Gap Filling of 3-D Microvascular Networks by Tensor Voting

Laurent Risser (1) , Franck Plouraboué (2) , Xavier Descombes (3)

1 IMT - Institut de Mathématiques de Toulouse UMR5219
2 IMFT - Institut de mécanique des fluides de Toulouse
3 MORPHEME - Morphologie et Images

We present a new algorithm which merges discontinuities in 3-D images of tubular structures presenting undesirable gaps. The application of the proposed method is mainly associated to large 3-D images of microvascular networks. In order to recover the real network topology, we need to fill the gaps between the closest discontinuous vessels. The algorithm presented in this paper aims at achieving this goal. This algorithm is based on the skeletonization of the segmented network followed by a tensor voting method. It permits to merge the most common kinds of discontinuities found in microvascular networks. It is robust, easy to use, and relatively fast. The microvascular network images were obtained using synchrotron tomography imaging at the European Synchrotron Radiation Facility. These images exhibit samples of intracortical networks. Representative results are illustrated.

Mots clés: Gap filling; Skeleton; Tensor voting; Vessel extraction; X-ray imaging

Domaines: Imagerie médicale; Mécanique des fluides [physics.class-ph]

Laurent Risser, Franck Plouraboué, Xavier Descombes. Gap Filling of 3-D Microvascular Networks by Tensor Voting. IEEE Transactions on Medical Imaging, 2008, 27 (5), pp.674-687. ⟨10.1109/TMI.2007.913248⟩. ⟨hal-01576034⟩