You could do a binary fill holes operation and then subtract the previous image from the result - you'd have only the small "holes" evident inside normal RBC's shown in your example. Then remove any labeled regions in contact with those holes.
You'll probably have a few spurious ones left over, and this might accidentally remove a sickle cell here or there (can't see any in the example, but I'm sure it's possible). However, with minimal tweaking this would remove most normal RBCs from the thresholded image shown.
Speaking in the abstract sans example code, here, but it seems like you could get pretty far this way.
I'll let someone else chime in about separating cells in contact.
Josh
On Tuesday, December 9, 2014 1:58:43 PM UTC-6, Claiborne Morton 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