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Optimized Parallelization for Nonlocal Means Based Low Dose CT Image Processing

Abstract : 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 I/O 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
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Contributor : Laurent Jonchère <>
Submitted on : Friday, June 3, 2016 - 2:04:21 PM
Last modification on : Monday, March 9, 2020 - 3:19:21 PM

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Libo Zhang, Benqiang Yang, Zhikun Zhuang, Yining Hu, Yang Chen, et al.. Optimized Parallelization for Nonlocal Means Based Low Dose CT Image Processing. Computational and Mathematical Methods in Medicine, Hindawi Publishing Corporation, 2015, 2015, pp.790313. ⟨10.1155/2015/790313⟩. ⟨hal-01326302⟩



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