Shape Based Segmentation of Anatomical Structures in Magnetic Resonance Images

  • Kilian M. Pohl
  • John Fisher
  • Ron Kikinis
  • W. Eric L. Grimson
  • William M. Wells
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)


Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases, segmentation is largely performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We present an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior information. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. Structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the maximum a posteriori probability estimation problem. We demonstrate the approach on 20 brain magnetic resonance images showing superior performance, particularly in cases where purely image based methods fail.


Logistic Function Expectation Maximization Principle Component Analysis Shape Model Automatic Segmentation 
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 2005

Authors and Affiliations

  • Kilian M. Pohl
    • 1
  • John Fisher
    • 1
  • Ron Kikinis
    • 2
  • W. Eric L. Grimson
    • 1
  • William M. Wells
    • 2
  1. 1.Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Surgical Planning LaboratoryHarvard Medical School, and Brigham and Women’s HospitalBostonUSA

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