Effective Method for Leukocytes Classification and Segmentation in Blood Smear Images

Abstract: Blood is one of the important component of the body. It consists of RBC, WBC and Platelets. Detection and counting of white blood cells (WBC) in blood samples provides valuable information to hematologists, to identify various types of hematic pathologies such as AIDS and blood cancer (Leukemia). But performing this task manually prone to error and time consuming. An automatic detection and classification of WBC images can enhance the accuracy and speed up the detection of WBCs. In this paper, we propose an efficient framework for localization of WBCs within microscopic blood smear images using a multi-class ensemble classification mechanism. In the proposed framework, the nuclei are first segmented, followed by extraction of features such as texture, statistical, and wavelet features. Finally, the detected WBCs are classified into five classes including basophil, eosinophil, neutrophil, lymphocyte, and monocyte. The proposed method improves the segmentation performance when compared to other state-of-the-art segmentation methods. Keywords: Hematology; Image Segmentation; Image Classification;multi-class ensemble.