Abstract
Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. We present a survey of vessel extraction techniques and algorithms. We put the various vessel extraction approaches and techniques in perspective by means of a classification of the existing research. While we have mainly targeted the extraction of blood vessels, neurosvascular structure in particular, we have also reviewed some of the segmentation methods for the tubular objects that show similar characteristics to vessels. We have divided vessel segmentation algorithms and techniques into six main categories: (1) pattern recognition techniques, (2) model-based approaches, (3) tracking-based approaches, (4) artificial intelligence-based approaches, (5) neural network-based approaches, and (6) tube-like object detection approaches. Some of these categories are further divided into subcategories. We have also created tables to compare the papers in each category against such criteria as dimensionality, input type, preprocessing, user interaction, and result type.
- Agin, G. and Binford, T. 1976. Computer description of curved objects. IEEE Trans. Comput. C-25, 439--449.]]Google Scholar
- Armande, N., Monga, O., and Montesinos, P. 1995. Extraction of thin nets in grey-level images. In Proceedings of Scandinavian Conference on Image Analysis. 287--295.]]Google Scholar
- Armande, N., Montesinos, P., and Monga, O. 1999. Thin nets extraction using multi-scale approach. Comput. Vis. Image Understand. 73, 2, 248--257.]] Google Scholar
- Ayache, N. 1994. Medical computer vision, virtual reality and robotics. Image Vis. Comput. 13, 4, 295--313.]]Google Scholar
- Ayache, N., Guéziec, A., Thirion, J., and Gourdon, A. 1993. Evaluating 3-d registration of ct-scan images using crest lines. Math. Meth. Med. Imag. II 2035, 06 (July), 60--71.]]Google Scholar
- Aylward, S. and Bullitt, E. 2001. Analysis of the parameter space of a metric for registering 3d vascular images. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention.]] Google Scholar
- Aylward, S. and Bullitt, E. 2002. Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Trans. Med. Image. 21, 2 (Feb.), 61--75.]]Google Scholar
- Aylward, S., Jomier, J., Weeks, S., and Bullitt, E. 2002. Registration and analysis of vascular images. Int. J. Comput. Vision.]] Google Scholar
- Aylward, S., Pizer, S., Bullitt, E., and Eberl, D. 1996. Intensity ridge and widths for tubular object segmentation and description. In Proceedings of the Workshop on Math. Methods in Biomed. Image Analysis. 131--138.]] Google Scholar
- Binford, T. 1971. Visual perception by computer. In IEEE Conf. on Sys. and Controls.]]Google Scholar
- Bombardier, V., Jaluent, M., Bubel, A., and Bremont, J. 1997. Cooperation of two fuzzy segmentation operators for digital subtracted angiograms analysis. In IEEE Conference on Fuzzy Systems. Vol. 2. 1057--1062.]]Google Scholar
- Bors, A. G. and Pitas, I. 1998. Object segmentation and modeling in volumetric images. In Proc. Wksp on Non-Linear Model Based Image Analysis. 295--300.]]Google Scholar
- Buhler, K., Felkel, P., and La Cruz, A. 2002. Geometric methods for vessel visualization and quantification---A survey. Tech. Rep. TR_VRVis_2002_035, VRVis Research Center,Vienna, Austria.]]Google Scholar
- Bullitt, E. and Aylward, S. 2001. Analysis of time-varying images using 3d vascular models. In Proc. Applied Imagery Pat. Recog. Works. 9--14.]] Google Scholar
- Bullitt, E., Aylward, S., Liu, A., Stone, J., Mukherji, S., Coffey, C., Gerig, G., and Pizer, S. 1999. 3d graph description of the intracerebral vasculature from segmented mra and test of accuracy by copariosn with x-ray angiograms. Inf. Proc. Med. Imag. 1613, 308--321.]] Google Scholar
- Bullitt, E., Aylward, S., Smith, K. Mukherji, S., Jiroutek, M., and Muller, K. 2001. Symbolic description of intracerebral vessels segmented from MRA and evaluation by comparison with x-ray angiograms. IEEE Med. Image Anal. 5, 157--169.]]Google Scholar
- Canny, J. 1983. Finding edges and lines in images. Tech. Rep. 720, MITAIL.]] Google Scholar
- Caselles, V., Catte, F., Coll, T., and Dibos, F. 1993. A geometric model for active contours in image processing. Numer. Math. 66, 1, 1--32.]]Google Scholar
- Chan, R., Karl, W., and Lees, R. 2000. A new model-based technique for enhanced small-vessel measurements in x-ray cine-angiograms. IEEE Trans. on Med. Image 19, 3 (March), 243--255.]]Google Scholar
- Chandrinos, K. V., Pilu, M., Fisher, R. B., and Trahanias, P. E. 1998. Image processing techniques for the quantification of atherosclerotic changes. In Mediterranian Conf. Medical and Bio. Eng. and Computing.]]Google Scholar
- Chaudhuri, S., Katz, N., Nelson, M., and Goldbaum, M. 1989. Detection of blood vessels in retinal images using two dimensional blood vessel filters. IEEE Trans. on Med. Img. 8, 3 (Sept.).]]Google Scholar
- Chen, J., Sato, Y., and Tamura, S. 1998. Orientation space filtering for multiple orientation line segmentation. In Proc. of the IEEE Conf. on CVPR. 311--317.]] Google Scholar
- Chen, J., Sato, Y., and Tamura, S. 2000. Orientation space filtering for multiple orientation line segmentation. PAMI 22, 5 (May), 417--429.]] Google Scholar
- Chen, Q., Stock, K., Prasad, P., and Hatabu, H. 1999. Fast magnetic resonance imaging techniques. European J. of Radio. 29, 2 (Feb.), 90--100.]]Google Scholar
- Chwialkowski, M., Ibrahim, Y., Hong, F., and Peshock, R. 1996. A method for fully automated quantitative analysis of arterial flow using flow-sensitized MR images. Comp. Med. Imaging and Graphics 20, 5, 365--378.]]Google Scholar
- Clak, J. 1991. Neural network modelling. Physics in Med. and Bio. 36, 1259--1317.]]Google Scholar
- Clarke, L., Velthuizen, R., Camacho, M., Heine, J., Vaidyanathan, M., Hall, L., and Thatcher, R. 1995. MRI segmentation: Methods and applications. Magne. Reson. Imaging 13, 3, 343--368.]]Google Scholar
- Cote, B., Hart, W., Goldbaum, M., Kube, P., and Nelson, M. 1994. Classifiction of blood vessels in images of the ocular fundus. Tech. Rep. CS94-350, UCSD.]]Google Scholar
- Cronemeyer, J., Heising, G., and Orglmeister, R. 1992. A fast skeleton finder for parallel hardware. In IEEE Computers in Cardiology. 23--26.]]Google Scholar
- Davies, E. 1987. A high speed algorithm for circular object detection. Pattern Rec. Let. 6, 323--333.]] Google Scholar
- Do Carmo, M. 1976. Differential geometry of curves and surfaces. PH.]]Google Scholar
- Donizelli, M. 1998. Region-oriented segmentation of vascular structures from dsa images using mathematical morphology and binary region growing. In Proc. of the Works. Image Proces. for the Medicine. Vol. 12.]]Google Scholar
- Duncan, J. S. and Ayache, N. 2000. Medical image analysis: Progress over two decades and the challenges ahead. PAMI 22, 1 (Jan.), 85--105.]] Google Scholar
- Eiho, S. and Qian, Y. 1997. Detection of coronary artery tree using morphological operator. IEEE Comput. Cardiol. 24, 525--528.]]Google Scholar
- Felkel, P., Wegenkittl, R., and Kanitsar, A. 2001. Vessel tracking in peripheral CTA datasets---An overview. In Spring Conf. on Computer Graph. 232--239.]] Google Scholar
- Fessler, J. A. and Macovski, A. 1991. Object-based 3-d reconstruction of arterial trees from magnetic resonance angiograms. IEEE Trans. on Med. Image 10, 1 (Mar.), 25--39.]]Google Scholar
- Figueiredo, M. and Leitao, J. 1995. A nonsmoothing approach to the estimation of vessel contours in angiograms. IEEE Trans. on Med. Image 14, 162--172.]]Google Scholar
- Fritsch, D., Eberly, D., Pizer, S., and McAuliffe, M. 1995. Simulated cores and their application in medical imaging. Inf. Proc. Med. Imaging, 365--368.]]Google Scholar
- Fritsch, D., Pizer, S., Morse, B., Eberly, D., and Liu, A. 1994. The multiscale medial axis and its applications in image registration. Pattern Rec. Let. 15, 5, 445--452.]] Google Scholar
- Geiger, D., Gupta, A., Costa, L., and Vlontzos, J. 1995. Dynamic programming for detecting, tracking, and matching deformable contours. PAMI 17, 3, 294--302.]] Google Scholar
- Goldbaum, M., Moezzi, S., Taylor, A., Chatterjee, S., Boyd, J., Hunter, E., and Jain, R. 1996. Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images. In IEEE Int. Conf. on Image Processing. Vol. 3.]]Google Scholar
- Grimson, W., Lozano-Perez, T., Nobel, N., and White, S. 1993. An automatic tube inspection system that finds cylinders in range data. In Proc. of the IEEE Conf. on CVPR. 446--452.]]Google Scholar
- Guéziec, A. and Ayache, N. 1994. Smoothing and matching of 3-d space curves. Int. J. of Comp. Vision 12, 1 (Jan.), 79--104.]] Google Scholar
- Guéziec, A., Pennec, X., and Ayache, N. 1997. Medical image registration using geometric hashing. IEEE Computational Sci. Eng., Special Issue on Geometric Hashing 4, 4, 29--41, Oct--Dec.]] Google Scholar
- Gullberg, G. and Zeng, G. 1992. A cone-beam filtered backpropagation reconstruction algorithm for cardiac single photon emission computed tomography. IEEE Trans. on Med. Img. MI-11, 91--101.]]Google Scholar
- Guo, D. and Richardson, P. 1998. Automatic vessel extraction from angiogram images. IEEE Comput. Cardiol. 25, 441--444.]]Google Scholar
- Haris, K., Efstratiadis, S. N., Maglaveras, N., Gourassas, J., Pappas, C., and Louridas, G. 1997a. Automated coronary artey extraction using watersheds. IEEE Comput. Cardiol. 24, 741--744.]]Google Scholar
- Haris, K., Efstratiadis, S., Maglaveras, N., and Pappas, C. 1997b. Semi-automatic extraction of vascular networks in angiograms. In IEEE Conf. Eng. in Medicine and Bio. 1067--1068.]]Google Scholar
- Hart, M. and Holley, L. 1993. A method of automated coronary artey tracking in unsubtracted angiograms. In IEEE Comput. Cardiol. 93--96.]]Google Scholar
- Hart, W. E., Goldbaum, M., Cote, B., Kube, P., and Nelson, M. R. 1997. Automated measurement of retinal vascular tortuosity. In Proc AMIA Fall Conference.]]Google Scholar
- Haykin, S. 1994. Neural Networks: A Comprehensive Foundation. Mcmillan College, New York.]] Google Scholar
- Higgins, W., Sypra, W., Karwoski, R., and Ritman, E. 1996. System for analyzing hig-resolution three-dimensional coronary angiograms. IEEE Trans. on Med. Image 15, 377--385.]]Google Scholar
- Higgins, W. E., Spyra, W. J. T., Ritman, E. L., Kim, Y., and Spelman, F. A. 1989. Automatic extraction of the arterial tree from 3-d angiograms. In IEEE Conf. Eng. in Medicine and Bio. Vol. 2. 563--564.]]Google Scholar
- Hoover, A., Kouznetsova, V., and Goldbaum, M. 2000. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. on Med. Image 19, 3 (March), 203--210.]]Google Scholar
- Hu, Y., Rogers, W., Coast, D., Kramer, C., and Reichek, N. 1998. Vessel boundary extraction based on a global and local deformable physical model with variable stiffness. Magne. Reson. Imaging 16, 8, 943--951.]]Google Scholar
- Huang, Q. and Stockman, G. 1993. Generalized tube model: Recognizing 3d elongated objects from 2d intensity images. In Proc. of the IEEE Conf. on CVPR. 104--109.]]Google Scholar
- Hunter, I., Soraghan, J., and McDonagh, T. 1995. Fully automatic left ventricular boundary extraction in echocardiographic images. In IEEE Computers in Cardiology. 741--744.]]Google Scholar
- Jain, R., Kasturi, R., and Schunck, B. 1995. Machine Vision. McGH.]] Google Scholar
- Kalitzin, S., Ter haar Romaney, B., Salden, A., Nacken, P., and Viergever, M. 1998. Topological numbers and singularities in scalar images: Scale-space evolution properties. J. Math. Imaging and Vis. 9, 253--269.]] Google Scholar
- Kass, M., Witkin, A., and Terzoopoulos, D. 1988. Snakes: Active contour models. Int. J. Comput. Vision 1, 4, 321--331.]]Google Scholar
- Kawata, Y., Niki, N., and Kumazaki, T. 1995a. An approach for detecting blood vessel diseases from cone-beam CT image. In IEEE Int. Conf. on Image Processing. 500--503.]] Google Scholar
- Kawata, Y., Niki, N., Kumazaki, T., and Moonier, P. 1995b. Characteristics measurement for blood vessel diseases detection based on cone-beam ct images. In IEEE Nuclear Science Symposium and Medical Imaging Conference. Vol. 3. 1660--1664.]]Google Scholar
- Kayikcioglu, T. and Mitra, S. 1992. Unique determination of shape and area of coronary arterial cross-section from biplane angiograms. In IEEE Comp.-Based Med. Sys. 596--603.]]Google Scholar
- Kayikcioglu, T. and Mitra, S. 1993. A new method for estimating dimensions and 3-d reconstruction of coronary arterial trees from biplane angiograms. In IEEE Comp.-Based Med. Sys. 153--158.]]Google Scholar
- Kirbas, C. and Quek, F. 2002. 3d wave propagation and traceback in vascular extraction. In IEEE Eng. in Medicine and Bio. and Biomed. Eng. Soc.]]Google Scholar
- Kitamura, K., Tobis, J., and Sklansky, J. 1988a. Biplane analysis of atheromatous coronary arteries. In Proc. Int. Conf. Pattern Rec. Vol. 2. 1277--1281.]]Google Scholar
- Kitamura, K., Tobis, J., and Sklansky, J. 1988b. Estimating the 3-d skeletons and transverse areas of coronary arteries from biplane angiograms. IEEE Trans. on Med. Img. 7, 173--187.]]Google Scholar
- Klein, A., Egglin, T., Pollak, J., Lee, F., and Amini, A. 1994. Identifying vascular features with orientation specific filters and b-spline snakes. In IEEE Computers in Cardiology. 113--116.]]Google Scholar
- Klein, A., Lee, F., and Amini, A. 1997. Quantitative coronary angiography with deformable spline models. IEEE Trans. on Med. Img. 16, 468--482.]]Google Scholar
- Koenderink, J. 1990. Solid shapes. MITP.]] Google Scholar
- Kohonen, T. 1995. Self-organizing Maps. Springer-Verlag, New York.]] Google Scholar
- Koller, T. M., Gerig, G., Székely, G., and Dettwiler, D. 1995. Multiscale detection of curvilinear structures in 2d and 3d image data. In Int. Conf. on Comp. Vision. 864--869.]] Google Scholar
- Kompatsiaris, I., Tzovaras, D., Koutkias, V., and Strintzis, M. 2000. Deformable boundary detection of stents in angiographic images. IEEE Trans. on Med. Img. 19, 6 (June), 652--662.]]Google Scholar
- Kottke, D. and Sun, Y. 1990a. Region splitting of medical images based upon bimodality analysis. In IEEE Eng. Conf. in Medicine and Bio. Vol. 12. 154--155.]]Google Scholar
- Kottke, D. and Sun, Y. 1990b. Segmentation of coronary arteriograms by iterative ternary classsification. IEEE Trans. on Biomed. Engr. 37, 778--785.]]Google Scholar
- Kozerke, S., Botnar, R., Oyre, S., Scheidegger, M. B., Pedersen, E., and Boesinger, P. 1999. Automatic vessel segmentation using active contours in cine phase contrast flow measurements. J. of Mag. Res. Imaging 10, 1 (July), 41--51.]]Google Scholar
- Krissian, K., Malandain, G., and Ayache, N. 1996. Directional anisotropic diffusion applied to segmentation of vessels in 3d images. Tech. Rep. 3064, INRIA.]]Google Scholar
- Krissian, K., Malandain, G., and Ayache, N. 1998. Model-based multiscale detection and reconstruction of 3d vessels. Tech. Rep. 3442, INRIA.]]Google Scholar
- Krissian, K., Malandain, G., Ayache, N., Vaillant, R., and Trousset, Y. 1998a. Model-based multiscale detection of 3d vessels. Proc. of the IEEE Conf. on CVPR, 722--727.]] Google Scholar
- Krissian, K., Malandain, G., Ayache, N., Vaillant, R., and Trousset, Y. 1998b. Model-based multiscale detection of 3d vessels. In Proc. IEEE Workshop Biomed. Image Anal. 202--208.]] Google Scholar
- Krissian, K., Malandain, G., Ayache, N., Vaillant, R., and Trousset, Y. 1999. Model based detection of tubular structures in 3d images. Tech. Rep. 3736, INRIA.]]Google Scholar
- Lecornu, L., Roux, C., and Jacq, J. 1994. Extraction of vessel contours in angiograms by simultaneous tracking of the two edges. In IEEE Conf. Eng. in Medicine and Bio. Vol. 1. 678--679.]]Google Scholar
- Lindeberg, T. 1994. Scale-Space theory in Computer Vision. Kluwer Academic Publishers, Dordrecht, Netherlands.]] Google Scholar
- Lindeberg, T. 1996. Edge detection and ridge detection with automatic scale selection. In Proc. of the IEEE Conf. on CVPR. 465.]] Google Scholar
- Liu, I. and Sun, Y. 1993. Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme. IEEE Trans. on Med. Img. 12, 334--341.]]Google Scholar
- Lorenz, C., Carse, I. C., Buzug, T. M., Fassnacht, C., and Weese, J. 1997. Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2d and 3d medical images. In Joint Conf. Comp. Vision, Vir. Reality and Robotics in Medicine and Robotics, and Comput.-Assisted Surgery. 213--222.]] Google Scholar
- Lu, S. and Eiho, S. 1993. Automatic detection of the coronary arterial contours with sub-branches from an x-ray angiogram. In IEEE Computers in Cardiology. 575--578.]]Google Scholar
- Luo, H., Qiang, L., Acharya, R., and Gaborski, R. 2000. Robust snake model. In Proc. IEEE Conf. on CVPR. Vol. 1. 452--457.]]Google Scholar
- Malladi, R., Sethian, J. A., and Vemuri, B. C. 1995. Shape modeling with front propagation: A level set approach. PAMI 17, 2 (Feb.), 158--175.]] Google Scholar
- Mao, F., Ruan, S., Bruno, A., Toumoulin, C., Collorec, R., and Haigron, P. 1992. Extraction of structural features in digital subtraction angiography. In IEEE Int. Biomed. Eng. Days. 166--169.]]Google Scholar
- Martelli, A. 1976. An application of heuristic search methods to edge and contour detection. In Commun. ACM 19, 73--83.]] Google Scholar
- Mayer, H., Laptev, I., Baumgartner, A., and Steger, C. 1997. Automatic road extraction based on multi-scale modeling, context, and snakes. IEEE Trans. on Med. Image 32, 47--56.]]Google Scholar
- McInerney, T. and Terzopoulos, D. 1995. Topologically adaptable snakes. In Int. Conf. on Comp. Vision. 840--845.]] Google Scholar
- McInerney, T. and Terzopoulos, D. 1996. Deformable models in medical image analysis: A survey. IEEE Medical Image Analysis 1, 2, 91--108.]]Google Scholar
- McInerney, T. and Terzopoulos, D. 1997. Medical image segmentation using topologically adaptable surfaces. In Conf. Comp. Vision, Vir. Reality and Robotics in Medicine and Robotics. Vol. 1205. 23--32.]] Google Scholar
- Miller, J., Breen, D., Lorensen, W., O'Bara, R., and Wozny, M. 1991. Geometrically deformed models: A method for extracting closed geometric models from volume data. CG 25, 4 (July), 217--226.]] Google Scholar
- Molina, C., Prause, G., Radeva, P., and Sonka, M. 1998. 3-d catheter path reconstruction from biplane angiograms. In SPIE. Vol. 3338. 504--512.]]Google Scholar
- Monga, O., Armande, N., and Montesinos, P. 1997. Thin nets and crest lines: Application to satellite data and medical images. Computer Vision and Image Understanding 66, 1.]] Google Scholar
- Monga, O., Lengagne, R., and Deriche, R. 1994a. Crest-lines extraction in volumetric 3d medical images: a multiscale approach. In Proc. Int. Conf. Pattern Rec.]]Google Scholar
- Monga, O., Lengagne, R., and Deriche, R. 1994b. Extraction of the zero-crossings of the curvature derivatives in volumic 3d medical images: A multi-scale approach. In Proc. of the IEEE Conf. on CVPR. 852--855.]]Google Scholar
- Nekovei, R. and Sun, Y. 1995. Back-propagation network and its configuration for blood vessel detection in angiograms. IEEE Trans. on Neural Nets 6, 1 (January), 64--72.]] Google Scholar
- Nguyen, T. and Sklansky, J. 1986a. Computing the skeleton of coronary arteries in cineangiograms. Comput. and Biomed. Res. 19, 428--444.]] Google Scholar
- Nguyen, T. and Sklansky, J. 1986b. A fast skeleton finder for coronary arteries. In Proc. Int. Conf. Pattern Rec. 481--483.]]Google Scholar
- Niki, N., Kawata, Y., Sato, H., and Kumazaki, T. 1993. 3d imaging of blood vessels using x-ray rotational angiographic system. IEEE Med. Imaging Conf. 3, 1873--1877.]]Google Scholar
- O'Brien, J. F. and Ezquerra, N. F. 1994. Automated segmentation of coronary vessels in angiographic image sequences utilizing temporal, spatial structural constraints. In Proc. SPIE Conf. Visualization in Biomed. Computing.]]Google Scholar
- O'Donnell, T., Boult, T. E., Fang, X., and Gupta, A. 1994. The extruded generalized cylinder: A deformable model for object recovery. In Proc. of the IEEE Conf. on CVPR. 174--181.]]Google Scholar
- O'Donnell, T., Gupta, A., and Boult, T. 1997. A new model for the recovery of cylindrical structures from medical image data. In Joint Conf. Comp. Vision, Vir. Reality and Robotics in Medicine and Robotics, and Comp.-Assisted Surgery. 223--232.]] Google Scholar
- Osher, S. and Sethian, J. A. 1988. Fronts propagating with curvature dependent speed: Algorithms based on hamilton-jacobi formulation. J. Computat. Phys. 79, 12--49.]] Google Scholar
- Otsu, N. 1979. A threshold selection method from gray-level histograms. IEEE Trans. on Syst., Man, and Cybernet. 9, 62--66.]]Google Scholar
- Park, S., Lee, J., Koo, J., Kwon, O., and Hong, S. 1997. Adaptive tracking algorithm based on direction field using ml estimation in angiogram. In IEEE Conference on Speech and Image Technologies for Computing and Telecommunications. Vol. 2. 671--675.]]Google Scholar
- Parker, D. L., Wu, J., and van Bree, R. E. 1988. Three dimensional vascular reconstruction from projections: A theoretical review. In IEEE Conf. Eng. in Medicine and Bio.]]Google Scholar
- Parvin, B. A., Penf, C., Johnston, W., and Maestre, F. M. 1994. Tracking of tubular objects for scientific applications. In Proc. of the IEEE Conf. on CVPR. 295--301.]]Google Scholar
- Pellot, C., Herment, A., and Sigelle, M. 1994. A 3d reconstruction of vascular structures from two x-ray angiograms using an adapted simulated annealing algorithm. IEEE Trans. on Med. Img. 13, 48--60.]]Google Scholar
- Perona, P. and Malik, J. 1990. Scale-space and edge detection using anisotropic diffusion. PAMI 12, 7 (July), 629--639.]] Google Scholar
- Petrocelli, R., Elion, J., and Manbeck, K. M. 1992. A new method for structure recognition in unsubtracted digital angiograms. In IEEE Computers in Cardiology. 207--210.]]Google Scholar
- Petrocelli, R., Manbeck, K., and Elion, J. 1993. Three dimensional structure recognition in digital angiograms using gauss-markov methods. In IEEE Computers in Cardiology. 101--104.]]Google Scholar
- Pham, D., Xu, C., and Prince, J. 2000. Current Methods in Medical Image Segmentation. Vol. 2. 315--338.]]Google Scholar
- Pitas, I. and Venetsanopoulos, A. 1990. Nonlinear Digital Filters: principles and applications. Kluver Academic, Norwell, Mass.]]Google Scholar
- Pizer, S., Morse, B., and Fritsch, D. 1998. Zoom-invariant vision of figural shape: the mathematics of cores. Computer Vision and Image Understanding 69, 55--71.]] Google Scholar
- Poli, R. and Valli, G. 1997. An algorithm for real-time vessel enhancement and detection. Comp. Methods and Prog. in Biomed. 52, 1 (Jan.), 1--22.]]Google Scholar
- Prinet, V., Monga, O., Ge, C. Xie, S., and Ma, S. 1996. Thin network extraction in 3d images: Application to medical angiograms. In Proc. Int. Conf. Pattern Rec. 386--390.]] Google Scholar
- Prinet, V., Monga, O., and Rocchisani, J. 1997. Multi-dimensional vessel extraction using crest lines. In IEEE Conf. Eng. in Medicine and Bio. Vol. 1. 393--394.]]Google Scholar
- Puig, P. 1998a. Cerebral blood vessels modeling. Tech. Rep. LSI-98-21-R, PICS.]]Google Scholar
- Puig, P. 1998b. Discrete medial axis transform for discrete objects. Tech. Rep. LSI-98-20-R, PICS.]]Google Scholar
- Quek, F. and Kirbas, C. 2001. Vessel extraction in medical images by wave propagation and traceback. IEEE Trans. on Med. Img. 20, 2 (Feb.), 117--131.]]Google Scholar
- Quek, F., Kirbas, C., and Charbel, F. 1999. Aim:attentionally-based interaction model for the interpretation of vascular angiograph. IEEE Trans. on Inf. Tech. in Biomed. 3, 2 (June), 139--150.]] Google Scholar
- Quek, F., Kirbas, C., and Charbel, F. 2001. Aim: An attentionally-based system for the interpretation of angiography. In Proc. IEEE Med. Imaging and Augmented Reality Conf. 168--173.]] Google Scholar
- Quek, F., Kirbas, C., and Gong, X. 2001. Simulated wave propagation and traceback in vascular extraction. In Proc. IEEE Med. Imaging and Augmented Reality Conf. 229--234.]] Google Scholar
- Ripley, B. 1996. Pattern Recognition and Neural Networks. Cambridge University Press.]] Google Scholar
- Ritchings, R. and Colchester, A. 1986. Detection of abnomalities on carotid angiograms. Pattern Rec. Let. 4, 367--374.]] Google Scholar
- Rosenfeld, A. and Smith, R. 1981. Thresholding using relaxation. PAMI 3, 598--606.]]Google Scholar
- Rost, U., Munkel, H., and Liedtke, C.-E. 1998. A knowledge based system for the configuration of image processing algorithms. Fachtagung Informations und Mikrosystem Technik.]]Google Scholar
- Rueckert, D. and Burger, P. 1995. Contour fitting using stochastic and probabilistic relaxation for cine mr images. In Computer Assisted Radiology. 137--142.]]Google Scholar
- Rueckert, D. and Burger, P. 1996. Shape-based tracking and analysis of the aorta in cardiac mr images using geometrically defornable templates. In Computer Assisted Radiology.]]Google Scholar
- Rueckert, D., Burger, P., Forbat, S. M., Mohiaddin, R. D., and Yang, G. Z. 1997. Automatic tracking of the aorta in cardiovascular mr images using deformable models. IEEE Trans. on Med. Img. 16, 5 (Oct.), 581--590.]]Google Scholar
- Sarry, L. and Boire, J. 2001. Three-dimensional tracking of coronary arteries from biplane angiographic sequences using parametrically deformable moodels. IEEE Trans. on Med. Img. 20, 12 (Dec.), 1341--1351.]]Google Scholar
- Sarwal, A. and Dhawan, A. 1994. 3-d reconstruction of coronary arteries. In IEEE Conf. Eng. in Medicine and Bio. Vol. 1. 504--505.]]Google Scholar
- Sato, Y., Araki, T., Hanayama, M., Naito, H., and Tamura, S. 1998a. A viewpoint determination system for stenosis diagnosis and quantification in coronary angiographic image acquisition. IEEE Trans. on Med. Img. 17, 121--137.]]Google Scholar
- Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., and Kikinis, R. 1998b. 3d multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. IEEE Medical Image Analysis 2, 2 (June), 143--168.]]Google Scholar
- Schmitt, H., Grass, M., Rasche, V., Schramm, O., Haehnel, S., and Sartor, K. 2002. An x-ray-based method for the determination of the contrast agent propagation in 3-d vessel structures. IEEE Trans. on Med. Img. 21, 3 (Mar.), 251--262.]]Google Scholar
- Sethian, J. 1999. Level Set Methods and Fast Marching Methods:Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Material Science. Cambridge University Press, Cambridge, UK.]]Google Scholar
- Sethian, J. A. 1996. A fast marching level set method for monotinically advancing fronts. In Proc. of Nat. Acad. of Sci. Vol. 93. 1591--1595.]]Google Scholar
- Shen, H. and Johnson, C. R. 1994. Semi-automatic image segmentation: A bimodal thresholding approach. Tech. Rep. UUCS-94-019, Univ. of Utah, Dept. of Comput. Science.]]Google Scholar
- Shiffman, S., Rubin, G. D., and Napel, S. 1996. Semiautomated Editing of Computed Tomography Sections for Visualization of Vasculature. Vol. 2707. SPIE.]]Google Scholar
- Smets, C., Verbeeck, G., Suetens, P., and Oosterlinck, A. 1988. A knowledge-based system for the delineation of blood vessels on subtraction angiograms. Pattern Rec. Lett. 8, 113--121.]] Google Scholar
- Sonka, M., Hlavac, V., and Boyle, R. 1999. Image Processing, Analysis, and Machine Vision. PWS Publishing.]] Google Scholar
- Sorantin, E., Halmai, C., Erdohelyi, B., Palagyi, K., Nyul, L., Olle, K., Geiger, B., Lindbichler, F., Friedrich, G., and Kiesler, K. 2002. Spiral-ct-based assessment of tracheal stenoses using 3-d-skeletonization. IEEE Trans. on Med. Img. 21, 3 (Mar.), 263--273.]]Google Scholar
- Stansfield, S. 1986. Angy: A rule-based expert system for automatic segmentation of coronory vessels from digital subtracted angiograms. PAMI 8, 3 (Mar.), 188--199.]] Google Scholar
- Stevenson, D. 1987. Working towards the automatic detection of blood vessels in x-ray angiograms. Pattern Rec. Lett. 6, 107--112.]] Google Scholar
- Stockett, M. and Soroka, B. 1992. Extracting spinal cord contours from transaxial mr images using computer vision techniques. In IEEE Comp.-Based Med. Sys. 1--8.]]Google Scholar
- Summers, P. and Bhalerao, A. 1995. Derivation of pressure gradients from magnetic resonance angiography using multi-resolution segmentation. In Proceedings of International Conference on Image Processing and its Applications. 404--408.]]Google Scholar
- Sun, Y. 1989. Automated identification of vessel contours in coronary arteriograms by an adaptive tracking algorithm. IEEE Trans. on Med. Img. 8, 78--88.]]Google Scholar
- Thackray, B. and Nelson, A. 1993. Semi-automatic segmentation of vascular network images using a rotating structuring element (rose) with mathematical morphology and dual feature thresholding. IEEE Trans. on Med. Img. 12, 385--392.]]Google Scholar
- Thirion, B., Bascle, B., Ramesh, V., and Navab, N. 2000. Fusion of color, shading and boundary infomation for factory pipe segmentation. In Proc. of the IEEE Conf. on CVPR. Vol. 2. 349--356.]]Google Scholar
- Toledo, R., Orriols, X., Binefa, X., Raveda, P., Vitria, J., and Villanueva, J. J. 2000. Tracking elongated structures using statistical snakes. In Proc. of the IEEE Conf. on CVPR. 157--162.]]Google Scholar
- Tolias, Y. and Panas, S. 1998. A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans. on Med. Img. 17, 263--273.]]Google Scholar
- Tozaki, T., Kawata, Y., Niki, N., Ohmatsu, H., and Moriyama, N. 1995. 3-d visualization of blood vessels and tumor using thin slice ct. In IEEE Nuclear Science Symposium and Medical Imaging Conference. Vol. 3. 1470--1474.]]Google Scholar
- Umbaugh, S. 1998. Computer Vision and Machine Processing. PHPTR.]]Google Scholar
- van der Weide, R., Bakker, C., and Viergever, M. 2001. Localization of intravascular devices with paramagnetic markers in mr images. IEEE Trans. on Med. Img. 20, 10 (October), 1061--1071.]]Google Scholar
- Wood, S., Qu, G., and Roloff, L. 1995. Detection and labeling of retinal vessels for longitidunal studies. In IEEE Int. Conf. on Image Processing. Vol. 3. 164--167.]] Google Scholar
- Xu, C., Pham, D., and Prince, J. 2000. Medical Image Segmentation Using Deformable Models. SPIE Press, Chapter 3, 129--174.]]Google Scholar
- Xu, C. and Prince, J. 1998. Snakes, shapes, and gradient vector flow. IEEE Trans. on Image Proces. 7, 359--369.]] Google Scholar
- Yim, P., Choyke, P., and Summers, R. 2000. Gray-scale skeletonization of small vessels in magnetic resonance angiography. IEEE Trans. on Med. Img. 19, 6 (June), 568--576.]]Google Scholar
- Zana, F. and Klein, J. 1997. Robust segmentation of vessels from retinal angiography. In IEEE International Conference on Digital Signal Processing. Vol. 2. 1087--1090.]]Google Scholar
- Zerroug, M. and Nevatia, R. 1993. Quasi-invariant properties and 3-d shape recovery of non-constant generalized cylinders. In Proc. of the IEEE Conf. on CVPR. 96--103.]]Google Scholar
- Zhou, L., Rzeszotarski, M., Singerman, L., and Chokreff, J. 1994. The detection and quantification of retinopathy using digital angiograms. IEEE Trans. on Med. Img. 13, 619--626.]]Google Scholar
Index Terms
- A review of vessel extraction techniques and algorithms
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