Global denoising for 3D MRI

Ao Feng; Jing Peng; Xi Wu

doi:10.1117/12.2243969

29 August 2016 Global denoising for 3D MRI

Ao Feng, Jing Peng, Xi Wu

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.

Citation Download Citation

Ao Feng, Jing Peng, and Xi Wu "Global denoising for 3D MRI", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100331Q (29 August 2016); https://doi.org/10.1117/12.2243969

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