TRACKING OF CORONARY ARTERIES IN ANGIOGRAM SEQUENCE BY STRUCTURAL MATCHING OF JUNCTIONS

Coronary artery disease is the most common form of heart disease and is a leading cause of death worldwide. The standard diagnostic tool for coronary artery disease is x-ray angiography. Angiograms are 2D images. To better understand the 3D structure of coronary arteries, angiogram sequences are often taken at different view points. However, a 3D model is stationary and lacks dynamic information of coronary arteries. A 4D (3D plus time) model provides complete information about the coronary arteries. To obtain a 4D model, tracking the coronary arteries within the angiogram sequences is a crucial step. We propose a novel algorithm that tracks blood vessels automatically by using the graph of junctions. We define the junction descriptor as a vector of the angles and widths of every branch of the junction. Junctions are tracked by matching the descriptors in successive angiogram frames. The structural consistency of the junction matches is then verified by the structure of the graph of junctions and inconsistent matches are excluded. In the case that the matching fails due to significant noise in the angiogram, the algorithm estimates the location of these junctions using its neigboring junction in the graph structure. The algorithm is applied to six angiogram sequences that are taken for one patient in different view points. Experiment results show that the proposed algorithm can track the junctions accurately and automatically.

[1]  Ifeoma Nwogu,et al.  Fast Temporal Tracking and 3D Reconstruction of a Single Coronary Vessel , 2007, 2007 IEEE International Conference on Image Processing.

[2]  Guido Gerig,et al.  Multiscale detection of curvilinear structures in 2-D and 3-D image data , 1995, Proceedings of IEEE International Conference on Computer Vision.

[3]  H D McIntosh,et al.  Assessment of regional myocardial performance from biplane coronary cineangiograms. , 1971, The American journal of cardiology.

[4]  M. Garreau,et al.  A knowledge-based approach for 3-D reconstruction and labeling of vascular networks from biplane angiographic projections. , 1991, IEEE transactions on medical imaging.

[5]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Max A. Viergever,et al.  Scale and the differential structure of images , 1992, Image Vis. Comput..

[7]  Jie Tian,et al.  A Partial Intensity Invariant Feature Descriptor for Multimodal Retinal Image Registration , 2010, IEEE Transactions on Biomedical Engineering.

[8]  Farida Cheriet,et al.  Robust Coronary Artery Tracking from Fluoroscopic Image Sequences , 2007, ICIAR.

[9]  Nicholas Ayache,et al.  Preprocessing : data selection , pseudo ECG III . 3 − D centerlines reconstruction , 2011 .

[10]  Alok Gupta,et al.  Optimal polyline tracking for artery motion compensation in coronary angiography , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[11]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[12]  Zheng Sun,et al.  Analysis of Coronary Arterial Dynamics from X-ray Angiographic Sequences , 2009, ISCID.

[13]  A. K. Klein,et al.  Identifying vascular features with orientation specific filters and B-spline snakes , 1994, Computers in Cardiology 1994.

[14]  Shan Lu,et al.  Automatic detection of the coronary arterial contours with sub-branches from an X-ray angiogram , 1993, Proceedings of Computers in Cardiology Conference.

[15]  Pascal Haigron,et al.  Extraction of structural features in digital subtraction angiography , 1992, Proceedings of the 1992 International Biomedical Engineering Days.

[16]  J A Sethian,et al.  A fast marching level set method for monotonically advancing fronts. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Michael H. Goldbaum,et al.  Automated measurement of retinal vascular tortuosity , 1997, AMIA.

[18]  Jiaquan Xu,et al.  Deaths: final data for 2007. , 2010, National vital statistics reports : from the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System.

[19]  Aggelos K. Katsaggelos,et al.  Motion estimation of skeletonized angiographic images using elastic registration , 1994, IEEE Trans. Medical Imaging.

[20]  Elliot R. McVeigh,et al.  Three-dimensional motion tracking of coronary arteries in biplane cineangiograms , 2003, IEEE Transactions on Medical Imaging.

[21]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[22]  Rui Liao,et al.  3-D reconstruction of the coronary artery tree from multiple views of a rotational X-ray angiography , 2010, The International Journal of Cardiovascular Imaging.

[23]  John D. Carroll,et al.  3-D reconstruction of coronary arterial tree to optimize angiographic visualization , 2000, IEEE Transactions on Medical Imaging.

[24]  Francis K. H. Quek,et al.  A review of vessel extraction techniques and algorithms , 2004, CSUR.

[25]  Jürgen Weese,et al.  Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images , 1997, CVRMed.

[26]  Jean Meunier,et al.  Structural method for tracking coronary arteries in coronary cineangiograms , 2004, First Canadian Conference on Computer and Robot Vision, 2004. Proceedings..

[27]  Panos Liatsis,et al.  Automatic 3D Reconstruction of Coronary Artery Centerlines from Monoplane X-ray Angiogram Images , 2008 .

[28]  Paul Suetens,et al.  A knowledge-based system for the delineation of blood vessels on subtraction angiograms , 1988, Pattern Recognit. Lett..

[29]  Morton H. Friedman,et al.  Vessel tracking by template string in angiography , 2001, International Symposium on Multispectral Image Processing and Pattern Recognition.

[30]  Francis K. H. Quek,et al.  Vessel extraction in medical images by wave-propagation and traceback , 2001, IEEE Transactions on Medical Imaging.

[31]  I E Magnin,et al.  Improved vessel visualization in MR angiography by nonlinear anisotropic filtering , 1997, Magnetic resonance in medicine.

[32]  René M. Botnar,et al.  Automatic vessel segmentation using active contours in cine phase contrast flow measurements , 1999, Journal of magnetic resonance imaging : JMRI.

[33]  Jiang Wang,et al.  An Iterative Refinement DSA Image Registration Algorithm Using Structural Image Quality Measure , 2009, 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[34]  J. Koenderink.  The structure of images , 2004, Biological Cybernetics.

[35]  Daniel Rueckert,et al.  Contour fitting using stochastic and probabilistic relaxation for Cine MR Images , 1995 .

[36]  Yongtian Wang,et al.  Multiresolution Elastic Registration of X-Ray Angiography Images Using Thin-Plate Spline , 2007, IEEE Transactions on Nuclear Science.

[37]  Nong Sang,et al.  Multiscale Centerline Extraction of Angiogram Vessels Using Gabor Filters , 2004, CIS.

[38]  M. Hart,et al.  A method of automated coronary artery tracking in unsubtracted angiograms , 1993, Proceedings of Computers in Cardiology Conference.

[39]  J Hale,et al.  An Algorithm for MR Angiography Image Enhancement , 1995, Magnetic resonance in medicine.

[40]  Michael H. Goldbaum,et al.  Registering retinal images using automatically selected control point pairs , 1994, Proceedings of 1st International Conference on Image Processing.

[41]  J.L. Coatrieux,et al.  Vessel extraction in coronary X-ray Angiography , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[42]  Y. Bentoutou,et al.  An invariant approach for image registration in digital subtraction angiography , 2002, Pattern Recognit..

[43]  Yanghai Tsin,et al.  A Deformation Tracking Approach to 4D Coronary Artery Tree Reconstruction , 2009, MICCAI.

[44]  Jerry L. Prince,et al.  Generalized gradient vector flow external forces for active contours , 1998, Signal Process..