Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2037715.2037786 | SIGGRAPH Posters |
Keywords | Field | DocType |
feature-preserving smoothing,tomographic data,computer tomography,diffusion kernel,anisotropic diffusion,fast gpu implementation,complex topology,diffusion tensor,ct data,multiscale feature-preserving smoothing,limited number,directional histogram,computed tomography,reverse engineering | Voxel,Anisotropic diffusion,Computer vision,Diffusion MRI,Computer graphics (images),Medical imaging,Computer science,Tomography,Smoothing,Artificial intelligence,Contouring,Edge-preserving smoothing | Conference |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nassim Jibai | 1 | 0 | 0.34 |
| Cyril Soler | 2 | 528 | 31.97 |
| Kartic Subr | 3 | 199 | 15.28 |
| Nicolas Holzschuch | 4 | 607 | 40.15 |