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Development of High-Performance Algorithms for the Segmentation of Fundus Images Using a Graphics Processing Unit

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Abstract

Diabetic retinopathy is one of the dangerous fundus diseases that leads to irreversible loss of vision. In the case of untimely or incorrect treatment, blindness occurs. Currently, laser coagulation is a common treatment method. An ophthalmologist uses a laser to apply a series of burns to the retina. The success of the operation depends entirely on the experience of the doctor. The automatic formation of a preliminary plan of coagulates allows us to solve a number of problems related to the operation, such as long manual placement of coagulates or adjustment of laser power. Thus, the probability of a doctor’s error is reduced, and the preparation time for the operation is significantly reduced. One of the key stages in the formation of the plan is the segmentation of the fundus image. This stage is carried out with the help of texture features, the calculation of which takes a long time. In relation to this, this study proposes high-performance algorithms for the segmentation of fundus images using CUDA technologies, which significantly speed up sequential versions and outperform parallel algorithms.

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Funding

This work was funded by the Russian Foundation for Basic Research (project no. 19-29-01135) and the Ministry of Science and Higher Education of the Russian Federation within a government project of the Federal Research Center Crystallography and Photonics of the Russian Academy of Sciences.

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Authors and Affiliations

  1. Image Processing Systems Institute, Branch of the Federal Research Center Crystallography and Photonics, Russian Academy of Sciences, 443001, Samara, Russia

    N. Yu. Ilyasova, A. S. Shirokanev & N. S. Demin

Authors
  1. N. Yu. Ilyasova
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  2. A. S. Shirokanev
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  3. N. S. Demin
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Corresponding authors

Correspondence to N. Yu. Ilyasova, A. S. Shirokanev or N. S. Demin.

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This article is a completely original work by the authors, it has not been previously published, and it will not be published in other publications.

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Additional information

Nataly Yurievna Ilyasova. Born 1966. Graduated with honors from Korolyov Samara State Aerospace University (SSAU) (1991). She received a Candidate’s degree (1997) and a Doctoral degree (2015) in Technical Sciences. She is a Senior Researcher at the Image Processing Systems Institute of the Russian Academy of Sciences–Branch of the FSRC Crystallography and Photonics RAS and a Professor at SSAU’s Technical Cybernetics subdepartment. Research interests: digital signals, image processing, pattern recognition, artificial intelligence, and biomedical imaging and analysis. Laureate of the regional prize in the field of science and technology on the topic “Computer systems for processing medical diagnostic images in medical institutions of the Samara region.” She is the author of more than 160 scientific papers, including 80 articles in the field of computer systems for analysis of diagnostic images, information systems for biomedical applications and 3 monographs published with coauthors. https://ssau.ru/english/staff/64603001-ilyasova-natalya-y

Alexandr Sergeevich Shirokanev graduated (2017) with a master’s degree in Applied Mathematics and Informatics. He is a postgraduate student of Samara University. Junior researcher at the Image Processing Systems Institute of the Russian Academy of Sciences–Branch of the FSRC Crystallography and Photonics RAS. Until 2017, he worked as an engineer at the Department of Technical Cybernetics at Samara University. Research interests: medical data mining, digital image processing, mathematical modeling, and numerical methods. He is the author of more than 40 publications in the field of intelligent analysis of fundus data to improve the quality of diagnosis and treatment of diabetic retinopathy. He won the Young Scientist competition. https://ssau.ru/english/staff/427263695-shirokanev-alexandr-s

Nikita Sergeevich Demin. Postgraduate student at Samara University and an Assistant of the Technical Cybernetics Department at Samara University, Russia. Graduated from Samara University in 2019 with a Master’s degree in Applied Mathematics and Informatics. Research interests: digital image processing, mathematical modeling, pattern recognition, and artificial intelligence. https://ssau.ru/english/staff/335805566-demin-nikita-s

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Ilyasova, N.Y., Shirokanev, A.S. & Demin, N.S. Development of High-Performance Algorithms for the Segmentation of Fundus Images Using a Graphics Processing Unit. Pattern Recognit. Image Anal. 31, 529–538 (2021). https://doi.org/10.1134/S1054661821030135

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