Blood microscopic image segmentation using rough sets

Abstract

Hematological disorders are mostly identified based on characterization of blood parameters i.e. erythrocytes, leukocytes and platelets. Microscopic examination of leukocytes in blood slides is the most frequent laboratory investigation performed for malignancy detection. Hematological examination of blood is an indispensable technique still today and solely depends on human visual interpretation. Such examination are subjected to inter and intra-observer variations, slowness, tiredness and operator experience. Accurate and authentic diagnosis of hematological neoplasia can help in the planning of suitable surgery and chemotherapy, and generally improve the quality of patient care. Microscopy cell image analysis is a tool which facilitates conventional blood examination for disease detection using quantitative microscopy. Thus microscopic image analysis serves as an impressive diagnostic tool for hematological disease (leukemia, malaria, psoriasis, AIDS etc) recognition. The present paper aims at leukocyte or white blood cell (WBC) segmentation which can assist in acute leukemia detection. A rough set based clustering approach is followed for color based segmentation of WBC. The segmented nucleus and cytoplasm can be used for feature extraction which can lead to classification of a leukocyte into mature lymphocyte or lymphoblast.