Efficient Kernel Density Estimation Of Shape And Intensity Priors For Level Set Segmentation

  • Daniel Cremers
  • Mikael Rousson
Part of the Topics in Biomedical Engineering. International Book Series book series (ITBE)

We propose a nonlinear statistical shape model for level set segmentation that can be efficiently implemented. Given a set of training shapes, we perform a kernel density estimation in the low-dimensional subspace spanned by the training shapes. In this way, we are able to combine an accurate model of the statistical shape distribution with efficient optimization in a finite-dimensional subspace. In a Bayesian inference framework, we integrate the nonlinear shape model with a nonparametric intensity model and a set of pose parameters that are estimated in a more direct data-driven manner than in previously proposed level set methods. Quantitative results show superior performance (regarding runtime and segmentation accuracy) of the proposed nonparametric shape prior over existing approaches.


Kernel Density Active Contour Kernel Density Estimation Statistical Shape Segmentation Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Daniel Cremers
    • 1
  • Mikael Rousson
    • 2
  1. 1.Department of Computer ScienceUniversity of BonnGermany
  2. 2.Department of Imaging and VisualizationSiemens Corporate ResearchPrincetonUSA

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