Conditional Variability of Statistical Shape Models Based on Surrogate Variables

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)


We propose to increment a statistical shape model with surrogate variables such as anatomical measurements and patient-related information, allowing conditioning the shape distribution to follow prescribed anatomical constraints. The method is applied to a shape model of the human femur, modeling the joint density of shape and anatomical parameters as a kernel density. Results show that it allows for a fast, intuitive and anatomically meaningful control on the shape deformations and an effective conditioning of the shape distribution, allowing the analysis of the remaining shape variability and relations between shape and anatomy. The approach can be further employed for initializing elastic registration methods such as Active Shape Models, improving their regularization term and reducing the search space for the optimization.


Conditional Distribution Shape Distribution Femur Length Surrogate Variable Human Femur 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Computer Vision LaboratoryETHZZürichSwitzerland
  2. 2.ARTORG Center for Biomedical Engineering ResearchUniversity of BernBernSwitzerland

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