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Segmentation of 3D tubular objects with adaptive front propagation and minimal tree extraction for 3D medical imaging

Deschamps, Thomas; Cohen, Laurent D. (2007), Segmentation of 3D tubular objects with adaptive front propagation and minimal tree extraction for 3D medical imaging, Computer Methods in Biomechanics and Biomedical Engineering, 10, 4, p. 289-305. http://dx.doi.org/10.1080/10255840701328239

Type
Article accepté pour publication ou publié
Date
2007
Nom de la revue
Computer Methods in Biomechanics and Biomedical Engineering
Volume
10
Numéro
4
Éditeur
Taylor and Francis
Pages
289-305
Identifiant publication
http://dx.doi.org/10.1080/10255840701328239
Métadonnées
Afficher la notice complète
Auteur(s)
Deschamps, Thomas
Cohen, Laurent D.
Résumé (EN)
We present a new fast approach for segmentation of thin branching structures, like vascular trees, based on Fast-Marching (FM) and Level Set (LS) methods. FM allows segmentation of tubular structures by inflating a “long balloon” from a user given single point. However, when the tubular shape is rather long, the front propagation may blow up through the boundary of the desired shape close to the starting point. Our contribution is focused on a method to propagate only the useful part of the front while freezing the rest of it. We demonstrate its ability to segment quickly and accurately tubular and tree-like structures. We also develop a useful stopping criterion for the causal front propagation. We finally derive an efficient algorithm for extracting an underlying 1D skeleton of the branching objects, with minimal path techniques. Each branch being represented by its centerline, we automatically detect the bifurcations, leading to the “Minimal Tree” representation. This so-called “Minimal Tree” is very useful for visualization and quantification of the pathologies in our anatomical data sets. We illustrate our algorithms by applying it to several arteries datasets.
Mots-clés
Virtual endoscopy; 3D medical imaging; Skeletonization; Minimal paths; Fast-marching; Segmentation

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