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A Novel Multispectral Imaging Analysis Method for White Blood Cell Detection

  • Hongbo Zhang
  • Libo Zeng
  • Hengyu Ke
  • Hong Zheng
  • Qiongshui Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3611)

Abstract

This paper presents a novel approach for automatic detection of white blood cells in bone marrow microscopic images. Far more different from traditional color imaging analysis methods, a multispectral imaging techniques for image analysis is introduced. Multispectral image can not only show the spatial features of a cell, but also reveal the unique spectral information of each pixel. The supported vector machine (SVM) classifier is employed to train the spectrum vector of a pixel, and the output of the classifier can indicate the class type of the pixel: nucleus, erythrocytes, cytoplasm and background. Experimental results show that, compared with any other method previously reported, our method is more robust, precise and insensitive to smear staining and illumination condition.

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References

  1. 1.
    Hengen, H., Spoor, S., Pandit, M.: Analysis of Blood and Bone Marrow Smears Using Digital Image Processing Techniques. In: Proc. SPIE. Int. Soc. Opt. Eng., pp. 624–635 (2002)Google Scholar
  2. 2.
    Park, J.S., Keller, J.M.: Fuzzy Patch Label Relaxation in Bone Marrow Cell Segmentation. Proc. IEEE. Int. Conf. Syst. Man. Cybern. 2, 1133–1138 (1997)Google Scholar
  3. 3.
    Sobrevilla, P., Montseny, E., Keller, J.: White Blood Cell Detection in Bone Marrow Images. In: Annu. Conf. North. Am. Fuzzy. Inf. Process. Soc. NAFIPS, pp. 403–407 (1999)Google Scholar
  4. 4.
    Keller, J.M., Gader, P.D., Sohn, S., Caldwell, C.W.: Soft Counting Networks for Bone Marrow Differentials. In: Proc. IEEE. Int. Conf. Syst. Man. Cybern., vol. 5, pp. 3425–3428 (2001)Google Scholar
  5. 5.
    Gat, N.: Imaging Spectroscopy Using Tunable Filters: A Review. In: Proc. SPIE. Int. Soc. Opt. Eng., pp. 50–64 (2000)Google Scholar
  6. 6.
    Levenson, R.M., Hoyt, C.C.: Spectral Imaging and Microscopy. Am. Lab. 32, 26–33 (2000)Google Scholar
  7. 7.
    Cortes, C., Vapnik, V.: Support-Vector Networks. Mach. Learn. 20, 273 (1995)zbMATHGoogle Scholar
  8. 8.
    Hsu, C.W., Lin, C.J.: A Simple Decomposition Method for Support Vector Machines. Mach. Learn. 46, 291–314 (2002)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hongbo Zhang
    • 1
  • Libo Zeng
    • 1
  • Hengyu Ke
    • 1
  • Hong Zheng
    • 1
  • Qiongshui Wu
    • 1
  1. 1.School of Electronic InformationWuhan UniversityWuhanP.R. China

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