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

: Kirschner, Matthias; Wesarg, Stefan

Magnor, M.A.:
Vision, Modeling, and Visualization Workshop 2009. Proceedings : November 16 - 18, 2009 Braunschweig, Germany
Magdeburg: Otto-von-Guericke-Universität Magdeburg, 2009
ISBN: 978-3-9804874-8-1
Workshop Vision, Modeling, and Visualization (VMV) <14, 2009, Braunschweig>
Conference Paper
Fraunhofer IGD ()
surface parameterization; model based segmentations; statistical shape models (SSM); medical image processing; point correspondence; cardiology

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.