Rotation Invariant Non-Local Means for Noise Reduction in Magnetic Resonance Images

doi:10.11938/cjmr20172582

Abstract

Abstract: Averaging of multiple scans is often used in magnetic resonance imaging (MRI) to increase the signal-to-noise ratio (SNR). However, image averaging often results in movement-induced blurs of the edges and tissue details. A matched and weighted averaging (MWA) method has been proposed by our group to obtain images with reduced blurring effects in signal averaging. Here a rotation-invariant non-local means (RINLM) algorithm was proposed, which used circular patches consisted of series of rings with equal area, instead of square patches, to search for similar patches in the images. Compared with the non-local means (NLM) algorithm, the RINLM algorithm was capable of finding more similar patches in the images containing many rotated local structure. This method was used to process noisy images to improve the SNR, and validated using both phantom images and in vivo MR images. The results demonstrated that the method could improve the SNR, while better preserving the edges and details of the images.

Key words: magnetic resonance imaging (MRI), non-local means (NLM), rotation invariance, image denoising

CLC Number:

O482.53

ZHANG Bo, XIE Hai-bin, YAN Xu, LI Wen-jing, YANG Guang. Rotation Invariant Non-Local Means for Noise Reduction in Magnetic Resonance Images[J]. Chinese Journal of Magnetic Resonance, 2018, 35(2): 162-169.

References

[1] MOHAN J, KRISHNAVENI V, GUO Y H, et al. A survey on the magnetic resonance image denoising methods[J]. Biomed Signal Proces, 2014, 9:56-69.
[2] DONOHO D L, JOHNSTONE I M. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994, 81(3):425-455.
[3] DONOHO D L. De-noising by soft-thresholding[J]. IEEE T Inform Theory, 1995, 3(41):613-627.
[4] PIZURICA A, WINK A M, VANSTEENKISTE E, et al. A review of wavelet denoising in MRI and ultrasound brain imaging[J]. Current Med Imaging Rev, 2006, 2(2):247-260.
[5] MANJON J V, COUPEB P, BUADES A, et al. New methods for MRI denoising based on sparseness and self-similarity[J]. Med Image Anal, 2012, 16(1):18-27.
[6] BAO L J, ROBINI M, LIU W Y, et al. Structure-adaptive sparse denoising for diffusion-tensor MRI[J]. Med Image Anal, 2013, 17(4):442-457.
[7] AHARON M, ELAD M, BRUCKSTEIN A. rmK-SVD:an algorithm for designing over complete dictionaries for sparse representation[J]. IEEE T Signal Proces, 2006, 54(11):4311-4322.
[8] ELAD M, AHARON M. Image denoising via sparse and redundant representations over learned dictionaries[J]. IEEE Trans Image Process, 2006, 15(12):3736-3745.
[9] BUADES A, COLL B, MOREL J M, et al. A review of image denoising algorithms, with a new one, Multiscale Model[J]. Siam Journal on Multiscale Modeling & Simulation, 2005, 4(2):490-530.
[10] MANJÓN J V, COUPÉ P, BUADES A, et al. New methods for MRI denoising based on sparseness and self-similarity[J]. Med Image Anal, 2012, 16(1):18-27.
[11] CAI B, LIU W, ZHENG Z, et al. A new similarity measure for non-local means denoising[M]. ZHA H, CHEN X, WANG L, et al. Computer vision. communications in computer and information science. Heidelberg:Springer, 2015, 546:306-316.
[12] LI H J, SUEN C Y. A novel Non-local means image denoising method based on grey theory[J]. Pattern Recogn, 2016, 49:237-248.
[13] PRASATH V B S, KALAVATHI P. Adaptive nonlocal filtering for brain MRI restoration[M]. THAMPI S, BANDYOPADHYAY S, KRISHNAN S, et al. Advances in signal processing and intelligent recognition systems. Switzerland:Springer International Publishing, 2016, 425:571-580.
[14] MANJON J V, COUPE P, MARTI-BONMATI L, et al. Adaptive non-local means denoising of MR images with spatially varying noise levels[J]. J Magn Reson Imaging, 2010, 31(1):192-203.
[15] COUPE P, YGER P, PRIMA S, et al. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images[J]. IEEE Trans Med Imaging, 2008, 27(4):425-441.
[16] MAHMOUDI M, SAPIRO G. Fast image and video denoising via nonlocal means of similar neighborhoods[J]. IEEE Signal Proc Let, 2005, 12(12):839-842.
[17] LI W J, XIE H B, YAN X, et al. MR image average based on local offset correction[J]. Chinese J Magn Reson, 2017, 34(3):294-301. 李文静, 谢海滨, 严序, 等. 基于局部位移校正的磁共振图像相干平均[J]. 波谱学杂志, 2017, 34(3):294-301.
[18] HE L L, GREENSHIELDS I R. A nonlocal maximum likelihood estimation method for rician noise reduction in MR images[J]. IEEE Trans Med Imaging, 2009, 28(2):165-172.
[19] SALMON J. On two parameters for denoising with non-local means[J]. IEEE Signal Proc Let, 2010, 17(3):269-272.
[20] VAN DE VILLE D, KOCHER M. Nonlocal means with dimensionality reduction and sure-based parameter selection[J]. IEEE T Image Process, 2011, 20(9):2683-2690.
[21] http://brainweb.bic.mni.mcgill.ca/brainweb/selection_normal.html[OL]
[22] WANG Z, SIMONCELLI E P, BOVIK A C, et al. Multiscale structural similarity for image quality assessment[C]. Pacific Grove:Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 2004. DOI:10.1109/ACSSC.2003. 1292216