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Research and Development of the Method for Automating the Diagnostic Analysis of Human Fundus Images Produced by Optical Coherent Tomography Angiography

  • SELECTED PAPERS OF THE 8th INTERNATIONAL WORKSHOP “IMAGE MINING. THEORY AND APPLICATIONS”
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Abstract

This article presents the results of the joint work of specialists in the field of image analysis and ophthalmologists on the task of analyzing images obtained by the method of optical coherence tomography angiography. A descriptive algorithmic scheme for analyzing images obtained using optical coherence tomography angiography is built to automate the detection of pathological changes in the morphometric characteristics of the fundus. The algorithmic scheme is based on the methods of image processing, analysis, and recognition. The previously developed feature space is supplemented and modified, based on which it is possible to identify pathological changes in the structure of the choroid plexuses of the human retina. It was possible to improve the accuracy of classifying images of healthy and pathological eyes, as well as significantly increasing the accuracy of classification of borderline cases. Software is created that makes it possible to accurately carry out differential diagnostics of the normal state of vessels from the pathological one in the offline mode, which increases its diagnostic value. It is planned to achieve higher classification accuracy results for all three cases.

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Funding

This study was partially supported by the Russian Foundation for Basic Research (grant no. 20-07-01031).

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Authors and Affiliations

  1. Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 119333, Moscow, Russia

    I. B. Gurevich, V. V. Yashina & A. T. Tleubaev

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  1. I. B. Gurevich
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  2. V. V. Yashina
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  3. A. T. Tleubaev
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Corresponding authors

Correspondence to I. B. Gurevich, V. V. Yashina or A. T. Tleubaev.

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This article is an entirely original work of its authors; it has not been published before and will not be sent to other publications unless the PRIA Editorial Board decides not to accept it for publication.

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Igor B. Gurevich. Born August 24, 1938. Dr.-Eng. diploma engineer (Automatic Control and Electrical Engineering), 1961, National Research University “Moscow Power Engineering Institute, Moscow, USSR; Cand. Sci. (Mathematical Ccybernetics), 1975, Moscow Institute of Physics and Technology (National Research University), Moscow, USSR. Leading Researcher at the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, Russia. He has worked since 1960 to date as an engineer, researcher, and lecturer in industry, research institutions, medicine, and universities, and since 1985 in the USSR/Russian Academy of Sciences. Area of expertise: mathematical theory of image analysis, image-mining, image understanding, mathematical theory of pattern recognition, theoretical computer science, medical informatics, applications of pattern recognition and image analysis techniques in biology, medicine and in automation of scientific research, and knowledge-based systems.

I.B. Gurevich proposed, proved, and developed with his pupils the descriptive approach to image analysis and recognition (DAIA). Within DAIA a new class of image algebra was introduced, defined, and investigated (descriptive image algebras); new types of image models were introduced, classified, and investigated; axioms of descriptive theory of image analysis were introduced; a common model of image recognition process was defined and investigated; new settings of image analysis and recognition problems were introduced; the notion of image equivalence was introduced and investigated; new classes of image recognition algorithms were defined and investigated; and an image formalization space was introduced, defined, and investigated.

The listed results were used in the development of software kits for image analysis and recognition, as well as for the solution of important and difficult applied problems of automated biomedical image analysis.

I.B. Gurevich is the author of 2 monographs and 307 papers in peer reviewed journals and proceedings indexed in the Web of Science, Scopus, and the Russian Science Citation Index on the platform of the Web of Science, 31 invited papers at international conferences, and holder of 8 patents. Web of Science: 22 papers; Scopus: 76 papers and 287 citations in 148 documents; Hirsh index, 10; Russian Science Citation Index on the platform of Web of Science: 129 papers; 910 citations; and Hirsh index, 11.

Vice-Chairman of the National Committee for Pattern Recognition and Image Analysis of the Russian Academy of Sciences, Member of the International Association for Pattern Recognition (IAPR) Governing Board (representative from RF), and IAPR Fellow. He has been the PI of 63 R&D projects as part of national and international research programs. Vice-Editor-in-Chief of the Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, the international journal of the RAS, member of editorial boards of several international scientific journals, and member of the program and technical committees of many international scientific conferences. Teaching experience: Moscow State University, RF (Assistant Professor), Dresden Technical University, Germany (Visiting Professor), and George Mason University, United States (Research Fellow). He was supervisor of 6 PhD students and many graduate and master students.

Vera V. Yashina. Born September 13, 1980. Diploma in mathematics, Moscow State University (2002). Cand. Sci. (Theoretical Foundations of Informatics), 2009, Dorodnicyn Computing Center of the Russian Academy of Sciences, Moscow. Leading Researcher at the Department “Recognition, security and analysis of information” at the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow. She has worked from 2001 to date at the Russian Academy of Sciences. Scientific expertise: mathematical theory of image analysis, image algebras, models, and medical informatics.

Her main results were obtained in the mathematical theory of image analysis: descriptive image algebras with one ring were defined, classified, and investigated; a new topological image formalization space was specified and investigated; and descriptive generating trees were defined, classified, and investigated. The listed results were applied in biomedical imaging analysis.

She is the scientific secretary of the National Committee for Pattern Recognition and Image Analysis of the Presidium of the Russian Academy of Sciences. She is a member of the Educational and Membership Committees of the International Association for Pattern Recognition. She is a Vice Chair of Technical Committee no. 16 “Algebraic and Discrete Mathematical Techniques in Pattern Recognition and Image Analysis” of the International Association for Pattern Recognition. She has been a member of many R&D projects as part of national and international research programs. Member of the Editorial Board of Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, the international journal of the RAS. Author of 79 papers in peer-reviewed journals, as well as conference and workshop proceedings. Web of Science: 11 papers; Hirsh index, 4; Scopus: 40 papers, 162 citations in 75 papers; Hirsh index, 8; Russian Science Citation Index on the platform of the Web of Science: 56 papers; 255 citations; Hirsh index, 9. She has received several awards for presenting the best young scientist papers at international conferences. Teaching experience: Moscow State University, Moscow, Russia. She has supervised several graduate and master’s students.

Adil T. Tleubaev. Born February 13, 1994. Graduated from Moscow State University in computational mathematics and cybernetics in 2018. Research interests: pattern recognition, image analysis, machine learning

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Gurevich, I.B., Yashina, V.V. & Tleubaev, A.T. Research and Development of the Method for Automating the Diagnostic Analysis of Human Fundus Images Produced by Optical Coherent Tomography Angiography. Pattern Recognit. Image Anal. 32, 533–544 (2022). https://doi.org/10.1134/S1054661822030154

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