An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images

Kaixin Chen; Xiao Lin; Xing Hu; Jiayao Wang; Han Zhong; Linhua Jiang

doi:10.1186/s12880-019-0407-4

BMC Medical Imaging (Jan 2020)

An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images

Kaixin Chen,
Xiao Lin,
Xing Hu,
Jiayao Wang,
Han Zhong,
Linhua Jiang

Affiliations

Kaixin Chen: Shanghai Key Lab of Modern Optical Systems, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology
Xiao Lin: Shanghai Key Lab of Modern Optical Systems, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology
Xing Hu: Shanghai Key Lab of Modern Optical Systems, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology
Jiayao Wang: Shanghai Key Lab of Modern Optical Systems, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology
Han Zhong: Shanghai Key Lab of Modern Optical Systems, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology
Linhua Jiang: Shanghai Key Lab of Modern Optical Systems, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology

DOI: https://doi.org/10.1186/s12880-019-0407-4
Journal volume & issue: Vol. 20, no. 1
pp. 1 – 9

Abstract

Read online

Abstract Background The Rician noise formed in magnetic resonance (MR) imaging greatly reduced the accuracy and reliability of subsequent analysis, and most of the existing denoising methods are suitable for Gaussian noise rather than Rician noise. Aiming to solve this problem, we proposed fuzzy c-means and adaptive non-local means (FANLM), which combined the adaptive non-local means (NLM) with fuzzy c-means (FCM), as a novel method to reduce noise in the study. Method The algorithm chose the optimal size of search window automatically based on the noise variance which was estimated by the improved estimator of the median absolute deviation (MAD) for Rician noise. Meanwhile, it solved the problem that the traditional NLM algorithm had to use a fixed size of search window. Considering the distribution characteristics for each pixel, we designed three types of search window sizes as large, medium and small instead of using a fixed size. In addition, the combination with the FCM algorithm helped to achieve better denoising effect since the improved the FCM algorithm divided the membership degrees of images and introduced the morphological reconstruction to preserve the image details. Results The experimental results showed that the proposed algorithm (FANLM) can effectively remove the noise. Moreover, it had the highest peak signal-noise ratio (PSNR) and structural similarity (SSIM), compared with other three methods: non-local means (NLM), linear minimum mean square error (LMMSE) and undecimated wavelet transform (UWT). Using the FANLM method, the image details can be well preserved with the noise being mostly removed. Conclusion Compared with the traditional denoising methods, the experimental results showed that the proposed approach effectively suppressed the noise and the edge details were well retained. However, the FANLM method took an average of 13 s throughout the experiment, and its computational cost was not the shortest. Addressing these can be part of our future research.

Published in BMC Medical Imaging

ISSN: 1471-2342 (Online)
Publisher: BMC
Country of publisher: United Kingdom
LCC subjects: Medicine: Medicine (General): Medical technology
Website: http://bmcmedimaging.biomedcentral.com

About the journal

Abstract

Keywords