By Topic

Deformable template and distribution mixture-based data modeling for the endocardial contour tracking in an echographic sequence

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

2 Author(s)
Mignotte, M. ; INRIA, France ; Meunier, J.

We present a new method to shape-based segmentation of deformable anatomical structures in medical images and validate this approach by detecting and tracking the endocardial border in an echographic image sequence. To this end, a global prior knowledge of the endocardial contour is captured by a prototype template with a set of admissible deformations to take into account its inherent natural variability over time. In this approach, the data likelihood model rely on an accurate statistical modeling of the grey level distribution of each class present in the image. The parameters of this distribution mixture are given by a preliminary estimation step which takes into account the distribution shape of each class. Then the tracking problem is stated in a Bayesian framework where it ends up as an optimization problem. This one is then efficiently solved by a genetic algorithm combined with a steepest ascent procedure. This technique has been successfully applied on synthetic images and on a real echocardiographic image sequence

Published in:

Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.  (Volume:1 )

Date of Conference:

1999