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Computational and Mathematical Methods in Medicine
Volume 2015 (2015), Article ID 232389, 11 pages
Research Article

A MRI Denoising Method Based on 3D Nonlocal Means and Multidimensional PCA

Liu Chang,1,2 Gao ChaoBang,1,2 and Yu Xi1,2

1School of Computer Science, Chengdu University, Chengdu 610106, China
2Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan, Chengdu, China

Received 14 June 2015; Revised 12 August 2015; Accepted 24 August 2015

Academic Editor: Anne Humeau-Heurtier

Copyright © 2015 Liu Chang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recently nonlocal means (NLM) and its variants have been applied in the various scientific fields extensively due to its simplicity and desirable property to conserve the neighborhood information. The two-stage MRI denoising algorithm proposed in this paper is based on 3D optimized blockwise version of NLM and multidimensional PCA (MPCA). The proposed algorithm takes full use of the block representation advantageous of NLM3D to restore the noisy slice from different neighboring slices and employs MPCA as a postprocessing step to remove noise further while preserving the structural information of 3D MRI. The experiments have demonstrated that the proposed method has achieved better visual results and evaluation criteria than 3D-ADF, NLM3D, and OMNLM_LAPCA.