Hi Stefanie, Wow, those results *do* look weird! =) Why are they not the same shape as the image? Could you send us the complete code you used?
In the meantime, there's a few things to change. `cut_threshold` is probably the single most fragile algorithm you can use with a RAG. You want to be using merge_hierarchical. You can do this either on a "color graph", which merges regions according to color similarity (and is most similar to the statistical region merging of Fiji): http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_rag_merge.html#sphx-glr-auto-examples-segmentation-plot-rag-merge-py or on a *boundary graph*, which first detects "edges" in the image and then merges regions progressively according to the mean edge value: http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_rag_boundary.html#sphx-glr-auto-examples-segmentation-plot-rag-boundary-py http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_boundary_merge.html#sphx-glr-auto-examples-segmentation-plot-boundary-merge-py My intuition about this image is that your best choice is to use a sobel filter: http://scikit-image.org/docs/dev/auto_examples/edges/plot_edge_filter.html#sphx-glr-auto-examples-edges-plot-edge-filter-py followed by compact watershed: http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_compact_watershed.html#sphx-glr-auto-examples-segmentation-plot-compact-watershed-py then by region boundary merging: http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_boundary_merge.html#sphx-glr-auto-examples-segmentation-plot-boundary-merge-py The edges look pretty sharp, too. You might even get good results with Canny: http://scikit-image.org/docs/dev/auto_examples/edges/plot_canny.html#sphx-glr-auto-examples-edges-plot-canny-py I hope all this helps! Juan. On 15 November 2016 at 7:11:14 pm, Stefanie Lück (lue...@gmail.com) wrote: Dear all, sorry about the worse problem description! I tried this example <http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_rag_mean_color.html#sphx-glr-auto-examples-segmentation-plot-rag-mean-color-py> with different parameters for SLIC and graph.cut_threshold but none of them gave me satisfying results. I attached the original image, the ImageJ SRM (Q=25) output and the RAG output (segmentation.slic(img, compactness=20; n_segments=400), graph.cut_threshold(labels1, g, 10)) The results are quite different, obviously I am doing something wrong. My aim is to segment each leaf separately. At the moment I am using felzenszwalb, which gives me quite reasonable results. However I would like to try everything possible and therefore I would appreciate some tips. Thank you for the explanation of the algorithm, that was helpful. Best regards, Stefanie 2016-11-15 1:51 GMT+01:00 Juan Nunez-Iglesias <jni.s...@gmail.com>: > Hi Stefanie, > > Sorry, these responses should all be CCd to the list, so that others can > benefit from the discussion — my bad for dropping that thread. Could you > please: > > - provide an example segmentation where skimage is doing worse than Fiji > - provide the script and parameter settings for both > > Then we can help troubleshoot. I don’t know what you mean by the results > “were very strange”, for example, so it’s hard to diagnose the problem. =) > > Starting to merge directly from pixels, as the Fiji plugin does, is > expensive and can be error prone. SLIC is a fast, initial pixel merging > step, from which we can merge regions according to various criteria. With > the right parameters, SLIC + RAG mean color agglomeration should give quite > similar results to Fiji’s approach… > > Juan. > > On 15 November 2016 at 2:39:45 am, Stefanie Lück (lue...@gmail.com) wrote: > > Hi Juan, > > thank you for your reply! I have seen and tested the RAG examples but I > did not understand the SLIC step and the results were very strange... Is > there any advantage? I am using SLIC anyway at the moment but > the statistical region merging of ImageJ gives me better results. > > Thanks > Stefanie > > > > 2016-11-14 13:44 GMT+01:00 Juan Nunez-Iglesias <jni.s...@gmail.com>: > >> Hi Stefanie! >> >> Have a look at skimage.future.graph! There are some relevant examples in >> the gallery, too: >> >> http://scikit-image.org/docs/dev/auto_examples/#segmentation-of-objects >> >> The future.graph API is still experimental (that’s why it’s in “future”), >> so we really appreciate any feedback you have about it! >> >> Juan. >> >> >> >> On 14 November 2016 at 8:21:12 pm, Stefanie Lück (lue...@gmail.com) >> wrote: >> >> Hi! >> >> I am looking for a statistical region merging segmentation. Is there >> anything like this in skimage? >> >> Thanks in advance, >> Stefanie >> _______________________________________________ >> scikit-image mailing list >> scikit-image@python.org >> https://mail.python.org/mailman/listinfo/scikit-image >> >> >
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