29 August 2016 Global denoising for 3D MRI
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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100331Q (2016) https://doi.org/10.1117/12.2243969
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
Abstract
Denoising is the primary preprocessing step before subsequent clinical diagnostic analysis of MRI data. Common patch-based denoising methods rely heavily on the degree of patch matching, which limits their performance by the necessity of finding sufficiently similar patches. In this paper, we propose a global filtering framework, in which each voxel is restored with information from the whole 3D image. This global filter is not restricted to any specific patchbased filter, as it is a low-rank approximation using the Nyström method combined with a low sampling rate and a kmeans clustering adaptive sampling scheme. Experiments demonstrate that this method utilizes information effectively from the whole image for denoising, and the framework can be applied on top of most patch-based methods to further improve the performance.
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Ao Feng, Jing Peng, Xi Wu, "Global denoising for 3D MRI", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100331Q (29 August 2016); doi: 10.1117/12.2243969; https://doi.org/10.1117/12.2243969
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