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Gap Filling of 3-D Microvascular Networks by Tensor Voting

Risser, Laurent and Plouraboué, Franck and Descombes , Xavier Gap Filling of 3-D Microvascular Networks by Tensor Voting. (2008) IEEE Transactions on Medical Imaging, vol. 27 (n° 5). pp. 674-687. ISSN 0278-0062

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Official URL: http://dx.doi.org/10.1109/TMI.2007.913248

Abstract

We present a new algorithm which merges disconti- nuities 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.

Item Type:Article
Additional Information:Thanks to IEEE editor. The original PDF is available at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=42
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT
French research institutions > Institut National de la Recherche en Informatique et en Automatique - INRIA
Université de Toulouse > Université Paul Sabatier-Toulouse III - UPS
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Deposited By: Franck PLOURABOUE

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