THE IMPORTANCE OF DIGITAL PROCESSING OF OCT IMAGES IN THE DIAGNOSIS OF EYE DISEASES

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Published: 2025-12-05
DOI: https://doi.org/10.66073/ReFocus-Conf/446
Keywords:
OCT, speckle noise, gaussian noise, salt and pepper noise, Gaussian filter, Sobel filter

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Yusupov Ozod Rabbimovich
Head of the Department of "Software Engineering", Samarkand State University named after Sharof Rashidov
Shamsieva Khabiba Gafurovna
PhD student at the Research Institute for the Development of Digital Technologies and Artificial Intelligence

Abstract

This work studies the problem of speckle noise in optical coherence tomography (OCT) images. The noise in medical images, in particular in OCT images, is analyzed and effective approaches to reduce it are considered. The application of traditional filtering methods - Gaussian and Sobel filters to Gaussian noise, speckle noise, and salt and pepper noise is considered. It is stated that by reducing the above-mentioned noises through the presented formulas, the image quality is improved, the layers can be seen more clearly, and the subsequent stages (segmentation, classification, diagnosis) serve to optimize the data. As a result, it is studied that the efficiency of detecting eye diseases is increased by pre-processing OCT images

Article Details

Issue

Vol. 1 No. 1 (2025): Republican scientific-practical conference on “New Paradigms in Science, Technology and Education”

Section

Articles
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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Yusupov Ozod Rabbimovich, & Shamsieva Khabiba Gafurovna. (2025). THE IMPORTANCE OF DIGITAL PROCESSING OF OCT IMAGES IN THE DIAGNOSIS OF EYE DISEASES. Partner Conferences of the International Scientific Journal Research Focus, 1(1), 602-605. https://doi.org/10.66073/ReFocus-Conf/446

References

1. H. Zhang, E. Essa, X. Xie. Automatic vessel lumen segmentation in optical coherence tomography (OCT) images. Applied Soft Computing Journal 88 (2020) 106042.

2. Shaima Ibraheem Jabbar. Advanced shape detection in Optical Coherence Tomography (OCT) imaging. Ain Shams Engineering Journal 15 (2024) 102981.

3. M. Nawaz, A. Uvaliyev, Kh. Bibi, H. Wei, S. Mu Dalike Abaxi, A. Masood, Peilun Shi, Ho-Pui Ho, Wu Yuan. Unraveling the complexity of Optical Coherence Tomography image segmentation using machine and deep learning techniques: A review. Computerized Medical Imaging and Graphics 108 (2023)

4. Mingming Yang, Junhui Du, Ruichan Lv. CRAT: Advanced transformer-based deep learning algorithms in OCT image classification. Biomedical Signal Processing and Control 104 (2025) 107544.

5. Mirzayev N., Shamsiyeva X.G. Ko‘z kasalliklarida fundus tasvirlar sifatini baholash va segmentatsiyalash algoritmlari. Raqamli tеxnologiyalarning nazariy va amaliy masalalari xalqaro jurnali, 2024 № 7(4), ISSN 2181-3086, 73-79 b.