莱斯校正的NLM算法在扩散加权图像中的应用

Application of Rician Correction Non-local Mean Algorithm in Diffusion-weighted Image

扩散张量成像技术是一种非侵入活体获取脑白质结构的技术,其广泛应用于人体大脑的神经纤维跟踪.扩散张量图像是由扩散加权图像计算得到的,而扩散加权图像对噪声较为敏感,从而影响后续处理.扩散加权图像具有两个特点,一是图像的自相似性程度高,纹理和结构具有重复出现的特性且细节纹理较多,二是图像中所含噪声为莱斯噪声.基于这两个特点,我们提出了莱斯校正的非局部均值滤波算法.并将此算法应用于扩散加权图像的降噪中.算法首先针对图像中的莱斯噪声进行莱斯校正,然后再对校正后的图像使用非局部均值滤波器对其进行降噪.为了验证本文算法,通过实验将本文算法与传统的降噪算法进行比较.实验结果表明,本文算法能够更有效的减少扩散加权图像中的噪声,更好的保存了图像的纹理细节,提高了数据准确度.

Diffusion tensor imaging is a noninvasive technique to acquire white matter structures of the brain,which is widely used in the tracking of nerve fibers in the human brain. Diffusion tensor images are calculated by diffusion-weighted images. Diffusion-weighted images are more sensitive to noise,thus this will affect subsequent processing. Diffusion-weighted images have two characteristics.One is the high degree of self-similarity of the images,and the rich feature details. The other is that the noise contained in the image is Rician noise. Based on these characteristics,a Rician correction non-local means filter algorithm is proposed for the diffusion-weighted images denoising. And the proposed algorithm is applied to the diffusion-weighted images denoising. Firstly,the algorithm performs a Rician correction on the Rician noise in the images,and secondly it uses the non-local mean filter to reduce the noise on the corrected image. In order to verify our algorithm,the proposed algorithm is compared with some traditional denoising algorithms through experiments. The experimental results showthat the proposed algorithm can not only reduce the noise in the diffusion-weighted images,but also preserve much better the image local structures. The accuracy of data are improved.