def annot_to_vtk(annot_file, vtk_file, output_vtk=''):
"""
Load a FreeSurfer .annot file and save as a VTK format file.
Parameters
----------
annot_file : string
name of FreeSurfer .annot file
vtk_file : string
name of VTK surface file
output_vtk : string
name of output VTK file, where each vertex is assigned
the corresponding shape value
Returns
-------
labels : list
integers (one label per vertex)
output_vtk : string
name of output VTK file, where each vertex is assigned
the corresponding shape value
Examples
--------
>>> import os
>>> from mindboggle.utils.io_free import annot_to_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> annot_file = os.path.join(path, 'arno', 'freesurfer', 'lh.aparc.annot')
>>> vtk_file = os.path.join(path, 'arno', 'freesurfer', 'lh.pial.vtk')
>>> output_vtk = ''
>>> #
>>> labels, output_vtk = annot_to_vtk(annot_file, vtk_file, output_vtk)
>>> #
>>> # View:
>>> from mindboggle.utils.plots import plot_vtk
>>> plot_vtk(output_vtk)
"""
import os
import nibabel as nb
from mindboggle.utils.io_vtk import rewrite_scalars
labels, colortable, names = nb.freesurfer.read_annot(annot_file)
if not output_vtk:
output_vtk = os.path.join(os.getcwd(),
os.path.basename(annot_file).strip('.annot') + '.vtk')
rewrite_scalars(vtk_file, output_vtk, labels, 'Labels')
if not os.path.exists(output_vtk):
raise(IOError(output_vtk + " not found"))
return labels, output_vtk
def curvature_to_vtk(surface_file, vtk_file, output_vtk):
"""
Convert FreeSurfer curvature, thickness, or convexity file to VTK format.
Parameters
----------
surface_file : string
name of FreeSurfer surface file
vtk_file : string
name of VTK surface file
output_vtk : string
name of output VTK file
Returns
-------
output_vtk : string
name of output VTK file, where each vertex is assigned
the corresponding shape value
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import curvature_to_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> surface_file = os.path.join(path, 'arno', 'freesurfer', 'lh.thickness')
>>> vtk_file = os.path.join(path, 'arno', 'freesurfer', 'lh.pial.vtk')
>>> output_vtk = ''
>>> #
>>> curvature_to_vtk(surface_file, vtk_file, output_vtk)
>>> #
>>> # View:
>>> from mindboggle.utils.plots import plot_vtk
>>> plot_vtk('lh.thickness.vtk')
"""
import os
import nibabel as nb
from mindboggle.utils.io_vtk import rewrite_scalars
curvature_values = nb.freesurfer.read_morph_data(surface_file)
scalar_names = os.path.basename(surface_file)
if not output_vtk:
output_vtk = os.path.join(os.getcwd(),
os.path.basename(surface_file)+'.vtk')
rewrite_scalars(vtk_file, output_vtk, curvature_values, scalar_names)
if not os.path.exists(output_vtk):
raise(IOError(output_vtk + " not found"))
return output_vtk
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_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 extract_subfolds(depth_file,
folds,
min_size=10,
depth_factor=0.25,
depth_ratio=0.1,
tolerance=0.01,
save_file=False):
"""
Use depth to segment folds into subfolds in a triangular surface mesh.
Note ::
The function extract_sulci() performs about the same whether folds
or subfolds are used as input. The latter leads to some loss of
small subfolds and possibly holes for small subfolds in the middle
of other subfolds.
Note about the watershed() function:
The watershed() function performs individual seed growing from deep seeds,
repeats segmentation from the resulting seeds until each seed's segment
touches a boundary. The function segment() fills in the rest. Finally
segments are joined if their seeds are too close to each other.
Despite these precautions, the order of seed selection in segment() could
possibly influence the resulting borders between adjoining segments.
[The propagate() function is slower and insensitive to depth,
but is not biased by seed order.]
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
min_size : integer
minimum number of vertices for a subfold
depth_factor : float
watershed() depth_factor:
factor to determine whether to merge two neighboring watershed catchment
basins -- they are merged if the Euclidean distance between their basin
seeds is less than this fraction of the maximum Euclidean distance
between points having minimum and maximum depths
depth_ratio : float
watershed() depth_ratio:
the minimum fraction of depth for a neighboring shallower
watershed catchment basin (otherwise merged with the deeper basin)
tolerance : float
watershed() tolerance:
tolerance for detecting differences in depth between vertices
save_file : Boolean
save output VTK file?
Returns
-------
subfolds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_subfolds : int
number of subfolds
subfolds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.utils.mesh import find_neighbors_from_file
>>> from mindboggle.features.folds import extract_subfolds
>>> from mindboggle.utils.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> min_size = 10
>>> depth_factor = 0.5
>>> depth_ratio = 0.1
>>> tolerance = 0.01
>>> #
>>> subfolds, n_subfolds, subfolds_file = extract_subfolds(depth_file,
>>> folds, min_size, depth_factor, depth_ratio, tolerance, True)
>>> #
>>> # View:
>>> rewrite_scalars(depth_file, 'subfolds.vtk', subfolds, 'subfolds', subfolds)
>>> plot_surfaces('subfolds.vtk')
"""
import os
import numpy as np
from time import time
from mindboggle.utils.io_vtk import rewrite_scalars, read_vtk
from mindboggle.utils.mesh import find_neighbors
from mindboggle.utils.segment import segment, propagate, watershed
print("Segment folds into subfolds")
t0 = time()
#-------------------------------------------------------------------------
# Load depth values for all vertices
#-------------------------------------------------------------------------
faces, lines, indices, points, npoints, depths, \
name, input_vtk = read_vtk(depth_file, return_first=True, return_array=True)
#-------------------------------------------------------------------------
# Find neighbors for each vertex
#-------------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
#-------------------------------------------------------------------------
# Segment folds into "watershed basins"
#-------------------------------------------------------------------------
indices_folds = [i for i, x in enumerate(folds) if x != -1]
subfolds, seed_indices = watershed(depths,
points,
indices_folds,
neighbor_lists,
min_size,
depth_factor=0.25,
depth_ratio=0.1,
tolerance=0.01,
regrow=True)
# Print statement
n_subfolds = len([x for x in np.unique(subfolds) if x != -1])
print(' ...Extracted {0} subfolds ({1:.2f} seconds)'.format(
n_subfolds,
time() - t0))
#-------------------------------------------------------------------------
# Return subfolds, number of subfolds, file name
#-------------------------------------------------------------------------
if save_file:
subfolds_file = os.path.join(os.getcwd(), 'subfolds.vtk')
rewrite_scalars(depth_file, subfolds_file, subfolds, 'subfolds',
subfolds)
if not os.path.exists(subfolds_file):
raise (IOError(subfolds_file + " not found"))
else:
subfolds_file = None
return subfolds, n_subfolds, subfolds_file
def extract_subfolds(depth_file, folds, min_size=10, depth_factor=0.25,
depth_ratio=0.1, tolerance=0.01, save_file=False):
"""
Use depth to segment folds into subfolds in a triangular surface mesh.
Note ::
The function extract_sulci() performs about the same whether folds
or subfolds are used as input. The latter leads to some loss of
small subfolds and possibly holes for small subfolds in the middle
of other subfolds.
Note about the watershed() function:
The watershed() function performs individual seed growing from deep seeds,
repeats segmentation from the resulting seeds until each seed's segment
touches a boundary. The function segment() fills in the rest. Finally
segments are joined if their seeds are too close to each other.
Despite these precautions, the order of seed selection in segment() could
possibly influence the resulting borders between adjoining segments.
[The propagate() function is slower and insensitive to depth,
but is not biased by seed order.]
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
min_size : integer
minimum number of vertices for a subfold
depth_factor : float
watershed() depth_factor:
factor to determine whether to merge two neighboring watershed catchment
basins -- they are merged if the Euclidean distance between their basin
seeds is less than this fraction of the maximum Euclidean distance
between points having minimum and maximum depths
depth_ratio : float
watershed() depth_ratio:
the minimum fraction of depth for a neighboring shallower
watershed catchment basin (otherwise merged with the deeper basin)
tolerance : float
watershed() tolerance:
tolerance for detecting differences in depth between vertices
save_file : Boolean
save output VTK file?
Returns
-------
subfolds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_subfolds : int
number of subfolds
subfolds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.utils.mesh import find_neighbors_from_file
>>> from mindboggle.features.folds import extract_subfolds
>>> from mindboggle.utils.plots import plot_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> min_size = 10
>>> depth_factor = 0.5
>>> depth_ratio = 0.1
>>> tolerance = 0.01
>>> #
>>> subfolds, n_subfolds, subfolds_file = extract_subfolds(depth_file,
>>> folds, min_size, depth_factor, depth_ratio, tolerance, True)
>>> #
>>> # View:
>>> rewrite_scalars(depth_file, 'subfolds.vtk', subfolds, 'subfolds', subfolds)
>>> plot_vtk('subfolds.vtk')
"""
import os
import numpy as np
from time import time
from mindboggle.utils.io_vtk import rewrite_scalars, read_vtk
from mindboggle.utils.mesh import find_neighbors
from mindboggle.utils.segment import segment, propagate, watershed
print("Segment folds into subfolds")
t0 = time()
#-------------------------------------------------------------------------
# Load depth values for all vertices
#-------------------------------------------------------------------------
faces, lines, indices, points, npoints, depths, \
name, input_vtk = read_vtk(depth_file, return_first=True, return_array=True)
#-------------------------------------------------------------------------
# Find neighbors for each vertex
#-------------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
#-------------------------------------------------------------------------
# Segment folds into "watershed basins"
#-------------------------------------------------------------------------
indices_folds = [i for i,x in enumerate(folds) if x > -1]
subfolds, seed_indices = watershed(depths, points, indices_folds,
neighbor_lists, min_size, depth_factor=0.25,
depth_ratio=0.1, tolerance=0.01, regrow=True)
# Print statement
n_subfolds = len([x for x in np.unique(subfolds) if x != -1])
print(' ...Extracted {0} subfolds ({1:.2f} seconds)'.
format(n_subfolds, time() - t0))
#-------------------------------------------------------------------------
# Return subfolds, number of subfolds, file name
#-------------------------------------------------------------------------
if save_file:
subfolds_file = os.path.join(os.getcwd(), 'subfolds.vtk')
rewrite_scalars(depth_file, subfolds_file, subfolds, 'subfolds', subfolds)
if not os.path.exists(subfolds_file):
raise(IOError(subfolds_file + " not found"))
else:
subfolds_file = None
return subfolds, n_subfolds, subfolds_file
sulci_file = os.path.join(data_path, 'arno', 'features', 'sulci.vtk')
faces, lines, indices, points, npoints, sulci, name, input_vtk = read_vtk(sulci_file)
sulcus_ID = 1
sulcus_indices = [i for i,x in enumerate(sulci) if x == sulcus_ID]
sulcus_faces = remove_faces(faces, sulcus_indices)
sulcus_neighbor_lists = find_neighbors(sulcus_faces, len(points))
G=nx.Graph()
G.add_nodes_from(sulcus_indices)
for i, sulcus_neighbor_list in enumerate(sulcus_neighbor_lists):
G.add_edges_from([[i,x] for x in sulcus_neighbor_list])
adjacency_matrix = nx.adjacency_matrix(G, nodelist=None, weight='weight')
indices_to_connect = [0, len(sulcus_indices)-1]
adjacency_matrix2, W, Path, Degree, TreeNbr = min_span_tree(adjacency_matrix,
indices_to_connect)
# Write results to vtk file and view:
MST = np.zeros(len(points))
MST[W] = 1
rewrite_scalars(sulci_file, 'test_min_span_tree.vtk', MST, 'MST', MST)
Terminal, Branching = [], []
for vtx in xrange(0,Num):
if Degree[vtx] ==1:
Terminal.append(vtx)
elif Degree[vtx] > 2:
Branching.append(vtx)
Path, NodeColor = prune(Path, Degree, TreeNbr, Terminal, Branching, indices_to_connect, sulcus_indices)
#endpoints = [i for i,x in enumerate(Degree) if x == 1]
def segment_fundi(fundus_per_fold, sulci=[], vtk_file='', save_file=False):
"""
Segment fundi by sulcus definitions.
Parameters
----------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
sulci : numpy array or list of integers
sulcus number for each vertex, used to filter and label fundi
vtk_file : string (if save_file)
VTK file with sulcus number for each vertex
save_file : Boolean
save output VTK file?
Returns
-------
fundus_per_sulcus : list of integers
fundus numbers for all vertices, labeled by sulcus
(-1 for non-fundus vertices)
n_fundi : integer
number of fundi
fundus_per_sulcus_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more sulci:
>>> single_fold = True
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.features.fundi import extract_fundi, segment_fundi
>>> from mindboggle.utils.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> sulci, name = read_scalars(vtk_file, True, True)
>>> curv_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'travel_depth_rescaled.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file, True, True)
>>> if single_fold:
>>> fold_number = 2 #11
>>> folds[folds != fold_number] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> fundus_per_fold, o1, o2 = extract_fundi(folds, curv_file, depth_file, min_separation, erode_ratio, erode_min_size, save_file)
>>> o1, o2, fundus_per_sulcus_file = segment_fundi(fundus_per_fold, sulci, vtk_file, save_file)
>>> #
>>> # View:
>>> plot_surfaces(fundus_per_sulcus_file)
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from mindboggle.utils.io_vtk import rewrite_scalars
if isinstance(sulci, list):
sulci = np.array(sulci)
#-------------------------------------------------------------------------
# Create fundi by segmenting fold fundi with overlapping sulcus labels:
#-------------------------------------------------------------------------
indices = [i for i, x in enumerate(fundus_per_fold) if x != -1]
if indices and np.size(sulci):
fundus_per_sulcus = -1 * np.ones(len(sulci))
fundus_per_sulcus[indices] = sulci[indices]
n_fundi = len([x for x in np.unique(fundus_per_sulcus) if x != -1])
else:
fundus_per_sulcus = []
n_fundi = 0
if n_fundi == 1:
sdum = 'sulcus fundus'
else:
sdum = 'sulcus fundi'
print(' Segmented {0} {1}'.format(n_fundi, sdum))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
fundus_per_sulcus_file = None
if n_fundi > 0:
fundus_per_sulcus = [int(x) for x in fundus_per_sulcus]
if save_file and os.path.exists(vtk_file):
fundus_per_sulcus_file = os.path.join(os.getcwd(),
'fundus_per_sulcus.vtk')
rewrite_scalars(vtk_file, fundus_per_sulcus_file,
fundus_per_sulcus, 'fundus_per_sulcus',
fundus_per_sulcus)
if not os.path.exists(fundus_per_sulcus_file):
raise (IOError(fundus_per_sulcus_file + " not found"))
return fundus_per_sulcus, n_fundi, fundus_per_sulcus_file
def extract_sulci(labels_file, folds_or_file, hemi, sulcus_label_pair_lists,
unique_sulcus_label_pairs, min_boundary=1, sulcus_names=[]):
"""
Identify sulci from folds in a brain surface according to a labeling
protocol that includes a list of label pairs defining each sulcus.
A fold is a group of connected, deep vertices.
Steps for each fold ::
1. Remove fold if it has fewer than two labels.
2. Remove fold if its labels do not contain a sulcus label pair.
3. Find vertices with labels that are in only one of the fold's
label boundary pairs. Assign the vertices the sulcus with the
label pair if they are connected to the label boundary for that pair.
4. If there are remaining vertices, segment into sets of vertices
connected to label boundaries, and assign a unique ID to each segment.
Parameters
----------
labels_file : string
file name for surface mesh VTK containing labels for all vertices
folds_or_file : list or string
fold number for each vertex or name of VTK file containing folds scalars
hemi : string
hemisphere ('lh' or 'rh')
sulcus_label_pair_lists : list of two lists of multiple lists of integer pairs
list containing left and right lists, each with multiple lists of
integer pairs corresponding to label boundaries / sulcus / fundus
unique_sulcus_label_pairs : list of unique pairs of integers
unique label pairs
min_boundary : integer
minimum number of vertices for a sulcus label boundary segment
sulcus_names : list of strings
names of sulci
Returns
-------
sulci : list of integers
sulcus numbers for all vertices (-1 for non-sulcus vertices)
n_sulci : integers
number of sulci
sulci_file : string
name of output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.labels.protocol import dkt_protocol
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.utils.plots import plot_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> # Load labels, folds, neighbor lists, and sulcus names and label pairs
>>> labels_file = os.path.join(path, 'arno', 'labels', 'relabeled_lh.DKTatlas40.gcs.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds_or_file, name = read_scalars(folds_file)
>>> protocol = 'DKT31'
>>> hemi = 'lh'
>>> sulcus_names, sulcus_label_pair_lists, unique_sulcus_label_pairs,
... label_names, label_numbers, cortex_names, cortex_numbers,
... noncortex_names, noncortex_numbers = dkt_protocol(protocol)
>>> min_boundary = 10
>>> #
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
>>> hemi, sulcus_label_pair_lists, unique_sulcus_label_pairs,
>>> min_boundary, sulcus_names)
>>> # View:
>>> plot_vtk('sulci.vtk')
"""
import os
from time import time
import numpy as np
from mindboggle.utils.io_vtk import read_scalars, read_vtk, rewrite_scalars
from mindboggle.utils.mesh import find_neighbors
from mindboggle.labels.labels import extract_borders
from mindboggle.utils.segment import propagate, segment
# Load fold numbers if folds_or_file is a string
if isinstance(folds_or_file, str):
folds, name = read_scalars(folds_or_file)
elif isinstance(folds_or_file, list):
folds = folds_or_file
if hemi == 'lh':
sulcus_label_pair_lists = sulcus_label_pair_lists[0]
elif hemi == 'rh':
sulcus_label_pair_lists = sulcus_label_pair_lists[1]
else:
print("Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors
faces, foo1, foo2, points, npoints, labels, foo3, foo4 = read_vtk(labels_file)
neighbor_lists = find_neighbors(faces, npoints)
# Array of sulcus IDs for fold vertices, initialized as -1.
# Since we do not touch gyral vertices and vertices whose labels
# are not in the label list, or vertices having only one label,
# their sulcus IDs will remain -1.
sulci = -1 * np.ones(npoints)
#-------------------------------------------------------------------------
# Loop through folds
#-------------------------------------------------------------------------
fold_numbers = [int(x) for x in np.unique(folds) if x > -1]
n_folds = len(fold_numbers)
print("Extract sulci from {0} folds...".format(n_folds))
t0 = time()
for n_fold in fold_numbers:
fold = [i for i,x in enumerate(folds) if x == n_fold]
len_fold = len(fold)
# List the labels in this fold (greater than zero)
fold_labels = [labels[x] for x in fold]
unique_fold_labels = [int(x) for x in np.unique(fold_labels) if x > 0]
#---------------------------------------------------------------------
# NO MATCH -- fold has fewer than two labels
#---------------------------------------------------------------------
if len(unique_fold_labels) < 2:
# Ignore: sulci already initialized with -1 values
if not unique_fold_labels:
print(" Fold {0} ({1} vertices): NO MATCH -- fold has no labels".
format(n_fold, len_fold))
else:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has only one label ({2})".
format(n_fold, len_fold, unique_fold_labels[0]))
# Ignore: sulci already initialized with -1 values
else:
# Find all label boundary pairs within the fold
indices_fold_pairs, fold_pairs, unique_fold_pairs = extract_borders(
fold, labels, neighbor_lists, ignore_values=[],
return_label_pairs=True)
# Find fold label pairs in the protocol (pairs are already sorted)
fold_pairs_in_protocol = [x for x in unique_fold_pairs
if x in unique_sulcus_label_pairs]
if unique_fold_labels:
print(" Fold {0} labels: {1} ({2} vertices)".format(n_fold,
', '.join([str(x) for x in unique_fold_labels]), len_fold))
#-----------------------------------------------------------------
# NO MATCH -- fold has no sulcus label pair
#-----------------------------------------------------------------
if not fold_pairs_in_protocol:
print(" Fold {0}: NO MATCH -- fold has no sulcus label pair".
format(n_fold, len_fold))
#-----------------------------------------------------------------
# Possible matches
#-----------------------------------------------------------------
else:
print(" Fold {0} label pairs in protocol: {1}".format(n_fold,
', '.join([str(x) for x in fold_pairs_in_protocol])))
# Labels in the protocol (includes repeats across label pairs)
labels_in_pairs = [x for lst in fold_pairs_in_protocol for x in lst]
# Labels that appear in one or more than one sulcus label boundary
unique_labels = []
nonunique_labels = []
for label in np.unique(labels_in_pairs):
if len([x for x in labels_in_pairs if x == label]) == 1:
unique_labels.append(label)
else:
nonunique_labels.append(label)
#-------------------------------------------------------------
# Vertices whose labels are in only one sulcus label pair
#-------------------------------------------------------------
# Find vertices with a label that is in only one of the fold's
# label pairs (the other label in the pair can exist
# in other pairs). Assign the vertices the sulcus with the label
# pair if they are connected to the label boundary for that pair.
#-------------------------------------------------------------
if len(unique_labels):
for pair in fold_pairs_in_protocol:
# If one or both labels in label pair is/are unique
unique_labels_in_pair = [x for x in pair if x in unique_labels]
n_unique = len(unique_labels_in_pair)
if n_unique:
ID = [i for i,x in enumerate(sulcus_label_pair_lists)
if pair in x][0]
# Construct seeds from label boundary vertices
# (fold_pairs and pair already sorted)
indices_pair = [x for i,x in enumerate(indices_fold_pairs)
if fold_pairs[i] == pair]
# Identify vertices with unique label(s) in pair
indices_unique_labels = [fold[i]
for i,x in enumerate(fold_labels)
if x in unique_sulcus_label_pairs]
# Propagate from seeds to labels in label pair
sulci2 = segment(indices_unique_labels, neighbor_lists,
min_region_size=1,
seed_lists=[indices_pair],
keep_seeding=False,
spread_within_labels=True,
labels=labels)
sulci[sulci2 > -1] = ID
# Print statement
if n_unique == 1:
ps1 = '1 label'
else:
ps1 = 'Both labels'
if len(sulcus_names):
ps2 = sulcus_names[ID]
else:
ps2 = ''
print(" {0} unique to one fold pair: {1} {2}".
format(ps1, ps2, unique_labels_in_pair))
#-------------------------------------------------------------
# Vertex labels shared by multiple label pairs
#-------------------------------------------------------------
# Propagate labels from label borders to vertices with labels
# that are shared by multiple label pairs in the fold.
#-------------------------------------------------------------
if len(nonunique_labels):
# For each label shared by different label pairs
for label in nonunique_labels:
# Print statement
print(" Propagate sulcus label borders with label {0}".
format(int(label)))
# Construct seeds from label boundary vertices
seeds = -1 * np.ones(len(points))
for ID, label_pair_list in enumerate(sulcus_label_pair_lists):
label_pairs = [x for x in label_pair_list if label in x]
for label_pair in label_pairs:
indices_pair = [x for i,x in enumerate(indices_fold_pairs)
if np.sort(fold_pairs[i]).tolist() == label_pair]
if indices_pair:
# Do not include short boundary segments
if min_boundary > 1:
indices_pair2 = []
seeds2 = segment(indices_pair, neighbor_lists)
for seed2 in range(int(max(seeds2))+1):
iseed2 = [i for i,x in enumerate(seeds2)
if x == seed2]
if len(iseed2) >= min_boundary:
indices_pair2.extend(iseed2)
else:
if len(iseed2) == 1:
print(" Remove assignment "
"of ID {0} from 1 vertex".
format(seed2))
else:
print(" Remove assignment "
"of ID {0} from {1} vertices".
format(seed2, len(iseed2)))
indices_pair = indices_pair2
# Assign sulcus IDs to seeds
seeds[indices_pair] = ID
# Identify vertices with the label
label_array = -1 * np.ones(len(points))
indices_label = [fold[i] for i,x in enumerate(fold_labels)
if x == label]
if len(indices_label):
label_array[indices_label] = 1
# Propagate from seeds to vertices with label
#indices_seeds = []
#for seed in range(int(max(seeds))+1):
# indices_seeds.append([i for i,x in enumerate(seeds)
# if x == seed])
#sulci2 = segment(indices_label, neighbor_lists,
# 50, indices_seeds, False, True, labels)
sulci2 = propagate(points, faces,
label_array, seeds, sulci,
max_iters=10000,
tol=0.001, sigma=5)
sulci[sulci2 > -1] = sulci2[sulci2 > -1]
#-------------------------------------------------------------------------
# Print out assigned sulci
#-------------------------------------------------------------------------
sulcus_numbers = [int(x) for x in np.unique(sulci) if x > -1]
n_sulci = len(sulcus_numbers)
print("Extracted {0} sulci from {1} folds ({2:.1f}s):".
format(n_sulci, n_folds, time()-t0))
if len(sulcus_names):
for sulcus_number in sulcus_numbers:
print(" {0}: {1}".format(sulcus_number, sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in sulcus_numbers]))
#-------------------------------------------------------------------------
# Print out unresolved sulci
#-------------------------------------------------------------------------
unresolved = [i for i in range(len(sulcus_label_pair_lists))
if i not in sulcus_numbers]
if len(unresolved) == 1:
print("The following sulcus is unaccounted for:")
else:
print("The following {0} sulci are unaccounted for:".format(len(unresolved)))
if len(sulcus_names):
for sulcus_number in unresolved:
print(" {0}: {1}".format(sulcus_number, sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in unresolved]))
#-------------------------------------------------------------------------
# Return sulci, number of sulci, and file name
#-------------------------------------------------------------------------
sulci_file = os.path.join(os.getcwd(), 'sulci.vtk')
rewrite_scalars(labels_file, sulci_file, sulci, 'sulci', sulci)
sulci.tolist()
return sulci, n_sulci, sulci_file
def 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 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 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 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 segment_fundi(fundus_per_fold, sulci=[], vtk_file='', save_file=False):
"""
Segment fundi by sulcus definitions.
Parameters
----------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
sulci : numpy array or list of integers
sulcus number for each vertex, used to filter and label fundi
vtk_file : string (if save_file)
VTK file with sulcus number for each vertex
save_file : Boolean
save output VTK file?
Returns
-------
fundus_per_sulcus : list of integers
fundus numbers for all vertices, labeled by sulcus
(-1 for non-fundus vertices)
n_fundi : integer
number of fundi
fundus_per_sulcus_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more sulci:
>>> single_fold = True
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.features.fundi import extract_fundi, segment_fundi
>>> from mindboggle.utils.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> sulci, name = read_scalars(vtk_file, True, True)
>>> curv_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'travel_depth_rescaled.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file, True, True)
>>> if single_fold:
>>> fold_number = 2 #11
>>> folds[folds != fold_number] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> fundus_per_fold, o1, o2 = extract_fundi(folds, curv_file, depth_file, min_separation, erode_ratio, erode_min_size, save_file)
>>> o1, o2, fundus_per_sulcus_file = segment_fundi(fundus_per_fold, sulci, vtk_file, save_file)
>>> #
>>> # View:
>>> plot_surfaces(fundus_per_sulcus_file)
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from mindboggle.utils.io_vtk import rewrite_scalars
if isinstance(sulci, list):
sulci = np.array(sulci)
#-------------------------------------------------------------------------
# Create fundi by segmenting fold fundi with overlapping sulcus labels:
#-------------------------------------------------------------------------
indices = [i for i,x in enumerate(fundus_per_fold) if x != -1]
if indices and np.size(sulci):
fundus_per_sulcus = -1 * np.ones(len(sulci))
fundus_per_sulcus[indices] = sulci[indices]
n_fundi = len([x for x in np.unique(fundus_per_sulcus) if x != -1])
else:
fundus_per_sulcus = []
n_fundi = 0
if n_fundi == 1:
sdum = 'sulcus fundus'
else:
sdum = 'sulcus fundi'
print(' Segmented {0} {1}'.format(n_fundi, sdum))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
fundus_per_sulcus_file = None
if n_fundi > 0:
fundus_per_sulcus = [int(x) for x in fundus_per_sulcus]
if save_file and os.path.exists(vtk_file):
fundus_per_sulcus_file = os.path.join(os.getcwd(),
'fundus_per_sulcus.vtk')
rewrite_scalars(vtk_file, fundus_per_sulcus_file,
fundus_per_sulcus, 'fundus_per_sulcus',
fundus_per_sulcus)
if not os.path.exists(fundus_per_sulcus_file):
raise(IOError(fundus_per_sulcus_file + " not found"))
return fundus_per_sulcus, n_fundi, fundus_per_sulcus_file
def extract_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 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 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 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 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 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 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 extract_folds(depth_file, min_fold_size=50, tiny_depth=0.001, save_file=False):
"""
Use depth to extract folds from a triangular surface mesh.
Steps ::
1. Compute histogram of depth measures.
2. Define a depth threshold and find the deepest vertices.
3. Segment deep vertices as an initial set of folds.
4. Remove small folds.
5. Find and fill holes in the folds.
6. Renumber folds.
Step 2 ::
To extract an initial set of deep vertices from the surface mesh,
we anticipate that there will be a rapidly decreasing distribution
of low depth values (on the outer surface) with a long tail
of higher depth values (in the folds), so we smooth the histogram's
bin values, convolve to compute slopes, and find the depth value
for the first bin with slope = 0. This is our threshold.
Step 5 ::
The folds could have holes in areas shallower than the depth threshold.
Calling fill_holes() could accidentally include very shallow areas
(in an annulus-shaped fold, for example), so we call fill_holes() with
the argument exclude_range set close to zero to retain these areas.
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
min_fold_size : integer
minimum fold size (number of vertices)
tiny_depth : float
largest non-zero depth value that will stop a hole from being filled
save_file : Boolean
save output VTK file?
Returns
-------
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_folds : int
number of folds
depth_threshold : float
threshold defining the minimum depth for vertices to be in a fold
bins : list of integers
histogram bins: each is the number of vertices within a range of depth values
bin_edges : list of floats
histogram bin edge values defining the bin ranges of depth values
folds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> import os
>>> import numpy as np
>>> import pylab
>>> from scipy.ndimage.filters import gaussian_filter1d
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.utils.mesh import find_neighbors_from_file
>>> from mindboggle.utils.plots import plot_vtk
>>> from mindboggle.features.folds import extract_folds
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> neighbor_lists = find_neighbors_from_file(depth_file)
>>> min_fold_size = 50
>>> tiny_depth = 0.001
>>> save_file = True
>>> #
>>> folds, n_folds, thr, bins, bin_edges, folds_file = extract_folds(depth_file,
>>> min_fold_size, tiny_depth, save_file)
>>> #
>>> # View folds:
>>> plot_vtk('folds.vtk')
>>> # Plot histogram and depth threshold:
>>> depths, name = read_scalars(depth_file)
>>> nbins = np.round(len(depths) / 100.0)
>>> a,b,c = pylab.hist(depths, bins=nbins)
>>> pylab.plot(thr*np.ones((100,1)), np.linspace(0, max(bins), 100), 'r.')
>>> pylab.show()
>>> # Plot smoothed histogram:
>>> bins_smooth = gaussian_filter1d(bins.tolist(), 5)
>>> pylab.plot(range(len(bins)), bins, '.', range(len(bins)), bins_smooth,'-')
>>> pylab.show()
"""
import os
import sys
import numpy as np
from time import time
from scipy.ndimage.filters import gaussian_filter1d
from mindboggle.utils.io_vtk import rewrite_scalars, read_vtk
from mindboggle.utils.mesh import find_neighbors
from mindboggle.utils.morph import fill_holes
from mindboggle.utils.segment import segment
do_fill_holes = True
print("Extract folds in surface mesh")
t0 = time()
#-------------------------------------------------------------------------
# Load depth values for all vertices
#-------------------------------------------------------------------------
faces, lines, indices, points, npoints, depths, name, input_vtk = read_vtk(depth_file,
return_first=True, return_array=True)
#-------------------------------------------------------------------------
# Find neighbors for each vertex
#-------------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
#-------------------------------------------------------------------------
# Compute histogram of depth measures
#-------------------------------------------------------------------------
min_vertices = 10000
if npoints > min_vertices:
nbins = np.round(npoints / 100.0)
else:
sys.err(" Expecting at least {0} vertices to create depth histogram".
format(min_vertices))
bins, bin_edges = np.histogram(depths, bins=nbins)
#-------------------------------------------------------------------------
# Anticipating that there will be a rapidly decreasing distribution
# of low depth values (on the outer surface) with a long tail of higher
# depth values (in the folds), smooth the bin values (Gaussian), convolve
# to compute slopes, and find the depth for the first bin with slope = 0.
#-------------------------------------------------------------------------
bins_smooth = gaussian_filter1d(bins.tolist(), 5)
window = [-1, 0, 1]
bin_slopes = np.convolve(bins_smooth, window, mode='same') / (len(window) - 1)
ibins0 = np.where(bin_slopes == 0)[0]
if ibins0.shape:
depth_threshold = bin_edges[ibins0[0]]
else:
depth_threshold = np.median(depths)
#-------------------------------------------------------------------------
# Find the deepest vertices
#-------------------------------------------------------------------------
indices_deep = [i for i,x in enumerate(depths) if x >= depth_threshold]
if indices_deep:
#---------------------------------------------------------------------
# Segment deep vertices as an initial set of folds
#---------------------------------------------------------------------
print(" Segment vertices deeper than {0:.2f} as folds".format(depth_threshold))
t1 = time()
folds = segment(indices_deep, neighbor_lists)
# Slightly slower alternative -- fill boundaries:
#regions = -1 * np.ones(len(points))
#regions[indices_deep] = 1
#folds = segment_by_filling_borders(regions, neighbor_lists)
print(' ...Segmented folds ({0:.2f} seconds)'.format(time() - t1))
#---------------------------------------------------------------------
# Remove small folds
#---------------------------------------------------------------------
if min_fold_size > 1:
print(' Remove folds smaller than {0}'.format(min_fold_size))
unique_folds = [x for x in np.unique(folds) if x > -1]
for nfold in unique_folds:
indices_fold = [i for i,x in enumerate(folds) if x == nfold]
if len(indices_fold) < min_fold_size:
folds[indices_fold] = -1
#---------------------------------------------------------------------
# Find and fill holes in the folds
# Note: Surfaces surrounded by folds can be mistaken for holes,
# so exclude_range includes outer surface values close to zero.
#---------------------------------------------------------------------
if do_fill_holes:
print(" Find and fill holes in the folds")
folds = fill_holes(folds, neighbor_lists, values=depths,
exclude_range=[0, tiny_depth])
#---------------------------------------------------------------------
# Renumber folds so they are sequential
#---------------------------------------------------------------------
renumber_folds = -1 * np.ones(len(folds))
fold_numbers = [int(x) for x in np.unique(folds) if x > -1]
for i_fold, n_fold in enumerate(fold_numbers):
fold = [i for i,x in enumerate(folds) if x == n_fold]
renumber_folds[fold] = i_fold
folds = renumber_folds
n_folds = i_fold + 1
# Print statement
print(' ...Extracted {0} folds ({1:.2f} seconds)'.
format(n_folds, time() - t0))
else:
print(' No deep vertices')
#-------------------------------------------------------------------------
# Return folds, number of folds, file name
#-------------------------------------------------------------------------
if save_file:
folds_file = os.path.join(os.getcwd(), 'folds.vtk')
rewrite_scalars(depth_file, folds_file, folds, 'folds', folds)
if not os.path.exists(folds_file):
raise(IOError(folds_file + " not found"))
else:
folds_file = None
return folds.tolist(), n_folds, depth_threshold, bins, bin_edges, folds_file
def 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
def extract_folds(depth_file,
min_fold_size=50,
tiny_depth=0.001,
save_file=False):
"""
Use depth to extract folds from a triangular surface mesh.
Steps ::
1. Compute histogram of depth measures.
2. Define a depth threshold and find the deepest vertices.
3. Segment deep vertices as an initial set of folds.
4. Remove small folds.
5. Find and fill holes in the folds.
6. Renumber folds.
Step 2 ::
To extract an initial set of deep vertices from the surface mesh,
we anticipate that there will be a rapidly decreasing distribution
of low depth values (on the outer surface) with a long tail
of higher depth values (in the folds), so we smooth the histogram's
bin values, convolve to compute slopes, and find the depth value
for the first bin with slope = 0. This is our threshold.
Step 5 ::
The folds could have holes in areas shallower than the depth threshold.
Calling fill_holes() could accidentally include very shallow areas
(in an annulus-shaped fold, for example), so we call fill_holes() with
the argument exclude_range set close to zero to retain these areas.
Parameters
----------
depth_file : string
surface mesh file in VTK format with faces and depth scalar values
min_fold_size : integer
minimum fold size (number of vertices)
tiny_depth : float
largest non-zero depth value that will stop a hole from being filled
save_file : Boolean
save output VTK file?
Returns
-------
folds : list of integers
fold numbers for all vertices (-1 for non-fold vertices)
n_folds : int
number of folds
depth_threshold : float
threshold defining the minimum depth for vertices to be in a fold
bins : list of integers
histogram bins: each is the number of vertices within a range of depth values
bin_edges : list of floats
histogram bin edge values defining the bin ranges of depth values
folds_file : string (if save_file)
name of output VTK file with fold IDs (-1 for non-fold vertices)
Examples
--------
>>> import os
>>> import numpy as np
>>> import pylab
>>> from scipy.ndimage.filters import gaussian_filter1d
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.utils.mesh import find_neighbors_from_file
>>> from mindboggle.utils.plots import plot_surfaces
>>> from mindboggle.features.folds import extract_folds
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> neighbor_lists = find_neighbors_from_file(depth_file)
>>> min_fold_size = 50
>>> tiny_depth = 0.001
>>> save_file = True
>>> #
>>> folds, n_folds, thr, bins, bin_edges, folds_file = extract_folds(depth_file,
>>> min_fold_size, tiny_depth, save_file)
>>> #
>>> # View folds:
>>> plot_surfaces('folds.vtk')
>>> # Plot histogram and depth threshold:
>>> depths, name = read_scalars(depth_file)
>>> nbins = np.round(len(depths) / 100.0)
>>> a,b,c = pylab.hist(depths, bins=nbins)
>>> pylab.plot(thr*np.ones((100,1)), np.linspace(0, max(bins), 100), 'r.')
>>> pylab.show()
>>> # Plot smoothed histogram:
>>> bins_smooth = gaussian_filter1d(bins.tolist(), 5)
>>> pylab.plot(range(len(bins)), bins, '.', range(len(bins)), bins_smooth,'-')
>>> pylab.show()
"""
import os
import sys
import numpy as np
from time import time
from scipy.ndimage.filters import gaussian_filter1d
from mindboggle.utils.io_vtk import rewrite_scalars, read_vtk
from mindboggle.utils.mesh import find_neighbors
from mindboggle.utils.morph import fill_holes
from mindboggle.utils.segment import segment
do_fill_holes = True
print("Extract folds in surface mesh")
t0 = time()
#-------------------------------------------------------------------------
# Load depth values for all vertices
#-------------------------------------------------------------------------
faces, lines, indices, points, npoints, depths, name, input_vtk = read_vtk(
depth_file, return_first=True, return_array=True)
#-------------------------------------------------------------------------
# Find neighbors for each vertex
#-------------------------------------------------------------------------
neighbor_lists = find_neighbors(faces, npoints)
#-------------------------------------------------------------------------
# Compute histogram of depth measures
#-------------------------------------------------------------------------
min_vertices = 10000
if npoints > min_vertices:
nbins = np.round(npoints / 100.0)
else:
sys.err(" Expecting at least {0} vertices to create depth histogram".
format(min_vertices))
bins, bin_edges = np.histogram(depths, bins=nbins)
#-------------------------------------------------------------------------
# Anticipating that there will be a rapidly decreasing distribution
# of low depth values (on the outer surface) with a long tail of higher
# depth values (in the folds), smooth the bin values (Gaussian), convolve
# to compute slopes, and find the depth for the first bin with slope = 0.
#-------------------------------------------------------------------------
bins_smooth = gaussian_filter1d(bins.tolist(), 5)
window = [-1, 0, 1]
bin_slopes = np.convolve(bins_smooth, window,
mode='same') / (len(window) - 1)
ibins0 = np.where(bin_slopes == 0)[0]
if ibins0.shape:
depth_threshold = bin_edges[ibins0[0]]
else:
depth_threshold = np.median(depths)
#-------------------------------------------------------------------------
# Find the deepest vertices
#-------------------------------------------------------------------------
indices_deep = [i for i, x in enumerate(depths) if x >= depth_threshold]
if indices_deep:
#---------------------------------------------------------------------
# Segment deep vertices as an initial set of folds
#---------------------------------------------------------------------
print(" Segment vertices deeper than {0:.2f} as folds".format(
depth_threshold))
t1 = time()
folds = segment(indices_deep, neighbor_lists)
# Slightly slower alternative -- fill boundaries:
#regions = -1 * np.ones(len(points))
#regions[indices_deep] = 1
#folds = segment_by_filling_borders(regions, neighbor_lists)
print(' ...Segmented folds ({0:.2f} seconds)'.format(time() - t1))
#---------------------------------------------------------------------
# Remove small folds
#---------------------------------------------------------------------
if min_fold_size > 1:
print(' Remove folds smaller than {0}'.format(min_fold_size))
unique_folds = [x for x in np.unique(folds) if x != -1]
for nfold in unique_folds:
indices_fold = [i for i, x in enumerate(folds) if x == nfold]
if len(indices_fold) < min_fold_size:
folds[indices_fold] = -1
#---------------------------------------------------------------------
# Find and fill holes in the folds
# Note: Surfaces surrounded by folds can be mistaken for holes,
# so exclude_range includes outer surface values close to zero.
#---------------------------------------------------------------------
if do_fill_holes:
print(" Find and fill holes in the folds")
folds = fill_holes(folds,
neighbor_lists,
values=depths,
exclude_range=[0, tiny_depth])
#---------------------------------------------------------------------
# Renumber folds so they are sequential
#---------------------------------------------------------------------
renumber_folds = -1 * np.ones(len(folds))
fold_numbers = [int(x) for x in np.unique(folds) if x != -1]
for i_fold, n_fold in enumerate(fold_numbers):
fold = [i for i, x in enumerate(folds) if x == n_fold]
renumber_folds[fold] = i_fold
folds = renumber_folds
n_folds = i_fold + 1
# Print statement
print(' ...Extracted {0} folds ({1:.2f} seconds)'.format(
n_folds,
time() - t0))
else:
print(' No deep vertices')
folds = [int(x) for x in folds]
#-------------------------------------------------------------------------
# Return folds, number of folds, file name
#-------------------------------------------------------------------------
if save_file:
folds_file = os.path.join(os.getcwd(), 'folds.vtk')
rewrite_scalars(depth_file, folds_file, folds, 'folds', folds)
if not os.path.exists(folds_file):
raise (IOError(folds_file + " not found"))
else:
folds_file = None
return folds, n_folds, depth_threshold, bins, bin_edges, folds_file
