Rician Noise Removal by Non-Local Means Filtering for Low Signal-to-Noise Ratio MRI: Applications to DT-MRI

Image denoising, a significant research area in the field of medical image processing, makes an effort to recover the original image from its noise corrupted image. The Pulse Coupled Neural Networks (PCNN) works well against denoising a noisy image. Generally, image denoising techniques are directly applied on the pixels. From the literature review, it is reported that denoising after frequency domain transformation is performing better since noise removal is applied over the coefficients. Motivated by this, in this paper, a new technique called the Static Thresholded Pulse Coupled Neural Network (ST-PCNN) is proposed by combining PCNN with traditional filtering or threshold shrinkage technique in Contourlet Transform domain. Four different existing PCNN architectures, such as Neuromime Structure, Intersecting Cortical Model, Unit-Linking Model and Multichannel Model are considered for comparative analysis. The filters such as Wiener, Median, Average, Gaussian and threshold shrinkage techniques such as Sure Shrink, HeurShrink, Neigh Shrink, BayesShrink are used. For noise removal, a mixture of Speckle and Gaussian noise is considered for a CT skull image. A mixture of Rician and Gaussian noise is considered for MRI brain image. A mixture of Speckle and Salt and Pepper noise is considered for a Mammogram image. The Performance Metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Index (IQI), Universal Image Quality Index (UQI), Image Enhancement Filter (IEF), Structural Content (SC), Correlation Coefficient (CC), and Weighted Signal-to-Noise Ratio (WSNR) and Visual Signal-to-Noise Ratio (VSNR) are used to evaluate the performance of denoising.

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Fuzzy similarity based non local means filter for Rician noise removal

Multimedia Tools and Applications ◽

10.1007/s11042-014-1867-8 ◽

2014 ◽

Vol 74 (15) ◽

pp. 5533-5556 ◽

Cited By ~ 11

Author(s):

Muhammad Sharif ◽

Ayyaz Hussain ◽

Muhammad Arfan Jaffar ◽

Tae-Sun Choi

Keyword(s):

Noise Removal ◽

Rician Noise ◽

Local Means ◽

Fuzzy Similarity ◽

Non Local

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Noise Removal from Brain MRI Images Using Adaptive Bayesian Shrinkage

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8446 ◽

2020 ◽

Vol 17 (4) ◽

pp. 1818-1825

Author(s):

S. Josephine ◽

S. Murugan

Keyword(s):

Signal To Noise Ratio ◽

Brain Mri ◽

Noise Removal ◽

Wavelet Thresholding ◽

Signal To Noise ◽

Pixel Intensity ◽

Noisy Pixel ◽

Shrinkage Method ◽

Bayesian Shrinkage ◽

Noise Ratio

In MR machine, surface coils, especially phased-arrays are used extensively for acquiring MR images with high spatial resolution. The signal intensities on images acquired using these coils have a non-uniform map due to coil sensitivity profile. Although these smooth intensity variations have little impact on visual diagnosis, they become critical issues when quantitative information is needed from the images. Sometimes, medical images are captured by low signal to noise ratio (SNR). The low SNR makes it difficult to detect anatomical structures because tissue characterization fails on those images. Hence, denoising are essential processes before further processing or analysis will be conducted. They found that the noise in MR image is of Rician distribution. Hence, general filters cannot be used to remove these types of noises. The linear spatial filtering technique blurs the object boundaries and degrades the sharp details. The existing works proved that Wavelet based works eliminates the noise coefficient that called wavelet thresholding. Wavelet thresholding estimates the noise level from high frequency content and estimates the threshold value by comparing the estimated noisy wavelet coefficient with other wavelet coefficients and eliminate the noisy pixel intensity value. Bayesian Shrinkage rule is one of the widely used methods. It uses for Gaussian type of noise, the proposed method introduced some adaptive technique in Bayesian Shrinkage method to remove Rician type of noises from MRI images. The results were verified using quantitative parameters such as Peak Signal to Noise Ratio (PSNR). The proposed Adaptive Bayesian Shrinkage Method (ABSM) outperformed existing methods.

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TRAVELING SIGNAL‐TO‐NOISE RATIO AND SIGNAL POWER ESTIMATES

Geophysics ◽

10.1190/1.1439874 ◽

1967 ◽

Vol 32 (3) ◽

pp. 485-493 ◽

Cited By ~ 4

Author(s):

S. M. Simpson

Keyword(s):

Seismic Noise ◽

Signal To Noise Ratio ◽

Signal Strength ◽

Noise Removal ◽

Statistical Properties ◽

Signal Power ◽

Signal To Noise ◽

Direct Estimation ◽

Noise Ratio

Undesirable seismic noise of a nondeterministic type must be destroyed by making use of its statistical properties. Averaging of one sort or another provides methods for performing this noise removal. Our purpose here is to present a method for direct estimation of signal strength versus seismogram time, with stepout as a parameter. After describing the method and its expected behavior to some extent, we illustrate its application to a set of three noisy records.

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Improving Signal-to-Noise Ratio in Susceptibility Weighted Imaging: A Novel Multicomponent Non-Local Approach

PLoS ONE ◽

10.1371/journal.pone.0126835 ◽

2015 ◽

Vol 10 (6) ◽

pp. e0126835 ◽

Cited By ~ 15

Author(s):

Pasquale Borrelli ◽

Giuseppe Palma ◽

Enrico Tedeschi ◽

Sirio Cocozza ◽

Marco Comerci ◽

...

Keyword(s):

Signal To Noise Ratio ◽

Susceptibility Weighted Imaging ◽

Signal To Noise ◽

Local Approach ◽

Non Local ◽

Noise Ratio

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Development of Stand Alone Application Tool for Processing and Quality Measurement of Weld Imperfection Image Captured by μ-Focused Digital Radiography Using MATLAB- Based Graphical User Interface

The Journal of Engineering Research [TJER] ◽

10.24200/tjer.vol9iss2pp64-79 ◽

2012 ◽

Vol 9 (2) ◽

pp. 64 ◽

Cited By ~ 2

Author(s):

PZ Nadila ◽

YHP Manurung ◽

SA Halim ◽

SK Abas ◽

G Tham ◽

...

Keyword(s):

Image Processing ◽

User Interface ◽

Image Quality ◽

Digital Radiography ◽

Graphical User Interface ◽

Signal To Noise Ratio ◽

Quality Measurement ◽

Noise Removal ◽

Signal To Noise ◽

Noise Ratio

Digital radiography incresingly is being applied in the fabrication industry. Compared to film- based radiography, digitally radiographed images can be acquired with less time and fewer exposures. However, noises can simply occur on the digital image resulting in a low-quality result. Due to this and the system’s complexity, parameters’ sensitivity, and environmental effects, the results can be difficult to interpret, even for a radiographer. Therefore, the need of an application tool to improve and evaluate the image is becoming urgent. In this research, a user-friendly tool for image processing and image quality measurement was developed. The resulting tool contains important components needed by radiograph inspectors in analyzing defects and recording the results. This tool was written by using image processing and the graphical user interface development environment and compiler (GUIDE) toolbox available in Matrix Laboratory (MATLAB) R2008a. In image processing methods, contrast adjustment, and noise removal, edge detection was applied. In image quality measurement methods, mean square error (MSE), peak signal-to-noise ratio (PSNR), modulation transfer function (MTF), normalized signal-to-noise ratio (SNRnorm), sensitivity and unsharpness were used to measure the image quality. The graphical user interface (GUI) wass then compiled to build a Windows, stand-alone application that enables this tool to be executed independently without the installation of MATLAB.

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A Bayesian Multiresolution Approach for Noise Removal in Medical Magnetic Resonance Images

Journal of Intelligent Systems ◽

10.1515/jisys-2017-0402 ◽

2018 ◽

Vol 29 (1) ◽

pp. 189-201 ◽

Cited By ~ 9

Author(s):

Sima Sahu ◽

Harsh Vikram Singh ◽

Basant Kumar ◽

Amit Kumar Singh

Keyword(s):

Magnetic Resonance ◽

Bayesian Approach ◽

Gaussian Noise ◽

Signal To Noise Ratio ◽

Noise Removal ◽

White Gaussian Noise ◽

Error Estimator ◽

Wavelet Coefficients ◽

Signal To Noise ◽

Noise Ratio

Abstract A Bayesian approach using wavelet coefficient modeling is proposed for de-noising additive white Gaussian noise in medical magnetic resonance imaging (MRI). In a parallel acquisition process, the magnetic resonance image is affected by white Gaussian noise, which is additive in nature. A normal inverse Gaussian probability distribution function is taken for modeling the wavelet coefficients. A Bayesian approach is implemented for filtering the noisy wavelet coefficients. The maximum likelihood estimator and median absolute deviation estimator are used to find the signal parameters, signal variances, and noise variances of the distribution. The minimum mean square error estimator is used for estimating the true wavelet coefficients. The proposed method is simulated on MRI. Performance and image quality parameters show that the proposed method has the capability to reduce the noise more effectively than other state-of-the-art methods. The proposed method provides 8.83%, 2.02%, 6.61%, and 30.74% improvement in peak signal-to-noise ratio, structure similarity index, Pratt’s figure of merit, and Bhattacharyya coefficient, respectively, over existing well-accepted methods. The effectiveness of the proposed method is evaluated by using the mean squared difference (MSD) parameter. MSD shows the degree of dissimilarity and is 0.000324 for the proposed method, which is less than that of the other existing methods and proves the effectiveness of the proposed method. Experimental results show that the proposed method is capable of achieving better signal-to-noise ratio performance than other tested de-noising methods.

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Noise Level and Similarity Analysis for Computed Tomographic Thoracic Image with Fast Non-Local Means Denoising Algorithm

Applied Sciences ◽

10.3390/app10217455 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7455

Author(s):

Bae-Guen Kim ◽

Seong-Hyeon Kang ◽

Chan Rok Park ◽

Hyun-Woo Jeong ◽

Youngjin Lee

Keyword(s):

Noise Reduction ◽

Simulation Study ◽

Signal To Noise Ratio ◽

Ct Images ◽

Male Adult ◽

Computed Tomographic ◽

Local Means ◽

Thoracic Computed Tomography ◽

Non Local ◽

Noise Ratio

Although conventional denoising filters have been developed for noise reduction from digital images, these filters simultaneously cause blurring in the images. To address this problem, we proposed the fast non-local means (FNLM) denoising algorithm which would preserve the edge information of objects better than conventional denoising filters. In this study, we obtained thoracic computed tomography (CT) images from a male adult mesh (MASH) phantom modeled by computer and a five-year-old phantom to perform both the simulation study and the practical study. Subsequently, the FNLM denoising algorithm and conventional denoising filters, such as the Gaussian, median, and Wiener filters, were applied to the MASH phantom image adding Gaussian noise with a standard deviation of 0.002 and practical CT images. Finally, the results were compared quantitatively in terms of the coefficient of variation (COV), contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and correlation coefficient (CC). The results showed that the FNLM denoising algorithm was more efficient than the conventional denoising filters. In conclusion, through the simulation study and the practical study, this study demonstrated the feasibility of the FNLM denoising algorithm for noise reduction from thoracic CT images.

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An Image Filter Based on Multiobjective Genetic Algorithm and Shearlet Transformation

Mathematical Problems in Engineering ◽

10.1155/2013/463760 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Zhi-yong Fan ◽

Quan-sen Sun ◽

Ze-xuan Ji ◽

Kai Hu

Keyword(s):

Genetic Algorithm ◽

Wavelet Decomposition ◽

Signal To Noise Ratio ◽

Target Function ◽

Threshold Value ◽

Rician Noise ◽

Signal To Noise ◽

Multiobjective Genetic Algorithm ◽

Noise Ratio ◽

Magnetic Resonance Imaging Mri

Rician noise pollutes magnetic resonance imaging (MRI) data, making data’s postprocessing difficult. In order to remove this noise and avoid loss of details as much as possible, we proposed a filter algorithm using both multiobjective genetic algorithm (MOGA) and Shearlet transformation. Firstly, the multiscale wavelet decomposition is applied to the target image. Secondly, the MOGA target function is constructed by evaluation methods, such as signal-to-noise ratio (SNR) and mean square error (MSE). Thirdly, MOGA is used with optimal coefficients of Shearlet wavelet threshold value in a different scale and a different orientation. Finally, the noise-free image could be obtained through inverse wavelet transform. At the end of the paper, experimental results show that this proposed algorithm eliminates Rician noise more effectively and yields better peak signal-to-noise ratio (PSNR) gains compared with other traditional filters.

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