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

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