Denoising 3D MR images by the enhanced non-local means filter for Rician noise

Liu, Hong; Yang, Cihui; Pan, Ning; Song, Enmin; Green, Richard

doi:10.1016/j.mri.2010.06.023

Cited by 79 publications

(45 citation statements)

References 22 publications

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“…Fourth order PDE filters (You et al, 2000), adaptive fourth order PDE filter (Samsonov et al, 2004), fourth order complex PDE filters (Rajan et al, 2009). Non local means filter (NLM) (Buades et al, 2005), fast NLM filter (Coupé et al, 2006), Block wise optimised NLM filter (Coupé et al, 2008), Unbiased NLM filter (Manjón et al, 2008), dynamic NLM filter (Gal et al, 2010), enhanced NLM filter (Liu et al, 2010), adaptive NLM filter (Manjón et al, 2010). Combination of domain and range filters (Tomasi et al, 1998), bilateral domain and range filters , trilateral domain and range filters .…”

Section: Introductionmentioning

confidence: 99%

Modified complex diffusion based nonlinear filter for restoration and enhancement of magnetic resonance images

Yadav

Srivastava

2017

IJBET

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Abstract:In this paper, a new method is proposed for removal of Rician noise from MRI. Method is casted into a variational framework, consists of two terms wherein the first term is a data likelihood term and the second term is a prior function. The first term is obtained by minimising the negative log likelihood of Rician pdf. Owing to ill-posedness of the likelihood term, a prior function is introduced which is a nonlinear complex diffusion based filter. A regularisation parameter is used to balance the trade-off between data fidelity term and prior. The performance analysis of the proposed method with other standard methods is presented for Brain Web dataset. The values of performance measures such as PSNR, RMSE, SSIM, and CP are presented for various noise levels. From the simulation results, it is observed that the proposed method is performing better in comparison to other methods in consideration.Keywords: Rician noise reduction; 2D MR images; Rician's PDF; nonlinear partial differential equation based filter.Reference to this paper should be made as follows: Yadav, R.B., Srivastava, S. and Srivastava, R. (2017) 'Modified complex diffusion based nonlinear filter for restoration and enhancement of magnetic resonance images', Int.

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Section: Introductionmentioning

confidence: 99%

Modified complex diffusion based nonlinear filter for restoration and enhancement of magnetic resonance images

Yadav

Srivastava

2017

IJBET

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“…To address this bias, Manjon et al proposed an unbiased NLM (UNLM) estimate of MR images in the presence of Rician noise by subtracting the bias from the squared value of the filtered images [18]. A more theoretically reasonable approach is the Rician NLM (RNLM) filter which removes the Rician bias from the average of squared intensities in images [19], [20]. The RNLM approach was also adopted in the later work of Manjon et al [21], [22].…”

Section: Introductionmentioning

confidence: 99%

“…The RNLM approach was also adopted in the later work of Manjon et al [21], [22]. The NLM algorithm [23] and its Rician-adapted versions [18], [19], [20], [21], [22] show improved denoising accuracy compared with the wavelet and anisotropic diffusion filters when applied to MR images. However, this algorithm and its versions may lead to the blurring or loss of small high-contrast particle details contained in MR images.…”

Section: Introductionmentioning

confidence: 99%

“…Through extensive experiments, we find that the blurring of sharp particles in NLM-based algorithms is related to the weighting strategy of the central pixel. To avoid over-weighting of the central pixel from very high self-similarity, the weight of the central pixel was proposed to be assigned the maximum weight of the non-central pixels in the search window [15], and this strategy was adopted in the RNLM filter [19], [20], [21], [22]. However, when the intensity of the central pixel is significantly different from those of all other pixels in the search window, that is, when the central pixel cannot find similar pixel in the search window, the aforementioned central pixel weighting strategy [15] causes the reduced contribution of the central pixel to the weighted average output.…”

Section: Introductionmentioning

confidence: 99%

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Denoising MR Images Using Non-Local Means Filter with Combined Patch and Pixel Similarity

Zhang

Hou

et al. 2014

PLoS ONE

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Denoising is critical for improving visual quality and reliability of associative quantitative analysis when magnetic resonance (MR) images are acquired with low signal-to-noise ratios. The classical non-local means (NLM) filter, which averages pixels weighted by the similarity of their neighborhoods, is adapted and demonstrated to effectively reduce Rician noise without affecting edge details in MR magnitude images. However, the Rician NLM (RNLM) filter usually blurs small high-contrast particle details which might be clinically relevant information. In this paper, we investigated the reason of this particle blurring problem and proposed a novel particle-preserving RNLM filter with combined patch and pixel (RNLM-CPP) similarity. The results of experiments on both synthetic and real MR data demonstrate that the proposed RNLM-CPP filter can preserve small high-contrast particle details better than the original RNLM filter while denoising MR images.

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“…In magnetic resonance imaging (MRI), noise filtering is widely used to improve image quality, [1][2][3] which is necessitated by many applications. For example, noise filtering is very important as a pre-processing tool in image segmentation, 4,5 which is useful for the detection of many diseases including brain tumors.…”

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confidence: 99%

Application of Interpolation Method to Magnetic Resonance Imaging for Removal of Radio-Frequency Noise

Cho¹,

Ahn

et al. 2011

Bulletin of the Korean Chemical Society

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Denoising 3D MR images by the enhanced non-local means filter for Rician noise

Cited by 79 publications

References 22 publications

Modified complex diffusion based nonlinear filter for restoration and enhancement of magnetic resonance images

Modified complex diffusion based nonlinear filter for restoration and enhancement of magnetic resonance images

Denoising MR Images Using Non-Local Means Filter with Combined Patch and Pixel Similarity

Application of Interpolation Method to Magnetic Resonance Imaging for Removal of Radio-Frequency Noise

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