目的 噪声会降低磁共振图像(MRI)的质量,影响视觉效果和计算机辅助诊断,针对MRI噪声的莱斯(Rician)分布特性,设计一种有效的MRI去噪算法. 方法 在双树复小波(DT-CWT)域结合双边滤波器(BF)和基于Stein无偏误差估计的邻域收缩法(NeighShrinkSURE)、二变量收缩法(BivariateShrink)设计一种有效的磁共振图像MRI去噪算法.所设计的算法充分考虑了MRI的噪声分布特性以及小波系数的层间和层内相关性,其性能优劣主要取决于平方MRI的双树复小波系数的噪声标准差估计的准确程度,其次和双边滤波器的参数以及两种收缩方法的占比有关.为了使各算法的协同发挥最好的性能,以均方误差(MSE)、峰值信噪比(PSNR)和结构相似度(SSIM)作为图像质量评价指标,修正DT-CWT系数的噪声标准差,确定最佳双边滤波参数以及两种收缩方法的占比.在双树复小波域结合3种方法设计了一种有效的MRI去噪算法.结果 与现有几种算法进行图像去噪比较实验.在视觉效果、去噪指标PSNR和SSIM以及耗时方面,本文算法综合性能优于现有的几种MRI去噪算法,PSNR提高了约0.51~1dB,SSIM提高了约5%~10%.结论 双树复小波域的去噪优于基本小波去噪,利用层内和层间相关性的去噪算法很好去除了噪声,双边滤波器的使用增强了低频部分的图像,使得本文算法在MRI莱斯噪声的去除上获得了较好的表现,在去噪的同时能够保留边缘和细节信息.

Objective 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 . Method 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. Result 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%. Conclusion 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.