Noise in magnetic resonance images(MRI) lowers its quality

affect the visual effect and computer aided diagnosis. This paper designs an effective MRI denoising algorithm aim to remove Rician noise in MRI . In the complex wavelet domain through dual-tree complex wavelet transform(DT-CWT)

combined with Bilateral Filter (BF) and NeighShrink based on Stein's unbiased risk estimation(NeighShrinkSURE)、BivariateShrink

this paper designs an effective MRI denoising algorithm which fully consider the noise distribution characteristic in MRI and wavelet coefficient's inter and intra-scale dependencies. The performance of this method mainly depends on the estimation precision of the noise standard deviation in the coefficients of square MRI after DT-CWT transform

then relate to the parameters of BF and two shrink methods' weight factors. In order to make the cooperative method show the best performance

this paper takes mean square error (MSE) 、peak signal-to-noise ratio(PSNR)、structural similarity index (SSIM) as the image quality evaluation indexes to correct traditional noise standard deviation estimation method

determine the parameters in BF and the weight factor between two shrink methods. This paper designs an effective algorithm that combins three methods in the dual tree complex wavelet domain. The experimental results of image denoising show that in the aspect of visual quality

indicators PSNR and SSIM and elspsed time

the proposed method's comprehensive performance is superior to several traditional MRI denoising algorithms

the PSNR ratio has improved by approximately 0.51~1dB

the SSIM ratio has improved approximately 5%~10%. Denoising through DT-CWT transform is superior to the basic wavelet transform

the filtering accounts for inter-scale dependency and neighboring similarities

the use of bilateral filter enhances the low frequency part of image

aiming at removing Rician noise in MRI

the proposed algorithm has better noise reduction while preserve image's margin and detail.