A novel implementation for brain MRI noise reduction

In this paper proposed algorithm is based on Dual Tree-CWT and Nonlocal Mean filtering processes is used to eliminate Rician noise from the brain magnetic resonance images. The noise reduction is done using two stage processes, first sparse DT-CWT is applied, which allows for distinction of data dir...

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Bibliographic Details
Main Authors: Devi, G. Naga Rama, Velliangiri, S., Alagumuthukrishnan, S.
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Brain
Image degradation
Magnetic resonance imaging
Medical imaging
Noise levels
Noise reduction
Online Access:Get full text
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Summary:In this paper proposed algorithm is based on Dual Tree-CWT and Nonlocal Mean filtering processes is used to eliminate Rician noise from the brain magnetic resonance images. The noise reduction is done using two stage processes, first sparse DT-CWT is applied, which allows for distinction of data directionality in the transform space and then Rotational invariant version of Non-Local Mean filter is applied. The proposed algorithm is tested with different Rician noise levels of brain MR Images. Even the Image is degraded by 15% Rician noise the PSNR and SSIM obtained are 23dB and 0.93 which is a better performance as compared to Anisotropic Diffusion Filter (ADF), Non local Maximum Likelihood (NLML), Nutrosophic Set Median Filter (NS median).
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0058059