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Proceedings Paper

Optimal landmark distributions for statistical shape model construction
Author(s): Tobias Heimann; Ivo Wolf; Hans-Peter Meinzer
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Paper Abstract

Minimizing the description length (MDL) is one of the most promising methods to automatically generate 3D statistical shape models. By modifying an initial landmark distribution according to the MDL cost function, points across the different training shapes are brought into correspondence. A drawback of the current approach is that the user has no influence on the final landmark positions, which often do not represent the modeled shape adequately. We extend an existing remeshing technique to work with statistical shape models and show how the landmark distribution can be modified anytime during the model construction phase. This procedure is guided by a control map in parameter space that can be set up to produce any desired point distribution, e.g. equally spaced landmarks. To compare our remeshed models with the original approach, we generalize the established generalization and specificity measures to be independent of the underlying landmark distribution. This is accomplished by switching the internal metric from landmark distances to the Tanimoto coefficient, a volumetric overlap measure. In a concluding evaluation, we generate models for two medical datasets with and without landmark redistribution. As the outcome reveals, redistributing landmarks to an equally spaced distribution during the model construction phase improves the quality of the resulting models significantly if the shapes feature prominent bulges or other complex geometry.

Paper Details

Date Published: 10 March 2006
PDF: 11 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61441J (10 March 2006);
Show Author Affiliations
Tobias Heimann, German Cancer Research Ctr. (Germany)
Ivo Wolf, German Cancer Research Ctr. (Germany)
Hans-Peter Meinzer, German Cancer Research Ctr. (Germany)

Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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