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Co-dimension 2 Geodesic Active Contours for MRA Segmentation

  • Liana M. Lorigo
  • Olivier Faugeras
  • W. E. L. Grimson
  • Renaud Keriven
  • Ron Kikinis
  • Carl-Fredrik Westin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1613)

Abstract

Automatic and semi-automatic magnetic resonance angiography (MRA) segmentation techniques can potentially save radiologists large amounts of time required for manual segmentation and can facilitate further data analysis. The proposed MRA segmentation method uses a mathematical modeling technique which is well-suited to the complicated curve-like structure of blood vessels. We define the segmentation task as an energy minimization over all 3D curves and use a level set method to search for a solution. Our approach is an extension of previous level set segmentation techniques to higher co-dimension.

Keywords

Magnetic Resonance Angiography Maximum Intensity Projection Magnetic Resonance Angiography Image Geodesic Active Contour Mathematical Modeling Technique 
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 1999

Authors and Affiliations

  • Liana M. Lorigo
    • 1
  • Olivier Faugeras
    • 1
    • 2
  • W. E. L. Grimson
    • 1
  • Renaud Keriven
    • 3
  • Ron Kikinis
    • 4
  • Carl-Fredrik Westin
    • 4
  1. 1.MIT Artificial Intelligence LaboratoryCambridgeUSA
  2. 2.INRIA, Sophia AntipolisFrance
  3. 3.CermicsENPCFrance
  4. 4.Brigham & Women’s HospitalHarvard Medical SchoolBostonUSA

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