Analysis of Filtering Approaches to Brain MRI in Spatial Domain | Abstract

International Journal of Medical Research & Health Sciences (IJMRHS)
ISSN: 2319-5886 Indexed in: ESCI (Thomson Reuters)


Analysis of Filtering Approaches to Brain MRI in Spatial Domain

Author(s):Vedant Shukla*, Prasad Khandekar and Arti Khaparde

Non-linear filters are used to filter out the artifacts and noise present in MR data. The balance between signal preservation and noise reduction makes restoration of MR data a complex task. Application of non-linear filters such as median and Non-Local Means Filter (NLM) filter converts the right-skewed Rician Distribution into un-skewed Gaussian distribution. NLM filter gives better results than Bilateral and Median filter. As the distribution is un-skewed after the application of non-linear filters, standard linear filters such as Gaussian and Wiener filters were applied and results were drawn. A linear combination of NLM and Gaussian filter gives satisfactory results. The experimentation was performed on 40 clinical images and the NLM filter was found to have the best results. The image quality indices used for comparison are Peak Signal-to-Noise Ratio (PSNR), Root Mean Squared Error (RMSE), Structural Similarity Index (SSIM), and entropy. The experimentation was performed on MATLAB 2020a.

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