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…
On 15 November 2016 at 2:39:45 am, Stefanie Lück (email@example.com) wrote:
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.
2016-11-14 13:44 GMT+01:00 Juan Nunez-Iglesias firstname.lastname@example.org:
Have a look at skimage.future.graph! There are some relevant examples in the gallery, too:
The future.graph API is still experimental (that’s why it’s in “future”), so we really appreciate any feedback you have about it!
On 14 November 2016 at 8:21:12 pm, Stefanie Lück (email@example.com) wrote:
I am looking for a statistical region merging segmentation. Is there anything like this in skimage?
Thanks in advance, Stefanie _______________________________________________ scikit-image mailing list firstname.lastname@example.org https://mail.python.org/mailman/listinfo/scikit-image