_prop:
indices_seeds = []
for seed in [x for x in np.unique(seeds) if x != background_value]:
indices_seeds.append([i for i, x in enumerate(seeds) if x == seed])
sulci2 = segment_regions(
indices_label,
neighbor_lists,
50,
indices_seeds,
False,
True,
labels,
[],
[],
"",
background_value,
verbose,
)
else:
label_array = background_value * np.ones(npoints)
label_array[indices_label] = 1
sulci2 = propagate(
points,
faces,
label_array,
seeds,
sulci,
max_iters=10000,
tol=0.001,
sigma=5,
background_value=background_value,
verbose=verbose,
)
sulci[sulci2 != background_value] = sulci2[sulci2 != background_value]
sulcus_numbers = [int(x) for x in np.unique(sulci) if x != background_value]
n_sulci = len(sulcus_numbers)
# ------------------------------------------------------------------------
# Print statements
# ------------------------------------------------------------------------
if verbose:
if n_sulci == 1:
sulcus_str = "sulcus"
else:
sulcus_str = "sulci"
if n_folds == 1:
folds_str = "fold"
else:
folds_str = "folds"
print("Extracted {0} {1} from {2} {3} ({4:.1f}s):".format(n_sulci, sulcus_str, n_folds, folds_str, time() - t0))
if sulcus_names:
for sulcus_number in sulcus_numbers:
print(" {0}: {1}".format(sulcus_number, sulcus_names[sulcus_number]))
elif sulcus_numbers:
print(" " + ", ".join([str(x) for x in sulcus_numbers]))
unresolved = [i for i in range(len(pair_lists)) if i not in sulcus_numbers]
if len(unresolved) == 1:
print("The following sulcus is unaccounted for:")
else:
print("The following {0} sulci are unaccounted for:".format(len(unresolved)))
if sulcus_names:
for sulcus_number in unresolved:
print(" {0}: {1}".format(sulcus_number, sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in unresolved]))
# ------------------------------------------------------------------------
# Return sulci, number of sulci, and file name
# ------------------------------------------------------------------------
sulci = [int(x) for x in sulci]
sulci_file = os.path.join(os.getcwd(), "sulci.vtk")
rewrite_scalars(labels_file, sulci_file, sulci, "sulci", [], background_value)
if not os.path.exists(sulci_file):
raise IOError(sulci_file + " not found")
return sulci, n_sulci, sulci_file
def rescale_by_neighborhood(input_vtk,
indices=[],
nedges=10,
p=99,
set_max_to_1=True,
save_file=False,
output_filestring='rescaled_scalars',
background_value=-1):
"""
Rescale the scalar values of a VTK file by a percentile value
in each vertex's surface mesh neighborhood.
Parameters
----------
input_vtk : string
name of VTK file with a scalar value for each vertex
indices : list of integers (optional)
indices of scalars to normalize
nedges : integer
number or edges from vertex, defining the size of its neighborhood
p : float in range of [0,100]
percentile used to normalize each scalar
set_max_to_1 : Boolean
set all rescaled values greater than 1 to 1.0?
save_file : Boolean
save output VTK file?
output_filestring : string (if save_file)
name of output file
background_value : integer
background value
Returns
-------
rescaled_scalars : list of floats
rescaled scalar values
rescaled_scalars_file : string (if save_file)
name of output VTK file with rescaled scalar values
Examples
--------
>>> import os
>>> from mindboggle.guts.mesh import rescale_by_neighborhood
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_vtk = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> indices = []
>>> nedges = 10
>>> p = 99
>>> set_max_to_1 = True
>>> save_file = True
>>> output_filestring = 'rescaled_scalars'
>>> background_value = -1
>>> #
>>> rescaled_scalars, rescaled_scalars_file = rescale_by_neighborhood(input_vtk,
>>> indices, nedges, p, set_max_to_1, save_file, output_filestring, background_value)
>>> #
>>> # View rescaled scalar values per fold:
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> #
>>> rewrite_scalars(rescaled_scalars_file, rescaled_scalars_file,
>>> rescaled_scalars, 'rescaled_depths', folds)
>>> plot_surfaces(rescaled_scalars_file)
"""
import os
import numpy as np
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors_from_file, find_neighborhood
# Load scalars and vertex neighbor lists:
scalars, name = read_scalars(input_vtk, True, True)
if not indices:
indices = [i for i, x in enumerate(scalars) if x != background_value]
print(" Rescaling {0} scalar values by neighborhood...".format(
len(indices)))
neighbor_lists = find_neighbors_from_file(input_vtk)
# Loop through vertices:
rescaled_scalars = scalars.copy()
for index in indices:
# Determine the scalars in the vertex's neighborhood:
neighborhood = find_neighborhood(neighbor_lists, [index], nedges)
# Compute a high neighborhood percentile to normalize vertex's value:
normalization_factor = np.percentile(scalars[neighborhood], p)
rescaled_scalar = scalars[index] / normalization_factor
rescaled_scalars[index] = rescaled_scalar
# Make any rescaled value greater than 1 equal to 1:
if set_max_to_1:
rescaled_scalars[[x for x in indices if rescaled_scalars[x] > 1.0]] = 1
rescaled_scalars = rescaled_scalars.tolist()
#-------------------------------------------------------------------------
# Return rescaled scalars and file name
#-------------------------------------------------------------------------
if save_file:
rescaled_scalars_file = os.path.join(os.getcwd(),
output_filestring + '.vtk')
rewrite_scalars(input_vtk, rescaled_scalars_file, rescaled_scalars,
'rescaled_scalars')
if not os.path.exists(rescaled_scalars_file):
raise (IOError(rescaled_scalars_file + " not found"))
else:
rescaled_scalars_file = None
return rescaled_scalars, rescaled_scalars_file
def compute_likelihood(trained_file, depth_file, curvature_file, folds,
save_file=False):
"""
Compute likelihoods based on input values, folds, and estimated parameters.
Compute likelihood values for a given VTK surface mesh file, after training
on distributions of depth and curvature values from multiple files.
Parameters
----------
trained_file : pickle compressed file
contains the following dictionaries containing lists of floats
(estimates of depth or curvature means, sigmas, and weights
trained on fold vertices either on or off sulcus label borders)
depth_border, curv_border, depth_nonborder, curv_nonborder
depth_file : string
VTK surface mesh file with depth values in [0,1] for all vertices
curvature_file : string
VTK surface mesh file with curvature values in [-1,1] for all vertices
folds : list of integers
fold number for all vertices (-1 for non-fold vertices)
save_file : Boolean
save output VTK file?
Returns
-------
likelihoods : list of floats
likelihood values for all vertices (0 for non-fold vertices)
likelihoods_file : string (if save_file)
name of output VTK file with likelihood scalars
(-1 for non-fold vertices)
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.shapes.likelihood import compute_likelihood
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> trained_file = os.path.join(path, 'atlases', 'depth_curv_border_nonborder_parameters.pkl')
>>> #depth_file = os.path.join(path, 'arno', 'shapes', 'travel_depth_rescaled.vtk')
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> curvature_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> save_file = True
>>> #
>>> compute_likelihood(trained_file, depth_file, curvature_file, folds, save_file)
>>> # View:
>>> plot_surfaces('likelihoods.vtk', folds_file)
"""
import os
import numpy as np
from math import pi
import cPickle as pickle
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
# Initialize variables:
tiny = 0.000000001
L = np.zeros(len(folds))
probs_border = np.zeros(len(folds))
probs_nonborder = np.zeros(len(folds))
# Load estimated depth and curvature distribution parameters:
depth_border, curv_border, depth_nonborder, curv_nonborder = pickle.load(
open(trained_file, "r"))
# Load depths, curvatures:
depths, name = read_scalars(depth_file, True, True)
curvatures, name = read_scalars(curvature_file, True, True)
# Prep for below:
n = 2
twopiexp = (2*pi)**(n/2)
border_sigmas = depth_border['sigmas'] * curv_border['sigmas']
nonborder_sigmas = depth_nonborder['sigmas'] * curv_nonborder['sigmas']
norm_border = 1 / (twopiexp * border_sigmas + tiny)
norm_nonborder = 1 / (twopiexp * nonborder_sigmas + tiny)
I = [i for i,x in enumerate(folds) if x != -1]
N = depth_border['sigmas'].shape[0]
for j in range(N):
# Border:
expB = depth_border['weights'][j] * \
((depths[I]-depth_border['means'][j])**2) / \
depth_border['sigmas'][j]**2
expB += curv_border['weights'][j] * \
((curvatures[I]-curv_border['means'][j])**2) / \
curv_border['sigmas'][j]**2
expB = -expB / 2
probs_border[I] = probs_border[I] + norm_border[j] * np.exp(expB)
# Non-border:
expNB = depth_nonborder['weights'][j] * \
((depths[I]-depth_nonborder['means'][j])**2) / \
depth_nonborder['sigmas'][j]**2
expNB += curv_nonborder['weights'][j] * \
((curvatures[I]-curv_nonborder['means'][j])**2) / \
curv_nonborder['sigmas'][j]**2
expNB = -expNB / 2
probs_nonborder[I] = probs_nonborder[I] + norm_nonborder[j] * np.exp(expNB)
likelihoods = probs_border / (probs_nonborder + probs_border + tiny)
likelihoods = likelihoods.tolist()
#-------------------------------------------------------------------------
# Return likelihoods and output file name
#-------------------------------------------------------------------------
if save_file:
likelihoods_file = os.path.join(os.getcwd(), 'likelihoods.vtk')
rewrite_scalars(depth_file, likelihoods_file, likelihoods,
'likelihoods', likelihoods)
if not os.path.exists(likelihoods_file):
raise(IOError(likelihoods_file + " not found"))
else:
likelihoods_file = None
return likelihoods, likelihoods_file
def extract_subfolds(depth_file, folds, min_size=10, depth_factor=0.25,
depth_ratio=0.1, tolerance=0.01, save_file=False):
"""
Use depth to segment folds into subfolds in a triangular surface mesh.
Note ::
The function extract_sulci() performs about the same whether folds
or subfolds are used as input. The latter leads to some loss of
small subfolds and possibly holes for small subfolds in the middle
of other subfolds.
Note about the watershed() function:
The watershed() function performs individual seed growing from deep seeds,
repeats segmentation from the resulting seeds until each seed's segment
touches a boundary. The function segment() fills in the rest. Finally
segments are joined if their seeds are too close to each other.
Despite these precautions, the order of seed selection in segment() could
possibly influence the resulting borders between adjoining segments.
[The propagate() function is slower and insensitive to depth,
but is not biased by seed order.]
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
min_size : integer
minimum number of vertices for a subfold
depth_factor : float
watershed() depth_factor:
factor to determine whether to merge two neighboring watershed catchment
basins -- they are merged if the Euclidean distance between their basin
seeds is less than this fraction of the maximum Euclidean distance
between points having minimum and maximum depths
depth_ratio : float
watershed() depth_ratio:
the minimum fraction of depth for a neighboring shallower
watershed catchment basin (otherwise merged with the deeper basin)
tolerance : float
watershed() tolerance:
tolerance for detecting differences in depth between vertices
save_file : Boolean
save output VTK file?
Returns
-------
subfolds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_subfolds : int
number of subfolds
subfolds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.guts.mesh import find_neighbors_from_file
>>> from mindboggle.features.folds import extract_subfolds
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> min_size = 10
>>> depth_factor = 0.5
>>> depth_ratio = 0.1
>>> tolerance = 0.01
>>> #
>>> subfolds, n_subfolds, subfolds_file = extract_subfolds(depth_file,
>>> folds, min_size, depth_factor, depth_ratio, tolerance, True)
>>> #
>>> # View:
>>> rewrite_scalars(depth_file, 'subfolds.vtk', subfolds, 'subfolds', subfolds)
>>> plot_surfaces('subfolds.vtk')
"""
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import rewrite_scalars, read_vtk
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import segment, propagate, watershed
print("Segment folds into subfolds")
t0 = time()
#-------------------------------------------------------------------------
# Load depth values for all vertices
#-------------------------------------------------------------------------
faces, lines, indices, points, npoints, depths, \
name, input_vtk = read_vtk(depth_file, return_first=True,
return_array=True)
#-------------------------------------------------------------------------
# Find neighbors for each vertex
#-------------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
#-------------------------------------------------------------------------
# Segment folds into "watershed basins"
#-------------------------------------------------------------------------
indices_folds = [i for i,x in enumerate(folds) if x != -1]
subfolds, seed_indices = watershed(depths, points, indices_folds,
neighbor_lists, min_size, depth_factor=0.25,
depth_ratio=0.1, tolerance=0.01, regrow=True)
# Print statement
n_subfolds = len([x for x in np.unique(subfolds) if x != -1])
print(' ...Extracted {0} subfolds ({1:.2f} seconds)'.
format(n_subfolds, time() - t0))
#-------------------------------------------------------------------------
# Return subfolds, number of subfolds, file name
#-------------------------------------------------------------------------
if save_file:
subfolds_file = os.path.join(os.getcwd(), 'subfolds.vtk')
rewrite_scalars(depth_file, subfolds_file,
subfolds, 'subfolds', subfolds)
if not os.path.exists(subfolds_file):
raise(IOError(subfolds_file + " not found"))
else:
subfolds_file = None
return subfolds, n_subfolds, subfolds_file
def plot_mask_surface(vtk_file, mask_file='', nonmask_value=-1,
masked_output='', remove_nonmask=False,
program='vtkviewer',
use_colormap=False, colormap_file=''):
"""
Use vtkviewer or mayavi2 to visualize VTK surface mesh data.
If a mask_file is provided, a temporary masked file is saved,
and it is this file that is viewed.
If using vtkviewer, can optionally provide colormap file
or set $COLORMAP environment variable.
Parameters
----------
vtk_file : string
name of VTK surface mesh file
mask_file : string
name of VTK surface mesh file to mask vtk_file vertices
nonmask_value : integer
nonmask (usually background) value
masked_output : string
temporary masked output file name
remove_nonmask : Boolean
remove vertices that are not in mask? (otherwise assign nonmask_value)
program : string {'vtkviewer', 'mayavi2'}
program to visualize VTK file
use_colormap : Boolean
use Paraview-style XML colormap file set by $COLORMAP env variable?
colormap_file : string
use colormap in given file if use_colormap==True? if empty and
use_colormap==True, use file set by $COLORMAP environment variable
Examples
--------
>>> import os
>>> from mindboggle.mio.plots import plot_mask_surface
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> mask_file = os.path.join(path, 'test_one_label.vtk')
>>> nonmask_value = 0 #-1
>>> masked_output = ''
>>> remove_nonmask = True
>>> program = 'vtkviewer'
>>> use_colormap = True
>>> colormap_file = '' #'/software/surface_cpp_tools/colormap.xml'
>>> plot_mask_surface(vtk_file, mask_file, nonmask_value, masked_output, remove_nonmask, program, use_colormap, colormap_file)
"""
import os
import numpy as np
from mindboggle.guts.mesh import remove_faces, reindex_faces_points
from mindboggle.guts.utilities import execute
from mindboggle.mio.plots import plot_surfaces
from mindboggle.mio.vtks import read_scalars, rewrite_scalars, \
read_vtk, write_vtk
#-------------------------------------------------------------------------
# Filter mesh with non-background values from a second (same-size) mesh:
#-------------------------------------------------------------------------
if mask_file:
mask, name = read_scalars(mask_file, True, True)
if not masked_output:
masked_output = os.path.join(os.getcwd(), 'temp.vtk')
file_to_plot = masked_output
#---------------------------------------------------------------------
# Remove nonmask-valued vertices:
#---------------------------------------------------------------------
if remove_nonmask:
#-----------------------------------------------------------------
# Load VTK files:
#-----------------------------------------------------------------
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file, True, True)
#-----------------------------------------------------------------
# Find mask indices, remove nonmask faces, and reindex:
#-----------------------------------------------------------------
Imask = [i for i,x in enumerate(mask) if x != nonmask_value]
mask_faces = remove_faces(faces, Imask)
mask_faces, points, \
original_indices = reindex_faces_points(mask_faces, points)
#-----------------------------------------------------------------
# Write VTK file with scalar values:
#-----------------------------------------------------------------
if np.ndim(scalars) == 1:
scalar_type = type(scalars[0]).__name__
elif np.ndim(scalars) == 2:
scalar_type = type(scalars[0][0]).__name__
else:
print("Undefined scalar type!")
write_vtk(file_to_plot, points, [], [], mask_faces,
scalars[original_indices].tolist(), scalar_names,
scalar_type=scalar_type)
else:
scalars, name = read_scalars(vtk_file, True, True)
scalars[mask == nonmask_value] = nonmask_value
rewrite_scalars(vtk_file, file_to_plot, scalars)
else:
file_to_plot = vtk_file
#-------------------------------------------------------------------------
# Display with vtkviewer.py:
#-------------------------------------------------------------------------
if program == 'vtkviewer':
plot_surfaces(file_to_plot, use_colormap=use_colormap,
colormap_file=colormap_file)
#-------------------------------------------------------------------------
# Display with mayavi2:
#-------------------------------------------------------------------------
elif program == 'mayavi2':
cmd = ["mayavi2", "-d", file_to_plot, "-m", "Surface", "&"]
execute(cmd, 'os')
def extract_folds(depth_file, depth_threshold=2, min_fold_size=50,
save_file=False, output_file='', background_value=-1,
verbose=False):
"""
Use depth threshold to extract folds from a triangular surface mesh.
A fold is a group of connected, deep vertices. To extract folds,
a depth threshold is used to segment deep vertices of the surface mesh.
We have observed in the histograms of travel depth measures of cortical
surfaces that there is a rapidly decreasing distribution of low depth
values (corresponding to the outer surface, or gyral crowns) with a
long tail of higher depth values (corresponding to the folds).
The find_depth_threshold function therefore computes a histogram of
travel depth measures, smooths the histogram's bin values, convolves
to compute slopes, and finds the depth value for the first bin with
zero slope. The extract_folds function uses this depth value, segments
deep vertices, and removes extremely small folds (empirically set at 50
vertices or fewer out of a total mesh size of over 100,000 vertices).
Steps ::
1. Segment deep vertices as an initial set of folds.
2. Remove small folds.
3. Renumber folds.
Note ::
Removed option: Find and fill holes in the folds:
Folds could have holes in areas shallower than the depth threshold.
Calling fill_holes() could accidentally include very shallow areas
(in an annulus-shaped fold, for example).
However, we could include the argument exclude_range to check for
any values from zero to min_hole_depth; holes would not be filled
if they were to contain values within this range.
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
depth_threshold : float
threshold defining the minimum depth for vertices to be in a fold
min_fold_size : integer
minimum fold size (number of vertices)
save_file : bool
save output VTK file?
output_file : string
name of output file in VTK format
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_folds : int
number of folds
folds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> from mindboggle.features.folds import extract_folds
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> depth_threshold = 2.36089
>>> min_fold_size = 50
>>> save_file = True
>>> output_file = 'extract_folds.vtk'
>>> background_value = -1
>>> verbose = False
>>> folds, n_folds, folds_file = extract_folds(depth_file,
... depth_threshold, min_fold_size, save_file, output_file,
... background_value, verbose)
>>> n_folds
33
>>> lens = [len([x for x in folds if x == y]) for y in range(n_folds)]
>>> lens[0:10]
[726, 67241, 2750, 5799, 1151, 6360, 1001, 505, 228, 198]
View folds (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> plot_surfaces('extract_folds.vtk') # doctest: +SKIP
View folds without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> rewrite_scalars(depth_file, 'extract_folds_no_background.vtk', folds,
... 'just_folds', folds, -1) # doctest: +SKIP
>>> plot_surfaces('extract_folds_no_background.vtk') # doctest: +SKIP
"""
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import rewrite_scalars, read_vtk
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import segment_regions
if verbose:
print("Extract folds in surface mesh")
t0 = time()
# ------------------------------------------------------------------------
# Load depth values for all vertices
# ------------------------------------------------------------------------
points, indices, lines, faces, depths, scalar_names, npoints, \
input_vtk = read_vtk(depth_file, return_first=True, return_array=True)
# ------------------------------------------------------------------------
# Find the deepest vertices
# ------------------------------------------------------------------------
indices_deep = [i for i,x in enumerate(depths) if x >= depth_threshold]
if indices_deep:
# --------------------------------------------------------------------
# Find neighbors for each vertex
# --------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
# --------------------------------------------------------------------
# Segment deep vertices as an initial set of folds
# --------------------------------------------------------------------
if verbose:
print(" Segment vertices deeper than {0:.2f} as folds".format(depth_threshold))
t1 = time()
folds = segment_regions(indices_deep, neighbor_lists, 1, [], False,
False, [], [], [], '', background_value, False)
if verbose:
print(' ...Segmented folds ({0:.2f} seconds)'.format(time() - t1))
# --------------------------------------------------------------------
# Remove small folds
# --------------------------------------------------------------------
if min_fold_size > 1:
if verbose:
print(' Remove folds smaller than {0}'.format(min_fold_size))
unique_folds = [x for x in np.unique(folds)
if x != background_value]
for nfold in unique_folds:
indices_fold = [i for i,x in enumerate(folds) if x == nfold]
if len(indices_fold) < min_fold_size:
folds[indices_fold] = background_value
# --------------------------------------------------------------------
# Find and fill holes in the folds
# Note: Surfaces surrounded by folds can be mistaken for holes,
# so exclude_range includes outer surface values close to zero.
# --------------------------------------------------------------------
# folds = fill_holes(folds, neighbor_lists, values=depths,
# exclude_range=[0, min_hole_depth])
# --------------------------------------------------------------------
# Renumber folds so they are sequential.
# NOTE: All vertices are included (-1 for non-fold vertices).
# --------------------------------------------------------------------
renumber_folds = background_value * np.ones(npoints)
fold_numbers = [x for x in np.unique(folds) if x != background_value]
for i_fold, n_fold in enumerate(fold_numbers):
fold_indices = [i for i,x in enumerate(folds) if x == n_fold]
renumber_folds[fold_indices] = i_fold
folds = renumber_folds
folds = [int(x) for x in folds]
n_folds = i_fold + 1
# Print statement
if verbose:
print(' ...Extracted {0} folds ({1:.2f} seconds)'.
format(n_folds, time() - t0))
else:
if verbose:
print(' No deep vertices')
# ------------------------------------------------------------------------
# Return folds, number of folds, file name
# ------------------------------------------------------------------------
if save_file:
if output_file:
folds_file = output_file
else:
folds_file = os.path.join(os.getcwd(), 'folds.vtk')
rewrite_scalars(depth_file, folds_file, folds, 'folds', [],
background_value)
if not os.path.exists(folds_file):
raise IOError(folds_file + " not found")
else:
folds_file = None
return folds, n_folds, folds_file
def extract_fundi(folds,
curv_file,
depth_file,
min_separation=10,
erode_ratio=0.1,
erode_min_size=1,
save_file=False,
verbose=False):
"""
Extract fundi from folds.
A fundus is a branching curve that runs along the deepest and most
highly curved portions of a fold.
Steps ::
1. Find fundus endpoints (outer anchors) with find_outer_anchors().
2. Include inner anchor points.
3. Connect anchor points using connect_points_erosion();
inner anchors are removed if they result in endpoints.
Parameters
----------
folds : numpy array or list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : bool
save output VTK file?
verbose : bool
print statements?
Returns
-------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
n_fundi_in_folds : integer
number of fundi
fundus_per_fold_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> single_fold = True
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> curv_file = fetch_data(urls['left_mean_curvature'])
>>> depth_file = fetch_data(urls['left_travel_depth'])
>>> folds_file = fetch_data(urls['left_folds'])
>>> folds, name = read_scalars(folds_file, True, True)
>>> if single_fold:
... fold_number = 2 #11
... folds[folds != fold_number] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> verbose = False
>>> o1, o2, fundus_per_fold_file = extract_fundi(folds, curv_file,
... depth_file, min_separation, erode_ratio, erode_min_size,
... save_file, verbose)
>>> if single_fold:
... lens = [len([x for x in o1 if x == 2])]
... else:
... lens = [len([x for x in o1 if x == y]) for y in range(o2)]
>>> lens[0:10]
[115]
View result (skip test):
>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces(fundus_per_fold_file) # doctest: +SKIP
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.compute import median_abs_dev
from mindboggle.guts.paths import find_max_values
from mindboggle.guts.mesh import find_neighbors_from_file
from mindboggle.guts.mesh import find_complete_faces
from mindboggle.guts.paths import find_outer_anchors
from mindboggle.guts.paths import connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
points, indices, lines, faces, curvs, scalar_names, npoints, \
input_vtk = read_vtk(curv_file, True, True)
depths, name = read_scalars(depth_file, True, True)
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
#-------------------------------------------------------------------------
# Loop through folds:
#-------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != -1]
if verbose:
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".format(
len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i, x in enumerate(folds) if x == fold_ID]
if indices_fold:
if verbose:
print(' Fold {0}:'.format(int(fold_ID)))
#-----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints:
#-----------------------------------------------------------------
verbose = False
outer_anchors, tracks = find_outer_anchors(indices_fold,
neighbor_lists, values,
depths, min_separation,
verbose)
#-----------------------------------------------------------------
# Find inner anchor points:
#-----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation,
thr)
#-----------------------------------------------------------------
# Connect anchor points to create skeleton:
#-----------------------------------------------------------------
B = -1 * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B,
neighbor_lists,
outer_anchors,
inner_anchors,
values,
erode_ratio,
erode_min_size,
save_steps=[],
save_vtk='',
verbose=False)
if skeleton:
skeletons.extend(skeleton)
#-----------------------------------------------------------------
# Remove fundus vertices if they complete triangle faces:
#-----------------------------------------------------------------
Iremove = find_complete_faces(skeletons, faces)
if Iremove:
skeletons = list(frozenset(skeletons).difference(Iremove))
indices_skel = [x for x in skeletons if folds[x] != -1]
fundus_per_fold = -1 * np.ones(npoints)
fundus_per_fold[indices_skel] = folds[indices_skel]
n_fundi_in_folds = len([x for x in np.unique(fundus_per_fold) if x != -1])
if n_fundi_in_folds == 1:
sdum = 'fold fundus'
else:
sdum = 'fold fundi'
if verbose:
print(' ...Extracted {0} {1}; {2} total ({3:.2f} seconds)'.format(
n_fundi_in_folds, sdum, n_fundi_in_folds,
time() - t1))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
if n_fundi_in_folds > 0:
fundus_per_fold = [int(x) for x in fundus_per_fold]
if save_file:
fundus_per_fold_file = os.path.join(os.getcwd(),
'fundus_per_fold.vtk')
rewrite_scalars(curv_file, fundus_per_fold_file, fundus_per_fold,
'fundi', folds)
if not os.path.exists(fundus_per_fold_file):
raise IOError(fundus_per_fold_file + " not found")
else:
fundus_per_fold_file = None
return fundus_per_fold, n_fundi_in_folds, fundus_per_fold_file
def extract_subfolds(depth_file,
folds,
min_size=10,
depth_factor=0.25,
depth_ratio=0.1,
tolerance=0.01,
save_file=False):
"""
Use depth to segment folds into subfolds in a triangular surface mesh.
Note ::
The function extract_sulci() performs about the same whether folds
or subfolds are used as input. The latter leads to some loss of
small subfolds and possibly holes for small subfolds in the middle
of other subfolds.
Note about the watershed() function:
The watershed() function performs individual seed growing from deep seeds,
repeats segmentation from the resulting seeds until each seed's segment
touches a boundary. The function segment() fills in the rest. Finally
segments are joined if their seeds are too close to each other.
Despite these precautions, the order of seed selection in segment() could
possibly influence the resulting borders between adjoining segments.
[The propagate() function is slower and insensitive to depth,
but is not biased by seed order.]
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
min_size : integer
minimum number of vertices for a subfold
depth_factor : float
watershed() depth_factor:
factor to determine whether to merge two neighboring watershed catchment
basins -- they are merged if the Euclidean distance between their basin
seeds is less than this fraction of the maximum Euclidean distance
between points having minimum and maximum depths
depth_ratio : float
watershed() depth_ratio:
the minimum fraction of depth for a neighboring shallower
watershed catchment basin (otherwise merged with the deeper basin)
tolerance : float
watershed() tolerance:
tolerance for detecting differences in depth between vertices
save_file : Boolean
save output VTK file?
Returns
-------
subfolds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_subfolds : int
number of subfolds
subfolds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.guts.mesh import find_neighbors_from_file
>>> from mindboggle.features.folds import extract_subfolds
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> min_size = 10
>>> depth_factor = 0.5
>>> depth_ratio = 0.1
>>> tolerance = 0.01
>>> #
>>> subfolds, n_subfolds, subfolds_file = extract_subfolds(depth_file,
>>> folds, min_size, depth_factor, depth_ratio, tolerance, True)
>>> #
>>> # View:
>>> rewrite_scalars(depth_file, 'subfolds.vtk', subfolds, 'subfolds', subfolds)
>>> plot_surfaces('subfolds.vtk')
"""
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import rewrite_scalars, read_vtk
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import segment, propagate, watershed
print("Segment folds into subfolds")
t0 = time()
#-------------------------------------------------------------------------
# Load depth values for all vertices
#-------------------------------------------------------------------------
points, indices, lines, faces, depths, scalar_names, npoints, \
input_vtk = read_vtk(depth_file, return_first=True, return_array=True)
#-------------------------------------------------------------------------
# Find neighbors for each vertex
#-------------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
#-------------------------------------------------------------------------
# Segment folds into "watershed basins"
#-------------------------------------------------------------------------
indices_folds = [i for i, x in enumerate(folds) if x != -1]
subfolds, seed_indices = watershed(depths,
points,
indices_folds,
neighbor_lists,
min_size,
depth_factor=0.25,
depth_ratio=0.1,
tolerance=0.01,
regrow=True)
# Print statement
n_subfolds = len([x for x in np.unique(subfolds) if x != -1])
print(' ...Extracted {0} subfolds ({1:.2f} seconds)'.format(
n_subfolds,
time() - t0))
#-------------------------------------------------------------------------
# Return subfolds, number of subfolds, file name
#-------------------------------------------------------------------------
if save_file:
subfolds_file = os.path.join(os.getcwd(), 'subfolds.vtk')
rewrite_scalars(depth_file, subfolds_file, subfolds, 'subfolds',
subfolds)
if not os.path.exists(subfolds_file):
raise (IOError(subfolds_file + " not found"))
else:
subfolds_file = None
return subfolds, n_subfolds, subfolds_file
def rescale_by_neighborhood(input_vtk,
indices=[],
nedges=10,
p=99,
set_max_to_1=True,
save_file=False,
output_filestring='rescaled_scalars',
background_value=-1):
"""
Rescale the scalar values of a VTK file by a percentile value
in each vertex's surface mesh neighborhood.
Parameters
----------
input_vtk : string
name of VTK file with a scalar value for each vertex
indices : list of integers (optional)
indices of scalars to normalize
nedges : integer
number or edges from vertex, defining the size of its neighborhood
p : float in range of [0,100]
percentile used to normalize each scalar
set_max_to_1 : bool
set all rescaled values greater than 1 to 1.0?
save_file : bool
save output VTK file?
output_filestring : string (if save_file)
name of output file
background_value : integer
background value
Returns
-------
rescaled_scalars : list of floats
rescaled scalar values
rescaled_scalars_file : string (if save_file)
name of output VTK file with rescaled scalar values
Examples
--------
>>> import numpy as np
>>> from mindboggle.guts.mesh import rescale_by_neighborhood
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.plots import plot_surfaces
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_vtk = fetch_data(urls['left_travel_depth'])
>>> indices = []
>>> nedges = 10
>>> p = 99
>>> set_max_to_1 = True
>>> save_file = True
>>> output_filestring = 'rescaled_scalars'
>>> background_value = -1
>>> rescaled, rescaled_file = rescale_by_neighborhood(input_vtk,
... indices, nedges, p, set_max_to_1, save_file, output_filestring,
... background_value)
>>> scalars1, name = read_scalars(input_vtk)
>>> print('{0:0.5f}, {1:0.5f}'.format(max(scalars1), max(rescaled)))
34.95560, 1.00000
>>> print('{0:0.5f}, {1:0.5f}'.format(np.mean(scalars1), np.mean(rescaled)))
7.43822, 0.44950
View rescaled scalar values on surface (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> plot_surfaces(rescaled_file) # doctest: +SKIP
"""
import os
import numpy as np
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors_from_file, find_neighborhood
# Load scalars and vertex neighbor lists:
scalars, name = read_scalars(input_vtk, True, True)
if not indices:
indices = [i for i, x in enumerate(scalars) if x != background_value]
#print(" Rescaling {0} scalar values by neighborhood...".format(len(indices)))
neighbor_lists = find_neighbors_from_file(input_vtk)
# Loop through vertices:
rescaled_scalars = scalars.copy()
for index in indices:
# Determine the scalars in the vertex's neighborhood:
neighborhood = find_neighborhood(neighbor_lists, [index], nedges)
# Compute a high neighborhood percentile to normalize vertex's value:
normalization_factor = np.percentile(scalars[neighborhood], p)
rescaled_scalar = scalars[index] / normalization_factor
rescaled_scalars[index] = rescaled_scalar
# Make any rescaled value greater than 1 equal to 1:
if set_max_to_1:
rescaled_scalars[[x for x in indices if rescaled_scalars[x] > 1.0]] = 1
rescaled_scalars = rescaled_scalars.tolist()
#-------------------------------------------------------------------------
# Return rescaled scalars and file name
#-------------------------------------------------------------------------
if save_file:
rescaled_scalars_file = os.path.join(os.getcwd(),
output_filestring + '.vtk')
rewrite_scalars(input_vtk, rescaled_scalars_file, rescaled_scalars,
'rescaled_scalars')
if not os.path.exists(rescaled_scalars_file):
raise IOError(rescaled_scalars_file + " not found")
else:
rescaled_scalars_file = None
return rescaled_scalars, rescaled_scalars_file
