This paper presents a level set based method for segmenting the outer vessel wall and plaque components of the carotid
artery in CTA. The method employs a GentleBoost classification framework that classifies pixels as calcified region or
not, and inside or outside the vessel wall. The combined result of both classifications is used to construct a speed
function for level set based segmentation of the outer vessel wall; the segmented lumen is used to initialize the level set.
The method has been optimized on 20 datasets and evaluated on 80 datasets for which manually annotated data was
available as reference. The average Dice similarity of the outer vessel wall segmentation was 92%, which compares
favorably to previous methods.
A novel 2D slice based automatic method for model based segmentation of the outer vessel wall of the common carotid artery in CTA data set is introduced. The method utilizes a lumen segmentation and AdaBoost, a fast and robust machine learning algorithm, to initially classify (mark) regions outside and inside the vessel wall using the distance from the lumen and intensity profiles sampled radially from the gravity center of the lumen. A similar method using the distance from the lumen and the image intensity as features is used to classify calcium regions. Subsequently, an ellipse shaped deformable model is fitted to the classification result. The method has achieved smaller detection error than the inter observer variability, and the method is robust against variation of the training data sets.