MICCAI 2003: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003 pp 771-779 | Cite as
Groupwise Non-rigid Registration Using Polyharmonic Clamped-Plate Splines
Conference paper
Abstract
This paper introduces a novel groupwise data-driven algorithm for non-rigid registration. The motivation behind the algorithm is to enable the analysis of groups of registered images; to this end, the algorithm automatically constructs a low-dimensional, common representation of the warp fields. We demonstrate the algorithm on an example set of 2D medical images, and show that we can obtain good registration across the set, with automatic detection and correction of misaligned examples, whilst still maintaining a low-dimensional representation.
Keywords
Reference Image Free Image Nonrigid Image Registration Pairwise Registration Warp Distance
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.
Download
to read the full conference paper text
References
- 1.Boggio, T.: Sulle funzioni di green d’ordine m. Rendiconti – Circolo Matematico di Palermo 20, 97–135 (1905)MATHCrossRefGoogle Scholar
- 2.Bookstein, F.L.: Principal Warps: Thin-Plate Splines and the Decomposition of Deformations. IEEE PAMI 11(6), 567–585 (1989)MATHGoogle Scholar
- 3.Bro-Nielsen, M., Gramkow, C.: Fast fluid registration of medical images. In: Höhne, K.H., Kikinis, R. (eds.) VBC 1996. LNCS, vol. 1131, pp. 267–276. Springer, Heidelberg (1996)Google Scholar
- 4.Chandrashekara, R., Mohiaddin, R.H., Rueckert, D.: Analysis of myocardial motion in tagged MR images using nonrigid image registration. In: Proceedings of 6th Medical Image Understanding and Analysis, pp. 1–4 (2002)Google Scholar
- 5.Chefd’Hotel, C., Hermosillo, G., Faugeras, O.: A variational approach to multimodal image matching. In: Proceedings of IEEE Workshop on Variational and Level Set Methods (VLSM 2001), pp. 21–28 (2001)Google Scholar
- 6.Davies, R., Twining, C.J., Cootes, T.F., Waterton, J.C., Taylor, C.J.: 3D statistical shape models using direct optimisation of description length. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 3–20. Springer, Heidelberg (2002)CrossRefGoogle Scholar
- 7.Forsey, D.R., Bartels, R.H.: Hierarchical B-spline refinement. ACM Transactions in Computer Graphics 22(4), 205–212 (1988)CrossRefGoogle Scholar
- 8.Gee, J., Reivich, M., Bajcsy, R.: Elastically deforming 3D atlas to match anatomical brain images. Journal of Computer Assisted Tomography 17(2), 225–236 (1993)CrossRefGoogle Scholar
- 9.Guimond, A., Meunier, J., Thirion, J.-P.: Average brain models: A convergence study. Technical Report RR-3731, INRIA, Sophia Antipolis (1999)Google Scholar
- 10.LeBriquer, L., Gee, J.: Design of a statistical model of brain shape. In: Duncan, J.S., Gindi, G. (eds.) IPMI 1997. LNCS, vol. 1230, pp. 477–482. Springer, Heidelberg (1997)Google Scholar
- 11.Marsland, S., Twining, C.: Constructing data-driven optimal representations for iterative pairwise non-rigid registration. Biomedical Image Registration (2003)Google Scholar
- 12.Rohlfing, C.R., Maurer Jr., T.: Intensity-based non-rigid registration using adaptive multilevel free-form deformation with an incompressibility constraint. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 111–119. Springer, Heidelberg (2001)CrossRefGoogle Scholar
- 13.Rohr, K., Stiehl, H.S., Sprengel, R., Buzug, T.M., Weese, J., Kuhn, M.H.: Landmark-based elastic registration using approximating thin-plate splines. IEEE Transactions on medical imaging 20(6), 526–534 (2001)CrossRefGoogle Scholar
- 14.Rueckert, D., Frangi, A.F., Schnabel, J.A.: Automatic construction of 3D statistical deformation models using non-rigid registration. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 77–84. Springer, Heidelberg (2001)CrossRefGoogle Scholar
- 15.Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Non-rigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
- 16.Studholme, C., Hill, D., Hawkes, D.: Automated three-dimenensional registration of magnetic resonance and positron emission tomography by multiresolution optimisation of voxel similarity measures. Medical Physics 24(1), 25–35 (1997)CrossRefGoogle Scholar
- 17.Twining, C.J., Marsland, S.: Constructing diffeomorphic representations of non-rigid registrations of medical images. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 413–425. Springer, Heidelberg (2003)CrossRefGoogle Scholar
- 18.Twining, C.J., Marsland, S., Taylor, C.J.: Measuring geodesic distances on the space of bounded diffeomorphims. In: Proceedings of BMVC 2002 (2002)Google Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2003