Pairwise active appearance model and its application to echocardiography tracking

Authors:
S. Kevin Zhou;Jie Shao;Bogdan Georgescu;Dorin Comaniciu
Affiliations:
Integrated Data Systems, Siemens Corporate Research, Inc., Princeton, NJ;Center for Automation Research, University of Maryland, College Park, MD;Integrated Data Systems, Siemens Corporate Research, Inc., Princeton, NJ;Integrated Data Systems, Siemens Corporate Research, Inc., Princeton, NJ
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2006

Citing 4
Cited 0

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Abstract

We propose a pairwise active appearance model (PAAM) to characterize statistical regularities in shape, appearance, and motion presented by a target that undergoes a series of motion phases, such as the left ventricle in echocardiography. The PAAM depicts the transition in motion phase through a Markov chain and the transition in both shape and appearance through a conditional Gaussian distribution. We learn from a database the joint Gaussian distribution of the shapes and appearances belonging to two consecutive motion phases (i.e., a pair of motion phases), from which we analytically compute the conditional Gaussian distribution. We utilize the PAAM in tracking the left ventricle contour in echocardiography and obtain improved tracking results in terms of localization accuracy when compared with expert-specified contours.