Automatic detection and counting of platelets in microscopic image

• Autorzy: Burduk R. , Krawczyk B.
• Języki publikacji: EN

Abstrakty

EN

In this paper we present a machine learning-based approach for detecting platelet cells in microscopic smear images. Counting how many platelets appeared in each smear image is one of the basic tasks done in many laboratories. In many cases this is still done by a human — laboratory technician. Due to very small size and often great quantity of those cells, precise estimating of the number of platelets is not a trivial task. As in all man-dependent problems the whole process is very sensitive to errors, time-consuming and its accuracy is limited by human perception. We propose alternative, fully automatic solution that is free of those drawbacks. Our idea is based on the combination of techniques driven from two fields of modern computer science: the image analysis and pattern recognition ⁄ machine learning. It not only reduces the error rate, but, what is more important, also decreases the time needed for each smear image analysis. The obtained results are very satisfying and our solution is more precise than estimation based on human perception. This will improve the quality of laboratory work and allow to save time that can be spent on other important tasks.

Słowa kluczowe

EN

pattern recognition; bioinformatic; machine learning; image analysis; platelet

PL

rozpoznawanie wzorców; nauczanie maszynowe; analiza obrazu

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2010
• Tom: Vol. 16
• Strony: 173--178
• Opis fizyczny: Bibliogr. 11 poz., rys., tab.

Twórcy

autor

Burduk R.

• Department of Systems and Computer Networks, Wroclaw University of Technology, Poland

autor

Krawczyk B.

Bibliografia

• [1] BANKMAN I., Handbook of medical image processing and analysis, John Hopkins University, 2008.
• [2] CRISTIANINI N., SHAWE-TAYLOR J., An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, 2000.
• [3] CSEKE I., A fast segmentation scheme for white blood cell images, Proceedings of the IAPR, 1992.
• [4] International Conference on Image, Speech and Signal Analysis.
• [5] KATZ A. R. J., Image analysis and supervised learning in the automated differentiation of white blood cells from microscopic imag., RMIT University, 2000.
• [6] LE M., BRETSCHNEIDER T. R., KUSS C., PREISER P. R., A novel semi-automatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears, BMC Cell Biology, 2008.
• [7] LEZORAY O., CARDOT H., Cooperation of color pixel classification schemes and color watershed: a study for microscopic images, IEEE Transactions on Image Processing, 2002, pp. 783–789.
• [8] MICHELI-TZANAKOU E., SHEIKH H., ZHU B., Neural networks and blood cell identification, 1997.
• [9] Journal of Medical Systems, Vol. 21, pp. 201–210.
• [10] SURI S. S., WILSON D. L., LAXMINARAYAN S., Handbook of biomedical image analysis, Vol. 1-3, Kluwer Academic, 2005.
• [11] VAPNIK V., Statistical learning theory, Wiley, 1998.