A Non-Local Means Filtering Algorithm for Restoration of Rician Distributed MRI

Denoising algorithms for medical images are generally constrained owing to the dependence on type of noise model as well as introduction of artifacts (in terms of removal of fine structures) upon restoration. In context of magnitude Magnetic Resonance Images (MRI), where noise is approximated as Rician instead of additive Gaussian; denoising algorithms fail to produce satisfactory results. This paper presents a Non-Local Means (NLM) based filtering algorithm for denoising Rician distributed MRI. The proposed denoising algorithm utilizes the concept of self-similarity which considers the weighted average of all the pixels by identifying the similar and dissimilar windows based on Euclidean distance for MRI restoration. Simulations are carried out on MRI contaminated with different levels of Rician noise and are evaluated on the basis of Peak Signal-to-Noise Ratio (

PSNR

) and Structural Similarity (SSIM) as quality parameters. Performance of the proposed algorithm has shown significant results when compared to the other state of the art MRI denoising algorithms.