TY - GEN
T1 - Automatic initialization algorithm for carotid artery segmentation in CTA images
AU - Sanderse, Martijn
AU - Marquering, Henk A.
AU - Hendriks, Emile A.
AU - Van Lugt, Aad Der
AU - Reiber, Johan H.C.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33744807759&partnerID=8YFLogxK
U2 - 10.1007/11566489_104
DO - 10.1007/11566489_104
M3 - Conference proceeding
C2 - 16686039
AN - SCOPUS:33744807759
SN - 3540293264
SN - 9783540293262
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 846
EP - 853
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings
T2 - 8th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005
Y2 - 26 October 2005 through 29 October 2005
ER -