def double_propagate(img, label, FIR_THRES, SEC_THRES, OPEN):
thres = img > FIR_THRES
thres2 = img > SEC_THRES
open_img = opening(img, np.ones((OPEN, OPEN)))
img = img - open_img
labels_out, distances = propagate(img, label, thres, 1)
label = labels_out
labels_out, distances = propagate(img, label, thres2, 1)
labels_out = np.uint16(labels_out)
return labels_out
def segment(self, img, include_intermediate_results=False, **kwargs):
assert img.ndim == 3, 'Expecting 3D image, got shape {}'.format(
img.shape)
img = ndi.median_filter(img, size=(1, 3, 3))
img = img_as_float(img)
img = util.invert(img)
img_mz = img.max(axis=0)
img_mz = exposure.rescale_intensity(img_mz, out_range=(0, 1))
peaks, img_dog, sigmas = blob_dog(img_mz,
min_sigma=8,
max_sigma=128,
sigma_ratio=1.6,
overlap=.25,
threshold=1.75)
img_pk = np.zeros(img_mz.shape, dtype=bool)
img_pk[peaks[:, 0].astype(int), peaks[:, 1].astype(int)] = True
img_pk = morphology.label(img_pk)
# Get mask to conduct segmentation over
img_pm = self.get_primary_object_mask(
img, morphology.binary_dilation(img_pk > 0, morphology.disk(32)))
img_dt = ndi.distance_transform_edt(img_pm)
# Use propogation rather than watershed as it often captures a much more accurate boundary
img_obj = propagate.propagate(img_mz, img_pk, img_pm,
.01)[0].astype(np.uint16)
img_bnd = img_obj * segmentation.find_boundaries(
img_obj, mode='inner', background=0)
img_seg = [img_obj, img_obj, img_bnd, img_bnd]
if include_intermediate_results:
to_uint16 = lambda im: exposure.rescale_intensity(
im, out_range='uint16').astype(np.uint16)
img_seg += [
to_uint16(img_mz),
to_uint16(img_dog[0]),
to_uint16(img_dog[1]),
img_pm.astype(np.uint16),
img_pk.astype(np.uint16)
]
# Stack and add new axis to give to (z, ch, h, w)
img_seg = np.stack(img_seg)[np.newaxis]
assert img_seg.dtype == np.uint16, 'Expecting 16bit result, got type {}'.format(
img_seg.dtype)
assert img_seg.ndim == 4, 'Expecting 4D result, got shape {}'.format(
img_seg.shape)
return img_seg
def propagate_and_cleanup(img,
nuclabel,
all_label,
mask,
prop_param=1.0,
nuc_overlap=4):
label = all_label.copy()
label[all_label > 0] = nuclabel.max() + 1
label[nuclabel > 0] = 0
label += nuclabel
labels_out, distances = propagate(img, label, mask, prop_param)
mask = calc_mask_exclude_overlap(all_label, nuc_overlap)
labels_out[mask] = 0
labels_out[labels_out == nuclabel.max()] = 0
mask = calc_mask_exclude_overlap(labels_out, 1)
labels_out[mask] = 0
return labels_out
def get_skeleton_points(self, obj):
'''Get points by skeletonizing the objects and decimating'''
ii = []
jj = []
total_skel = np.zeros(obj.shape, bool)
for labels, indexes in obj.get_labels():
colors = morph.color_labels(labels)
for color in range(1, np.max(colors) + 1):
labels_mask = colors == color
skel = morph.skeletonize(
labels_mask,
ordering=distance_transform_edt(labels_mask) *
poisson_equation(labels_mask))
total_skel = total_skel | skel
n_pts = np.sum(total_skel)
if n_pts == 0:
return np.zeros(0, np.int32), np.zeros(0, np.int32)
i, j = np.where(total_skel)
if n_pts > self.max_points.value:
#
# Decimate the skeleton by finding the branchpoints in the
# skeleton and propagating from those.
#
markers = np.zeros(total_skel.shape, np.int32)
branchpoints = \
morph.branchpoints(total_skel) | morph.endpoints(total_skel)
markers[branchpoints] = np.arange(np.sum(branchpoints)) + 1
#
# We compute the propagation distance to that point, then impose
# a slightly arbitarary order to get an unambiguous ordering
# which should number the pixels in a skeleton branch monotonically
#
ts_labels, distances = propagate(np.zeros(markers.shape), markers,
total_skel, 1)
order = np.lexsort((j, i, distances[i, j], ts_labels[i, j]))
#
# Get a linear space of self.max_points elements with bounds at
# 0 and len(order)-1 and use that to select the points.
#
order = order[np.linspace(0,
len(order) - 1,
self.max_points.value).astype(int)]
return i[order], j[order]
return i, j
def get_skeleton_points(self, obj):
'''Get points by skeletonizing the objects and decimating'''
ii = []
jj = []
total_skel = np.zeros(obj.shape, bool)
for labels, indexes in obj.get_labels():
colors = morph.color_labels(labels)
for color in range(1, np.max(colors) + 1):
labels_mask = colors == color
skel = morph.skeletonize(
labels_mask,
ordering = distance_transform_edt(labels_mask) *
poisson_equation(labels_mask))
total_skel = total_skel | skel
n_pts = np.sum(total_skel)
if n_pts == 0:
return np.zeros(0, np.int32), np.zeros(0, np.int32)
i, j = np.where(total_skel)
if n_pts > self.max_points.value:
#
# Decimate the skeleton by finding the branchpoints in the
# skeleton and propagating from those.
#
markers = np.zeros(total_skel.shape, np.int32)
branchpoints = \
morph.branchpoints(total_skel) | morph.endpoints(total_skel)
markers[branchpoints] = np.arange(np.sum(branchpoints))+1
#
# We compute the propagation distance to that point, then impose
# a slightly arbitarary order to get an unambiguous ordering
# which should number the pixels in a skeleton branch monotonically
#
ts_labels, distances = propagate(np.zeros(markers.shape),
markers, total_skel, 1)
order = np.lexsort((j, i, distances[i, j], ts_labels[i, j]))
#
# Get a linear space of self.max_points elements with bounds at
# 0 and len(order)-1 and use that to select the points.
#
order = order[
np.linspace(0, len(order)-1, self.max_points.value).astype(int)]
return i[order], j[order]
return i, j
def do_measurements(self, workspace, image_name, object_name,
center_object_name, center_choice,
bin_count_settings, dd):
'''Perform the radial measurements on the image set
workspace - workspace that holds images / objects
image_name - make measurements on this image
object_name - make measurements on these objects
center_object_name - use the centers of these related objects as
the centers for radial measurements. None to use the
objects themselves.
center_choice - the user's center choice for this object:
C_SELF, C_CENTERS_OF_OBJECTS or C_EDGES_OF_OBJECTS.
bin_count_settings - the bin count settings group
d - a dictionary for saving reusable partial results
returns one statistics tuple per ring.
'''
assert isinstance(workspace, cpw.Workspace)
assert isinstance(workspace.object_set, cpo.ObjectSet)
bin_count = bin_count_settings.bin_count.value
wants_scaled = bin_count_settings.wants_scaled.value
maximum_radius = bin_count_settings.maximum_radius.value
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
objects = workspace.object_set.get_objects(object_name)
labels, pixel_data = cpo.crop_labels_and_image(objects.segmented,
image.pixel_data)
nobjects = np.max(objects.segmented)
measurements = workspace.measurements
assert isinstance(measurements, cpmeas.Measurements)
heatmaps = {}
for heatmap in self.heatmaps:
if heatmap.object_name.get_objects_name() == object_name and \
image_name == heatmap.image_name.get_image_name() and \
heatmap.get_number_of_bins() == bin_count:
dd[id(heatmap)] = \
heatmaps[MEASUREMENT_ALIASES[heatmap.measurement.value]] = \
np.zeros(labels.shape)
if nobjects == 0:
for bin in range(1, bin_count + 1):
for feature in (F_FRAC_AT_D, F_MEAN_FRAC, F_RADIAL_CV):
feature_name = (
(feature + FF_GENERIC) % (image_name, bin, bin_count))
measurements.add_measurement(
object_name, "_".join([M_CATEGORY, feature_name]),
np.zeros(0))
if not wants_scaled:
measurement_name = "_".join([M_CATEGORY, feature,
image_name, FF_OVERFLOW])
measurements.add_measurement(
object_name, measurement_name, np.zeros(0))
return [(image_name, object_name, "no objects", "-", "-", "-", "-")]
name = (object_name if center_object_name is None
else "%s_%s" % (object_name, center_object_name))
if dd.has_key(name):
normalized_distance, i_center, j_center, good_mask = dd[name]
else:
d_to_edge = distance_to_edge(labels)
if center_object_name is not None:
#
# Use the center of the centering objects to assign a center
# to each labeled pixel using propagation
#
center_objects = workspace.object_set.get_objects(center_object_name)
center_labels, cmask = cpo.size_similarly(
labels, center_objects.segmented)
pixel_counts = fix(scind.sum(
np.ones(center_labels.shape),
center_labels,
np.arange(1, np.max(center_labels) + 1, dtype=np.int32)))
good = pixel_counts > 0
i, j = (centers_of_labels(center_labels) + .5).astype(int)
ig = i[good]
jg = j[good]
lg = np.arange(1, len(i) + 1)[good]
if center_choice == C_CENTERS_OF_OTHER:
#
# Reduce the propagation labels to the centers of
# the centering objects
#
center_labels = np.zeros(center_labels.shape, int)
center_labels[ig, jg] = lg
cl, d_from_center = propagate(np.zeros(center_labels.shape),
center_labels,
labels != 0, 1)
#
# Erase the centers that fall outside of labels
#
cl[labels == 0] = 0
#
# If objects are hollow or crescent-shaped, there may be
# objects without center labels. As a backup, find the
# center that is the closest to the center of mass.
#
missing_mask = (labels != 0) & (cl == 0)
missing_labels = np.unique(labels[missing_mask])
if len(missing_labels):
all_centers = centers_of_labels(labels)
missing_i_centers, missing_j_centers = \
all_centers[:, missing_labels - 1]
di = missing_i_centers[:, np.newaxis] - ig[np.newaxis, :]
dj = missing_j_centers[:, np.newaxis] - jg[np.newaxis, :]
missing_best = lg[np.argsort((di * di + dj * dj,))[:, 0]]
best = np.zeros(np.max(labels) + 1, int)
best[missing_labels] = missing_best
cl[missing_mask] = best[labels[missing_mask]]
#
# Now compute the crow-flies distance to the centers
# of these pixels from whatever center was assigned to
# the object.
#
iii, jjj = np.mgrid[0:labels.shape[0], 0:labels.shape[1]]
di = iii[missing_mask] - i[cl[missing_mask] - 1]
dj = jjj[missing_mask] - j[cl[missing_mask] - 1]
d_from_center[missing_mask] = np.sqrt(di * di + dj * dj)
else:
# Find the point in each object farthest away from the edge.
# This does better than the centroid:
# * The center is within the object
# * The center tends to be an interesting point, like the
# center of the nucleus or the center of one or the other
# of two touching cells.
#
i, j = maximum_position_of_labels(d_to_edge, labels, objects.indices)
center_labels = np.zeros(labels.shape, int)
center_labels[i, j] = labels[i, j]
#
# Use the coloring trick here to process touching objects
# in separate operations
#
colors = color_labels(labels)
ncolors = np.max(colors)
d_from_center = np.zeros(labels.shape)
cl = np.zeros(labels.shape, int)
for color in range(1, ncolors + 1):
mask = colors == color
l, d = propagate(np.zeros(center_labels.shape),
center_labels,
mask, 1)
d_from_center[mask] = d[mask]
cl[mask] = l[mask]
good_mask = cl > 0
if center_choice == C_EDGES_OF_OTHER:
# Exclude pixels within the centering objects
# when performing calculations from the centers
good_mask = good_mask & (center_labels == 0)
i_center = np.zeros(cl.shape)
i_center[good_mask] = i[cl[good_mask] - 1]
j_center = np.zeros(cl.shape)
j_center[good_mask] = j[cl[good_mask] - 1]
normalized_distance = np.zeros(labels.shape)
if wants_scaled:
total_distance = d_from_center + d_to_edge
normalized_distance[good_mask] = (d_from_center[good_mask] /
(total_distance[good_mask] + .001))
else:
normalized_distance[good_mask] = \
d_from_center[good_mask] / maximum_radius
dd[name] = [normalized_distance, i_center, j_center, good_mask]
ngood_pixels = np.sum(good_mask)
good_labels = labels[good_mask]
bin_indexes = (normalized_distance * bin_count).astype(int)
bin_indexes[bin_indexes > bin_count] = bin_count
labels_and_bins = (good_labels - 1, bin_indexes[good_mask])
histogram = coo_matrix((pixel_data[good_mask], labels_and_bins),
(nobjects, bin_count + 1)).toarray()
sum_by_object = np.sum(histogram, 1)
sum_by_object_per_bin = np.dstack([sum_by_object] * (bin_count + 1))[0]
fraction_at_distance = histogram / sum_by_object_per_bin
number_at_distance = coo_matrix((np.ones(ngood_pixels), labels_and_bins),
(nobjects, bin_count + 1)).toarray()
object_mask = number_at_distance > 0
sum_by_object = np.sum(number_at_distance, 1)
sum_by_object_per_bin = np.dstack([sum_by_object] * (bin_count + 1))[0]
fraction_at_bin = number_at_distance / sum_by_object_per_bin
mean_pixel_fraction = fraction_at_distance / (fraction_at_bin +
np.finfo(float).eps)
masked_fraction_at_distance = masked_array(fraction_at_distance,
~object_mask)
masked_mean_pixel_fraction = masked_array(mean_pixel_fraction,
~object_mask)
# Anisotropy calculation. Split each cell into eight wedges, then
# compute coefficient of variation of the wedges' mean intensities
# in each ring.
#
# Compute each pixel's delta from the center object's centroid
i, j = np.mgrid[0:labels.shape[0], 0:labels.shape[1]]
imask = i[good_mask] > i_center[good_mask]
jmask = j[good_mask] > j_center[good_mask]
absmask = (abs(i[good_mask] - i_center[good_mask]) >
abs(j[good_mask] - j_center[good_mask]))
radial_index = (imask.astype(int) + jmask.astype(int) * 2 +
absmask.astype(int) * 4)
statistics = []
for bin in range(bin_count + (0 if wants_scaled else 1)):
bin_mask = (good_mask & (bin_indexes == bin))
bin_pixels = np.sum(bin_mask)
bin_labels = labels[bin_mask]
bin_radial_index = radial_index[bin_indexes[good_mask] == bin]
labels_and_radii = (bin_labels - 1, bin_radial_index)
radial_values = coo_matrix((pixel_data[bin_mask],
labels_and_radii),
(nobjects, 8)).toarray()
pixel_count = coo_matrix((np.ones(bin_pixels), labels_and_radii),
(nobjects, 8)).toarray()
mask = pixel_count == 0
radial_means = masked_array(radial_values / pixel_count, mask)
radial_cv = np.std(radial_means, 1) / np.mean(radial_means, 1)
radial_cv[np.sum(~mask, 1) == 0] = 0
for measurement, feature, overflow_feature in (
(fraction_at_distance[:, bin], MF_FRAC_AT_D, OF_FRAC_AT_D),
(mean_pixel_fraction[:, bin], MF_MEAN_FRAC, OF_MEAN_FRAC),
(np.array(radial_cv), MF_RADIAL_CV, OF_RADIAL_CV)):
if bin == bin_count:
measurement_name = overflow_feature % image_name
else:
measurement_name = feature % (image_name, bin + 1, bin_count)
measurements.add_measurement(object_name,
measurement_name,
measurement)
if feature in heatmaps:
heatmaps[feature][bin_mask] = measurement[bin_labels - 1]
radial_cv.mask = np.sum(~mask, 1) == 0
bin_name = str(bin + 1) if bin < bin_count else "Overflow"
statistics += [(image_name, object_name, bin_name, str(bin_count),
round(np.mean(masked_fraction_at_distance[:, bin]), 4),
round(np.mean(masked_mean_pixel_fraction[:, bin]), 4),
round(np.mean(radial_cv), 4))]
return statistics
def segment(self, img_nuc, img_memb=None,
marker_dilation=1, marker_min_size=16,
memb_min_dist=5, memb_max_dist=10, memb_hole_size=16,
memb_sigma=1, memb_gamma=None, memb_tresh_method='li', memb_propagation_regularization=.05,
batch_size=DEFAULT_BATCH_SIZE, return_masks=False):
if not self.initialized:
self.initialize()
if (memb_min_dist or 0) >= (memb_max_dist or np.inf):
raise ValueError(
'Membrane min distance argument (memb_min_dist = {}) used to set minimum cell boundary '
'must be <= maximum cell boundary distance from nucleus (memb_max_dist = {})'
.format(memb_min_dist, memb_max_dist)
)
# Convert images to segment or otherwise analyze to 8-bit
img_nuc = _to_uint8(img_nuc, 'nucleus')
if img_memb is not None:
img_memb = _to_uint8(img_memb, 'membrane')
# Add z dimension (equivalent to batch dim in this case) if not present
if img_nuc.ndim == 2:
img_nuc = np.expand_dims(img_nuc, 0)
if img_nuc.ndim != 3:
raise ValueError('Must provide image as ZHW or HW (image shape given = {})'.format(img_nuc.shape))
# Make predictions on image converted to 0-1 and with trailing channel dimension to give NHWC;
# Result has shape NHWC where C=3 and C1 = bg, C2 = interior, C3 = border
img_pred = self.predict(np.expand_dims(img_nuc / 255., -1), batch_size)
assert img_pred.shape[-1] == 3, \
'Expecting 3 outputs in predictions (shape = {})'.format(img_pred.shape)
img_seg_list, img_bin_list = [], []
nz = img_nuc.shape[0]
for i in range(nz):
# Use nuclei interior mask as watershed markers
img_bin_nucm = np.argmax(img_pred[i], axis=-1) == 1
# Remove markers (which determine number of cells) below the given size
if marker_min_size > 0:
img_bin_nucm = morphology.remove_small_objects(img_bin_nucm, min_size=marker_min_size)
# Define the entire nucleus as a slight dilation of the markers noting that this
# works better than using the union of predicted interiors and predicted boundaries
# (which are too thick)
img_bin_nuci = img_bin_nucm
if marker_dilation > 0:
img_bin_nuci = cv2.dilate(
img_bin_nucm.astype(np.uint8), morphology.disk(marker_dilation)).astype(np.bool)
# Determine the overall mask to segment across by dilating nuclei by some fixed amount
# or if possible, using the given cell membrane image
img_bin_mask = self.get_segmentation_mask(
img_bin_nuci, img_memb=img_memb[i] if img_memb is not None else None,
min_dist=memb_min_dist, max_dist=memb_max_dist, hole_size=memb_hole_size,
method=memb_tresh_method, sigma=memb_sigma, gamma=memb_gamma)
# Label the nuclei markers (which determines number of cells to output)
# *Note: It is important to keep this separate from nuclei interior as single or double pixel
# gaps between nuclei are common when densely packed
img_bin_nucm_label = morphology.label(img_bin_nucm)
# Create labeled cell image
if img_memb is None or memb_propagation_regularization is None:
# Run watershed using markers and expanded nuclei / cell mask
img_basin = -1 * ndimage.distance_transform_edt(img_bin_nucm)
img_cell_seg = segmentation.watershed(img_basin, img_bin_nucm_label, mask=img_bin_mask)
else:
# Before running propagation segmentation, make sure that the input image is 0-1 float
# as the regularization threshold is calibrated to work only with data in that range
img_cell_seg, _ = propagate.propagate(
img_as_float(img_memb[i]), img_bin_nucm_label,
img_bin_mask, memb_propagation_regularization
)
# Generate nucleus segmentation based on cell segmentation and nucleus mask
# and relabel nuclei objections using corresponding cell labels
img_nuc_seg = (img_cell_seg > 0) & img_bin_nuci
img_nuc_seg = img_nuc_seg * img_cell_seg
# Add labeled images to results
assert img_cell_seg.dtype == img_nuc_seg.dtype, \
'Cell segmentation dtype {} != nucleus segmentation dtype {}'\
.format(img_cell_seg.dtype, img_nuc_seg.dtype)
img_seg_list.append(np.stack([img_cell_seg, img_nuc_seg], axis=0))
# Add mask images to results, if requested
if return_masks:
img_bin_list.append(np.stack([img_bin_nuci, img_bin_nucm, img_bin_mask], axis=0))
assert nz == len(img_seg_list)
if return_masks:
assert nz == len(img_bin_list)
# Stack final segmentation image as (z, c, h, w)
img_seg = np.stack(img_seg_list, axis=0)
img_bin = np.stack(img_bin_list, axis=0) if return_masks else None
assert img_seg.ndim == 4, 'Expecting 4D segmentation image but shape is {}'.format(img_seg.shape)
# Return (in this order) labeled volumes, prediction volumes, mask volumes
return img_seg, img_pred, img_bin
def run(self, workspace):
'''Run the module on the image set'''
seed_objects_name = self.seed_objects_name.value
skeleton_name = self.image_name.value
seed_objects = workspace.object_set.get_objects(seed_objects_name)
labels = seed_objects.segmented
labels_count = np.max(labels)
label_range = np.arange(labels_count, dtype=np.int32) + 1
skeleton_image = workspace.image_set.get_image(
skeleton_name, must_be_binary=True)
skeleton = skeleton_image.pixel_data
if skeleton_image.has_mask:
skeleton = skeleton & skeleton_image.mask
try:
labels = skeleton_image.crop_image_similarly(labels)
except:
labels, m1 = cpo.size_similarly(skeleton, labels)
labels[~m1] = 0
#
# The following code makes a ring around the seed objects with
# the skeleton trunks sticking out of it.
#
# Create a new skeleton with holes at the seed objects
# First combine the seed objects with the skeleton so
# that the skeleton trunks come out of the seed objects.
#
# Erode the labels once so that all of the trunk branchpoints
# will be within the labels
#
#
# Dilate the objects, then subtract them to make a ring
#
my_disk = morph.strel_disk(1.5).astype(int)
dilated_labels = grey_dilation(labels, footprint=my_disk)
seed_mask = dilated_labels > 0
combined_skel = skeleton | seed_mask
closed_labels = grey_erosion(dilated_labels,
footprint=my_disk)
seed_center = closed_labels > 0
combined_skel = combined_skel & (~seed_center)
#
# Fill in single holes (but not a one-pixel hole made by
# a one-pixel image)
#
if self.wants_to_fill_holes:
def size_fn(area, is_object):
return (~ is_object) and (area <= self.maximum_hole_size.value)
combined_skel = morph.fill_labeled_holes(
combined_skel, ~seed_center, size_fn)
#
# Reskeletonize to make true branchpoints at the ring boundaries
#
combined_skel = morph.skeletonize(combined_skel)
#
# The skeleton outside of the labels
#
outside_skel = combined_skel & (dilated_labels == 0)
#
# Associate all skeleton points with seed objects
#
dlabels, distance_map = propagate.propagate(np.zeros(labels.shape),
dilated_labels,
combined_skel, 1)
#
# Get rid of any branchpoints not connected to seeds
#
combined_skel[dlabels == 0] = False
#
# Find the branchpoints
#
branch_points = morph.branchpoints(combined_skel)
#
# Odd case: when four branches meet like this, branchpoints are not
# assigned because they are arbitrary. So assign them.
#
# . .
# B.
# .B
# . .
#
odd_case = (combined_skel[:-1, :-1] & combined_skel[1:, :-1] &
combined_skel[:-1, 1:] & combined_skel[1, 1])
branch_points[:-1, :-1][odd_case] = True
branch_points[1:, 1:][odd_case] = True
#
# Find the branching counts for the trunks (# of extra branches
# eminating from a point other than the line it might be on).
#
branching_counts = morph.branchings(combined_skel)
branching_counts = np.array([0, 0, 0, 1, 2])[branching_counts]
#
# Only take branches within 1 of the outside skeleton
#
dilated_skel = scind.binary_dilation(outside_skel, morph.eight_connect)
branching_counts[~dilated_skel] = 0
#
# Find the endpoints
#
end_points = morph.endpoints(combined_skel)
#
# We use two ranges for classification here:
# * anything within one pixel of the dilated image is a trunk
# * anything outside of that range is a branch
#
nearby_labels = dlabels.copy()
nearby_labels[distance_map > 1.5] = 0
outside_labels = dlabels.copy()
outside_labels[nearby_labels > 0] = 0
#
# The trunks are the branchpoints that lie within one pixel of
# the dilated image.
#
if labels_count > 0:
trunk_counts = fix(scind.sum(branching_counts, nearby_labels,
label_range)).astype(int)
else:
trunk_counts = np.zeros((0,), int)
#
# The branches are the branchpoints that lie outside the seed objects
#
if labels_count > 0:
branch_counts = fix(scind.sum(branch_points, outside_labels,
label_range))
else:
branch_counts = np.zeros((0,), int)
#
# Save the endpoints
#
if labels_count > 0:
end_counts = fix(scind.sum(end_points, outside_labels, label_range))
else:
end_counts = np.zeros((0,), int)
#
# Calculate the distances
#
total_distance = morph.skeleton_length(
dlabels * outside_skel, label_range)
#
# Save measurements
#
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
feature = "_".join((C_NEURON, F_NUMBER_TRUNKS, skeleton_name))
m.add_measurement(seed_objects_name, feature, trunk_counts)
feature = "_".join((C_NEURON, F_NUMBER_NON_TRUNK_BRANCHES,
skeleton_name))
m.add_measurement(seed_objects_name, feature, branch_counts)
feature = "_".join((C_NEURON, F_NUMBER_BRANCH_ENDS, skeleton_name))
m.add_measurement(seed_objects_name, feature, end_counts)
feature = "_".join((C_NEURON, F_TOTAL_NEURITE_LENGTH, skeleton_name))
m[seed_objects_name, feature] = total_distance
#
# Collect the graph information
#
if self.wants_neuron_graph:
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
intensity_image = workspace.image_set.get_image(
self.intensity_image_name.value)
edge_graph, vertex_graph = self.make_neuron_graph(
combined_skel, dlabels,
trunk_mask,
branch_points & ~trunk_mask,
end_points,
intensity_image.pixel_data)
image_number = workspace.measurements.image_set_number
edge_path, vertex_path = self.get_graph_file_paths(m, m.image_number)
workspace.interaction_request(
self, m.image_number, edge_path, edge_graph,
vertex_path, vertex_graph, headless_ok=True)
if self.show_window:
workspace.display_data.edge_graph = edge_graph
workspace.display_data.vertex_graph = vertex_graph
workspace.display_data.intensity_image = intensity_image.pixel_data
#
# Make the display image
#
if self.show_window or self.wants_branchpoint_image:
branchpoint_image = np.zeros((skeleton.shape[0],
skeleton.shape[1],
3))
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
branch_mask = branch_points & (outside_labels != 0)
end_mask = end_points & (outside_labels != 0)
branchpoint_image[outside_skel, :] = 1
branchpoint_image[trunk_mask | branch_mask | end_mask, :] = 0
branchpoint_image[trunk_mask, 0] = 1
branchpoint_image[branch_mask, 1] = 1
branchpoint_image[end_mask, 2] = 1
branchpoint_image[dilated_labels != 0, :] *= .875
branchpoint_image[dilated_labels != 0, :] += .1
if self.show_window:
workspace.display_data.branchpoint_image = branchpoint_image
if self.wants_branchpoint_image:
bi = cpi.Image(branchpoint_image,
parent_image=skeleton_image)
workspace.image_set.add(self.branchpoint_image_name.value, bi)
def run(self, workspace):
'''Run the module on the image set'''
seed_objects_name = self.seed_objects_name.value
skeleton_name = self.image_name.value
seed_objects = workspace.object_set.get_objects(seed_objects_name)
labels = seed_objects.segmented
labels_count = np.max(labels)
label_range = np.arange(labels_count, dtype=np.int32) + 1
skeleton_image = workspace.image_set.get_image(skeleton_name,
must_be_binary=True)
skeleton = skeleton_image.pixel_data
if skeleton_image.has_mask:
skeleton = skeleton & skeleton_image.mask
try:
labels = skeleton_image.crop_image_similarly(labels)
except:
labels, m1 = cpo.size_similarly(skeleton, labels)
labels[~m1] = 0
#
# The following code makes a ring around the seed objects with
# the skeleton trunks sticking out of it.
#
# Create a new skeleton with holes at the seed objects
# First combine the seed objects with the skeleton so
# that the skeleton trunks come out of the seed objects.
#
# Erode the labels once so that all of the trunk branchpoints
# will be within the labels
#
#
# Dilate the objects, then subtract them to make a ring
#
my_disk = morph.strel_disk(1.5).astype(int)
dilated_labels = grey_dilation(labels, footprint=my_disk)
seed_mask = dilated_labels > 0
combined_skel = skeleton | seed_mask
closed_labels = grey_erosion(dilated_labels, footprint=my_disk)
seed_center = closed_labels > 0
combined_skel = combined_skel & (~seed_center)
#
# Fill in single holes (but not a one-pixel hole made by
# a one-pixel image)
#
if self.wants_to_fill_holes:
def size_fn(area, is_object):
return (~is_object) and (area <= self.maximum_hole_size.value)
combined_skel = morph.fill_labeled_holes(combined_skel,
~seed_center, size_fn)
#
# Reskeletonize to make true branchpoints at the ring boundaries
#
combined_skel = morph.skeletonize(combined_skel)
#
# The skeleton outside of the labels
#
outside_skel = combined_skel & (dilated_labels == 0)
#
# Associate all skeleton points with seed objects
#
dlabels, distance_map = propagate.propagate(np.zeros(labels.shape),
dilated_labels,
combined_skel, 1)
#
# Get rid of any branchpoints not connected to seeds
#
combined_skel[dlabels == 0] = False
#
# Find the branchpoints
#
branch_points = morph.branchpoints(combined_skel)
#
# Odd case: when four branches meet like this, branchpoints are not
# assigned because they are arbitrary. So assign them.
#
# . .
# B.
# .B
# . .
#
odd_case = (combined_skel[:-1, :-1] & combined_skel[1:, :-1]
& combined_skel[:-1, 1:] & combined_skel[1, 1])
branch_points[:-1, :-1][odd_case] = True
branch_points[1:, 1:][odd_case] = True
#
# Find the branching counts for the trunks (# of extra branches
# emanating from a point other than the line it might be on).
#
branching_counts = morph.branchings(combined_skel)
branching_counts = np.array([0, 0, 0, 1, 2])[branching_counts]
#
# Only take branches within 1 of the outside skeleton
#
dilated_skel = scind.binary_dilation(outside_skel, morph.eight_connect)
branching_counts[~dilated_skel] = 0
#
# Find the endpoints
#
end_points = morph.endpoints(combined_skel)
#
# We use two ranges for classification here:
# * anything within one pixel of the dilated image is a trunk
# * anything outside of that range is a branch
#
nearby_labels = dlabels.copy()
nearby_labels[distance_map > 1.5] = 0
outside_labels = dlabels.copy()
outside_labels[nearby_labels > 0] = 0
#
# The trunks are the branchpoints that lie within one pixel of
# the dilated image.
#
if labels_count > 0:
trunk_counts = fix(
scind.sum(branching_counts, nearby_labels,
label_range)).astype(int)
else:
trunk_counts = np.zeros((0, ), int)
#
# The branches are the branchpoints that lie outside the seed objects
#
if labels_count > 0:
branch_counts = fix(
scind.sum(branch_points, outside_labels, label_range))
else:
branch_counts = np.zeros((0, ), int)
#
# Save the endpoints
#
if labels_count > 0:
end_counts = fix(scind.sum(end_points, outside_labels,
label_range))
else:
end_counts = np.zeros((0, ), int)
#
# Calculate the distances
#
total_distance = morph.skeleton_length(dlabels * outside_skel,
label_range)
#
# Save measurements
#
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
feature = "_".join((C_OBJSKELETON, F_NUMBER_TRUNKS, skeleton_name))
m.add_measurement(seed_objects_name, feature, trunk_counts)
feature = "_".join(
(C_OBJSKELETON, F_NUMBER_NON_TRUNK_BRANCHES, skeleton_name))
m.add_measurement(seed_objects_name, feature, branch_counts)
feature = "_".join(
(C_OBJSKELETON, F_NUMBER_BRANCH_ENDS, skeleton_name))
m.add_measurement(seed_objects_name, feature, end_counts)
feature = "_".join(
(C_OBJSKELETON, F_TOTAL_OBJSKELETON_LENGTH, skeleton_name))
m[seed_objects_name, feature] = total_distance
#
# Collect the graph information
#
if self.wants_objskeleton_graph:
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
intensity_image = workspace.image_set.get_image(
self.intensity_image_name.value)
edge_graph, vertex_graph = self.make_objskeleton_graph(
combined_skel, dlabels, trunk_mask,
branch_points & ~trunk_mask, end_points,
intensity_image.pixel_data)
image_number = workspace.measurements.image_set_number
edge_path, vertex_path = self.get_graph_file_paths(
m, m.image_number)
workspace.interaction_request(self,
m.image_number,
edge_path,
edge_graph,
vertex_path,
vertex_graph,
headless_ok=True)
if self.show_window:
workspace.display_data.edge_graph = edge_graph
workspace.display_data.vertex_graph = vertex_graph
workspace.display_data.intensity_image = intensity_image.pixel_data
#
# Make the display image
#
if self.show_window or self.wants_branchpoint_image:
branchpoint_image = np.zeros(
(skeleton.shape[0], skeleton.shape[1], 3))
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
branch_mask = branch_points & (outside_labels != 0)
end_mask = end_points & (outside_labels != 0)
branchpoint_image[outside_skel, :] = 1
branchpoint_image[trunk_mask | branch_mask | end_mask, :] = 0
branchpoint_image[trunk_mask, 0] = 1
branchpoint_image[branch_mask, 1] = 1
branchpoint_image[end_mask, 2] = 1
branchpoint_image[dilated_labels != 0, :] *= .875
branchpoint_image[dilated_labels != 0, :] += .1
if self.show_window:
workspace.display_data.branchpoint_image = branchpoint_image
if self.wants_branchpoint_image:
bi = cpi.Image(branchpoint_image, parent_image=skeleton_image)
workspace.image_set.add(self.branchpoint_image_name.value, bi)
def run(self, workspace):
assert isinstance(workspace, cpw.Workspace)
image_name = self.image_name.value
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
workspace.display_data.statistics = []
img = image.pixel_data
mask = image.mask
objects = workspace.object_set.get_objects(self.primary_objects.value)
global_threshold = None
if self.method == M_DISTANCE_N:
has_threshold = False
else:
thresholded_image = self.threshold_image(image_name, workspace)
has_threshold = True
#
# Get the following labels:
# * all edited labels
# * labels touching the edge, including small removed
#
labels_in = objects.unedited_segmented.copy()
labels_touching_edge = np.hstack(
(labels_in[0, :], labels_in[-1, :], labels_in[:,
0], labels_in[:,
-1]))
labels_touching_edge = np.unique(labels_touching_edge)
is_touching = np.zeros(np.max(labels_in) + 1, bool)
is_touching[labels_touching_edge] = True
is_touching = is_touching[labels_in]
labels_in[(~is_touching) & (objects.segmented == 0)] = 0
#
# Stretch the input labels to match the image size. If there's no
# label matrix, then there's no label in that area.
#
if tuple(labels_in.shape) != tuple(img.shape):
tmp = np.zeros(img.shape, labels_in.dtype)
i_max = min(img.shape[0], labels_in.shape[0])
j_max = min(img.shape[1], labels_in.shape[1])
tmp[:i_max, :j_max] = labels_in[:i_max, :j_max]
labels_in = tmp
if self.method in (M_DISTANCE_B, M_DISTANCE_N):
if self.method == M_DISTANCE_N:
distances, (i, j) = scind.distance_transform_edt(
labels_in == 0, return_indices=True)
labels_out = np.zeros(labels_in.shape, int)
dilate_mask = distances <= self.distance_to_dilate.value
labels_out[dilate_mask] =\
labels_in[i[dilate_mask],j[dilate_mask]]
else:
labels_out, distances = propagate(img, labels_in,
thresholded_image, 1.0)
labels_out[distances > self.distance_to_dilate.value] = 0
labels_out[labels_in > 0] = labels_in[labels_in > 0]
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out
#
# Create the final output labels by removing labels in the
# output matrix that are missing from the segmented image
#
segmented_labels = objects.segmented
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
elif self.method == M_PROPAGATION:
labels_out, distance = propagate(img, labels_in, thresholded_image,
self.regularization_factor.value)
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out.copy()
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
elif self.method == M_WATERSHED_G:
#
# First, apply the sobel filter to the image (both horizontal
# and vertical). The filter measures gradient.
#
sobel_image = np.abs(scind.sobel(img))
#
# Combine the image mask and threshold to mask the watershed
#
watershed_mask = np.logical_or(thresholded_image, labels_in > 0)
watershed_mask = np.logical_and(watershed_mask, mask)
#
# Perform the first watershed
#
labels_out = watershed(sobel_image,
labels_in,
np.ones((3, 3), bool),
mask=watershed_mask)
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out.copy()
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
elif self.method == M_WATERSHED_I:
#
# invert the image so that the maxima are filled first
# and the cells compete over what's close to the threshold
#
inverted_img = 1 - img
#
# Same as above, but perform the watershed on the original image
#
watershed_mask = np.logical_or(thresholded_image, labels_in > 0)
watershed_mask = np.logical_and(watershed_mask, mask)
#
# Perform the watershed
#
labels_out = watershed(inverted_img,
labels_in,
np.ones((3, 3), bool),
mask=watershed_mask)
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
if self.wants_discard_edge and self.wants_discard_primary:
#
# Make a new primary object
#
lookup = scind.maximum(segmented_out, objects.segmented,
range(np.max(objects.segmented) + 1))
lookup = fix(lookup)
lookup[0] = 0
lookup[lookup != 0] = np.arange(np.sum(lookup != 0)) + 1
segmented_labels = lookup[objects.segmented]
segmented_out = lookup[segmented_out]
new_objects = cpo.Objects()
new_objects.segmented = segmented_labels
if objects.has_unedited_segmented:
new_objects.unedited_segmented = objects.unedited_segmented
if objects.has_small_removed_segmented:
new_objects.small_removed_segmented = objects.small_removed_segmented
new_objects.parent_image = objects.parent_image
primary_outline = outline(segmented_labels)
if self.wants_primary_outlines:
out_img = cpi.Image(primary_outline.astype(bool),
parent_image=image)
workspace.image_set.add(self.new_primary_outlines_name.value,
out_img)
else:
primary_outline = outline(objects.segmented)
secondary_outline = outline(segmented_out)
#
# Add the objects to the object set
#
objects_out = cpo.Objects()
objects_out.unedited_segmented = small_removed_segmented_out
objects_out.small_removed_segmented = small_removed_segmented_out
objects_out.segmented = segmented_out
objects_out.parent_image = image
objname = self.objects_name.value
workspace.object_set.add_objects(objects_out, objname)
if self.use_outlines.value:
out_img = cpi.Image(secondary_outline.astype(bool),
parent_image=image)
workspace.image_set.add(self.outlines_name.value, out_img)
object_count = np.max(segmented_out)
#
# Add measurements
#
measurements = workspace.measurements
cpmi.add_object_count_measurements(measurements, objname, object_count)
cpmi.add_object_location_measurements(measurements, objname,
segmented_out)
#
# Relate the secondary objects to the primary ones and record
# the relationship.
#
children_per_parent, parents_of_children = \
objects.relate_children(objects_out)
measurements.add_measurement(self.primary_objects.value,
cpmi.FF_CHILDREN_COUNT % objname,
children_per_parent)
measurements.add_measurement(
objname, cpmi.FF_PARENT % self.primary_objects.value,
parents_of_children)
image_numbers = np.ones(len(parents_of_children), int) *\
measurements.image_set_number
mask = parents_of_children > 0
measurements.add_relate_measurement(
self.module_num, R_PARENT, self.primary_objects.value,
self.objects_name.value, image_numbers[mask],
parents_of_children[mask], image_numbers[mask],
np.arange(1,
len(parents_of_children) + 1)[mask])
#
# If primary objects were created, add them
#
if self.wants_discard_edge and self.wants_discard_primary:
workspace.object_set.add_objects(
new_objects, self.new_primary_objects_name.value)
cpmi.add_object_count_measurements(
measurements, self.new_primary_objects_name.value,
np.max(new_objects.segmented))
cpmi.add_object_location_measurements(
measurements, self.new_primary_objects_name.value,
new_objects.segmented)
for parent_objects, parent_name, child_objects, child_name in (
(objects, self.primary_objects.value, new_objects,
self.new_primary_objects_name.value),
(new_objects, self.new_primary_objects_name.value, objects_out,
objname)):
children_per_parent, parents_of_children = \
parent_objects.relate_children(child_objects)
measurements.add_measurement(
parent_name, cpmi.FF_CHILDREN_COUNT % child_name,
children_per_parent)
measurements.add_measurement(child_name,
cpmi.FF_PARENT % parent_name,
parents_of_children)
if self.show_window:
object_area = np.sum(segmented_out > 0)
workspace.display_data.object_pct = \
100 * object_area / np.product(segmented_out.shape)
workspace.display_data.img = img
workspace.display_data.segmented_out = segmented_out
workspace.display_data.primary_labels = objects.segmented
workspace.display_data.global_threshold = global_threshold
workspace.display_data.object_count = object_count
def run(self, workspace):
assert isinstance(workspace, cpw.Workspace)
image_name = self.image_name.value
image = workspace.image_set.get_image(image_name,
must_be_grayscale = True)
workspace.display_data.statistics = []
img = image.pixel_data
mask = image.mask
objects = workspace.object_set.get_objects(self.primary_objects.value)
global_threshold = None
if self.method == M_DISTANCE_N:
has_threshold = False
else:
thresholded_image = self.threshold_image(image_name, workspace)
has_threshold = True
#
# Get the following labels:
# * all edited labels
# * labels touching the edge, including small removed
#
labels_in = objects.unedited_segmented.copy()
labels_touching_edge = np.hstack(
(labels_in[0,:], labels_in[-1,:], labels_in[:,0], labels_in[:,-1]))
labels_touching_edge = np.unique(labels_touching_edge)
is_touching = np.zeros(np.max(labels_in)+1, bool)
is_touching[labels_touching_edge] = True
is_touching = is_touching[labels_in]
labels_in[(~ is_touching) & (objects.segmented == 0)] = 0
#
# Stretch the input labels to match the image size. If there's no
# label matrix, then there's no label in that area.
#
if tuple(labels_in.shape) != tuple(img.shape):
tmp = np.zeros(img.shape, labels_in.dtype)
i_max = min(img.shape[0], labels_in.shape[0])
j_max = min(img.shape[1], labels_in.shape[1])
tmp[:i_max, :j_max] = labels_in[:i_max, :j_max]
labels_in = tmp
if self.method in (M_DISTANCE_B, M_DISTANCE_N):
if self.method == M_DISTANCE_N:
distances,(i,j) = scind.distance_transform_edt(labels_in == 0,
return_indices = True)
labels_out = np.zeros(labels_in.shape,int)
dilate_mask = distances <= self.distance_to_dilate.value
labels_out[dilate_mask] =\
labels_in[i[dilate_mask],j[dilate_mask]]
else:
labels_out, distances = propagate(img, labels_in,
thresholded_image,
1.0)
labels_out[distances>self.distance_to_dilate.value] = 0
labels_out[labels_in > 0] = labels_in[labels_in>0]
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out
#
# Create the final output labels by removing labels in the
# output matrix that are missing from the segmented image
#
segmented_labels = objects.segmented
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
elif self.method == M_PROPAGATION:
labels_out, distance = propagate(img, labels_in,
thresholded_image,
self.regularization_factor.value)
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out.copy()
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
elif self.method == M_WATERSHED_G:
#
# First, apply the sobel filter to the image (both horizontal
# and vertical). The filter measures gradient.
#
sobel_image = np.abs(scind.sobel(img))
#
# Combine the image mask and threshold to mask the watershed
#
watershed_mask = np.logical_or(thresholded_image, labels_in > 0)
watershed_mask = np.logical_and(watershed_mask, mask)
#
# Perform the first watershed
#
labels_out = watershed(sobel_image,
labels_in,
np.ones((3,3),bool),
mask=watershed_mask)
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out.copy()
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
elif self.method == M_WATERSHED_I:
#
# invert the image so that the maxima are filled first
# and the cells compete over what's close to the threshold
#
inverted_img = 1-img
#
# Same as above, but perform the watershed on the original image
#
watershed_mask = np.logical_or(thresholded_image, labels_in > 0)
watershed_mask = np.logical_and(watershed_mask, mask)
#
# Perform the watershed
#
labels_out = watershed(inverted_img,
labels_in,
np.ones((3,3),bool),
mask=watershed_mask)
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
if self.wants_discard_edge and self.wants_discard_primary:
#
# Make a new primary object
#
lookup = scind.maximum(segmented_out,
objects.segmented,
range(np.max(objects.segmented)+1))
lookup = fix(lookup)
lookup[0] = 0
lookup[lookup != 0] = np.arange(np.sum(lookup != 0)) + 1
segmented_labels = lookup[objects.segmented]
segmented_out = lookup[segmented_out]
new_objects = cpo.Objects()
new_objects.segmented = segmented_labels
if objects.has_unedited_segmented:
new_objects.unedited_segmented = objects.unedited_segmented
if objects.has_small_removed_segmented:
new_objects.small_removed_segmented = objects.small_removed_segmented
new_objects.parent_image = objects.parent_image
primary_outline = outline(segmented_labels)
if self.wants_primary_outlines:
out_img = cpi.Image(primary_outline.astype(bool),
parent_image = image)
workspace.image_set.add(self.new_primary_outlines_name.value,
out_img)
else:
primary_outline = outline(objects.segmented)
secondary_outline = outline(segmented_out)
#
# Add the objects to the object set
#
objects_out = cpo.Objects()
objects_out.unedited_segmented = small_removed_segmented_out
objects_out.small_removed_segmented = small_removed_segmented_out
objects_out.segmented = segmented_out
objects_out.parent_image = image
objname = self.objects_name.value
workspace.object_set.add_objects(objects_out, objname)
if self.use_outlines.value:
out_img = cpi.Image(secondary_outline.astype(bool),
parent_image = image)
workspace.image_set.add(self.outlines_name.value, out_img)
object_count = np.max(segmented_out)
#
# Add measurements
#
measurements = workspace.measurements
cpmi.add_object_count_measurements(measurements, objname, object_count)
cpmi.add_object_location_measurements(measurements, objname,
segmented_out)
#
# Relate the secondary objects to the primary ones and record
# the relationship.
#
children_per_parent, parents_of_children = \
objects.relate_children(objects_out)
measurements.add_measurement(self.primary_objects.value,
cpmi.FF_CHILDREN_COUNT%objname,
children_per_parent)
measurements.add_measurement(objname,
cpmi.FF_PARENT%self.primary_objects.value,
parents_of_children)
image_numbers = np.ones(len(parents_of_children), int) *\
measurements.image_set_number
mask = parents_of_children > 0
measurements.add_relate_measurement(
self.module_num, R_PARENT,
self.primary_objects.value, self.objects_name.value,
image_numbers[mask], parents_of_children[mask],
image_numbers[mask],
np.arange(1, len(parents_of_children) + 1)[mask])
#
# If primary objects were created, add them
#
if self.wants_discard_edge and self.wants_discard_primary:
workspace.object_set.add_objects(new_objects,
self.new_primary_objects_name.value)
cpmi.add_object_count_measurements(measurements,
self.new_primary_objects_name.value,
np.max(new_objects.segmented))
cpmi.add_object_location_measurements(measurements,
self.new_primary_objects_name.value,
new_objects.segmented)
for parent_objects, parent_name, child_objects, child_name in (
(objects, self.primary_objects.value,
new_objects, self.new_primary_objects_name.value),
(new_objects, self.new_primary_objects_name.value,
objects_out, objname)):
children_per_parent, parents_of_children = \
parent_objects.relate_children(child_objects)
measurements.add_measurement(parent_name,
cpmi.FF_CHILDREN_COUNT%child_name,
children_per_parent)
measurements.add_measurement(child_name,
cpmi.FF_PARENT%parent_name,
parents_of_children)
if self.show_window:
object_area = np.sum(segmented_out > 0)
workspace.display_data.object_pct = \
100 * object_area / np.product(segmented_out.shape)
workspace.display_data.img = img
workspace.display_data.segmented_out = segmented_out
workspace.display_data.primary_labels = objects.segmented
workspace.display_data.global_threshold = global_threshold
workspace.display_data.object_count = object_count
def do_measurements(self, workspace, image_name, object_name,
center_object_name, center_choice,
bin_count_settings, dd):
'''Perform the radial measurements on the image set
workspace - workspace that holds images / objects
image_name - make measurements on this image
object_name - make measurements on these objects
center_object_name - use the centers of these related objects as
the centers for radial measurements. None to use the
objects themselves.
center_choice - the user's center choice for this object:
C_SELF, C_CENTERS_OF_OBJECTS or C_EDGES_OF_OBJECTS.
bin_count_settings - the bin count settings group
d - a dictionary for saving reusable partial results
returns one statistics tuple per ring.
'''
assert isinstance(workspace, cpw.Workspace)
assert isinstance(workspace.object_set, cpo.ObjectSet)
bin_count = bin_count_settings.bin_count.value
wants_scaled = bin_count_settings.wants_scaled.value
maximum_radius = bin_count_settings.maximum_radius.value
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
objects = workspace.object_set.get_objects(object_name)
labels, pixel_data = cpo.crop_labels_and_image(objects.segmented,
image.pixel_data)
nobjects = np.max(objects.segmented)
measurements = workspace.measurements
assert isinstance(measurements, cpmeas.Measurements)
heatmaps = {}
for heatmap in self.heatmaps:
if heatmap.object_name.get_objects_name() == object_name and \
image_name == heatmap.image_name.get_image_name() and \
heatmap.get_number_of_bins() == bin_count:
dd[id(heatmap)] = \
heatmaps[MEASUREMENT_ALIASES[heatmap.measurement.value]] = \
np.zeros(labels.shape)
if nobjects == 0:
for bin in range(1, bin_count + 1):
for feature in (F_FRAC_AT_D, F_MEAN_FRAC, F_RADIAL_CV):
feature_name = (
(feature + FF_GENERIC) % (image_name, bin, bin_count))
measurements.add_measurement(
object_name, "_".join([M_CATEGORY, feature_name]),
np.zeros(0))
if not wants_scaled:
measurement_name = "_".join([M_CATEGORY, feature,
image_name, FF_OVERFLOW])
measurements.add_measurement(
object_name, measurement_name, np.zeros(0))
return [(image_name, object_name, "no objects", "-", "-", "-", "-")]
name = (object_name if center_object_name is None
else "%s_%s" % (object_name, center_object_name))
if dd.has_key(name):
normalized_distance, i_center, j_center, good_mask = dd[name]
else:
d_to_edge = distance_to_edge(labels)
if center_object_name is not None:
#
# Use the center of the centering objects to assign a center
# to each labeled pixel using propagation
#
center_objects = workspace.object_set.get_objects(center_object_name)
center_labels, cmask = cpo.size_similarly(
labels, center_objects.segmented)
pixel_counts = fix(scind.sum(
np.ones(center_labels.shape),
center_labels,
np.arange(1, np.max(center_labels) + 1, dtype=np.int32)))
good = pixel_counts > 0
i, j = (centers_of_labels(center_labels) + .5).astype(int)
ig = i[good]
jg = j[good]
lg = np.arange(1, len(i) + 1)[good]
if center_choice == C_CENTERS_OF_OTHER:
#
# Reduce the propagation labels to the centers of
# the centering objects
#
center_labels = np.zeros(center_labels.shape, int)
center_labels[ig, jg] = lg
cl, d_from_center = propagate(np.zeros(center_labels.shape),
center_labels,
labels != 0, 1)
#
# Erase the centers that fall outside of labels
#
cl[labels == 0] = 0
#
# If objects are hollow or crescent-shaped, there may be
# objects without center labels. As a backup, find the
# center that is the closest to the center of mass.
#
missing_mask = (labels != 0) & (cl == 0)
missing_labels = np.unique(labels[missing_mask])
if len(missing_labels):
all_centers = centers_of_labels(labels)
missing_i_centers, missing_j_centers = \
all_centers[:, missing_labels - 1]
di = missing_i_centers[:, np.newaxis] - ig[np.newaxis, :]
dj = missing_j_centers[:, np.newaxis] - jg[np.newaxis, :]
missing_best = lg[np.argsort((di * di + dj * dj,))[:, 0]]
best = np.zeros(np.max(labels) + 1, int)
best[missing_labels] = missing_best
cl[missing_mask] = best[labels[missing_mask]]
#
# Now compute the crow-flies distance to the centers
# of these pixels from whatever center was assigned to
# the object.
#
iii, jjj = np.mgrid[0:labels.shape[0], 0:labels.shape[1]]
di = iii[missing_mask] - i[cl[missing_mask] - 1]
dj = jjj[missing_mask] - j[cl[missing_mask] - 1]
d_from_center[missing_mask] = np.sqrt(di * di + dj * dj)
else:
# Find the point in each object farthest away from the edge.
# This does better than the centroid:
# * The center is within the object
# * The center tends to be an interesting point, like the
# center of the nucleus or the center of one or the other
# of two touching cells.
#
i, j = maximum_position_of_labels(d_to_edge, labels, objects.indices)
center_labels = np.zeros(labels.shape, int)
center_labels[i, j] = labels[i, j]
#
# Use the coloring trick here to process touching objects
# in separate operations
#
colors = color_labels(labels)
ncolors = np.max(colors)
d_from_center = np.zeros(labels.shape)
cl = np.zeros(labels.shape, int)
for color in range(1, ncolors + 1):
mask = colors == color
l, d = propagate(np.zeros(center_labels.shape),
center_labels,
mask, 1)
d_from_center[mask] = d[mask]
cl[mask] = l[mask]
good_mask = cl > 0
if center_choice == C_EDGES_OF_OTHER:
# Exclude pixels within the centering objects
# when performing calculations from the centers
good_mask = good_mask & (center_labels == 0)
i_center = np.zeros(cl.shape)
i_center[good_mask] = i[cl[good_mask] - 1]
j_center = np.zeros(cl.shape)
j_center[good_mask] = j[cl[good_mask] - 1]
normalized_distance = np.zeros(labels.shape)
if wants_scaled:
total_distance = d_from_center + d_to_edge
normalized_distance[good_mask] = (d_from_center[good_mask] /
(total_distance[good_mask] + .001))
else:
normalized_distance[good_mask] = \
d_from_center[good_mask] / maximum_radius
dd[name] = [normalized_distance, i_center, j_center, good_mask]
ngood_pixels = np.sum(good_mask)
good_labels = labels[good_mask]
bin_indexes = (normalized_distance * bin_count).astype(int)
bin_indexes[bin_indexes > bin_count] = bin_count
labels_and_bins = (good_labels - 1, bin_indexes[good_mask])
histogram = coo_matrix((pixel_data[good_mask], labels_and_bins),
(nobjects, bin_count + 1)).toarray()
sum_by_object = np.sum(histogram, 1)
sum_by_object_per_bin = np.dstack([sum_by_object] * (bin_count + 1))[0]
fraction_at_distance = histogram / sum_by_object_per_bin
number_at_distance = coo_matrix((np.ones(ngood_pixels), labels_and_bins),
(nobjects, bin_count + 1)).toarray()
object_mask = number_at_distance > 0
sum_by_object = np.sum(number_at_distance, 1)
sum_by_object_per_bin = np.dstack([sum_by_object] * (bin_count + 1))[0]
fraction_at_bin = number_at_distance / sum_by_object_per_bin
mean_pixel_fraction = fraction_at_distance / (fraction_at_bin +
np.finfo(float).eps)
masked_fraction_at_distance = masked_array(fraction_at_distance,
~object_mask)
masked_mean_pixel_fraction = masked_array(mean_pixel_fraction,
~object_mask)
# Anisotropy calculation. Split each cell into eight wedges, then
# compute coefficient of variation of the wedges' mean intensities
# in each ring.
#
# Compute each pixel's delta from the center object's centroid
i, j = np.mgrid[0:labels.shape[0], 0:labels.shape[1]]
imask = i[good_mask] > i_center[good_mask]
jmask = j[good_mask] > j_center[good_mask]
absmask = (abs(i[good_mask] - i_center[good_mask]) >
abs(j[good_mask] - j_center[good_mask]))
radial_index = (imask.astype(int) + jmask.astype(int) * 2 +
absmask.astype(int) * 4)
statistics = []
for bin in range(bin_count + (0 if wants_scaled else 1)):
bin_mask = (good_mask & (bin_indexes == bin))
bin_pixels = np.sum(bin_mask)
bin_labels = labels[bin_mask]
bin_radial_index = radial_index[bin_indexes[good_mask] == bin]
labels_and_radii = (bin_labels - 1, bin_radial_index)
radial_values = coo_matrix((pixel_data[bin_mask],
labels_and_radii),
(nobjects, 8)).toarray()
pixel_count = coo_matrix((np.ones(bin_pixels), labels_and_radii),
(nobjects, 8)).toarray()
mask = pixel_count == 0
radial_means = masked_array(radial_values / pixel_count, mask)
radial_cv = np.std(radial_means, 1) / np.mean(radial_means, 1)
radial_cv[np.sum(~mask, 1) == 0] = 0
for measurement, feature, overflow_feature in (
(fraction_at_distance[:, bin], MF_FRAC_AT_D, OF_FRAC_AT_D),
(mean_pixel_fraction[:, bin], MF_MEAN_FRAC, OF_MEAN_FRAC),
(np.array(radial_cv), MF_RADIAL_CV, OF_RADIAL_CV)):
if bin == bin_count:
measurement_name = overflow_feature % image_name
else:
measurement_name = feature % (image_name, bin + 1, bin_count)
measurements.add_measurement(object_name,
measurement_name,
measurement)
if feature in heatmaps:
heatmaps[feature][bin_mask] = measurement[bin_labels - 1]
radial_cv.mask = np.sum(~mask, 1) == 0
bin_name = str(bin + 1) if bin < bin_count else "Overflow"
statistics += [(image_name, object_name, bin_name, str(bin_count),
round(np.mean(masked_fraction_at_distance[:, bin]), 4),
round(np.mean(masked_mean_pixel_fraction[:, bin]), 4),
round(np.mean(radial_cv), 4))]
return statistics