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Noise reduction by adaptive-SIN filtering for retinal OCT images

Optical coherence tomography (OCT) images is widely used in ophthalmic examination, but their qualities are often affected by noises. Shearlet transform has shown its effectiveness in removing image noises because of its edge-preserving property and directional sensitivity. In the paper, we propose...

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Autores principales: Hu, Yan, Ren, Jianfeng, Yang, Jianlong, Bai, Ruibing, Liu, Jiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8484270/
https://www.ncbi.nlm.nih.gov/pubmed/34593894
http://dx.doi.org/10.1038/s41598-021-98832-w
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author Hu, Yan
Ren, Jianfeng
Yang, Jianlong
Bai, Ruibing
Liu, Jiang
author_facet Hu, Yan
Ren, Jianfeng
Yang, Jianlong
Bai, Ruibing
Liu, Jiang
author_sort Hu, Yan
collection PubMed
description Optical coherence tomography (OCT) images is widely used in ophthalmic examination, but their qualities are often affected by noises. Shearlet transform has shown its effectiveness in removing image noises because of its edge-preserving property and directional sensitivity. In the paper, we propose an adaptive denoising algorithm for OCT images. The OCT noise is closer to the Poisson distribution than the Gaussian distribution, and shearlet transform assumes additive white Gaussian noise. We hence propose a square-root transform to redistribute the OCT noise. Different manufacturers and differences between imaging objects may influence the observed noise characteristics, which make predefined thresholding scheme ineffective. We propose an adaptive 3D shearlet image filter with noise-redistribution (adaptive-SIN) scheme for OCT images. The proposed adaptive-SIN is evaluated on three benchmark datasets using quantitative evaluation metrics and subjective visual inspection. Compared with other algorithms, the proposed algorithm better removes noise in OCT images and better preserves image details, significantly outperforming in terms of both quantitative evaluation and visual inspection. The proposed algorithm effectively transforms the Poisson noise to Gaussian noise so that the subsequent shearlet transform could optimally remove the noise. The proposed adaptive thresholding scheme optimally adapts to various noise conditions and hence better remove the noise. The comparison experimental results on three benchmark datasets against 8 compared algorithms demonstrate the effectiveness of the proposed approach in removing OCT noise.
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spelling pubmed-84842702021-10-01 Noise reduction by adaptive-SIN filtering for retinal OCT images Hu, Yan Ren, Jianfeng Yang, Jianlong Bai, Ruibing Liu, Jiang Sci Rep Article Optical coherence tomography (OCT) images is widely used in ophthalmic examination, but their qualities are often affected by noises. Shearlet transform has shown its effectiveness in removing image noises because of its edge-preserving property and directional sensitivity. In the paper, we propose an adaptive denoising algorithm for OCT images. The OCT noise is closer to the Poisson distribution than the Gaussian distribution, and shearlet transform assumes additive white Gaussian noise. We hence propose a square-root transform to redistribute the OCT noise. Different manufacturers and differences between imaging objects may influence the observed noise characteristics, which make predefined thresholding scheme ineffective. We propose an adaptive 3D shearlet image filter with noise-redistribution (adaptive-SIN) scheme for OCT images. The proposed adaptive-SIN is evaluated on three benchmark datasets using quantitative evaluation metrics and subjective visual inspection. Compared with other algorithms, the proposed algorithm better removes noise in OCT images and better preserves image details, significantly outperforming in terms of both quantitative evaluation and visual inspection. The proposed algorithm effectively transforms the Poisson noise to Gaussian noise so that the subsequent shearlet transform could optimally remove the noise. The proposed adaptive thresholding scheme optimally adapts to various noise conditions and hence better remove the noise. The comparison experimental results on three benchmark datasets against 8 compared algorithms demonstrate the effectiveness of the proposed approach in removing OCT noise. Nature Publishing Group UK 2021-09-30 /pmc/articles/PMC8484270/ /pubmed/34593894 http://dx.doi.org/10.1038/s41598-021-98832-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Hu, Yan
Ren, Jianfeng
Yang, Jianlong
Bai, Ruibing
Liu, Jiang
Noise reduction by adaptive-SIN filtering for retinal OCT images
title Noise reduction by adaptive-SIN filtering for retinal OCT images
title_full Noise reduction by adaptive-SIN filtering for retinal OCT images
title_fullStr Noise reduction by adaptive-SIN filtering for retinal OCT images
title_full_unstemmed Noise reduction by adaptive-SIN filtering for retinal OCT images
title_short Noise reduction by adaptive-SIN filtering for retinal OCT images
title_sort noise reduction by adaptive-sin filtering for retinal oct images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8484270/
https://www.ncbi.nlm.nih.gov/pubmed/34593894
http://dx.doi.org/10.1038/s41598-021-98832-w
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