Hey Adam, I'm *guessing* the IJ method is: 1. compute the thresholded background image (ie background labeled "True") 2. compute the Euclidean distance transform (scipy.ndimage.distance_transform_edt) 3. compute the local maxima (find_local_max) and set them as seeds 4. compute watershed, using the foreground as mask. All of those functions are available in scipy/scikit-image. If you get good results, a gallery example of this would certainly be appreciated! =) However, my experience with such methods is that they only work well for reasonably sparse, perfectly spherical particles. As to removing particles on the edge, I would use a bool mask with only the edges selected, then np.unique(), then remove them manually in a for loop. I agree that it's a bit laborious... Perhaps a separate function to do this could be added to the API... On Thu, Feb 19, 2015 at 11:04 AM, Adam Hughes <hughesadam87@gmail.com> wrote:

Hi, In ImageJ, one can select watershedding to break up connected regions of particles. Are there any examples of using watershed in this capacity in scikit image? All of the examples I see seem to use watershedding to do segmentation, not to break connected particles in an already-segmented black and white image. Also, is there a straightforward way to remove particles on a the edge of an image? Sorry, googling is failing me, but I know this is possible. Thanks -- You received this message because you are subscribed to the Google Groups "scikit-image" group. To unsubscribe from this group and stop receiving emails from it, send an email to scikit-image+unsubscribe@googlegroups.com. For more options, visit https://groups.google.com/d/optout.