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.

Noise reduction in magnetic resonance images using adaptive non‐local means filtering

2013 ◽  
Vol 49 (5) ◽  
pp. 324-326 ◽  
Author(s):  
B. Kang ◽  
O. Choi ◽  
J.D. Kim ◽  
D. Hwang
Keyword(s):  
Magnetic Resonance ◽  
Noise Reduction ◽  
Magnetic Resonance Images ◽  
Local Means ◽  
Non Local

scholarly journals Denoising magnetic resonance images using collaborative non-local means

2016 ◽  
Vol 177 ◽  
pp. 215-227 ◽  
Author(s):  
Geng Chen ◽  
Pei Zhang ◽  
Yafeng Wu ◽  
Dinggang Shen ◽  
Pew-Thian Yap
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Local Means ◽  
Non Local

MRI EDGE DETECTION AND NOISE REDUCTION WITH HYBRID SYSTEM BASED ON WAVELET AND CURVELET

2015 ◽  
Vol 27 (03) ◽  
pp. 1550024
Author(s):  
Saba Zahmati ◽  
Mohammad Mahdi Khalilzadeh ◽  
Mohsen Foroughipour
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Magnetic Resonance ◽  
Edge Detection ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Magnetic Resonance Images ◽  
Mr Images ◽  
Noise Elimination ◽  
Time Frequency

In recent years, multi-scale transform application in image processing especially for magnetic resonance (MR) images has been raised. Wavelet transform is introduced as a useful tool in image processing and it is capable of effectively removing noise from magnetic resonance images. The main problem with wavelet transform is that it is not able to distinguish one dimensional (1D) or higher dimentional discontinuities in images. In other words, since the wavelet transform is two dimensional (2D), it is considered as a separable transform, it is solely able to identify pointwise discontinuity in images. A proposed solution for this issue is an inseparable transform which is named curvelet. Time frequency transform based noise elimination methods, usually rely on thresholding. There are two important factors involved in thresholding: (1) a method to determine the threshold limit, (2) the implementation of the threshold. In curvelet method, by setting a hard threshold at low levels of noise the obtained similarity index is 0.9254. The proposed method for noise elimination and edge detection in this paper is applying curvelet transform in combination with wavelet transform, which on average leads to 10% improvement compared with wavelet method. The results show the efficiency of this method in different parts of image processing on simulated and actual MR images.

Rician noise removal from MR images using novel adapted selective non-local means filter

2012 ◽  
Vol 72 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Sultan Zia ◽  
M. Arfan Jaffar ◽  
Anwar M. Mirza ◽  
Tae-Sun Choi
Keyword(s):  
Noise Removal ◽  
Rician Noise ◽  
Mr Images ◽  
Local Means ◽  
Non Local

scholarly journals Comparative analysis of Median filter family for Removing High-Density Noise in Magnetic Resonance Images

2019 ◽  
pp. 2246-2256
Author(s):  
Nada Jasim Habeeb
Keyword(s):  
Comparative Analysis ◽  
Magnetic Resonance ◽  
Impulse Noise ◽  
Median Filter ◽  
Similarity Index ◽  
Noise Removal ◽  
Magnetic Resonance Images ◽  
High Density ◽  
Switching Median Filter ◽  
Filter Noise

Magnetic Resonance Imaging (MRI) is a medical indicative test utilized for taking images of the tissue points of interest of the human body. During image acquisition, MRI images can be damaged by many noise signals such as impulse noise. One reason for this noise may be a sharp or sudden disturbance in the image signal. The removal of impulse noise is one of the real difficulties. As of late, numerous image de-noising methods were produced for removing the impulse noise from images. Comparative analysis of known and modern methods of median filter family is presented in this paper. These filters can be categorized as follows: Standard Median Filter; Adaptive Median Filter; Progressive Switching Median Filter; Noise Adaptive Fuzzy Switching Median Filter; and Different Applied Median Filter. The de-noising technique performance for each one is evaluated and compared using Peak Signal Noise Ratio, Structural Similarity index Metric, and Beta metric as quantitative metrics.  The experimental results showed that the latest de-noising technique, Different Applied Median Filter (DAMF), produced better results in removing impulse noise compared with the other de-noising techniques. However, this filter produced de-noised image with nonlinear edges in high-density noise. As a result, noise removal from images is one of the low-level images processing which is considered as a first step in many image applications. Therefore, the efficiency of any image processed depends on the efficiency of noise removal technique.

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