Skip to main content
SpringerLink
Log in

Registration and Analysis of Vascular Images

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

We have developed a method for rigidly aligning images of tubes. This paper presents an evaluation of the consistency of that method for three-dimensional images of human vasculature. Vascular images may contain alignment ambiguities, poorly corresponding vascular networks, and non-rigid deformations, yet the Monte Carlo experiments presented in this paper show that our method registers vascular images with sub-voxel consistency in a matter of seconds. Furthermore, we show that the method's insensitivity to non-rigid deformations enables the localization, quantification, and visualization of those deformations.

Our method aligns a source image with a target image by registering a model of the tubes in the source image directly with the target image. Time can be spent to extract an accurate model of the tubes in the source image. Multiple target images can then be registered with that model without additional extractions.

Our registration method builds upon the principles of our tubular object segmentation work that combines dynamic-scale central ridge traversal with radius estimation. In particular, our registration method's consistency stems from incorporating multi-scale ridge and radius measures into the model-image match metric. Additionally, the method's speed is due in part to the use of coarse-to-fine optimization strategies that are enabled by measures made during model extraction and by the parameters inherent to the model-image match metric.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Alperin, N., Levin, D.N., and Pelizzari, C.A. 1994. Retrospective registration of x-ray angiograms withMRimages by using vessels as intrinsic landmarks. Journal of Magnetic Resonance Imaging, 4:139–144.

    Google Scholar 

  • Aylward, S.R., Bullitt, E., Pizer, S.M., and Eberly, D. 1996. Intensity ridge and widths for tubular object segmentation and registration. IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 131–138.

  • Aylward, S.R., Weeks, S., and Bullitt, E. 2001. Analysis of the parameter space of a metric for registering 3D vascular images. MICCAI, pp. 932–939.

  • Aylward, S.R. and Bullitt, E. 2002. A comparison of methods for tubular-object centerline extraction. IEEE Transactions on Medical Imaging, 21(2):61–75.

    Google Scholar 

  • Aylward, S.R., Jomier, J., Guyon, J.P., and Weeks, S. 2002. Intra-operative 3D ultrasound augmentation. IEEE International Symposium on Biomedical Imaging, Washington, DC, p. 4.

  • Besl, P.J. and McKay, N.D. 1992. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14:239–256.

    Google Scholar 

  • Box, G.E.P., Hunter, W.G., and Hunter, J.S. 1978. Statistics for Experiments. Wiley Series in Probability and Mathematical Statistics. John Wiley and Sons: New York, NY.

    Google Scholar 

  • Bullitt, E., Liu, A., Aylward, S.R., and Pizer, S.M. 1997. Reconstruction of the intracerebral vasculature from MRA and a pair of projection views. Information Processing in Medical Imaging, Poultney, VT, pp. 537–542.

  • Bullitt, E., Liu, A., Aylward, S.R., Coffey, C., Stone, J., Mukherji, S., and Pizer, S.M. 1999. Registration of 3d cerebral vessels with 2d digital angiograms: Clinical evaluation. Academic Radiology, 6:539–546.

    Google Scholar 

  • Bullitt, E., Aylward, S.R., Bernard, E., and Gerig, G. 2001. Computer-assisted visualization of arteriovenous malformations on the home pc. Neurosurgery, 48:576–583.

    Google Scholar 

  • Bullitt, E., Aylward, S.R., Smith, K., Mukherji, S., Jiroutek, M., and Muller, K. 2001. Symbolic description of intracerebral vessels segmented from magnetic resonance angiograms and evaluation by comparison with X-ray angiograms. Medical Image Analysis, 5:157–169.

    Google Scholar 

  • Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., and Marchal, G. 1995. Automated multi-modality image registration based on information theory. Information Processing in Medical Imaging, pp. 263–274.

  • Du, Y., Parker, D., and Davis, W. 1995. Improved vessel visualization in MR angiography by nonlinear anisotropic filtering. Journal of Magnetic Resonance Imaging, 5:353–359.

    Google Scholar 

  • Dryden, I. and Mardia, K. 1998. Statistical Shape Analysis. John Wiley and Sons: New York, NY.

    Google Scholar 

  • Eberly, D. 1996. Ridges in Image and Data Analysis, Computational Imaging and Vision, vol. 7. Kluwer Academic Publishers: Dordrecht.

    Google Scholar 

  • Frangi, A.F., Niessen, W.J., Hoogeveen, R.M., Van Walsum, T., and Viergever, M.A. 1999. Model-based quantitation of 3-d magnetic resonance angiographic images. IEEE Transactions on Medical Imaging, 18(10):946–956.

    Google Scholar 

  • Fritsch, D.S., Eberly, D., Pizer, S.M., and McAuliffe, M.J. 1995. Stimulated cores and their applications in medical imaging. Information Processing in Medical Imaging, Kluwer Series in Computational Imaging and Vision, pp. 385–368.

  • Gao, L., Heath, D.G., Kuszyk, B.S.W., and Fishman, E.K. 1996. Automatic liver segmentation technique for three-dimensional visualization of CT data. Radiology, 201(2):359–364.

    Google Scholar 

  • Ge, Y., Maurer Jr, C.R., and Fitzpatrick, J.M. 1996. Surface-based 3-D image registration using the Iterative Closest Point algorithm with a closest point transform. Medical Imaging 1996: Image Processing, Proceedings of SPIE.

  • Gerig, G., Koller, T., Szekely, G., Brechbuhler, C., and Kubler, O. 1993. Symbolic description of 3-d structures applied to cerebral vessel tree obtained from MR angiography volume data. Information Processing in Medical Imaging'93, Lecture Notes in Computer Science 687, pp. 94–111.

    Google Scholar 

  • Haris, K., Efstraatiadis, S.N., Maglaveras, N., Pappas, C., Gourassas, J., and Louridas, G. 1999. Model-based morphological segmentation and labeling of coronary angiograms. IEEE Transactions on Medical Imaging, 18(10):1003–1015.

    Google Scholar 

  • Herline, A.J., Herring, J.L., Stefansic, J.D., Chapman, W.C., Galloway, R.L., and Dawant, B.M. 2000. Surface registration for use in interactive, image-guided liver surgery. Computer Aided Surgery, 5(1):11–17.

    Google Scholar 

  • Hill, D., Batchelor, P., Holden, M., and Hawkes, D. 2001. Medical image registration. Phys. Med. Biol., 46.

  • Hoogeveen, R.M., Bakker, C.J.G., and Viergever, M.A. 1998. Limits to the accuracy of vessel diameter measurement in MR angiography. Journal of Magnetic Resonance Imaging, 8.

  • Ibanez, L., Nyg, L., Gee, J., and Aylward, S. 2002. Registration patterns: The generic framework for image registration of the insight toolkit. IEEE International Symposium on Biomedical Imaging, p. 4.

  • Koller, T., Gerig, G., Szekely, G., and Dettwiler, D. 1995. Multiscale detection of curvilinear structures in 2-d and 3-d image data. Proceedings of the 5th International Conference on Computer Vision, Boston, MA, pp. 864–869.

  • Lindeberg, T. 1994. Scale-Space Theory in Computer Vision, Kluwer Academic Publishers: Dordrecht, Netherlands.

    Google Scholar 

  • Lorenz, C., Carlsen, 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. CVRMed-MRCAS, Springer-Verlag, pp. 233–242.

  • Lorigo, L.M., Faugeras, O., Grimson, W.E.L., Keriven, R., Kikinis, R., and Westin, C.F. 1999. Co-dimension 2 geodesic active contours for MRA segmentation. Information Processing in Medical Imaging' 99, Lecture Notes in Computer Science 1613, pp. 126–139.

    Google Scholar 

  • Masutani, Y., Schiemann, T., and Hohne, K.H. 1998. Vascular shape segmentation and structure extraction using a shape-based region-growing model. Proceedings of Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science 1496, pp. 1242–1249.

    Google Scholar 

  • McInerney, T. and Terzopoulos, D. 1999. Topology adaptive deformable surfaces for medical image volume segmentation. IEEE Transactions on Medical Imaging, 18(10):840–850.

    Google Scholar 

  • Morse, B.S., Pizer, S.M., and Fritsch, D.S. 1994. Robust object representation through object-relevant use of scale. Medical Imaging'94: Image Processing, vol. 2167, SPIE, 1994, pp. 104–115.

    Google Scholar 

  • Orkisz, M.M., Bresson, C., Magnin, I.E., Champin, O., and Douek, P.C. 1997. Improved vessel visualization in MR angiography by nonlinear anisotropic filtering. Magentic Resonance Imaging, 37.

  • Park, W., Hoffman, E.A., and Sonka, M. 1998. Segmentation of intrathoracic airway trees: A fuzzy logic approach. IEEE Transactions on Medical Imaging, 17(4):489–497.

    Google Scholar 

  • Pizer, S.M., Eberly, D., Morse, B.S., and Fritsch, D.S. 1998. Zoom-invariant vision of figural shape: The mathematics of cores. Computer Vision and Image Understanding, 69:55–71.

    Google Scholar 

  • Porter, B.C., Rubens, D.J., Strang, J.G., Smith, J., Totterman, S., and Parker, K.J. 2001. 3D registration and fusion of ultrasound and mri using major vessels as fiducial markers. IEEE Transactions on Medical Imaging, 20(4).

  • Reuze, P., Coatrieux, J.L., Luo, L.M., and Dillenserger, J.L. 1993. A 3-d moment based approach for blood vessel detection and quantification in MRA. Technology and Health Care, 1: 181–188.

    Google Scholar 

  • Roche, A., Pennec, X., Malandain, G., and Ayache, N. 2001. Rigid registration of 3D ultrasound with MR Images: A new approach combining intensity and gradient information. IEEE Transactions on Medical Imaging, 20(10):1038–1049.

    Google Scholar 

  • Rohlfing, T., Maurer, C.R., Walter, G., and Zhong, J. 2001. Modeling liver motion and deformation during the respiratory cycle using intensity-based free-form registration of gated MR images. Proc. SPIE Medical Imaging 2001: Image Processing, pp. 337–348.

  • Rueckert, D., Sonoda, L.L., Hayes, C., Hill, D.L.G., Leach, M.O., and Hawkes, D.J. 1999. Nonrigid registration using free-form deformations: Applications to breast MR images. IEEE Trans. on Medical Imaging, 18(8):712–721.

    Google Scholar 

  • Sato, Y., Nakajima, S., Atsumi, H., Koller, T., Gerig, G., Yoshida, S., and Kikinis, R. 1997. 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. CVRMed-MRCAS'97, Springer-Verlag, pp. 213–222.

  • Schnabel, J., Rueckert, D., Quist, M., Blackall, J.M., Castellano-Smith, A.D., Hartkens, T., Penney, G.P., Hall, W.A., Liu, H., Truwit, C.L., Gerritsen, F.A., Hill, D.L.G., and Hawkes, D.J. 2001. A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations. MICCAI 2001:573–581.

    Google Scholar 

  • Sobol, L.M. 1994. A Primer for the Monte Carlo Method. CRC Press: Boca Raton.

    Google Scholar 

  • Soler, L., Delingette, H., Malandain, G., Ayache, N., Koehl, C., Clement, J.M., Dourthe, O., and Marescaux, J. 2000. An automatic virtual patient reconstruction from CT-scans for hepatic surgical planning. Medicine Meets Virtual Reality, IOS Press, pp. 316–322.

  • van den Elsen, P. and Maint, A. 1994. Registering images using correlation of geometrical features. IEEE Trans. Med. Imaging, 14:384–396.

    Google Scholar 

  • Viola, P. and Wells, W.M. III. 1997. Alignment by maximization of mutual information. Int. J. of Computer Vision, 24(2):137–154.

    Google Scholar 

  • West, J., Fitzpatrick, J.M., Wang, M.Y., Dawant, B.M., Maurer, C.R., Kessler, R.M., and Maciunas, R.J. 1999. Retrospective intermodality registration techniques for images of the head: Surface-based versus volume-based. IEEE Trans. on Medical Imaging, 18(2):144–150.

    Google Scholar 

  • Wilson, D.L. and Noble, J.A. 1999. An adaptive segmentation algorithm for time-of-flight MRA data. IEEE Transactions on Medical Imaging, 18(10):938–945.

    Google Scholar 

  • Yim, P.J., Choyke, P.L., and Summers, R.M. 2000. Gray-scale skeletonization of small vessels in magnetic resonance angiography. IEEE Transactions on Medical Imaging, 19(6):568–576.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer-Aided Diagnosis and Display Laboratory, The University of North Carolina at Chapel Hill, USA

    Stephen R. Aylward, Julien Jomier, Sue Weeks & Elizabeth Bullitt

Authors
  1. Stephen R. Aylward
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julien Jomier
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sue Weeks
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elizabeth Bullitt
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Aylward, S.R., Jomier, J., Weeks, S. et al. Registration and Analysis of Vascular Images. International Journal of Computer Vision 55, 123–138 (2003). https://doi.org/10.1023/A:1026126900358

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1026126900358

Search

Navigation