Denoising of 3D magnetic resonance images using non-local PCA and Transform-Domain Filter

The Magnetic Resonance Imaging (MRI) technology used in clinical diagnosis demands high Peak Signal-to-Noise ratio (PSNR) and improved resolution for accurate analysis and treatment monitoring. However, MRI data is often corrupted by random noise which degrades the quality of Magnetic Resonance (MR) images. Denoising is a paramount challenge as removing noise causes reduction in the fine details of MRI images. We have developed a novel algorithm which employs Principal Component Analysis (PCA) decomposition and Wiener filtering. We have proposed a two stage approach. In first stage, non-local PCA thresholding is applied on noisy image and second stage uses Wiener filter over this filtered image. Our algorithm is implemented using MATLAB and performance is measured via PSNR. The proposed approach has also been compared with related state-of-art methods. Moreover, we present both qualitative and quantitative results which prove that proposed algorithm gives superior denoising performance.