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
2 Service de Radiologie [CHU Caen]

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.

Domaines: Traitement des images [eess.IV]

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, ⟨10.1109/IAI.2000.839579⟩. ⟨hal-00960750⟩