def setUp(self):
# define parameters
args = {
'reference_mu_lab': [8.63234435, -0.11501964, 0.03868433],
'reference_std_lab': [0.57506023, 0.10403329, 0.01364062],
'min_radius': 12,
'max_radius': 30,
'foreground_threshold': 60,
'min_nucleus_area': 80,
'local_max_search_radius': 10,
}
args = collections.namedtuple('Parameters', args.keys())(**args)
# read input image
input_image_file = os.path.join(TEST_DATA_DIR, 'Easy1.png')
im_input = skimage.io.imread(input_image_file)[:, :, :3]
# perform color normalization
im_input_nmzd = htk_cnorm.reinhard(im_input, args.reference_mu_lab,
args.reference_std_lab)
# perform color decovolution
w = htk_cdeconv.rgb_separate_stains_macenko_pca(
im_input_nmzd, im_input_nmzd.max())
im_stains = htk_cdeconv.color_deconvolution(im_input_nmzd, w).Stains
nuclei_channel = htk_cdeconv.find_stain_index(
htk_cdeconv.stain_color_map['hematoxylin'], w)
im_nuclei_stain = im_stains[:, :, nuclei_channel].astype(np.float)
cytoplasm_channel = htk_cdeconv.find_stain_index(
htk_cdeconv.stain_color_map['eosin'], w)
im_cytoplasm_stain = im_stains[:, :,
cytoplasm_channel].astype(np.float)
# segment nuclei
im_nuclei_seg_mask = cli_utils.detect_nuclei_kofahi(
im_nuclei_stain, args)
# perform connected component analysis
nuclei_rprops = skimage.measure.regionprops(im_nuclei_seg_mask)
# compute nuclei features
fdata_nuclei = htk_features.compute_nuclei_features(
im_nuclei_seg_mask,
im_nuclei_stain,
im_cytoplasm=im_cytoplasm_stain)
self.im_input = im_input
self.im_input_nmzd = im_input_nmzd
self.im_nuclei_stain = im_nuclei_stain
self.im_nuclei_seg_mask = im_nuclei_seg_mask
self.nuclei_rprops = nuclei_rprops
self.fdata_nuclei = fdata_nuclei
def compute_tile_nuclei_features(slide_path, tile_position, args, it_kwargs,
src_mu_lab=None, src_sigma_lab=None):
# get slide tile source
ts = large_image.getTileSource(slide_path)
# get requested tile
tile_info = ts.getSingleTile(
tile_position=tile_position,
format=large_image.tilesource.TILE_FORMAT_NUMPY,
**it_kwargs)
# get tile image
im_tile = tile_info['tile'][:, :, :3]
# perform color normalization
im_nmzd = htk_cnorm.reinhard(im_tile,
args.reference_mu_lab,
args.reference_std_lab,
src_mu=src_mu_lab,
src_sigma=src_sigma_lab)
# perform color decovolution
w = cli_utils.get_stain_matrix(args)
im_stains = htk_cdeconv.color_deconvolution(im_nmzd, w).Stains
im_nuclei_stain = im_stains[:, :, 0].astype(np.float)
# segment nuclei
im_nuclei_seg_mask = cli_utils.detect_nuclei_kofahi(im_nuclei_stain, args)
# generate nuclei annotations
nuclei_annot_list = cli_utils.create_tile_nuclei_annotations(
im_nuclei_seg_mask, tile_info, args.nuclei_annotation_format)
# compute nuclei features
if args.cytoplasm_features:
im_cytoplasm_stain = im_stains[:, :, 1].astype(np.float)
else:
im_cytoplasm_stain = None
fdata = htk_features.compute_nuclei_features(
im_nuclei_seg_mask, im_nuclei_stain, im_cytoplasm_stain,
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,
)
fdata.columns = ['Feature.' + col for col in fdata.columns]
return nuclei_annot_list, fdata