ANA HEp-2 cells image classification using number, size, shape and localization of targeted cell regions

Gennady V. Ponomarev, Vladimir L. Arlazarov, Mikhail S. Gelfand, Marat D. Kazanov

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)

Abstract

The ANA HEp-2 medical test is a powerful tool in autoimmune disease diagnostics. The last step of this test, the interpretation of immunofluorescent images by trained experts, represents a potential source of errors and could theoretically be replaced by automated methods. Here we present a fully automatic method for recognition of types of immunofluorescent images produced by the ANA HEp-2 medical test. The proposed method makes use of the difference in number, size, shape and localization of cell regions that are targeted by the antinuclear antibodies - the humoral components of immune system that bind human antigens as a result of the immune system malfunction. The method extracts morphological properties of stained cell regions using a combination of thresholding-based and thresholding-less approaches and applies a conventional machine-learning algorithm for image classification.

Original languageEnglish
Pages (from-to)2360-2366
Number of pages7
JournalPattern Recognition
Volume47
Issue number7
DOIs
Publication statusPublished - Jul 2014
Externally publishedYes

Keywords

  • Antinuclear antibodies
  • HEp-2 cells
  • Image classification
  • Immunofluorescent images

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