Jud, Christoph. Object segmentation by fitting statistical shape models : a Kernel-based approach with application to wisdom tooth segmentation from CBCT images. 2014, PhD Thesis, University of Basel, Faculty of Science.
Official URL: http://edoc.unibas.ch/diss/DissB_10884
To tackle these problems, we follow on kernel-based approaches to registration and shape modeling. We introduce a kernel, which considers landmarks as an additional prior in image registration. This allows
to locally improve the registration accuracy. We present a Demons-like registration method with an inhomogeneous regularization which allows to apply such a landmark kernel.
For modeling the shape variation, we construct a kernel comprising a generic smoothness and an empirical sample covariance. With this combined kernel, we increase the flexibility of the statistical shape model. We make use of a reproducing kernel Hilbert space framework for registration, where we apply this combined kernel as reproducing kernel. To make the approach computationally feasible, we perform a low-rank
approximation of the specific kernel function.
Because of a heterogeneous appearance inside the wisdom tooth, fitting the statistical model to plain intensity images is difficult. We build a nonparametric appearance model, based on random forest regression, which abstracts the raw images to semantic probability maps. Hence, the misleading structures become semantic values, which greatly simplificates the shape model fitting.
|Committee Members:||Roth, Volker|
|Faculties and Departments:||05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Computergraphik Bilderkennung (Vetter)|
|Bibsysno:||Link to catalogue|
|Number of Pages:||122 S.|
|Last Modified:||30 Jun 2016 10:56|
|Deposited On:||08 Sep 2014 13:13|
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