A contrast-enhanced trilateral filter for MR image denoising

It is well known that the noise in magnetic resonance (MR) magnitude images obeys a Rician distribution. Denoising of MR images is of importance for clinical diagnosis and computerized analysis, such as tissue classification, segmentation, and registration. We propose a post-acquisition denoising algorithm in an attempt to automatically remove the random fluctuations and bias introduced by Rician noise. It replaces the intensity value on each pixel with an average value weighted by the geometric, radiometric, and median-metric components between neighboring pixels associated with an entropy function. Moreover, fuzzy functions are introduced to adaptively compute the parameters in terms of local intensity variations. The contrast-enhanced results indicate that this new filter outperformed several existing methods in providing greater noise reduction and clearer structure boundaries in MR images.