def compute_fundus_distances(label_boundary_fundi, fundi, folds, points, n_fundi):
"""
Create a fundus distance matrix.
Compute the minimum distance from each label boundary vertex corresponding
to a fundus in the Desikan-Killiany-Tourville cortical labeling protocol
to all of the fundus vertices in the same fold.
Parameters
----------
label_boundary_fundi : list of integers
indices to fundi in sulcus protocol
fundi : list of integers
indices of vertices
folds : list of integers
indices to vertices
points : list of lists of three floats
coordinates
n_fundi : int
number of fundi
Returns
-------
distances : numpy array
distance value for each vertex (zero where there is no vertex)
distance_matrix : numpy array
rows are for vertices and columns for fundi (-1 default value)
"""
import numpy as np
from mindboggle.utils.compute import point_distance
npoints = len(points)
distances = np.zeros(npoints)
distance_matrix = -1 * np.ones((npoints, n_fundi))
sum_distances = 0
num_distances = 0
# For each label boundary fundus point
for i_label_point, fundus_ID in enumerate(label_boundary_fundi):
if fundus_ID > 0:
# Find (indices of) fundus points in the same fold
I_fundus_points = [i for i,x in enumerate(fundi)
if x > 0
if folds[i] == folds[i_label_point]]
# Find the closest fundus point to the label boundary fundus point
d, i = point_distance(points[i_label_point],
points[I_fundus_points])
distances[i_label_point] = d
distance_matrix[i_label_point, fundus_ID - 1] = d
sum_distances += d
num_distances += 1
if i_label_point % 1000 == 0 and num_distances > 0:
percent_done = 100.0 * float(i_label_point) / float(npoints)
mean_distance = sum_distances / num_distances
print('Done: {0} pct, mean dist {1}, {2}'.format(
percent_done, mean_distance, i_label_point))
mean_distance = sum_distances / num_distances
print('Done: 100 pct, mean dist {0}'.format(mean_distance))
return distances, distance_matrix
def watershed(depths, points, indices, neighbor_lists, min_size=1,
depth_factor=0.25, depth_ratio=0.1, tolerance=0.01, regrow=True):
"""
Segment vertices of a surface mesh into contiguous "watershed basins"
by seed growing from an iterative selection of the deepest vertices.
Steps ::
1. Grow segments from an iterative selection of the deepest seeds.
2. Regrow segments from the resulting seeds, until each seed's
segment touches a boundary.
3. Use the segment() function to fill in the rest.
4. Merge segments if their seeds are too close to each other
or their depths are very different.
Note ::
Despite the above precautions, the order of seed selection in segment()
could possibly influence the resulting borders between adjoining
segments (vs. propagate(), which is slower and insensitive to depth,
but is not biased by seed order).
Parameters
----------
depths : numpy array of floats
depth values for all vertices (default -1)
points : list of lists of floats
each element is a list of 3-D coordinates of a vertex on a surface mesh
indices : list of integers
indices to mesh vertices to be segmented
min_size : index
the minimum number of vertices in a basin
neighbor_lists : list of lists of integers
each list contains indices to neighboring vertices for each vertex
depth_factor : float
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
the minimum fraction of depth for a neighboring shallower
watershed catchment basin (otherwise merged with the deeper basin)
tolerance : float
tolerance for detecting differences in depth between vertices
regrow : Boolean
regrow segments from watershed seeds?
Returns
-------
segments : list of integers
region numbers for all vertices (default -1)
seed_indices : list of integers
list of indices to seed vertices
Examples
--------
>>> # Perform watershed segmentation on the deeper portions of a surface:
>>> import os
>>> import numpy as np
>>> from mindboggle.utils.mesh import find_neighbors
>>> from mindboggle.utils.plots import plot_vtk
>>> from mindboggle.utils.segment import watershed, segment
>>> from mindboggle.utils.io_vtk import read_vtk, read_scalars, rewrite_scalars
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> faces, lines, indices, points, npoints, depths, name, input_vtk = read_vtk(depth_file,
>>> return_first=True, return_array=True)
>>> indices = np.where(depths > 0.01)[0] # high to speed up
>>> neighbor_lists = find_neighbors(faces, npoints)
>>> min_size = 50
>>> depth_factor = 0.25
>>> depth_ratio = 0.1
>>> tolerance = 0.01
>>> regrow = True
>>> #
>>> segments, seed_indices = watershed(depths, points,
>>> indices, neighbor_lists, min_size, depth_factor, depth_ratio,
>>> tolerance, regrow)
>>> #
>>> # Write results to vtk file and view:
>>> rewrite_scalars(depth_file, 'watershed.vtk',
>>> segments, 'segments', segments)
>>> plot_vtk('watershed.vtk')
>>> # View watershed seeds:
>>> seeds = -1 * np.ones(len(depths))
>>> for i, s in enumerate(seed_indices):
>>> seeds[s] = i
>>> rewrite_scalars(depth_file, 'watershed_seeds.vtk',
>>> seeds, 'seeds', seeds)
>>> plot_vtk('watershed_seeds.vtk')
"""
import numpy as np
from time import time
from mindboggle.labels.labels import extract_borders
from mindboggle.utils.segment import segment
from mindboggle.utils.compute import point_distance
# Make sure argument is a list
if isinstance(indices, np.ndarray):
indices.tolist()
print('Segment {0} vertices by a surface watershed algorithm'.
format(len(indices)))
verbose = False
merge = True
t0 = time()
tiny = 0.000001
use_depth_ratio = True
#-------------------------------------------------------------------------
# Find the borders of the given mesh vertices (indices):
#-------------------------------------------------------------------------
D = np.ones(len(depths))
D[indices] = 2
borders, foo1, foo2 = extract_borders(range(len(depths)), D,
neighbor_lists, ignore_values=[], return_label_pairs=False)
#-------------------------------------------------------------------------
# Select deepest vertex as initial seed:
#-------------------------------------------------------------------------
index_deepest = indices[np.argmax(depths[indices])]
seed_list = [index_deepest]
basin_depths = []
original_indices = indices[:]
#-------------------------------------------------------------------------
# Loop until all vertices have been segmented.
# This limits the number of possible seeds:
#-------------------------------------------------------------------------
segments = -1 * np.ones(len(depths))
seed_indices = []
seed_points = []
all_regions = []
region = []
counter = 0
terminate = False
while not terminate:
# Add seeds to region:
region.extend(seed_list)
all_regions.extend(seed_list)
# Remove seeds from vertices to segment:
indices = list(frozenset(indices).difference(seed_list))
if indices:
# Identify neighbors of seeds:
neighbors = []
[neighbors.extend(neighbor_lists[x]) for x in seed_list]
# Select neighbors that have not been previously selected
# and are among the vertices to segment:
old_seed_list = seed_list[:]
seed_list = list(frozenset(neighbors).intersection(indices))
seed_list = list(frozenset(seed_list).difference(all_regions))
# For each vertex, select neighbors that are shallower:
seed_neighbors = []
for seed in old_seed_list:
seed_neighbors.extend([x for x in neighbor_lists[seed]
if depths[x] - tolerance <= depths[seed]])
seed_list = list(frozenset(seed_list).intersection(seed_neighbors))
else:
seed_list = []
# If there are no seeds remaining:
if not len(seed_list):
# If there is at least min_size points, assign counter to
# segmented region, store index, and increment counter:
if len(region) >= min_size:
segments[region] = counter
seed_indices.append(index_deepest)
seed_points.append(points[index_deepest])
counter += 1
# Compute basin depth (max - min):
Imax = region[np.argmax(depths[region])]
Imin = region[np.argmin(depths[region])]
max_depth = point_distance(points[Imax], [points[Imin]])[0]
basin_depths.append(max_depth)
# If vertices left to segment, re-initialize parameters:
if indices:
# Initialize new region/basin:
region = []
# Select deepest unsegmented vertex as new seed
# if its rescaled depth is close to 1:
index_deepest = indices[np.argmax(depths[indices])]
seed_list = [index_deepest]
# Termination criteria:
if not len(indices):
terminate = True
# Display current number and size of region:
if verbose:
print(" {0} vertices remain".format(len(indices)))
print(' ...Segmented {0} initial watershed regions ({1:.2f} seconds)'.
format(counter, time() - t0))
#-------------------------------------------------------------------------
# Regrow from (deep) watershed seeds, stopping at borders:
#-------------------------------------------------------------------------
if regrow:
print(' Regrow segments from watershed seeds, stopping at borders')
indices = original_indices[:]
segments = -1 * np.ones(len(depths))
all_regions = []
for iseed, seed_index in enumerate(seed_indices):
seed_list = [seed_index]
region = []
terminate = False
while not terminate:
# Add seeds to region:
region.extend(seed_list)
all_regions.extend(seed_list)
# Remove seeds from vertices to segment:
indices = list(frozenset(indices).difference(seed_list))
if indices:
# Identify neighbors of seeds:
neighbors = []
[neighbors.extend(neighbor_lists[x]) for x in seed_list]
# Select neighbors that have not been previously selected
# and are among the vertices to segment:
old_seed_list = seed_list[:]
seed_list = list(frozenset(neighbors).intersection(indices))
seed_list = list(frozenset(seed_list).difference(all_regions))
# For each vertex, select neighbors that are shallower:
seed_neighbors = []
for seed in old_seed_list:
seed_neighbors.extend([x for x in neighbor_lists[seed]
if depths[x] - tolerance <= depths[seed]])
seed_list = list(frozenset(seed_list).intersection(seed_neighbors))
# Remove seed list if it contains a border vertex:
if seed_list:
if list(frozenset(seed_list).intersection(borders)):
seed_list = []
else:
seed_list = []
# Terminate growth for this seed if the seed_list is empty:
if not len(seed_list):
terminate = True
# If there is at least min_size points, store index:
if len(region) >= min_size:
segments[region] = iseed
# Display current number and size of region:
if verbose:
print(" {0} vertices remain".format(len(indices)))
#---------------------------------------------------------------------
# Continue growth until there are no more vertices to segment:
#---------------------------------------------------------------------
# Note: As long as keep_seeding=False, the segment values in `segments`
# are equal to the order of the `basin_depths` and `seed_points` below.
seed_lists = [[i for i,x in enumerate(segments) if x==s]
for s in np.unique(segments) if s!=-1]
segments = segment(indices, neighbor_lists, min_region_size=1,
seed_lists=seed_lists, keep_seeding=False, spread_within_labels=False,
labels=[], label_lists=[], values=[], max_steps='', verbose=False)
print(' ...Regrew {0} watershed regions from seeds ({1:.2f} seconds)'.
format(iseed+1, time() - t0))
#-------------------------------------------------------------------------
# Merge watershed catchment basins:
#-------------------------------------------------------------------------
if merge:
# Extract segments pairs at borders between watershed basins:
print(' Merge watershed catchment basins with deeper neighboring basins')
if verbose:
print(' Extract basin borders')
foo1, foo2, pairs = extract_borders(original_indices, segments,
neighbor_lists, ignore_values=[-1],
return_label_pairs=True)
# Sort basin depths (descending order) -- return segment indices:
Isort = np.argsort(basin_depths).tolist()
Isort.reverse()
# Find neighboring basins to each of the sorted basins:
if verbose:
print(" Find neighboring basins")
basin_pairs = []
for index in Isort:
index_neighbors = [int(list(frozenset(x).difference([index]))[0])
for x in pairs if index in x]
if index_neighbors:
# Store neighbors whose depth is less than a fraction of the
# basin's depth and farther away than half the basin's depth:
if use_depth_ratio:
index_neighbors = [[x, index] for x in index_neighbors
if basin_depths[x] / (basin_depths[index]+tiny) < depth_ratio
if point_distance(seed_points[x], [seed_points[index]])[0] >
depth_factor * max([basin_depths[x], basin_depths[index]])]
# Store neighbors farther away than half the basin's depth:
else:
index_neighbors = [[x, index] for x in index_neighbors
if point_distance(seed_points[x], [seed_points[index]])[0] >
depth_factor * max([basin_depths[x], basin_depths[index]])]
if index_neighbors:
basin_pairs.extend(index_neighbors)
# Merge shallow watershed catchment basins:
if basin_pairs:
if verbose:
print(' Merge basins with deeper neighboring basins')
for basin_pair in basin_pairs:
segments[np.where(segments == basin_pair[0])] = basin_pair[1]
# Renumber segments so they are sequential:
renumber_segments = segments.copy()
segment_numbers = [int(x) for x in np.unique(segments) if x != -1]
for i_segment, n_segment in enumerate(segment_numbers):
segment = [i for i,x in enumerate(segments) if x == n_segment]
renumber_segments[segment] = i_segment
segments = renumber_segments
# Print statement:
print(' ...Merged segments to form {0} watershed regions ({1:.2f} seconds)'.
format(i_segment + 1, time() - t0))
return segments.tolist(), seed_indices
def source_to_target_distances(sourceIDs,
targetIDs,
points,
segmentIDs=[],
excludeIDs=[-1]):
"""
Create a Euclidean distance matrix between source and target points.
Compute the Euclidean distance from each source point to
its nearest target point, optionally within each segment.
Example::
Compute fundus-to-feature distances, the minimum distance
from each label boundary vertex (corresponding to a fundus
in the DKT cortical labeling protocol) to all of the
feature vertices in the same fold.
Parameters
----------
sourceIDs : list of N integers (N is the number of vertices)
source IDs, where any ID not in excludeIDs is a source point
targetIDs : list of N integers (N is the number of vertices)
target IDs, where any ID not in excludeIDs is a target point
points : list of N lists of three floats (N is the number of vertices)
coordinates of all vertices
segmentIDs : list of N integers (N is the number of vertices)
segment IDs, where each ID not in excludeIDs is considered a
different segment (unlike above, where value in sourceIDs or
targetIDs doesn't matter, so long as its not in excludeIDs);
source/target distances are computed within each segment
excludeIDs : list of integers
IDs to exclude
Returns
-------
distances : numpy array
distance value for each vertex (default -1)
distance_matrix : numpy array [#points by maximum segment ID + 1]
distances organized by segments (columns)
"""
import numpy as np
from mindboggle.utils.compute import point_distance
if isinstance(points, list):
points = np.asarray(points)
npoints = len(points)
# Extract unique segment IDs (or use all points as a single segment):
if np.size(segmentIDs):
segments = [x for x in np.unique(segmentIDs) if x not in excludeIDs]
else:
segmentIDs = np.zeros(npoints)
segments = [0]
nsegments = max(segments) + 1
# Initialize outputs:
distances = -1 * np.ones(npoints)
distance_matrix = -1 * np.ones((npoints, nsegments))
# For each segment:
for segment in segments:
segment_indices = [i for i, x in enumerate(segmentIDs) if x == segment]
# Find all source points in the segment:
source_indices = [
i for i, x in enumerate(sourceIDs) if x not in excludeIDs
if i in segment_indices
]
# Find all target points in the segment:
target_indices = [
i for i, x in enumerate(targetIDs) if x not in excludeIDs
if i in segment_indices
]
if source_indices and target_indices:
# For each source point in the segment:
for isource in source_indices:
# Find the closest target point:
d, i = point_distance(points[isource], points[target_indices])
distances[isource] = d
distance_matrix[isource, segment] = d
return distances, distance_matrix
def source_to_target_distances(sourceIDs, targetIDs, points,
segmentIDs=[], excludeIDs=[-1]):
"""
Create a Euclidean distance matrix between source and target points.
Compute the Euclidean distance from each source point to
its nearest target point, optionally within each segment.
Example::
Compute fundus-to-feature distances, the minimum distance
from each label boundary vertex (corresponding to a fundus
in the DKT cortical labeling protocol) to all of the
feature vertices in the same fold.
Parameters
----------
sourceIDs : list of N integers (N is the number of vertices)
source IDs, where any ID not in excludeIDs is a source point
targetIDs : list of N integers (N is the number of vertices)
target IDs, where any ID not in excludeIDs is a target point
points : list of N lists of three floats (N is the number of vertices)
coordinates of all vertices
segmentIDs : list of N integers (N is the number of vertices)
segment IDs, where each ID not in excludeIDs is considered a
different segment (unlike above, where value in sourceIDs or
targetIDs doesn't matter, so long as its not in excludeIDs);
source/target distances are computed within each segment
excludeIDs : list of integers
IDs to exclude
Returns
-------
distances : numpy array
distance value for each vertex (default -1)
distance_matrix : numpy array [#points by maximum segment ID + 1]
distances organized by segments (columns)
"""
import numpy as np
from mindboggle.utils.compute import point_distance
if isinstance(points, list):
points = np.asarray(points)
npoints = len(points)
# Extract unique segment IDs (or use all points as a single segment):
if np.size(segmentIDs):
segments = [x for x in np.unique(segmentIDs) if x not in excludeIDs]
else:
segmentIDs = np.zeros(npoints)
segments = [0]
nsegments = max(segments) + 1
# Initialize outputs:
distances = -1 * np.ones(npoints)
distance_matrix = -1 * np.ones((npoints, nsegments))
# For each segment:
for segment in segments:
segment_indices = [i for i,x in enumerate(segmentIDs)
if x == segment]
# Find all source points in the segment:
source_indices = [i for i,x in enumerate(sourceIDs)
if x not in excludeIDs
if i in segment_indices]
# Find all target points in the segment:
target_indices = [i for i,x in enumerate(targetIDs)
if x not in excludeIDs
if i in segment_indices]
if source_indices and target_indices:
# For each source point in the segment:
for isource in source_indices:
# Find the closest target point:
d, i = point_distance(points[isource],
points[target_indices])
distances[isource] = d
distance_matrix[isource, segment] = d
return distances, distance_matrix
gene_mni = G['mni']
path = os.environ['MINDBOGGLE_DATA']
genes = []
gene_values = []
for i in range(25):
print(i)
input_vtk = os.path.join(path, 'allen', 'labels_traveldepth' + str(i) + '.vtk')
if os.path.exists(input_vtk):
faces, lines, indices, points, npoints, depths, name, input_vtk = read_vtk(input_vtk)
I = [i for i,x in enumerate(depths) if x>-1]
gene = 0
value = 0
print(len(I))
points2 = np.array(points)
points2 = points2[I]
for point in points2:
mind, minI = point_distance(point, gene_mni)
if np.max(values[minI]) > value:
gene = minI
value = np.max(values[minI])
genes.append(gene)
gene_values.append(value)
else:
genes.append(0)
gene_values.append(0)