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.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Risser L,Plouraboue F,Descombes Xdoi
10.1109/TMI.2007.913248subject
Has Abstractpub_date
2008-05-01 00:00:00pages
674-87issue
5eissn
0278-0062issn
1558-254Xjournal_volume
27pub_type
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