Cart (Loading....) | Create Account
Close category search window
 

Statistical shape models for segmentation and structural analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Gerig, G. ; Dept. of Comput. Sci., North Carolina Univ., Chapel Hill, NC, USA ; Styner, M. ; Szekely, G.

Biomedical imaging of large patient populations, both cross-sectionally and longitudinally, is becoming a standard technique for noninvasive, in-vivo studies of the pathophysiology of diseases and for monitoring drug treatment. In radiation oncology, imaging and extraction of anatomical organ geometry is a routine procedure for therapy planning an monitoring, and similar procedures are vital for surgical planning and image-guided therapy. Bottlenecks of today's studies, often processed by labor-intensive manual region drawing, are the lack of efficient, reliable tools for three-dimensional organ segmentation and for advanced morphologic characterization. This paper discusses current research and development focused towards building of statistical shape models, used for automatic model-based segmentation and for shape analysis and discrimination. We build statistical shape models which describe the geometric variability and image intensity characteristics of anatomical structures. New segmentations are obtained by model deformation driven by local image match forces and constrained by the training statistics. Two complimentary representations for 3D shape are discussed and compared, one based on global surface parametrization and a second one on medial manifold description. The discussion will be guided by presenting a most recent study to construct a statistical shape model of the caudate structure.

Published in:

Biomedical Imaging, 2002. Proceedings. 2002 IEEE International Symposium on

Date of Conference:

2002

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.