Abstract
The paper presents the automatic system for white blood cell recognition on the basis of the image of bone marrow smear. The paper proposes the complete system solving all problems, beginning from cell extraction using watershed algorithm, generation of different features based on texture, geometry and the statistical description of image intensity, feature selection using Linear Support Vector Machine and final classification by applying Gaussian kernel Support Vector Machine. The results of numerical experiments of recognition of 10 classes of blood cells of patients suffering from leukaemia have shown that the proposed system is sufficiently accurate so as to find practical application in the hospital practice.
This is a preview of subscription content, log in via an institution to check access.
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
Beksac, M., Beksac, M.S., Tippi, V.B., Duru, H.A., Karakas, M.U., Nurcakar, A.: An Artificial Inteligent Diagnostic System on Differential Recognition of Hematopietic Cells from Microscopic Images. Cytometry 30, 145–150 (1997)
Duda, R.O., Hart, P.E., Stork, P.: Pattern Classification and Scene Analysis. Wiley, Hoboken (2003)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene Selection for Cancer Classification Using Support Vector Machines. Machine Learning 46, 389–422 (2002)
Hsu, C.W., Lin, C.J.: A Comparison Methods for Multi Class Support Vector Machines. IEEE Trans. Neural Networks 13, 415–425 (2002)
Lewandowski, K., Hellmann, A.: Haematology Atlas. Multimedia Medical Publisher, Gdansk (2001)
Matlab user manual – Image processing toolbox. MathWorks, Natick (1999)
Markiewicz, T., Osowski, S., Mariańska, B., Moszczyński, L.: Automatic Recognition of the Blood Cells of Myelogenous Leukemia Using SVM. In: IJCNN, Montreal, pp. 2496–2501 (2005)
Osowski, S., Markiewicz, T.: Support Vector Machine for Recognition of White Blood Cell of Leukemia. In: Camps-Valls, G., Rojo-Alvarez, L., Martinez-Ramon, M. (eds.) Kernel Methods in Bioengineering, Communication and Image Processing, pp. 81–108. Idea Group, Hershey (2007)
Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
Schurmann, J.: Pattern Classification, a Unified View of Statistical and Neural Approaches. Wiley, Hoboken (1996)
Soille, P.: Morphological Image Analysis, Principles and Applications. Springer, Berlin (2003)
Theera-Umpon, N., Gader, P.: System-level Training of Neural Networks for Counting White Blood Cells. IEEE Trans. SMS-C 32, 48–53 (2002)
Ushizima, D.M., Lorena, A.C., de Carvalho, A.C.P.: Support Vector Machines Applied to White Blood Recognition. In: V Int. Conf. Hybrid Intelligent Systems, Rio de Janeiro, pp. 234–237 (2005)
Vapnik, V.: Statistical Learning Theory. Wiley, Hoboken (1998)
Wagner, T.: Texture Analysis. In: Jahne, B., Haussecker, H., Geisser, P. (eds.) Handbook of Computer Vision and Application, pp. 275–309. Academic Press, London (1999)
DiffMaster Octavia DM96, Cellavision AB, http://www.cellavision.com
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Markiewicz, T., Osowski, S., Mariańska, B. (2007). White Blood Cell Automatic Counting System Based on Support Vector Machine. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_36
Download citation
DOI: https://doi.org/10.1007/978-3-540-71629-7_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71590-0
Online ISBN: 978-3-540-71629-7
eBook Packages: Computer ScienceComputer Science (R0)