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Gap Filling of 3-D Microvascular Networks by Tensor Voting

Laurent Risser 1 Franck Plouraboué 2 Xavier Descombes 3
3 MORPHEME - Morphologie et Images
CRISAM - Inria Sophia Antipolis - Méditerranée , IBV - Institut de Biologie Valrose : U1091, Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : 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.
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Submitted on : Tuesday, August 22, 2017 - 10:33:38 AM
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Laurent Risser, Franck Plouraboué, Xavier Descombes. Gap Filling of 3-D Microvascular Networks by Tensor Voting. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2008, 27 (5), pp.674-687. ⟨10.1109/TMI.2007.913248⟩. ⟨hal-01576034⟩



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