NDLI: Echocardiogram view classification using edge filtered scale-invariant motion features

Content Provider	IEEE Xplore Digital Library
Author	Kumar, R. Fei Wang Beymer, D. Syeda-Mahmood, T.
Copyright Year	2009
Description	Author affiliation: Almaden Res. Center, IBM, San Jose, CA, USA (Fei Wang; Beymer, D.; Syeda-Mahmood, T.) \|\| Dept. of Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL, USA (Kumar, R.)
Abstract	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.
Starting Page	723
Ending Page	730
File Size	1377669
Page Count	8
File Format	PDF
ISBN	9781424439928
ISSN	10636919
DOI	10.1109/CVPR.2009.5206838
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Probes Videos Testing Voting Ultrasonic imaging Heart Anatomy Image edge detection Image motion analysis Kinetic theory
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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