def find_segmented_regions(seeds, autof_stack, imsave):
min_autof_proj = min_intensity_projection(autof_stack)
equal_autof = equalize_adaptive(min_autof_proj, "equal_autof")
smoothed_autof = gaussian_filter(equal_autof, sigma=5, name="smooth_autof")
edge_autof = find_edges(smoothed_autof, name="edge_autof")
thresh_autof = threshold_otsu(smoothed_autof, mult=0.6, name="thresh_autof")
# ndfeed = skimage.img_as_uint(edge_autof.image_array & thresh_autof)
# imsave('ndfeed.png', ndfeed)
# altseg = watershed_ift(ndfeed, seeds.image_array)
# imsave('altseg.png', altseg)
# segmentation = watershed_with_seeds(smoothed_autof, ImageArray(altseg, 'atseg'),
segmentation = watershed_with_seeds(smoothed_autof, seeds, mask_image=thresh_autof)
# my_maker = make_named_transform('hughbert')
# my_filter = my_maker(filter_segmentation)
# filtered_segmentation = my_filter(segmentation)
filtered_segmentation = filter_segmentation(segmentation)
re_watershed = watershed_with_seeds(
smoothed_autof, filtered_segmentation, mask_image=thresh_autof, name="re_watershed"
)
return re_watershed
def generate_segmentation_seeds(nuclear_stack):
"""Given the nuclear fluorescence channel, find markers representing the
locations of those nuclei so that they can be used to seed a segmentation.
"""
normed_stack = normalise_stack(nuclear_stack)
max_nuclear_proj = max_intensity_projection(normed_stack)
eq_proj = equalize_adaptive(max_nuclear_proj, n_tiles=16, name="equalized_nuclear_proj")
gauss = gaussian_filter(eq_proj, sigma=3)
edges = find_edges(gauss, name="seed_edges")
thresh = threshold_otsu(edges, mult=1)
nosmall = remove_small_objects(thresh, min_size=500)
# dilated = dilate_simple(nosmall)
connected_components = find_connected_components(nosmall, background=0, name="conn_seeds")
seeds = component_centroids(connected_components, name="seed_centroids")
return seeds
def find_probe_locations(stack_dir, imsave, pchannel):
"""Find probe locations. Given a path, we construct a z stack from the first
channel of the images in that path, and then find probes within that stack.
Returns a list of coordinate pairs, representing x, y locations of probes.
"""
zstack = Stack.from_path(stack_dir, channel=pchannel)
# For comparative purposes (so we save the image)
projection = max_intensity_projection(zstack)
# Normalise each image in the stack
norm_stack = normalise_stack(zstack)
# Now take a maximum intensity projection
norm_projection = max_intensity_projection(norm_stack, 'norm_projection')
# Find edges should show the circle-like probes as annuli
edges = find_edges(norm_projection)
# Find a suitable template image for matching
template = find_best_template(edges, imsave)
match_result = match_template(edges.image_array, template, pad_input=True)
imsave('stage2_match.png', match_result)
# Set a threshold for matched locations
match_thresh = 0.6
print "t,c"
for t in np.arange(0.1, 1, 0.05):
print "{},{}".format(t, len(np.where(match_result > t)[0]))
locs = np.where(match_result > match_thresh)
annotated_edges = grayscale_to_rgb(edges.image_array)
annotated_edges[locs] = edges.image_array.max(), 0, 0
imsave('annotated_edges.png', annotated_edges)
# Find the centroids of the locations where we think there's a probe
cloc_array = match_result > match_thresh
ia_locs = ImageArray(cloc_array, name='new_cloc')
connected_components = find_connected_components(ia_locs)
centroids = component_centroids(connected_components)
probe_locs = zip(*np.where(centroids.image_array != 0))
generate_probe_loc_image(norm_projection, probe_locs, imsave)
return probe_locs
