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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/6593

Title: Automatic detection and classification of leukaemia cells
Authors: Ismail, Waidah
Advisors: Swift, S
Payne, A
Keywords: Classification
Heuristic search
Cellular automata
Leukaemia cells
Neural network
Publication Date: 2012
Publisher: Brunel University, School of Information Systems, Computing and Mathematics
Abstract: Today, there is 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 centred on processing of images of blood and bone marrow smears of patients suffering from leukaemia, 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. Poor diet and unhealthy lifestyle may trigger or contribute to these changes, although the underlying mechanism is often unknown. Importantly, many cancer types including leukaemia are curable and patient survival and treatment can be improved, subject to prompt diagnosis. In particular, this study focuses on Acute Myeloid Leukaemia (AML), which can be of eight distinct types (M0 to M7), with the main objective to develop a methodology to automatically detect and classify leukaemia cells into one of the above types. The data was collected from the Department of Haematology, Universiti Sains Malaysia, in Malaysia. Three main methods, namely Cellular Automata, Heuristic Search and classification using Neural Networks are facilitated. In the case of Cellular Automata, an improved method based on the 8-neighbourhood and rules were developed to remove noise from images and estimate the radius of the potential blast cells contained in them. The proposed methodology selects the starting points, corresponding to potential blast cells, for the subsequent seeded heuristic search. The Seeded Heuristic employs a new fitness function for blast cell detection. Furthermore, the WEKA software is utilised for classification of blast cells and hence images, into AML subtypes. As a result accuracy of 97.22% was achieved in the classification of blasts into M3 and other 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, that is, images of blood samples from patients suffering from leukaemia.
Description: This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.
Sponsorship: The Institute of Higher Education of Malaysia and the Universiti Sains Islam Malaysia (USIM).
URI: http://bura.brunel.ac.uk/handle/2438/6593
Appears in Collections:Computer Science
Dept of Computer Science Theses

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