Statistical Shape Modeling Using MDL Incorporating Shape, Appearance, and Expert Knowledge

  • Aaron D. Ward
  • Ghassan Hamarneh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4791)


We propose a highly automated approach to the point correspondence problem for anatomical shapes in medical images. Manual landmarking is performed on a small subset of the shapes in the study, and a machine learning approach is used to elucidate the characteristic shape and appearance features at each landmark. A classifier trained using these features defines a cost function that drives key landmarks to anatomically meaningful locations after MDL-based correspondence establishment. Results are shown for artificial examples as well as real data.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Aaron D. Ward
    • 1
  • Ghassan Hamarneh
    • 1
  1. 1.Medical Image Analysis Lab, School of Computing Science, Simon Fraser UniversityCanada

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