By Topic

Segmentation of 2D stress echocardiography sequences using rest-based patient-specific prior information

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)
Zabair, A.T. ; Dept. of Eng. Sci., Univ. of Oxford, Oxford, UK ; Noble, J.A.

In stress echocardiography, the heart is imaged at rest and again when stressed to observe the change in function between these two states; the idea being that abnormalities will be exaggerated and therefore easier to identify in stress, but importantly this is referenced to the rest state. Despite the development of segmentation and tracking techniques for the heart at rest, there is little literature on the same for the stressed heart. First we propose a patient-specific segmentation technique that gives a prediction of stress dataset segmentation given rest dataset segmentation for a healthy heart through the use of a global motion model based on Canonical Correlation Analysis (CCA). Secondly, we refine this prior segmentation using texture measures from the rest dataset as reference parameters for maximum likelihood estimation of the boundary in the stress dataset. Results show that for 52 out of 78 datasets, our model gives better results than using the technique described in.

Published in:

Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on

Date of Conference:

14-17 April 2010