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Probabilistic Fitting of Active Shape Models

Morel-Forster, Andreas and Gerig, Thomas and Lüthi, Marcel and Vetter, Thomas. (2018) Probabilistic Fitting of Active Shape Models. In: International Workshop on Shape in Medical Imaging, 11167. pp. 137-146.

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Official URL: https://edoc.unibas.ch/69081/

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Abstract

Active Shape Models (ASMs) are a classical and widely used approach for fitting shape models to images. In this paper, we propose a fully probabilistic interpretation of ASM fitting as Bayesian inference. To infer the posterior, we use the Metropolis-Hastings algorithm. We then use the maximum a posteriori sample as the segmentation result. Our approach has several advantages compared to classical ASM fitting: (1) We are left with fewer parameters that we need to choose. (2) It is less prone to get trapped in local minima. (3) It becomes straightforward to extend the approach to include additional information, such as expert annotations. (4) It is even simpler to implement than the classical ASM fitting method. We apply our algorithm to the SLIVER dataset and show that it achieves a higher segmentation accuracy than the standard ASM app- roach. We further demonstrate the flexibility and expressivity of the framework by integrating experts annotations along parts of the outline to further increase the accuracy. The code used for fitting is based on open-source software and made available to the community.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Computergraphik Bilderkennung (Vetter)
UniBasel Contributors:Vetter, Thomas and Morel, Andreas and Gerig, Thomas and Lüthi, Marcel
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:Springer
Note:Publication type according to Uni Basel Research Database: Conference paper
Language:English
Identification Number:
Last Modified:27 Mar 2019 15:51
Deposited On:27 Feb 2019 11:11

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