Abstract
This paper presents a method for the identification of leukaemia cells within images of blood smear microscope slides, which is currently a time consuming manual process. The work presented is the first stage of a procedure aimed at classifying the sub-types of Acute Myeloid Leukaemia. This paper utilises the techniques of Otsu, Cellular Automata and heuristic search and highlights a comparison between random and seeded searches. We present a novel Cellular Automata based technique that helps to remove noise from the images and additionally locates good starting points for candidate white blood cells. Our results are based on real world image data from a Haematology Department, and our analysis shows promising initial results.
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Ismail, W., Hassan, R., Swift, S. (2010). Detecting Leukaemia (AML) Blood Cells Using Cellular Automata and Heuristic Search. In: Cohen, P.R., Adams, N.M., Berthold, M.R. (eds) Advances in Intelligent Data Analysis IX. IDA 2010. Lecture Notes in Computer Science, vol 6065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13062-5_7
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DOI: https://doi.org/10.1007/978-3-642-13062-5_7
Publisher Name: Springer, Berlin, Heidelberg
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