Automatic initialization algorithm for carotid artery segmentation in CTA images.
Analysis of CT datasets is commonly time consuming because of the required manual interaction. We present a novel and fast automatic initialization algorithm to detect the carotid arteries providing a fully automated approach of the segmentation and centerline detection. First, the volume of interest (VOI) is estimated using a shoulder landmark. The carotid arteries are subsequently detected in axial slices of the VOI by applying a circular Hough transform. To select carotid arteries related signals in the Hough space, a 3-D, direction dependent hierarchical clustering is used. To allow a successful detection for a wide range of vessel diameters, a feedback architecture was introduced. The algorithm was designed and optimized using a training set of 20 patients and subsequently evaluated using 31 test datasets. The detection algorithm, including VOI estimation, correctly detects 88% of the carotid arteries. Even though not all carotid arteries have been correctly detected, the results are very promising.
Sanderse
Martijn
M
Dept. of Radiology, Div. of Image Processing, LUMC, Leiden, The Netherlands. M.Sanderse@lumc.nl
Marquering
Henk A
HA
Hendriks
Emile A
EA
van der Lugt
Aad
A
Reiber
Johan H C
JH