Fuzzy C Means Detection of Leukemia Based on Morphological Contour Segmentation

AbstractDue to complex nature of blood smear images and imitation of similar signs of other disorders makes difficult to detect leukemia. It also needs more time to diagnose and sometimes susceptible to errors. In order to solve this issues fuzzy C means cluster optimization of leukemia detection based on morphological contour segmentation is proposed in this paper. This paper introduces the new approach for leukemia detection which consist of (1) contrast enhancement to highlights the nuclei, (2) morphological contour segmentation, and (3) Fuzzy C means detection of leukemia. The contract enhancement is done by simple addition and subtraction operation to separate the nuclei. The morphological contour segmentation detects the edges of nuclei and eliminate the normal white blood cells from the microscopic blood image. Then the texture, geometry, color and statistical features of nuclei is evaluated to determines the various factors of leukemia. Finally it is trained by Fuzzy C mean clustering of single row feature vector of each cell is used to classify leukemia from white blood cells. This makes the proposed algorithm better results in accuracy and time consumption when compare to normal hematologist's visual classification