Uncertainty-Driven Non-parametric Knowledge-Based Segmentation: The Corpus Callosum Case

  • Maxime Taron
  • Nikos Paragios
  • Marie-Pierre Jolly
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3752)


In this paper we propose a novel variational technique for the knowledge based segmentation of two dimensional objects. One of the elements of our approach is the use of higher order implicit polynomials to represent shapes. The most important contribution is the estimation of uncertainties on the registered shapes, which can be used with a variable bandwidth kernel-based non-parametric density estimation process to model prior knowledge about the object of interest. Such a non-linear model with uncertainty measures is integrated with an adaptive visual-driven data term that aims to separate the object of interest from the background. Promising results obtained for the segmentation of the corpus callosum in MR mid-sagittal brain slices demonstrate the potential of such a framework.


Corpus Callosum Active Shape Model Local Registration Variable Bandwidth Free Form Deformation 
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 2005

Authors and Affiliations

  • Maxime Taron
    • 1
  • Nikos Paragios
    • 1
  • Marie-Pierre Jolly
    • 2
  1. 1.CERTIS – Ecole Nationale des Ponts et ChausseesChamps-sur-MarneFrance
  2. 2.Imaging & Visualization DepartmentSiemens Corporate ResearchPrincetonUSA

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