[scikit-image] statistical region merging

[Juan Nunez-Iglesias]

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 (luecks at 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.soma at 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 (luecks at 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.soma at 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 (luecks at 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 at python.org
>> https://mail.python.org/mailman/listinfo/scikit-image
>>
>>
>
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