Implicit Active Shape Models for 3D Segmentation in MR Imaging

  • Mikaël Rousson
  • Nikos Paragios
  • Rachid Deriche
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3216)


Extraction of structures of interest in medical images is often an arduous task because of noisy or incomplete data. However, hand-segmented data are often available and most of the structures to be extracted have a similar shape from one subject to an other. Then, the possibility of modeling a family of shapes and restricting the new structure to be extracted within this class is of particular interest. This approach is commonly implemented using active shape models [2] and the definition of the image term is the most challenging component of such an approach. In parallel, level set methods [8] define a powerful optimization framework, that can be used to recover objects of interest by the propagation of curves or surfaces. They can support complex topologies, considered in higher dimensions, are implicit, intrinsic and parameter free. In this paper we re-visit active shape models and introduce a level set variant of them. Such an approach can account for prior shape knowledge quite efficiently as well as use data/image terms of various form and complexity. Promising results on the extraction of brain ventricles in MR images demonstrate the potential of our approach.


Active Shape Model Learning Space Object Extraction Active Region Model Lateral Brain Ventricle 
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 2004

Authors and Affiliations

  • Mikaël Rousson
    • 1
  • Nikos Paragios
    • 2
  • Rachid Deriche
    • 1
  1. 1.I.N.R.I.A. Sophia AntipolisFrance
  2. 2.Ecole Nationale des Ponts et ChausséesParisFrance

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