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

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 au...

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Bibliographic Details
Published in:Pattern recognition 2014-07, Vol.47 (7), p.2360-2366
Main Authors: Ponomarev, Gennady V., Arlazarov, Vladimir L., Gelfand, Mikhail S., Kazanov, Marat D.
Format: Article
Language:English
Subjects:
Algorithms
Antinuclear antibodies
HEp-2 cells
Image classification
Immune systems
Immunofluorescent images
Localization
Medical
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Summary: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. •An automatic method for classification of ANA HEp-2 cells images is proposed.•The proposed method utilizes for recognition a morphological properties of the targeted cell domains.•The method applies both a binarization and threshold-less approaches for feature extraction.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2013.09.027