Denoising Diffusion Tensor Images with Shearlet

Diffusion tensor imaging (DTI) is known to be the best non-invasive imaging modality in providing anatomical information as white-matter fiber bundles. However, the Gaussian noise introduced into the diffusion tensor images can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Gaussian noise, many denoising methods have been presented. In this paper, a shearlet based denosing strategy is introduced. To evaluate the efficiency of the proposed shearlet based denoising method in accounting for the Gaussian noise introduced into the images, the peak to peak signal-to-noise ratio (PSNR), signal-to-mean squared error ratio (SMSE) and edge keeping index (Beta) metrics are adopted. The experiment results acquired from both the synthetic and real data indicate the good performance of our proposed filter.