Concave point extraction: A novel method for WBC segmentation in ALL images

Abstract

Analysis of hematological disorder of leukocyte (WBC) is common in medical diagnoses. The counting and classification of blood cells enables the estimation and identification of large number of diseases. Morphological analysis of blood cells requires skill operators, is time-consuming, and less accurate. This paper discusses the Computer-Aided Acute Lymphoblastic Leukemia (ALL) diagnosis technique based on image examination. Here, methods for the segmentation and categorization of white blood cell (WBC) in the interior of acute lymphoblastic leukemia (ALL) smear images are presented. The segmentation method consists of contour evidence extrication to detect the apparent part of each entity and contour estimation to evaluate the final object contours. Then, some attributes were extracted from each segmented cell. In this paper, detection of hematological disorders like leukemia (blood cancer) is carried out which is based on geometric, texture, and mixed features. Classification is done to categories each segmented cell to be normal (benign) or abnormal (malignant).