Discriminative power of lymphoid cell features: Factor analysis approach

Igor Gurevich, Dmitry Harazishvili, Irina Jemova, Alexey Nefyodov, Anastasia Trykova, Ivan Vorobjev

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

The new results of the research in the field of automation of hematopoietic 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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsAlberto Sanfeliu, Jose Ruiz-Shulcloper
PublisherSpringer Verlag
Pages298-305
Number of pages8
ISBN (Print)354020590X, 9783540205906
DOIs
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2905
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Gurevich, I., Harazishvili, D., Jemova, I., Nefyodov, A., Trykova, A., & Vorobjev, I. (2003). Discriminative power of lymphoid cell features: Factor analysis approach. In A. Sanfeliu, & J. Ruiz-Shulcloper (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 298-305). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2905). Springer Verlag. https://doi.org/10.1007/978-3-540-24586-5_36