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Optimal Initialization for 3D Correspondence Optimization: An Evaluation Study

Kirschner, Matthias and Gollmer, Sebastian T. and Wesarg, Stefan and Buzug, Thorsten M. (2011):
Optimal Initialization for 3D Correspondence Optimization: An Evaluation Study.
In: Lecture Notes in Computer Science (LNCS); 6801, pp. 308-319, Springer, Berlin, Heidelberg, New York, Information Processing in Medical Imaging, [Conference or Workshop Item]

Abstract

The identification of corresponding landmarks across a set of training shapes is a prerequisite for statistical shape model (SSM) construction. We automatically establish 3D correspondence using one new and several known alternative approaches for consistent, shape-preserving, spherical parameterization. The initial correspondence determined by all employed methods is refined by optimizing a groupwise objective function. The quality of all models before and after optimization is thoroughly evaluated using several data sets of clinically relevant, anatomical objects of varying complexity. Correspondence quality is benchmarked in terms of the SSMs' specificity and generalization ability, which are measured using different surface based distance functions. We find that our new approach performs best for complex objects. Furthermore, all new and previously published methods of our own allow for (i) building SSMs that are significantly better than the well-known SPHARM method, (ii) establishing quasi-optimal correspondence for low and moderately complex objects without additional optimization, and (iii) considerably speeding up convergence, thus, providing means for practical, fast, and accurate SSM construction.

Item Type: Conference or Workshop Item
Erschienen: 2011
Creators: Kirschner, Matthias and Gollmer, Sebastian T. and Wesarg, Stefan and Buzug, Thorsten M.
Title: Optimal Initialization for 3D Correspondence Optimization: An Evaluation Study
Language: English
Abstract:

The identification of corresponding landmarks across a set of training shapes is a prerequisite for statistical shape model (SSM) construction. We automatically establish 3D correspondence using one new and several known alternative approaches for consistent, shape-preserving, spherical parameterization. The initial correspondence determined by all employed methods is refined by optimizing a groupwise objective function. The quality of all models before and after optimization is thoroughly evaluated using several data sets of clinically relevant, anatomical objects of varying complexity. Correspondence quality is benchmarked in terms of the SSMs' specificity and generalization ability, which are measured using different surface based distance functions. We find that our new approach performs best for complex objects. Furthermore, all new and previously published methods of our own allow for (i) building SSMs that are significantly better than the well-known SPHARM method, (ii) establishing quasi-optimal correspondence for low and moderately complex objects without additional optimization, and (iii) considerably speeding up convergence, thus, providing means for practical, fast, and accurate SSM construction.

Series Name: Lecture Notes in Computer Science (LNCS); 6801
Publisher: Springer, Berlin, Heidelberg, New York
Uncontrolled Keywords: Forschungsgruppe Medical Computing (MECO), Statistical shape models (SSM), Evaluation, Optimization, Spherical parameterization, Point correspondence
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: Information Processing in Medical Imaging
Date Deposited: 12 Nov 2018 11:16
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