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_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 stats_per_label(values,
labels,
include_labels=[],
exclude_labels=[],
weights=[],
precision=1):
"""
Compute various statistical measures across vertices per label,
optionally using weights (such as surface area per vertex).
Example (area-weighted mean):
average value = sum(a_i * v_i) / total_surface_area,
where *a_i* and *v_i* are the area and value for each vertex *i*.
Note ::
This function is different than means_per_label() in two ways:
1. It computes more than simply the (weighted) mean and sdev.
2. It only accepts 1-D arrays of values.
Reference
---------
Weighted skewness and kurtosis unbiased by sample size
Lorenzo Rimoldini, arXiv:1304.6564 (2013)
http://arxiv.org/abs/1304.6564
Parameters
----------
values : numpy array of individual or lists of integers or floats
values for all vertices
labels : list or array of integers
label for each value
include_labels : list of integers
labels to include
exclude_labels : list of integers
labels to be excluded
weights : numpy array of floats
weights to compute weighted statistical measures
precision : integer
number of decimal places to consider weights
Returns
-------
medians : list of floats
median for each label
mads : list of floats
median absolute deviation for each label
means : list of floats
mean for each label
sdevs : list of floats
standard deviation for each label
skews : list of floats
skew for each label
kurts : list of floats
kurtosis value for each label
lower_quarts : list of floats
lower quartile for each label
upper_quarts : list of floats
upper quartile for each label
label_list : list of integers
list of unique labels
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.utils.compute import stats_per_label
>>> data_path = os.environ['MINDBOGGLE_DATA']
>>> values_file = os.path.join(data_path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> area_file = os.path.join(data_path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> labels_file = os.path.join(data_path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> values, name = read_scalars(values_file, True, True)
>>> areas, name = read_scalars(area_file, True, True)
>>> labels, name = read_scalars(labels_file)
>>> include_labels = []
>>> exclude_labels = [-1]
>>> weights = areas
>>> precision = 1
>>> stats_per_label(values, labels, include_labels, exclude_labels, weights, precision)
"""
import numpy as np
from scipy.stats import skew, kurtosis, scoreatpercentile
from mindboggle.utils.compute import weighted_to_repeated_values, median_abs_dev
# Make sure arguments are numpy arrays:
if not isinstance(values, np.ndarray):
values = np.asarray(values)
if not isinstance(weights, np.ndarray):
weights = np.asarray(weights)
# Initialize all statistical lists:
if include_labels:
label_list = include_labels
else:
label_list = np.unique(labels)
label_list = [int(x) for x in label_list if int(x) not in exclude_labels]
medians = []
mads = []
means = []
sdevs = []
skews = []
kurts = []
lower_quarts = []
upper_quarts = []
# Extract all vertex indices for each label:
for label in label_list:
I = [i for i, x in enumerate(labels) if x == label]
if I:
# Get the vertex values:
X = values[I]
if len([x for x in X if x != 0]):
# If there are as many weights as values, apply the weights to the values:
if np.size(weights) == np.size(values):
W = weights[I]
sumW = np.sum(W)
# If the sum of the weights and the standard deviation is non-zero,
# compute all statistics of the weighted values:
if sumW > 0:
Xdiff = X - np.mean(X)
Xstd = np.sqrt(np.sum(W * Xdiff**2) / sumW)
means.append(np.sum(W * X) / sumW)
sdevs.append(Xstd)
if Xstd > 0:
skews.append(
(np.sum(W * Xdiff**3) / sumW) / Xstd**3)
kurts.append((np.sum(W * Xdiff**4) / sumW) /
Xstd**4 - 3)
else:
skews.append(skew(X))
kurts.append(kurtosis(X))
X = weighted_to_repeated_values(X, W, precision)
# If the sum of the weights equals zero, simply compute the statistics:
else:
means.append(np.mean(X))
sdevs.append(np.std(X))
skews.append(skew(X))
kurts.append(kurtosis(X))
# If there are no (or not enough) weights, simply compute the statistics:
else:
means.append(np.mean(X))
sdevs.append(np.std(X))
skews.append(skew(X))
kurts.append(kurtosis(X))
# Compute median, median absolute deviation, and lower and upper quartiles:
if np.size(X):
medians.append(np.median(X))
mads.append(median_abs_dev(X))
lower_quarts.append(scoreatpercentile(X, 25))
upper_quarts.append(scoreatpercentile(X, 75))
# If the weights are all smaller than the precision, then X will disappear,
# so set the above statistics (in the 'if' block) to zero:
else:
medians.append(0)
mads.append(0)
lower_quarts.append(0)
upper_quarts.append(0)
# If all values are equal to zero, set all statistics to zero:
else:
medians.append(0)
mads.append(0)
means.append(0)
sdevs.append(0)
skews.append(0)
kurts.append(0)
lower_quarts.append(0)
upper_quarts.append(0)
# If there are no vertices for the label, set all statistics to zero:
else:
medians.append(0)
mads.append(0)
means.append(0)
sdevs.append(0)
skews.append(0)
kurts.append(0)
lower_quarts.append(0)
upper_quarts.append(0)
return medians, mads, means, sdevs, skews, kurts, \
lower_quarts, upper_quarts, label_list
def stats_per_label(values, labels, exclude_labels, weights=[], precision=1):
"""
Compute various statistical measures across vertices per label,
optionally using weights (such as surface area per vertex).
Example (area-weighted mean):
average value = sum(a_i * v_i) / total_surface_area,
where *a_i* and *v_i* are the area and value for each vertex *i*.
Note ::
This function is different than means_per_label() in two ways:
1. It computes more than simply the (weighted) mean and sdev.
2. It only accepts 1-D arrays of values.
Reference
---------
Weighted skewness and kurtosis unbiased by sample size
Lorenzo Rimoldini, arXiv:1304.6564 (2013)
http://arxiv.org/abs/1304.6564
Parameters
----------
values : numpy array of individual or lists of integers or floats
values for all vertices
labels : list or array of integers
label for each value
exclude_labels : list of integers
labels to be excluded
weights : numpy array of floats
weights to compute weighted statistical measures
precision : integer
number of decimal places to consider weights
Returns
-------
medians : list of floats
median for each label
mads : list of floats
median absolute deviation for each label
means : list of floats
mean for each label
sdevs : list of floats
standard deviation for each label
skews : list of floats
skew for each label
kurts : list of floats
kurtosis value for each label
lower_quarts : list of floats
lower quartile for each label
upper_quarts : list of floats
upper quartile for each label
label_list : list of integers
list of unique labels
Examples
--------
>>> import os
>>> from mindboggle.utils.io_vtk import read_scalars
>>> from mindboggle.utils.compute import stats_per_label
>>> data_path = os.environ['MINDBOGGLE_DATA']
>>> values_file = os.path.join(data_path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> area_file = os.path.join(data_path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> labels_file = os.path.join(data_path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> values, name = read_scalars(values_file, True, True)
>>> areas, name = read_scalars(area_file, True, True)
>>> labels, name = read_scalars(labels_file)
>>> exclude_labels = [-1]
>>> weights = areas
>>> precision = 1
>>> stats_per_label(values, labels, exclude_labels, weights, precision)
"""
import numpy as np
from scipy.stats import skew, kurtosis, scoreatpercentile
from mindboggle.utils.compute import weighted_to_repeated_values, median_abs_dev
# Make sure arguments are numpy arrays:
if not isinstance(values, np.ndarray):
values = np.asarray(values)
if not isinstance(weights, np.ndarray):
weights = np.asarray(weights)
# Initialize all statistical lists:
label_list = np.unique(labels)
label_list = [int(x) for x in label_list if int(x) not in exclude_labels]
medians = []
mads = []
means = []
sdevs = []
skews = []
kurts = []
lower_quarts = []
upper_quarts = []
# Extract all vertex indices for each label:
for label in label_list:
I = [i for i,x in enumerate(labels) if x == label]
if I:
# Get the vertex values:
X = values[I]
if len([x for x in X if x != 0]):
# If there are as many weights as values, apply the weights to the values:
if np.size(weights) == np.size(values):
W = weights[I]
sumW = np.sum(W)
# If the sum of the weights and the standard deviation is non-zero,
# compute all statistics of the weighted values:
if sumW > 0:
Xdiff = X - np.mean(X)
Xstd = np.sqrt(np.sum(W * Xdiff**2) / sumW)
means.append(np.sum(W * X) / sumW)
sdevs.append(Xstd)
if Xstd > 0:
skews.append((np.sum(W * Xdiff**3) / sumW) / Xstd**3)
kurts.append((np.sum(W * Xdiff**4) / sumW) / Xstd**4 - 3)
else:
skews.append(skew(X))
kurts.append(kurtosis(X))
X = weighted_to_repeated_values(X, W, precision)
# If the sum of the weights equals zero, simply compute the statistics:
else:
means.append(np.mean(X))
sdevs.append(np.std(X))
skews.append(skew(X))
kurts.append(kurtosis(X))
# If there are no (or not enough) weights, simply compute the statistics:
else:
means.append(np.mean(X))
sdevs.append(np.std(X))
skews.append(skew(X))
kurts.append(kurtosis(X))
# Compute median, median absolute deviation, and lower and upper quartiles:
if np.size(X):
medians.append(np.median(X))
mads.append(median_abs_dev(X))
lower_quarts.append(scoreatpercentile(X, 25))
upper_quarts.append(scoreatpercentile(X, 75))
# If the weights are all smaller than the precision, then X will disappear,
# so set the above statistics (in the 'if' block) to zero:
else:
medians.append(0)
mads.append(0)
lower_quarts.append(0)
upper_quarts.append(0)
# If all values are equal to zero, set all statistics to zero:
else:
medians.append(0)
mads.append(0)
means.append(0)
sdevs.append(0)
skews.append(0)
kurts.append(0)
lower_quarts.append(0)
upper_quarts.append(0)
# If there are no vertices for the label, set all statistics to zero:
else:
medians.append(0)
mads.append(0)
means.append(0)
sdevs.append(0)
skews.append(0)
kurts.append(0)
lower_quarts.append(0)
upper_quarts.append(0)
return medians, mads, means, sdevs, skews, kurts, \
lower_quarts, upper_quarts, label_list
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