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Computer Aided Detection and Classification of Acute Lymphoblastic Leukemia Cell Subtypes Based on Microscopic Image Analysis Publisher Pubmed



Moradiamin M1 ; Memari A2 ; Samadzadehaghdam N1 ; Kermani S3 ; Talebi A4
Authors
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Authors Affiliations
  1. 1. Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
  2. 2. Department of Electrical Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran
  3. 3. Department of Biomedical Engineering, Faculty of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran
  4. 4. Department of Pathology School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Microscopy Research and Technique Published:2016


Abstract

Acute lymphoblastic leukemia (ALL) is a cancer that starts from the early version of white blood cells called lymphocytes in the bone marrow. It can spread to different parts of the body rapidly and if not treated, would probably be deadly within a couple of months. Leukemia cells are categorized into three types of L1, L2, and L3. The cancer is detected through screening of blood and bone marrow smears by pathologists. But manual examination of blood samples is a time-consuming and boring procedure as well as limited by human error risks. So to overcome these limitations a computer-aided detection system, capable of discriminating cancer from noncancer cases and identifying the cancerous cell subtypes, seems to be necessary. In this article an automatic detection method is proposed; first cell nucleus is segmented by fuzzy c-means clustering algorithm. Then a rich set of features including geometric, first- and second-order statistical features are obtained from the nucleus. A principal component analysis is used to reduce feature matrix dimensionality. Finally, an ensemble of SVM classifiers with different kernels and parameters is applied to classify cells into four groups, that is noncancerous, L1, L2, and L3. Results show that the proposed method can be used as an assistive diagnostic tool in laboratories. © 2016 Wiley Periodicals, Inc.
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