An Adaptive Hierarchical Method Based on Wavelet and Adaptive Filtering for MRI Denoising

MRI is one of the most powerful techniques to study the internal structure of the body. MRI image quality is affected by various noises. Noises in MRI are usually thermal and mainly due to the motion of charged particles in the coil. Noise in MRI images also cause a limitation in the study of visual images as well as computer analysis of the images. In this paper, first, it is proved that probability density function (PDF) of MRI Images is rician because of the process of image capturing and MRI hardware. Based on the review of later works in this area, it is determined that rician denoising in wavelet domain is better. It was concluded that the remaining noise in the final output of the conventional methods of wavelet domain, is Gaussian and can be greatly reduced with a Gaussian adaptive filter. In the proposed method the histogram of input and output image difference in first step of denoising routine is using for an adaptive estimation of remained Gaussian noise in output. Based on this estimation, a Gaussian filter designed and the output image was filtered again. The results showed that the final image quality will improve considerably. Rather than visual criteria, for proving the improvement the SSIM between main and filtered image is shown. In similar situations, the proposed algorithm is always better than the others.