In this paper, a novel active contour model is proposed for vessel tree
segmentation. First, we introduce a region competition-based active contour model
exploiting the gaussian mixture model, which mainly segments thick vessels.
Second, we define a vascular vector field to evolve the active contour along its
center line into the thin and weak vessels. The vector field is derived from the
eigenanalysis of the Hessian matrix of the image intensity in a multiscale
framework. Finally, a dual curvature strategy, which uses a vesselness
measure-dependent function selecting between a minimal principal curvature and a
mean curvature criterion, is added to smoothen the surface of the vessel without
changing its shape. The developed model is used to extract the liver and lung
vessel tree as well as the coronary artery from high-resolution volumetric
computed tomography images. Comparisons are made with several classical active
contour models and manual extraction. The experiments show that our model is more
accurate and robust than these classical models and is, therefore, more suited
for automatic vessel tree extraction.
Original languageEnglish
Pages (from-to)1023-1032
JournalIEEE Trans Biomed Eng
Volume58
Issue number4
Publication statusPublished - 2011

    Research areas

  • vessel tree segmentation

ID: 2038048