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A Generic Probabilistic Active Shape Model for Organ Segmentation

  • Andreas Wimmer
  • Grzegorz Soza
  • Joachim Hornegger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)

Abstract

Probabilistic models are extensively used in medical image segmentation. Most of them employ parametric representations of densities and make idealizing assumptions, e.g. normal distribution of data. Often, such assumptions are inadequate and limit a broader application. We propose here a novel probabilistic active shape model for organ segmentation, which is entirely built upon non-parametric density estimates. In particular, a nearest neighbor boundary appearance model is complemented by a cascade of boosted classifiers for region information and combined with a shape model based on Parzen density estimation. Image and shape terms are integrated into a single level set equation. Our approach has been evaluated for 3-D liver segmentation using a public data base originating from a competition (http://sliver07.org). With an average surface distance of 1.0 mm and an average volume overlap error of 6.5 %, it outperforms other automatic methods and provides accuracy close to interactive ones. Since no adaptions specific to liver segmentation have been made, our probabilistic active shape model can be applied to other segmentation tasks easily.

Keywords

Shape Model Active Shape Model Medical Image Segmentation Liver Segmentation Shape Term 
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-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Andreas Wimmer
    • 1
    • 2
  • Grzegorz Soza
    • 2
  • Joachim Hornegger
    • 1
  1. 1.Chair of Pattern Recognition, Department of Computer ScienceFriedrich-Alexander UniversityErlangen-Nuremberg
  2. 2.Siemens Healthcare SectorComputed TomographyForchheimGermany

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