Noise Removal from Brain MRI Images Using Adaptive Bayesian Shrinkage

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.

Static Thresholded Pulse Coupled Neural Networks in Contourlet Domain — A New Framework for Medical Image Denoising

2020 ◽  
Vol 20 (03) ◽  
pp. 2050025
Author(s):  
S. Shajun Nisha ◽  
S. P. Raja ◽  
A. Kasthuri
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Signal To Noise ◽  
Coupled Neural Networks ◽  
Noise Ratio ◽  
Pulse Coupled Neural Networks ◽  
Image Quality Index

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.

TRAVELING SIGNAL‐TO‐NOISE RATIO AND SIGNAL POWER ESTIMATES

Geophysics ◽  
1967 ◽  
Vol 32 (3) ◽  
pp. 485-493 ◽  
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.

scholarly journals 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

2012 ◽  
Vol 9 (2) ◽  
pp. 64 ◽  
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. 

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

2018 ◽  
Vol 29 (1) ◽  
pp. 189-201 ◽  
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.

scholarly journals Analisis Pembobotan T2 Turbo Spin Echo (TSE) brain MRI Potongan Axial dengan Penggunaan Sensitivity Encoding (SENSE) dan Tanpa Penggunaan Sense : Evaluasi pada Signal to Noise Ratio (SNR) dan Scan Time

2016 ◽  
Vol 2 (2) ◽  
pp. 148-153
Author(s):  
Fani Susanto ◽  
A. Gunawan Santoso ◽  
Bagus Abimanyu
Keyword(s):  
Signal To Noise Ratio ◽  
Spin Echo ◽  
Brain Mri ◽  
Statistical Test ◽  
Turbo Spin Echo ◽  
Signal To Noise ◽  
Turbo Spin ◽  
Sensitivity Encoding ◽  
Scan Time ◽  
Noise Ratio

Background: On examination brain MRI often finds non-cooperative patients, requiring rapid acquisition techniques. The parallel imaging sensitivity encoding (SENSE) technique utilizes spatial RF coated phased array information to reduce acquisition time by reducing the K space sampling line to produce good quality and spatial resolution, but has a limitation of signal-to-noise ratio (SNR) reduction. SENSE is used with MRI sequence pulses one of them turbo spin echo (TSE). The purpose of this study was to determine the difference of SNR and scan time on TSE T2 weighting brain MRI axial slices between use SENSE and without SENSE.Methods: This research is quantitative study with experimental approach. The data were collected from May to June 2016 at the Radiology Installation of Premier Bintaro Hospital by calculating the SNR through the software for the region of interest (ROI) and calculating the scan time through the scan timer on the workstation monitor. Data analysis was done by statistical test with SPPS 16 application using paired T-test and descriptiveResults: From the result of statistical test, it is known that SNR at TSE T2 weighting between with and without SENSE is obtained p-value 0,000 (p 0, 05). This is because the encoding of the both image are different, On TSE T2 weighting image without SENSE there is the use 1800 pulses approaching the effective TE so the shallow gradient produces maximum echo, while on TSE T2 weighting with SENSE there is a reduction of phase encoding row in K space and the presence of g-factor causes the SNR to decrease. From descriptive analysis result, is known that scan time on TSE T2 weighting between with and without SENSE usage is obtained by reduction of scan time for 1 minute 24 seconds (49, 01%). This is because the acquisition technique between the both image are different, on the TSE T2 weighting  without SENSE there is ETL in charging K space, whereas on the TSE T2 Weighting  with  SENSE there is R-factor causing the sampling not to fill all K space so that scanning time is reduced.Conclusion: There are SNR and scan time differences on TSE T2 weighting brain MRI of the axial slices with SENSE and without SENSE usage.

scholarly journals Fast and High-Resolution Neonatal Brain MRI Through Super-Resolution Reconstruction From Acquisitions With Variable Slice Selection Direction

2021 ◽  
Vol 15 ◽  
Author(s):  
Yao Sui ◽  
Onur Afacan ◽  
Ali Gholipour ◽  
Simon K. Warfield
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Brain Mri ◽  
Super Resolution ◽  
Neonatal Brain ◽  
Signal To Noise ◽  
High Quality ◽  
Noise Ratio

The brain of neonates is small in comparison to adults. Imaging at typical resolutions such as one cubic mm incurs more partial voluming artifacts in a neonate than in an adult. The interpretation and analysis of MRI of the neonatal brain benefit from a reduction in partial volume averaging that can be achieved with high spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is slow, which increases the potential for motion artifact, and suffers from reduced signal-to-noise ratio. The purpose of this study is thus that using super-resolution reconstruction in conjunction with fast imaging protocols to construct neonatal brain MRI images at a suitable signal-to-noise ratio and with higher spatial resolution than can be practically obtained by direct Fourier encoding. We achieved high quality brain MRI at a spatial resolution of isotropic 0.4 mm with 6 min of imaging time, using super-resolution reconstruction from three short duration scans with variable directions of slice selection. Motion compensation was achieved by aligning the three short duration scans together. We applied this technique to 20 newborns and assessed the quality of the images we reconstructed. Experiments show that our approach to super-resolution reconstruction achieved considerable improvement in spatial resolution and signal-to-noise ratio, while, in parallel, substantially reduced scan times, as compared to direct high-resolution acquisitions. The experimental results demonstrate that our approach allowed for fast and high-quality neonatal brain MRI for both scientific research and clinical studies.

scholarly journals Analisis TSE Factor Terhadap Signal to Noise Ratio dan Contrast to Noise Ratio pada Pembobotan T2 Turbo Spin Echo Potongan Axial MRI Brain

2015 ◽  
Vol 3 (2) ◽  
pp. 271-276 ◽  
Author(s):  
Novelsa Chintya Prabawati ◽  
Siti Masrochah ◽  
Sri Mulyati
Keyword(s):  
White Matter ◽  
Linear Regression ◽  
Grey Matter ◽  
Signal To Noise Ratio ◽  
Brain Mri ◽  
Signal To Noise ◽  
Mri Examination ◽  
Regression Test ◽  
Coefficient Corrélation ◽  
Noise Ratio

Background: TSE factor is parameters that affect Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR). TSE factor for brain MRI examination is a long TSE factor. There are differences when using TSE factor. At the theory, the brain MRI examination is using TSE factor ≥16 while at Siloam  Surabaya  Hospital was using TSE factor 14. The writer ever seen some noises at brain MRI image therefore the radiographer doing modification of TSE factor. The purpose of this research are to determine the influence of modification in the TSE factor value against SNR and CNR and to define the SNR and CNR optimum from that.Methods: This research is a quantitative study with an experimental approach. This research was done by MRI Philips Achieva 1,5 T with 10 modification TSE factor (8, 10, 12, 14, 16, 18, 20, 22, 24 and 26). SNR and CNR obtained by measurement of ROI in the grey matter, white matter and CSF with the result an average signal and compared with the average standard deviation of the background image. Data was analyzed by linear regression test to know the influence of TSE factor against SNR and CNR and data was analyzed by descriptive test mean rank to obtain the optimum TSE factor value.Result: The result showed that there was the inluence of TSE factor to SNR and CNR at T2W TSE axial brain. There was a significant correlation between TSE factor with all of area SNR and CNR with coefficient correlation of SNR grey matter r=0,591, with coefficient correlation of SNR white matter r=0,604, with coefficient correlation of SNR CSF r=0,687, with coefficient correlation of CNR CSF–grey matter r=0,690, with coefficient correlation of CNR CSF-white matter r=0,658. The significant value of linear regression test is (0,000*) p value (0,05). TSE factor optimum value at T2W TSE axial brain was TSE factor value 10 for SNR with mean rank SNR 45,05 and TSE factor value 8 for CNR with mean rank CNR 35,43.Conclusion: There was the influence of TSE factor to SNR and CNR at T2W TSE axial brain. TSE factor optimum value in brain MRI T2W TSE axial is 10 to SNR and TSE factor 8 to CNR.

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