Localization of 3D Anatomical Structures Using Random Forests and Discrete Optimization

  • René Donner
  • Erich Birngruber
  • Helmut Steiner
  • Horst Bischof
  • Georg Langs
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6533)

Abstract

In this paper we propose a method for the automatic localization of complex anatomical structures using interest points derived from Random Forests and matching based on discrete optimization. During training landmarks are annotated in a set of example volumes. A sparse elastic model encodes the geometric constraints of the landmarks. A Random Forest classifier learns the local appearance around the landmarks based on Haar-like 3D descriptors. During search we classify all voxels in the query volume. This yields probabilities for each voxel that indicate its correspondence with the landmarks. Mean-shift clustering obtains a subset of 3D interest points at the locations with the highest similarity in a local neighboorhood. We encode these points together with the conformity of the connecting edges to the learnt geometric model in a Markov Random Field. By solving the discrete optimization problem the most probable locations for each model landmark are found in the query volume. On a set of 8 hand CTs we show that this approach is able to consistently localize the model landmarks (finger tips, joints, etc) despite the complex and repetitive structure of the object.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • René Donner
    • 1
    • 2
  • Erich Birngruber
    • 1
  • Helmut Steiner
    • 1
  • Horst Bischof
    • 2
  • Georg Langs
    • 3
  1. 1.Computational Image Analysis and Radiology Lab, Department of RadiologyMedical University of ViennaAustria
  2. 2.Institute for Computer Graphics and VisionGraz University of TechnologyAustria
  3. 3.Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyCambridgeUSA

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