Coupled-Contour Tracking through Non-orthogonal Projections and Fusion for Echocardiography View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2004

AUTHORS

Xiang Sean Zhou , Dorin Comaniciu , Sriram Krishnan

ABSTRACT

Existing methods for incorporating subspace model constraints in contour tracking use only partial information from the measurements and model distribution. We propose a complete fusion formulation for robust contour tracking, optimally resolving uncertainties from heteroscedastic measurement noise, system dynamics, and a subspace model. The resulting non-orthogonal subspace projection is a natural extension of the traditional model constraint using orthogonal projection. We build models for coupled double-contours, and exploit information from the ground truth initialization through a strong model adaptation. Our framework is applied for tracking in echocardiograms where the noise is heteroscedastic, each heart has distinct shape, and the relative motions of epi- and endocardial borders reveal crucial diagnostic features. The proposed method significantly outperforms the traditional shape-space-constrained tracking algorithm. Due to the joint fusion of heteroscedastic uncertainties, the strong model adaptation, and the coupled tracking of double-contours, robust performance is observed even on the most challenging cases. More... »

PAGES

336-349

Book

TITLE

Computer Vision - ECCV 2004

ISBN

978-3-540-21984-2
978-3-540-24670-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-24670-1_26

DOI

http://dx.doi.org/10.1007/978-3-540-24670-1_26

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1002194116


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