Hierarchical automated clustering of cloud point set by ellipsoidal skeleton: application to organ geometric modeling from CT-scan images

Frederic Banegas; Dominique Michelucci; Marc Roelens; Marc Jaeger

doi:10.1117/12.348517

21 May 1999 Hierarchical automated clustering of cloud point set by ellipsoidal skeleton: application to organ geometric modeling from CT-scan images

Frederic Banegas, Dominique Michelucci, Marc Roelens, Marc Jaeger

Author Affiliations +

Proceedings Volume 3661, Medical Imaging 1999: Image Processing; (1999) https://doi.org/10.1117/12.348517
Event: Medical Imaging '99, 1999, San Diego, CA, United States

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.

Citation Download Citation

Frederic Banegas, Dominique Michelucci, Marc Roelens, and Marc Jaeger "Hierarchical automated clustering of cloud point set by ellipsoidal skeleton: application to organ geometric modeling from CT-scan images", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); https://doi.org/10.1117/12.348517

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available