Comparative study of shape, intensity and texture features and support vector machine for white blood cell classification

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

Abstrakty

EN

The complete blood count (CBC) is widely used test for counting and categorizing various peripheral particles in the blood. The main goal of the paper is to count and classify white blood cells (leukocytes) in microscopic images into five major categories using features such as shape, intensity and texture features. The first critical step of counting and classification procedure involves segmentation of individual cells in cytological images of thin blood smears. The quality of segmentation has significant impact on the cell type identification, but poor quality, noise, and/or low resolution images make segmentation less reliable. We analyze the performance of our system for three different sets of features and we determine that the best performance is achieved by wavelet features using the Dual-Tree Complex Wavelet Transform (DT-CWT) which is based on multi-resolution characteristics of the image. These features are combined with the Support Vector Machine (SVM) which classifies white blood cells into their five primary types. This approach was validated with experiments conducted on digital normal blood smear images with low resolution.

Słowa kluczowe

EN

Complete Blood Count (CBC); White Blood Cell Classification; dual-tree complex wavelet; Transform (DT-CWT); Shape; intensity and texture features; kernel PCA; support vector machine (SVM); (SVM)

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Journal of Theoretical and Applied Computer Science
• Rocznik: 2013
• Tom: Vol. 7, nr 1
• Strony: 20--35
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Habibzadeh M.

• Department of Computer Science & Software Engineering, Concordia University, Montréal, Québec

autor

Krzyżak A.

• Department of Computer Science & Software Engineering, Concordia University, Montréal, Québec

autor

Fevens T.

• Department of Computer Science & Software Engineering, Concordia University, Montréal, Québec

Bibliografia

• [1] Ramoser, H., Laurain, V., Bischof, H., Ecker, R.: Leukocyte segmentation and classification in blood-smear images. In: 27th IEEE Annual Conference Engineering in Medicine and Biology, pp. 3371–3374. Shanghai, China, 2005.
• [2] Ushizima, D., Lorena, A., de Carvalho, A.: Support Vector Machines Applied to White Blood Cell Recognition. In: 5th International Conference on Hybrid Intelligent Systems, pp. 379–384. Rio de Janeiro, Brazil, 2005.
• [3] Bentley, S., Lewis, S.: The use of an image analyzing computer for the quantification of red cell morphological characteristics. British Journal of Hematology, 29, pp. 81–88, 1975.
• [4] Rowan, R., England, J. M.: Automated examination of the peripheral blood smear. In: Automation and quality assurance in hematology, chapter 5, pp. 129–177. Blackwell Scientific Oxford, 1986.
• [5] Dorini, L., Minetto, R., Leite, N.: Semi-automatic white blood cell segmentation based on multiscale analysis. IEEE Transactions on Information Technology in Biomedicine, 17(1), pp. 250–256, 2013. ISSN 2168-2194.
• [6] Shitong,W., Min,W.: A new detection algorithm (NDA) based on fuzzy cellular neural networks for white blood cell detection. IEEE Transactions on Information Technology in Biomedicine, 10(1), pp. 5–10, 2006.
• [7] Theera-Umpon, N., Dhompongsa, S.: Morphological Granulometric Features of Nucleus in Automatic Bone Marrow White Blood Cell Classification. IEEE Transactions on Information Technology in Biomedicine, 11(3), pp. 353–359, 2007.
• [8] 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, pp. 2461–2466. Washington, DC, USA, 2001.
• [9] Lezoray, O., Elmoataz, A., Cardot, H., Gougeon, G., Lecluse, M., Elie, H., Revenu, M.: Segmentation of cytological images using color and mathematical morphology. Acta Stereologica, 18(1), pp. 1–14, 1999.
• [10] Kumar, B., Joseph, D., Sreenivas, T.: Teager energy based blood cell segmentation. In: 14th International Conference on Digital Signal Processing, pp. 619–622. Santorini, Greece, 2002.
• [11] Sinha, N., Ramakrishnan, A.: Automation of differential blood count. In: IEEE International Conference on Convergent Technologies for Asia-Pacific Region, pp. 547–551. Bangalore, India, 2003.
• [12] Comaniciu, D., Meer, P.: Cell image segmentation for diagnostic pathology. In: Advanced algorithmic approaches to medical image segmentation, pp. 541-558. Springer, New York, NY USA, 2002
• [13] Jiang, K., Liao, Q.-M., Dai, S.-Y.: A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering. In: IEEE International Conference on Machine Learning and Cybernetics, pp. 2820–2825. Xi’an, China, 2003.
• [14] Chan, H., Li-Jun, J., Jiang, B.: Wavelet transform and morphology image segmentation algorism for blood cell. In: 4th IEEE International Conference on Industrial Electronics and Applications, pp. 542 –545. Xi’an, China, 2009.
• [15] Selesnick, I., Baraniuk, R., Kingsbury, N.: The dual-tree complex wavelet transform. IEEE Signal Processing Magazine, 22(6), pp. 123 – 151, 2005. ISSN 1053-5888.
• [16] Ben-Hur, A., Weston, J.: A User’s Guide to Support Vector Machines. In: Carugo, O., Eisenhaber, F. (eds.), Data Mining Techniques for the Life Sciences, volume 609 of Methods in Molecular Biology, pp. 223–239. Humana Press, 2010. ISBN 978-1-60327-241-4.
• [17] Habibzadeh, M., Krzyżak, A., Fevens, T.: Application of pattern recognition techniques for the analysis of thin blood smear images. Journal of Medical Informatics & Technologies, 18, pp. 29–40, 2011.
• [18] Habibzadeh, M., Krzyżak, A., Fevens, T., Sadr, A.: Counting of RBCs and WBCs in noisy normal blood smear microscopic images. In: SPIE Medical Imaging : Computer-Aided Diagnosis, volume 7963, p. 79633I. Orlando, FL, USA, 2011.
• [19] Y. Rathi, S. D., Tannenbaum, A.: Statistical shape analysis using kernel PCA. In: SPIE Conferences: IS&T Electronic Imaging, volume 6064, pp. 425–432. San Jose, CA, USA, 2006.
• [20] Hu, M.: Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8(2), pp. 179–187, 1962. ISSN 0096-1000.
• [21] Muralidharan, R., Chandrasekar, C.: Scale invariant feature extraction for identifying an object in the image using Moment invariants. In: International Conference on Communication and Computational Intelligence (INCOCCI), pp. 452–456. 2010.
• [22] Rodenacker, K., Bengtsson, E.: A feature set for cytometry on digitized microscopic images. Analytical Cellular Pathology, 25(1), pp. 1–36, 2001.
• [23] Haralick, R., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics, SMC-3(6), pp. 610–621, 1973. ISSN 0018-9472.
• [24] Kingsbury, N.: Design of Q-shift complex wavelets for image processing using frequency domain energy minimization. In: International Conference on Image Processing (ICIP), volume 1, pp. I – 1013–16. 2003. ISSN 1522-4880.
• [25] Selesnick, I.: The double-density dual-tree DWT. IEEE Transactions on Signal Processing, 52(5), pp. 1304 – 1314, 2004. ISSN 1053-587X.
• [26] Habibzadeh, M., Krzyżak, A., Fevens, T.: Analysis of White Blood Cell Differential Counts Using Dual-Tree Complex Wavelet Transform and Support Vector Machine Classifier. In: ICCVG International Conference on Computer Vision and Graphics, volume 7594, pp. 414–422. Springer, Warsaw, Poland, 2012.
• [27] Kingsbury, N.: Complex wavelets for shift invariant analysis and filtering of signals. Applied and Computational Harmonic Analysis, 10(3), pp. 234 – 253, 2001.
• [28] Jolliffe, I.: Principal Component Analysis. Springer-Verlag (New York Inc), 2 edition, 2002.
• [29] Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley-Interscience, New York, 2 edition, 2001.
• [30] Habibzadeh, M., Krzyżak, A., Fevens, T.: White Blood Cell Differential Counts Using Concolutional Neural Networks for Low Resolution Images. In: Artificial Intelligence and Soft Computing, volume 7895 of Lecture Notes in Computer Science, pp. 263–274. Springer Berlin Heidelberg, 2013. ISBN 978-3-642-38609-1.