DocumentCode
2830986
Title
Accurate detection of 3D tubular tree structures
Author
Flasque, Nicolas ; Desvignes, Michel ; Constans, Jean-marc ; Revenu, M. Arinette
Author_Institution
Greyc Image, ISMRA, Caen, France
Volume
3
fYear
2000
fDate
2000
Firstpage
436
Abstract
The spatial resolution of Magnetic Resonance Angiography (MRA) makes it a powerful tool for diagnosis and surgical planning. However, image interpretation and visualization tools are missing, and three-dimensional measurements are not usually accessible. Flexible visualization of the whole vascular tree and precise quantification of phenomena like carotid stenosis are applications of an automated processing of MRA. Building an accurate model of a tubular objects network such as bronchi or blood vessels can provide a substantial help for 3D visualization and quantification. The authors present a tracking algorithm of centrelines that makes very few assumptions on the structure grey-level profile. The main originality of this work is the accurate 3D centreline tracking process which provides subvoxel accuracy and deals with bifurcations. This approach has been successfully applied to the cerebral vasculature in MRA images
Keywords
biomedical MRI; blood vessels; brain; medical image processing; 3D tubular tree structures; accurate detection; bifurcations; bronchi; carotid stenosis; cerebral vasculature; diagnosis; image interpretation; magnetic resonance angiography; medical diagnostic imaging; spatial resolution; structure grey-level profile; subvoxel accuracy; surgical planning; three-dimensional measurements; visualization tools; Angiography; Biomedical imaging; Blood vessels; Buildings; Magnetic resonance; Respiratory system; Spatial resolution; Surgery; Tree data structures; Visualization;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Processing, 2000. Proceedings. 2000 International Conference on
Conference_Location
Vancouver, BC
ISSN
1522-4880
Print_ISBN
0-7803-6297-7
Type
conf
DOI
10.1109/ICIP.2000.899428
Filename
899428
Link To Document