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21 March 2014 Statistical shape and appearance models without one-to-one correspondences
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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.
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Jan Ehrhardt, Julia Krüger, and Heinz Handels "Statistical shape and appearance models without one-to-one correspondences", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90340U (21 March 2014);

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