Noise reduction by adaptive-SIN filtering for retinal OCT images

Abstract 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...

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Autores principales:	Yan Hu, Jianfeng Ren, Jianlong Yang, Ruibing Bai, Jiang Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0fb52e54279a4ec1ad764d129edb782a
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spelling	oai:doaj.org-article:0fb52e54279a4ec1ad764d129edb782a2021-12-02T19:16:47ZNoise reduction by adaptive-SIN filtering for retinal OCT images10.1038/s41598-021-98832-w2045-2322https://doaj.org/article/0fb52e54279a4ec1ad764d129edb782a2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98832-whttps://doaj.org/toc/2045-2322Abstract 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.Yan HuJianfeng RenJianlong YangRuibing BaiJiang LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yan Hu Jianfeng Ren Jianlong Yang Ruibing Bai Jiang Liu Noise reduction by adaptive-SIN filtering for retinal OCT images
description	Abstract 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.
format	article
author	Yan Hu Jianfeng Ren Jianlong Yang Ruibing Bai Jiang Liu
author_facet	Yan Hu Jianfeng Ren Jianlong Yang Ruibing Bai Jiang Liu
author_sort	Yan Hu
title	Noise reduction by adaptive-SIN filtering for retinal OCT images
title_short	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_sort	noise reduction by adaptive-sin filtering for retinal oct images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0fb52e54279a4ec1ad764d129edb782a
work_keys_str_mv	AT yanhu noisereductionbyadaptivesinfilteringforretinaloctimages AT jianfengren noisereductionbyadaptivesinfilteringforretinaloctimages AT jianlongyang noisereductionbyadaptivesinfilteringforretinaloctimages AT ruibingbai noisereductionbyadaptivesinfilteringforretinaloctimages AT jiangliu noisereductionbyadaptivesinfilteringforretinaloctimages
_version_	1718376897587445760

Noise reduction by adaptive-SIN filtering for retinal OCT images

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