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 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 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 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 concatenate_sulcus_scalars(scalar_files, fold_files, label_files):
"""
Prepare data for estimating scalar distributions along and outside fundi.
Extract (e.g., depth, curvature) scalar values in folds, along sulcus
label boundaries as well as outside the sulcus label boundaries.
Concatenate these scalar values across multiple files.
Parameters
----------
scalar_files : list of strings
names of surface mesh VTK files with scalar values to concatenate
fold_files : list of strings (corr. to each list in scalar_files)
VTK files with fold numbers as scalars (-1 for non-fold vertices)
label_files : list of strings (corr. to fold_files)
VTK files with label numbers (-1 for unlabeled vertices)
Returns
-------
border_scalars : list of floats
concatenated scalar values within folds along sulcus label boundaries
nonborder_scalars : list of floats
concatenated scalar values within folds outside sulcus label boundaries
Examples
--------
>>> # Concatenate (duplicate) depth scalars:
>>> import os
>>> from mindboggle.shapes.likelihood import concatenate_sulcus_scalars
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'depth_rescaled.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> labels_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> scalar_files = [depth_file, depth_file]
>>> fold_files = [folds_file, folds_file]
>>> label_files = [labels_file, labels_file]
>>> #
>>> S = concatenate_sulcus_scalars(scalar_files, fold_files, label_files)
"""
import numpy as np
from mindboggle.utils.io_vtk import read_scalars
from mindboggle.utils.mesh import find_neighbors_from_file
from mindboggle.labels.labels import extract_borders
from mindboggle.labels.protocol import dkt_protocol
protocol = 'DKT25'
sulcus_names, sulcus_label_pair_lists, unique_sulcus_label_pairs, \
label_names, label_numbers, cortex_names, cortex_numbers, \
noncortex_names, noncortex_numbers = dkt_protocol(protocol)
# Prepare (non-unique) list of sulcus label pairs:
protocol_label_pairs = [x for lst in sulcus_label_pair_lists for x in lst]
border_scalars = []
nonborder_scalars = []
# Loop through files with the scalar values:
for ifile, scalar_file in enumerate(scalar_files):
print(scalar_file)
# Load scalars, folds, and labels:
folds_file = fold_files[ifile]
labels_file = label_files[ifile]
scalars, name = read_scalars(scalar_file, True, True)
if scalars.shape:
folds, name = read_scalars(folds_file)
labels, name = read_scalars(labels_file)
indices_folds = [i for i,x in enumerate(folds) if x != -1]
neighbor_lists = find_neighbors_from_file(labels_file)
# Find all label border pairs within the folds:
indices_label_pairs, label_pairs, unique_pairs = extract_borders(
indices_folds, labels, neighbor_lists, ignore_values=[-1],
return_label_pairs=True)
indices_label_pairs = np.array(indices_label_pairs)
# Find vertices with label pairs in the sulcus labeling protocol:
Ipairs_in_protocol = [i for i,x in enumerate(label_pairs)
if x in protocol_label_pairs]
indices_label_pairs = indices_label_pairs[Ipairs_in_protocol]
indices_outside_pairs = list(frozenset(indices_folds).difference(
indices_label_pairs))
# Store scalar values in folds along label border pairs:
border_scalars.extend(scalars[indices_label_pairs].tolist())
# Store scalar values in folds outside label border pairs:
nonborder_scalars.extend(scalars[indices_outside_pairs].tolist())
return border_scalars, nonborder_scalars
def concatenate_sulcus_scalars(scalar_files, fold_files, label_files):
"""
Prepare data for estimating scalar distributions along and outside fundi.
Extract (e.g., depth, curvature) scalar values in folds, along sulcus
label boundaries as well as outside the sulcus label boundaries.
Concatenate these scalar values across multiple files.
Parameters
----------
scalar_files : list of strings
names of surface mesh VTK files with scalar values to concatenate
fold_files : list of strings (corr. to each list in scalar_files)
VTK files with fold numbers as scalars (-1 for non-fold vertices)
label_files : list of strings (corr. to fold_files)
VTK files with label numbers (-1 for unlabeled vertices)
Returns
-------
border_scalars : list of floats
concatenated scalar values within folds along sulcus label boundaries
nonborder_scalars : list of floats
concatenated scalar values within folds outside sulcus label boundaries
Examples
--------
>>> # Concatenate (duplicate) depth scalars:
>>> import os
>>> from mindboggle.shapes.likelihood import concatenate_sulcus_scalars
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'depth_rescaled.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> labels_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> scalar_files = [depth_file, depth_file]
>>> fold_files = [folds_file, folds_file]
>>> label_files = [labels_file, labels_file]
>>> #
>>> S = concatenate_sulcus_scalars(scalar_files, fold_files, label_files)
"""
import numpy as np
from mindboggle.utils.io_vtk import read_scalars
from mindboggle.utils.mesh import find_neighbors_from_file
from mindboggle.utils.segment import extract_borders
from mindboggle.LABELS import DKTprotocol
dkt = DKTprotocol()
# Prepare (non-unique) list of sulcus label pairs:
protocol_label_pairs = [
x for lst in dkt.sulcus_label_pair_lists for x in lst
]
border_scalars = []
nonborder_scalars = []
# Loop through files with the scalar values:
for ifile, scalar_file in enumerate(scalar_files):
print(scalar_file)
# Load scalars, folds, and labels:
folds_file = fold_files[ifile]
labels_file = label_files[ifile]
scalars, name = read_scalars(scalar_file, True, True)
if scalars.shape:
folds, name = read_scalars(folds_file)
labels, name = read_scalars(labels_file)
indices_folds = [i for i, x in enumerate(folds) if x != -1]
neighbor_lists = find_neighbors_from_file(labels_file)
# Find all label border pairs within the folds:
indices_label_pairs, label_pairs, unique_pairs = extract_borders(
indices_folds,
labels,
neighbor_lists,
ignore_values=[-1],
return_label_pairs=True)
indices_label_pairs = np.array(indices_label_pairs)
# Find vertices with label pairs in the sulcus labeling protocol:
Ipairs_in_protocol = [
i for i, x in enumerate(label_pairs)
if x in protocol_label_pairs
]
indices_label_pairs = indices_label_pairs[Ipairs_in_protocol]
indices_outside_pairs = list(
frozenset(indices_folds).difference(indices_label_pairs))
# Store scalar values in folds along label border pairs:
border_scalars.extend(scalars[indices_label_pairs].tolist())
# Store scalar values in folds outside label border pairs:
nonborder_scalars.extend(scalars[indices_outside_pairs].tolist())
return border_scalars, nonborder_scalars
def 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
