Avoiding Mesh Folding in 3D Optimal Surface Segmentation

  • Christian Bauer
  • Shanhui Sun
  • Reinhard Beichel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)


The segmentation of 3D medical images is a challenging problem that benefits from incorporation of prior shape information. Optimal Surface Segmentation (OSS) has been introduced as a powerful and flexible framework that allows segmenting the surface of an object based on a rough initial prior with robustness against local minima. When applied to general 3D meshes, conventional search profiles constructed for the OSS may overlap resulting in defective segmentation results due to mesh folding. To avoid this problem, we propose to use the Gradient Vector Flow field to guide the construction of non-overlapping search profiles. As shown in our evaluation on segmenting lung surfaces, this effectively solves the mesh folding problem and decreases the average absolute surface distance error from 0.82±0.29 mm (mean±standard deviation) to 0.79±0.24 mm.


Segmentation Result Initial Vector Active Shape Model Gradient Vector Flow Surface Normal Direction 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christian Bauer
    • 1
    • 2
  • Shanhui Sun
    • 1
    • 2
  • Reinhard Beichel
    • 1
    • 2
    • 3
  1. 1.Deptartment of Electrical and Computer EngineeringThe University of IowaIowa CityUSA
  2. 2.The Iowa Institute for Biomedical ImagingThe University of IowaIowa CityUSA
  3. 3.Deptartment of Internal MedicineThe University of IowaIowa CityUSA

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