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Proceedings Paper

Curve evolution methods for dynamic tomography with unknown dynamic models
Author(s): Yonggang Shi; William Clement Karl; David A. Castanon
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Paper Abstract

In this paper, we propose a variational framework for tomographic reconstruction of dynamic objects with unknown dynamic models. This is an extension of our previous work on dynamic tomography using curve evolution methods where the shape dynamics are known a priori. We assume the dynamic model of the shape is a parameterized affine transform and propose a variational framework that incorporates information from observed data, intensity dynamics, spatial smoothness prior, and the dynamical shape model. A coordinate descent algorithm based on a curve evolution method is then proposed for the joint estimation of the intensities, object boundary sequences, and the unknown dynamic model parameters. For implementation of the curve evolution and parameter estimation process, we use efficient level set methods.

Paper Details

Date Published: 15 May 2003
PDF: 9 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481883
Show Author Affiliations
Yonggang Shi, Boston Univ. (United States)
William Clement Karl, Boston Univ. (United States)
David A. Castanon, Boston Univ. (United States)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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