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An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images

BACKGROUND: The Rician noise formed in magnetic resonance (MR) imaging greatly reduced the accuracy and reliability of subsequent analysis, and most of the existing denoising methods are suitable for Gaussian noise rather than Rician noise. Aiming to solve this problem, we proposed fuzzy c-means and...

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Autores principales: Chen, Kaixin, Lin, Xiao, Hu, Xing, Wang, Jiayao, Zhong, Han, Jiang, Linhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6945655/
https://www.ncbi.nlm.nih.gov/pubmed/31906873
http://dx.doi.org/10.1186/s12880-019-0407-4
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author Chen, Kaixin
Lin, Xiao
Hu, Xing
Wang, Jiayao
Zhong, Han
Jiang, Linhua
author_facet Chen, Kaixin
Lin, Xiao
Hu, Xing
Wang, Jiayao
Zhong, Han
Jiang, Linhua
author_sort Chen, Kaixin
collection PubMed
description BACKGROUND: The Rician noise formed in magnetic resonance (MR) imaging greatly reduced the accuracy and reliability of subsequent analysis, and most of the existing denoising methods are suitable for Gaussian noise rather than Rician noise. Aiming to solve this problem, we proposed fuzzy c-means and adaptive non-local means (FANLM), which combined the adaptive non-local means (NLM) with fuzzy c-means (FCM), as a novel method to reduce noise in the study. METHOD: The algorithm chose the optimal size of search window automatically based on the noise variance which was estimated by the improved estimator of the median absolute deviation (MAD) for Rician noise. Meanwhile, it solved the problem that the traditional NLM algorithm had to use a fixed size of search window. Considering the distribution characteristics for each pixel, we designed three types of search window sizes as large, medium and small instead of using a fixed size. In addition, the combination with the FCM algorithm helped to achieve better denoising effect since the improved the FCM algorithm divided the membership degrees of images and introduced the morphological reconstruction to preserve the image details. RESULTS: The experimental results showed that the proposed algorithm (FANLM) can effectively remove the noise. Moreover, it had the highest peak signal-noise ratio (PSNR) and structural similarity (SSIM), compared with other three methods: non-local means (NLM), linear minimum mean square error (LMMSE) and undecimated wavelet transform (UWT). Using the FANLM method, the image details can be well preserved with the noise being mostly removed. CONCLUSION: Compared with the traditional denoising methods, the experimental results showed that the proposed approach effectively suppressed the noise and the edge details were well retained. However, the FANLM method took an average of 13 s throughout the experiment, and its computational cost was not the shortest. Addressing these can be part of our future research.
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spelling pubmed-69456552020-01-07 An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images Chen, Kaixin Lin, Xiao Hu, Xing Wang, Jiayao Zhong, Han Jiang, Linhua BMC Med Imaging Research Article BACKGROUND: The Rician noise formed in magnetic resonance (MR) imaging greatly reduced the accuracy and reliability of subsequent analysis, and most of the existing denoising methods are suitable for Gaussian noise rather than Rician noise. Aiming to solve this problem, we proposed fuzzy c-means and adaptive non-local means (FANLM), which combined the adaptive non-local means (NLM) with fuzzy c-means (FCM), as a novel method to reduce noise in the study. METHOD: The algorithm chose the optimal size of search window automatically based on the noise variance which was estimated by the improved estimator of the median absolute deviation (MAD) for Rician noise. Meanwhile, it solved the problem that the traditional NLM algorithm had to use a fixed size of search window. Considering the distribution characteristics for each pixel, we designed three types of search window sizes as large, medium and small instead of using a fixed size. In addition, the combination with the FCM algorithm helped to achieve better denoising effect since the improved the FCM algorithm divided the membership degrees of images and introduced the morphological reconstruction to preserve the image details. RESULTS: The experimental results showed that the proposed algorithm (FANLM) can effectively remove the noise. Moreover, it had the highest peak signal-noise ratio (PSNR) and structural similarity (SSIM), compared with other three methods: non-local means (NLM), linear minimum mean square error (LMMSE) and undecimated wavelet transform (UWT). Using the FANLM method, the image details can be well preserved with the noise being mostly removed. CONCLUSION: Compared with the traditional denoising methods, the experimental results showed that the proposed approach effectively suppressed the noise and the edge details were well retained. However, the FANLM method took an average of 13 s throughout the experiment, and its computational cost was not the shortest. Addressing these can be part of our future research. BioMed Central 2020-01-06 /pmc/articles/PMC6945655/ /pubmed/31906873 http://dx.doi.org/10.1186/s12880-019-0407-4 Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Chen, Kaixin
Lin, Xiao
Hu, Xing
Wang, Jiayao
Zhong, Han
Jiang, Linhua
An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images
title An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images
title_full An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images
title_fullStr An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images
title_full_unstemmed An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images
title_short An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images
title_sort enhanced adaptive non-local means algorithm for rician noise reduction in magnetic resonance brain images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6945655/
https://www.ncbi.nlm.nih.gov/pubmed/31906873
http://dx.doi.org/10.1186/s12880-019-0407-4
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