AUTOMATIC SEGMENTATION & CLASSIFICATION OF ACUTE LEUKEMIA CELLS IN MICROSCOPIC IMAGES

Abstract

Today, there are a substantial number of software and research groups that focus on the development of image processing software to extract useful information from medical images, in order to assist and improve patient diagnosis. The work presented in this thesis is centered on processing of images of blood and bone marrow smears of patients suffering from Leukemia, a common type of cancer. In general, cancer is due to aberrant gene expression, which is caused by either mutations or epigenetic changes in DNA. An unhealthy lifestyle may trigger or contribute to these changes, although the underlying mechanism is often unknown. Importantly, many cancer types including Leukemia are curable and patient survival and treatment can be improved, subject to prompt diagnosis. In particular, this study focuses on Acute Myeloid Leukemia (AML), which can be of eight distinct types (M0 to M7), with the main objective to develop a methodology to automatically detect and classify Leukemia cells into one of the above types. The data was collected from the Department of Hematology, International medical center, in Cairo. The main methods are image processing and Data classification. In image processing, a method was developed to remove noise from images, segment the Leukemia cells and enhance the segmented images. The proposed methodology selects the starting points, corresponding to potential blast cells. Furthermore, the WEKA software is utilized for classification of blast cells and hence images, into AML subtypes. Finally, these algorithms are integrated into an automated system for image processing. In brief, the research presented in this thesis, involves the use of advanced computational techniques for processing and classification of medical images, images of blood samples from patients suffering from Leukemia.