Segment highly packed nuclei with dotted high intensity signal inside each object I collected high magnification images on cell nuclei and I need to segment them (red circle in the image). The edges of the nuclei are visible but inside each nuclear object there are bright spots of positive signal (Not an imaging artefact, if I will retake the images it will be the same).  



I tested different way to segment the image (using python scripts based on scikit-image, scipy.ndimage and opencv):
- Threshold->morphology (close/opening)-> watershed (after gaussian filtering or median filtering to try to remove the dots)
- Graph-cuts (Al-Kofahi algorithm implemented in the Farsight package: https://github.com/gbkedar/farsight)

All the methods lead to oversegmentation because of the internal bright spots.  

To solve that I thought was a good idea to isolate the dots with a laplacian of gaussian and used the opencv function cv.inpaint to fill the dots with the intensity of the surrounded pixel values. The result was not good either. 

The questions are:
- Do you think that the best strategy is to isolate the bright dots and then try to replace them with the surrounding signal?
- Any strategy that will lead to a better result? 

Any help will be greatly appreciated
I wouldn't bother with correct bright pixel segmentation.
I think such a strategy would add complexity and another stage in the algorithm which might add errors at the eventual product.

I would use some form of scaling function for the pixel value so that the bright pixels would blend-in better while maintaining contrast in the darker pixels. One such function could be the log function:


  im = log(double(im));




Another one might be the square root.
You might even get away with using a max value for the image , e.g. 


  thr = 150; im(im> thr) = thr;


or a combination of both, for instance a capped transform function
thr = 100; 
tform_im = img.^0.5;
tform_im_scaled = tform_im - min(tform_im(img>thr)) + thr;
img(img>thr) = tform_im_scaled(img>thr);

 

Either way good luck!