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Study of noise reduction algorithms at various artificial noises on MRI images

Keywords:

A.R.A. Abdulraqeb – Post-graduate Student, Vladimir State University named after A.&N. Stoletovs. E-mail: atef_alsanawy@hotmail.com L.T. Sushkova – Dr. Sc. (Eng.), Head of Department, Vladimir State University named after A.&N. Stoletovs. E-mail: ludm@vlsu.ru M.M. Abounassif – Consultant Diagnostic Radiology, King Saud Medical City, MOH, Riyadh, Saudi Arabia. E-mail: mabounassif@ksmc.med.sa P.J. Parameaswari – Ph.D., Biostatistician, King Saud Medical City, MOH, Riyadh, Saudi Arabia. E-mail: parameaswari@ksmc.med.sa M.A. Muteb – Ph.D., Research Center Director, King Saud Medical City, MOH, Riyadh, Saudi Arabia. E-mail: parameaswari@ksmc.med.sa


The aim of this research was to compare the often used denoising algorithm on MRI images of patients of specialized clinics from two different geographical locations (Vladimir & Riyadh) and find the optimal algorithm for the preprocessing stage for further segmentation, and treatment planning. The original images were noised separately by adding Gaussian noise with zero mean and 0.01 variance, multiplicative noise with zero mean and 0.04 variance, salt and pepper with zero mean and 0.05 variance and later denoised by Average, Gaussian, Wiener, Median, wavelet Haar filters. The denoising algorithms were compared with Peak signal to noise Ratio (PSNR). It was found that in the case of images noised by Gaussian and Salt and Pepper noises, median filter reduced majority of the noises and had the maximum average of PSNR value. In the case of images without noise, Gaussian filter with a standard deviation of 0.5 had the maximum average of PSNR value. In the case of images noised by the multiplicative noise, the average filter had the maximum average of PSNR value followed by median and Gaussian filters.
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