Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2015 (2015), Article ID 790313, 11 pages
Research Article

Optimized Parallelization for Nonlocal Means Based Low Dose CT Image Processing

1Department of Radiology, General Hospital of Shenyang Military Area Command, Shenhe District, Shenyang 110840, China
2Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China
3The Key Laboratory of Computer Network and Information Integration, Southeast University and Ministry of Education, Nanjing 210096, China
4Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), 35000 Rennes, France

Received 19 July 2014; Revised 19 September 2014; Accepted 3 October 2014

Academic Editor: Yi Gao

Copyright © 2015 Libo Zhang 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.


Low dose CT (LDCT) images are often significantly degraded by severely increased mottled noise/artifacts, which can lead to lowered diagnostic accuracy in clinic. The nonlocal means (NLM) filtering can effectively remove mottled noise/artifacts by utilizing large-scale patch similarity information in LDCT images. But the NLM filtering application in LDCT imaging also requires high computation cost because intensive patch similarity calculation within a large searching window is often required to be used to include enough structure-similarity information for noise/artifact suppression. To improve its clinical feasibility, in this study we further optimize the parallelization of NLM filtering by avoiding the repeated computation with the row-wise intensity calculation and the symmetry weight calculation. The shared memory with fast speed is also used in row-wise intensity calculation for the proposed method. Quantitative experiment demonstrates that significant acceleration can be achieved with respect to the traditional straight pixel-wise parallelization.