How do we extract the features for each independent object in an image?

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I have a tissue image and there are around 100 cells in it. I also have the corresponding mask image which includes those 100 cells. How do I calculate the radiomics features for each cell?

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Hi @mozdag

Yes, you can do this using SlicerRadiomics extension module.

  1. Load your tissue image and the corresponding mask as a lablel image in Slicer.

  2. In the SlicerRadiomics module, you can then specify your Input Image Volume (tissue image), and Input Regions (your converted labelimage) and then run the module.

HTH
Andinet

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3

Note that if you want to have features separately for the individual cells, you will need to assign a unique label value for each of the cell segmentations.

You can do this using the “Islands” effect of Segment Editor:

image
image822Ă—1059 172 KB

4

Thank you for your response. I won’t be using the Slicer app. I’ll have to use some library such as connected component:

from cc3d import connected_components
labels_out = connected_components(data_mask)

I believe, labels_out is a set of those ROI regions for each cell? Another point is that my images will be 2D. Have any of you got an idea on this?

I still run this code and got error: Attribute Error; image has no attribute “shape”.

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5

There are some additional attributes you need to make sure are set, did you check this documentation page: https://pyradiomics.readthedocs.io/en/latest/features.html#module-radiomics.shape2D ?

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As to splitting connected components and extracting features, check out this issue, which deals with the same problem.

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