inria-00611915, version 1
Multiscale Feature-Preserving Smoothing of Tomographic Data
Nassim Jibai
1, 2Cyril Soler a, 1, 2Kartic Subr 3Nicolas Holzschuch a, 1, 2
ACM SIGGRAPH 2011 Posters (2011)
Résumé : Computer tomography (CT) has wide application in medical imaging and reverse engineering. Due to the limited number of projections used in reconstructing the volume, the resulting 3D data is typically noisy. Contouring such data, for surface extraction, yields surfaces with localised artifacts of complex topology. To avoid such artifacts, we propose a method for feature-preserving smoothing of CT data. The smoothing is based on anisotropic diffusion, with a diffusion tensor designed to smooth noise up to a given scale, while preserving features. We compute these diffusion kernels from the directional histograms of gradients around each voxel, using a fast GPU implementation.
- a – INRIA
- 1 : Laboratoire Jean Kuntzmann (LJK)
- CNRS : UMR5224 – Université Joseph Fourier - Grenoble I – Université Pierre Mendès-France - Grenoble II – Institut Polytechnique de Grenoble - Grenoble Institute of Technology
- 2 : ARTIS (INRIA Grenoble Rhône-Alpes / LJK Laboratoire Jean Kuntzmann)
- CNRS : FR71 – INRIA – Laboratoire Jean Kuntzmann – CNRS : UMR5224 – Université Joseph Fourier - Grenoble I – Institut National Polytechnique de Grenoble (INPG)
- 3 : Computer science department [University College London] (UCL-CS)
- University College of London (UCL)
- Domaine : Informatique/Géométrie algorithmique
- Mots-clés : Feature-preserving smoothing – anisotropic diffusion
- inria-00611915, version 1
- http://hal.inria.fr/inria-00611915
- oai:hal.inria.fr:inria-00611915
- Contributeur : Nassim Jibai
- Soumis le : Lundi 12 Septembre 2011, 14:58:00
- Dernière modification le : Lundi 21 Novembre 2011, 11:11:45
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