Abstract
In this paper, we present a new and efficient multi-modal 3D-3D vascular registration algorithm, which transforms the 3D-3D registration problem into a multiple 2D-3D vascular registration problem. Along each orthogonal axis, projected 2D image from a segmented binary 3D floating volume is compared with maximum intensity projection (MIP) image of the reference volume. At the preprocessing stage of the floating image volume, vessels are segmented and represented by a number of spheres with centers located at the skeleton points of the vessels and radii equal to the distance from the skeleton points to their closest boundary. To generate projected images from the binary 3D volume, instead of using the conventional ray-casting technique, the spheres are projected to the three orthogonal projection planes. The discrepancy between the projected image and the reference MIP image is measured by a relatively simple similarity measure, sum of squared differences (SSD). By visual comparison, we found that the performances of our method and the Mutual Information (MI)-based method are visually comparable. Moreover, based on the experimental results, our method for 3D-3D vascular registration is more computationally efficient than the MI-based method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chung, A.C.S., Wells III, W.M., et al.: Multi-modal Image Registration by Minimising Kullback-Leibler Distance. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2489, pp. 525–532. Springer, Heidelberg (2002)
Chan, H.-M., Chung, A.C.S., et al.: Multi-modal image registration by minimizing Kullback-Leibler distance between expected and observed joint class histograms. In: CVPR 2003 (2003) (to appear)
Chan, H.-M., Chung, A.C.S., Yu, S.C.H.: 2D-3D Vascular Registration Algorithm between Digital Subtraction Angiographic (DSA) and Magnetic Resonance Angiographic (MRA) Images. In: MICCAI 2003 (2003) (Submitted)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley & Sons, Inc., Chichester (1991)
Kullback, S.: Information Theory and Statistics. Dover Publications, Inc., New York (1968)
Wells, W., Viola, P., et al.: Multi-modal Volume Registration by Maximization of Mutual Information. Medical Image Analysis 1, 32–52 (1996)
Colignon, A., et al.: Automated multi-modality image registration based on information theory. In: IPMI, pp. 263–274. Kluwer Academic Publisher, Dordrecht (1995)
Wyatt, P., Noble, J.: MAP MRF Joint Segmentation and Registration. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 580–587. Springer, Heidelberg (2002)
Cosman, E.: Rigid Registration of MR and Biplanar Fluoroscopy, Master Thesis, Dept. of Electrical Engineering and Computer Engineering. MIT, Cambridge (2000)
Press, W.H., Teukolsky, S.A., et al.: Numerical Recipes in C, 2nd edn., pp. 402–405, 412-420. Cambridge University Press, Kluwer Academic Publisher (1995)
Kita, Y., Wilson, D.L., et al.: Real-time Registration of 3D Cerebral Vessels to X-rayAngiograms. In: MICCAI 1997, pp. 1125–1133 (1997)
Liu, A., Bullitt, E., et al.: 3D/2D Registration via skeletal near projective invariance intubular objectives. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 952–963. Springer, Heidelberg (1998)
Feldmar, J., Ayache, N., et al.: 3D-2D projective registration of free-form curves and surfaces, INRIA, Report # 2434 (1994)
Feldmar, J., Malandain, G., et al.: Matching 3D MR Angiography Data and 2D X-ray Angiograms. In: Troccaz, J., Mösges, R., Grimson, W.E.L. (eds.) CVRMed-MRCAS 1997, CVRMed 1997, and MRCAS 1997. LNCS, vol. 1205, pp. 129–138. Springer, Heidelberg (1997)
Roche, A., Malandain, G., et al.: Multimodal image registration by maximization of the Correlation Ratio, INRIA, Report # 3378 (1998)
McLaughlin, R.A., Hipwell, J., et al.: A comparison of 2D-3D intensity-based registration and feature-based registration for neurointerventions. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2489, pp. 517–524. Springer, Heidelberg (2002)
Fisher, R., Perkins, S., et al. (2000), http://www.dai.ed.ac.uk/HIPR2/skeleton.htm
Gagvani, N.: Skeletons and Volume Thinning in Visualization., MS. Thesis, Dept. of Electrical and Computer Engineering, Rutgers University, New Brunswick, New Jersey (1997)
Shahrokni, A., Soltanian-Zadeh, H., et al.: Fast skeletonization algorithm for 3-D elongated objects. Proceedings of SPIE 4322, 323–330 (2001)
Palagyi, K., Sorantin, R., et al.: A Sequential 3D Thinning Algorithm and Its Medical Applications. In: IPMI 2001, pp. 409–415 (2001)
Zhou, Y., Toga, A.: Efficient Skeletonization of Volumetric Objects. IEEE Transactions on Visualization and Computer Graphics, 196–209 (1999)
Jain, A.K.: Fundamentals of Digital Image Processing, pp. 381–389. Prentice-Hall, Englewood Cliffs (1989)
Watt, A., Watt, M.: Advanced Animation and Rendering Techniques: Theory and Practice. Addison-Wesley, Reading (1992)
Pennec, X.: Toward a generic framework for recognition based on uncertain geometric features. Videre: Journal of Computer Vision Research 1(2), 58–87 (1998)
Maurer Jr., C.R., Maciunas, R.J., Fitzpatrick, J.M.: Registration of head CT images to physical space using multiple geometrical features. In: Proc. SPIE Medical Imaging 1998, San Diego, CA, February 1998, vol. 3338, pp. 72–80 (1998)
Aylward, S., Bullitt, E.: Initialization, Noise, Singularities, and Scale in Height-Ridge Traversal for Tubular Object Centerline Extraction. IEEE Transactions on Medical Imaging, 61–75 (February 2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chan, HM., Chung, A.C.S. (2003). Efficient 3D-3D Vascular Registration Based on Multiple Orthogonal 2D Projections. In: Gee, J.C., Maintz, J.B.A., Vannier, M.W. (eds) Biomedical Image Registration. WBIR 2003. Lecture Notes in Computer Science, vol 2717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39701-4_32
Download citation
DOI: https://doi.org/10.1007/978-3-540-39701-4_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20343-8
Online ISBN: 978-3-540-39701-4
eBook Packages: Springer Book Archive