In order to improve the quality of the Magnetic Resonance (MR) image

a denoising algorithm for MR image using Dual-Tree Contourlet (DT-Contourlet) transform is proposed. First

the distribution model of the noise in MR image is investigated

and that such noise obeys Rician distribution; based on this

a new method to estimate the noise parameters of the squared magnitude MR image is derived. Then

the pyramidal dual-tree directional filter banks of DT-Contourlet is analyzed to show that DT-Contourlet maintains the flexibility direction selectivity of the contourlet transform

and overcomes the limitation of the contourlet which namely lack of shift invariance．After that

in the DT-contourlet domain

by calculating the Variance Homogeneity Measurement (VHM)

the locally adaptive window is determined to compute the shrinkage factor to shrink the DT-contourlet coefficients of the squared magnitude MR image. Finally

the denoising algorithm to MR image is implemented via the inverse DT-Contourlet transform. Experimental results show that the performance of the proposed algorithm is superior to the traditional algorithms in terms of the Peak Signal to Noise Ratio (PSNR); for simulated MR images with different noise variances

the new algorithm outperforms wavelet-based and contourlet-based algorithms by 2.13dB and 0.91dB averagely. As for visual quality

the proposed algorithm could reduce the noise in MR image effectively and retain more details simultaneously.