DWI denoising method based on BEMD and adaptive wiener filter

Diffusion weighted image (DWI), based on which a diffusion tensor image (DTI) is calculated, is affected by several artifacts and noise that complicate the analysis and interpretation of DTI. As DWI is multi-boundary in nature, to get accurate boundary signals of DWI is particularly important. A new method combing bidimensional empirical mode decomposition (BEMD) with adaptive wiener filter is proposed. Through the BEMD method the degraded image is decomposed to detail part and residual part. The detail part of the image contains the boundary signal and the noise of the degraded image, and residual part describe the image tendency. Then, the adaptive wiener filter is applied to remove the noise in the detail part of the DWI and the residual part is handled. At last, the denoised detail image and residual are combined to form the denoised DWI. The method is performed on the real DWI data. Experiment results positively show that this method removed noise effectively and kept the boundary of DWI successfully.