Affinity-based constraint optimization for nearly-automatic vessel segmentation

O. Cooper, M. Freiman*, L. Joskowicz, D. Lischinski

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We present an affinity-based optimization method for nearly-automatic vessels segmentation in CTA scans. The desired segmentation is modeled as a function that minimizes a quadratic affinity-based functional. The functional incorporates intensity and geometrical vessel shape information and a smoothing constraint. Given a few user-defined seeds, the minimum of the functional is obtained by solving a single set of linear equations. The binary segmentation is then obtained by applying a user-selected threshold. The advantages of our method are that it requires fewer initialization seeds, is robust, and yields better results than existing graph-based interactive segmentation methods. Experimental results on 20 vessel segments including the carotid arteries bifurcation and noisy parts of the carotid yield a mean symmetric surface error of 0.54mm (std=0.28).

Original languageAmerican English
Title of host publicationMedical Imaging 2010
Subtitle of host publicationImage Processing
EditionPART 1
StatePublished - 2010
EventMedical Imaging 2010: Image Processing - San Diego, CA, United States
Duration: 14 Feb 201016 Feb 2010

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
NumberPART 1
ISSN (Print)1605-7422


ConferenceMedical Imaging 2010: Image Processing
Country/TerritoryUnited States
CitySan Diego, CA


  • Vessels segmentation
  • optimization


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