Leukocyte segmentation in tissue images using differential evolution algorithm

An automatic segmentation of leukocytes can assist pharmaceutical companies to take decisions in the discovery of drugs and encourages for development of automated leukocyte recognition system. Segmentation of leukocytes in tissue images is a complex process due to the presence of various noise effects, large variability in the images and shape of the nuclei. Surprisingly, rare efforts have been made to automate the segmentation of leukocytes in various disease models on hematoxylin and eosin (H&E) stained tissue images. The present work proposes a novel strategy based on differential evolution (DE) algorithm to segment the leukocytes from the images of mice skin sections stained with H&E staining and acquired at 40×magnification. The proposed strategy is a first inline report used in such type of image database. Further, the proposed strategy is compared with well-known segmentation algorithms. The results show that the proposed strategy outperforms the traditional image segmentation techniques.