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I have raw microscopy images like this: Raw fluorescent images

And I want to segment the objects, as you see some of them are really close and I have a great range of intensity values.

background: 700 a.u.

fluorescent shapes: from 7000 to 32000 a.u.

To segment them I use Otsu binary segmentation from skimage package (without prior processing of the image)

thresh, imgthresh=cv2.threshold(image, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)

The result is pretty good, but still fails in detecting the brightest shapes as individual objects. segmentation and object detection after otsu thresholding

I have tried a lot of things: watershed algorithm, image preprocessing (blurring), eroding , adaptive thresholding, but nothing works properly since the main problem is the difference in fluorescent values of the image.

Any smart idea on how to solve this?

Claudia Salat
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1 Answers1

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Because your data have such a large range in intensity values, single histogram based methods on the whole image (e.g. Otsu) are going to have a little trouble accomplishing this task. I think that your best bet is going to be either:

  1. threshold_multiotsu: and choose number of classes based on number of 'clusters' of intensities. Unfortunately, you will likely need to alter the number of classes on an image by image basis so this isn't super robust.
  2. threshold_local: I know you said that you tried this but you might revisit this and alter the block_size parameter until you get something that looks reasonable. Based on your example images (and assuming a little bit about why the objects in your example images are green) it looks like that objects in close spatial proximity to one another generally have similar intensity values. Furthermore, you likely won't have to go through and alter the parameters as much as you would in option 1.

I suspect that these will be the simplest and most straight forward approaches but you could also delve into identifying the object edges using something from skimage.feature and then filling objects. Maybe something like outline here: https://scikit-image.org/docs/stable/auto_examples/features_detection/plot_blob.html. This will be a bit more involved, but these methods should be more robust with identifying objects with largely varied intensity values.

If all else fails you can try a couple of SOTA packages. The main ones that I am thinking of are https://github.com/stardist/stardist and https://github.com/MouseLand/cellpose but these seem like a bit of overkill based on your example data here.

tdaugird
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