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

Echocardiogram view classification using edge filtered scale-invariant motion features

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

4 Author(s)
Kumar, R. ; Dept. of Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL, USA ; Fei Wang ; Beymer, D. ; Syeda-Mahmood, T.

In an 2D echocardiogram exam, an ultrasound probe samples the heart with 2D slices. Changing the orientation and position on the probe changes the slice viewpoint, altering the cardiac anatomy being imaged. The determination of the probe viewpoint forms an essential step in automatic cardiac echo image analysis. In this paper we present a system for automatic view classification that exploits cues from both cardiac structure and motion in echocardiogram videos. In our framework, each image from the echocardiogram video is represented by a set of novel salient features. We locate these features at scale invariant points in the edge-filtered motion magnitude images and encode them using local spatial, textural and kinetic information. Training in our system involves learning a hierarchical feature dictionary and parameters of a pyramid matching kernel based support vector machine. While testing, each image, classified independently, casts a votes towards parent video classification and the viewpoint with maximum votes wins. Through experiments on a large database of echocardiograms obtained from both diseased and control subjects, we show that our technique consistently outperforms state-of-the-art methods in the popular four-view classification test. We also present results for eight-view classification to demonstrate the scalability of our framework.

Published in:

Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on

Date of Conference:

20-25 June 2009

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.