DTI Image Denoising Based on Complex Shearlet Domain and Complex Diffusion Anisotropic Filtering
Diffusion tensor imaging (DTI) is an imaging modality that has developed in recent years. It is a non-invasive technique and needn’t contrast medium. However, the SNR of DTI data is relatively low and clinically polluted by noise, which can bring serious impacts on tensor calculating, fiber tracking and other post-processing. In order to reduce the influence of noise on DTI images and improve the efficiency of diffusion tensor imaging effectively, a new DTI denoising scheme is proposed by combining the complex Shearlet transform and complex diffusion anisotropic filtering. The experiment results acquired from the simulated and real data prove the good performance of the presented algorithm.
KeywordsDiffusion tensor imaging Complex shearlet transform Complex diffusion anisotropic filtering
This work was supported by Natural Science Foundation of China under grant 61401308 and 61572063, Natural Science Foundation of Hebei Province under grant F2016201142 and F2016201187, Science research project of Hebei Province under grant QN2016085 and ZC2016040, Natural Science Foundation of Hebei University under grant 2014-303, Post-graduate’s Innovation Fund Project of Hebei University under grant X201710.
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