Classification of acute leukemia using image processing and machine learning techniques / Hayan Tareq Abdul Wahhab - Wahhab, Hayan Tareq Abdul (2015)
Medical diagnosis is the procedure of identifying a disease by critical analysis of its symptoms and is often aided by a series of laboratory tests of varying complexity. Accurate medical diagnosis is essential in order to provide the most effective treatment option. The work presented in this thesis is focused on processing of peripheral blood smear images of patients suffering from leukemia based on blast cells morphology. Leukemia, a blood cancer, is one of the commonest malignancies affecting both adults and children. It is a disease in which digital image processing and machine learning techniques can play a prominent role in its diagnostic process. Leukemia is classified as either acute or chronic based on the rapidity of the disease progression. Acute leukemia can be further classified to acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) based on the cell lineage. The treatment protocol is allocated based on the leukemia type. Fortunately, leukemia like many other cancer types are curable and patient survival and treatment can be improved, subject to accurate diagnosis. In particular, this research focuses on Acute Leukemia, which can be of two distinct types (ALL, AML), with the main objective to develop a methodology to detect and classify Acute Leukemia blast cells into one of the above types based on image processing and machine learning techniques using peripheral blood smear images. The methodology presented in this research consisted of several stages namely, image acquisition, image segmentation, feature extraction/selection and, classification. The data was collected from two different sources, University of Malaya Medical Center (UMMC), Malaysia and M. Tettamanti Research Center for childhood leukemia and hematological diseases, Italy. The image segmentation addressed several key issues in blast cells segmentation including, the blast cell localization, sub-imaging, color variation and segregation of touching cells. This stage was accomplished using several image processing techniques including, color transformation, mathematical morphology, thresholding, and watershed segmentation. The seeded region growing was used to further segment the blast cell into nucleus and cytoplasm, respectively. This combination resulted in a new algorithm we named CBCSA. Based on the Relative Ultimate Measurement Accuracy for Area, the proposed algorithm was able to achieve an accuracy of 96% and 94% in the extraction of the blast cell region and the nuclear region, respectively. Various types of features were employed to address the blast cell’s morphology, including shape, texture and color. In total, 601 features were extracted from each blast cell, and its nucleus: 31of these were shape-based features, while 534 were texture-based features and 36 were color-based features. Artificial Neural Network and Support Vector Machine were used to classify blast cells into either ALL or AML according to the extracted features. As a result, an accuracy rate of 96.93% was achieved in the classification of blasts cells. The resulting system will subsequently act as a second reader after the manual screening of peripheral blood smears. It is believed that this system would increase the diagnostic accuracy and consistency of the hematologist and laboratory practitioner in the daily diagnostic routine.