A Robust Non Local Means Maximum Likelihood Estimation Method for Rician Noise Reduction in MR Images

Denoising is one of the most important preprocessing task in medical image analysis. It has a great role in the clinical diagnosis and computerized analysis. When SNR is low, medical images follows a Rician noise distribution which is signal dependent. In the literature, only few works focus on the edge preserving quality of MR images. Our aim is to estimate the noise free signal from MR magnitude images by focusing on preserving edges and tissue boundaries. The proposed method is an improvisation over non local means maximum likelihood approach for Rician noise reduction in MR images. Our method focus on a robust estimator function (Geman-McClure function) for weight calculation, and is compared with the existing methods in terms of PSNR ratio, visual quality comparison and by SSIM values. The proposed method outperforms the state-of-the art methods in preserving fine structural details and edge boundaries.