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Analysis of White Blood Cell Differential Counts Using Dual-Tree Complex Wavelet Transform and Support Vector Machine Classifier

  • Mehdi Habibzadeh
  • Adam Krzyżak
  • Thomas Fevens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7594)

Abstract

A widely used pathological screening test for blood smears is the complete blood count which classifies and counts peripheral particles into their various types. We particularly interested in the classification and counting of the five main types of white blood cells (leukocytes) in a clinical setting where the quality of microscopic imagery may be poor. A critical first step in the medical analysis of cytological images of thin blood smears is the segmentation of individual cells. The quality of the segmentation has a great influence on the cell type identification, but for poor quality, noisy, and/or low resolution images, segmentation is correspondingly less reliable. In this paper, we compensate for less accurate segmentation by extracting features based on wavelets using the Dual-Tree Complex Wavelet Transform (DT-CWT) which is based on multi-resolution characteristics of the image. These features then form the basis of classification of white blood cells into their five primary types with a Support Vector Machine (SVM) that performs classification by constructing hyper-planes in a high multi-dimensional space that separates cases of different classes. This approach was validated with experiments conducted on poor quality, normal blood smear images.

Keywords

Support Vector Machine Discrete Wavelet Transform Confusion Matrice Kernel Principal Component Analysis Complex Wavelet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    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)Google Scholar
  2. 2.
    Bentley, S., Lewis, S.: The use of an image analyzing computer for the quantification of red cell morphological characteristics. British Journal of Hematology 29, 81–88 (1975)CrossRefGoogle Scholar
  3. 3.
    Chan, H., Li-Jun, J., Jiang, B.: Wavelet transform and morphology image segmentation algorism for blood cell. In: 4th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 542–545 (May 2009)Google Scholar
  4. 4.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience (November 2001)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Habibzadeh, M., Krzyżak, A., Fevens, T.: Comparative Analysis of White Blood Cell Differential Counts using CNN and SVM with K-PCA Classifiers (2012) (manuscript)Google Scholar
  7. 7.
    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, pp. 79633I–1 – 79633I–11 (February 2011)Google Scholar
  8. 8.
    Kingsbury, N.: Design of q-shift complex wavelets for image processing using frequency domain energy minimization. In: International Conference on Image Processing (ICIP), vol. 1, pp. I – 1013–16 (2003)Google Scholar
  9. 9.
    Lauer, F., Suen, C.Y., Bloch, G.: A trainable feature extractor for handwritten digit recognition. Journal of Pattern Recognition 40(6), 1816–1824 (2007)CrossRefzbMATHGoogle Scholar
  10. 10.
    Montseny, E., Sobrevilla, P., Romani, S.: A fuzzy approach to white blood cells segmentation in color bone marrow images. In: IEEE International Conference on Fuzzy Systems, vol. 1, pp. 173–178 (2004)Google Scholar
  11. 11.
    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, pp. 3371–3374 (September 2005)Google Scholar
  12. 12.
    Rowan, R., England, J.M.: Automated examination of the peripheral blood smear. In: Automation and Quality Assurance in Hematology. ch. 5, pp. 129–177. Blackwell Scientific, Oxford (1986)Google Scholar
  13. 13.
    Sabino, D.M.U., Costa, L.F., Rizzatti, E.G., Zago, M.A.: Toward leukocyte recognition using morphometry, texture and color. In: IEEE International Symposium on Biomedical Imaging: Nano to Macro, vol. 1, pp. 121–124 (April 2004)Google Scholar
  14. 14.
    Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.C.: The dual-tree complex wavelet transform. IEEE Signal Processing Magazine 22(6), 123–151 (2005)CrossRefGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    Ushizima, D.M., Lorena, A.C., de Carvalho, A.C.P.L.F.: Support Vector Machines Applied to White Blood Cell Recognition. In: International Conference on Hybrid Intelligent Systems, Los Alamitos, CA, USA, pp. 379–384 (2005)Google Scholar
  17. 17.
    Yampri, P., Pintavirooj, C., Daochai, S., Teartulakarn, S.: White Blood Cell Classification based on the Combination of Eigen Cell and Parametric Feature Detection. In: 1st IEEE Conference on Industrial Electronics and Applications, pp. 1–4 (May 2006)Google Scholar
  18. 18.
    Dambreville, S., Rathi, Y., Tannenbaum, A.: Statistical shape analysis using kernel PCA. In: SPIE Electronic Imaging (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mehdi Habibzadeh
    • 1
  • Adam Krzyżak
    • 1
  • Thomas Fevens
    • 1
  1. 1.Dept. of Computer Science & Software EngineeringConcordia UniversityMontréalCanada

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