Segmentation of the outer vessel wall of the common carotid artery in CTA.
A novel method is presented for carotid artery vessel wall segmentation in computed tomography angiography (CTA) data. First the carotid lumen is semi-automatically segmented using a level set approach initialized with three seed points. Subsequently, calcium regions located within the vessel wall are automatically detected and classified using multiple features in a GentleBoost framework. Calcium regions segmentation is used to improve localization of the outer vessel wall because it is an easier task than direct outer vessel wall segmentation. In a third step, pixels outside the lumen area are classified as vessel wall or background, using the same GentleBoost framework with a different set of image features. Finally, a 2-D ellipse shape deformable model is fitted to a cost image derived from both the calcium and vessel wall classifications. The method has been validated on a dataset of 60 CTA images. The experimental results show that the accuracy of the method is comparable to the interobserver variability.
Vukadinovic
Danijela
D
Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC, 3015GE Rotterdam, The Netherlands. d.vukadinovic@erasmusmc.nl
van Walsum
Theo
T
Manniesing
Rashindra
R
Rozie
Sietske
S
Hameeteman
Reinhard
R
de Weert
Thomas T
TT
van der Lugt
Aad
A
Niessen
Wiro J
WJ
D000465:Algorithms; D000792:Angiography / Q000379:methods*; D001499:Bayes Theorem; D017536:Carotid Artery, Common / Q000033:anatomy & histology* / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016011:Normal Distribution; D014057:Tomography, X-Ray Computed / Q000379:methods*