Brain MR Image Denoising for Rician Noise Using Intrinsic Geometrical Information

Abstract

A new image denoising algorithm based on nonsubsampled contourlet transform is presented. Magnetic Resonance (MR) images corrupted by Rician noise are transformed into multi-scale and multi-directional contour information, where a nonlinear mapping function is used to modify the contour coefficients at each level. The denoising is achieved by improving edge sharpness and inhibiting the background noise. Experiments show the proposed algorithm preserves the intrinsic geometrical information of the noised MR image and can be effectively applied to T1-, T2-, and PD-weighted MR images without any parameter tuning under diverse noise levels.