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An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images.

Pierrick Coupé 1, 2, * Pierre yger 1, 2, 3 Sylvain Prima 1, 2 Pierre Hellier 1, 2 Charles Kervrann 4, 5 Christian Barillot 1, 2 
* Corresponding author
2 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
5 VISTAS - Spatio-Temporal Vision and Learning
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image quality and to improve the performance of all the tasks needed for quantitative imaging analysis. The method proposed in this paper is based on a 3-D optimized blockwise version of the nonlocal (NL)-means filter (Buades, et al., 2005). The NL-means filter uses the redundancy of information in the image under study to remove the noise. The performance of the NL-means filter has been already demonstrated for 2-D images, but reducing the computational burden is a critical aspect to extend the method to 3-D images. To overcome this problem, we propose improvements to reduce the computational complexity. These different improvements allow to drastically divide the computational time while preserving the performances of the NL-means filter. A fully automated and optimized version of the NL-means filter is then presented. Our contributions to the NL-means filter are: 1) an automatic tuning of the smoothing parameter; 2) a selection of the most relevant voxels; 3) a blockwise implementation; and 4) a parallelized computation. Quantitative validation was carried out on synthetic datasets generated with BrainWeb (Collins, et al., 1998). The results show that our optimized NL-means filter outperforms the classical implementation of the NL-means filter, as well as two other classical denoising methods [anisotropic diffusion (Perona and Malik, 1990)] and total variation minimization process (Rudin, et al., 1992) in terms of accuracy (measured by the peak signal-to-noise ratio) with low computation time. Finally, qualitative results on real data are presented .
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Submitted on : Wednesday, September 5, 2007 - 11:04:00 AM
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Pierrick Coupé, Pierre yger, Sylvain Prima, Pierre Hellier, Charles Kervrann, et al.. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images.. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2008, 27 (4), pp.425-41. ⟨10.1109/TMI.2007.906087⟩. ⟨inserm-00169658⟩



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