Tubular Objects Network Detection from 3D Images

Nicolas Flasque 1 Michel Desvignes 1 Jean-Marc Constans 2 Marinette Revenu 1
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : We present an approach to the tree representation of a tubular object network. The full 3D tracking algorithm for a single tubular structure is detailed. Detection of bifurcations by a connectivity approach is then exposed. We show subvoxel accuracy and reliable orientation estimation for the tracking process on synthetic images. Bifurcations are also well detected on a complex synthetic image. Finally, applications of this method to real 3D medical images are shown. The method is particularly suited for processing magnetic resonance angiography of the brain and neck.
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Communication dans un congrès
4th IEEE Southwest Symposium on Image Analysis and Interpretation, 2000, Austin, Texas, United States. pp.96-100, 2000, 〈10.1109/IAI.2000.839579〉
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https://hal.archives-ouvertes.fr/hal-00960750
Contributeur : Image Greyc <>
Soumis le : mardi 18 novembre 2014 - 13:53:21
Dernière modification le : lundi 4 mars 2019 - 11:18:08
Document(s) archivé(s) le : jeudi 19 février 2015 - 10:05:12

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SSIAI-Flasque-2000.pdf
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Nicolas Flasque, Michel Desvignes, Jean-Marc Constans, Marinette Revenu. Tubular Objects Network Detection from 3D Images. 4th IEEE Southwest Symposium on Image Analysis and Interpretation, 2000, Austin, Texas, United States. pp.96-100, 2000, 〈10.1109/IAI.2000.839579〉. 〈hal-00960750〉

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