Study of denoising algorithm for MR image based on Contourlet Transform

It is more difficult to remove the noise and keep the edge information in the same time in the brain MR image than the ordinary image since the boundaries of encephalic tissue are highly complicated. As one kind of the multi-scale and multidirectional geometrical analysis method, the Contourlet Transform (CT) is an optimal representation of the contour and texture information in an image and overcomes the drawbacks of the Wavelet Transform (WT). Due to the lack of translation invariance of the CT, a Cycle Spinning (CS) technique was employed to smooth Gibbs-like artifacts. In this study, Combined the CT and the CS, the CTCS with different threshold for each direction of each scale was investigated to remove the noise in the brain MR image. Theoretical analysis and experimental results showed that the CTCS is outperforms the CT and the WT both visually and in terms of the SNR.