Adaptive Nonlocal Filtering for Brain MRI Restoration

Brain magnetic resonance images (MRI) plays a crucial role in neuroscience and medical diagnosis. Denoising brain MRI images is an important pre-processing step required in many of the automatic computed aided-diagnosis systems in neuroscience. Recently, nonlocal means (NLM) and variants of these filters, which are widely used in Gaussian noise removal from digital image processing, have been adapted to handle Rician noise which occur in MRI. One of the crucial ingredient for the successful image filtering with NLM is the patch similarity. In this work we consider the use of fuzzy Gaussian mixture model (FGMM) for determining the patch similarity in NLM instead of the usual Euclidean distance. Experimental results with different noise levels on synthetic and brain MRI images are given to highlight the advantage of the proposed approach. Comparison with other image filtering methods our scheme obtains better results in terms of peak signal to noise ratio and structure preservation.