TY - GEN
T1 - Ridge-based automatic vascular centerline tracking in X-ray angiographic images
AU - Xiao, Ruoxiu
AU - Yang, Jian
AU - Li, Tong
AU - Liu, Yue
PY - 2013
Y1 - 2013
N2 - The extraction of vascular trees is very important for quantitative analysis of vascular structures. As angiographic image is the integration of X-ray through the whole body anatomy on the image plane, vascular structure loses most 3-D topological information. Hence, accurate vascular structure detection is of great help for clinical diagnosis. In this paper, a fully automatic vascular centerline extraction method is proposed. A self-adaptive morphological operator is combined with a multi-scale enhancement filter to enhance tubular-like structures. Then, points with local maximum intensity are extracted as seed points, while the initial track directions are determined by detecting prominent ridge points in the predefined range. By iteratively searching the connected ridge points, the centerlines are gradually extracted by connecting the ridge points. By statistically counting of connected components, fake connections are efficiently removed. And bifurcation points are discriminated from centerline skeletons by determining the connections of each centerline point. Our approach is automatic completely. Experimental results show that the proposed algorithm is very effective for the extraction of centerlines from angiographic images.
AB - The extraction of vascular trees is very important for quantitative analysis of vascular structures. As angiographic image is the integration of X-ray through the whole body anatomy on the image plane, vascular structure loses most 3-D topological information. Hence, accurate vascular structure detection is of great help for clinical diagnosis. In this paper, a fully automatic vascular centerline extraction method is proposed. A self-adaptive morphological operator is combined with a multi-scale enhancement filter to enhance tubular-like structures. Then, points with local maximum intensity are extracted as seed points, while the initial track directions are determined by detecting prominent ridge points in the predefined range. By iteratively searching the connected ridge points, the centerlines are gradually extracted by connecting the ridge points. By statistically counting of connected components, fake connections are efficiently removed. And bifurcation points are discriminated from centerline skeletons by determining the connections of each centerline point. Our approach is automatic completely. Experimental results show that the proposed algorithm is very effective for the extraction of centerlines from angiographic images.
KW - Angiographic image
KW - Automatic extraction
KW - Centerline
KW - Coronary artery
UR - http://www.scopus.com/inward/record.url?scp=84892933945&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-36669-7_96
DO - 10.1007/978-3-642-36669-7_96
M3 - Conference contribution
AN - SCOPUS:84892933945
SN - 9783642366680
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 793
EP - 800
BT - Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers
T2 - 3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012
Y2 - 15 October 2012 through 17 October 2012
ER -