Separating conjoined blobs

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• loop moments area for 2d coverage. Divide total by mode(?) for count.
  • or use threshold to mask original, then target gradient values inrange between platelets in 2nd pass mask.
  • or convexhull defects could subdivide blobs in an inexact way. depending on need of your application it may have some purpose in secondary factors. I am not in mutant science.
  • or assuming a red blood cell is the spotted one, isolate those (RETR_CCOMP)... then from it's center draw a circle of average size. filled to mask or stroke to separate.

What is practical for your purpose? Can you present user UI controls for different model estimates? You may accept multiple samples of same image and process x times for more accuracy, especially if it's a partial view.

... You might accept hsv is the defining factor, in addition to scale. Careful kmeans tests may isolate 3 centers - white background, purple, and red. Derive area from this and compare to expectations. Inrange can moderate the sample spectrum. Be aware that radial hsv red channel spans linear color model. You might consider output frames as slices of a histogram.

My idea is the following:

• Fill the "hollow circles" in the binary image: using the negative of the first image in post, make a connected component analysis. The first connected component is the background, the others are the isolated areas inside the blood cells.

  Set these areas to 255 on the original binarized image. So you'll have a well segmented image.

• Compute the distance transform on this binary image. Use the maxima to detect the blood cellls.