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Area Preserving Parameterisation of Shapes with Spherical Topology

Kirschner, Matthias and Wesarg, Stefan (2009):
Area Preserving Parameterisation of Shapes with Spherical Topology.
In: GI-Edition - Lecture Notes in Informatics (LNI); P-154, pp. 1257-1266, Gesellschaft für Informatik, Bonn, Informatik 2009. Im Focus das Leben, [Conference or Workshop Item]

Abstract

Statistical shape models are powerful tools for model-based segmentation and have been successfully applied to the segmentation of various structures in medical images. Though the segmentation algorithms based on statistical shape models are simple, finding corresponding landmarks for the construction of the models is a challenging optimisation task. State-of-the-art algorithms that solve the correspondence problem require a representation of the training shapes in a suitable parameter space. The mapping of a shape to a parameter space can introduce large area distortions so that simple sampling techniques can not reconstruct the original shapes sufficiently well. In this paper, we propose an algorithm to construct area preserving parameterisations of shapes with spherical topology. Using our approach, good reconstructions of the original shapes can be achieved by uniform sampling. In contrast to previously published methods that use a black box optimisation approach, we exploit knowledge about the shortcomings of initial parameterisations.

Item Type: Conference or Workshop Item
Erschienen: 2009
Creators: Kirschner, Matthias and Wesarg, Stefan
Title: Area Preserving Parameterisation of Shapes with Spherical Topology
Language: English
Abstract:

Statistical shape models are powerful tools for model-based segmentation and have been successfully applied to the segmentation of various structures in medical images. Though the segmentation algorithms based on statistical shape models are simple, finding corresponding landmarks for the construction of the models is a challenging optimisation task. State-of-the-art algorithms that solve the correspondence problem require a representation of the training shapes in a suitable parameter space. The mapping of a shape to a parameter space can introduce large area distortions so that simple sampling techniques can not reconstruct the original shapes sufficiently well. In this paper, we propose an algorithm to construct area preserving parameterisations of shapes with spherical topology. Using our approach, good reconstructions of the original shapes can be achieved by uniform sampling. In contrast to previously published methods that use a black box optimisation approach, we exploit knowledge about the shortcomings of initial parameterisations.

Series Name: GI-Edition - Lecture Notes in Informatics (LNI); P-154
Publisher: Gesellschaft für Informatik, Bonn
Uncontrolled Keywords: Forschungsgruppe Medical Computing (MECO), Surface parameterization, Model based segmentations, Statistical shape models (SSM), Point correspondence
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: Informatik 2009. Im Focus das Leben
Date Deposited: 12 Nov 2018 11:16
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