Rician噪声水平场的估计及其在MR图像去噪中的应用  

Estimation of Spatially Variable Level Field of Rician Noise and its Application to MR Image Denoising

作　　者：余丽玲[1] 阳维[1] 冯衍秋[1] 刘闽[2] 冯前进[1] 陈武凡[1] 

机构地区：[1]南方医科大学生物医学工程学院,广州510515 [2]深圳出入境检验检疫局工业品检测技术中心,深圳518067

出　　处：《中国生物医学工程学报》2013年第5期532-538,共7页Chinese Journal of Biomedical Engineering

基　　金：国家自然科学青年基金(81101109);国家重点基础研究发展计划(973计划)(2010CB732505)

摘　　要：针对MR图像中空间变化Rician噪声的抑制问题,提出了一种噪声水平场的估计方法,同时结合方差稳定变换和BM3D算法实现MR图像的去噪。噪声水平场通过Rician噪声水平的局部估计和稀疏性约束模型进行估计,利用噪声水平场对噪声图像幅值进行空间自适应方差稳定变换,使得噪声与信号幅值和空间位置无关,采用BM3D算法即可实现对噪声的抑制,最后通过方差稳定逆变换得到无偏的去噪图像。仿真实验中,噪声水平场估计的平均相对误差小于0.2%,利用空间自适应方差稳定变换进行去噪,相比方差稳定变换,去噪图像的峰值信噪比可提高2 dB;采用真实乳腺MR图像进行去噪实验,利用自适应方差稳定变换可得到较高的Q度量。结果表明,所提出的方法能有效估计Rician噪声水平场,并用于抑制MR图像中空间变化的噪声。The levels of Rician noise in MR images vary spatially. A method to estimate the noise level field （NLF） was proposed in this paper for denoising the spatially variable noise. The NLF was fitted using the estimations of local noise levels and a NLF model with the sparsity constraint. Then, the noisy MR images were made to be homoscedastic by the spatially adaptive variance-stabilization transformations with the estimated NLF. Thus, BM3D algorithm was adopted to suppress the noise in the transformed images. Experimental results on the synthetic and real images demonstrate that the proposed method effectively estimates the NLF and the estimated NLF is useful for denoising the spatially variable Rician noise. The mean relative error of the estimated noise levels was less than 0.2%. Compared with other denoising methods for MR images, the method with NLF performed better and PSNR was improve about 2 dB. The results demonstrate that the proposed method can effectively estimate the Rician noise level field and can be used to suppress the spatially variable Rician noise.

关 键 词：Rician噪声 噪声水平场 方差稳定变换 BM3D 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]