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

Jan Ehrhardt, Julia Krüger, Heinz Handels


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.
Original languageEnglish
Title of host publicationMedical Imaging 2014: Image Processing
EditorsSebastien Ourselin, Martin A. Styner
Publication date21.03.2014
ISBN (Print)9780819498274
Publication statusPublished - 21.03.2014
EventSPIE Medical Imaging 2014, Image Processing
- San Diego, United States
Duration: 15.02.201420.02.2014


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