By Topic

Statistical shape influence in geodesic active contours

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)
Leventon, M.E. ; Artificial Intelligence Lab., MIT, Cambridge, MA, USA ; Grimson, W.E.L. ; Faugeras, O.

A novel method of incorporating shape information into the image segmentation process is presented. We introduce a representation for deformable shapes and define a probability distribution over the variances of a set of training shapes. The segmentation process embeds an initial curve as the zero level set of a higher dimensional surface, and evolves the surface such that the zero level set converges on the boundary of the object to be segmented. At each step of the surface evolution, we estimate the maximum a posteriori (MAP) position and shape of the object in the image, based on the prior shape information and the image information. We then evolve the surface globally; towards the MAP estimate, and locally based on image gradients and curvature. Results are demonstrated on synthetic data and medical imagery in 2D min 3D

Published in:

Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on  (Volume:1 )

Date of Conference:

2000