Paper Title :Detection and Classification of Sub-Types of Acute Lymphocytic Leukemia Cells

Author :Hsu Htet Aung, Kyi Kyi Maw

Article Citation :Hsu Htet Aung ,Kyi Kyi Maw , (2018 ) " Detection and Classification of Sub-Types of Acute Lymphocytic Leukemia Cells " , International Journal of Advances in Science, Engineering and Technology(IJASEAT) , pp. 28-33, Volume-6, Issue-3, Spl. Iss-1

Abstract : Acute Lymphocytic Leukemia is a type of blood cancer which affects a group of white blood cells called lymphocytes, causing the production of a vase amount of immature lymphocytes in the bone marrow. The microscope examination for the morphological analysis of white blood cells is the initial screening step for the diagnosis of leukemia and it is done by skillful pathologists. The problems in the diagnosis procedure arise due to the similarity of the malignant cell structures between the other types of leukemia, causing the inconsistent prognosis result. Therefore, the accuracy of the manual observation highly depends on the experience of the pathologists. In order to eliminate some drawbacks of manual examination, a computer-assisted diagnosis system is proposed by performing the morphology analysis of the peripheral blood smear microscope images. The methodology presented in this work consists of four main stages: (1) Pre-processing (2) Segmentation (3) Feature Extraction (4) Classification. In pre-processing step, the input image is applied color contrast adjustment, conversion to L*a*b color space and guided filtering for noise removal. Zack’s algorithm is used for segmenting the leucocytes. Marker-controlled watershed is adopted for separation of touching cells. Segmentation of nucleus region is achieved using K-Means clustering. Shape and textures features are extracted using Gray Level Co-occurrence Matrix(GLCM) and histogram-based texture features methods. Finally, One-vs-all multi-Support Vector Machine is used to classify the cells into L1,L2 and normal lymphocyte. Index terms - Acute Lymphocytic Leukemia, Guided Filtering, GLCM, K-Means Clustering, Support Vector Machine.

Type : Research paper

Published : Volume-6, Issue-3, Spl. Iss-1

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