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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hassan, R.: Molecular Diagnosis in Acute Leukaemia: Hospital University’s experience. In: International Joint Symposium Frontier In Biomedical Sciences: From Genes to Applications, Faculty of Medicine, Gadjah Mada University, Yogyakarta, Indonesia (2008)
Vardiman, J.W., Harris, N.L., Brunning, R.D.: The World Health Organization (WHO) classification of the myeloid neoplasms. Blood 100(7), 2292–2302 (2002)
Nipon, T.U., Gader, P.: System-level training of neural networks for counting white blood cells. IEEE Trans. SMS-C 32(1), 48–53 (2002)
Yi, D.H., Rashid, S., Cibas, E.S., Arrigg, P.G., Dana, M.R.: Acute unilateral leukemic hypopyon in an adult with relapsing acute lymphoblastic leukemia. Am. J. Ophthalmol. 139, 719–721 (2005)
Petrou, M., Petrou, C.: Image Processing, The Fundamentals, 2nd edn. John Wiley and Sons, Ltd, Chichester (2010)
Piuri, V., Scotti, F.: Morphological Classification of Blood Leucocytes by Microscope Images. In: IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Boston, MA (2004)
Jiang, K., Liao, Q.M., Xiong, Y.: A novel white blood cell segmentation scheme based on feature space clustering. Soft Computing - A Fusion of Foundations, Methodologies and Applications 10(1), 12–19 (2006)
Zamini, F., Safabakhsh, R.: An unsupervised GVF Snake Approach for White Blood Cell Segmentation based on Nucleus. In: International Conference on Signal Processing Proceedings (ICSP), Beijing (2006)
Yampri, P., Pintavirooj, C., Daochai, S., Teartulakarn, S.: White Blood Cell Classification based on the Combination of Eigen Cell and Parametric Feature Detection. In: 1st IEEE Conference on Industrial Electronics and Applications, Singapore (2006)
Zerfass, T., Schlarb, T., Elter, M.: Segmentation of leukocyte cells in bone marrow smears. In: 23rd International Symposium on Computer-Based Medical Systems (CBMS), Perth, WA (2010)
Borovicka, J.: Circle Detection Using Hough Transform. University of Bristol, Bristol
Mulchrone, K.F., Choudhury, K.R.: Fitting an Ellipse to an Arbitary Shape: Implications for Strain Analysis. Journal of Structural Geology 26, 143–153 (2004)
Ismail, W., Hassan, R., Swift, S.: Detecting Leukaemia (AML) Blood Cells Using Cellular Automata and Heuristic Search. In: Cohen, P.R., Adams, N.M., Berthold, M.R. (eds.) IDA 2010. LNCS, vol. 6065, pp. 54–66. Springer, Heidelberg (2010)
Ismail, W., Swift, S.: Detecting Leukaemia (AML) blood cells using Genetic Algorithms. In: SCOR 2010. Brunel University, London (2010)
Microsoft, n.d., Standard Template Library, http://msdn.microsoft.com/en-us/library/c191tb28.aspx (accessed March 23, 2012)
LibTIFF - TIFF Library and Utilities, http://www.libtiff.org/libtiff.html (accessed March 23, 2012)
NVIDIA CUDA C Programming Guide. NVIDIA Corporation, Santa Clara (2011)
Wen-Mei, H.W.: GPU Computing Gems Emerald Edition, 1st edn. Morgan Kaufmann, San Francisco (2011)
NVidia, CURAND Library. 1st edn. NVidia Corporation (2010)
Microsoft, GDI+, http://msdn.microsoft.com/en-us/library/ms533798v=vs.85.aspx (accessed March 23, 2012)
Otsu, N.: A Threshold Selection Method from Gray-Level Historgrams. IEEE Transactions on Systems, Man, and Cybernetics SMC-9(1), 62–67 (1979)
Bandini, S.: Cellular Automata. Future Generation Computer Systems 18(7) (2002)
Fang, L.: The New Adaptive Evolutionary Programing. In: IEEE Proceedings of the Ninth International Conference on Machine Learning and Cybernetics, Qingdao (2010)
Ghozeil, A., Fogel, B.D.: Discovering Patterns in Sptial Data Using Evolutionary Programming. In: GECCO Genetic and Evolutionary Computation Conference. MIT Press, Cambridge (1996)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1), 10–18 (2009)
Zhu, N., Wang, G., Yang, G., Dai, W.: A fats 2D Otsu Thresholding Algorithm Based on Improved Histogram. In: CCPR Chinese Conference on Pattern Recognition, Nanjing (2009)
Ismail, W.: Automatic Detection and Classification of Leukaemia Cells. PhD Thesis, School of Information systems, Computing and mathematics, Brunel University, London, UK (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Skrobanski, S., Pavlidis, S., Ismail, W., Hassan, R., Counsell, S., Swift, S. (2012). Use of General Purpose GPU Programming to Enhance the Classification of Leukaemia Blast Cells in Blood Smear Images. In: Hollmén, J., Klawonn, F., Tucker, A. (eds) Advances in Intelligent Data Analysis XI. IDA 2012. Lecture Notes in Computer Science, vol 7619. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34156-4_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-34156-4_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34155-7
Online ISBN: 978-3-642-34156-4
eBook Packages: Computer ScienceComputer Science (R0)