Confidence based anisotropic filtering of magnetic resonance images

Image filtering is an important off-line image processing technique to improve the signal-to-noise ratio (SNR) and/or contrast-to-noise ratio (CNR) of acquired images. The major drawback of filtering is that it often blurs the fine structures and object boundaries in the image along with noise. Anisotropic diffusive filtering techniques incorporate gradient information to blur homogeneous regions while preserving the boundaries and interesting structures. Unfortunately, their performance is limited in low contrast regions and around fuzzy boundaries. This paper introduces a multi-scale confidence based conductance function to address the limitations of anisotropic diffusive filtering. Experiments on phantom and magnetic resonance (MR) images have been performed using both our method and the gradient-based anisotropic diffusive filtering for comparison purposes.