def test_clog(self):
im_nuclei_stain = np.load(
os.path.join(TEST_DATA_DIR, 'Easy1_nuclei_stain.npy'))
im_nuclei_fgnd_mask = np.load(
os.path.join(TEST_DATA_DIR, 'Easy1_nuclei_fgnd_mask.npy'))
sigma_min = 10.0 / np.sqrt(2.0)
sigma_max = 40.0 / np.sqrt(2.0)
im_log_max, im_sigma_max = clog(im_nuclei_stain, im_nuclei_fgnd_mask,
sigma_min, sigma_max)
im_log_max_gtruth = np.load(
os.path.join(TEST_DATA_DIR, 'Easy1_clog_max.npy'))
np.testing.assert_array_almost_equal(im_log_max.astype('float16'),
im_log_max_gtruth,
decimal=4)
im_sigma_max_gtruth = np.load(
os.path.join(TEST_DATA_DIR, 'Easy1_clog_sigma_max.npy'))
np.testing.assert_array_almost_equal(im_sigma_max.astype(np.float16),
im_sigma_max_gtruth,
decimal=4)
def test_clog(self):
im_nuclei_stain_data = np.load(
os.path.join(TEST_DATA_DIR, 'Easy1_nuclei_stain.npz'))
im_nuclei_stain = im_nuclei_stain_data['Easy1_nuclei_stain']
im_nuclei_fgnd_mask_data = np.load(
os.path.join(TEST_DATA_DIR, 'Easy1_nuclei_fgnd_mask.npz'))
im_nuclei_fgnd_mask = im_nuclei_fgnd_mask_data[
'Easy1_nuclei_fgnd_mask']
sigma_min = 10.0 / np.sqrt(2.0)
sigma_max = 40.0 / np.sqrt(2.0)
im_log_max, im_sigma_max = clog(im_nuclei_stain, im_nuclei_fgnd_mask,
sigma_min, sigma_max)
im_log_max_gtruth_data = np.load(
os.path.join(TEST_DATA_DIR, 'Easy1_clog_max.npz'))
im_log_max_gtruth = im_log_max_gtruth_data['Easy1_clog_max']
assert_array_almost_equal_neighborhood_lines(im_log_max,
im_log_max_gtruth,
decimal=4)
compare_maxima(im_log_max, im_log_max_gtruth)
im_sigma_max_gtruth_data = np.load(
os.path.join(TEST_DATA_DIR, 'Easy1_clog_sigma_max.npz'))
im_sigma_max_gtruth = im_sigma_max_gtruth_data['Easy1_clog_sigma_max']
assert_array_almost_equal_neighborhood_lines(im_sigma_max,
im_sigma_max_gtruth,
decimal=4)
def test_clog(self):
im_nuclei_stain_data = np.load(
utilities.externaldata('data/Easy1_nuclei_stain.npz.sha512'))
im_nuclei_stain = im_nuclei_stain_data['Easy1_nuclei_stain']
im_nuclei_fgnd_mask_data = np.load(
utilities.externaldata('data/Easy1_nuclei_fgnd_mask.npz.sha512'))
im_nuclei_fgnd_mask = im_nuclei_fgnd_mask_data['Easy1_nuclei_fgnd_mask']
sigma_min = 10.0 / np.sqrt(2.0)
sigma_max = 40.0 / np.sqrt(2.0)
im_log_max, im_sigma_max = clog(
im_input=im_nuclei_stain, im_mask=im_nuclei_fgnd_mask,
sigma_min=sigma_min, sigma_max=sigma_max)
im_log_max_gtruth_data = np.load(os.path.join(utilities.externaldata(
'data/Easy1_clog_max.npz.sha512')))
im_log_max_gtruth = im_log_max_gtruth_data['Easy1_clog_max']
assert_array_almost_equal_neighborhood_lines(im_log_max, im_log_max_gtruth, decimal=4)
compare_maxima(im_log_max, im_log_max_gtruth)
im_sigma_max_gtruth_data = np.load(os.path.join(utilities.externaldata(
'data/Easy1_clog_sigma_max.npz.sha512')))
im_sigma_max_gtruth = im_sigma_max_gtruth_data['Easy1_clog_sigma_max']
assert_array_almost_equal_neighborhood_lines(im_sigma_max, im_sigma_max_gtruth, decimal=4)
def detect_nuclei_kofahi(im_input, args):
# perform color normalization
im_nmzd = htk_cnorm.reinhard(im_input, args.reference_mu_lab,
args.reference_std_lab)
# perform color decovolution
w = np.array([
stain_color_map[args.stain_1], stain_color_map[args.stain_2],
stain_color_map[args.stain_3]
]).T
im_stains = htk_cdeconv.color_deconvolution(im_nmzd, w).Stains
im_nuclei_stain = im_stains[:, :, 0].astype(np.float)
# segment foreground (assumes nuclei are darker on a bright background)
im_nuclei_fgnd_mask = sp.ndimage.morphology.binary_fill_holes(
im_nuclei_stain < args.foreground_threshold)
# run adaptive multi-scale LoG filter
im_log = htk_shape_filters.clog(im_nuclei_stain,
im_nuclei_fgnd_mask,
sigma_min=args.min_radius * np.sqrt(2),
sigma_max=args.max_radius * np.sqrt(2))
# apply local maximum clustering
im_nuclei_seg_mask, seeds, max = htk_seg.nuclear.max_clustering(
im_log, im_nuclei_fgnd_mask, args.local_max_search_radius)
# filter out small objects
im_nuclei_seg_mask = htk_seg.label.area_open(
im_nuclei_seg_mask, args.min_nucleus_area).astype(np.int)
return im_nuclei_seg_mask
def main(args):
#
# Read Input Image
#
print('>> Reading input image')
imInput = skimage.io.imread(args.inputImageFile)[:, :, :3]
#
# Perform color normalization
#
print('>> Performing color normalization')
# compute mean and stddev of input in LAB color space
Mu, Sigma = htk_color_conversion.lab_mean_std(imInput)
# perform reinhard normalization
imNmzd = htk_color_normalization.reinhard(imInput, Mu, Sigma)
#
# Perform color deconvolution
#
print('>> Performing color deconvolution')
stainColor_1 = stainColorMap[args.stain_1]
stainColor_2 = stainColorMap[args.stain_2]
stainColor_3 = stainColorMap[args.stain_3]
W = np.array([stainColor_1, stainColor_2, stainColor_3]).T
imDeconvolved = htk_color_deconvolution.ColorDeconvolution(imNmzd, W)
imNucleiStain = imDeconvolved.Stains[:, :, 0].astype(np.float)
#
# Perform nuclei segmentation
#
print('>> Performing nuclei segmentation')
# segment foreground
imFgndMask = sp.ndimage.morphology.binary_fill_holes(
imNucleiStain < args.foreground_threshold)
# run adaptive multi-scale LoG filter
imLog = htk_shape_filters.clog(imNucleiStain, imFgndMask,
sigma_min=args.min_radius * np.sqrt(2),
sigma_max=args.max_radius * np.sqrt(2))
imNucleiSegMask, Seeds, Max = htk_seg.nuclear.max_clustering(
imLog, imFgndMask, args.local_max_search_radius)
# filter out small objects
imNucleiSegMask = htk_seg.label.area_open(
imNucleiSegMask, args.min_nucleus_area).astype(np.int)
#
# Generate annotations
#
objProps = skimage.measure.regionprops(imNucleiSegMask)
print 'Number of nuclei = ', len(objProps)
# create basic schema
annotation = {
"name": "Nuclei",
"description": "Nuclei bounding boxes from a segmentation algorithm",
"attributes": {
"algorithm": {
"color_normalization": "reinhard",
"color_deconvolution": "ColorDeconvolution",
"nuclei_segmentation": ["cLOG",
"MaxClustering",
"FilterLabel"]
}
},
"elements": []
}
# add each nucleus as an element into the annotation schema
for i in range(len(objProps)):
c = [objProps[i].centroid[1], objProps[i].centroid[0], 0]
width = objProps[i].bbox[3] - objProps[i].bbox[1] + 1
height = objProps[i].bbox[2] - objProps[i].bbox[0] + 1
cur_bbox = {
"type": "rectangle",
"center": c,
"width": width,
"height": height,
"rotation": 0,
"fillColor": "rgba(255, 255, 255, 0)",
"lineWidth": 2,
"lineColor": "rgb(34, 139, 34)"
}
annotation["elements"].append(cur_bbox)
#
# Save output segmentation mask
#
print('>> Outputting nuclei segmentation mask')
skimage.io.imsave(args.outputNucleiMaskFile, imNucleiSegMask)
#
# Save output annotation
#
print('>> Outputting nuclei annotation')
with open(args.outputNucleiAnnotationFile, 'w') as annotationFile:
json.dump(annotation, annotationFile, indent=2, sort_keys=False)
def main(args):
#
# Read Input Image
#
print('>> Reading input image')
im_input = skimage.io.imread(args.inputImageFile)[:, :, :3]
#
# Perform color normalization
#
print('>> Performing color normalization')
# compute mean and stddev of input in LAB color space
mu, sigma = htk_ccvt.lab_mean_std(im_input)
# perform reinhard normalization
im_nmzd = htk_cnorm.reinhard(im_input, mu, sigma)
#
# Perform color deconvolution
#
print('>> Performing color deconvolution')
stain_color_1 = stain_color_map[args.stain_1]
stain_color_2 = stain_color_map[args.stain_2]
stain_color_3 = stain_color_map[args.stain_3]
w = np.array([stain_color_1, stain_color_2, stain_color_3]).T
im_stains = htk_cdeconv.color_deconvolution(im_nmzd, w).Stains
im_nuclei_stain = im_stains[:, :, 0].astype(np.float)
#
# Perform nuclei segmentation
#
print('>> Performing nuclei segmentation')
# segment foreground
im_fgnd_mask = sp.ndimage.morphology.binary_fill_holes(
im_nuclei_stain < args.foreground_threshold)
# run adaptive multi-scale LoG filter
im_log = htk_shape_filters.clog(im_nuclei_stain, im_fgnd_mask,
sigma_min=args.min_radius * np.sqrt(2),
sigma_max=args.max_radius * np.sqrt(2))
im_nuclei_seg_mask, seeds, max = htk_seg.nuclear.max_clustering(
im_log, im_fgnd_mask, args.local_max_search_radius)
# filter out small objects
im_nuclei_seg_mask = htk_seg.label.area_open(
im_nuclei_seg_mask, args.min_nucleus_area).astype(np.int)
#
# Generate annotations
#
obj_props = skimage.measure.regionprops(im_nuclei_seg_mask)
print 'Number of nuclei = ', len(obj_props)
# create basic schema
annotation = {
"name": "Nuclei",
"description": "Nuclei bounding boxes from a segmentation algorithm",
"attributes": {
"algorithm": {
"color_normalization": "reinhard",
"color_deconvolution": "ColorDeconvolution",
"nuclei_segmentation": ["cLOG",
"MaxClustering",
"FilterLabel"]
}
},
"elements": []
}
# add each nucleus as an element into the annotation schema
for i in range(len(obj_props)):
c = [obj_props[i].centroid[1], obj_props[i].centroid[0], 0]
width = obj_props[i].bbox[3] - obj_props[i].bbox[1] + 1
height = obj_props[i].bbox[2] - obj_props[i].bbox[0] + 1
cur_bbox = {
"type": "rectangle",
"center": c,
"width": width,
"height": height,
"rotation": 0,
"fillColor": "rgba(255, 255, 255, 0)",
"lineWidth": 2,
"lineColor": "rgb(34, 139, 34)"
}
annotation["elements"].append(cur_bbox)
#
# Save output segmentation mask
#
print('>> Outputting nuclei segmentation mask')
skimage.io.imsave(args.outputNucleiMaskFile, im_nuclei_seg_mask)
#
# Save output annotation
#
print('>> Outputting nuclei annotation')
with open(args.outputNucleiAnnotationFile, 'w') as annotation_file:
json.dump(annotation, annotation_file, indent=2, sort_keys=False)
def test_segment_nuclei_kofahi(self):
input_image_file = os.path.join(TEST_DATA_DIR, 'Easy1.png')
ref_image_file = os.path.join(TEST_DATA_DIR, 'L1.png')
# read input image
im_input = skimage.io.imread(input_image_file)[:, :, :3]
# read reference image
im_reference = skimage.io.imread(ref_image_file)[:, :, :3]
# get mean and stddev of reference image in lab space
mean_ref, std_ref = htk_cvt.lab_mean_std(im_reference)
# perform color normalization
im_nmzd = htk_cnorm.reinhard(im_input, mean_ref, std_ref)
# perform color decovolution
stain_color_map = {
'hematoxylin': [0.65, 0.70, 0.29],
'eosin': [0.07, 0.99, 0.11],
'dab': [0.27, 0.57, 0.78],
'null': [0.0, 0.0, 0.0]
}
w = htk_cdeconv.rgb_separate_stains_macenko_pca(im_nmzd, im_nmzd.max())
im_stains = htk_cdeconv.color_deconvolution(im_nmzd, w).Stains
nuclei_channel = htk_cdeconv.find_stain_index(stain_color_map['hematoxylin'], w)
im_nuclei_stain = im_stains[:, :, nuclei_channel].astype(np.float)
# segment foreground (assumes nuclei are darker on a bright background)
im_nuclei_fgnd_mask = sp.ndimage.morphology.binary_fill_holes(
im_nuclei_stain < 60)
# run adaptive multi-scale LoG filter
im_log, im_sigma_max = htk_shape_filters.clog(
im_nuclei_stain, im_nuclei_fgnd_mask,
sigma_min=20 / np.sqrt(2), sigma_max=30 / np.sqrt(2))
# apply local maximum clustering
im_nuclei_seg_mask, seeds, maxima = htk_seg.nuclear.max_clustering(
im_log, im_nuclei_fgnd_mask, 10)
# filter out small objects
im_nuclei_seg_mask = htk_seg.label.area_open(
im_nuclei_seg_mask, 80).astype(np.uint8)
# perform connected component analysis
obj_props = skimage.measure.regionprops(im_nuclei_seg_mask)
num_nuclei = len(obj_props)
# check if segmentation mask matches ground truth
gtruth_mask_file = os.path.join(TEST_DATA_DIR,
'Easy1_nuclei_seg_kofahi_adaptive.npy')
im_gtruth_mask = np.load(gtruth_mask_file)
obj_props_gtruth = skimage.measure.regionprops(im_gtruth_mask)
num_nuclei_gtruth = len(obj_props_gtruth)
assert(num_nuclei == num_nuclei_gtruth)
np.testing.assert_allclose(im_nuclei_seg_mask, im_gtruth_mask)
def main(args):
#
# Read Input Image
#
print('>> Reading input image')
imInput = skimage.io.imread(args.inputImageFile)[:, :, :3]
#
# Perform color normalization
#
print('>> Performing color normalization')
# compute mean and stddev of input in LAB color space
Mu, Sigma = htk_color_conversion.lab_mean_std(imInput)
# perform reinhard normalization
imNmzd = htk_color_normalization.reinhard(imInput, Mu, Sigma)
#
# Perform color deconvolution
#
print('>> Performing color deconvolution')
stainColor_1 = stainColorMap[args.stain_1]
stainColor_2 = stainColorMap[args.stain_2]
stainColor_3 = stainColorMap[args.stain_3]
W = np.array([stainColor_1, stainColor_2, stainColor_3]).T
imDeconvolved = htk_color_deconvolution.ColorDeconvolution(imNmzd, W)
imNucleiStain = imDeconvolved.Stains[::2, ::2, 0].astype(np.float)
#
# Perform nuclei segmentation
#
print('>> Performing nuclei segmentation')
# segment foreground
imFgndMask = sp.ndimage.morphology.binary_fill_holes(
imNucleiStain < args.foreground_threshold)
# run adaptive multi-scale LoG filter
imLog = htk_shape_filters.clog(imNucleiStain, imFgndMask,
sigma_min=args.min_radius * np.sqrt(2),
sigma_max=args.max_radius * np.sqrt(2))
imNucleiSegMask, Seeds, Max = htk_seg.nuclear.max_clustering(
imLog, imFgndMask, args.local_max_search_radius)
# filter out small objects
imNucleiSegMask = htk_seg.label.area_open(
imNucleiSegMask, args.min_nucleus_area).astype(np.int)
#
# Perform feature extraction
#
print('>> Performing feature extraction')
im_nuclei = imDeconvolved.Stains[::2, ::2, 0]
if args.cytoplasm_features:
im_cytoplasm = imDeconvolved.Stains[::2, ::2, 1]
else:
im_cytoplasm = None
df = htk_features.ComputeNucleiFeatures(
imNucleiSegMask, im_nuclei, im_cytoplasm,
fsd_bnd_pts=args.fsd_bnd_pts,
fsd_freq_bins=args.fsd_freq_bins,
cyto_width=args.cyto_width,
num_glcm_levels=args.num_glcm_levels,
morphometry_features_flag=args.morphometry_features,
fsd_features_flag=args.fsd_features,
intensity_features_flag=args.intensity_features,
gradient_features_flag=args.gradient_features,
)
#
# Create HDF5 file
#
print('>> Writing HDF5 file')
hdf = pd.HDFStore(args.outputFile)
hdf.put('d1', df, format='table', data_columns=True)
print '--- Object x Features = ', hdf['d1'].shape
def main(args):
#
# Read Input Image
#
print('>> Reading input image')
im_input = skimage.io.imread(args.inputImageFile)[:, :, :3]
#
# Perform color normalization
#
print('>> Performing color normalization')
# compute mean and stddev of input in LAB color space
mu, sigma = htk_ccvt.lab_mean_std(im_input)
# perform reinhard normalization
im_nmzd = htk_cnorm.reinhard(im_input, mu, sigma)
#
# Perform color deconvolution
#
print('>> Performing color deconvolution')
stain_color_1 = stain_color_map[args.stain_1]
stain_color_2 = stain_color_map[args.stain_2]
stain_color_3 = stain_color_map[args.stain_3]
w = np.array([stain_color_1, stain_color_2, stain_color_3]).T
im_stains = htk_cdeconv.color_deconvolution(im_nmzd, w).Stains
im_nuclei_stain = im_stains[:, :, 0].astype(np.float)
#
# Perform nuclei segmentation
#
print('>> Performing nuclei segmentation')
# segment foreground
im_fgnd_mask = sp.ndimage.morphology.binary_fill_holes(
im_nuclei_stain < args.foreground_threshold)
# run adaptive multi-scale LoG filter
im_log = htk_shape_filters.clog(im_nuclei_stain,
im_fgnd_mask,
sigma_min=args.min_radius * np.sqrt(2),
sigma_max=args.max_radius * np.sqrt(2))
im_nuclei_seg_mask, seeds, max = htk_seg.nuclear.max_clustering(
im_log, im_fgnd_mask, args.local_max_search_radius)
# filter out small objects
im_nuclei_seg_mask = htk_seg.label.area_open(
im_nuclei_seg_mask, args.min_nucleus_area).astype(np.int)
#
# Perform feature extraction
#
print('>> Performing feature extraction')
im_nuclei = im_stains[:, :, 0]
if args.cytoplasm_features:
im_cytoplasm = im_stains[:, :, 1]
else:
im_cytoplasm = None
df = htk_features.ComputeNucleiFeatures(
im_nuclei_seg_mask,
im_nuclei,
im_cytoplasm,
fsd_bnd_pts=args.fsd_bnd_pts,
fsd_freq_bins=args.fsd_freq_bins,
cyto_width=args.cyto_width,
num_glcm_levels=args.num_glcm_levels,
morphometry_features_flag=args.morphometry_features,
fsd_features_flag=args.fsd_features,
intensity_features_flag=args.intensity_features,
gradient_features_flag=args.gradient_features,
)
#
# Create HDF5 file
#
print('>> Writing HDF5 file')
hdf = pd.HDFStore(args.outputFile)
hdf.put('d1', df, format='table', data_columns=True)
print '--- Object x Features = ', hdf['d1'].shape
