scholarly journals Adaptive non-local means denoising of MR images with spatially varying noise levels

2009 ◽  
Vol 31 (1) ◽  
pp. 192-203 ◽  
Author(s):  
José V. Manjón ◽  
Pierrick Coupé ◽  
Luis Martí-Bonmatí ◽  
D. Louis Collins ◽  
Montserrat Robles
Keyword(s):  
Mr Images ◽  
Noise Levels ◽  
Local Means ◽  
Non Local ◽  
Spatially Varying

Laplacian based non-local means denoising of MR images with Rician noise

2013 ◽  
Vol 31 (9) ◽  
pp. 1599-1610 ◽  
Author(s):  
Hemalata V. Bhujle ◽  
Subhasis Chaudhuri
Keyword(s):  
Rician Noise ◽  
Mr Images ◽  
Local Means ◽  
Non Local

Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images

2020 ◽  
Vol 13 (4) ◽  
pp. 14-31
Author(s):  
Nikita Joshi ◽  
Sarika Jain ◽  
Amit Agarwal
Keyword(s):  
Magnetic Resonance ◽  
Total Variation ◽  
Similarity Index ◽  
Noise Removal ◽  
Magnetic Resonance Images ◽  
Variation Method ◽  
Mr Images ◽  
Denoising Method ◽  
Local Means ◽  
Non Local

Magnetic resonance (MR) images suffer from noise introduced by various sources. Due to this noise, diagnosis remains inaccurate. Thus, removal of noise becomes a very important task when dealing with MR images. In this paper, a denoising method has been discussed that makes use of non-local means filter and discrete total variation method. The proposed approach has been compared with other noise removal techniques like non-local means filter, anisotropic diffusion, total variation, and discrete total variation method, and it proves to be effective in reducing noise. The performance of various denoising methods is compared on basis of metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), universal image quality index (UQI), and structure similarity index (SSIM) values. This method has been tested for various noise levels, and it outperformed other existing noise removal techniques, without blurring the image.

scholarly journals Image Denoising Using Non-Local Means (NLM) Approach in Magnetic Resonance (MR) Imaging: A Systematic Review

2020 ◽  
Vol 10 (20) ◽  
pp. 7028
Author(s):  
Yeong-Cheol Heo ◽  
Kyuseok Kim ◽  
Youngjin Lee
Keyword(s):  
Systematic Review ◽  
Magnetic Resonance ◽  
Mr Imaging ◽  
Noise Reduction ◽  
Research Result ◽  
Reduction Algorithm ◽  
Mr Images ◽  
Local Means ◽  
Non Local ◽  
Noise Reduction Algorithm

The non-local means (NLM) noise reduction algorithm is well known as an excellent technique for removing noise from a magnetic resonance (MR) image to improve the diagnostic accuracy. In this study, we undertook a systematic review to determine the effectiveness of the NLM noise reduction algorithm in MR imaging. A systematic literature search was conducted of three databases of publications dating from January 2000 to March 2020; of the 82 publications reviewed, 25 were included in this study. The subjects were categorized into four major frameworks and analyzed for each research result. Research in NLM noise reduction for MR images has been increasing worldwide; however, it was found to have slightly decreased since 2016. It was found that the NLM technique was most frequently used on brain images taken using the general MR imaging technique; these were most frequently performed during simultaneous real and simulated experimental studies. In particular, comparison parameters were frequently used to evaluate the effectiveness of the algorithm on MR images. The ultimate goal is to provide an accurate method for the diagnosis of disease, and our conclusion is that the NLM noise reduction algorithm is a promising method of achieving this goal.

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

2010 ◽  
Vol 28 (10) ◽  
pp. 1485-1496 ◽  
Author(s):  
Hong Liu ◽  
Cihui Yang ◽  
Ning Pan ◽  
Enmin Song ◽  
Richard Green
Keyword(s):  
Rician Noise ◽  
Mr Images ◽  
Local Means ◽  
Non Local

Bayesian approach for Rician non-local means denoising in MR images

2015 ◽  
Vol 63 (6) ◽  
pp. 303-314
Author(s):  
D. W. Kim ◽  
C. Kim ◽  
D. H. Lim
Keyword(s):  
Bayesian Approach ◽  
Mr Images ◽  
Local Means ◽  
Non Local

scholarly journals Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4161
Author(s):  
Jan Kubicek ◽  
Michal Strycek ◽  
Martin Cerny ◽  
Marek Penhaker ◽  
Ondrej Prokop ◽  
...  
Keyword(s):  
Optimization Procedure ◽  
Mr Images ◽  
Relevant Variable ◽  
Median Filters ◽  
Spatial Image ◽  
Local Means ◽  
Tissue Identification ◽  
Image Area ◽  
Local Mean ◽  
Non Local

In the area of musculoskeletal MR images analysis, the image denoising plays an important role in enhancing the spatial image area for further processing. Recent studies have shown that non-local means (NLM) methods appear to be more effective and robust when compared with conventional local statistical filters, including median or average filters, when Rician noise is presented. A significant limitation of NLM is the fact that thy have the tendency to suppress tiny objects, which may represent clinically important information. For this reason, we provide an extensive quantitative and objective analysis of a novel NLM algorithm, taking advantage of pixel and patch similarity information with the optimization procedure for optimal filter parameters selection to demonstrate a higher robustness and effectivity, when comparing with NLM and conventional local means methods, including average and median filters. We provide extensive testing on variable noise generators with dynamical noise intensity to objectively demonstrate the robustness of the method in a noisy environment, which simulates relevant, variable and real conditions. This work also objectively evaluates the potential and benefits of the application of NLM filters in contrast to conventional local-mean filters. The final part of the analysis is focused on the segmentation performance when an NLM filter is applied. This analysis demonstrates a better performance of tissue identification with the application of smoothing procedure under worsening image conditions.

Sign in / Sign up

Export Citation Format

Share Document