Use of general purpose GPU programming to enhance the classification of leukaemia blast cells in blood smear images

Authors:
Stefan Skrobanski;Stelios Pavlidis;Waidah Ismail;Rosline Hassan;Steve Counsell;Stephen Swift
Affiliations:
Arithmetica Ltd, London, UK;Department of Information Systems and Computing, Brunel University, London, UK;Department of Information Systems and Computing, Brunel University, London, UK;Department of Haematology, School of Medical Sciences, Universiti Sains Malaysia, Malaysia;Department of Information Systems and Computing, Brunel University, London, UK;Department of Information Systems and Computing, Brunel University, London, UK
Venue:
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Year:
2012

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Abstract

Leukaemia is a life threatening form of cancer, which causes an uncontrollable increase in the production of malformed white blood cells, termed blasts, inhibiting the body's ability to fight infection. Given the variety of leukaemia types and the disease's nature, prompt diagnosis is essential for the choice of appropriate, timely patient treatment. Currently, however, the diagnostic process is time consuming and laborious. To target this issue we propose a methodology based on an existing system, for automated blast detection and diagnosis from a set of blood smear images, utilising a mixture of image processing, cellular automata, heuristic search and classification techniques. Our system builds upon this work, by employing General Purpose Graphical Processing Unit programming, to shorten execution times. Additionally, we utilise an enhanced ellipse-fitting algorithm for blast cell detection, yielding more information from captured cells. We show that the methodology is efficient, producing highly accurate classification results.