Kirschner, Matthias ; Wesarg, Stefan (2009)
Propagation of Shape Parameterisation for the Construction of a Statistical Shape Model of the Left Ventricle.
Proceedings of the Vision, Modeling, and Visualization Workshop 2009.
Conference or Workshop Item, Bibliographie
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 ; Wesarg, Stefan |
| Type of entry: | Bibliographie |
| Title: | Propagation of Shape Parameterisation for the Construction of a Statistical Shape Model of the Left Ventricle |
| Language: | English |
| Date: | 2009 |
| Publisher: | Otto-von-Guericke-Universität, Magdeburg |
| Event Title: | Proceedings of the Vision, Modeling, and Visualization Workshop 2009 |
| 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. |
| 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 |
| Date Deposited: | 12 Nov 2018 11:16 |
| Last Modified: | 12 Nov 2018 11:16 |
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