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2010
Conference Paper
Titel
3D statistical shape model building using consistent parameterization
Abstract
We propose a new correspondence optimisation algorithm for building 3D statistical shape models (SSMs) of genus-0 shapes. The main contribution of our work is the use of parameter space propagation to generate consistent spherical parameterisations of the training shapes. We present evaluation results for two data sets: A set of 30 liver shapes from different patients, and a set of 25 left ventricles covering the cardiac cycle of a single patient. Our evaluation shows that the use of parameter space propagation improves the robustness of correspondence optimisation algorithms and lead to fast convergence times.