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)
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)
if center_choice == C_CENTERS_OF_OTHER:
#
# Reduce the propagation labels to the centers of
# the centering objects
#
ig = i[good]
jg = j[good]
lg = np.arange(1, len(i)+1)[good]
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.lexsort((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)
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 run_on_objects(self, object_name, workspace):
"""Run, computing the area measurements for a single map of objects"""
objects = workspace.get_objects(object_name)
assert isinstance(objects, cpo.Objects)
#
# Do the ellipse-related measurements
#
i, j, l = objects.ijv.transpose()
centers, eccentricity, major_axis_length, minor_axis_length, \
theta, compactness =\
ellipse_from_second_moments_ijv(i, j, 1, l, objects.indices, True)
del i
del j
del l
self.record_measurement(workspace, object_name, F_ECCENTRICITY,
eccentricity)
self.record_measurement(workspace, object_name, F_MAJOR_AXIS_LENGTH,
major_axis_length)
self.record_measurement(workspace, object_name, F_MINOR_AXIS_LENGTH,
minor_axis_length)
self.record_measurement(workspace, object_name, F_ORIENTATION,
theta * 180 / np.pi)
self.record_measurement(workspace, object_name, F_COMPACTNESS,
compactness)
is_first = False
if len(objects.indices) == 0:
nobjects = 0
else:
nobjects = np.max(objects.indices)
mcenter_x = np.zeros(nobjects)
mcenter_y = np.zeros(nobjects)
mextent = np.zeros(nobjects)
mperimeters = np.zeros(nobjects)
msolidity = np.zeros(nobjects)
euler = np.zeros(nobjects)
max_radius = np.zeros(nobjects)
median_radius = np.zeros(nobjects)
mean_radius = np.zeros(nobjects)
min_feret_diameter = np.zeros(nobjects)
max_feret_diameter = np.zeros(nobjects)
zernike_numbers = self.get_zernike_numbers()
zf = {}
for n, m in zernike_numbers:
zf[(n, m)] = np.zeros(nobjects)
if nobjects > 0:
chulls, chull_counts = convex_hull_ijv(objects.ijv,
objects.indices)
for labels, indices in objects.get_labels():
to_indices = indices - 1
distances = distance_to_edge(labels)
mcenter_y[to_indices], mcenter_x[to_indices] =\
maximum_position_of_labels(distances, labels, indices)
max_radius[to_indices] = fix(
scind.maximum(distances, labels, indices))
mean_radius[to_indices] = fix(
scind.mean(distances, labels, indices))
median_radius[to_indices] = median_of_labels(
distances, labels, indices)
#
# The extent (area / bounding box area)
#
mextent[to_indices] = calculate_extents(labels, indices)
#
# The perimeter distance
#
mperimeters[to_indices] = calculate_perimeters(labels, indices)
#
# Solidity
#
msolidity[to_indices] = calculate_solidity(labels, indices)
#
# Euler number
#
euler[to_indices] = euler_number(labels, indices)
#
# Zernike features
#
zf_l = cpmz.zernike(zernike_numbers, labels, indices)
for (n, m), z in zip(zernike_numbers, zf_l.transpose()):
zf[(n, m)][to_indices] = z
#
# Form factor
#
ff = 4.0 * np.pi * objects.areas / mperimeters**2
#
# Feret diameter
#
min_feret_diameter, max_feret_diameter = \
feret_diameter(chulls, chull_counts, objects.indices)
else:
ff = np.zeros(0)
for f, m in ([(F_AREA, objects.areas), (F_CENTER_X, mcenter_x),
(F_CENTER_Y, mcenter_y), (F_EXTENT, mextent),
(F_PERIMETER, mperimeters), (F_SOLIDITY, msolidity),
(F_FORM_FACTOR, ff), (F_EULER_NUMBER, euler),
(F_MAXIMUM_RADIUS, max_radius),
(F_MEAN_RADIUS, mean_radius),
(F_MEDIAN_RADIUS, median_radius),
(F_MIN_FERET_DIAMETER, min_feret_diameter),
(F_MAX_FERET_DIAMETER, max_feret_diameter)] +
[(self.get_zernike_name((n, m)), zf[(n, m)])
for n, m in zernike_numbers]):
self.record_measurement(workspace, object_name, f, m)
def run_on_objects(self,object_name, workspace):
"""Run, computing the area measurements for a single map of objects"""
objects = workspace.get_objects(object_name)
assert isinstance(objects, cpo.Objects)
#
# Do the ellipse-related measurements
#
i, j, l = objects.ijv.transpose()
centers, eccentricity, major_axis_length, minor_axis_length, \
theta, compactness =\
ellipse_from_second_moments_ijv(i, j, 1, l, objects.indices, True)
del i
del j
del l
self.record_measurement(workspace, object_name,
F_ECCENTRICITY, eccentricity)
self.record_measurement(workspace, object_name,
F_MAJOR_AXIS_LENGTH, major_axis_length)
self.record_measurement(workspace, object_name,
F_MINOR_AXIS_LENGTH, minor_axis_length)
self.record_measurement(workspace, object_name, F_ORIENTATION,
theta * 180 / np.pi)
self.record_measurement(workspace, object_name, F_COMPACTNESS,
compactness)
is_first = False
if len(objects.indices) == 0:
nobjects = 0
else:
nobjects = np.max(objects.indices)
mcenter_x = np.zeros(nobjects)
mcenter_y = np.zeros(nobjects)
mextent = np.zeros(nobjects)
mperimeters = np.zeros(nobjects)
msolidity = np.zeros(nobjects)
euler = np.zeros(nobjects)
max_radius = np.zeros(nobjects)
median_radius = np.zeros(nobjects)
mean_radius = np.zeros(nobjects)
min_feret_diameter = np.zeros(nobjects)
max_feret_diameter = np.zeros(nobjects)
zernike_numbers = self.get_zernike_numbers()
zf = {}
for n,m in zernike_numbers:
zf[(n,m)] = np.zeros(nobjects)
if nobjects > 0:
chulls, chull_counts = convex_hull_ijv(objects.ijv, objects.indices)
for labels, indices in objects.get_labels():
to_indices = indices-1
distances = distance_to_edge(labels)
mcenter_y[to_indices], mcenter_x[to_indices] =\
maximum_position_of_labels(distances, labels, indices)
max_radius[to_indices] = fix(scind.maximum(
distances, labels, indices))
mean_radius[to_indices] = fix(scind.mean(
distances, labels, indices))
median_radius[to_indices] = median_of_labels(
distances, labels, indices)
#
# The extent (area / bounding box area)
#
mextent[to_indices] = calculate_extents(labels, indices)
#
# The perimeter distance
#
mperimeters[to_indices] = calculate_perimeters(labels, indices)
#
# Solidity
#
msolidity[to_indices] = calculate_solidity(labels, indices)
#
# Euler number
#
euler[to_indices] = euler_number(labels, indices)
#
# Zernike features
#
zf_l = cpmz.zernike(zernike_numbers, labels, indices)
for (n,m), z in zip(zernike_numbers, zf_l.transpose()):
zf[(n,m)][to_indices] = z
#
# Form factor
#
ff = 4.0 * np.pi * objects.areas / mperimeters**2
#
# Feret diameter
#
min_feret_diameter, max_feret_diameter = \
feret_diameter(chulls, chull_counts, objects.indices)
else:
ff = np.zeros(0)
for f, m in ([(F_AREA, objects.areas),
(F_CENTER_X, mcenter_x),
(F_CENTER_Y, mcenter_y),
(F_EXTENT, mextent),
(F_PERIMETER, mperimeters),
(F_SOLIDITY, msolidity),
(F_FORM_FACTOR, ff),
(F_EULER_NUMBER, euler),
(F_MAXIMUM_RADIUS, max_radius),
(F_MEAN_RADIUS, mean_radius),
(F_MEDIAN_RADIUS, median_radius),
(F_MIN_FERET_DIAMETER, min_feret_diameter),
(F_MAX_FERET_DIAMETER, max_feret_diameter)] +
[(self.get_zernike_name((n,m)), zf[(n,m)])
for n,m in zernike_numbers]):
self.record_measurement(workspace, object_name, f, m)
def do_measurements(self, workspace, image_name, object_name,
center_object_name, bin_count,
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.
bin_count - bin the object into this many concentric rings
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)
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)
if nobjects == 0:
for bin in range(1, bin_count+1):
for feature in (FF_FRAC_AT_D, FF_MEAN_FRAC, FF_RADIAL_CV):
measurements.add_measurement(object_name,
M_CATEGORY + "_" + feature %
(image_name, bin, bin_count),
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:
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]
center_labels = np.zeros(center_labels.shape, int)
center_labels[ig,jg] = labels[ig,jg] ## TODO: This is incorrect when objects are annular. Retrieves label# = 0
cl,d_from_center = propagate(np.zeros(center_labels.shape),
center_labels,
labels != 0, 1)
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
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)
total_distance = d_from_center + d_to_edge
normalized_distance[good_mask] = (d_from_center[good_mask] /
(total_distance[good_mask] + .001))
dd[name] = [normalized_distance, i_center, j_center, good_mask]
ngood_pixels = np.sum(good_mask)
good_labels = objects.segmented[good_mask]
bin_indexes = (normalized_distance * bin_count).astype(int)
labels_and_bins = (good_labels-1,bin_indexes[good_mask])
histogram = coo_matrix((image.pixel_data[good_mask], labels_and_bins),
(nobjects, bin_count)).toarray()
sum_by_object = np.sum(histogram, 1)
sum_by_object_per_bin = np.dstack([sum_by_object]*bin_count)[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)).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)[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):
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 in ((fraction_at_distance[:,bin], MF_FRAC_AT_D),
(mean_pixel_fraction[:,bin], MF_MEAN_FRAC),
(np.array(radial_cv), MF_RADIAL_CV)):
measurements.add_measurement(object_name,
feature %
(image_name, bin+1, bin_count),
measurement)
radial_cv.mask = np.sum(~mask,1)==0
statistics += [(image_name, object_name, str(bin+1), 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