Segmentation of heavily clustered cell nuclei in histopathological images

Abstract

Automated cell nuclei determination of stained images is of uttermost importance for diagnosis. In this work, we have proposed a novel efficient and accurate image segmentation technique for densely clustered overlapping cell nuclei. Firstly, we have extracted the cell body (foreground) from the background using global thresholding followed by local thresholding. Then, we have employed the fusion of seeded region growing technique and level-set algorithm. The initial seed points need to be selected accurately and precisely in order to generate appropriate outcomes from region growing framework. Initial contours for level-set evolution relies heavily on an output of this adaptive region growing approach and some morphological operations. Finally, Global Gaussian distribution with several means and variances is employed in an enhanced edge-based level-set approach for precise nuclei segmentation. We have performed our analysis on Nissl stained EMF exposed, and SHAM exposed cell images. The proposed framework is very much capable of extracting the cell nuclei from stained cell images. Experimental outcomes reveal that our approach has out-performed existing state of art techniques for cell nuclei extraction and segmentation.