def test_01_03_crop_labels(self):
labels, image = cpo.crop_labels_and_image(np.zeros((10, 30)),
np.zeros((10,20)))
self.assertEqual(tuple(labels.shape), (10,20))
self.assertEqual(tuple(image.shape), (10,20))
labels, image = cpo.crop_labels_and_image(np.zeros((20, 20)),
np.zeros((10, 20)))
self.assertEqual(tuple(labels.shape), (10,20))
self.assertEqual(tuple(image.shape), (10,20))
def test_01_03_crop_labels(self):
labels, image = cpo.crop_labels_and_image(np.zeros((10, 30)),
np.zeros((10, 20)))
self.assertEqual(tuple(labels.shape), (10, 20))
self.assertEqual(tuple(image.shape), (10, 20))
labels, image = cpo.crop_labels_and_image(np.zeros((20, 20)),
np.zeros((10, 20)))
self.assertEqual(tuple(labels.shape), (10, 20))
self.assertEqual(tuple(image.shape), (10, 20))
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(self, workspace):
if self.show_window:
workspace.display_data.col_labels = (
"Image","Object","Feature","Mean","Median","STD")
workspace.display_data.statistics = statistics = []
for image_name in [img.name for img in self.images]:
image = workspace.image_set.get_image(image_name.value,
must_be_grayscale=True)
for object_name in [obj.name for obj in self.objects]:
# Need to refresh image after each iteration...
img = image.pixel_data
if image.has_mask:
masked_image = img.copy()
masked_image[~image.mask] = 0
else:
masked_image = img
objects = workspace.object_set.get_objects(object_name.value)
nobjects = objects.count
integrated_intensity = np.zeros((nobjects,))
integrated_intensity_edge = np.zeros((nobjects,))
mean_intensity = np.zeros((nobjects,))
mean_intensity_edge = np.zeros((nobjects,))
std_intensity = np.zeros((nobjects,))
std_intensity_edge = np.zeros((nobjects,))
min_intensity = np.zeros((nobjects,))
min_intensity_edge = np.zeros((nobjects,))
max_intensity = np.zeros((nobjects,))
max_intensity_edge = np.zeros((nobjects,))
mass_displacement = np.zeros((nobjects,))
lower_quartile_intensity = np.zeros((nobjects,))
median_intensity = np.zeros((nobjects,))
mad_intensity = np.zeros((nobjects,))
upper_quartile_intensity = np.zeros((nobjects,))
cmi_x = np.zeros((nobjects,))
cmi_y = np.zeros((nobjects,))
max_x = np.zeros((nobjects,))
max_y = np.zeros((nobjects,))
for labels, lindexes in objects.get_labels():
lindexes = lindexes[lindexes != 0]
labels, img = cpo.crop_labels_and_image(labels, img)
_, masked_image = cpo.crop_labels_and_image(labels, masked_image)
outlines = cpmo.outline(labels)
if image.has_mask:
_, mask = cpo.crop_labels_and_image(labels, image.mask)
masked_labels = labels.copy()
masked_labels[~mask] = 0
masked_outlines = outlines.copy()
masked_outlines[~mask] = 0
else:
masked_labels = labels
masked_outlines = outlines
lmask = masked_labels > 0 & np.isfinite(img) # Ignore NaNs, Infs
has_objects = np.any(lmask)
if has_objects:
limg = img[lmask]
llabels = labels[lmask]
mesh_y, mesh_x = np.mgrid[0:masked_image.shape[0],
0:masked_image.shape[1]]
mesh_x = mesh_x[lmask]
mesh_y = mesh_y[lmask]
lcount = fix(nd.sum(np.ones(len(limg)), llabels, lindexes))
integrated_intensity[lindexes-1] = \
fix(nd.sum(limg, llabels, lindexes))
mean_intensity[lindexes-1] = \
integrated_intensity[lindexes-1] / lcount
std_intensity[lindexes-1] = np.sqrt(
fix(nd.mean((limg - mean_intensity[llabels-1])**2,
llabels, lindexes)))
min_intensity[lindexes-1] = fix(nd.minimum(limg, llabels, lindexes))
max_intensity[lindexes-1] = fix(
nd.maximum(limg, llabels, lindexes))
# Compute the position of the intensity maximum
max_position = np.array( fix(nd.maximum_position(limg, llabels, lindexes)), dtype=int )
max_position = np.reshape( max_position, ( max_position.shape[0], ) )
max_x[lindexes-1] = mesh_x[ max_position ]
max_y[lindexes-1] = mesh_y[ max_position ]
# The mass displacement is the distance between the center
# of mass of the binary image and of the intensity image. The
# center of mass is the average X or Y for the binary image
# and the sum of X or Y * intensity / integrated intensity
cm_x = fix(nd.mean(mesh_x, llabels, lindexes))
cm_y = fix(nd.mean(mesh_y, llabels, lindexes))
i_x = fix(nd.sum(mesh_x * limg, llabels, lindexes))
i_y = fix(nd.sum(mesh_y * limg, llabels, lindexes))
cmi_x[lindexes-1] = i_x / integrated_intensity[lindexes-1]
cmi_y[lindexes-1] = i_y / integrated_intensity[lindexes-1]
diff_x = cm_x - cmi_x[lindexes-1]
diff_y = cm_y - cmi_y[lindexes-1]
mass_displacement[lindexes-1] = \
np.sqrt(diff_x * diff_x+diff_y*diff_y)
#
# Sort the intensities by label, then intensity.
# For each label, find the index above and below
# the 25%, 50% and 75% mark and take the weighted
# average.
#
order = np.lexsort((limg, llabels))
areas = lcount.astype(int)
indices = np.cumsum(areas) - areas
for dest, fraction in (
(lower_quartile_intensity, 1.0/4.0),
(median_intensity, 1.0/2.0),
(upper_quartile_intensity, 3.0/4.0)):
qindex = indices.astype(float) + areas * fraction
qfraction = qindex - np.floor(qindex)
qindex = qindex.astype(int)
qmask = qindex < indices + areas-1
qi = qindex[qmask]
qf = qfraction[qmask]
dest[lindexes[qmask]-1] = (
limg[order[qi]] * (1 - qf) +
limg[order[qi + 1]] * qf)
#
# In some situations (e.g. only 3 points), there may
# not be an upper bound.
#
qmask = (~qmask) & (areas > 0)
dest[lindexes[qmask]-1] = limg[order[qindex[qmask]]]
#
# Once again, for the MAD
#
madimg = np.abs(limg - median_intensity[llabels-1])
order = np.lexsort((madimg, llabels))
qindex = indices.astype(float) + areas / 2.0
qfraction = qindex - np.floor(qindex)
qindex = qindex.astype(int)
qmask = qindex < indices + areas-1
qi = qindex[qmask]
qf = qfraction[qmask]
mad_intensity[lindexes[qmask]-1] = (
madimg[order[qi]] * (1 - qf) +
madimg[order[qi + 1]] * qf)
qmask = (~qmask) & (areas > 0)
mad_intensity[lindexes[qmask]-1] = madimg[order[qindex[qmask]]]
emask = masked_outlines > 0
eimg = img[emask]
elabels = labels[emask]
has_edge = len(eimg) > 0
if has_edge:
ecount = fix(nd.sum(
np.ones(len(eimg)), elabels, lindexes))
integrated_intensity_edge[lindexes-1] = \
fix(nd.sum(eimg, elabels, lindexes))
mean_intensity_edge[lindexes-1] = \
integrated_intensity_edge[lindexes-1] / ecount
std_intensity_edge[lindexes-1] = \
np.sqrt(fix(nd.mean(
(eimg - mean_intensity_edge[elabels-1])**2,
elabels, lindexes)))
min_intensity_edge[lindexes-1] = fix(
nd.minimum(eimg, elabels, lindexes))
max_intensity_edge[lindexes-1] = fix(
nd.maximum(eimg, elabels, lindexes))
m = workspace.measurements
for category, feature_name, measurement in \
((INTENSITY, INTEGRATED_INTENSITY, integrated_intensity),
(INTENSITY, MEAN_INTENSITY, mean_intensity),
(INTENSITY, STD_INTENSITY, std_intensity),
(INTENSITY, MIN_INTENSITY, min_intensity),
(INTENSITY, MAX_INTENSITY, max_intensity),
(INTENSITY, INTEGRATED_INTENSITY_EDGE, integrated_intensity_edge),
(INTENSITY, MEAN_INTENSITY_EDGE, mean_intensity_edge),
(INTENSITY, STD_INTENSITY_EDGE, std_intensity_edge),
(INTENSITY, MIN_INTENSITY_EDGE, min_intensity_edge),
(INTENSITY, MAX_INTENSITY_EDGE, max_intensity_edge),
(INTENSITY, MASS_DISPLACEMENT, mass_displacement),
(INTENSITY, LOWER_QUARTILE_INTENSITY, lower_quartile_intensity),
(INTENSITY, MEDIAN_INTENSITY, median_intensity),
(INTENSITY, MAD_INTENSITY, mad_intensity),
(INTENSITY, UPPER_QUARTILE_INTENSITY, upper_quartile_intensity),
(C_LOCATION, LOC_CMI_X, cmi_x),
(C_LOCATION, LOC_CMI_Y, cmi_y),
(C_LOCATION, LOC_MAX_X, max_x),
(C_LOCATION, LOC_MAX_Y, max_y)):
measurement_name = "%s_%s_%s"%(category,feature_name,
image_name.value)
m.add_measurement(object_name.value,measurement_name,
measurement)
if self.show_window and len(measurement) > 0:
statistics.append((image_name.value, object_name.value,
feature_name,
np.round(np.mean(measurement),3),
np.round(np.median(measurement),3),
np.round(np.std(measurement),3)))
def run(self, workspace):
if self.show_window:
workspace.display_data.col_labels = ("Image", "Object", "Feature",
"Mean", "Median", "STD")
workspace.display_data.statistics = statistics = []
for image_name in [img.name for img in self.images]:
image = workspace.image_set.get_image(image_name.value,
must_be_grayscale=True)
for object_name in [obj.name for obj in self.objects]:
# Need to refresh image after each iteration...
img = image.pixel_data
if image.has_mask:
masked_image = img.copy()
masked_image[~image.mask] = 0
else:
masked_image = img
objects = workspace.object_set.get_objects(object_name.value)
nobjects = objects.count
integrated_intensity = np.zeros((nobjects, ))
integrated_intensity_edge = np.zeros((nobjects, ))
mean_intensity = np.zeros((nobjects, ))
mean_intensity_edge = np.zeros((nobjects, ))
std_intensity = np.zeros((nobjects, ))
std_intensity_edge = np.zeros((nobjects, ))
min_intensity = np.zeros((nobjects, ))
min_intensity_edge = np.zeros((nobjects, ))
max_intensity = np.zeros((nobjects, ))
max_intensity_edge = np.zeros((nobjects, ))
mass_displacement = np.zeros((nobjects, ))
lower_quartile_intensity = np.zeros((nobjects, ))
median_intensity = np.zeros((nobjects, ))
mad_intensity = np.zeros((nobjects, ))
upper_quartile_intensity = np.zeros((nobjects, ))
cmi_x = np.zeros((nobjects, ))
cmi_y = np.zeros((nobjects, ))
max_x = np.zeros((nobjects, ))
max_y = np.zeros((nobjects, ))
for labels, lindexes in objects.get_labels():
lindexes = lindexes[lindexes != 0]
labels, img = cpo.crop_labels_and_image(labels, img)
_, masked_image = cpo.crop_labels_and_image(
labels, masked_image)
outlines = cpmo.outline(labels)
if image.has_mask:
_, mask = cpo.crop_labels_and_image(labels, image.mask)
masked_labels = labels.copy()
masked_labels[~mask] = 0
masked_outlines = outlines.copy()
masked_outlines[~mask] = 0
else:
masked_labels = labels
masked_outlines = outlines
lmask = masked_labels > 0 & np.isfinite(
img) # Ignore NaNs, Infs
has_objects = np.any(lmask)
if has_objects:
emask = masked_outlines > 0
limg = img[lmask]
eimg = img[emask]
llabels = labels[lmask]
elabels = labels[emask]
mesh_y, mesh_x = np.mgrid[0:masked_image.shape[0],
0:masked_image.shape[1]]
mesh_x = mesh_x[lmask]
mesh_y = mesh_y[lmask]
lcount = fix(
nd.sum(np.ones(len(limg)), llabels, lindexes))
ecount = fix(
nd.sum(np.ones(len(eimg)), elabels, lindexes))
integrated_intensity[lindexes-1] = \
fix(nd.sum(limg, llabels, lindexes))
integrated_intensity_edge[lindexes-1] = \
fix(nd.sum(eimg, elabels, lindexes))
mean_intensity[lindexes-1] = \
integrated_intensity[lindexes-1] / lcount
mean_intensity_edge[lindexes-1] = \
integrated_intensity_edge[lindexes-1] / ecount
std_intensity[lindexes - 1] = np.sqrt(
fix(
nd.mean(
(limg - mean_intensity[llabels - 1])**2,
llabels, lindexes)))
std_intensity_edge[lindexes - 1] = np.sqrt(
fix(
nd.mean((eimg -
mean_intensity_edge[elabels - 1])**2,
elabels, lindexes)))
min_intensity[lindexes - 1] = fix(
nd.minimum(limg, llabels, lindexes))
min_intensity_edge[lindexes - 1] = fix(
nd.minimum(eimg, elabels, lindexes))
max_intensity[lindexes - 1] = fix(
nd.maximum(limg, llabels, lindexes))
max_intensity_edge[lindexes - 1] = fix(
nd.maximum(eimg, elabels, lindexes))
# Compute the position of the intensity maximum
max_position = np.array(fix(
nd.maximum_position(limg, llabels, lindexes)),
dtype=int)
max_position = np.reshape(max_position,
(max_position.shape[0], ))
max_x[lindexes - 1] = mesh_x[max_position]
max_y[lindexes - 1] = mesh_y[max_position]
# The mass displacement is the distance between the center
# of mass of the binary image and of the intensity image. The
# center of mass is the average X or Y for the binary image
# and the sum of X or Y * intensity / integrated intensity
cm_x = fix(nd.mean(mesh_x, llabels, lindexes))
cm_y = fix(nd.mean(mesh_y, llabels, lindexes))
i_x = fix(nd.sum(mesh_x * limg, llabels, lindexes))
i_y = fix(nd.sum(mesh_y * limg, llabels, lindexes))
cmi_x[lindexes -
1] = i_x / integrated_intensity[lindexes - 1]
cmi_y[lindexes -
1] = i_y / integrated_intensity[lindexes - 1]
diff_x = cm_x - cmi_x[lindexes - 1]
diff_y = cm_y - cmi_y[lindexes - 1]
mass_displacement[lindexes-1] = \
np.sqrt(diff_x * diff_x+diff_y*diff_y)
#
# Sort the intensities by label, then intensity.
# For each label, find the index above and below
# the 25%, 50% and 75% mark and take the weighted
# average.
#
order = np.lexsort((limg, llabels))
areas = lcount.astype(int)
indices = np.cumsum(areas) - areas
for dest, fraction in ((lower_quartile_intensity,
1.0 / 4.0), (median_intensity,
1.0 / 2.0),
(upper_quartile_intensity,
3.0 / 4.0)):
qindex = indices.astype(float) + areas * fraction
qfraction = qindex - np.floor(qindex)
qindex = qindex.astype(int)
qmask = qindex < indices + areas - 1
qi = qindex[qmask]
qf = qfraction[qmask]
dest[lindexes[qmask] -
1] = (limg[order[qi]] * (1 - qf) +
limg[order[qi + 1]] * qf)
#
# In some situations (e.g. only 3 points), there may
# not be an upper bound.
#
qmask = (~qmask) & (areas > 0)
dest[lindexes[qmask] -
1] = limg[order[qindex[qmask]]]
#
# Once again, for the MAD
#
madimg = limg - median_intensity[llabels - 1]
order = np.lexsort((limg, llabels))
qindex = indices.astype(float) + areas / 2.0
qfraction = qindex - np.floor(qindex)
qindex = qindex.astype(int)
qmask = qindex < indices + areas - 1
qi = qindex[qmask]
qf = qfraction[qmask]
mad_intensity[lindexes[qmask] -
1] = (madimg[order[qi]] * (1 - qf) +
madimg[order[qi + 1]] * qf)
qmask = (~qmask) & (areas > 0)
mad_intensity[lindexes[qmask] -
1] = madimg[order[qindex[qmask]]]
m = workspace.measurements
for category, feature_name, measurement in \
((INTENSITY, INTEGRATED_INTENSITY, integrated_intensity),
(INTENSITY, MEAN_INTENSITY, mean_intensity),
(INTENSITY, STD_INTENSITY, std_intensity),
(INTENSITY, MIN_INTENSITY, min_intensity),
(INTENSITY, MAX_INTENSITY, max_intensity),
(INTENSITY, INTEGRATED_INTENSITY_EDGE, integrated_intensity_edge),
(INTENSITY, MEAN_INTENSITY_EDGE, mean_intensity_edge),
(INTENSITY, STD_INTENSITY_EDGE, std_intensity_edge),
(INTENSITY, MIN_INTENSITY_EDGE, min_intensity_edge),
(INTENSITY, MAX_INTENSITY_EDGE, max_intensity_edge),
(INTENSITY, MASS_DISPLACEMENT, mass_displacement),
(INTENSITY, LOWER_QUARTILE_INTENSITY, lower_quartile_intensity),
(INTENSITY, MEDIAN_INTENSITY, median_intensity),
(INTENSITY, MAD_INTENSITY, mad_intensity),
(INTENSITY, UPPER_QUARTILE_INTENSITY, upper_quartile_intensity),
(C_LOCATION, LOC_CMI_X, cmi_x),
(C_LOCATION, LOC_CMI_Y, cmi_y),
(C_LOCATION, LOC_MAX_X, max_x),
(C_LOCATION, LOC_MAX_Y, max_y)):
measurement_name = "%s_%s_%s" % (category, feature_name,
image_name.value)
m.add_measurement(object_name.value, measurement_name,
measurement)
if self.show_window and len(measurement) > 0:
statistics.append(
(image_name.value, object_name.value, feature_name,
np.round(np.mean(measurement),
3), np.round(np.median(measurement), 3),
np.round(np.std(measurement), 3)))
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
