Statistical Shape and Appearance Models without One-to-One Correspondences

Jan Ehrhardt, Julia Krüger, Heinz Handels

Abstract

One-to-one correspondences are fundamental for the creation of classical statistical shape and appearance models. At the same time, the identification of these correspondences is the weak point of such model-based methods. Hufnagel et al.1 proposed an alternative method using correspondence probabilities instead of exact one-to- one correspondences for a statistical shape model. In this work, we extended the approach by incorporating appearance information into the model. For this purpose, we introduce a point-based representation of image data combining position and appearance information. Then, we pursue the concept of probabilistic correspondences and use a maximum a-posteriori (MAP) approach to derive a statistical shape and appearance model. The model generation as well as the model fitting can be expressed as a single global optimization criterion with respect to model parameters. In a first evaluation, we show the feasibility of the proposed approach and evaluate the model generation and model-based segmentation using 2D lung CT slices.
OriginalspracheEnglisch
TitelMedical Imaging 2014: Image Processing
Redakteure/-innenSebastien Ourselin, Martin A. Styner
Band9034
Herausgeber (Verlag)SPIE
Erscheinungsdatum21.03.2014
Seiten90340U
ISBN (Print)9780819498274
DOIs
PublikationsstatusVeröffentlicht - 21.03.2014
VeranstaltungSPIE Medical Imaging 2014, Image Processing
- San Diego, USA / Vereinigte Staaten
Dauer: 15.02.201420.02.2014
https://spie.org/about-spie/press-room/mi14-news

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