Fuzzy similarity based non local means filter for Rician noise removal

Rician noise contaminated Magnetic Resonance (MR) Images can effect the accuracy of quantitative analysis. For accurate analysis of MR data, noise smoothing is considered as an important pre-processing step. In this article, a novel Fuzzy Similarity based Non-Local Means (FSNLM) filter has been proposed for the removal of Rician noise from MR images. Proposed technique consists of three major modules: Pre-processing, Fuzzy similarity and Fuzzy restoration. In pre-processing module, some important statistical parameters are identified. These parameters are then used by the fuzzy similarity mechanism to find non-local homogeneous neighboring pixels. Selected homogeneous pixels play an important role during fuzzy logic based restoration process for the estimation of noise-free pixels. The proposed scheme FSNLM has been tested on simulated and real data sets, and compared with state-of-the-art filters based on well known global and local quantitative measures such as root-mean-squared-error (RMSE), peak-signal-to-noise-ratio (PSNR), structural-similarity-index-measure (SSIM), and figure-of-merit (FOM). Experimental results show that the proposed noise filtering technique is more effective than the existing methods, both at low and high densities of Rician noise.