MRI Denoising using Visual System Response Model Simulating PCNN

Abstract

A novel improved Non-local Means (NLM) filtering scheme for MR images is proposed based on a simplified pulse-coupled neural network (PCNN) which follows mammal's visual/perceptual response characteristics. The proposed scheme is a two-stage filtering technique. In the first stage, the noisy MR image is passed through a PCNN and the firing time output (time signature) of the PCNN is recorded. The time signature is used to pre-select a subset of pixels in non-local vicinities, depending on their similarities with the pixel of interest. In the second stage, improved unbiased NLM (IUNLM) is applied to denoise the pixel of interest only considering the contributions of the pre-selected subset of pixels in the non-local vicinities. Extensive experiments and comparisons with state-of-the-art MRI denoising techniques were carried out to evaluate the performance of the proposed scheme. The results suggest supporting evidence of the effectiveness of the proposed scheme in MR image denoising.