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An Efficient Optimization Framework for Multi-Region Segmentation based on Lagrangian Duality

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Author

  • Johannes Ulén
  • Petter Strandmark
  • Fredrik Kahl

Summary, in English

We introduce a multi-region model for simultaneous segmentation of medical images. In contrast to many other models, geometric constraints such as inclusion and exclusion between the regions are enforced, which makes it possible to correctly segment different regions even if the intensity distributions are identical. We efficiently optimize the model using a combination of graph cuts and Lagrangian duality which is faster and more memory efficient than current state of the art. As the method is based on global optimization techniques, the resulting segmentations are independent of initialization. We apply our framework to the segmentation of the left and right ventricles, myocardium and the left ventricular papillary muscles in MRI and to lung segmentation in full-body X-ray CT. We evaluate our approach on a publicly available benchmark with competitive results.

Publishing year

2013

Language

English

Pages

178-188

Publication/Series

IEEE Transactions on Medical Imaging

Volume

32

Issue

2

Document type

Journal article

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Computer Vision and Robotics (Autonomous Systems)
  • Mathematics

Keywords

  • Cardiac segmentation
  • discrete optimization
  • image segmentation
  • lung segmentation

Status

Published

Research group

  • Mathematical Imaging Group

ISBN/ISSN/Other

  • ISSN: 1558-254X