Analysis of the Parameter Space of a Metric for Registering 3D Vascular Images

  • Stephen R. Aylward
  • Sue Weeks
  • Elizabeth Bullitt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2208)


We present a new metricfor registering 3D images of vasculature, and we analyze the rigid-body transformation parameter space of that metric and its derivatives. To quantify and direct a source image’s alignment with a target image, this new vascular-image registration system models the vessels in the source image and makes measurements in the target image at a sparse set of transformed points from the centerlines of those models. The system is fast and effective because the measures made at the transformed centerline points incorporate the general geometric properties of tubes and specific model-quality information calculated during the vessel model generation process. Additionally, by adjusting the sample density or scaling the centerline point measures, coarse-to- fine registration strategies are directly enabled. We present visualizations of the metrica nd its derivatives over a range of mis-registrations given different sample densities and different measure scalings using magnetic resonance angiograms, x-ray computed tomography images, and 3D ultrasound images.


  1. 1.
    K Harris, SN Efstraatiadis, N Maglaveras, C Pappas, J Gourassas, and G Louridas, “Model-based morphological segmentation and labeling of coronary angiograms.” IEEE Transactions on Medical Imaging, 18(10):1003–1015, October 1999CrossRefGoogle Scholar
  2. 2.
    E Bullitt, A Liu, S Aylward, C Coffey, J Stone S Mukherji, and S Pizer, “Registration of 3D Cerebral Vessels with 2D Digital Angiograms: Clinical Evaluation,” Academic Radiology, 6:539–546 1999CrossRefGoogle Scholar
  3. 3.
    N Alperin, DN Levin, and CA Pelizzari, “Retrospective registration of x-ray angiograms with MR images by using vessels as intrinsiclan dmarks,” Journal of MagneticR esonance Imaging, 4:139–144 1994CrossRefGoogle Scholar
  4. 4.
    SR Aylward, E Bullitt, SM Pizer, and D Eberly, “Intensity Ridges and Widths for Tubular Object Segmentation and Registration,” in IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, 131–138 1996Google Scholar
  5. 5.
    PJ Yim, PL Choyke, and RM Summers, “Gray-scale skeletonization of small vessels in magnetic resonance angiography,” IEEE Transactions on Medical Imaging, 19(6); 568–576, June 2000.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Stephen R. Aylward
    • 1
  • Sue Weeks
    • 1
  • Elizabeth Bullitt
    • 1
  1. 1.Department of Radiology Computer-Aided Diagnosis and Display LabThe University of North CarolinaChapel HillUSA

Personalised recommendations