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Proceedings Paper

Hierarchical automated clustering of cloud point set by ellipsoidal skeleton: application to organ geometric modeling from CT-scan images
Author(s): Frederic Banegas; Dominique Michelucci; Marc Roelens; Marc Jaeger
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Paper Abstract

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.

Paper Details

Date Published: 21 May 1999
PDF: 11 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348517
Show Author Affiliations
Frederic Banegas, Ecole Nationale Superieure des Mines de St. Etienne (France)
Dominique Michelucci, Ecole Nationale Superieure des Mines de St. Etienne (France)
Marc Roelens, Ecole Nationale Superieure des Mines de St. Etienne (France)
Marc Jaeger, CIRAD-MEB (France)


Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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