Abstract
The Complete Blood Count (CBC) is a medical diagnostic test concerned with identifying and counting basic blood cells such as red blood cells (RBC), white blood cells (WBC) and platelets. The computerized automation of CBC has been a challenging problem in medical diagnostics. In this work we describe a subcomponent system for the CBC to perform the automatic classification of WBC cells into one of five WBC types in low resolution cytological images. We describe feature extraction and consider three classifiers: a support vector machine (SVM) using standard intensity and histogram features, an SVM with features extracted by a kernel principal component analysis of the intensity and histogram features, and a convolutional neural network (CNN) which takes the entire image as input. The proposed classifiers were compared through experiments conducted on low resolution cytological images of normal blood smears. The best results were obtained with the CNN solution with recognition rates either higher or comparable to the SVM-based classifiers for all five types of WBCs.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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. Methods in Molecular Biology, vol. 609, pp. 223–239. Humana Press (2010)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
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, Xi’an, China, May 25-28, pp. 542–545 (2009)
Comaniciu, D., Meer, P.: Cell image segmentation for diagnostic pathology. In: Advanced Algorithmic Approaches to Medical Image Segmentation, pp. 541–558. Springer, New York (2002)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)
Dorini, L.B., Minetto, R., Leite, N.: Semi-automatic white blood cell segmentation based on multiscale analysis. IEEE Transactions on Information Technology in Biomedicine (2012) (to appear)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience (November 2001)
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, 29–40 (2011)
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: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 414–422. Springer, Heidelberg (2012)
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, Orlando, FL, USA, February 12-17, vol. 7963, p. 79633I (2011)
Hamghalam, M., Motameni, M., Kelishomi, A.E.: Leukocyte segmentation in giemsa-stained image of peripheral blood smears based on active contour. In: IEEE International Conference on Signal Processing Systems, Los Alamitos, CA, USA, May 15-17, pp. 103–106 (2009)
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, Xi’an, China, November 2-5, pp. 2820–2825 (2003)
Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer-Verlag, New York Inc. (2002)
Kumar, B.R., Joseph, D.K., Sreenivas, T.V.: Teager energy based blood cell segmentation. In: 14th International Conference on Digital Signal Processing, Santorini, Greece, July 1-3, pp. 619–622 (2002)
Lauer, F., Suen, C.Y., Bloch, G.: A trainable feature extractor for handwritten digit recognition. Pattern Recognition 40(6), 1816–1824 (2007)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
LeCun, Y., Huang, F.-J., Bottou, L.: Learning methods for generic object recognition with invariance to pose and lighting. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, USA, June 27-July 2, vol. 2, pp. II-97–II-104 (2004)
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), 1–14 (1999)
Ogiela, M.R., Tadeusiewicz, R.: Syntactic reasoning and pattern recognition for analysis of coronary artery images. Artif. Intell. Med. 26(1-2), 145–159 (2002)
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, Washington, DC, USA, July 15-19, pp. 2461–2466 (2001)
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, Shanghai, China, September 1-4, pp. 3371–3374 (2005)
Rathi, Y., Dambreville, S., Tannenbaum, A.: Statistical shape analysis using kernel PCA. In: SPIE Conferences: IS&T Electronic Imaging, San Jose, CA, Jan. 15-19, vol. 6064, p. 60641B (2006)
Rodenacker, K., Bengtsson, E.: A feature set for cytometry on digitized microscopic images. Analytical Cellular Pathology 25(1), 1–36 (2001)
Shitong, W., Min, W.: A new detection algorithm (NDA) based on fuzzy cellular neural networks for white blood cell detection. IEEE Trans. on Information Technology in Biomedicine 10(1), 5–10 (2006)
Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: 7th International Conference on Document Analysis and Recognition, Washington, DC, USA, August 3-6, pp. 958–963 (2003)
Sinha, N., Ramakrishnan, A.G.: Automation of differential blood count. In: IEEE Inter’l Conf. on Convergent Technologies for Asia-Pacific Region, Bangalore, India, October 15-17, pp. 547–551 (2003)
Skubalska-Rafajłowicz, E.: Pattern recognition algorithms based on space-filling curves and orthogonal expansions. IEEE Transactions on Information Theory 47(5), 1915–1927 (2001)
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), 353–359 (2007)
Ushizima, D.M., Lorena, A.C., de Carvalho, A.C.P.L.F.: Support Vector Machines Applied to White Blood Cell Recognition. In: 5th International Conference on Hybrid Intelligent Systems, November 6-9, pp. 379–384. Rio de Janeiro, Brazil (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Habibzadeh, M., Krzyżak, A., Fevens, T. (2013). White Blood Cell Differential Counts Using Convolutional Neural Networks for Low Resolution Images. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7895. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38610-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-38610-7_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38609-1
Online ISBN: 978-3-642-38610-7
eBook Packages: Computer ScienceComputer Science (R0)