Primal/Dual Linear Programming and Statistical Atlases for Cartilage Segmentation

  • Ben Glocker
  • Nikos Komodakis
  • Nikos Paragios
  • Christian Glaser
  • Georgios Tziritas
  • Nassir Navab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4792)


In this paper we propose a novel approach for automatic segmentation of cartilage using a statistical atlas and efficient primal/dual linear programming. To this end, a novel statistical atlas construction is considered from registered training examples. Segmentation is then solved through registration which aims at deforming the atlas such that the conditional posterior of the learned (atlas) density is maximized with respect to the image. Such a task is reformulated using a discrete set of deformations and segmentation becomes equivalent to finding the set of local deformations which optimally match the model to the image. We evaluate our method on 56 MRI data sets (28 used for the model and 28 used for evaluation) and obtain a fully automatic segmentation of patella cartilage volume with an overlap ratio of 0.84 with a sensitivity and specificity of 94.06% and 99.92%, respectively.


Automatic Segmentation Patella Cartilage Smoothness Term Dual Feasible Solution Tibial Cartilage Volume 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ben Glocker
    • 1
    • 2
  • Nikos Komodakis
    • 2
    • 4
  • Nikos Paragios
    • 2
  • Christian Glaser
    • 3
  • Georgios Tziritas
    • 4
  • Nassir Navab
    • 1
  1. 1.Computer Aided Medical Procedures (CAMP), Technische Universität München 
  2. 2.GALEN Group, Laboratoire de Mathématiques Appliquées aux Systèmes, Ecole Centrale de Paris 
  3. 3.Department of Clinical Radiology, Ludwig-Maximilians-Universität München 
  4. 4.Computer Science Department, University of Crete 

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