Multi-level approach for statistical appearance models with probabilistic correspondences

Julia Krüger; Jan Ehrhardt; Heinz Handels

doi:10.1117/12.2214885

21 March 2016 Multi-level approach for statistical appearance models with probabilistic correspondences

Julia Krüger, Jan Ehrhardt, Heinz Handels

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 978433 (2016) https://doi.org/10.1117/12.2214885
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Statistical shape and appearance models are often based on the accurate identification of one-to-one correspondences in a training data set. At the same time, the determination of these corresponding landmarks is the most challenging part of such methods. Hufnagel et al.¹ developed an alternative method using correspondence probabilities for a statistical shape model. In Krüuger et al.^{2, 3} we propose the use of probabilistic correspondences for statistical appearance models by incorporating appearance information into the framework. We employ a point-based representation of image data combining position and appearance information. The model is optimized and adapted by a maximum a-posteriori (MAP) approach deriving a single global optimization criterion with respect to model parameters and observation dependent parameters that directly affects shape and appearance information of the considered structures. Because initially unknown correspondence probabilities are used and a higher number of degrees of freedom is introduced to the model a regularization of the model generation process is advantageous. For this purpose we extend the derived global criterion by a regularization term which penalizes implausible topological changes. Furthermore, we propose a multi-level approach for the optimization, to increase the robustness of the model generation process.

Citation Download Citation

Julia Krüger, Jan Ehrhardt, and Heinz Handels "Multi-level approach for statistical appearance models with probabilistic correspondences", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978433 (21 March 2016); https://doi.org/10.1117/12.2214885

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