Dear all, I have been playing around with the watershed segmentation by markers with the code proposed as example: http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_watershed.h... Unfortunately, if we use for example floating values for the radii of the circles (like r1, r2 = 20.7, 24.7), the separation is not perfect, as it gives 4 labels. If we use the chamfer distance transform instead of the Euclidean distance transform, it is even worse. It appears that the markers detection by regional maximum (peak_local_max) fails in the presence of plateaus. Its algorithm is basically D(I)==I, where D is the morphological dilation. A better algorithm would be to use morphological reconstruction (see SOILLE, Pierre. /Morphological image analysis: principles and applications/. Springer Science & Business Media, 2003, p202, Eq 6.13). A proposition of the code can be the following (it should deal with float values): import numpy as np from skimage import morphology def rmax(I): """ This avoids plateaus problems of peak_local_max I: original image, float values returns: binary array, with True for the maxima """ I = I.astype('float'); I = I / np.max(I) * 2**31; I = I.astype('int32'); rec = morphology.reconstruction(I-1, I); maxima = I - rec; return maxima>0 This code is relatively fast. Notice that the matlab function imregionalmax seem to work the same way (the help is not explicit, but the results on a few tests seem to be similar). I am afraid I do not have time to integrate it on gitlab, but this should be a good start if someone wants to work on it. If you see any problem with this code, please correct it. thank you best regards -- Yann