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Pairwise Active Appearance Model and Its Application to Echocardiography Tracking

  • S. Kevin Zhou
  • Jie Shao
  • Bogdan Georgescu
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)

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.

Keywords

Motion Phase Landmark Point Active Appearance Model Active Shape Model Appearance Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • S. Kevin Zhou
    • 1
  • Jie Shao
    • 2
  • Bogdan Georgescu
    • 1
  • Dorin Comaniciu
    • 1
  1. 1.Integrated Data SystemsSiemens Corporate Research, Inc.PrincetonUSA
  2. 2.Center for Automation ResearchUniversity of MarylandCollege ParkUSA

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