Study on MRI Denoising Based on NLM and LMMSE Estimation

WU Juan; JING Bin; JING Junyao; WU Bin; SUN Nana

doi:10.3969/j.issn.1674-1633.20241267

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China Medical Devices ›› 2025, Vol. 40 ›› Issue (2) : 35-39. DOI: 10.3969/j.issn.1674-1633.20241267

RESEARCH WORK

Study on MRI Denoising Based on NLM and LMMSE Estimation

WU Juan¹, JING Bin², JING Junyao³, WU Bin¹, SUN Nana¹

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Abstract

Objective To propose a Rician noise removal algorithm for magnetic resonance imaging (MRI). Methods Firstly, the noise level of MRI was estimated by local variance statistics, and then the image was restored by linear least mean square error estimation and non-local means filtering. Results The proposed denoising method was verified qualitatively and quantitatively by using simulated brain MRI. The results showed that when the noise variance of the denoising algorithm was 15, the mean square error, peak signal-to-noise ratio and mean signal-to-noise ratio of different slices were 70.07, 29.78 dB and 21.95 dB successively, and the results of non-local means filtering were 82.17, 29.11 dB and 21.28 dB successively. The results of linear least mean square error estimation were 108.16, 27.80 dB and 19.97 dB successively. It could be seen that the proposed algorithm was superior to other algorithms. Compared with the traditional non-local means filtering, the proposed algorithm also had a certain improvement in edge protection and improved the denoising effect of linear least mean square error estimation at high noise levels. Conclusion The algorithm proposed in this paper can effectively realize the restoration of noisy MRI signal and provide a reliable guarantee for the subsequent image processing and application.

Key words

MRI; denoising; non-local means; linear minimum mean square error; Rician noise; self-adaption; iteration

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WU Juan, JING Bin, JING Junyao, et al. Study on MRI Denoising Based on NLM and LMMSE Estimation[J]. China Medical Devices, 2025, 40(2): 35-39 https://doi.org/10.3969/j.issn.1674-1633.20241267

References

[1] 田刚. 基于非局部均值的MRI去噪研究[D]. 西安: 西安电子科技大学, 2014.
Tian G. A study on MRI denoising based on NLM[D]. Xi’an: Xidian University, 2014.
[2] Nowak RD. Wavelet-based Rician noise removal for magnetic resonance imaging[J]. IEEE Trans Image Process, 1999, 8(10): 1408-1419.
[3] Perez G, Conci A, Moreno AB, et al. Presenting novel denoising techniques for brain MRI[A]. 2012 19th International Conference on Systems, Signals and Image Processing: IWSSIP 2012[C]. Vienna, Austria, IEEE, 2012: 244-247.
[4] Rajeesh J, Moni RS, Kumar SP, et al. Rician noise removal on MRI using wave atom transform with histogram based noise variance estimation[A]. Communication Control and Computing Technologies (ICCCCT)[C]. 2010: 531-535.
[5] Aja-Fernandez S, Alberola-Lopez C, Westin CF. Noise and signal estimation in magnitude MRI and Rician distributed images: a LMMSE approach[J]. IEEE Trans Image Process, 2008, 17(8): 1383-1398.
[6] Sudeep PV, Palanisamy P, Rajan J. A Hybrid model for Rician noise reduction in MRI[A]. International Conference on Advanced Computing[C]. IEEE Computer Society, 2013.
[7] Sharif M, Jaffar MA, Mahmood MT. Genetic programming based composite filter for Rician noise reduction[A]. Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics[C]. IEEE, 2013: 1317-1322.
[8] Nair JJ, Mohan N. A robust non local means maximum likelihood estimation method for Rician noise reduction in MR images[A]. 2014 International Conference on Communication and Signal Processing (ICCSP)[C]. 2014: 848-852.
[9] Rajan J, Audekerke JV, Linden AVD, et al. An adaptive non local maximum likelihood estimation method for denoising magnetic resonance images[A]. 2012 9th IEEE International Symposium Biomedical Imaging (ISBI)[C]. 2012: 1136-1139.
[10] Iftikhar MA, Rathore S, Jalil A, et al. A novel extension to non-local means algorithm: application to brain MRI de-noising[A]. 16th International Multi Topic Conference (INMIC)[C]. 2013: 195-200.
[11] Foi A. Noise estimation and removal in MR imaging: the variance-stabilization approach[A]. 2011 IEEE International Symposium on Biomedical Imaging: from Nano to Macro (ISBI 2011)[C]. Chicago, Illinois, USA, IEEE, 2011: 1809-1814.
[12] Mohan J, Krishnaveni V, Yanhui G. Validating the Neutrosophic approach of MRI denoising based on structural similarity[A]. IET Conference on Image Processing (IPR 2012)[C]. London, UK, Institute of Electrical and Electronics Engineers, 2012: 1-6.
[13] Ray D. Noise reduction and image enhancement of MRI using adaptive multiscale data condensation[A]. 2012 1st International Conference Recent Advances in Information Technology (RAIT)[C]. Dhanbad, India, IEEE, 2012: 107-113.
[14] Cocosco CA, Kollokian V, Kwan KS, et al. BrainWeb: online interface to a 3D MRI simulated brain database[J]. NeuroImage, 1997, 5(4).
[15] Buades A, Coll B, Morel JM. A review of image denoising algorithms, with a new one[J]. Multiscale model simul, 2005, 4(2): 490-530.
[16] Manjón JV, Carbonell-Caballero J, Lull JJ, et al. MRI denoising using non-local means[J]. Med Image Anal, 2008, 12(4): 514-523.
[17] Garg G, Juneja M. A survey of denoising techniques for multiparametric prostate MRI[J]. Multimed Tools Appl, 2019, 78(8): 12689-12722.
[18] 章毓晋. 图像处理和分析教程[M]. 北京: 人民邮电出版社, 2009.
[19] Rudin LI, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D, 1992, 60(1-4): 259-268.
[20] Golshan HM, Hasanzadeh RP, Yousefzadeh SC. An MRI denoising method using image data redundancy and local SNR estimation[J]. Magn Reson Imaging, 2013, 31(7): 1206-1217.