Posted by
Stoyan Pavlov on
Jan 09, 2018; 4:39am
URL: http://imagej.273.s1.nabble.com/Weka-Trainable-Segmentation-alternative-tp5019833p5019847.html
Dear Adrian,
As mentioned before Weka Segmentation performs pixel classification, so the
best result you can hope to achieve is to segment islets from the exocrine
tissue, but not to classify the islets. Ilastik ( the interactive learning
and segmentation toolkit;
http://ilastik.org/ ) is a tool for image
classification and segmentation similar to the weka segmentation, but it
has multiple workflows. I think in your case might be worth to try the
object classification workflow: first you train the classifier to recognize
islets from exocrine tissue using pixel classification, in a second pass
the workflow is trained to classify the detected objects based on multiple
selectable features.
Best of luck,
Stoyan
На 6.01.2018 г. 1:56 пр.об. "Adrián Villalba" <
[hidden email]> написа:
> Dear all,
>
> I am trying to use the Weka Trainable Segmentation plugin in order to
> classify islets of Langerhans in Hematoxilin/Eosin tissues. There are four
> different types of islets depending on insulitis (immune cell infiltration
> within the islet) as i show you in an attached JPG-picture (just to show
> you the expected result, not to manipulate).
>
> My goal is to do it automatically in imageJ, rather tan manual scoring of
> pictures. So i thought it would be a good idea to use the Trainable Weka
> Segmentation plugin just to train the algorithm to do it automatically but
> it fails. (I cannot attach the classifier.model archive because it is
> rejected by the mailing list conditions).
>
> I think that maybe it is not a proble for the Weka, instead being a
> conceptual problem and that maybe you could know another imageJ tool to
> pursuit that goal.
>
> Thank you very much in advance!
>
> --
>
> - Adrián Villalba Felipe.
>
https://es.linkedin.com/in/adrianvillalba>
> --
> ImageJ mailing list:
http://imagej.nih.gov/ij/list.html>
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