Hey Clay,

Application is everything. =) Depending on your downstream question, one approach might be preferred over another. If your goal is to count normal vs sickle cells, *and* you want to do so often, you could:

- use regionprops to get *a lot* of features from your data. There's eccentricity but that'll pick up those clumps of normal cells. You can also compute Euler number (the number of holes in a shape), area, and maybe solidity. 
- then run the regions through clustering in scikit-learn. I think you'll get clusters corresponding to sickle cells, normal cells, clumps of 2 normal cells, clumps of a normal and a sickle cell, etc.
- then count based on the number of regions in each cluster times the number of cells corresponding to that cluster.

Of course this might be too complicated... But simpler approaches will probably have a significant error rate (as you have noticed).


On Wed, Dec 10, 2014 at 7:01 AM, Claiborne Morton <claiborne.morton@gmail.com> wrote:

Hello Scikit community, 

I'm looking for help using particle descriptors to identify sickle cells in the attached image. As you can see the sickle cells are the long, thin cells. I have two issues, the first is that in some cases the sickle cells are in contact with other healthy cells. I am trying to find a way of separating (or water-shedding) the cells so that each "particle" is actually one cell. The second issue is that I am dealing with is trying to find shape descriptors that will allow me to accurately distinguish the sickle cells from the healthy cells. 

I used an eccentricity filter on the original image to remove all of the cells with eccentricity less than 0.6. Making this any higher results in removal of sickle cells. What other descriptors might be used for further differentiation?

Thanks, Clay 

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