Discriminative Power of Lymphoid Cell Features: Factor Analysis Approach

  • Igor Gurevich
  • Dmitry Harazishvili
  • Irina Jernova
  • Alexey Nefyodov
  • Anastasia Trykova
  • Ivan Vorobjev
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

The new results of the research in the field of automation of hemato-poietic tumor diagnostics by analysis of the images of cytological specimens are presented. Factor analysis of numerical diagnostically important features used for the description of lymphoma cell nucleus was carried out in order to evaluate the significance of the features and to reduce the considered feature space. The following results were obtained: a) the proposed features were classified; b) the feature set composed of 47 elements was reduced to 8 informative factors; c) the extracted factors allowed to distinguish some groups of patients. This implies that received factors have substantial medical meaning. The results presented in the paper confirm the advisability of involving factor analysis in the automated system for morphological analysis of the cytological specimens in order to create a complex model of phenomenon investigated.

Keywords

Chronic Lymphocytic Leukemia Lymphoid Tumor Cytological Specimen Hematopoietic Tumor Factor Analysis Approach 
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.

References

  1. 1.
    Churakova, J.V., Gurevich, I.B., Hilkov, A.V., Jernova, I.A., Kharazishvili, D.V., Nefyodov, A.V., Sheval, E.V.: Selection of Diagnostically Valuable Features for Morphological Analysis of Blood Cells. Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications 2, 382–383 (2003)Google Scholar
  2. 2.
    Elkina, V.N., Zagoruiko, N.G.: Some Classification Algorithms Developed at Novosibirsk. In: Simon, J.C. (ed.) Intelligence Artificielle, Reconnaissance des Formes. R.A.I.R.O. Informatique/ Computer Science, vol. 1, pp. 37–46 (1978)Google Scholar
  3. 3.
    Gurevich, I.B., Khilkov, A.V., Murashov, D.M., et al.: Black Square Version 1.0: Programm Development System for Automation of Scientific Research and Education. Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications 4, 609–634 (1999)Google Scholar
  4. 4.
    Gurevich, I.B., Harazishvili, D.V., Jernova, I.A., Nefyodov, A.V., Vorobjev, I.A.: Information Technology for the Morphological Analysis of the Lymphoid Cell Nuclei. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 541–548. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Zhuravlev, Y.I., Gurevitch, I.B.: Pattern Recognition and Image Recognition. Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications in the USSR 2, 149–181 (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Igor Gurevich
    • 1
  • Dmitry Harazishvili
    • 2
  • Irina Jernova
    • 1
  • Alexey Nefyodov
    • 1
  • Anastasia Trykova
    • 1
  • Ivan Vorobjev
    • 2
  1. 1.Scientific Council “Cybernetics” of the Russian Academy of SciencesMoscowRussian Federation
  2. 2.Hematological Scientific Center of the Russian Academy of Medical SciencesMoscowRussian Federation

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