Abstract | ||
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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. |
Year | DOI | Venue |
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2015 | 10.1007/s11042-014-1867-8 | Multimedia Tools and Applications |
Keywords | DocType | Volume |
Medical image restoration,Magnetic resonance imaging,Image denoising,Rician noise,Fuzzy logic | Journal | 74 |
Issue | ISSN | Citations |
15 | 1380-7501 | 7 |
PageRank | References | Authors |
0.46 | 16 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Muhammad Sharif | 1 | 317 | 37.96 |
Ayyaz Hussain | 2 | 103 | 11.60 |
Muhammad Arfan Jaffar | 3 | 24 | 3.80 |
Tae-sun Choi | 4 | 670 | 60.26 |