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A fast technique for motion correction in DAS using a feature-based, irregular grid

  • Erik H. W. Meijering
  • Karel J. Zuiderveld
  • Max A. Viergever
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)

Abstract

In clinical practice, Digital Subtraction Angiography (DSA) is a powerful technique for the visualization of blood vessels in the human body. However, due to patient motion the diagnostic relevance of the images is often reduced by the introduction of artifacts. In this paper, we propose a new approach to the registration of DSA images, which is both effective, and very fast. The computational speed of our algorithm is achieved by applying a gradient based control point selection mechanism, which allows for a more efficient positioning of a reduced number of control points as compared to approaches based on regular grids. The results of preliminary experiments with several clinical data sets clearly show the applicability of the algorithm.

Keywords

Control Point Digital Subtraction Angiography Delaunay Triangulation Gradient Magnitude Subtraction Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    W.A. Chilcote, M.T. Modic, W.A. Pavlicek, J.R. Little, A.J. Furian, P.M. Duchesneau & M.A. Weinstein. Digital subtraction angiography of the carotid arteries: A comparitive study in 100 patients. Radiology, vol. 139, no. 2 (1981), pp. 287–295.CrossRefPubMedGoogle Scholar
  2. 2.
    W.R. Brody, D.R. Enzmann, L.-S. Deutsch, A. Hall & N. Pelc. Intravenous carotid arteriography using line-scanned digital radiography. Radiology, vol. 139, no. 2 (1981), pp. 297–300.CrossRefPubMedGoogle Scholar
  3. 3.
    L.G. Brown. A survey of image registration techniques. ACM Computing Surveys, vol. 24, no. 4 (1992), pp. 325–376.CrossRefGoogle Scholar
  4. 4.
    J.B.A. Maintz & M.A. Viergever. A survey of medical image registration. Medical Image Analysis, vol. 2, no. 1 (1998), pp. 1–36.CrossRefPubMedGoogle Scholar
  5. 5.
    A. Venot & V. Leclerc. Automated correction of patient motion and gray values prior to subtraction in digitized angiography. IEEE Transactions on Medical Imaging, vol. 3, no. 4 (1984), pp. 179–186.CrossRefPubMedGoogle Scholar
  6. 6.
    K.J. Zuiderveld, B.M. ter Haar Romeny & M.A. Viergever. Fast rubber sheet masking for digital subtraction angiography. In Science and Engineering of Medical Imaging, M.A. Viergever (ed.), vol. 1137 of Proceedings of SPIE, The International Society for Optical Engineering, Bellingham, Washington, USA, 1989, pp. 22–30.CrossRefGoogle Scholar
  7. 7.
    L. van Tran & J. Sklansky. Flexible mask subtraction for digital angiography. IEEE Transactions on Medical Imaging, vol. 11, no. 3 (1992), pp. 407–415.CrossRefPubMedGoogle Scholar
  8. 8.
    G.S. Cox & G. de Jager. Automatic registration of temporal image pairs for digital subtraction angiography. In Image Processing, vol. 2167 of Proceedings of SPIE, The International Society for Optical Engineering, Bellingham, Washington, USA, 1994, pp. 188–199.Google Scholar
  9. 9.
    J.F. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6 (1986), pp. 679–698.CrossRefPubMedGoogle Scholar
  10. 10.
    T.M. Buzug, J. Weese, C. Fassnacht & C. Lorenz. Image registration: Convex weighting functions for histogram-based similarity measures. In CVRMed-MRCAS ’97, J. Troccaz, E. Grimson & R. Mösges (eds.), vol. 1205 of Lecture Notes in Computer Science, Springer-Verlag, Berlin, Germany, 1997, pp. 203–212.Google Scholar
  11. 11.
    T.M. Buzug, J. Weese, C. Lorenz & W. Beil. Histogram-based image registration for digital subtraction angiography. In Image Analysis and Processing (ICIAP ’97), A. Del Bimbo (ed.), vol. 1311 of Lecture Notes in Computer Science, Springer-Verlag, Berlin, Germany, 1997, pp. 380–387.Google Scholar
  12. 12.
    D.F. Watson. Computing the n-dimensional Delaunay tessellation with application to Voronoi polytopes. The Computer Journal, vol. 24, no. 2 (1981), pp. 167–172.CrossRefGoogle Scholar
  13. 13.
    J.M. Fitzpatrick. The existence of geometrical density-image transformations corresponding to object motion. Computer Vision, Graphics and Image Processing, vol. 44, no. 2 (1988), pp. 155–174.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Erik H. W. Meijering
    • 1
  • Karel J. Zuiderveld
    • 1
  • Max A. Viergever
    • 1
  1. 1.Image Sciences InstituteUtrecht UniversityCX UtrechtThe Netherlands

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