International Journal of Computer Vision

, Volume 31, Issue 2–3, pp 227–246

Image Registration for Digital Subtraction Angiography

  • Erik H.W. Meijering
  • Karel J. Zuiderveld
  • Max A. Viergever
Article

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.

digital subtraction angiography motion correction registration matching warping 

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References

  1. 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.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. 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.Google Scholar
  4. Beier, T. and Neely, S. 1992. Feature-based image metamorphosis. Computer Graphics (SIGGRAPH' 92 Conference Proceedings), 26(2): 35–42.Google Scholar
  5. Brody, W.R. 1981. Hybrid subtraction for improved arteriography. Radiology, 141(3): 828–831.Google Scholar
  6. Brody, W.R. 1982. Digital subtraction angiography. IEEE Transactions on Nuclear Science, 29(3): 1176–1180.Google Scholar
  7. 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.Google Scholar
  8. Brown, L.G. 1992. A survey of image registration techniques. ACM Computing Surveys, 24(4): 325–376.CrossRefGoogle Scholar
  9. 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.Google Scholar
  10. 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.Google Scholar
  11. 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.Google Scholar
  12. Canny, J.F. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6): 679–698.Google Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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.Google Scholar
  15. 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.Google Scholar
  16. 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.Google Scholar
  17. Davis, L.S., Wu, Z. and Sun, H. 1983. Contour-based motion estimation. Computer Vision, Graphics and Image Processing, 23(3): 313–326.Google Scholar
  18. 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.Google Scholar
  19. 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.Google Scholar
  20. Flusser, J. 1992. An adaptive method for image registration. Pattern Recognition, 25(1): 45–54.CrossRefGoogle Scholar
  21. Fogel, S.V. 1991. The estimation of velocity vector fields from time-varying image sequences. CVGIP: Image Understanding, 53(3): 253–287.CrossRefGoogle Scholar
  22. 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.Google Scholar
  23. Goshtasby, A. 1986. Piecewise linear mapping functions for image registration. Pattern Recognition, 19(6): 459–466.CrossRefGoogle Scholar
  24. Goshtasby, A. 1987. Piecewise cubic mapping functions for image registration. Pattern Recognition, 20(5): 525–533.CrossRefGoogle Scholar
  25. 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.Google Scholar
  26. Heckbert, P.S. 1986. Survey of texture mapping. IEEE Computer Graphics and Applications, 6(11): 56–67.Google Scholar
  27. 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.Google Scholar
  28. Hildreth, E.C. 1984. The computation of the velocity field. Proceedings of the Royal Society of London, Series B, 221: 189–220.Google Scholar
  29. 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.Google Scholar
  30. Horn, B.K.P. and Schunck, B.G. 1981. Determining optical flow. Artificial Intelligence, 17: 185–203.CrossRefGoogle Scholar
  31. 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.Google Scholar
  32. 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.Google Scholar
  33. 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.Google Scholar
  34. 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.Google Scholar
  35. 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.Google Scholar
  36. 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.Google Scholar
  37. 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.Google Scholar
  38. Maintz, J.B.A. and Viergever, M.A. 1998. Asurvey of medical image registration. Medical Image Analysis, 2(1): 1–36.CrossRefGoogle Scholar
  39. 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.CrossRefGoogle Scholar
  40. Marr, D. and Hildreth, E.C. 1980. Theory of edge detection. Proceedings of the Royal Society of London, Series B, 207: 187–217.Google Scholar
  41. 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.Google Scholar
  42. Neider, J., Davis, T. and Woo, M. 1995. Open GL Programming Guide. Addison-Wesley: Reading, Massachusetts, USA.Google Scholar
  43. 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.Google Scholar
  44. Ousterhout, J.K. 1994. Tcl and the Tk Toolkit. Professional Computing Series, Addison-Wesley: Reading, Massachusetts, USA.Google Scholar
  45. 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.Google Scholar
  46. 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.Google Scholar
  47. Pratt, W.K. 1974. Correlation techniques of image registration. IEEE transactions on Aerospace and Electronic Systems, 10: 353–358.Google Scholar
  48. Ruprecht, D. 1994. Geometrische Deformationen als Werkzeug in der Graphischen Datenverarbeitung. PhD thesis, Universität Dortmund, Fachbereich Informatik, Lehrstuhl VII-Graphische Systeme, Germany.Google Scholar
  49. Ruprecht, D. and Müller, H. 1995. Image warping with scattered data interpolation. IEEE Computer Graphics and Applications, 15(2): 37–43.CrossRefGoogle Scholar
  50. Shi, J. and Tomasi, C. 1994. Good features to track. IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600.Google Scholar
  51. 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.Google Scholar
  52. Stroustrup, B. 1991. The C++ Programming Language. 2nd edn, Addison-Wesley: Reading, Massachusetts, USA.Google Scholar
  53. Szeliski, R. and Coughlan, J. 1997. Spline-based image registration. International Journal of Computer Vision, 22(3): 199–218.CrossRefGoogle Scholar
  54. 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.Google Scholar
  55. 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.CrossRefGoogle Scholar
  56. van Tran, L. and Sklansky, J. 1992. Flexible mask subtraction for digital angiography. IEEE Transactions on Medical Imaging, 11(3): 407–415.CrossRefGoogle Scholar
  57. 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.Google Scholar
  58. 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.Google Scholar
  59. 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.CrossRefGoogle Scholar
  60. 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.Google Scholar
  61. Watson, D.F. 1981. Computing the n-dimensional Delaunay tessellation with application to Voronoi polytopes. The Computer Journal, 24(2): 167–172.Google Scholar
  62. Watson, D.F. and Philip, G.M. 1984. Survey: Systematic triangulations. Computer Vision, Graphics and Image Processing, 26(2): 217–223.Google Scholar
  63. Wolberg, G. 1990. Digital Image Warping. IEEE Computer Society Press: Washington, USA.Google Scholar
  64. 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.Google Scholar
  65. 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.Google Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • Erik H.W. Meijering
    • 1
  • Karel J. Zuiderveld
    • 1
  • Max A. Viergever
    • 1
  1. 1.Image Sciences InstituteUniversity Hospital UtrechtUtrechtThe Netherlands

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