Computer-Aided Acute Lymphoblastic Leukemia Diagnosis System Based on Image Analysis

Abstract

Leukemia is a kind of cancer that basically begins in the bone marrow. It is caused by excessive production of leukocytes that replace normal blood cells. This chapter presents Computer-Aided Acute Lymphoblastic Leukemia (ALL) diagnosis system based on image analysis. It presented to identify the cells ALL by segmenting each cell in the microscopic images, and then classify each segmented cell to be normal or affected. A well-known dataset was used in this chapter (ALL-IDB2). The dataset contains 260 cell images: 130 normal and 130 affected by ALL. The proposed system starts by segmenting the white blood cells. This process includes sub-processes such as conversion from RGB to CMYK color model, histogram equalization, thresholding by Zack technique, and background removal operation. Then some features were extracted from each cell, each of them represents aspects of a cell. The extracted features include color, texture, and shape features. Then each feature set was exposed to three data normalization techniques z-score, min-max, and grey-scaling to narrow down the gap between the features values. Finally, different classifiers were used to validate the proposed system. The proposed diagnosing system achieved acceptable accuracies when tested by well-known classifiers; however, K-NN achieved the best classification accuracy.