Abstract
In this paper we present a new method for constructing diffeomorphic statistical deformation models in arbitrary dimensional images with a nonlinear generative model and a linear parameter space. Our deformation model is a modified version of the diffeomorphic model introduced by Cootes et al. The modifications ensure that no boundary restriction has to be enforced on the parameter space to prevent folds or tears in the deformation field. For straightforward statistical analysis, principal component analysis and sparse methods, we assume that the parameters for a class of deformations lie on a linear manifold and that the distance between two deformations are given by the metric introduced by the L2-norm in the parameter space. The chosen L2-norm is shown to have a clear and intuitive interpretation on the usual nonlinear manifold. Our model is validated on a set of MR images of corpus callosum with ground truth in form of manual expert annotations, and compared to Cootes's model. We anticipate applications in unconstrained diffeomorphic synthesis of images, e.g. for tracking, segmentation, registration or classification purposes.
Original language | English |
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Title of host publication | 2007 IEEE 11th International Conference on Computer Vision |
Publisher | IEEE |
Publication date | 2007 |
Pages | 2626-2633 |
ISBN (Print) | 978-1-4244-1631-8 |
DOIs | |
Publication status | Published - 2007 |
Event | Workshop on Non-rigid Registration and Tracking through learning - Rio de Janeiro, Brazil Duration: 14 Oct 2007 → 20 Oct 2007 |
Workshop
Workshop | Workshop on Non-rigid Registration and Tracking through learning |
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Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 14/10/2007 → 20/10/2007 |
Other | Part of 2007 IEEE 11th International Conference on Computer Vision |