Vessel Wall Segmentation Using Implicit Models and Total Curvature Penalizers

  • Rodrigo Moreno
  • Chunliang Wang
  • Örjan Smedby
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)


This paper proposes an automatic segmentation method of vessel walls that combines an implicit 3D model of the vessels and a total curvature penalizer in a level set evolution scheme. First, the lumen is segmented by alternating a model-guided level set evolution and a recalculation of the model itself. Second, the level set of the lumen is evolved with a term that aims at penalizing the total curvature and with a prior that forces the outer layer of the vessel towards the outside of the lumen. The model term is deactivated during this step. Finally, in a third step, the model term is reactivated in order to impose a smooth change of the radius along the vessel. Once the two segmentations have been computed, stenoses are detected and quantified at the thickest locations of the segmented vessel wall. Preliminary results show that the proposed method compares favorably with respect to the state-of-the-art both for synthetic and real CTA datasets.


Compute Tomography Angiography Compute Tomography Coronary Angiography Total Curvature Implicit Model Vessel 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 2013

Authors and Affiliations

  • Rodrigo Moreno
    • 1
  • Chunliang Wang
    • 1
  • Örjan Smedby
    • 1
  1. 1.Center for Medical Imaging Science and Visualization (CMIV), Department of Medical and Health Sciences (IMH)Linköping UniversityLinköpingSweden

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