ANA HEp-2 cells image classification using number,size, shape and localization of targeted cell regions |
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Authors: | Gennady V Ponomarev Vladimir L Arlazarov Mikhail S Gelfand Marat D Kazanov |
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Affiliation: | 1. A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Science, Russia;2. Institute for Systems Analysis, Russia Academy of Science, Russia;3. M.V. Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, Russia |
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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. |
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Keywords: | Immunofluorescent images HEp-2 cells Antinuclear antibodies Image classification |
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