Abstract
In clinical practice, Digital Subtraction Angiography (DSA) is a powerful technique for the visualization of blood vessels in the human body. The diagnostic relevance of the images is often reduced by artifacts which arise from the misalignment of successive images in the sequence, due to patient motion. In order to improve the quality of the subtraction images, several registration techniques have been proposed. However, because of the required computation times, it has never led to algorithms that are fast enough so as to be acceptable for integration in clinical applications. In this paper, a new approach to the registration of digital angiographic images is proposed. It involves an edge-based selection of control points for which the displacement is computed by means of template matching, and from which the complete displacement vector field is constructed by means of interpolation. The final warping of the images according to the calculated displacement vector field is performed real-time by graphics hardware. Experimental results with several clinical data sets show that the proposed algorithm is both effective and very fast.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Aggerwal, J.K. and Nandhakumar, N. 1988. On the computation of motion from sequences of images — A review. Proceedings of the IEEE, 76(8): 917–935.
Althof, R., Wind, M.G.J. and Dobbins, J.T. 1997. A rapid and automatic image registration algorithm with subpixel accuracy. IEEE Transactions on Medical Imaging, 16(1): 308–316.
Barnea, D.I. and Silverman, H.F. 1972. A class of algorithms for fast digital image registration. IEEE Transactions on Computers, 21(2): 179–186.
Beier, T. and Neely, S. 1992. Feature-based image metamorphosis. Computer Graphics (SIGGRAPH' 92 Conference Proceedings), 26(2): 35–42.
Brody, W.R. 1981. Hybrid subtraction for improved arteriography. Radiology, 141(3): 828–831.
Brody, W.R. 1982. Digital subtraction angiography. IEEE Transactions on Nuclear Science, 29(3): 1176–1180.
Brody, W.R., Enzmann, D.R., Deutsch, L.-S., Hall, A. and Pelc, N. 1981. Intravenous carotid arteriography using line-scanned digital radiography. Radiology, 139(2): 297–300.
Brown, L.G. 1992. A survey of image registration techniques. ACM Computing Surveys, 24(4): 325–376.
Buzug, T.M., Lorenz, C. and Weese, J. 1997. Improvement of vessel segmentation by elastically compensated patient motion in digital subtraction angiography images. In G. Sommer, K. Daniilidis and J. Pauli (eds), Computer Analysis of Images and Patterns (CAIP' 97), volume 1296 of Lecture Notes in Computer Science, Springer-Verlag: Berlin, Germany, pp. 106–113.
Buzug, T.M. and Weese, J. 1996. Improving DSA images with an automatic algorithm based on template matching and an entropy measure. In H.U. Lemke, M.W. Vannier, K. Inamura and A.G. Farman (eds), Computer Assisted Radiology (CAR' 96), volume 1124 of International congress series, Elsevier Science: Amsterdam, The Netherlands, pp. 145–150.
Buzug, T.M., Weese, J., Fassnacht, C. and Lorenz, C. 1997. Image registration: Convex weighting functions for histogram-based similarity measures. In J. Troccaz, E. Grimson and R. Mösges (eds), CVRMed-MRCAS' 97, volume 1205 of Lecture Notes in Computer Science, Springer-Verlag: Berlin, Germany, pp. 203–212.
Canny, J.F. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6): 679–698.
Chiang, J.Y. and Sullivan, B.J. 1993. Coincident bit counting — A new criterion for image registration. IEEE Transactions on Medical Imaging, 12(1): 30–38.
Chilcote, W.A., Modic, M.T., Pavlicek, W.A., Little, J.R., Furian, A.J., Duchesneau, P.M. and Weinstein, M.A. 1981. Digital subtraction angiography of the carotid arteries: A comparitive study in 100 patients. Radiology, 139(2): 287–295.
Cox, G.S. 1995. Review: Template matching and measures of match in image processing. University of Cape Town, Department of Electrical Engineering, South Africa, Technical report.
Cox, G.S. and de Jager, G. 1994. Automatic registration of temporal image pairs for digital subtraction angiography. Image Processing, volume 2167 of Proceedings of SPIE, The International Society for Optical Engineering: Bellingham,Washington, USA, pp. 188–199.
Davis, L.S., Wu, Z. and Sun, H. 1983. Contour-based motion estimation. Computer Vision, Graphics and Image Processing, 23(3): 313–326.
Fitzpatrick, J.M. 1988. The existence of geometrical density-image transformations corresponding to object motion. Computer Vision, Graphics and Image Processing, 44(2): 155–174.
Fitzpatrick, J.M., Grefenstette, J.J., Pickens, D.R., Mazer, M. and Perry, J.M. 1988. A system for image registration in digital subtraction angiography. In C.N. de Graaf and M.A. Viergever (eds), Image Processing in Medical Imaging, Plenum Press, New York, USA, pp. 415–435.
Flusser, J. 1992. An adaptive method for image registration. Pattern Recognition, 25(1): 45–54.
Fogel, S.V. 1991. The estimation of velocity vector fields from time-varying image sequences. CVGIP: Image Understanding, 53(3): 253–287.
Foley, J., van Dam, A., Feiner, S.K. and Hughes, J.F. 1990. Computer Graphics: Principles and Practice. Systems Programming Series, 2nd edn, Addison-Wesley: Reading, Massachusetts, USA.
Goshtasby, A. 1986. Piecewise linear mapping functions for image registration. Pattern Recognition, 19(6): 459–466.
Goshtasby, A. 1987. Piecewise cubic mapping functions for image registration. Pattern Recognition, 20(5): 525–533.
Goshtasby, A., Stockman, G.C. and Page, C.V. 1986. A region based approach to digital image registration with subpixel accuracy. IEEE Transactions on Geoscience and Remote Sensing, 24(3): 390–399.
Heckbert, P.S. 1986. Survey of texture mapping. IEEE Computer Graphics and Applications, 6(11): 56–67.
Hildreth, E.C. 1983. The detection of intensity changes by computer and biological vision systems. Computer Vision, Graphics and Image Processing, 22(1): 1–27.
Hildreth, E.C. 1984. The computation of the velocity field. Proceedings of the Royal Society of London, Series B, 221: 189–220.
Hillman, B.J., Ovitt, T.W., Nudelman, S., Fischer, H.D., Frost, M.M., Capp, P., Roehrig, H. and Seeley, G. 1981. Digital video subtraction angiography of renal vascular abnormalities. Radiology, 139(2): 277–280.
Horn, B.K.P. and Schunck, B.G. 1981. Determining optical flow. Artificial Intelligence, 17: 185–203.
Hua, P. and Fram, I. 1993. Feature-based image registration for digital subtraction angiography. In M.H. Loew (ed.), Image Processing, volume 1898 of Proceedings of SPIE, The International Society for Optical Engineering: Bellingham, Washington, USA, pp. 24–31.
Kruger, R.A., Miller, F.J., Nelson, J.A., Liu, P.Y. and Bateman, W. 1982. Digital subtraction angiography using a temporal bandpass filter: Associated patient motion properties. Radiology, 145(2): 315–320.
Kruger, R.A., Mistretta, C.A., Crummy, A.B., Sackett, J.F., Goodsitt, M.M., Riederer, S.J., Houk, T.L., Shaw, C.-G. and Flemming, D. 1977. Digital K-edge subtraction radiography. Radiology, 125(1): 243–245.
Kruger, R.A., Nelson, J.A., Roy, D.G., Miller, F.J., Anderson, R.E. and Liu, P. 1983. Dynamic tomographic digital subtraction angiography using temporal filtration. Radiology, 147(3): 863–867.
Kruger, R.A., Sedaghati, M., Roy, D.G., Liu, P., Nelson, J.A., Kubal, W. and Rio, P. Del 1984. Tomosynthesis applied to digital subtraction angiography. Radiology, 152(3): 805–808.
Lawson, C.L. 1977. Software for C1 surface interpolation. In J.R. Rice (ed.), Mathematical Software III, Academic Press: New York, USA, pp. 161–194.
Lee, D.T. and Schachter, B.J. 1980. Two algorithms for constructing a Delaunay triangulation. International Journal of Computer and Information Sciences, 9(3): 219–242.
Maintz, J.B.A. and Viergever, M.A. 1998. Asurvey of medical image registration. Medical Image Analysis, 2(1): 1–36.
Mandava, V.R., Fitzpatrick, J.M. and Pickens, D.R. 1989. Adaptive search space scaling in digital image registration. IEEE Transactions on Medical Imaging, 8(3): 251–262.
Marr, D. and Hildreth, E.C. 1980. Theory of edge detection. Proceedings of the Royal Society of London, Series B, 207: 187–217.
Mistretta, C.A., Ort, M.G., Kelcz, F., Cameron, J.R., Sieband, M.P. and Crummy, A.B. 1973. Absorption edge fluoroscopy using quasimonoenergetic X-ray beams. Investigative Radiology, 8(6): 402–412.
Neider, J., Davis, T. and Woo, M. 1995. Open GL Programming Guide. Addison-Wesley: Reading, Massachusetts, USA.
Oung, H. and Smith, A.M. 1984. Real time motion detection in digital subtraction angiography. In A. Deurinckx, M.H. Loew and J.M.S Prewitt (eds), Proceedings of the International Symposium on Medical Images and Icons, IEEE Computer Society Press: Silver Spring, USA, pp. 336–339.
Ousterhout, J.K. 1994. Tcl and the Tk Toolkit. Professional Computing Series, Addison-Wesley: Reading, Massachusetts, USA.
Potel, M.J. and Gustafson, D.E. 1983. Motion correction for digital subtraction angiography. IEEE Frontiers of Engineering and Computing in Health Care: Proceedings of the 5th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 166–169.
Powell, M.J.D. 1964. An efficient method for finding the minimum of a function of several variables without calculating derivatives. The Computer Journal, 7: 155–162.
Pratt, W.K. 1974. Correlation techniques of image registration. IEEE transactions on Aerospace and Electronic Systems, 10: 353–358.
Ruprecht, D. 1994. Geometrische Deformationen als Werkzeug in der Graphischen Datenverarbeitung. PhD thesis, Universität Dortmund, Fachbereich Informatik, Lehrstuhl VII-Graphische Systeme, Germany.
Ruprecht, D. and Müller, H. 1995. Image warping with scattered data interpolation. IEEE Computer Graphics and Applications, 15(2): 37–43.
Shi, J. and Tomasi, C. 1994. Good features to track. IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600.
Stockman, G.C., Kopstein, S. and Benett, S. 1982. Matching images to models for registration and object detection via clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 4(3): 229–241.
Stroustrup, B. 1991. The C++ Programming Language. 2nd edn, Addison-Wesley: Reading, Massachusetts, USA.
Szeliski, R. and Coughlan, J. 1997. Spline-based image registration. International Journal of Computer Vision, 22(3): 199–218.
Tomasi, C. and Kanade, T. 1991. Shape and motion from image streams: A factorization method-Part 3. Detection and tracking of point features. Carnegie Mellon University, School of Computer Science, Pittsburgh, USA, Technical Report CMU-CS–91–132.
van den Elsen, P.A., Pol, E.-J.D. and Viergever, M.A. 1993. Medical image matching—A review with classification. IEEE Engineering in Medicine and Biology, 12(1): 26–39.
van Tran, L. and Sklansky, J. 1992. Flexible mask subtraction for digital angiography. IEEE Transactions on Medical Imaging, 11(3): 407–415.
Venot, A., Lebruchec, J.F. and Roucayrol, J.C. 1984. A new class of similarity measures for robust image registration. Computer Vision, Graphics and Image Processing, 28(2): 176–184.
Venot, A. and Leclerc, V. 1984. Automated correction of patient motion and gray values prior to subtraction in digitized angiography. IEEE Transactions on Medical Imaging, 3(4): 179–186.
Venot, A., Pronzato, L. and Walter, E. 1994. Comments about the coincident bit counting (CBC) criterion for image registration. IEEE Transactions on Medical Imaging, 13(3): 565–566.
Verhoeven, L.A.J. 1985. Digital Subtraction Angiography. The technique and an analysis of the physical factors influencing the image quality. PhD thesis, Delft University of Technology, The Netherlands.
Watson, D.F. 1981. Computing the n-dimensional Delaunay tessellation with application to Voronoi polytopes. The Computer Journal, 24(2): 167–172.
Watson, D.F. and Philip, G.M. 1984. Survey: Systematic triangulations. Computer Vision, Graphics and Image Processing, 26(2): 217–223.
Wolberg, G. 1990. Digital Image Warping. IEEE Computer Society Press: Washington, USA.
Yanagisawa, M., Shigemitsu, S. and Akatsuka, T. 1984. Registration of locally distorted images by multiwindow pattern matching and displacement interpolation: The proposal of an algorithm and its application to digital subtraction angiography. In M.D. Levine (ed.), Proceedings of the Seventh International Conference on Pattern Recognition, volume 2, IEEE Publishing Services: New York, USA, pp. 1288–1291.
Zuiderveld, K.J., ter Haar Romeny, B.M. and Viergever, M.A. 1989. Fast rubber sheet masking for digital subtraction angiography. In M.A. Viergever (ed.), Science and Engineering of Medical Imaging, volume 1137 of Proceedings of SPIE, The International Society for Optical Engineering: Bellingham, Washington, USA, pp. 22–30.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Meijering, E.H., Zuiderveld, K.J. & Viergever, M.A. Image Registration for Digital Subtraction Angiography. International Journal of Computer Vision 31, 227–246 (1999). https://doi.org/10.1023/A:1008074100927
Issue Date:
DOI: https://doi.org/10.1023/A:1008074100927