Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Leukocyte subtypes classification by means of image processing

, , , , , , , ,

DOI: http://dx.doi.org/10.15439/2016F80

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 309316 ()

Full text

Abstract. The classification of leukocyte subtypes is a routine method to diagnose many diseases, infections, and inflammations. By applying an automated cell counting procedure, it is possible to decrease analysis time and increase the number of analyzed cells per patient, thereby making the analysis more robust. Here we propose a method, which automatically differentiate between two white blood cell subtypes, which are present in blood in the highest fractions. We apply generalized pseudo-Zernike moments to transfer morphological information of the cells to features and subsequently to a classification model. The first results indicate that information from the morphology can be used to obtain efficient automatic classification, which was demonstrated for the leukocyte subtype classification of neutrophils and lymphocytes. The approach can be extended to other imaging modalities, like different types of staining, spectroscopic techniques, dark field or phase contrast microscopy.


  1. A. Kratz, M. Ferraro, P. M. Sluss, and K. B. Lewandrowski, “Normal reference laboratory values,” New England Journal of Medicine, vol. 351, no. 15, pp. 1548–1563, 2004. http://dx.doi.org/10.1056/NEJMcpc049016 PMID: 15470219.
  2. A. Ramoji, U. Neugebauer, T. Bocklitz, M. Foerster, M. Kiehntopf, M. Bauer, and J. Popp, “Toward a spectroscopic hemogram: Raman spectroscopic differentiation of the two most abundant leukocytes from peripheral blood,” Analytical Chemistry, vol. 84, no. 12, pp. 5335–5342, 2012. http://dx.doi.org/10.1021/ac3007363 PMID: 22721427.
  3. S. Khan, A. Khan, F. S. Khattak, and A. Naseem, “An accurate and cost effective approach to blood cell count,” International Journal of Computer Applications, vol. 50, no. 1, 2012. http://dx.doi.org/10.5120/7734-0682.
  4. L. Putzu and C. Di Ruberto, “White blood cells identification and counting from microscopic blood image,” International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, vol. 7, no. 1, pp. 20 – 27, 2013. http://waset.org/Publications?p=73
  5. M. M. G. Bhamare and D. Patil, “Automatic blood cell analysis by using digital image processing: A preliminary study,” in International Journal of Engineering Research and Technology, vol. 2, no. 9, ESRSA Publications. ESRSA Publications, 2013. http://www.ijert.org/view-pdf/5460/
  6. F. Sadeghian, Z. Seman, A. R. Ramli, B. A. Kahar, and M.-I. Saripan, “A framework for white blood cell segmentation in microscopic blood images using digital image processing,” Biological procedures online, vol. 11, no. 1, pp. 196–206, 2009. http://dx.doi.org/10.1007/s12575-009-9011-2.
  7. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” Systems, Man and Cybernetics, IEEE Transactions on, vol. SMC-3, no. 6, pp. 610–621, 1973. http://dx.doi.org/10.1109/TSMC.1973.4309314.
  8. M. Habibzadeh, A. Krzyżak, and T. Fevens, Comparative study of feature selection for white blood cell differential counts in low resolution images, ser. Lecture Notes in Computer Science. Springer International Publishing, 2014, vol. 8774, book section 20, pp. 216–227. ISBN 978-3-319-11655-6. http://dx.doi.org/10.1007/978-3-319-11656-3_20
  9. T. Xia, H. Zhu, H. Shu, P. Haigron, and L. Luo, “Image description with generalized pseudo-Zernike moments,” Journal of the Optical Society of America A, vol. 24, no. 1, pp. 50–59, 2007. http://dx.doi.org/10.1364/JOSAA.24.000050.
  10. S. O. Belkasim, M. Shridhar, and M. Ahmadi, “Pattern recognition with moment invariants: A comparative study and new results,” Pattern Recognition, vol. 24, no. 12, pp. 1117–1138, 1991. http://dx.doi.org/10.1016/0031-3203(91)90140-Z.
  11. C. Kan and M. D. Srinath, “Invariant character recognition with Zernike and orthogonal Fourier-Mellin moments,” Pattern Recognition, vol. 35, no. 1, pp. 143–154, 2002. http://dx.doi.org/10.1016/S0031-3203(00)00179-5.
  12. C. W. Chong, P. Raveendran, and R. Mukundan, “The scale invariants of pseudo-Zernike moments,” Pattern Analysis and Applications, vol. 6, no. 3, pp. 176–184, 2003. http://dx.doi.org/10.1007/s10044-002-0183-5.
  13. Y.-H. Pang, A. T. B. J, and D. N. C. L, “Enhanced pseudo Zernike moments in face recognition,” IEICE Electronics Express, vol. 2, no. 3, pp. 70–75, 2005. http://dx.doi.org/10.1587/elex.2.70.
  14. E. Walia, C. Singh, and N. Mittal, “Discriminative Zernike and pseudo Zernike moments for face recognition,” Int. J. Comput. Vis. Image Process., vol. 2, no. 2, pp. 12–35, 2012. http://dx.doi.org/10.4018/ijcvip.2012040102.
  15. J. Haddadnia, M. Ahmadi, and K. Faez, “An efficient feature extraction method with pseudo-Zernike moment in rbf neural network-based human face recognition system,” EURASIP Journal on Advances in Signal Processing, vol. 2003, pp. 890–901, 2003. http://dx.doi.org/10.1155/s1110865703305128.
  16. T. Bocklitz, E. Kämmer, S. Stöckel, D. Cialla-May, K. Weber, R. Zell, V. Deckert, and J. Popp, “Single virus detection by means of atomic force microscopy in combination with advanced image analysis,” Journal of Structural Biology, vol. 188, no. 1, pp. 30 – 38, 2014. http://dx.doi.org/10.1016/j.jsb.2014.08.008.
  17. R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2015. https://www.R-project.org/
  18. B. Rajwa, M. Dundar, A. Irvine, and T. Dang, IM: Orthogonal Moment Analysis, 2013, R package version 1.0. https://CRAN.R-project.org/package=IM
  19. D. Meyer, E. Dimitriadou, K. Hornik, A. Weingessel, and F. Leisch, e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien, 2015, R package version 1.6-7. https://CRAN.R-project.org/package=e1071
  20. Revolution Analytics and S. Weston, foreach: Provides Foreach Looping Construct for R, 2015, R package version 1.4.3. [Online]. Available: https://CRAN.R-project.org/package=foreach
  21. Revolution Analytics and S. Weston, doParallel: Foreach Parallel Adaptor for the ’parallel’ Package, 2015, R package version 1.0.10. https://CRAN.R-project.org/package=doParallel
  22. S. Urbanek, jpeg: Read and write JPEG images, 2014, R package version 0.1-8. https://CRAN.R-project.org/package=jpeg
  23. C. H. Teh and R. T. Chin, “On image analysis by the methods of moments,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 4, pp. 496–513, Jul 1988. http://dx.doi.org/10.1109/34.3913.