Hierarchical automated clustering of cloud point set by ellipsoidal skeleton. Application to organ geometric modeling from CT-scan images
We present a robust method for automatically constructing an ellipsoidal skeleton (e-skeleton) from a set of 3D points taken from NMR or TDM images. To ensure steadiness and accuracy, all points of the objects are taken into account, including the inner ones, which is different from the existing techniques. This skeleton will be essentially useful for object characterization, for comparisons between various measurements and as a basis for deformable models. It also provides good initial guess for surface reconstruction algorithms. On output of the entire process, we obtain an analytical description of the chosen entity, semantically zoomable (local features only or reconstructed surfaces), with any level of detail (LOD) by discretization step control in voxel or polygon format. This capability allows us to handle objects at interactive frame rates once the e-skeleton is computed. Each e-skeleton is stored as a multiscale CSG implicit tree.
| Main Authors: | , , , |
|---|---|
| Format: | conference_item biblioteca |
| Language: | eng |
| Published: |
SPIE
|
| Subjects: | U10 - Informatique, mathématiques et statistiques, imagerie, sciences médicales, modèle mathématique, radiographie, partie du corps, http://aims.fao.org/aos/agrovoc/c_36760, http://aims.fao.org/aos/agrovoc/c_4695, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_6428, http://aims.fao.org/aos/agrovoc/c_981, |
| Online Access: | http://agritrop.cirad.fr/401290/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | We present a robust method for automatically constructing an ellipsoidal skeleton (e-skeleton) from a set of 3D points taken from NMR or TDM images. To ensure steadiness and accuracy, all points of the objects are taken into account, including the inner ones, which is different from the existing techniques. This skeleton will be essentially useful for object characterization, for comparisons between various measurements and as a basis for deformable models. It also provides good initial guess for surface reconstruction algorithms. On output of the entire process, we obtain an analytical description of the chosen entity, semantically zoomable (local features only or reconstructed surfaces), with any level of detail (LOD) by discretization step control in voxel or polygon format. This capability allows us to handle objects at interactive frame rates once the e-skeleton is computed. Each e-skeleton is stored as a multiscale CSG implicit tree. |
|---|