Hi Juan, Thank you for your answer, I kind of do what you propose, although it's not very "elegant":
1) Using util.view_as_windows(), I make chunks with overlay. 2) jolib for parallel processing (I'd like to use skimage.util.apply_parallel but I'm not sure how I can use a fonction with more than 1 arg?) 3) this part is a little "bricolage", I take the overlap of adjacent regions and, using regionprops, I can find the "intensity" (label) of 1 region over the other one. Then I just relabel one of the region. 4) I stitch the block back together, again, it's not pretty, I just add up all the regions into a new array, not sure if there is a better way. So it works well and it's relatively easy to implement because I only have 4 chunks, I've to see how that will go for a big tile of images. Best, Cedric 2017-06-28 3:27 GMT-07:00 Juan Nunez-Iglesias <jni.s...@gmail.com>: > Hi Cedric, > > Stitching of separate regions is not a trivial problem… You’ll basically > have to: > > a) overlap the blocks by a certain amount. You might be able to use > skimage.util.apply_parallel more easily here than joblib. > b) compute which labels overlap the most between each neighbouring pair of > images > c) relabel as necessary to make those labels match > > It’s not super-hard, just a lot of annoying bookkeeping. If you come up > with a reasonably robust method to do this, I for one would very much > welcome such a contribution into the library! > > Here’s a paper from an old colleague of mine who had to deal this on a > large-scale problem, and they created a robust method to do the matching: > https://arxiv.org/pdf/1604.00385.pdf > (page 6) > > Juan. > > On 27 Jun 2017, 9:20 AM +1000, Cedric Espenel <cedric.espe...@gmail.com>, > wrote: > > Hi, > > I'm working on a project where I have to segment out nucleus of cells in a > volume. I use random_walker with define markers for every nuclei. If I try > to run it on the all volume the segmentation works well but it's very slow. > So I used util.view_as_blocks function from scikit to split my image and > markers and I loop over the different block using joblib: > > rw_dapi_chunks = Parallel(n_jobs = 4)(delayed(segmentation. > random_walker)(chunks[i,j], > chunks_markers[i,j], beta = 3000, > mode='cg_mg') for > i in range(2) for j in range(2)) > > > So I end up with a list of 4 images and if I combine these images (btw I'm > not sure what is the best way for doing that? I just add them up in an > empty array) everything work well, except that I end-up with nuclei with 2 > different labels: > > [image: Images intégrées 1] > Am I doing something wrong? Is there a way to split your images in block > for parallel computing but being able to get the labeling right at the end? > > Best, > > Cedric > _______________________________________________ > scikit-image mailing list > scikit-image@python.org > https://mail.python.org/mailman/listinfo/scikit-image > > > _______________________________________________ > scikit-image mailing list > scikit-image@python.org > https://mail.python.org/mailman/listinfo/scikit-image > >
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