def propagate_fundus_lines(surf_file, fundus_lines_file, thickness_file):
"""Propagate fundus lines to tile the surface.
Parameters
----------
surf_file: file containing the surface geometry in vtk format
fundus_lines_file: file containing scalars representing fundus lines
thickness_file: file containing cortical thickness scalar data
(for masking out the medial wall only)
Returns
-------
scalars indicating whether each vertex is part of the closed
fundus lines or not
"""
from mindboggle.utils.io_vtk import read_vtk, read_scalars
faces, _, _, points, num_points, fundus_lines, _, _ = read_vtk(
surf_file, return_first=True, return_array=True)
fundus_lines, _ = read_scalars(fundus_lines_file)
fundus_line_indices = [i for i, x in enumerate(fundus_lines) if x > 0.5]
thickness, _ = read_scalars(thickness_file,
return_first=True, return_array=True)
return propagate_fundus_lines(
points, faces, fundus_line_indices, thickness)
def histogram_of_vtk_scalars(vtk_file, nbins=100):
"""
Plot histogram of VTK surface mesh scalar values.
Parameters
----------
vtk_file : string
name of VTK file with scalar values to plot
nbins : integer
number of histogram bins
Examples
--------
>>> import os
>>> from mindboggle.utils.plots import histogram_of_vtk_scalars
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> histogram_of_vtk_scalars(vtk_file, nbins=500)
"""
import matplotlib.pyplot as plt
from mindboggle.utils.io_vtk import read_scalars
# Load values:
values, name = read_scalars(vtk_file)
# Histogram:
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.hist(values, nbins, normed=False, facecolor='gray', alpha=0.5)
plt.show()
def plot_scalar_histogram(vtk_file, nbins=100):
"""
Plot histogram of VTK surface mesh scalar values.
Inputs
------
vtk_file : string
name of VTK file
nbins : integer
number of histogram bins
Examples
--------
>>> import os
>>> from mindboggle.utils.plots import plot_scalar_histogram
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> plot_scalar_histogram(vtk_file, nbins=500)
"""
import matplotlib.pyplot as plt
from mindboggle.utils.io_vtk import read_scalars
# Load values:
values, name = read_scalars(vtk_file)
# Histogram:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(values, nbins, normed=False, facecolor='gray', alpha=0.5)
plt.show()
def plot_vtk(vtk_file, mask_file='', masked_output=''):
"""
Use mayavi2 to visualize VTK surface mesh data.
Inputs
------
vtk_file : string
name of VTK surface mesh file
Examples
--------
>>> import os
>>> from mindboggle.utils.plots import plot_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> mask_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> masked_output = ''
>>> plot_vtk(vtk_file, mask_file, masked_output)
"""
import os
# import subprocess
# Filter mesh with the non -1 values from a second (same-size) mesh:
if mask_file:
from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
scalars, name = read_scalars(vtk_file)
mask, name = read_scalars(mask_file)
if not masked_output:
masked_output = 'temp.vtk'
rewrite_scalars(vtk_file, masked_output, scalars, 'masked', mask)
cmd = ["mayavi2", "-d", masked_output, "-m", "Surface"]
else:
cmd = ["mayavi2", "-d", vtk_file, "-m", "Surface"]
# Note: subprocess won't allow me to put the command in the background:
# p = subprocess.Popen(cmd)
# p.communicate()
cmd = ' '.join(cmd) + ' &'
print(cmd)
os.system(cmd)
def plot_vtk(vtk_file, mask_file='', masked_output=''):
"""
Use mayavi2 to visualize VTK surface mesh data.
Inputs
------
vtk_file : string
name of VTK surface mesh file
Examples
--------
>>> import os
>>> from mindboggle.utils.plots import plot_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> mask_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> masked_output = ''
>>> plot_vtk(vtk_file, mask_file, masked_output)
"""
from mindboggle.utils.utils import execute
# Filter mesh with the non -1 values from a second (same-size) mesh:
if mask_file:
from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
scalars, name = read_scalars(vtk_file)
mask, name = read_scalars(mask_file)
if not masked_output:
masked_output = 'temp.vtk'
rewrite_scalars(vtk_file, masked_output, scalars, 'masked', mask)
cmd = ["mayavi2", "-d", masked_output, "-m", "Surface"]
else:
cmd = ["mayavi2", "-d", vtk_file, "-m", "Surface"]
cmd.extend('&')
execute(cmd, 'os')
def extract_sulci(labels_file,
folds_or_file,
hemi,
min_boundary=1,
sulcus_names=[]):
"""
Identify sulci from folds in a brain surface according to a labeling
protocol that includes a list of label pairs defining each sulcus.
A fold is a group of connected, deep vertices.
Steps for each fold ::
1. Remove fold if it has fewer than two labels.
2. Remove fold if its labels do not contain a sulcus label pair.
3. Find vertices with labels that are in only one of the fold's
label boundary pairs. Assign the vertices the sulcus with the label
pair if they are connected to the label boundary for that pair.
4. If there are remaining vertices, segment into sets of vertices
connected to label boundaries, and assign a unique ID to each set.
Parameters
----------
labels_file : string
file name for surface mesh VTK containing labels for all vertices
folds_or_file : list or string
fold number for each vertex / name of VTK file containing fold scalars
hemi : string
hemisphere abbreviation in {'lh', 'rh'} for sulcus labels
min_boundary : integer
minimum number of vertices for a sulcus label boundary segment
sulcus_names : list of strings
names of sulci
Returns
-------
sulci : list of integers
sulcus numbers for all vertices (-1 for non-sulcus vertices)
n_sulci : integers
number of sulci
sulci_file : string
output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.utils.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> # Load labels, folds, neighbor lists, and sulcus names and label pairs
>>> labels_file = os.path.join(path, 'arno', 'labels', 'relabeled_lh.DKTatlas40.gcs.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds_or_file, name = read_scalars(folds_file)
>>> hemi = 'lh'
>>> min_boundary = 10
>>> sulcus_names = []
>>> #
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file, hemi, min_boundary, sulcus_names)
>>> # View:
>>> plot_surfaces('sulci.vtk')
"""
import os
from time import time
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, read_vtk, rewrite_scalars
from mindboggle.utils.mesh import find_neighbors
from mindboggle.utils.segment import extract_borders, propagate, segment
from mindboggle.LABELS import DKTprotocol
# Load fold numbers if folds_or_file is a string:
if isinstance(folds_or_file, str):
folds, name = read_scalars(folds_or_file)
elif isinstance(folds_or_file, list):
folds = folds_or_file
dkt = DKTprotocol()
if hemi == 'lh':
pair_lists = dkt.left_sulcus_label_pair_lists
elif hemi == 'rh':
pair_lists = dkt.right_sulcus_label_pair_lists
else:
print("Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors:
faces, o1, o2, points, npoints, labels, o3, o4 = read_vtk(labels_file)
neighbor_lists = find_neighbors(faces, npoints)
# Array of sulcus IDs for fold vertices, initialized as -1.
# Since we do not touch gyral vertices and vertices whose labels
# are not in the label list, or vertices having only one label,
# their sulcus IDs will remain -1:
sulci = -1 * np.ones(npoints)
#-------------------------------------------------------------------------
# Loop through folds
#-------------------------------------------------------------------------
fold_numbers = [int(x) for x in np.unique(folds) if x != -1]
n_folds = len(fold_numbers)
print("Extract sulci from {0} folds...".format(n_folds))
t0 = time()
for n_fold in fold_numbers:
fold = [i for i, x in enumerate(folds) if x == n_fold]
len_fold = len(fold)
# List the labels in this fold:
fold_labels = [labels[x] for x in fold]
unique_fold_labels = [
int(x) for x in np.unique(fold_labels) if x != -1
]
#---------------------------------------------------------------------
# NO MATCH -- fold has fewer than two labels
#---------------------------------------------------------------------
if len(unique_fold_labels) < 2:
# Ignore: sulci already initialized with -1 values:
if not unique_fold_labels:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has no labels".format(
n_fold, len_fold))
else:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has only one label ({2})".format(
n_fold, len_fold, unique_fold_labels[0]))
# Ignore: sulci already initialized with -1 values
else:
# Find all label boundary pairs within the fold:
indices_fold_pairs, fold_pairs, unique_fold_pairs = \
extract_borders(fold, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
# Find fold label pairs in the protocol (pairs are already sorted):
fold_pairs_in_protocol = [
x for x in unique_fold_pairs
if x in dkt.unique_sulcus_label_pairs
]
if unique_fold_labels:
print(" Fold {0} labels: {1} ({2} vertices)".format(
n_fold, ', '.join([str(x) for x in unique_fold_labels]),
len_fold))
#-----------------------------------------------------------------
# NO MATCH -- fold has no sulcus label pair
#-----------------------------------------------------------------
if not fold_pairs_in_protocol:
print(" Fold {0}: NO MATCH -- fold has no sulcus label pair".
format(n_fold, len_fold))
#-----------------------------------------------------------------
# Possible matches
#-----------------------------------------------------------------
else:
print(" Fold {0} label pairs in protocol: {1}".format(
n_fold,
', '.join([str(x) for x in fold_pairs_in_protocol])))
# Labels in the protocol (includes repeats across label pairs):
labels_in_pairs = [
x for lst in fold_pairs_in_protocol for x in lst
]
# Labels that appear in one or more sulcus label boundary:
unique_labels = []
nonunique_labels = []
for label in np.unique(labels_in_pairs):
if len([x for x in labels_in_pairs if x == label]) == 1:
unique_labels.append(label)
else:
nonunique_labels.append(label)
#-------------------------------------------------------------
# Vertices whose labels are in only one sulcus label pair
#-------------------------------------------------------------
# Find vertices with a label that is in only one of the fold's
# label pairs (the other label in the pair can exist in other
# pairs). Assign the vertices the sulcus with the label pair
# if they are connected to the label boundary for that pair.
#-------------------------------------------------------------
if unique_labels:
for pair in fold_pairs_in_protocol:
# If one or both labels in label pair is/are unique:
unique_labels_in_pair = [
x for x in pair if x in unique_labels
]
n_unique = len(unique_labels_in_pair)
if n_unique:
ID = None
for i, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
if pair in pair_list:
ID = i
break
if ID:
# Seeds from label boundary vertices
# (fold_pairs and pair already sorted):
indices_pair = [
x for i, x in enumerate(indices_fold_pairs)
if fold_pairs[i] == pair
]
# Vertices with unique label(s) in pair:
indices_unique_labels = [
fold[i] for i, x in enumerate(fold_labels)
if x in dkt.unique_sulcus_label_pairs
]
# Propagate from seeds to labels in label pair:
sulci2 = segment(indices_unique_labels,
neighbor_lists,
min_region_size=1,
seed_lists=[indices_pair],
keep_seeding=False,
spread_within_labels=True,
labels=labels)
sulci[sulci2 != -1] = ID
# Print statement:
if n_unique == 1:
ps1 = '1 label'
else:
ps1 = 'Both labels'
if len(sulcus_names):
ps2 = sulcus_names[ID]
else:
ps2 = ''
print(" {0} unique to one fold pair: "
"{1} {2}".format(ps1, ps2,
unique_labels_in_pair))
#-------------------------------------------------------------
# Vertex labels shared by multiple label pairs
#-------------------------------------------------------------
# Propagate labels from label borders to vertices with labels
# that are shared by multiple label pairs in the fold.
#-------------------------------------------------------------
if len(nonunique_labels):
# For each label shared by different label pairs:
for label in nonunique_labels:
# Print statement:
print(" Propagate sulcus borders with label {0}".
format(int(label)))
# Construct seeds from label boundary vertices:
seeds = -1 * np.ones(len(points))
for ID, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
label_pairs = [x for x in pair_list if label in x]
for label_pair in label_pairs:
indices_pair = [
x for i, x in enumerate(indices_fold_pairs)
if np.sort(fold_pairs[i]).tolist() ==
label_pair
]
if indices_pair:
# Do not include short boundary segments:
if min_boundary > 1:
indices_pair2 = []
seeds2 = segment(
indices_pair, neighbor_lists)
useeds2 = [
x for x in np.unique(seeds2)
if x != -1
]
for seed2 in useeds2:
iseed2 = [
i for i, x in enumerate(seeds2)
if x == seed2
]
if len(iseed2) >= min_boundary:
indices_pair2.extend(iseed2)
else:
if len(iseed2) == 1:
print(" Remove "
"assignment "
"of ID {0} from "
"1 vertex".format(
seed2))
else:
print(
" Remove "
"assignment "
"of ID {0} from "
"{1} vertices".format(
seed2,
len(iseed2)))
indices_pair = indices_pair2
# Assign sulcus IDs to seeds:
seeds[indices_pair] = ID
# Identify vertices with the label:
label_array = -1 * np.ones(len(points))
indices_label = [
fold[i] for i, x in enumerate(fold_labels)
if x == label
]
if len(indices_label):
label_array[indices_label] = 1
# Propagate from seeds to vertices with label:
#indices_seeds = []
#for seed in range(int(max(seeds))+1):
# indices_seeds.append([i for i,x
# in enumerate(seeds)
# if x == seed])
#sulci2 = segment(indices_label, neighbor_lists,
# 50, indices_seeds, False, True,
# labels)
sulci2 = propagate(points,
faces,
label_array,
seeds,
sulci,
max_iters=10000,
tol=0.001,
sigma=5)
sulci[sulci2 != -1] = sulci2[sulci2 != -1]
#-------------------------------------------------------------------------
# Print out assigned sulci
#-------------------------------------------------------------------------
sulcus_numbers = [int(x) for x in np.unique(sulci) if x != -1]
# if not np.isnan(x)]
n_sulci = len(sulcus_numbers)
print("Extracted {0} sulci from {1} folds ({2:.1f}s):".format(
n_sulci, n_folds,
time() - t0))
if sulcus_names:
for sulcus_number in sulcus_numbers:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
elif sulcus_numbers:
print(" " + ", ".join([str(x) for x in sulcus_numbers]))
#-------------------------------------------------------------------------
# Print out unresolved sulci
#-------------------------------------------------------------------------
unresolved = [i for i in range(len(pair_lists)) if i not in sulcus_numbers]
if len(unresolved) == 1:
print("The following sulcus is unaccounted for:")
else:
print("The following {0} sulci are unaccounted for:".format(
len(unresolved)))
if sulcus_names:
for sulcus_number in unresolved:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in unresolved]))
#-------------------------------------------------------------------------
# Return sulci, number of sulci, and file name
#-------------------------------------------------------------------------
sulci = [int(x) for x in sulci]
sulci_file = os.path.join(os.getcwd(), 'sulci.vtk')
rewrite_scalars(labels_file, sulci_file, sulci, 'sulci', sulci)
if not os.path.exists(sulci_file):
raise (IOError(sulci_file + " not found"))
return sulci, n_sulci, sulci_file
def write_face_vertex_averages(input_file, area_file="", delimiter=","):
"""
Make table of average vertex values per face.
Parameters
----------
input_file : string
name of VTK file with scalars to average
area_file : string
name of VTK file with surface area scalar values
delimiter : string
delimiter between columns, such as ','
Returns
-------
output_table : string
output table filename
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.utils.io_table import write_face_vertex_averages
>>> path = os.environ['MINDBOGGLE_DATA']
>>> #input_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> #input_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> input_file = os.path.join(path, 'arno', 'shapes', 'lh.thickness.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> delimiter = ','
>>> #
>>> write_face_vertex_averages(input_file, area_file, delimiter)
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_vtk, read_scalars
from mindboggle.utils.io_table import write_columns
faces, lines, indices, points, npoints, scalars, name, input_vtk = read_vtk(input_file, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
# ---------------------------------------------------------------------
# For each face, average vertex values:
# ---------------------------------------------------------------------
output_table = os.path.join(os.getcwd(), "average_face_values.csv")
columns = []
for face in faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
# -----------------------------------------------------------------
# Write to table:
# -----------------------------------------------------------------
write_columns(columns, "", output_table, delimiter, quote=False)
return output_table
def write_shape_stats(
labels_or_file,
sulci=[],
fundi=[],
affine_transform_file="",
transform_format="itk",
area_file="",
mean_curvature_file="",
travel_depth_file="",
geodesic_depth_file="",
convexity_file="",
thickness_file="",
labels_spectra=[],
labels_spectra_IDs=[],
sulci_spectra=[],
sulci_spectra_IDs=[],
exclude_labels=[-1],
delimiter=",",
):
"""
Make tables of shape statistics per label, fundus, and/or sulcus.
Parameters
----------
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
sulci : list of integers
indices to sulci, one per vertex, with -1 indicating no sulcus
fundi : list of integers
indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_file : string
affine transform file to standard space
transform_format : string
format for transform file
Ex: 'txt' for text, 'itk' for ITK, and 'mat' for Matlab format
area_file : string
name of VTK file with surface area scalar values
mean_curvature_file : string
name of VTK file with mean curvature scalar values
travel_depth_file : string
name of VTK file with travel depth scalar values
geodesic_depth_file : string
name of VTK file with geodesic depth scalar values
convexity_file : string
name of VTK file with convexity scalar values
thickness_file : string
name of VTK file with thickness scalar values
labels_spectra : list of lists of floats
Laplace-Beltrami spectra for labeled regions
labels_spectra_IDs : list of integers
unique ID numbers (labels) for labels_spectra
sulci_spectra : list of lists of floats
Laplace-Beltrami spectra for sulci
sulci_spectra_IDs : list of integers
unique ID numbers (labels) for sulci_spectra
exclude_labels : list of lists of integers
indices to be excluded (in addition to -1)
delimiter : string
delimiter between columns, such as ','
Returns
-------
label_table : string
output table filename for label shapes
sulcus_table : string
output table filename for sulcus shapes
fundus_table : string
output table filename for fundus shapes
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.utils.io_table import write_shape_stats
>>> path = os.environ['MINDBOGGLE_DATA']
>>> labels_or_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> sulci_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> fundi_file = os.path.join(path, 'arno', 'features', 'fundi.vtk')
>>> sulci, name = read_scalars(sulci_file)
>>> fundi, name = read_scalars(fundi_file)
>>> affine_transform_file = os.path.join(path, 'arno', 'mri',
>>> # 'affine_to_template.mat')
>>> 't1weighted_brain.MNI152Affine.txt')
>>> #transform_format = 'mat'
>>> transform_format = 'itk'
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> mean_curvature_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> travel_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> geodesic_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.geodesic_depth.vtk')
>>> convexity_file = ''
>>> thickness_file = ''
>>> delimiter = ','
>>> #
>>> import numpy as np
>>> labels, name = read_scalars(labels_or_file)
>>> labels_spectra = [[1,2,3] for x in labels]
>>> labels_spectra_IDs = np.unique(labels).tolist()
>>> sulci_spectra = [[1,2,3] for x in sulci]
>>> sulci_spectra_IDs = np.unique(sulci).tolist()
>>> exclude_labels = [-1]
>>> #
>>> write_shape_stats(labels_or_file, sulci, fundi,
>>> affine_transform_file, transform_format, area_file,
>>> mean_curvature_file, travel_depth_file, geodesic_depth_file,
>>> convexity_file, thickness_file, labels_spectra,
>>> labels_spectra_IDs, sulci_spectra,
>>> sulci_spectra_IDs, exclude_labels, delimiter)
"""
import os
import numpy as np
from mindboggle.shapes.measure import means_per_label, stats_per_label, sum_per_label
from mindboggle.utils.io_vtk import read_scalars, read_vtk, apply_affine_transform
from mindboggle.utils.io_table import write_columns
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = labels_or_file.tolist()
elif isinstance(labels_or_file, list):
labels = labels_or_file
elif isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file)
if isinstance(sulci, np.ndarray):
sulci = sulci.tolist()
if isinstance(fundi, np.ndarray):
fundi = fundi.tolist()
# -------------------------------------------------------------------------
# Feature lists, shape names, and shape files:
# -------------------------------------------------------------------------
# Feature lists:
feature_lists = [labels, sulci, fundi]
feature_names = ["label", "sulcus", "fundus"]
spectra_lists = [labels_spectra, sulci_spectra]
spectra_ID_lists = [labels_spectra_IDs, sulci_spectra_IDs]
spectra_names = ["label spectrum", "sulcus spectrum"]
table_names = ["label_shapes.csv", "sulcus_shapes.csv", "fundus_shapes.csv"]
# Shape names corresponding to shape files below:
shape_names = ["area", "mean curvature", "travel depth", "geodesic depth", "convexity", "thickness"]
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [
area_file,
mean_curvature_file,
travel_depth_file,
geodesic_depth_file,
convexity_file,
thickness_file,
]
shape_arrays = []
column_names = []
first_pass = True
area_array = []
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
faces, lines, indices, points, npoints, scalars_array, name, input_vtk = read_vtk(
shape_file, True, True
)
points = np.array(points)
first_pass = False
if affine_transform_file:
affine_points, foo1 = apply_affine_transform(
affine_transform_file, points, transform_format, save_file=False
)
affine_points = np.array(affine_points)
else:
scalars_array, name = read_scalars(shape_file, True, True)
if scalars_array.size:
shape_arrays.append(scalars_array)
# Store area array:
if ishape == 0:
area_array = scalars_array.copy()
# Initialize table file names:
sulcus_table = None
fundus_table = None
# Loop through features / tables:
for itable, feature_list in enumerate(feature_lists):
table_column_names = []
# ---------------------------------------------------------------------
# For each feature, construct a table of average shape values:
# ---------------------------------------------------------------------
table_file = os.path.join(os.getcwd(), table_names[itable])
if feature_list:
feature_name = feature_names[itable]
columns = []
# -----------------------------------------------------------------
# Mean positions in the original space:
# -----------------------------------------------------------------
# Compute mean position per feature:
positions, sdevs, label_list, foo = means_per_label(points, feature_list, exclude_labels, area_array)
# Append mean position per feature to columns:
table_column_names.append("mean position")
columns.append(positions)
# -----------------------------------------------------------------
# Mean positions in standard space:
# -----------------------------------------------------------------
if affine_transform_file:
# Compute standard space mean position per feature:
standard_positions, sdevs, label_list, foo = means_per_label(
affine_points, feature_list, exclude_labels, area_array
)
# Append standard space mean position per feature to columns:
table_column_names.append("mean position in standard space")
columns.append(standard_positions)
# -----------------------------------------------------------------
# Loop through shape measures:
# -----------------------------------------------------------------
table_column_names.extend(column_names[:])
for ishape, shape_array in enumerate(shape_arrays):
shape_name = shape_names[ishape]
print(" Compute statistics on {0} {1}".format(feature_name, shape_name))
# Append shape names and values per feature to columns:
pr = feature_name + ": " + shape_name + ": "
if np.size(area_array):
po = " (weighted)"
else:
po = ""
# -------------------------------------------------------------
# Append total feature areas to columns:
# -------------------------------------------------------------
if ishape == 0 and np.size(area_array):
sums, label_list = sum_per_label(shape_array, feature_list, exclude_labels)
table_column_names.append(pr + "total")
columns.append(sums)
# -------------------------------------------------------------
# Append feature shape statistics to columns:
# -------------------------------------------------------------
else:
medians, mads, means, sdevs, skews, kurts, lower_quarts, upper_quarts, label_list = stats_per_label(
shape_array, feature_list, exclude_labels, area_array, precision=1
)
table_column_names.append(pr + "median" + po)
table_column_names.append(pr + "median absolute deviation" + po)
table_column_names.append(pr + "mean" + po)
table_column_names.append(pr + "standard deviation" + po)
table_column_names.append(pr + "skew" + po)
table_column_names.append(pr + "kurtosis" + po)
table_column_names.append(pr + "lower quartile" + po)
table_column_names.append(pr + "upper quartile" + po)
columns.append(medians)
columns.append(mads)
columns.append(means)
columns.append(sdevs)
columns.append(skews)
columns.append(kurts)
columns.append(lower_quarts)
columns.append(upper_quarts)
# -----------------------------------------------------------------
# Laplace-Beltrami spectra:
# -----------------------------------------------------------------
if itable in [0, 1]:
spectra = spectra_lists[itable]
spectra_name = spectra_names[itable]
spectra_IDs = spectra_ID_lists[itable]
# Order spectra into a list:
spectrum_list = []
for label in label_list:
if label in spectra_IDs:
spectrum = spectra[spectra_IDs.index(label)]
spectrum_list.append(spectrum)
else:
spectrum_list.append("")
# Append spectral shape name and values to relevant columns:
columns.append(spectrum_list)
table_column_names.append(spectra_name)
# -----------------------------------------------------------------
# Write labels/IDs and values to table:
# -----------------------------------------------------------------
# Write labels/IDs to table:
write_columns(label_list, feature_name, table_file, delimiter)
# Append columns of shape values to table:
if columns:
write_columns(columns, table_column_names, table_file, delimiter, quote=True, input_table=table_file)
else:
# Write something to table:
write_columns([], "", table_file, delimiter)
# ---------------------------------------------------------------------
# Return correct table file name:
# ---------------------------------------------------------------------
if itable == 0:
label_table = table_file
elif itable == 1:
sulcus_table = table_file
elif itable == 2:
fundus_table = table_file
return label_table, sulcus_table, fundus_table
def explode_scalars(input_indices_vtk, input_values_vtk='', output_stem='',
exclude_values=[-1], background_value=-1,
output_scalar_name='scalars',
remove_background_faces=True, reindex=True):
"""
Write out a separate VTK file for each integer (not in exclude_values)
in (the first) scalar list of an input VTK file.
Optionally write the values drawn from a second VTK file,
remove background values, and reindex indices.
Parameters
----------
input_indices_vtk : string
path of the input VTK file that contains indices as scalars
(assumes that the scalars are a list of floats or integers)
input_values_vtk : string
path of the input VTK file that contains values as scalars
output_stem : string
path and stem of the output VTK file
exclude_values : list or array
values to exclude
background_value : integer or float
background value in output VTK files
remove_background_faces : Boolean
remove all faces whose three vertices are not all a given index?
reindex : Boolean
reindex all indices in faces?
Examples
--------
>>> # Example 1: explode sulci with thickness values
>>> import os
>>> from mindboggle.utils.io_vtk import explode_scalars
>>> from mindboggle.utils.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_indices_vtk = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> input_values_vtk = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> output_stem = 'sulci_depth'
>>> #
>>> explode_scalars(input_indices_vtk, input_values_vtk, output_stem)
>>> #
>>> # View:
>>> example_vtk = os.path.join(os.getcwd(), output_stem + '0.vtk')
>>> plot_surfaces(example_vtk)
>>> #
>>> # Example 2: explode labels
>>> import os
>>> from mindboggle.utils.io_vtk import explode_scalars
>>> from mindboggle.utils.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_values_vtk = os.path.join(path, 'arno', 'labels',
>>> 'lh.labels.DKT25.manual.vtk')
>>> input_indices_vtk = input_values_vtk
>>> output_stem = 'label'
>>> exclude_values = [-1]
>>> background_value = -1,
>>> output_scalar_name = 'scalars'
>>> remove_background_faces = True
>>> reindex = True
>>> #
>>> explode_scalars(input_indices_vtk, input_values_vtk, output_stem,
>>> exclude_values, background_value,
>>> output_scalar_name, remove_background_faces, reindex)
>>> # View:
>>> example_vtk = os.path.join(os.getcwd(), output_stem + '2.vtk')
>>> plot_surfaces(example_vtk)
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, read_vtk, write_vtk
from mindboggle.utils.mesh import reindex_faces_points, remove_faces
# Load VTK file:
faces, lines, indices, points, npoints, scalars, scalar_names, \
foo1 = read_vtk(input_indices_vtk, True, True)
print("Explode the scalar list in {0}".
format(os.path.basename(input_indices_vtk)))
if input_values_vtk != input_indices_vtk:
values, name = read_scalars(input_values_vtk, True, True)
print("Explode the scalar list of values in {0} "
"with the scalar list of indices in {1}".
format(os.path.basename(input_values_vtk),
os.path.basename(input_indices_vtk)))
else:
values = np.copy(scalars)
# Loop through unique (non-excluded) scalar values:
unique_scalars = np.unique(scalars)
if all(unique_scalars==np.round(unique_scalars)):
unique_scalars = [int(x) for x in unique_scalars
if x not in exclude_values]
else:
unique_scalars = [x for x in unique_scalars
if x not in exclude_values]
for scalar in unique_scalars:
# Remove background (keep only faces with the scalar):
if remove_background_faces:
scalar_indices = [i for i,x in enumerate(scalars) if x == scalar]
scalar_faces = remove_faces(faces, scalar_indices)
else:
scalar_faces = faces
# Reindex:
if reindex:
scalar_faces, select_points, \
o1 = reindex_faces_points(scalar_faces, points)
else:
select_points = points
# Create array and indices for scalar value:
if reindex:
len_indices = len(select_points)
select_values = scalar * np.ones(len_indices)
else:
select_values = np.copy(values)
select_values[scalars != scalar] = background_value
len_indices = len([i for i,x in enumerate(select_values)
if x != background_value])
print(" Scalar {0}: {1} vertices".format(scalar, len_indices))
# Write VTK file with scalar values (list of values):
if np.ndim(select_values) == 1:
scalar_type = type(select_values[0]).__name__
elif np.ndim(select_values) == 2:
scalar_type = type(select_values[0][0]).__name__
else:
print("Undefined scalar type!")
output_vtk = os.path.join(os.getcwd(),
output_stem + str(scalar) + '.vtk')
write_vtk(output_vtk, select_points, indices, lines, scalar_faces,
select_values.tolist(), output_scalar_name,
scalar_type=scalar_type)
def extract_borders_2nd_surface(labels_file, mask_file="", values_file=""):
"""
Extract borders (between labels) on a surface.
Options: Mask out values; extract border values on a second surface.
Parameters
----------
labels_file : string
file name for surface mesh with labels
mask_file : string
file name for surface mesh with mask (>-1) values
values_file : string
file name for surface mesh with values to extract along borders
Returns
-------
border_file : string
file name for surface mesh with label borders (-1 background values)
border_values : numpy array
values for all vertices (-1 for vertices not along label borders)
Examples
--------
>>> # Extract depth values along label borders in sulci (mask):
>>> import os
>>> from mindboggle.labels.labels import extract_borders_2nd_surface
>>> from mindboggle.utils.plots import plot_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> labels_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> mask_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> values_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> #
>>> border_file, border_values = extract_borders_2nd_surface(labels_file, mask_file, values_file)
>>> #
>>> plot_vtk(border_file)
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, read_vtk, rewrite_scalars
from mindboggle.utils.mesh import find_neighbors
from mindboggle.labels.labels import extract_borders
# Load labeled surface file
faces, foo1, foo2, foo3, npoints, labels, foo4, foo5 = read_vtk(labels_file, return_first=True, return_array=True)
# Detect borders
neighbor_lists = find_neighbors(faces, npoints)
indices_borders, foo1, foo2 = extract_borders(range(npoints), labels, neighbor_lists)
# Filter values with label borders
border_values = -1 * np.ones(npoints)
if values_file:
values, name = read_scalars(values_file, return_first=True, return_array=True)
border_values[indices_borders] = values[indices_borders]
else:
border_values[indices_borders] = 1
# Mask values (for mask >-1)
if mask_file:
mask_values, name = read_scalars(mask_file)
else:
mask_values = []
# Write out label boundary vtk file
border_file = os.path.join(os.getcwd(), "borders_" + os.path.basename(labels_file))
rewrite_scalars(labels_file, border_file, border_values, "label_borders_in_mask", mask_values)
if not os.path.exists(border_file):
raise (IOError(border_file + " not found"))
return border_file, border_values
def 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.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.shapes.likelihood import compute_likelihood
>>> from mindboggle.utils.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.utils.io_vtk 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, combine_all_labels=False,
nedges=10, p=99, set_max_to_1=True, 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
combine_all_labels : Boolean
combine all labels (scalars not equal to -1) as one label?
nedges : integer (if norm_by_neighborhood)
number or edges from vertex, defining the size of its neighborhood
p : float in range of [0,100] (if norm_by_neighborhood)
percentile used to rescale 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
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.shapes.measure import rescale_by_label
>>> from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.utils.plots import plot_vtk
>>> 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')
>>> combine_all_labels = False
>>> nedges = 10
>>> p = 99
>>> set_max_to_1 = True
>>> save_file = True
>>> output_filestring = 'rescaled_scalars'
>>> #
>>> rescaled_scalars, rescaled_scalars_file = rescale_by_label(input_vtk,
>>> labels_or_file, combine_all_labels, nedges, p,
>>> set_max_to_1, 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_vtk(rescaled_scalars_file)
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
from mindboggle.utils.mesh import find_neighbors_from_file
# 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)
else:
rescaled_scalars_file = None
return rescaled_scalars, rescaled_scalars_file
def evaluate_deep_features(features_file, labels_file, sulci_file='', hemi='',
excludeIDs=[-1], output_vtk_name='', verbose=True):
"""
Evaluate deep surface features by computing the minimum distance from each
label boundary vertex to all of the feature vertices in the same sulcus,
and from each feature vertex to all of the label boundary vertices in the
same sulcus. The label boundaries run along the deepest parts of sulci
and correspond to fundi in the DKT cortical labeling protocol.
Parameters
----------
features_file : string
VTK surface file with feature numbers for vertex scalars
labels_file : string
VTK surface file with label numbers for vertex scalars
sulci_file : string
VTK surface file with sulcus numbers for vertex scalars
excludeIDs : list of integers
feature/sulcus/label IDs to exclude (background set to -1)
output_vtk_name : Boolean
if not empty, output a VTK file beginning with output_vtk_name that
contains a surface with mean distances as scalars
verbose : Boolean
print mean distances to standard output?
Returns
-------
feature_to_fundus_mean_distances : numpy array [number of features x 1]
mean distance from each feature to sulcus label boundary ("fundus")
feature_to_fundus_sd_distances : numpy array [number of features x 1]
standard deviations of feature-to-fundus distances
feature_to_fundus_mean_distances_vtk : string
VTK surface file containing feature_to_fundus_mean_distances
fundus_to_feature_mean_distances : numpy array [number of features x 1]
mean distances from each sulcus label boundary ("fundus") to feature
fundus_to_feature_sd_distances : numpy array [number of features x 1]
standard deviations of fundus-to-feature distances
fundus_to_feature_mean_distances_vtk : string
VTK surface file containing fundus_to_feature_mean_distances
"""
import os
import sys
import numpy as np
from mindboggle.utils.io_vtk import read_vtk, read_scalars, write_vtk
from mindboggle.utils.mesh import find_neighbors, remove_faces
from mindboggle.utils.segment import extract_borders
from mindboggle.utils.compute import source_to_target_distances
from mindboggle.LABELS import DKTprotocol
dkt = DKTprotocol()
#-------------------------------------------------------------------------
# Load labels, features, and sulci:
#-------------------------------------------------------------------------
faces, lines, indices, points, npoints, labels, scalar_names, \
input_vtk = read_vtk(labels_file, True, True)
features, name = read_scalars(features_file, True, True)
if sulci_file:
sulci, name = read_scalars(sulci_file, True, True)
# List of indices to sulcus vertices:
sulcus_indices = [i for i,x in enumerate(sulci) if x != -1]
segmentIDs = sulci
sulcus_faces = remove_faces(faces, sulcus_indices)
else:
sulcus_indices = range(len(labels))
segmentIDs = []
sulcus_faces = faces
#-------------------------------------------------------------------------
# Prepare neighbors, label pairs, fundus IDs, and outputs:
#-------------------------------------------------------------------------
# Calculate neighbor lists for all points:
print('Find neighbors to all vertices...')
neighbor_lists = find_neighbors(faces, npoints)
# Find label boundary points in any of the sulci:
print('Find label boundary points in any of the sulci...')
border_indices, border_label_tuples, unique_border_label_tuples = \
extract_borders(sulcus_indices, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
if not len(border_indices):
sys.exit('There are no label boundary points!')
# Initialize an array of label boundaries fundus IDs
# (label boundary vertices that define sulci in the labeling protocol):
print('Build an array of label boundary fundus IDs...')
label_boundary_fundi = -1 * np.ones(npoints)
if hemi == 'lh':
nsulcus_lists = len(dkt.left_sulcus_label_pair_lists)
else:
nsulcus_lists = len(dkt.right_sulcus_label_pair_lists)
feature_to_fundus_mean_distances = -1 * np.ones(nsulcus_lists)
feature_to_fundus_sd_distances = -1 * np.ones(nsulcus_lists)
fundus_to_feature_mean_distances = -1 * np.ones(nsulcus_lists)
fundus_to_feature_sd_distances = -1 * np.ones(nsulcus_lists)
feature_to_fundus_mean_distances_vtk = ''
fundus_to_feature_mean_distances_vtk = ''
#-------------------------------------------------------------------------
# Loop through sulci:
#-------------------------------------------------------------------------
# For each list of sorted label pairs (corresponding to a sulcus):
for isulcus, label_pairs in enumerate(dkt.sulcus_label_pair_lists):
# Keep the boundary points with label pair labels:
fundus_indices = [x for i,x in enumerate(border_indices)
if np.unique(border_label_tuples[i]).tolist()
in label_pairs]
# Store the points as sulcus IDs in the fundus IDs array:
if fundus_indices:
label_boundary_fundi[fundus_indices] = isulcus
if len(np.unique(label_boundary_fundi)) > 1:
#---------------------------------------------------------------------
# Construct a feature-to-fundus distance matrix and VTK file:
#---------------------------------------------------------------------
# Construct a distance matrix:
print('Construct a feature-to-fundus distance matrix...')
sourceIDs = features
targetIDs = label_boundary_fundi
distances, distance_matrix = source_to_target_distances(
sourceIDs, targetIDs, points, segmentIDs, excludeIDs)
# Compute mean distances for each feature:
nfeatures = min(np.shape(distance_matrix)[1], nsulcus_lists)
for ifeature in range(nfeatures):
feature_distances = [x for x in distance_matrix[:, ifeature]
if x != -1]
feature_to_fundus_mean_distances[ifeature] = \
np.mean(feature_distances)
feature_to_fundus_sd_distances[ifeature] = \
np.std(feature_distances)
if verbose:
print('Feature-to-fundus mean distances:')
print(feature_to_fundus_mean_distances)
print('Feature-to-fundus standard deviations of distances:')
print(feature_to_fundus_sd_distances)
# Write resulting feature-label boundary distances to VTK file:
if output_vtk_name:
feature_to_fundus_mean_distances_vtk = os.path.join(os.getcwd(),
output_vtk_name + '_feature_to_fundus_mean_distances.vtk')
print('Write feature-to-fundus distances to {0}...'.
format(feature_to_fundus_mean_distances_vtk))
write_vtk(feature_to_fundus_mean_distances_vtk, points,
[], [], sulcus_faces, [distances],
['feature-to-fundus_distances'], 'float')
#---------------------------------------------------------------------
# Construct a fundus-to-feature distance matrix and VTK file:
#---------------------------------------------------------------------
# Construct a distance matrix:
print('Construct a fundus-to-feature distance matrix...')
sourceIDs = label_boundary_fundi
targetIDs = features
distances, distance_matrix = source_to_target_distances(
sourceIDs, targetIDs, points, segmentIDs, excludeIDs)
# Compute mean distances for each feature:
nfeatures = min(np.shape(distance_matrix)[1], nsulcus_lists)
for ifeature in range(nfeatures):
fundus_distances = [x for x in distance_matrix[:, ifeature]
if x != -1]
fundus_to_feature_mean_distances[ifeature] = \
np.mean(fundus_distances)
fundus_to_feature_sd_distances[ifeature] = \
np.std(fundus_distances)
if verbose:
print('Fundus-to-feature mean distances:')
print(fundus_to_feature_mean_distances)
print('Fundus-to-feature standard deviations of distances:')
print(fundus_to_feature_sd_distances)
# Write resulting feature-label boundary distances to VTK file:
if output_vtk_name:
fundus_to_feature_mean_distances_vtk = os.path.join(os.getcwd(),
output_vtk_name + '_fundus_to_feature_mean_distances.vtk')
print('Write fundus-to-feature distances to {0}...'.
format(fundus_to_feature_mean_distances_vtk))
write_vtk(fundus_to_feature_mean_distances_vtk, points,
[], [], sulcus_faces, [distances],
['fundus-to-feature_distances'], 'float')
#-------------------------------------------------------------------------
# Return outputs:
#-------------------------------------------------------------------------
return feature_to_fundus_mean_distances, feature_to_fundus_sd_distances,\
feature_to_fundus_mean_distances_vtk,\
fundus_to_feature_mean_distances, fundus_to_feature_sd_distances,\
fundus_to_feature_mean_distances_vtk
def write_vertex_measures(output_table, labels_or_file, sulci=[], fundi=[],
affine_transform_file='', transform_format='itk',
area_file='', mean_curvature_file='', travel_depth_file='',
geodesic_depth_file='', freesurfer_convexity_file='',
freesurfer_thickness_file='', delimiter=','):
"""
Make a table of shape values per vertex.
Note ::
This function is tailored for Mindboggle outputs.
Parameters
----------
output_table : string
output file (full path)
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
sulci : list of integers
indices to sulci, one per vertex, with -1 indicating no sulcus
fundi : list of integers
indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_file : string
affine transform file to standard space
transform_format : string
format for transform file
Ex: 'txt' for text, 'itk' for ITK, and 'mat' for Matlab format
area_file : string
name of VTK file with surface area scalar values
mean_curvature_file : string
name of VTK file with mean curvature scalar values
travel_depth_file : string
name of VTK file with travel depth scalar values
geodesic_depth_file : string
name of VTK file with geodesic depth scalar values
freesurfer_convexity_file : string
name of VTK file with FreeSurfer convexity scalar values
freesurfer_thickness_file : string
name of VTK file with FreeSurfer thickness scalar values
delimiter : string
delimiter between columns, such as ','
Returns
-------
output_table : table file name for vertex shape values
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.utils.io_table import write_vertex_measures
>>> #
>>> output_table = ''#vertex_shapes.csv'
>>> path = os.environ['MINDBOGGLE_DATA']
>>> labels_or_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> sulci_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> fundi_file = os.path.join(path, 'arno', 'features', 'fundi.vtk')
>>> sulci, name = read_scalars(sulci_file)
>>> fundi, name = read_scalars(fundi_file)
>>> affine_transform_file = os.path.join(path, 'arno', 'mri',
>>> 't1weighted_brain.MNI152Affine.txt')
>>> transform_format = 'itk'
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> mean_curvature_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> travel_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> geodesic_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.geodesic_depth.vtk')
>>> freesurfer_convexity_file = ''
>>> freesurfer_thickness_file = ''
>>> delimiter = ','
>>> #
>>> write_vertex_measures(output_table, labels_or_file, sulci, fundi,
>>> affine_transform_file, transform_format, area_file,
>>> mean_curvature_file, travel_depth_file, geodesic_depth_file,
>>> freesurfer_convexity_file, freesurfer_thickness_file, delimiter)
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, read_vtk, \
apply_affine_transform
from mindboggle.utils.io_table import write_columns
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = [int(x) for x in labels_or_file]
elif isinstance(labels_or_file, list):
labels = labels_or_file
elif isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file)
if isinstance(sulci, np.ndarray):
sulci = [int(x) for x in sulci]
if isinstance(fundi, np.ndarray):
fundi = [int(x) for x in fundi]
if not labels and not sulci and not fundi:
import sys
sys.exit('No feature data to tabulate in write_vertex_measures().')
# Feature names and corresponding feature lists:
feature_names = ['Label', 'Sulcus', 'Fundus']
feature_lists = [labels, sulci, fundi]
# Shape names corresponding to shape files below:
shape_names = ['area', 'mean curvature', 'travel depth', 'geodesic depth',
'FreeSurfer convexity', 'FreeSurfer thickness']
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [area_file, mean_curvature_file, travel_depth_file,
geodesic_depth_file, freesurfer_convexity_file,
freesurfer_thickness_file]
# Append columns of per-vertex scalar values:
columns = []
column_names = []
for ifeature, values in enumerate(feature_lists):
if values:
columns.append(values)
column_names.append(feature_names[ifeature])
first_pass = True
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
u1, u2, u3, points, u4, scalars, u5, u6 = read_vtk(shape_file)
columns.append(points)
column_names.append('coordinates')
first_pass = False
if affine_transform_file and transform_format:
affine_points, \
foo1 = apply_affine_transform(affine_transform_file,
points, transform_format,
vtk_file_stem='')
columns.append(affine_points)
column_names.append('coordinates in standard space')
else:
scalars, name = read_scalars(shape_file)
if len(scalars):
columns.append(scalars)
column_names.append(shape_names[ishape])
# Prepend with column of indices and write table
if not output_table:
output_table = os.path.join(os.getcwd(), 'vertices.csv')
write_columns(range(len(columns[0])), 'Index', delimiter, quote=True,
input_table='', output_table=output_table)
write_columns(columns, column_names, delimiter, quote=True,
input_table=output_table, output_table=output_table)
if not os.path.exists(output_table):
raise(IOError(output_table + " not found"))
return output_table
def write_shape_stats(labels_or_file=[], sulci=[], fundi=[],
affine_transform_file='', transform_format='itk',
area_file='', normalize_by_area=True, mean_curvature_file='',
travel_depth_file='', geodesic_depth_file='',
freesurfer_convexity_file='', freesurfer_thickness_file='',
labels_spectra=[], labels_spectra_IDs=[],
sulci_spectra=[], sulci_spectra_IDs=[],
labels_zernike=[], labels_zernike_IDs=[],
sulci_zernike=[], sulci_zernike_IDs=[],
exclude_labels=[-1], delimiter=','):
"""
Make tables of shape statistics per label, sulcus, and/or fundus.
Note ::
This function is tailored for Mindboggle outputs.
Parameters
----------
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
sulci : list of integers
indices to sulci, one per vertex, with -1 indicating no sulcus
fundi : list of integers
indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_file : string
affine transform file to standard space
transform_format : string
format for transform file
Ex: 'txt' for text, 'itk' for ITK, and 'mat' for Matlab format
area_file : string
name of VTK file with surface area scalar values
normalize_by_area : Boolean
normalize all shape measures by area of label/feature?
mean_curvature_file : string
name of VTK file with mean curvature scalar values
travel_depth_file : string
name of VTK file with travel depth scalar values
geodesic_depth_file : string
name of VTK file with geodesic depth scalar values
freesurfer_convexity_file : string
name of VTK file with FreeSurfer convexity scalar values
freesurfer_thickness_file : string
name of VTK file with FreeSurfer thickness scalar values
labels_zernike : list of lists of floats
Laplace-Beltrami spectra for labeled regions
labels_spectra_IDs : list of integers
unique ID numbers (labels) for labels_spectra
sulci_spectra : list of lists of floats
Laplace-Beltrami spectra for sulci
sulci_spectra_IDs : list of integers
unique ID numbers (labels) for sulci_spectra
labels_zernike : list of lists of floats
Zernike moments for labeled regions
labels_zernike_IDs : list of integers
unique ID numbers (labels) for labels_zernike
sulci_zernike : list of lists of floats
Zernike moments for sulci
sulci_zernike_IDs : list of integers
unique ID numbers (labels) for sulci_zernike
exclude_labels : list of lists of integers
indices to be excluded (in addition to -1)
delimiter : string
delimiter between columns, such as ','
Returns
-------
label_table : string
output table filename for label shapes
sulcus_table : string
output table filename for sulcus shapes
fundus_table : string
output table filename for fundus shapes
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.utils.io_table import write_shape_stats
>>> path = os.environ['MINDBOGGLE_DATA']
>>> labels_or_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> sulci_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> fundi_file = os.path.join(path, 'arno', 'features', 'fundi.vtk')
>>> sulci, name = read_scalars(sulci_file)
>>> fundi, name = read_scalars(fundi_file)
>>> affine_transform_file = os.path.join(path, 'arno', 'mri', 't1weighted_brain.MNI152Affine.txt')
>>> #transform_format = 'mat'
>>> transform_format = 'itk'
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> normalize_by_area = True
>>> mean_curvature_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> travel_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> geodesic_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.geodesic_depth.vtk')
>>> freesurfer_convexity_file = ''
>>> freesurfer_thickness_file = ''
>>> delimiter = ','
>>> #
>>> labels, name = read_scalars(labels_or_file)
>>> labels_spectra = []
>>> labels_spectra_IDs = []
>>> sulci_spectra = []
>>> sulci_spectra_IDs = []
>>> labels_zernike = []
>>> labels_zernike_IDs = []
>>> sulci_zernike = []
>>> sulci_zernike_IDs = []
>>> exclude_labels = [-1]
>>> #
>>> write_shape_stats(labels_or_file, sulci, fundi,
>>> affine_transform_file, transform_format, area_file, normalize_by_area,
>>> mean_curvature_file, travel_depth_file, geodesic_depth_file,
>>> freesurfer_convexity_file, freesurfer_thickness_file,
>>> labels_spectra, labels_spectra_IDs,
>>> sulci_spectra, sulci_spectra_IDs,
>>> labels_zernike, labels_zernike_IDs,
>>> sulci_zernike, sulci_zernike_IDs,
>>> exclude_labels, delimiter)
"""
import os
import numpy as np
from mindboggle.utils.compute import means_per_label, stats_per_label, \
sum_per_label
from mindboggle.utils.io_vtk import read_scalars, read_vtk, \
apply_affine_transform
from mindboggle.utils.io_table import write_columns
from mindboggle.LABELS import DKTprotocol
dkt = DKTprotocol()
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = [int(x) for x in labels_or_file]
elif isinstance(labels_or_file, list):
labels = labels_or_file
elif isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file)
if isinstance(sulci, np.ndarray):
sulci = [int(x) for x in sulci]
if isinstance(fundi, np.ndarray):
fundi = [int(x) for x in fundi]
if not labels and not sulci and not fundi:
import sys
sys.exit('No feature data to tabulate in write_shape_stats().')
#-------------------------------------------------------------------------
# Feature lists, shape names, and shape files:
#-------------------------------------------------------------------------
# Feature lists:
feature_lists = [labels, sulci, fundi]
feature_names = ['Label', 'Sulcus', 'Fundus']
spectra_lists = [labels_spectra, sulci_spectra]
spectra_ID_lists = [labels_spectra_IDs, sulci_spectra_IDs]
zernike_lists = [labels_zernike, sulci_zernike]
zernike_ID_lists = [labels_zernike_IDs, sulci_zernike_IDs]
table_names = ['label_shapes.csv', 'sulcus_shapes.csv',
'fundus_shapes.csv']
# Shape names corresponding to shape files below:
shape_names = ['area', 'mean curvature', 'travel depth', 'geodesic depth',
'FreeSurfer convexity', 'FreeSurfer thickness']
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [area_file, mean_curvature_file, travel_depth_file,
geodesic_depth_file, freesurfer_convexity_file,
freesurfer_thickness_file]
shape_arrays = []
first_pass = True
area_array = []
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
faces, lines, indices, points, npoints, scalars_array, name, \
input_vtk = read_vtk(shape_file, True, True)
points = np.array(points)
first_pass = False
if affine_transform_file and transform_format:
affine_points, \
foo1 = apply_affine_transform(affine_transform_file,
points, transform_format,
vtk_file_stem='')
affine_points = np.array(affine_points)
else:
scalars_array, name = read_scalars(shape_file, True, True)
if scalars_array.size:
shape_arrays.append(scalars_array)
# Store area array:
if ishape == 0:
area_array = scalars_array.copy()
if normalize_by_area:
use_area = area_array
else:
use_area = []
# Initialize table file names:
label_table = ''
sulcus_table = ''
fundus_table = ''
# Loop through features / tables:
for itable, feature_list in enumerate(feature_lists):
column_names = []
#---------------------------------------------------------------------
# For each feature, construct a table of average shape values:
#---------------------------------------------------------------------
if feature_list:
feature_name = feature_names[itable]
columns = []
#-----------------------------------------------------------------
# Loop through shape measures:
#-----------------------------------------------------------------
column_names.extend(column_names[:])
for ishape, shape_array in enumerate(shape_arrays):
shape = shape_names[ishape]
print(' Compute statistics on {0} {1}...'.
format(feature_name, shape))
#-------------------------------------------------------------
# Append feature areas to columns:
#-------------------------------------------------------------
if ishape == 0 and np.size(area_array):
sums, label_list = sum_per_label(shape_array,
feature_list, exclude_labels)
column_names.append(shape)
columns.append(sums)
#-------------------------------------------------------------
# Append feature shape statistics to columns:
#-------------------------------------------------------------
else:
medians, mads, means, sdevs, skews, kurts, \
lower_quarts, upper_quarts, \
label_list = stats_per_label(shape_array,
feature_list, exclude_labels, area_array, precision=1)
column_names.append(shape + ': median')
column_names.append(shape + ': MAD')
column_names.append(shape + ': mean')
column_names.append(shape + ': SD')
column_names.append(shape + ': skew')
column_names.append(shape + ': kurtosis')
column_names.append(shape + ': 25%')
column_names.append(shape + ': 75%')
columns.append(medians)
columns.append(mads)
columns.append(means)
columns.append(sdevs)
columns.append(skews)
columns.append(kurts)
columns.append(lower_quarts)
columns.append(upper_quarts)
#-----------------------------------------------------------------
# Mean positions in the original space:
#-----------------------------------------------------------------
# Compute mean position per feature:
positions, sdevs, label_list, foo = means_per_label(points,
feature_list, exclude_labels, use_area)
# Append mean position per feature to columns:
column_names.append('mean position')
columns.append(positions)
#-----------------------------------------------------------------
# Mean positions in standard space:
#-----------------------------------------------------------------
if affine_transform_file and transform_format:
# Compute standard space mean position per feature:
standard_positions, sdevs, label_list, \
foo = means_per_label(affine_points,
feature_list, exclude_labels, use_area)
# Append standard space mean position per feature to columns:
column_names.append('mean position in standard space')
columns.append(standard_positions)
#-----------------------------------------------------------------
# Label names:
#-----------------------------------------------------------------
if itable == 0:
label_numbers = dkt.label_numbers
label_names = dkt.label_names
name_list = []
for label in label_list:
name_list.append(label_names[label_numbers.index(label)])
#-----------------------------------------------------------------
# Laplace-Beltrami spectra:
#-----------------------------------------------------------------
if itable in [0, 1]:
spectra = spectra_lists[itable]
if spectra:
spectra_IDs = spectra_ID_lists[itable]
# Order spectra into a list:
spectrum_list = []
for label in label_list:
if label in spectra_IDs:
spectrum = spectra[spectra_IDs.index(label)]
spectrum_list.append(spectrum)
else:
spectrum_list.append('')
# Append spectral shape name and values to relevant columns:
columns.append(spectrum_list)
column_names.append('Laplace-Beltrami spectra')
#-----------------------------------------------------------------
# Zernike moments:
#-----------------------------------------------------------------
if itable in [0, 1]:
zernike = zernike_lists[itable]
if zernike:
zernike_IDs = zernike_ID_lists[itable]
# Order zernike into a list:
spectrum_list = []
for label in label_list:
if label in zernike_IDs:
spectrum = zernike[zernike_IDs.index(label)]
spectrum_list.append(spectrum)
else:
spectrum_list.append('')
# Append Zernike shape name and values to relevant columns:
columns.append(spectrum_list)
column_names.append('Zernike moments')
#-----------------------------------------------------------------
# Write labels/IDs and values to table:
#-----------------------------------------------------------------
# Write labels/IDs to table:
output_table = os.path.join(os.getcwd(), table_names[itable])
output_table = write_columns(label_list, feature_name, delimiter,
quote=True, input_table='',
output_table=output_table)
if itable == 0:
write_columns(name_list, 'Label name', delimiter,
quote=True, input_table=output_table,
output_table=output_table)
# Append columns of shape values to table:
if columns:
write_columns(columns, column_names, delimiter,
quote=True, input_table=output_table,
output_table=output_table)
if not os.path.exists(output_table):
raise(IOError(output_table + " not found"))
#-----------------------------------------------------------------
# Return correct table file name:
#-----------------------------------------------------------------
if itable == 0:
label_table = output_table
elif itable == 1:
sulcus_table = output_table
elif itable == 2:
fundus_table = output_table
return label_table, sulcus_table, fundus_table
def zernike_moments_per_label(vtk_file, order=20, exclude_labels=[-1], area_file="", largest_segment=True):
"""
Compute the Zernike moments per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
order : integer
number of moments to compute
exclude_labels : list of integers
labels to be excluded
area_file : string
name of VTK file with surface area scalar values
largest_segment : Boolean
compute moments only for largest segment with a given label?
Returns
-------
descriptors_lists : list of lists of floats
Zernike descriptors per label
label_list : list of integers
list of unique labels for which moments are computed
Examples
--------
>>> # Moments for label 22 (postcentral) in Twins-2-1:
>>> import os
>>> from mindboggle.shapes.zernike.zernike import zernike_moments_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> order = 3
>>> exclude_labels = [0]
>>> largest_segment = True
>>> zernike_moments_per_label(vtk_file, order, exclude_labels, area_file,
>>> largest_segment)
([[7562.751480397972,
143262239.5171249,
1107670.7893994227,
28487908892.820065,
112922387.17238183,
10250734140.30357]],
[22])
>>> order = 10
>>> zernike_moments_per_label(vtk_file, order, exclude_labels, area_file,
>>> largest_segment)
([[7562.751480397972,
143262239.5171249,
3308874674202.293,
8.485211965384958e+16,
2.3330162566631947e+21,
6.743205749389719e+25,
1107670.7893994227,
28487908892.820065,
750581458956752.5,
2.08268406178679e+19,
6.041241636463012e+23,
112922387.17238183,
3771094165018.0186,
1.1436534456761454e+17,
3.475222918728238e+21,
1.0745294340540639e+26,
10250734140.30357,
429344737184365.75,
1.4944306620454633e+19,
4.98685998888202e+23,
889109957039.494,
4.5419095219797416e+16,
1.798809048329269e+21,
6.5720455808877056e+25,
76646448525991.2,
4.648745223427816e+18,
2.067942924550439e+23,
6705825311489244.0,
4.701251187236028e+20,
2.3147665646780795e+25,
5.969381989053711e+17,
4.728007168783364e+22,
5.360784767352255e+19,
4.7214146910478664e+24,
4.813773883638603e+21,
4.3049570618844856e+23]],
[22])
"""
from mindboggle.utils.io_vtk import read_vtk, read_scalars
from mindboggle.utils.mesh import remove_faces
from mindboggle.shapes.zernike.zernike import zernike_moments, zernike_moments_of_largest
# Read VTK surface mesh file:
faces, u1, u2, points, u4, labels, u5, u6 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[ulabels.append(int(x)) for x in labels if x not in ulabels if x not in exclude_labels]
label_list = []
descriptors_lists = []
for label in ulabels:
# if label==22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
label_indices = [i for i, x in enumerate(labels) if x == label]
print("{0} vertices for label {1}".format(len(label_indices), label))
# Remove background faces:
select_faces = remove_faces(faces, label_indices)
# Compute Zernike moments for the label:
if largest_segment:
exclude_labels_inner = [-1]
descriptors = zernike_moments_of_largest(points, select_faces, order, exclude_labels_inner, areas)
else:
descriptors = zernike_moments(points, select_faces, order)
# Append to a list of lists of spectra:
descriptors_lists.append(descriptors)
label_list.append(label)
return descriptors_lists, label_list
def spectrum_per_label(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization='area', area_file='',
largest_segment=True):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
largest_segment : Boolean
compute spectrum only for largest segment with a given label?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> spectrum_per_label(vtk_file, spectrum_size, exclude_labels, None,
>>> area_file, largest_segment)
([[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]],
[22])
"""
from mindboggle.utils.io_vtk import read_vtk, read_scalars
from mindboggle.utils.mesh import remove_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
faces, u1, u2, points, u4, labels, u5, u6 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i,x in enumerate(labels) if x == label]
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = remove_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size,
exclude_labels_inner,
normalization, areas)
else:
spectrum = fem_laplacian(pick_points, pick_faces,
spectrum_size, normalization)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def concatenate_sulcus_scalars(scalar_files, fold_files, label_files):
"""
Prepare data for estimating scalar distributions along and outside fundi.
Extract (e.g., depth, curvature) scalar values in folds, along sulcus
label boundaries as well as outside the sulcus label boundaries.
Concatenate these scalar values across multiple files.
Parameters
----------
scalar_files : list of strings
names of surface mesh VTK files with scalar values to concatenate
fold_files : list of strings (corr. to each list in scalar_files)
VTK files with fold numbers as scalars (-1 for non-fold vertices)
label_files : list of strings (corr. to fold_files)
VTK files with label numbers (-1 for unlabeled vertices)
Returns
-------
border_scalars : list of floats
concatenated scalar values within folds along sulcus label boundaries
nonborder_scalars : list of floats
concatenated scalar values within folds outside sulcus label boundaries
Examples
--------
>>> # Concatenate (duplicate) depth scalars:
>>> import os
>>> from mindboggle.shapes.likelihood import concatenate_sulcus_scalars
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'depth_rescaled.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> labels_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> scalar_files = [depth_file, depth_file]
>>> fold_files = [folds_file, folds_file]
>>> label_files = [labels_file, labels_file]
>>> #
>>> S = concatenate_sulcus_scalars(scalar_files, fold_files, label_files)
"""
import numpy as np
from mindboggle.utils.io_vtk import read_scalars
from mindboggle.utils.mesh import find_neighbors_from_file
from mindboggle.labels.labels import extract_borders
from mindboggle.labels.protocol import dkt_protocol
protocol = 'DKT25'
sulcus_names, sulcus_label_pair_lists, unique_sulcus_label_pairs, \
label_names, label_numbers, cortex_names, cortex_numbers, \
noncortex_names, noncortex_numbers = dkt_protocol(protocol)
# Prepare (non-unique) list of sulcus label pairs:
protocol_label_pairs = [x for lst in sulcus_label_pair_lists for x in lst]
border_scalars = []
nonborder_scalars = []
# Loop through files with the scalar values:
for ifile, scalar_file in enumerate(scalar_files):
print(scalar_file)
# Load scalars, folds, and labels:
folds_file = fold_files[ifile]
labels_file = label_files[ifile]
scalars, name = read_scalars(scalar_file, True, True)
if scalars.shape:
folds, name = read_scalars(folds_file)
labels, name = read_scalars(labels_file)
indices_folds = [i for i,x in enumerate(folds) if x != -1]
neighbor_lists = find_neighbors_from_file(labels_file)
# Find all label border pairs within the folds:
indices_label_pairs, label_pairs, unique_pairs = extract_borders(
indices_folds, labels, neighbor_lists, ignore_values=[-1],
return_label_pairs=True)
indices_label_pairs = np.array(indices_label_pairs)
# Find vertices with label pairs in the sulcus labeling protocol:
Ipairs_in_protocol = [i for i,x in enumerate(label_pairs)
if x in protocol_label_pairs]
indices_label_pairs = indices_label_pairs[Ipairs_in_protocol]
indices_outside_pairs = list(frozenset(indices_folds).difference(
indices_label_pairs))
# Store scalar values in folds along label border pairs:
border_scalars.extend(scalars[indices_label_pairs].tolist())
# Store scalar values in folds outside label border pairs:
nonborder_scalars.extend(scalars[indices_outside_pairs].tolist())
return border_scalars, nonborder_scalars
def extract_fundi(folds,
curv_file,
depth_file,
min_separation=10,
erode_ratio=0.1,
erode_min_size=1,
save_file=False):
"""
Extract fundi from folds.
A fundus is a branching curve that runs along the deepest and most
highly curved portions of a fold.
Steps ::
1. Find fundus endpoints (outer anchors) with find_outer_anchors().
2. Include inner anchor points.
3. Connect anchor points using connect_points_erosion();
inner anchors are removed if they result in endpoints.
Parameters
----------
folds : numpy array or list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : Boolean
save output VTK file?
Returns
-------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
n_fundi_in_folds : integer
number of fundi
fundus_per_fold_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> single_fold = True
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.utils.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> curv_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'travel_depth_rescaled.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file, True, True)
>>> if single_fold:
>>> fold_number = 2 #11
>>> folds[folds != fold_number] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> o1, o2, fundus_per_fold_file = extract_fundi(folds, curv_file,
... depth_file, min_separation, erode_ratio, erode_min_size, save_file)
>>> #
>>> # View:
>>> plot_surfaces(fundi_file)
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.utils.io_vtk import read_scalars, read_vtk, rewrite_scalars
from mindboggle.utils.compute import median_abs_dev
from mindboggle.utils.paths import find_max_values
from mindboggle.utils.mesh import find_neighbors_from_file, find_complete_faces
from mindboggle.utils.paths import find_outer_anchors, connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
faces, u1, u2, points, npoints, curvs, u3, u4 = read_vtk(
curv_file, True, True)
depths, name = read_scalars(depth_file, True, True)
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
#-------------------------------------------------------------------------
# Loop through folds:
#-------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != -1]
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".format(
len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i, x in enumerate(folds) if x == fold_ID]
if indices_fold:
print(' Fold {0}:'.format(int(fold_ID)))
#-----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints:
#-----------------------------------------------------------------
outer_anchors, tracks = find_outer_anchors(indices_fold,
neighbor_lists, values,
depths, min_separation)
#-----------------------------------------------------------------
# Find inner anchor points:
#-----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation,
thr)
#-----------------------------------------------------------------
# Connect anchor points to create skeleton:
#-----------------------------------------------------------------
B = -1 * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B,
neighbor_lists,
outer_anchors,
inner_anchors,
values,
erode_ratio,
erode_min_size,
save_steps=[],
save_vtk='')
if skeleton:
skeletons.extend(skeleton)
#-----------------------------------------------------------------
# Remove fundus vertices if they complete triangle faces:
#-----------------------------------------------------------------
Iremove = find_complete_faces(skeletons, faces)
if Iremove:
skeletons = list(frozenset(skeletons).difference(Iremove))
indices = [x for x in skeletons if folds[x] != -1]
fundus_per_fold = -1 * np.ones(npoints)
fundus_per_fold[indices] = folds[indices]
n_fundi_in_folds = len([x for x in np.unique(fundus_per_fold) if x != -1])
if n_fundi_in_folds == 1:
sdum = 'fold fundus'
else:
sdum = 'fold fundi'
print(' ...Extracted {0} {1}; {2} total ({3:.2f} seconds)'.format(
n_fundi_in_folds, sdum, n_fundi_in_folds,
time() - t1))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
if n_fundi_in_folds > 0:
fundus_per_fold = [int(x) for x in fundus_per_fold]
if save_file:
fundus_per_fold_file = os.path.join(os.getcwd(),
'fundus_per_fold.vtk')
rewrite_scalars(curv_file, fundus_per_fold_file, fundus_per_fold,
'fundi', folds)
if not os.path.exists(fundus_per_fold_file):
raise (IOError(fundus_per_fold_file + " not found"))
else:
fundus_per_fold_file = None
return fundus_per_fold, n_fundi_in_folds, fundus_per_fold_file
def 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.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.shapes.likelihood import compute_likelihood
>>> from mindboggle.utils.plots import plot_vtk
>>> 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_vtk('likelihoods.vtk', folds_file)
"""
import os
import numpy as np
from math import pi
import cPickle as pickle
from mindboggle.utils.io_vtk 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.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_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.
4. Optionally smooth with smooth_skeleton().
Parameters
----------
folds : numpy array or list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : Boolean
save output VTK file?
Returns
-------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
n_fundi_in_folds : integer
number of fundi
fundus_per_fold_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> single_fold = True
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.utils.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> curv_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'travel_depth_rescaled.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file, True, True)
>>> if single_fold:
>>> fold_number = 2 #11
>>> folds[folds != fold_number] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> o1, o2, fundus_per_fold_file = extract_fundi(folds, curv_file,
... depth_file, min_separation, erode_ratio, erode_min_size, save_file)
>>> #
>>> # View:
>>> plot_surfaces(fundi_file)
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.utils.io_vtk import read_scalars, read_vtk, rewrite_scalars
from mindboggle.utils.compute import median_abs_dev
from mindboggle.utils.paths import find_max_values
from mindboggle.utils.mesh import find_neighbors_from_file, find_complete_faces
from mindboggle.utils.paths import find_outer_anchors, connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
faces, u1,u2, points, npoints, curvs, u3,u4 = read_vtk(curv_file, True,True)
depths, name = read_scalars(depth_file, True, True)
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
#-------------------------------------------------------------------------
# Loop through folds:
#-------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != -1]
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".
format(len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i,x in enumerate(folds) if x == fold_ID]
if indices_fold:
print(' Fold {0}:'.format(int(fold_ID)))
#-----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints:
#-----------------------------------------------------------------
outer_anchors, tracks = find_outer_anchors(indices_fold,
neighbor_lists, values, depths, min_separation)
#-----------------------------------------------------------------
# Find inner anchor points:
#-----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation, thr)
#-----------------------------------------------------------------
# Connect anchor points to create skeleton:
#-----------------------------------------------------------------
B = -1 * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B, neighbor_lists,
outer_anchors, inner_anchors, values,
erode_ratio, erode_min_size, save_steps=[], save_vtk='')
if skeleton:
skeletons.extend(skeleton)
#-----------------------------------------------------------------
# Remove fundus vertices if they complete triangle faces:
#-----------------------------------------------------------------
Iremove = find_complete_faces(skeletons, faces)
if Iremove:
skeletons = list(frozenset(skeletons).difference(Iremove))
indices = [x for x in skeletons if folds[x] != -1]
fundus_per_fold = -1 * np.ones(npoints)
fundus_per_fold[indices] = folds[indices]
n_fundi_in_folds = len([x for x in np.unique(fundus_per_fold)
if x != -1])
if n_fundi_in_folds == 1:
sdum = 'fold fundus'
else:
sdum = 'fold fundi'
print(' ...Extracted {0} {1}; {2} total ({3:.2f} seconds)'.
format(n_fundi_in_folds, sdum, n_fundi_in_folds, time() - t1))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
if n_fundi_in_folds > 0:
fundus_per_fold = [int(x) for x in fundus_per_fold]
if save_file:
fundus_per_fold_file = os.path.join(os.getcwd(),
'fundus_per_fold.vtk')
rewrite_scalars(curv_file, fundus_per_fold_file, fundus_per_fold,
'fundi', folds)
if not os.path.exists(fundus_per_fold_file):
raise(IOError(fundus_per_fold_file + " not found"))
else:
fundus_per_fold_file = None
return fundus_per_fold, n_fundi_in_folds, fundus_per_fold_file
def 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.utils.mesh import rescale_by_label
>>> from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.utils.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.utils.io_vtk 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 realign_boundaries_to_fundus_lines(
surf_file, init_label_file, fundus_lines_file, out_label_file=None):
"""
Fix label boundaries to fundus lines.
Parameters
----------
surf_file : file containing the surface geometry in vtk format
init_label_file : file containing scalars that represent the
initial guess at labels
fundus_lines_file : file containing scalars representing fundus lines.
out_label_file : if specified, the realigned labels will be writen to
this file
Returns
-------
numpy array representing the realigned label for each surface vertex.
"""
# import os
import numpy as np
from mindboggle.labels.labels import extract_borders
import mindboggle.utils.graph as go
from mindboggle.utils.io_vtk import read_vtk, read_scalars, write_vtk
# import mindboggle.utils.kernels as kernels
from mindboggle.utils.mesh import find_neighbors
# from mindboggle.labels.protocol import dkt_protocol
#
# protocol = 'DKT25'
# sulcus_names, sulcus_label_pair_lists, unique_sulcus_label_pairs, \
# label_names, label_numbers, cortex_names, cortex_numbers, \
# noncortex_names, noncortex_numbers = dkt_protocol(protocol)
## read files
faces, _, indices, points, num_points, _, _, _ = read_vtk(
surf_file, return_first=True, return_array=True)
indices = range(num_points)
init_labels, _ = read_scalars(init_label_file,
return_first=True, return_array=True)
fundus_lines, _ = read_scalars(fundus_lines_file,
return_first=True, return_array=True)
## setup seeds from initial label boundaries
neighbor_lists = find_neighbors(faces, num_points)
# extract all vertices that are on a boundary between labels
boundary_indices, label_pairs, _ = extract_borders(
indices, init_labels, neighbor_lists,
return_label_pairs=True)
# split boundary vertices into segments with common boundary pairs.
boundary_segments = {}
for boundary_index, label_pair in zip(boundary_indices, label_pairs):
key = ((label_pair[0], label_pair[1]) if label_pair[0] < label_pair[1]
else (label_pair[1], label_pair[0]))
if key not in boundary_segments:
boundary_segments[key] = []
boundary_segments[key].append(boundary_index)
boundary_matrix, boundary_matrix_keys = _build_boundary_matrix(
boundary_segments, num_points)
# build the affinity matrix
affinity_matrix = go.weight_graph(
np.array(points), indices, np.array(faces), sigma=10, add_to_graph=False)
## propagate boundaries to fundus line vertices
learned_matrix = _propagate_labels(
affinity_matrix, boundary_matrix, boundary_indices, 1000, 1)
# assign labels to fundus line vertices based on highest probability
new_boundaries = -1 * np.ones(init_labels.shape)
fundus_line_indices = [i for i, x in enumerate(fundus_lines) if x > 0.5]
# TODO: this currently only works for fundus lines that tile the
# surface into connected components (which is fine when you want
# to test this method on fundus lines generated from manual
# labeling). However, to work on real data, fundus lines will
# need to be connected together using shortest paths.
# split surface into connected components
connected_component_faces = _remove_boundary_faces(
points, faces, fundus_line_indices)
# label components based on most probable label assignment
new_labels = _label_components(
connected_component_faces, num_points, boundary_indices, learned_matrix,
boundary_matrix_keys)
# propagate new labels to fill holes
label_matrix, label_map = _build_label_matrix(new_labels)
new_learned_matrix = _propagate_labels(
affinity_matrix, label_matrix,
[i for i in range(num_points) if new_labels[i] >= 0], 100, 1)
# assign most probable labels
for idx in [i for i in range(num_points) if new_labels[i] == -1]:
max_idx = np.argmax(new_learned_matrix[idx])
new_labels[idx] = label_map[max_idx]
# save
if out_label_file is not None:
write_vtk(out_label_file, points, faces=faces,
scalars=new_labels.tolist())
return new_labels
def 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.utils.mesh import rescale_by_neighborhood
>>> from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.utils.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.utils.io_vtk import read_scalars, rewrite_scalars
from mindboggle.utils.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 evaluate_deep_features(features_file,
labels_file,
sulci_file='',
hemi='',
excludeIDs=[-1],
output_vtk_name='',
verbose=True):
"""
Evaluate deep surface features by computing the minimum distance from each
label border vertex to all of the feature vertices in the same sulcus,
and from each feature vertex to all of the label border vertices in the
same sulcus. The label borders run along the deepest parts of sulci
and correspond to fundi in the DKT cortical labeling protocol.
Parameters
----------
features_file : string
VTK surface file with feature numbers for vertex scalars
labels_file : string
VTK surface file with label numbers for vertex scalars
sulci_file : string
VTK surface file with sulcus numbers for vertex scalars
excludeIDs : list of integers
feature/sulcus/label IDs to exclude (background set to -1)
output_vtk_name : Boolean
if not empty, output a VTK file beginning with output_vtk_name that
contains a surface with mean distances as scalars
verbose : Boolean
print mean distances to standard output?
Returns
-------
feature_to_border_mean_distances : numpy array [number of features x 1]
mean distance from each feature to sulcus label border
feature_to_border_sd_distances : numpy array [number of features x 1]
standard deviations of feature-to-border distances
feature_to_border_distances_vtk : string
VTK surface file containing feature-to-border distances
border_to_feature_mean_distances : numpy array [number of features x 1]
mean distances from each sulcus label border to feature
border_to_feature_sd_distances : numpy array [number of features x 1]
standard deviations of border-to-feature distances
border_to_feature_distances_vtk : string
VTK surface file containing border-to-feature distances
"""
import os
import sys
import numpy as np
from mindboggle.utils.io_vtk import read_vtk, read_scalars, write_vtk
from mindboggle.utils.mesh import find_neighbors, remove_faces
from mindboggle.utils.segment import extract_borders
from mindboggle.utils.compute import source_to_target_distances
from mindboggle.LABELS import DKTprotocol
dkt = DKTprotocol()
#-------------------------------------------------------------------------
# Load labels, features, and sulci:
#-------------------------------------------------------------------------
faces, lines, indices, points, npoints, labels, scalar_names, \
input_vtk = read_vtk(labels_file, True, True)
features, name = read_scalars(features_file, True, True)
if sulci_file:
sulci, name = read_scalars(sulci_file, True, True)
# List of indices to sulcus vertices:
sulcus_indices = [i for i, x in enumerate(sulci) if x != -1]
segmentIDs = sulci
sulcus_faces = remove_faces(faces, sulcus_indices)
else:
sulcus_indices = range(len(labels))
segmentIDs = []
sulcus_faces = faces
#-------------------------------------------------------------------------
# Prepare neighbors, label pairs, border IDs, and outputs:
#-------------------------------------------------------------------------
# Calculate neighbor lists for all points:
print('Find neighbors for all vertices...')
neighbor_lists = find_neighbors(faces, npoints)
# Find label border points in any of the sulci:
print('Find label border points in any of the sulci...')
border_indices, border_label_tuples, unique_border_label_tuples = \
extract_borders(sulcus_indices, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
if not len(border_indices):
sys.exit('There are no label border points!')
# Initialize an array of label border IDs
# (label border vertices that define sulci in the labeling protocol):
print('Build an array of label border IDs...')
label_borders = -1 * np.ones(npoints)
if hemi == 'lh':
nsulcus_lists = len(dkt.left_sulcus_label_pair_lists)
else:
nsulcus_lists = len(dkt.right_sulcus_label_pair_lists)
feature_to_border_mean_distances = -1 * np.ones(nsulcus_lists)
feature_to_border_sd_distances = -1 * np.ones(nsulcus_lists)
border_to_feature_mean_distances = -1 * np.ones(nsulcus_lists)
border_to_feature_sd_distances = -1 * np.ones(nsulcus_lists)
feature_to_border_distances_vtk = ''
border_to_feature_distances_vtk = ''
#-------------------------------------------------------------------------
# Loop through sulci:
#-------------------------------------------------------------------------
# For each list of sorted label pairs (corresponding to a sulcus):
for isulcus, label_pairs in enumerate(dkt.sulcus_label_pair_lists):
# Keep the border points with label pair labels:
label_pair_border_indices = [
x for i, x in enumerate(border_indices)
if np.unique(border_label_tuples[i]).tolist() in label_pairs
]
# Store the points as sulcus IDs in the border IDs array:
if label_pair_border_indices:
label_borders[label_pair_border_indices] = isulcus
if len(np.unique(label_borders)) > 1:
#---------------------------------------------------------------------
# Construct a feature-to-border distance matrix and VTK file:
#---------------------------------------------------------------------
# Construct a distance matrix:
print('Construct a feature-to-border distance matrix...')
sourceIDs = features
targetIDs = label_borders
distances, distance_matrix = source_to_target_distances(
sourceIDs, targetIDs, points, segmentIDs, excludeIDs)
# Compute mean distances for each feature:
nfeatures = min(np.shape(distance_matrix)[1], nsulcus_lists)
for ifeature in range(nfeatures):
feature_distances = [
x for x in distance_matrix[:, ifeature] if x != -1
]
feature_to_border_mean_distances[ifeature] = \
np.mean(feature_distances)
feature_to_border_sd_distances[ifeature] = \
np.std(feature_distances)
if verbose:
print('Feature-to-border mean distances:')
print(feature_to_border_mean_distances)
print('Feature-to-border standard deviations of distances:')
print(feature_to_border_sd_distances)
# Write resulting feature-label border distances to VTK file:
if output_vtk_name:
feature_to_border_distances_vtk = os.path.join(
os.getcwd(),
output_vtk_name + '_feature_to_border_mean_distances.vtk')
print('Write feature-to-border distances to {0}...'.format(
feature_to_border_distances_vtk))
write_vtk(feature_to_border_distances_vtk, points, [], [],
sulcus_faces, [distances],
['feature-to-border_distances'], 'float')
#---------------------------------------------------------------------
# Construct a border-to-feature distance matrix and VTK file:
#---------------------------------------------------------------------
# Construct a distance matrix:
print('Construct a border-to-feature distance matrix...')
sourceIDs = label_borders
targetIDs = features
distances, distance_matrix = source_to_target_distances(
sourceIDs, targetIDs, points, segmentIDs, excludeIDs)
# Compute mean distances for each feature:
nfeatures = min(np.shape(distance_matrix)[1], nsulcus_lists)
for ifeature in range(nfeatures):
border_distances = [
x for x in distance_matrix[:, ifeature] if x != -1
]
border_to_feature_mean_distances[ifeature] = \
np.mean(border_distances)
border_to_feature_sd_distances[ifeature] = \
np.std(border_distances)
if verbose:
print('border-to-feature mean distances:')
print(border_to_feature_mean_distances)
print('border-to-feature standard deviations of distances:')
print(border_to_feature_sd_distances)
# Write resulting feature-label border distances to VTK file:
if output_vtk_name:
border_to_feature_distances_vtk = os.path.join(
os.getcwd(),
output_vtk_name + '_border_to_feature_mean_distances.vtk')
print('Write border-to-feature distances to {0}...'.format(
border_to_feature_distances_vtk))
write_vtk(border_to_feature_distances_vtk, points, [], [],
sulcus_faces, [distances],
['border-to-feature_distances'], 'float')
#-------------------------------------------------------------------------
# Return outputs:
#-------------------------------------------------------------------------
return feature_to_border_mean_distances, feature_to_border_sd_distances,\
feature_to_border_distances_vtk,\
border_to_feature_mean_distances, border_to_feature_sd_distances,\
border_to_feature_distances_vtk
def 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.utils.mesh import rescale_by_label
>>> from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.utils.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.utils.io_vtk 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 realign_boundaries_to_fundus_lines(
surf_file, init_label_file, fundus_lines_file, thickness_file,
out_label_file=None):
"""
Fix label boundaries to fundus lines.
Parameters
----------
surf_file : file containing the surface geometry in vtk format
init_label_file : file containing scalars that represent the
initial guess at labels
fundus_lines_file : file containing scalars representing fundus lines.
thickness_file: file containing cortical thickness scalar data
(for masking out the medial wall only)
out_label_file : if specified, the realigned labels will be writen to
this file
Returns
-------
numpy array representing the realigned label for each surface vertex.
"""
import numpy as np
from mindboggle.utils.segment import extract_borders
import mindboggle.utils.graph as go
from mindboggle.utils.io_vtk import read_vtk, read_scalars, write_vtk
from mindboggle.utils.mesh import find_neighbors
import propagate_fundus_lines
## read files
faces, _, indices, points, num_points, _, _, _ = read_vtk(
surf_file, return_first=True, return_array=True)
indices = range(num_points)
init_labels, _ = read_scalars(init_label_file,
return_first=True, return_array=True)
fundus_lines, _ = read_scalars(fundus_lines_file,
return_first=True, return_array=True)
thickness, _ = read_scalars(thickness_file,
return_first=True, return_array=True)
# remove labels from vertices with zero thickness (get around
# DKT40 annotations having the label '3' for all the Corpus
# Callosum vertices).
cc_inds = [x for x in indices if thickness[x] < 0.001]
init_labels[cc_inds] = 0
## setup seeds from initial label boundaries
neighbor_lists = find_neighbors(faces, num_points)
# extract all vertices that are on a boundary between labels
boundary_indices, label_pairs, _ = extract_borders(
indices, init_labels, neighbor_lists,
return_label_pairs=True)
# split boundary vertices into segments with common boundary pairs.
boundary_segments = {}
for boundary_index, label_pair in zip(boundary_indices, label_pairs):
key = ((label_pair[0], label_pair[1]) if label_pair[0] < label_pair[1]
else (label_pair[1], label_pair[0]))
if key not in boundary_segments:
boundary_segments[key] = []
boundary_segments[key].append(boundary_index)
boundary_matrix, boundary_matrix_keys = _build_boundary_matrix(
boundary_segments, num_points)
# build the affinity matrix
affinity_matrix = go.weight_graph(
np.array(points), indices, np.array(faces), sigma=10, add_to_graph=False)
## propagate boundaries to fundus line vertices
learned_matrix = _propagate_labels(
affinity_matrix, boundary_matrix, boundary_indices, 100, 1)
# assign labels to fundus line vertices based on highest probability
new_boundaries = -1 * np.ones(init_labels.shape)
fundus_line_indices = [i for i, x in enumerate(fundus_lines) if x > 0.5]
# tile the surface into connected components delimited by fundus lines
closed_fundus_lines, _, _ = propagate_fundus_lines.propagate_fundus_lines(
points, faces, fundus_line_indices, thickness)
closed_fundus_line_indices = np.where(closed_fundus_lines > 0)[0]
# split surface into connected components
connected_component_faces = _remove_boundary_faces(
points, faces, closed_fundus_line_indices)
# label components based on most probable label assignment
new_labels = _label_components(
connected_component_faces, num_points, boundary_indices, learned_matrix,
boundary_matrix_keys)
# propagate new labels to fill holes
label_matrix, label_map = _build_label_matrix(new_labels)
new_learned_matrix = _propagate_labels(
affinity_matrix, label_matrix,
[i for i in range(num_points) if new_labels[i] >= 0], 100, 1)
# assign most probable labels
for idx in [i for i in range(num_points) if new_labels[i] == -1]:
max_idx = np.argmax(new_learned_matrix[idx])
new_labels[idx] = label_map[max_idx]
# save
if out_label_file is not None:
write_vtk(out_label_file, points, faces=faces,
scalars=[int(x) for x in new_labels], scalar_type='int')
return new_labels
def 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.utils.mesh import rescale_by_neighborhood
>>> from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.utils.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.utils.io_vtk import read_scalars, rewrite_scalars
from mindboggle.utils.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 write_vertex_measures(
table_file,
labels_or_file,
sulci=[],
fundi=[],
affine_transform_file="",
transform_format="itk",
area_file="",
mean_curvature_file="",
travel_depth_file="",
geodesic_depth_file="",
convexity_file="",
thickness_file="",
delimiter=",",
):
"""
Make a table of shape values per vertex.
Parameters
----------
table_file : output filename (without path)
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
sulci : list of integers
indices to sulci, one per vertex, with -1 indicating no sulcus
fundi : list of integers
indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_file : string
affine transform file to standard space
transform_format : string
format for transform file
Ex: 'txt' for text, 'itk' for ITK, and 'mat' for Matlab format
area_file : string
name of VTK file with surface area scalar values
mean_curvature_file : string
name of VTK file with mean curvature scalar values
travel_depth_file : string
name of VTK file with travel depth scalar values
geodesic_depth_file : string
name of VTK file with geodesic depth scalar values
convexity_file : string
name of VTK file with convexity scalar values
thickness_file : string
name of VTK file with thickness scalar values
delimiter : string
delimiter between columns, such as ','
Returns
-------
shape_table : table file name for vertex shape values
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.tables.all_shapes import write_vertex_measures
>>> #
>>> table_file = 'vertex_shapes.csv'
>>> path = os.environ['MINDBOGGLE_DATA']
>>> labels_or_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> sulci_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> fundi_file = os.path.join(path, 'arno', 'features', 'fundi.vtk')
>>> sulci, name = read_scalars(sulci_file)
>>> fundi, name = read_scalars(fundi_file)
>>> affine_transform_file = os.path.join(path, 'arno', 'mri',
>>> 't1weighted_brain.MNI152Affine.txt')
>>> transform_format = 'itk'
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> mean_curvature_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> travel_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> geodesic_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.geodesic_depth.vtk')
>>> convexity_file = ''
>>> thickness_file = ''
>>> delimiter = ','
>>> #
>>> write_vertex_measures(table_file, labels_or_file, sulci, fundi,
>>> affine_transform_file, transform_format, area_file,
>>> mean_curvature_file, travel_depth_file, geodesic_depth_file,
>>> convexity_file, thickness_file, delimiter)
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, read_vtk, apply_affine_transform
from mindboggle.utils.io_table import write_columns
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = labels_or_file.tolist()
elif isinstance(labels_or_file, list):
labels = labels_or_file
elif isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file)
if isinstance(sulci, np.ndarray):
sulci = sulci.tolist()
if isinstance(fundi, np.ndarray):
fundi = fundi.tolist()
# Feature names and corresponding feature lists:
feature_names = ["label", "sulcus", "fundus"]
feature_lists = [labels, sulci, fundi]
# Shape names corresponding to shape files below:
shape_names = ["area", "mean curvature", "travel depth", "geodesic depth", "convexity", "thickness"]
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [
area_file,
mean_curvature_file,
travel_depth_file,
geodesic_depth_file,
convexity_file,
thickness_file,
]
# Append columns of per-vertex scalar values:
columns = []
column_names = []
for ifeature, values in enumerate(feature_lists):
if values:
columns.append(values)
column_names.append(feature_names[ifeature])
first_pass = True
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
u1, u2, u3, points, u4, scalars, u5, u6 = read_vtk(shape_file)
columns.append(points)
column_names.append("coordinates")
first_pass = False
if affine_transform_file:
affine_points, foo1 = apply_affine_transform(affine_transform_file, points, transform_format)
columns.append(affine_points)
column_names.append("coordinates in standard space")
else:
scalars, name = read_scalars(shape_file)
if len(scalars):
columns.append(scalars)
column_names.append(shape_names[ishape])
# Prepend with column of indices and write table
shapes_table = os.path.join(os.getcwd(), table_file)
write_columns(range(len(columns[0])), "index", shapes_table, delimiter)
write_columns(columns, column_names, shapes_table, delimiter, quote=True, input_table=shapes_table)
return shapes_table
def spectrum_per_label(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization='area', area_file='',
largest_segment=True):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
largest_segment : Boolean
compute spectrum only for largest segment with a given label?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> spectrum_per_label(vtk_file, spectrum_size, exclude_labels, None,
>>> area_file, largest_segment)
([[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]],
[22])
"""
from mindboggle.utils.io_vtk import read_vtk, read_scalars
from mindboggle.utils.mesh import remove_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
faces, u1, u2, points, u4, labels, u5, u6 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i,x in enumerate(labels) if x == label]
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = remove_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size,
exclude_labels_inner,
normalization, areas)
else:
spectrum = fem_laplacian(pick_points, pick_faces,
spectrum_size, normalization)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def write_average_face_values_per_label(
input_indices_vtk, input_values_vtk="", area_file="", output_stem="", exclude_values=[-1], background_value=-1
):
"""
Write out a separate VTK file for each integer (>-1)
in (the first) scalar list of an input VTK file.
Optionally write the values drawn from a second VTK file.
Parameters
----------
input_indices_vtk : string
path of the input VTK file that contains indices as scalars
input_values_vtk : string
path of the input VTK file that contains values as scalars
output_stem : string
path and stem of the output VTK file
exclude_values : list or array
values to exclude
background_value : integer or float
background value in output VTK files
scalar_name : string
name of a lookup table of scalars values
Examples
--------
>>> import os
>>> from mindboggle.utils.io_table import write_average_face_values_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_indices_vtk = os.path.join(path, 'allen', 'labels', 'lh.DKTatlas100.gcs.vtk')
>>> input_values_vtk = os.path.join(path, 'allen', 'shapes', 'lh.thickness.vtk')
>>> area_file = os.path.join(path, 'allen', 'shapes', 'lh.pial.area.vtk')
>>> output_stem = 'labels_thickness'
>>> exclude_values = [-1]
>>> background_value = -1
>>> #
>>> write_average_face_values_per_label(input_indices_vtk,
>>> input_values_vtk, area_file, output_stem, exclude_values, background_value)
>>> #
>>> # View:
>>> #example_vtk = os.path.join(os.getcwd(), output_stem + '0.vtk')
>>> #from mindboggle.utils.plots import plot_vtk
>>> #plot_vtk(example_vtk)
"""
import os
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, read_vtk, write_vtk
from mindboggle.utils.io_table import write_columns
from mindboggle.utils.mesh import remove_faces
# Load VTK file:
faces, lines, indices, points, npoints, scalars, scalar_names, foo1 = read_vtk(input_indices_vtk, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
print("Explode the scalar list in {0}".format(os.path.basename(input_indices_vtk)))
if input_values_vtk != input_indices_vtk:
values, name = read_scalars(input_values_vtk, True, True)
print(
"Explode the scalar list of values in {0} "
"with the scalar list of indices in {1}".format(
os.path.basename(input_values_vtk), os.path.basename(input_indices_vtk)
)
)
else:
values = np.copy(scalars)
# Loop through unique (non-excluded) scalar values:
unique_scalars = [int(x) for x in np.unique(scalars) if x not in exclude_values]
for scalar in unique_scalars:
keep_indices = [x for sublst in faces for x in sublst]
new_faces = remove_faces(faces, keep_indices)
# Create array and indices for scalar value:
select_scalars = np.copy(scalars)
select_scalars[scalars != scalar] = background_value
scalar_indices = [i for i, x in enumerate(select_scalars) if x == scalar]
print(" Scalar {0}: {1} vertices".format(scalar, len(scalar_indices)))
# ---------------------------------------------------------------------
# For each face, average vertex values:
# ---------------------------------------------------------------------
output_table = os.path.join(os.getcwd(), output_stem + str(scalar) + ".csv")
columns = []
for face in new_faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
# -----------------------------------------------------------------
# Write to table:
# -----------------------------------------------------------------
write_columns(columns, "", output_table, delimiter=",", quote=False)
def spectrum_per_label(vtk_file, n_eigenvalues=3, exclude_labels=[-1],
normalization='area', area_file=''):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
n_eigenvalues : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Spectrum for label 22 (postcentral) in Twins-2-1:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import laplacian_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> n_eigenvalues = 6
>>> exclude_labels = [0] #[-1]
>>> laplacian_per_label(vtk_file, n_eigenvalues, exclude_labels,
>>> normalization=None, area_file=area_file)
Load "Labels" scalars from lh.labels.DKT25.manual.vtk
Load "scalars" scalars from lh.pial.area.vtk
7819 vertices for label 22
Reduced 290134 to 15230 triangular faces
Linear FEM Laplace-Beltrami spectrum:
([[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]],
[22])
"""
from mindboggle.utils.io_vtk import read_vtk, read_scalars
from mindboggle.utils.mesh import remove_faces
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
# Read VTK surface mesh file:
faces, u1, u2, points, u4, labels, u5, u6 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label==22:
# Determine the indices per label:
label_indices = [i for i,x in enumerate(labels) if x == label]
print('{0} vertices for label {1}'.format(len(label_indices), label))
# Remove background faces:
select_faces = remove_faces(faces, label_indices)
# Compute Laplace-Beltrami spectrum for the label:
spectrum = spectrum_of_largest(points, select_faces, n_eigenvalues,
exclude_labels, normalization, areas)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def spectrum_from_file(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization=None, area_file=''):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> spectrum_from_file(vtk_file, spectrum_size=6)
[4.829758648026223e-18,
0.00012841730024671977,
0.0002715181572272744,
0.00032051508471594173,
0.000470162807048644,
0.0005768904023010327]
>>> # Spectrum for Twins-2-1 left postcentral pial surface (22)
>>> # (after running explode_scalars() with reindex=True):
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'label22.vtk')
>>> spectrum_from_file(vtk_file, spectrum_size=6)
[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]
>>> # Loop thru all MB 101 brains
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> for hemidir in os.listdir(header):
>>> print hemidir
>>> sulci_file = os.path.join(header, hemidir, "sulci.vtk")
>>> spectrum = spectrum_from_file(sulci_file)
"""
from mindboggle.utils.io_vtk import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
faces, u1, u2, points, u4, u5, u6, u7 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas)
return spectrum
def plot_mask_surface(vtk_file, mask_file='', nonmask_value=-1,
masked_output='', remove_nonmask=False,
program='vtkviewer',
use_colormap=False, colormap_file=''):
"""
Use vtkviewer or mayavi2 to visualize VTK surface mesh data.
If a mask_file is provided, a temporary masked file is saved,
and it is this file that is viewed.
If using vtkviewer, can optionally provide colormap file
or set $COLORMAP environment variable.
Parameters
----------
vtk_file : string
name of VTK surface mesh file
mask_file : string
name of VTK surface mesh file to mask vtk_file vertices
nonmask_value : integer
nonmask (usually background) value
masked_output : string
temporary masked output file name
remove_nonmask : Boolean
remove vertices that are not in mask? (otherwise assign nonmask_value)
program : string {'vtkviewer', 'mayavi2'}
program to visualize VTK file
use_colormap : Boolean
use Paraview-style XML colormap file set by $COLORMAP env variable?
colormap_file : string
use colormap in given file if use_colormap==True? if empty and
use_colormap==True, use file set by $COLORMAP environment variable
Examples
--------
>>> import os
>>> from mindboggle.utils.plots import plot_mask_surface
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> mask_file = os.path.join(path, 'test_one_label.vtk')
>>> nonmask_value = 0 #-1
>>> masked_output = ''
>>> remove_nonmask = True
>>> program = 'vtkviewer'
>>> use_colormap = True
>>> colormap_file = '' #'/software/mindboggle_tools/colormap.xml'
>>> plot_mask_surface(vtk_file, mask_file, nonmask_value, masked_output, remove_nonmask, program, use_colormap, colormap_file)
"""
import os
import numpy as np
from mindboggle.utils.mesh import remove_faces, reindex_faces_points
from mindboggle.utils.utils import execute
from mindboggle.utils.plots import plot_surfaces
from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars, \
read_vtk, write_vtk
#-------------------------------------------------------------------------
# Filter mesh with non-background values from a second (same-size) mesh:
#-------------------------------------------------------------------------
if mask_file:
mask, name = read_scalars(mask_file, True, True)
if not masked_output:
masked_output = os.path.join(os.getcwd(), 'temp.vtk')
file_to_plot = masked_output
#---------------------------------------------------------------------
# Remove nonmask-valued vertices:
#---------------------------------------------------------------------
if remove_nonmask:
#-----------------------------------------------------------------
# Load VTK files:
#-----------------------------------------------------------------
faces, lines, indices, points, npoints, scalars, scalar_names, \
o1 = read_vtk(vtk_file, True, True)
#-----------------------------------------------------------------
# Find mask indices, remove nonmask faces, and reindex:
#-----------------------------------------------------------------
Imask = [i for i,x in enumerate(mask) if x != nonmask_value]
mask_faces = remove_faces(faces, Imask)
mask_faces, points, \
original_indices = reindex_faces_points(mask_faces, points)
#-----------------------------------------------------------------
# Write VTK file with scalar values:
#-----------------------------------------------------------------
if np.ndim(scalars) == 1:
scalar_type = type(scalars[0]).__name__
elif np.ndim(scalars) == 2:
scalar_type = type(scalars[0][0]).__name__
else:
print("Undefined scalar type!")
write_vtk(file_to_plot, points, [], [], mask_faces,
scalars[original_indices].tolist(), scalar_names,
scalar_type=scalar_type)
else:
scalars, name = read_scalars(vtk_file, True, True)
scalars[mask == nonmask_value] = nonmask_value
rewrite_scalars(vtk_file, file_to_plot, scalars)
else:
file_to_plot = vtk_file
#-------------------------------------------------------------------------
# Display with vtkviewer.py:
#-------------------------------------------------------------------------
if program == 'vtkviewer':
plot_surfaces(file_to_plot, use_colormap=use_colormap,
colormap_file=colormap_file)
#-------------------------------------------------------------------------
# Display with mayavi2:
#-------------------------------------------------------------------------
elif program == 'mayavi2':
cmd = ["mayavi2", "-d", file_to_plot, "-m", "Surface", "&"]
execute(cmd, 'os')
def extract_sulci(labels_file, folds_or_file, hemi, sulcus_label_pair_lists,
unique_sulcus_label_pairs, min_boundary=1, sulcus_names=[]):
"""
Identify sulci from folds in a brain surface according to a labeling
protocol that includes a list of label pairs defining each sulcus.
A fold is a group of connected, deep vertices.
Steps for each fold ::
1. Remove fold if it has fewer than two labels.
2. Remove fold if its labels do not contain a sulcus label pair.
3. Find vertices with labels that are in only one of the fold's
label boundary pairs. Assign the vertices the sulcus with the
label pair if they are connected to the label boundary for that pair.
4. If there are remaining vertices, segment into sets of vertices
connected to label boundaries, and assign a unique ID to each segment.
Parameters
----------
labels_file : string
file name for surface mesh VTK containing labels for all vertices
folds_or_file : list or string
fold number for each vertex or name of VTK file containing folds scalars
hemi : string
hemisphere ('lh' or 'rh')
sulcus_label_pair_lists : list of two lists of multiple lists of integer pairs
list containing left and right lists, each with multiple lists of
integer pairs corresponding to label boundaries / sulcus / fundus
unique_sulcus_label_pairs : list of unique pairs of integers
unique label pairs
min_boundary : integer
minimum number of vertices for a sulcus label boundary segment
sulcus_names : list of strings
names of sulci
Returns
-------
sulci : list of integers
sulcus numbers for all vertices (-1 for non-sulcus vertices)
n_sulci : integers
number of sulci
sulci_file : string
name of output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.labels.protocol import dkt_protocol
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.utils.plots import plot_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> # Load labels, folds, neighbor lists, and sulcus names and label pairs
>>> labels_file = os.path.join(path, 'arno', 'labels', 'relabeled_lh.DKTatlas40.gcs.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds_or_file, name = read_scalars(folds_file)
>>> protocol = 'DKT31'
>>> hemi = 'lh'
>>> sulcus_names, sulcus_label_pair_lists, unique_sulcus_label_pairs,
... label_names, label_numbers, cortex_names, cortex_numbers,
... noncortex_names, noncortex_numbers = dkt_protocol(protocol)
>>> min_boundary = 10
>>> #
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
>>> hemi, sulcus_label_pair_lists, unique_sulcus_label_pairs,
>>> min_boundary, sulcus_names)
>>> # View:
>>> plot_vtk('sulci.vtk')
"""
import os
from time import time
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, read_vtk, rewrite_scalars
from mindboggle.utils.mesh import find_neighbors
from mindboggle.labels.labels import extract_borders
from mindboggle.utils.segment import propagate, segment
# Load fold numbers if folds_or_file is a string
if isinstance(folds_or_file, str):
folds, name = read_scalars(folds_or_file)
elif isinstance(folds_or_file, list):
folds = folds_or_file
if hemi == 'lh':
sulcus_label_pair_lists = sulcus_label_pair_lists[0]
elif hemi == 'rh':
sulcus_label_pair_lists = sulcus_label_pair_lists[1]
else:
print("Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors
faces, foo1, foo2, points, npoints, labels, foo3, foo4 = read_vtk(labels_file)
neighbor_lists = find_neighbors(faces, npoints)
# Array of sulcus IDs for fold vertices, initialized as -1.
# Since we do not touch gyral vertices and vertices whose labels
# are not in the label list, or vertices having only one label,
# their sulcus IDs will remain -1.
sulci = -1 * np.ones(npoints)
#-------------------------------------------------------------------------
# Loop through folds
#-------------------------------------------------------------------------
fold_numbers = [int(x) for x in np.unique(folds) if x > -1]
n_folds = len(fold_numbers)
print("Extract sulci from {0} folds...".format(n_folds))
t0 = time()
for n_fold in fold_numbers:
fold = [i for i,x in enumerate(folds) if x == n_fold]
len_fold = len(fold)
# List the labels in this fold (greater than zero)
fold_labels = [labels[x] for x in fold]
unique_fold_labels = [int(x) for x in np.unique(fold_labels) if x > 0]
#---------------------------------------------------------------------
# NO MATCH -- fold has fewer than two labels
#---------------------------------------------------------------------
if len(unique_fold_labels) < 2:
# Ignore: sulci already initialized with -1 values
if not unique_fold_labels:
print(" Fold {0} ({1} vertices): NO MATCH -- fold has no labels".
format(n_fold, len_fold))
else:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has only one label ({2})".
format(n_fold, len_fold, unique_fold_labels[0]))
# Ignore: sulci already initialized with -1 values
else:
# Find all label boundary pairs within the fold
indices_fold_pairs, fold_pairs, unique_fold_pairs = extract_borders(
fold, labels, neighbor_lists, ignore_values=[],
return_label_pairs=True)
# Find fold label pairs in the protocol (pairs are already sorted)
fold_pairs_in_protocol = [x for x in unique_fold_pairs
if x in unique_sulcus_label_pairs]
if unique_fold_labels:
print(" Fold {0} labels: {1} ({2} vertices)".format(n_fold,
', '.join([str(x) for x in unique_fold_labels]), len_fold))
#-----------------------------------------------------------------
# NO MATCH -- fold has no sulcus label pair
#-----------------------------------------------------------------
if not fold_pairs_in_protocol:
print(" Fold {0}: NO MATCH -- fold has no sulcus label pair".
format(n_fold, len_fold))
#-----------------------------------------------------------------
# Possible matches
#-----------------------------------------------------------------
else:
print(" Fold {0} label pairs in protocol: {1}".format(n_fold,
', '.join([str(x) for x in fold_pairs_in_protocol])))
# Labels in the protocol (includes repeats across label pairs)
labels_in_pairs = [x for lst in fold_pairs_in_protocol for x in lst]
# Labels that appear in one or more than one sulcus label boundary
unique_labels = []
nonunique_labels = []
for label in np.unique(labels_in_pairs):
if len([x for x in labels_in_pairs if x == label]) == 1:
unique_labels.append(label)
else:
nonunique_labels.append(label)
#-------------------------------------------------------------
# Vertices whose labels are in only one sulcus label pair
#-------------------------------------------------------------
# Find vertices with a label that is in only one of the fold's
# label pairs (the other label in the pair can exist
# in other pairs). Assign the vertices the sulcus with the label
# pair if they are connected to the label boundary for that pair.
#-------------------------------------------------------------
if len(unique_labels):
for pair in fold_pairs_in_protocol:
# If one or both labels in label pair is/are unique
unique_labels_in_pair = [x for x in pair if x in unique_labels]
n_unique = len(unique_labels_in_pair)
if n_unique:
ID = [i for i,x in enumerate(sulcus_label_pair_lists)
if pair in x][0]
# Construct seeds from label boundary vertices
# (fold_pairs and pair already sorted)
indices_pair = [x for i,x in enumerate(indices_fold_pairs)
if fold_pairs[i] == pair]
# Identify vertices with unique label(s) in pair
indices_unique_labels = [fold[i]
for i,x in enumerate(fold_labels)
if x in unique_sulcus_label_pairs]
# Propagate from seeds to labels in label pair
sulci2 = segment(indices_unique_labels, neighbor_lists,
min_region_size=1,
seed_lists=[indices_pair],
keep_seeding=False,
spread_within_labels=True,
labels=labels)
sulci[sulci2 > -1] = ID
# Print statement
if n_unique == 1:
ps1 = '1 label'
else:
ps1 = 'Both labels'
if len(sulcus_names):
ps2 = sulcus_names[ID]
else:
ps2 = ''
print(" {0} unique to one fold pair: {1} {2}".
format(ps1, ps2, unique_labels_in_pair))
#-------------------------------------------------------------
# Vertex labels shared by multiple label pairs
#-------------------------------------------------------------
# Propagate labels from label borders to vertices with labels
# that are shared by multiple label pairs in the fold.
#-------------------------------------------------------------
if len(nonunique_labels):
# For each label shared by different label pairs
for label in nonunique_labels:
# Print statement
print(" Propagate sulcus label borders with label {0}".
format(int(label)))
# Construct seeds from label boundary vertices
seeds = -1 * np.ones(len(points))
for ID, label_pair_list in enumerate(sulcus_label_pair_lists):
label_pairs = [x for x in label_pair_list if label in x]
for label_pair in label_pairs:
indices_pair = [x for i,x in enumerate(indices_fold_pairs)
if np.sort(fold_pairs[i]).tolist() == label_pair]
if indices_pair:
# Do not include short boundary segments
if min_boundary > 1:
indices_pair2 = []
seeds2 = segment(indices_pair, neighbor_lists)
for seed2 in range(int(max(seeds2))+1):
iseed2 = [i for i,x in enumerate(seeds2)
if x == seed2]
if len(iseed2) >= min_boundary:
indices_pair2.extend(iseed2)
else:
if len(iseed2) == 1:
print(" Remove assignment "
"of ID {0} from 1 vertex".
format(seed2))
else:
print(" Remove assignment "
"of ID {0} from {1} vertices".
format(seed2, len(iseed2)))
indices_pair = indices_pair2
# Assign sulcus IDs to seeds
seeds[indices_pair] = ID
# Identify vertices with the label
label_array = -1 * np.ones(len(points))
indices_label = [fold[i] for i,x in enumerate(fold_labels)
if x == label]
if len(indices_label):
label_array[indices_label] = 1
# Propagate from seeds to vertices with label
#indices_seeds = []
#for seed in range(int(max(seeds))+1):
# indices_seeds.append([i for i,x in enumerate(seeds)
# if x == seed])
#sulci2 = segment(indices_label, neighbor_lists,
# 50, indices_seeds, False, True, labels)
sulci2 = propagate(points, faces,
label_array, seeds, sulci,
max_iters=10000,
tol=0.001, sigma=5)
sulci[sulci2 > -1] = sulci2[sulci2 > -1]
#-------------------------------------------------------------------------
# Print out assigned sulci
#-------------------------------------------------------------------------
sulcus_numbers = [int(x) for x in np.unique(sulci) if x > -1]
n_sulci = len(sulcus_numbers)
print("Extracted {0} sulci from {1} folds ({2:.1f}s):".
format(n_sulci, n_folds, time()-t0))
if len(sulcus_names):
for sulcus_number in sulcus_numbers:
print(" {0}: {1}".format(sulcus_number, sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in sulcus_numbers]))
#-------------------------------------------------------------------------
# Print out unresolved sulci
#-------------------------------------------------------------------------
unresolved = [i for i in range(len(sulcus_label_pair_lists))
if i not in sulcus_numbers]
if len(unresolved) == 1:
print("The following sulcus is unaccounted for:")
else:
print("The following {0} sulci are unaccounted for:".format(len(unresolved)))
if len(sulcus_names):
for sulcus_number in unresolved:
print(" {0}: {1}".format(sulcus_number, sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in unresolved]))
#-------------------------------------------------------------------------
# Return sulci, number of sulci, and file name
#-------------------------------------------------------------------------
sulci_file = os.path.join(os.getcwd(), 'sulci.vtk')
rewrite_scalars(labels_file, sulci_file, sulci, 'sulci', sulci)
sulci.tolist()
return sulci, n_sulci, sulci_file
sulcus_names, sulcus_label_pair_lists, unique_sulcus_label_pairs, \
label_names, label_numbers, cortex_names, cortex_numbers, \
noncortex_names, noncortex_numbers = dkt_protocol(protocol)
fundi_file = sys.argv[1]
folds_file = sys.argv[2]
labels_file = sys.argv[3]
print('***')
print('Input fundi:' + fundi_file)
print('Input folds:' + folds_file)
print('Input labels:' + labels_file)
print('***')
# Load fundi, folds, labels
fundi, name = read_scalars(fundi_file, return_arrays=True)
folds, name = read_scalars(folds_file, return_arrays=True)
faces, lines, indices, points, npoints, labels, scalar_names = load_vtk(labels_file, return_arrays=True)
# List of indices to fold vertices
fold_indices = [i for i,x in enumerate(folds) if x > 0]
# Calculate neighbor lists for all points
print('Find neighbors to all vertices...')
neighbor_lists = find_neighbors(faces, npoints)
# Prepare list of all unique sorted label pairs in the labeling protocol
print('Prepare a list of unique, sorted label pairs in the protocol...')
n_fundi = len(sulcus_label_pair_lists)
# Find label boundary points in any of the folds
def spectrum_from_file(vtk_file, n_eigenvalues=6, exclude_labels=[-1],
normalization=None, area_file=''):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
n_eigenvalues : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
Returns
-------
spectrum : list of floats
first n_eigenvalues of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> spectrum_from_file(vtk_file, n_eigenvalues=6)
Load "Labels" scalars from lh.labels.DKT25.manual.vtk
Linear FEM Laplace-Beltrami spectrum:
[4.829758648026221e-18,
0.00012841730024672036,
0.00027151815722727465,
0.00032051508471594065,
0.0004701628070486447,
0.0005768904023010338]
>>> # Spectrum for label 22 (postcentral) (after running explode_scalars()):
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'label22.vtk')
>>> spectrum_from_file(vtk_file, n_eigenvalues=6)
Load "scalars" scalars from label22.vtk
Linear FEM Laplace-Beltrami spectrum:
[6.3469513010430304e-18,
0.0005178862383467466,
0.0017434911095630806,
0.003667561767487689,
0.005429017880363778,
0.006309346984678918]
>>> # Loop thru all MB 101 brains
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> for hemidir in os.listdir(header):
>>> print hemidir
>>> sulci_file = os.path.join(header, hemidir, "sulci.vtk")
>>> spectrum = spectrum_from_file(sulci_file)
"""
from mindboggle.utils.io_vtk import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
faces, u1, u2, points, u4, u5, u6, u7 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, n_eigenvalues,
exclude_labels, normalization, areas)
return spectrum
def concatenate_sulcus_scalars(scalar_files, fold_files, label_files):
"""
Prepare data for estimating scalar distributions along and outside fundi.
Extract (e.g., depth, curvature) scalar values in folds, along sulcus
label boundaries as well as outside the sulcus label boundaries.
Concatenate these scalar values across multiple files.
Parameters
----------
scalar_files : list of strings
names of surface mesh VTK files with scalar values to concatenate
fold_files : list of strings (corr. to each list in scalar_files)
VTK files with fold numbers as scalars (-1 for non-fold vertices)
label_files : list of strings (corr. to fold_files)
VTK files with label numbers (-1 for unlabeled vertices)
Returns
-------
border_scalars : list of floats
concatenated scalar values within folds along sulcus label boundaries
nonborder_scalars : list of floats
concatenated scalar values within folds outside sulcus label boundaries
Examples
--------
>>> # Concatenate (duplicate) depth scalars:
>>> import os
>>> from mindboggle.shapes.likelihood import concatenate_sulcus_scalars
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'depth_rescaled.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> labels_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> scalar_files = [depth_file, depth_file]
>>> fold_files = [folds_file, folds_file]
>>> label_files = [labels_file, labels_file]
>>> #
>>> S = concatenate_sulcus_scalars(scalar_files, fold_files, label_files)
"""
import numpy as np
from mindboggle.utils.io_vtk import read_scalars
from mindboggle.utils.mesh import find_neighbors_from_file
from mindboggle.utils.segment import extract_borders
from mindboggle.LABELS import DKTprotocol
dkt = DKTprotocol()
# Prepare (non-unique) list of sulcus label pairs:
protocol_label_pairs = [
x for lst in dkt.sulcus_label_pair_lists for x in lst
]
border_scalars = []
nonborder_scalars = []
# Loop through files with the scalar values:
for ifile, scalar_file in enumerate(scalar_files):
print(scalar_file)
# Load scalars, folds, and labels:
folds_file = fold_files[ifile]
labels_file = label_files[ifile]
scalars, name = read_scalars(scalar_file, True, True)
if scalars.shape:
folds, name = read_scalars(folds_file)
labels, name = read_scalars(labels_file)
indices_folds = [i for i, x in enumerate(folds) if x != -1]
neighbor_lists = find_neighbors_from_file(labels_file)
# Find all label border pairs within the folds:
indices_label_pairs, label_pairs, unique_pairs = extract_borders(
indices_folds,
labels,
neighbor_lists,
ignore_values=[-1],
return_label_pairs=True)
indices_label_pairs = np.array(indices_label_pairs)
# Find vertices with label pairs in the sulcus labeling protocol:
Ipairs_in_protocol = [
i for i, x in enumerate(label_pairs)
if x in protocol_label_pairs
]
indices_label_pairs = indices_label_pairs[Ipairs_in_protocol]
indices_outside_pairs = list(
frozenset(indices_folds).difference(indices_label_pairs))
# Store scalar values in folds along label border pairs:
border_scalars.extend(scalars[indices_label_pairs].tolist())
# Store scalar values in folds outside label border pairs:
nonborder_scalars.extend(scalars[indices_outside_pairs].tolist())
return border_scalars, nonborder_scalars
def realign_boundaries_to_fundus_lines(surf_file,
init_label_file,
fundus_lines_file,
thickness_file,
out_label_file=None):
"""
Fix label boundaries to fundus lines.
Parameters
----------
surf_file : file containing the surface geometry in vtk format
init_label_file : file containing scalars that represent the
initial guess at labels
fundus_lines_file : file containing scalars representing fundus lines.
thickness_file: file containing cortical thickness scalar data
(for masking out the medial wall only)
out_label_file : if specified, the realigned labels will be writen to
this file
Returns
-------
numpy array representing the realigned label for each surface vertex.
"""
import numpy as np
from mindboggle.utils.segment import extract_borders
import mindboggle.utils.graph as go
from mindboggle.utils.io_vtk import read_vtk, read_scalars, write_vtk
from mindboggle.utils.mesh import find_neighbors
import propagate_fundus_lines
## read files
faces, _, indices, points, num_points, _, _, _ = read_vtk(
surf_file, return_first=True, return_array=True)
indices = range(num_points)
init_labels, _ = read_scalars(init_label_file,
return_first=True,
return_array=True)
fundus_lines, _ = read_scalars(fundus_lines_file,
return_first=True,
return_array=True)
thickness, _ = read_scalars(thickness_file,
return_first=True,
return_array=True)
# remove labels from vertices with zero thickness (get around
# DKT40 annotations having the label '3' for all the Corpus
# Callosum vertices).
cc_inds = [x for x in indices if thickness[x] < 0.001]
init_labels[cc_inds] = 0
## setup seeds from initial label boundaries
neighbor_lists = find_neighbors(faces, num_points)
# extract all vertices that are on a boundary between labels
boundary_indices, label_pairs, _ = extract_borders(indices,
init_labels,
neighbor_lists,
return_label_pairs=True)
# split boundary vertices into segments with common boundary pairs.
boundary_segments = {}
for boundary_index, label_pair in zip(boundary_indices, label_pairs):
key = ((label_pair[0],
label_pair[1]) if label_pair[0] < label_pair[1] else
(label_pair[1], label_pair[0]))
if key not in boundary_segments:
boundary_segments[key] = []
boundary_segments[key].append(boundary_index)
boundary_matrix, boundary_matrix_keys = _build_boundary_matrix(
boundary_segments, num_points)
# build the affinity matrix
affinity_matrix = go.weight_graph(np.array(points),
indices,
np.array(faces),
sigma=10,
add_to_graph=False)
## propagate boundaries to fundus line vertices
learned_matrix = _propagate_labels(affinity_matrix, boundary_matrix,
boundary_indices, 100, 1)
# assign labels to fundus line vertices based on highest probability
new_boundaries = -1 * np.ones(init_labels.shape)
fundus_line_indices = [i for i, x in enumerate(fundus_lines) if x > 0.5]
# tile the surface into connected components delimited by fundus lines
closed_fundus_lines, _, _ = propagate_fundus_lines.propagate_fundus_lines(
points, faces, fundus_line_indices, thickness)
closed_fundus_line_indices = np.where(closed_fundus_lines > 0)[0]
# split surface into connected components
connected_component_faces = _remove_boundary_faces(
points, faces, closed_fundus_line_indices)
# label components based on most probable label assignment
new_labels = _label_components(connected_component_faces, num_points,
boundary_indices, learned_matrix,
boundary_matrix_keys)
# propagate new labels to fill holes
label_matrix, label_map = _build_label_matrix(new_labels)
new_learned_matrix = _propagate_labels(
affinity_matrix, label_matrix,
[i for i in range(num_points) if new_labels[i] >= 0], 100, 1)
# assign most probable labels
for idx in [i for i in range(num_points) if new_labels[i] == -1]:
max_idx = np.argmax(new_learned_matrix[idx])
new_labels[idx] = label_map[max_idx]
# save
if out_label_file is not None:
write_vtk(out_label_file,
points,
faces=faces,
scalars=[int(x) for x in new_labels],
scalar_type='int')
return new_labels
def plot_mask_surface(vtk_file, mask_file='', nonmask_value=-1,
masked_output='', remove_nonmask=False,
program='vtkviewer',
use_colormap=False, colormap_file=''):
"""
Use vtkviewer or mayavi2 to visualize VTK surface mesh data.
If a mask_file is provided, a temporary masked file is saved,
and it is this file that is viewed.
If using vtkviewer, can optionally provide colormap file
or set $COLORMAP environment variable.
Parameters
----------
vtk_file : string
name of VTK surface mesh file
mask_file : string
name of VTK surface mesh file to mask vtk_file vertices
nonmask_value : integer
nonmask (usually background) value
masked_output : string
temporary masked output file name
remove_nonmask : Boolean
remove vertices that are not in mask? (otherwise assign nonmask_value)
program : string {'vtkviewer', 'mayavi2'}
program to visualize VTK file
use_colormap : Boolean
use Paraview-style XML colormap file set by $COLORMAP env variable?
colormap_file : string
use colormap in given file if use_colormap==True? if empty and
use_colormap==True, use file set by $COLORMAP environment variable
Examples
--------
>>> import os
>>> from mindboggle.utils.plots import plot_mask_surface
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> mask_file = os.path.join(path, 'test_one_label.vtk')
>>> nonmask_value = 0 #-1
>>> masked_output = ''
>>> remove_nonmask = True
>>> program = 'vtkviewer'
>>> use_colormap = True
>>> colormap_file = '' #'/software/mindboggle_tools/colormap.xml'
>>> plot_mask_surface(vtk_file, mask_file, nonmask_value, masked_output, remove_nonmask, program, use_colormap, colormap_file)
"""
import os
import numpy as np
from mindboggle.utils.mesh import remove_faces, reindex_faces_points
from mindboggle.utils.utils import execute
from mindboggle.utils.plots import plot_surfaces
from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars, \
read_vtk, write_vtk
#-------------------------------------------------------------------------
# Filter mesh with non-background values from a second (same-size) mesh:
#-------------------------------------------------------------------------
if mask_file:
mask, name = read_scalars(mask_file, True, True)
if not masked_output:
masked_output = os.path.join(os.getcwd(), 'temp.vtk')
file_to_plot = masked_output
#---------------------------------------------------------------------
# Remove nonmask-valued vertices:
#---------------------------------------------------------------------
if remove_nonmask:
#-----------------------------------------------------------------
# Load VTK files:
#-----------------------------------------------------------------
faces, lines, indices, points, npoints, scalars, scalar_names, \
o1 = read_vtk(vtk_file, True, True)
#-----------------------------------------------------------------
# Find mask indices, remove nonmask faces, and reindex:
#-----------------------------------------------------------------
Imask = [i for i,x in enumerate(mask) if x != nonmask_value]
mask_faces = remove_faces(faces, Imask)
mask_faces, points, \
original_indices = reindex_faces_points(mask_faces, points)
#-----------------------------------------------------------------
# Write VTK file with scalar values:
#-----------------------------------------------------------------
if np.ndim(scalars) == 1:
scalar_type = type(scalars[0]).__name__
elif np.ndim(scalars) == 2:
scalar_type = type(scalars[0][0]).__name__
else:
print("Undefined scalar type!")
write_vtk(file_to_plot, points, [], [], mask_faces,
scalars[original_indices].tolist(), scalar_names,
scalar_type=scalar_type)
else:
scalars, name = read_scalars(vtk_file, True, True)
scalars[mask == nonmask_value] = nonmask_value
rewrite_scalars(vtk_file, file_to_plot, scalars)
else:
file_to_plot = vtk_file
#-------------------------------------------------------------------------
# Display with vtkviewer.py:
#-------------------------------------------------------------------------
if program == 'vtkviewer':
plot_surfaces(file_to_plot, use_colormap=use_colormap,
colormap_file=colormap_file)
#-------------------------------------------------------------------------
# Display with mayavi2:
#-------------------------------------------------------------------------
elif program == 'mayavi2':
cmd = ["mayavi2", "-d", file_to_plot, "-m", "Surface", "&"]
execute(cmd, 'os')
def extract_fundi(folds, sulci, curv_file, depth_file, min_separation=10,
erode_ratio=0.1, erode_min_size=1, save_file=False):
"""
Extract fundi from folds.
A fundus is a branching curve that runs along the deepest and most
highly curved portions of a sulcus fold.
Steps ::
1. Find fundus endpoints (outer anchors) with find_outer_anchors().
2. Include inner anchor points.
3. Connect anchor points using connect_points_erosion().
4. Segment fundi by sulcus definitions.
Possible postprocessing step: smooth with smooth_skeleton().
Parameters
----------
folds : list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
sulci : list of integers
sulcus number for each vertex
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : Boolean
save output VTK file?
Returns
-------
fundi : list of integers
fundus numbers for all vertices (-1 for non-fundus vertices)
n_fundi : integer
number of fundi
fundi_file : string (if save_file)
name of output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> single_fold = True
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.utils.plots import plot_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> sulci_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> sulci, name = read_scalars(sulci_file, True, True)
>>> curv_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'travel_depth_rescaled.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file, True, True)
>>> if single_fold:
>>> fold_number = 2 #11
>>> folds[folds != fold_number] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> fundi, n_fundi, fundi_file = extract_fundi(folds, sulci, curv_file,
>>> depth_file, min_separation, erode_ratio, erode_min_size, save_file)
>>> #
>>> # View:
>>> plot_vtk(fundi_file)
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.utils.io_vtk import read_scalars, read_vtk, rewrite_scalars
from mindboggle.utils.compute import median_abs_dev
from mindboggle.utils.paths import find_max_values
from mindboggle.utils.mesh import find_neighbors_from_file
from mindboggle.utils.paths import find_outer_anchors, connect_points_erosion
# Load values, threshold, and neighbors:
u1,u2,u3, points, npoints, curvs, u4,u5 = read_vtk(curv_file, True,True)
depths, name = read_scalars(depth_file, True, True)
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
#-------------------------------------------------------------------------
# Loop through folds:
#-------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != -1]
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".
format(len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i,x in enumerate(folds) if x == fold_ID]
if indices_fold:
print(' Fold {0}:'.format(int(fold_ID)))
#-----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints :
#-----------------------------------------------------------------
outer_anchors, tracks = find_outer_anchors(indices_fold,
neighbor_lists, values, depths, min_separation)
#-----------------------------------------------------------------
# Find inner anchor points:
#-----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation, thr)
#-----------------------------------------------------------------
# Connect endpoints to create skeleton:
#-----------------------------------------------------------------
B = -1 * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B, neighbor_lists,
outer_anchors, inner_anchors, values,
erode_ratio, erode_min_size, save_steps=[], save_vtk='')
if skeleton:
skeletons.extend(skeleton)
#-------------------------------------------------------------------------
# Create fundi by segmenting skeletons with overlapping sulcus labels:
#-------------------------------------------------------------------------
fundi = -1 * np.ones(npoints)
indices = [x for x in skeletons if sulci[x] != -1]
fundi[indices] = sulci[indices]
n_fundi = len([x for x in np.unique(fundi) if x != -1])
if n_fundi == 1:
sdum = 'fundus'
else:
sdum = 'fundi'
print(' ...Extracted {0} {1} ({2:.2f} seconds)'.
format(n_fundi, sdum, time() - t1))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
fundi = fundi.tolist()
if save_file:
fundi_file = os.path.join(os.getcwd(), 'fundi.vtk')
rewrite_scalars(curv_file, fundi_file, fundi, 'fundi', folds)
else:
fundi_file = None
return fundi, n_fundi, fundi_file
table_file = os.path.join(os.getcwd(), label_name+'_'+shape_name+'.csv')
table_column_names = []
column_names = []
columns = []
for subject in subjects_list:
label_file = '/homedir/Desktop/hippo/workspace/Mindboggle/Labels/_hemi_'+\
hemi+'_subject_'+subject+'/DKT_annot_to_VTK/'+hemi+'.DKTatlas40.gcs.vtk'
shape_file = '/homedir/Desktop/hippo/results/shapes/_hemi_'+hemi+\
'_subject_'+subject+'/'+hemi+'.thickness.vtk'
area_file = '/homedir/Desktop/hippo/results/shapes/_hemi_'+hemi+\
'_subject_'+subject+'/'+hemi+'.pial.area.vtk'
area_array = []
if os.path.exists(label_file):
feature_array, name = read_scalars(label_file, True, True)
if os.path.exists(shape_file):
shape_array, name = read_scalars(shape_file, True, True)
if os.path.exists(label_file):
label_array, name = read_scalars(label_file, True, True)
if area_file and os.path.exists(area_file):
area_array, name = read_scalars(area_file, True, True)
#-----------------------------------------------------------------
# Loop through shape measures:
#-----------------------------------------------------------------
table_column_names.extend(column_names[:])
print(' Compute statistics on {0} {1}'.
format(label_name, shape_name))
#-------------------------------------------------------------
