Dieomorphic Statistical Deformation Models | Michael Sass Hansen, Mads Fogtmann Hansen, Rasmus Larsen
| | 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 diffieomorphic model 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 alidated on a set of MR images of corpus callosum with ground truth in form of manual expert annotations. We anticipate applications in unconstrained diffeomorphic synthesis of images, e.g. for tracking, segmentation,
registration or classification purposes. | | Type | Conference paper [Submitted] | | Conference | ICCV 2007, Workshop NRTL | | Year | 2007 | | Electronic version(s) | [pdf] | | BibTeX data | [bibtex] | | IMM Group(s) | Image Analysis & Computer Graphics |
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