White blood cells nucleus segmentation from microscopic images of strained peripheral blood film during leukemia and normal condition

Counting of White blood cells (WBC) & characterizing their nucleus can provide valuable information to the doctors in order to identify different diseases or stage of a particular disease. The manual method is a tiresome process and has a lot of inaccuracy. On the other hand, the machine (hematological analyzer) based method are very expensive. Digital image processing can be a less time consuming and cost effective method for counting and characterizing the WBC. In order to Count or characterize WBC by image processing, proper segmentation is the key challenge. In this paper we proposed an algorithm to segment WBC nucleus from microscopic images of stained peripheral blood film during leukemia and normal condition. The proposed algorithm involves different steps such as color space conversion, color thresholding, filtering, marker controlled watershed and different morphological operations. The accuracy of the result obtained is 88.57%.