Entropy based divergence for leukocyte image segmentation

This work aims to develop the divergence measures based on Renyi's and Yager's entropies for segmenting the leukocyte nuclei from microscopic image of peripheral blood smear. Such measure minimizes the separation between the actual and ideal thresholded image. Finally, these measures have been compared with Shannon entropy based divergence algorithm. In fact, it is observed here that Yager's measure provides better result in segmenting the leukocyte nuclei from the background of the image. The effectiveness of our proposed methods is demonstrated on blood cytopathological images of normal and chronic myelogenous leukemia (CML) samples.