A Rician Noise Prediction and Removal Model for MRI Head Scans using wavelet based Non-Local Median Filter

Abstract

In the medical imaging field, anatomical structure preservation is a difficult task during the denoising process. Magnetic resonance imaging (MRI) scanner corrupts the images by the Rician noise during the acquisition process. Rician noise affects the diagnosis and treatment planning for the subjects. Regenerating noise-free images is a time-consuming process with limited MR scanner resources available in developing countries. Therefore, the medical industry utilizes the advancement of computer-aided automatic denoising methods. This article presents a novel denoising method for Rician noise using a wavelet-based non-local median filtering (WNLMed) technique. The work contains three phases: noise estimation, wavelet thresholding using a lifting scheme, and non-local median filter (NLMed). Materials used for this experiment are collected from the Brainweb repositories and tested with validation metrics such as normalized absolute error (NAE), peak signal to noise ratio (PSNR), structure similarity index measure (SSIM), the figure of merit (FOM), and compared with the state-of-the-art methods. The method yields high PSNR value than other methods.