Volume 17, Issue 3 (11-2020)                   JSDP 2020, 17(3): 101-108 | Back to browse issues page

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Kazemitabar J, Tavakkoli M. A Bayesian approach for image denoising in MRI. JSDP. 2020; 17 (3) :101-108
URL: http://jsdp.rcisp.ac.ir/article-1-893-en.html
Babol Noshirvani University of Technology
Abstract:   (206 Views)
Magnetic Resonance Imaging (MRI) is a notable medical imaging technique that is based on Nuclear Magnetic Resonance (NMR). MRI is a safe imaging method with high contrast between soft tissues, which made it the most popular imaging technique in clinical applications. MR Imagechr('39')s visual quality plays a vital role in medical diagnostics that can be severely corrupted by existing noise during the acquisition process. Therefore, the denoising of these images has great importance in medical applications. During the last decades, lots of MR denoising approaches from various groups of techniques have been proposed that can be classified into two general groups of acquisition-based noise reduction and post-acquisition denoising methods. The first groupchr('39')s approaches will add imaging time and led to a much time-consuming process. The second groupchr('39')s issues are its complicated mathematical equations required for image denoising, in which stochastic algorithms are usually required to solve these complex equations.
This study aims to find an appropriate statical post-acquisition denoising MR imaging method based on the Bayesian technique. Finding the appropriate prior density function also has great importance since the Bayesian techniquechr('39')s performance is related to its prior density function. In this study, the uniform distribution has been applied as the prior density function. The prior uniform distribution function will reduce the Bayesian algorithm to its simplest possible state and lower computational complexity and time consumption. The proposed method can solve the numerical problems with an adequate timing process without complex algorithms and remove noise in less than 120 seconds on average in all cases. To quantitatively assess image improvement, we used the Structural Similarity Function (SSIM) in MATLAB. The similarity with this function shows an average improvement of more than 0.1 in all images. Considering the results, it can be concluded that combining the uniform distribution function as a prior density function and the Bayesian algorithm can significantly reduce the imagechr('39')s noise without the time and computational cost.
Full-Text [PDF 2213 kb]   (65 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2018/08/28 | Accepted: 2020/08/18 | Published: 2020/12/5 | ePublished: 2020/12/5

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