Application of pattern recognition techniques for the analysis of thin blood smear images

• Autorzy: Habibzadeh M. , Krzyżak A. , Fevens T.
• Języki publikacji: EN

Abstrakty

EN

In this paper we discuss applications of pattern recognition and image processing to automatic processing and analysis of histopathological images. We focus on counting of Red and White blood cells using microscopic images of blood smear samples. We provide literature survey and point out new challenges. We present an improved cell counting algorithm.

Słowa kluczowe

EN

CBC; microscopic medical denoising; binarization; segmentation; edge preservation; granulometry

PL

binaryzacja; segmentacja; granulometria

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2011
• Tom: Vol. 18
• Strony: 29--40
• Opis fizyczny: Bibliogr. 39 poz., rys., tab.

Twórcy

autor

Habibzadeh M.

• The Department of Computer Science and Software Engineering, Concordia University, Montréal, Québec, Canada H3G 1M8

autor

Krzyżak A.

autor

Fevens T.

Bibliografia

• [1] AL-MUHAIRY J., AL-ASSAF Y., Automatic white blood cell segmentation based on image processing, In 16th IFAC World Congress, 2005.
• [2] BABIC Z., MANDIC D., An efficient noise removal and edge preserving convolution filter, In 6th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service, Vol. 2, 2003, pp. 538-541.
• [3] BENTLEY S., LEWIS, S., The use of an image analyzing computer for the quantification of red cell morphological characteristics, British Journal of Hematology, Vol. 29, 1975, pp. 81–88.
• [4] BERGEN T., STECKHAN D., WITTENBERG T., ZERFASS T., Segmentation of leukocytes and erythrocytes in blood smear images, In 30th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, 2008, pp. 3075–3078.
• [5] BERNSEN J., Dynamic thresholding of grey-level images, In International Conference on Pattern Recognition, 1986, pp. 1251–1255.
• [6] BOBIER B., WIRTH M., Evaluation of Binarization Algorithms, Tech. rep., Department of Computing and Information Science, University of Guelph, Guelph, ON, 2008.
• [7] DI RUBERTO C., DEMPSTER A., KHAN S., JARRA B., Segmentation of blood images using morphological operators, In 15th International Conference on Pattern Recognition, IEEE, Barcelona, Spain, 2000, pp. 397–400.
• [8] DI RUBERTO C., DEMPSTER A., KHAN S., JARRA, B., Analysis of infected blood cell images using morphological operators, Image and Vision Computing, Vol. 20, No. 2, 2002, pp. 133–146.
• [9] FODOR I., KAMATH C., On denoising images using wavelet-based statistical techniques, Tech. rep., Lawrence Livermore National Laboratory, 2001, UCRL JC-142357.
• [10] GAUCH J., Image segmentation and analysis via multiscale gradient watershed hierarchies, IEEE Transactions on Image Processing, Vol. 8, No. 1, 1999, pp. 69–79.
• [11] GONZALEZ R., WOODS R., Digital Image Processing, 3rd Economy Ed., Prentice–Hall, 2008.
• [12] GOOLDMAN L., SCHAFER A., The peripheral blood smear, In Cecil Medicine, Eds., 24, Saunders Elsevier, Philadelphia, PA, 2011, ch. 160.
• [13] HABIBZADEH M., KRZYŻAK A., FEVENS T., SADR A., Counting of RBCs and WBCs in noisy normal blood smear microscopic images, In SPIE Medical Imaging, Vol. 7963, 2011, pp. 79633I.
• [14] HAMGHALAM M., MOTAMENI M., KELISHOMI A.E., Leukocyte segmentation in giemsa-stained image of peripheral blood smears based on active contour, In International Conference on Signal Processing Systems, IEEE Computer Society, Los Alamitos, CA, USA, 2009, pp. 103–106.
• [15] HONG V., PALUS H., PAULUS D., Edge preserving filters on color images, Lecture Notes in Computer Science Vol. 3039, 2004, pp. 34–40.
• [16] JIANG K., LIAO Q.M., DAI., S.Y., A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering, In Int. Conf. on Machine Learning and Cybernetics, Vol. 5, 2003, pp. 2820–2825.
• [17] KASS M., WITKIN A., TERZOPOULOS D., Snakes: Active contour models, International Journal of Computer Vision 4, 1988, pp. 321–331.
• [18] KUMAR B., JOSEPH D., SREENIVAS T., Teager energy based blood cell segmentation, In International Conference on Digital Signal Processing, Vol. 2, 2002, pp. 619–622.
• [19] KUWAHARA M., HACHIMURA K., EIHO S., KINOSHITA M., Processing of ri-angiocardiographic images, In Digital Processing of Biomedical Images, PRESTON K., ONOE M., Eds., Plenum Press, New York, 1976, pp. 187–203.
• [20] LIN Y.C., TSAI Y.P., HUNG Y.P., SHIH Z.C., Comparison between immersion-based and toboggan-based watershed image segmentation, IEEE Transactions on Image Processing, Vol. 15, No. 3, 2006, pp. 632–640.
• [21] MAKKAPATI V., Improved wavelet-based microscope autofocusing for blood smears by using segmentation, In IEEE Inter. Conf. on Automation Science & Engineering, 2009, pp. 208–211.
• [22] MESCHER A., Junqueira’s Basic Histology, 12th Edition: Text and Atlas., McGraw-Hill Medical, 2009.
• [23] NIBLACK W., An Introduction to Digital Image Processing, Prentice-Hall, Inc., USA, 1990.
• [24] NIKOLAOU N., PAPAMARKOS N., Color reduction for complex document images, International Journal of Imaging Systems and Technology, Vol. 19, No. 1, 2009, pp. 14–26.
• [25] ONGUN G., HALICI U., LEBLEBICIOGLU K., ATALAY V., BEKSAC S., BEKSAC M., An automated differential blood count system, In IEEE Int. Conf. on Engineering in Medicine and Biology Society, Vol. 3, 2001, pp. 2583–2586.
• [26] ONGUN G., HALICI U., LEBLEBICIOGLU K., ATALAY V., BEKSAC S., BEKSAC M., Automated contour detection in blood cell images by an efficient snake algorithm, Nonlinear Analysis-Theory Methods & Applications, Vol. 47, No. 9, 2001, pp. 5839–5847.
• [27] ONGUN G., HALICI U., LEBLEBICIOGLU K., ATALAY V., BEKSAC M., BEKSAC S., Feature extraction and classification of blood cells for an automated differential blood count system, In International Joint Conference on Neural Networks, Vol. 4, 2001, pp. 2461–2466.
• [28] OTSU N., A threshold selection method from gray-level histograms, IEEE Transactions on System, Man and Cybernetics, Vol. 9, No. 1, 1979, pp. 62–66.
• [29] PAPARI G., PETKOV N., CAMPISI P., Artistic edge and corner enhancing smoothing, IEEE Transactions on Image Processing, Vol. 16, No. 10, 2007, pp. 2449–2462.
• [30] PATIDAR P., GUPTA M., SRIVASATAVA S., NAGAWAT A., Image de-noising by various filters for different noise, International Journal of Computer Applications, Vol. 9, 2010, pp. 45–50.
• [31] PIURI V., SCOTTI F., Morphological classification of blood leucocytes by microscope images, In IEEE International Conference on Computational Intelligence Far Measurement Systems and Applications, Boston, MA, 2004.
• [32] SAULOVA J., PIETIKAINEN M., Adaptive document image binarization, Pattern Recognition, Vol. 33, No. 2, 2000, pp. 225–236.
• [33] SENDUR L., SELESNICK I., A bivariate shrinkage function for wavelet-based denoising, In IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 2, 2002, pp. 1261–1264.
• [34] SENDUR L., SELESNICK I., Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency, IEEE Transactions on Signal Processing, Vol. 50, No. 11, 2002, pp. 2744–2756.
• [35] SINHA N., RAMAKRISHNAN A., Automation of differential blood count, In Conf. on Convergent Technologies for Asia-Pacific Region, Vol. 2, 2003, pp. 547–551.
• [36] SPARAWLS P., Physical Principles of Medical Imaging Second Edition, Medical Physics Pub, 1995.
• [37] TOMASI, C., MANDUCHI R. Bilateral filtering for gray and color images, In Sixth International Conference on Computer Vision, 1998, pp. 839–846.
• [38] VINCENT L., Fast opening functions and morphological granulometries, Image Algebra and Morphological Image Processing V, SPIE Proceedings, Vol. 2300, 1994, pp. 253–267.
• [39] WANG M., CHU R., A novel white blood cell detection method based on boundary support vectors, In IEEE International Conference on Systems, Man and Cybernetics, SMC’09, IEEE Press, 2009, pp. 2595–2598.