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Propagation of Shape Parameterisation for the Construction of a Statistical Shape Model of the Left Ventricle

Kirschner, Matthias and Wesarg, Stefan (2009):
Propagation of Shape Parameterisation for the Construction of a Statistical Shape Model of the Left Ventricle.
pp. 313-320, Otto-von-Guericke-Universität, Magdeburg, Proceedings of the Vision, Modeling, and Visualization Workshop 2009, [Conference or Workshop Item]

Abstract

Statistical Shape Models (SSMs) have been successfully applied to both segmentation and the description of the dynamic behaviour of the heart. SSMs are learned from a set of training examples, which are represented by vectors of corresponding landmarks. While the construction of a SSMis simple when a landmark representation of the training shapes is available, the extraction of corresponding landmarks from training images or meshes of different sizes is difficult. Optimisation schemes that solve this so-called correspondence problem rely on a parameter space representation of the input shapes. These optimisation schemes tend to be sensitive to the initial parameterisation of the input shapes. In this work, we present an algorithm to produce a consistent spherical parameterisation for shapes of the left ventricle. Our algorithm propagates the spherical parameterisation of a root shape within seconds to all other shapes. We demonstrate the effectiveness of our approach by extracting a SSM from the parameterisations generated by our algorithm.

Item Type: Conference or Workshop Item
Erschienen: 2009
Creators: Kirschner, Matthias and Wesarg, Stefan
Title: Propagation of Shape Parameterisation for the Construction of a Statistical Shape Model of the Left Ventricle
Language: English
Abstract:

Statistical Shape Models (SSMs) have been successfully applied to both segmentation and the description of the dynamic behaviour of the heart. SSMs are learned from a set of training examples, which are represented by vectors of corresponding landmarks. While the construction of a SSMis simple when a landmark representation of the training shapes is available, the extraction of corresponding landmarks from training images or meshes of different sizes is difficult. Optimisation schemes that solve this so-called correspondence problem rely on a parameter space representation of the input shapes. These optimisation schemes tend to be sensitive to the initial parameterisation of the input shapes. In this work, we present an algorithm to produce a consistent spherical parameterisation for shapes of the left ventricle. Our algorithm propagates the spherical parameterisation of a root shape within seconds to all other shapes. We demonstrate the effectiveness of our approach by extracting a SSM from the parameterisations generated by our algorithm.

Publisher: Otto-von-Guericke-Universität, Magdeburg
Uncontrolled Keywords: Forschungsgruppe Medical Computing (MECO), Surface parameterization, Model based segmentations, Statistical shape models (SSM), Medical image processing, Point correspondence, Cardiology
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: Proceedings of the Vision, Modeling, and Visualization Workshop 2009
Date Deposited: 12 Nov 2018 11:16
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