Geometrically deformable templates for shape-based segmentation and tracking in cardiac MR images

Daniel Rueckert, Peter Burger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations

Abstract

We present a new approach to shape-based segmentation and tracking of multiple, deformable anatomical structures in cardiac MR images. We propose to use an energy-minimizing geometrically deformable template (GDT) which can deform into similar shapes under the influence of image forces. The degree of deformation of the template from its equilibrium shape is measured by a penalty function associated with mapping between the two shapes. In 2D, this term corresponds to the bending energy of an idealized thin-plate of metal. By minimizing this term along with the image energy terms of the classic deformable model, the deformable template is attracted towards objects in the image whose shape is similar to its equilibrium shape. This framework allows for the simultaneous segmentation of multiple deformable objects using intra-as well as inter-shape information. The energy minimization problem of the deformable template is formulated in a Bayesian framework and solved using relaxation techniques: Simulated Annealing (SA), a stochastic relaxation technique is used for segmentation while Iterated Conditional Modes (ICM), a deterministic relaxation technique is used for tracking. We present results of the algorithm applied to the reconstruction of the left and right ventricle of the human heart in 4D MR images.

Original languageEnglish
Title of host publicationEnergy Minimization Methods in Computer Vision and Pattern Recognition - International Workshop EMMCVPR 1997, Proceedings
EditorsEdwin R. Hancock, Marcello Pelillo
PublisherSpringer Verlag
Pages83-98
Number of pages16
ISBN (Print)3540629092, 9783540629092
DOIs
StatePublished - 1997
Externally publishedYes
EventInternational Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 1997 - Venice, Italy
Duration: 21 May 199723 May 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1223
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 1997
Country/TerritoryItaly
CityVenice
Period21/05/9723/05/97

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