from timagetk.plugins import auto_seeded_watershed from timagetk.visu.mplt import profile_hmin import platform if platform.uname()[1] == "RDP-M7520-JL": base_dir = '/data/Meristems/Carlos/SuperResolution/' elif platform.uname()[1] == "calculus": base_dir = '/projects/SamMaps/SuperResolution/LSM Airyscan/' else: raise ValueError("Unknown custom path to 'base_dir' for this system...") fname = 'SAM1-gfp-pi-stack-LSM800-Airyscan Processing-high_PI_conf_z-stack_reg.tif' base_fname, ext = splitext(fname) image = imread(base_dir + fname) iso_image = isometric_resampling(image, method='min', option='cspline') iso_image.shape iso_image = z_slice_contrast_stretch(iso_image, pc_min=1.5) iso_image = linear_filtering(iso_image, method="gaussian_smoothing", sigma=0.1, real=True) # xsh, ysh, zsh = iso_image.shape # mid_x, mid_y, mid_z = int(xsh/2.), int(ysh/2.), int(zsh/2.) # # selected_hmin = profile_hmin(iso_image, x=mid_x, z=mid_z, plane='x', zone=mid_z) for selected_hmin in np.arange(4000, 6000, 500): seg_im = auto_seeded_watershed(iso_image, selected_hmin, control='most')
from timagetk.util import data_path from timagetk.io import imread from timagetk.algorithms.exposure import z_slice_contrast_stretch from timagetk.algorithms.resample import isometric_resampling from timagetk.plugins import linear_filtering from timagetk.plugins.segmentation import auto_seeded_watershed from timagetk.visu.mplt import profile_hmin from skimage import img_as_float from skimage.color import label2rgb, gray2rgb int_img = imread(data_path('p58-t0-a0.lsm')) print int_img.shape print int_img.voxelsize int_img = isometric_resampling(int_img, method='min', option='cspline') int_img = z_slice_contrast_stretch(int_img, pc_min=1) int_img = linear_filtering(int_img, method="gaussian_smoothing", sigma=0.25, real=True) xsh, ysh, zsh = int_img.shape mid_x, mid_y, mid_z = int(xsh / 2.), int(ysh / 2.), int(zsh / 2.) h_min = profile_hmin(int_img, x=mid_x, z=mid_z, plane='x', zone=mid_z) seg_img = auto_seeded_watershed(int_img, hmin=h_min) print seg_img.shape label_rgb = label2rgb(seg_img, alpha=1, bg_label=1, bg_color=(0, 0, 0))
assert all([vxs[n] == ref_vxs for vxs in vxs_list]) except: raise ValueError( "Voxelsize missmatch along axis {} ({}) among list of images!". format(axis[n], n)) for im2crop_fname in im2crop_fnames: print "\n\n# - Reading image file {}...".format(im2crop_fname) im2crop = read_image(im2crop_fname) print "Done." # - Create output filename: out_fname = splitext_zip(im2crop_fname)[0] # - Performs isometric resampling if required: if iso: out_fname += '-iso' im2crop = isometric_resampling(im2crop) # - Get original image infos: shape, ori, vxs, md = get_infos(im2crop) print "\nGot original shape: {}".format(shape) print "Got original voxelsize: {}".format(vxs) # - Crop the image: bounding_box = [] for n in range(ndim): bounding_box.extend([lower_bounds[n], upper_bounds[n]]) im = crop_image(im2crop, bounding_box) # - Add cropping region to filename: for n, ax in enumerate(axis): if lower_bounds[n] != 0 or upper_bounds[n] != -1: out_fname += '-{}{}_{}'.format(ax, lower_bounds[n], upper_bounds[n])
def seg_pipe(img2seg, h_min, img2sub=None, iso=True, equalize=True, stretch=False, std_dev=0.8, min_cell_volume=20., back_id=1, to_8bits=False): """Define the sementation pipeline Parameters ---------- img2seg : str image to segment. h_min : int h-minima used with the h-transform function img2sub : str, optional image to subtract to the image to segment. iso : bool, optional if True (default), isometric resampling is performed after h-minima detection and before watershed segmentation equalize : bool, optional if True (default), intensity adaptative equalization is performed before h-minima detection stretch : bool, optional if True (default, False), intensity histogram stretching is performed before h-minima detection std_dev : float, optional real unit standard deviation used for Gaussian smoothing of the image to segment min_cell_volume : float, optional minimal volume accepted in the segmented image back_id : int, optional the background label to_8bits : bool, optional transform the image to segment as an unsigned 8 bits image for the h-transform and seed-labelleing steps Returns ------- seg_im : SpatialImage the labelled image obtained by seeded-watershed Notes ----- * Both 'equalize' & 'stretch' can not be True at the same time since they work on the intensity of the pixels; * Signal subtraction is performed after intensity rescaling (if any); * Linear filtering (Gaussian smoothing) is performed before h-minima transform for local minima detection; * Gaussian smoothing should be performed on isometric images, if the provided image is not isometric, we resample it before smoothing, then go back to original voxelsize; * In any case H-Transfrom is performed on the image with its native resolution to speed upd seed detection; * Same goes for connexe components detection (seed labelling); * Segmentation will be performed on the isometric images if iso is True, in such case we resample the image of detected seeds and use the isometric smoothed intensity image; """ t_start = time.time() # - Check we have only one intensity rescaling method called: try: assert equalize + stretch < 2 except AssertionError: raise ValueError( "Both 'equalize' & 'stretch' can not be True at once!") # - Check the standard deviation value for Gaussian smoothing is valid: try: assert std_dev <= 1. except AssertionError: raise ValueError( "Standard deviation for Gaussian smoothing should be superior or equal to 1!" ) ori_vxs = img2seg.voxelsize ori_shape = img2seg.shape if img2sub is not None: print "\n - Performing signal substraction..." img2seg = signal_subtraction(img2seg, img2sub) if equalize: print "\n - Performing z-slices adaptative histogram equalisation on the intensity image to segment..." img2seg = z_slice_equalize_adapthist(img2seg) if stretch: print "\n - Performing z-slices histogram contrast stretching on the intensity image to segment..." img2seg = z_slice_contrast_stretch(img2seg) print "\n - Automatic seed detection...".format(h_min) # morpho_radius = 1.0 # asf_img = morphology(img2seg, max_radius=morpho_radius, method='co_alternate_sequential_filter') # ext_img = h_transform(asf_img, h=h_min, method='h_transform_min') min_vxs = min(img2seg.voxelsize) if std_dev < min_vxs: print " -- Isometric resampling prior to Gaussian smoothing...".format( std_dev) img2seg = isometric_resampling(img2seg) print " -- Gaussian smoothing with std_dev={}...".format(std_dev) iso_smooth_img = linear_filtering(img2seg, std_dev=std_dev, method='gaussian_smoothing', real=True) if std_dev < min_vxs: print " -- Down-sampling a copy back to original voxelsize (to use with `h-transform`)..." smooth_img = resample(iso_smooth_img, ori_vxs) if not np.allclose(ori_shape, smooth_img.shape): print "WARNING: shape missmatch after down-sampling from isometric image:" print " -- original image shape: {}".format(ori_shape) print " -- down-sampled image shape: {}".format(smooth_img.shape) else: print " -- Copying original image (to use with `h-transform`)..." smooth_img = iso_smooth_img if not iso: del iso_smooth_img # no need to keep this image after this step! print " -- H-minima transform with h-min={}...".format(h_min) if to_8bits: ext_img = h_transform(smooth_img.to_8bits(), h=h_min, method='h_transform_min') else: ext_img = h_transform(smooth_img, h=h_min, method='h_transform_min') if iso: smooth_img = iso_smooth_img # no need to keep both images after this step! print " -- Region labelling: connexe components detection..." seed_img = region_labeling(ext_img, low_threshold=1, high_threshold=h_min, method='connected_components') print "Detected {} seeds!".format(len(np.unique(seed_img)) - 1) # '0' is in the list! del ext_img # no need to keep this image after this step! print "\n - Performing seeded watershed segmentation..." if iso: seed_img = isometric_resampling(seed_img, option='label') if to_8bits: seg_im = segmentation(smooth_img.to_8bits(), seed_img, method='seeded_watershed') else: seg_im = segmentation(smooth_img, seed_img, method='seeded_watershed') # seg_im[seg_im == 0] = back_id print "Detected {} labels!".format(len(np.unique(seg_im))) if min_cell_volume > 0.: from vplants.tissue_analysis.spatial_image_analysis import SpatialImageAnalysis print "\n - Performing cell volume filtering..." spia = SpatialImageAnalysis(seg_im, background=None) vol = spia.volume() too_small_labels = [ k for k, v in vol.items() if v < min_cell_volume and k != 0 ] if too_small_labels != []: print "Detected {} labels with a volume < {}µm2".format( len(too_small_labels), min_cell_volume) print " -- Removing seeds leading to small cells..." spia = SpatialImageAnalysis(seed_img, background=None) seed_img = spia.get_image_without_labels(too_small_labels) print " -- Performing final seeded watershed segmentation..." seg_im = segmentation(smooth_img, seed_img, method='seeded_watershed') # seg_im[seg_im == 0] = back_id print "Detected {} labels!".format(len(np.unique(seg_im))) print "\nDone in {}s".format(round(time.time() - t_start, 3)) return seg_im