Using landmarks as a deformation prior for hybrid image registration

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6835 LNCS, Issue , 2011, Pages 196-205

  Lüthi, Marcel ^a   Jud, Christoph ^a   Vetter, Thomas ^a  

^a UNIVERSITY OF BASEL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC REGISTRATION ALGORITHM; GAUSSIAN PROCESS REGRESSION; GAUSSIAN PROCESSES; HARD CONSTRAINTS; LANDMARK ACCURACY; LARGE CLASS; OPTIMIZATION PROBLEMS; REGISTRATION SCHEME; REPRODUCING KERNEL HILBERT SPACES;

ALGORITHMS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); OPTIMIZATION; PATTERN RECOGNITION;

DEFORMATION;

EID: 80052982808     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23123-0_20     Document Type: Conference Paper

References (17)

1. Christensen, G.E., Miller, M.I., Vannier, M.W., Grenander, U.: Individualizing neuro-anatomical atlases using a massively parallel computer. Computer 29(1), 32-38 (1996)
2. Fischer, B., Modersitzki, J.: Combination of automatic non-rigid and landmark based registration: the best of both worlds. Medical Imaging, 1037-1048 (2003)
3. Haber, E., Heldmann, S., Modersitzki, J.: A Scale-Space approach to landmark constrained image registration. Scale Space and Variational Methods in Computer Vision, 612-623 (2009)
4. Hein, M., Bousquet, O.: Kernels, associated structures and generalizations. Max- Planck-Institut fuer biologische Kybernetik, Technical Report (2004)
5. Johnson, H.J., Christensen, G.E.: Consistent landmark and intensity-based image registration. IEEE Transactions on Medical Imaging 21(5), 450-461 (2002)
6. Lu, H., Cattin, P., Reyes, M.: A hybrid multimodal non-rigid registration of MR images based on diffeomorphic demons. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5951-5954 (2010)
7. Micchelli, C.A., Pontil, M.: On learning vector-valued functions. Neural Computation 17(1), 177-204 (2005)
8. Papademetris, X., Jackowski, A.P., Schultz, R.T., Staib, L.H., Duncan, J.S.: Integrated intensity and point-feature nonrigid registration. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 763-770. Springer, Heidelberg (2004)
9. Papenberg, N., Olesch, J., Lange, T., Schlag, P.M., Fischer, B.: Landmark constrained non-parametric image registration with isotropic tolerances. Bildverarbeitung für die Medizin 2009, 122-126 (2009)
10. Rasmussen, C.E., Williams, C.K.: Gaussian processes for machine learning. Springer, Heidelberg (2006)
11. Schaback, R.: Creating surfaces from scattered data using radial basis functions. Mathematical Methods for Curves and Surfaces, 477-496 (1995)
12. Schölkopf, B., Herbrich, R., Smola, A.: A generalized representer theorem. In: Computational Learning Theory, pp. 416-426 (2001)
13. Schölkopf, B., Steinke, F., Blanz, V.: Object correspondence as a machine learning problem. In: ICML 2005: Proceedings of the 22nd International Conference on Machine Learning, pp. 776-783. ACM Press, New York (2005)
14. Sorzano, C.O.S., Thevenaz, P., Unser, M.: Elastic registration of biological images using vector-spline regularization. IEEE Transactions on Biomedical Engineering 52(4), 652-663 (2005)
15. Unser, M.: Splines: A perfect fit for signal and image processing. IEEE Signal Processing Magazine 16(6), 22-38 (1999)
16. Wahba, G.: Spline models for observational data. Society for Industrial Mathematics (1990)
17. Wörz, S., Rohr, K.: Hybrid spline-based elastic image registration using analytic solutions of the navier equation. Bildverarbeitung für die Medizin 2007, 151-155 (2007)