Title: A magnetic resonance imaging denoising technique using non-local means and unsupervised learning
Authors: Bo Peng; Lei Xie; Tao Wu
Addresses: School of Electronics and Information Engineering, Sichuan University, Sichuan, 610065, China ' School of Computer Science, Chengdu University of Information Technology, Sichuan, 610021, China ' School of Computer Science, Chengdu University of Information Technology, Sichuan, 610021, China
Abstract: We propose a new non-local mean (NLM) algorithm using unsupervised learning and k-means clustering for denoising magnetic resonance (MR) images. Our technique improves image processing speeds with enhanced denoising performance on multiple types of images. The calculation of similarity weights at the cluster level improves computational efficiency. We conducted experiments with brain MR images of various sizes, including three T1- and T2-weighted images. Three quality metrics show that our algorithm achieves moderate improvements in denoising accuracy with significant reductions in execution time. The proposed method processed the sample data in one-fifth of the time of the original NLM method. Compared to several state-of-the-art methods, our method offers improved peak signal-to-noise ratios (PSNRs) for samples with large amounts of noise.
Keywords: magnetic resonance imaging; MRI; image denoising; non-local mean; NLM; k-means; unsupervised learning.
International Journal of Information and Communication Technology, 2020 Vol.16 No.2, pp.152 - 161
Received: 23 Jan 2019
Accepted: 07 Mar 2019
Published online: 06 Mar 2020 *