Abstract
This paper presents a learning method to select best geometric features for deformable brain registration. Best geometric features are selected for each brain location, and used to reduce the ambiguity in image matching during the deformable registration. Best geometric features are obtained by solving an energy minimization problem that requires the features of corresponding points in the training samples to be similar, and the features of a point to be different from those of nearby points. By incorporating those learned best features into the framework of HAMMER registration algorithm, we achieved about 10% improvement of accuracy in estimating the simulated deformation fields, compared to that obtained by HAMMER. Also, on real MR brain images, we found visible improvement of registration in cortical regions.
This work is supported by NSFC (National Science Foundation of China) 60271033.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Wang, Y., Staib, L.H.: Elastic model-based non-rigid registration incorporating statistical shape information. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1162–1173. Springer, Heidelberg (1998)
Gee, J.C., Reivich, M., Bajcsy, R.: Elastically deforming 3D atlas to match anatomical brain images. Journal of Computer Assisted Tomography 17, 225–236 (1993)
Christensen, G.E., Johnson, H.J.: Consistent Image Registration. IEEE Trans on Med. Imaging 20, 568–582 (2001)
Dawant, B.M., Hartmann, S.L., Gadamsetty, S.: Brain Atlas Deformation in the Presence of Large Space-occupying Tumours. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 589–596. Springer, Heidelberg (1999)
Thompson, P., Toga, A.W.: A surface-based technique for warping three-dimensional images of the brain. IEEE Trans on Med. Imaging 15, 402–417 (1996)
Wells, I., William, M., Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. Medical Image Analysis 1, 35–51 (1996)
Studholme, C., Hill, D.L.G., DJ, H.: Multiresolution voxel similarity measures for MR-PET registration. In: Proc. IPMI, Ile de Berder, France, pp. 287–298 (1995)
Shen, D., Davatzikos, C.: HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE Trans on Med. Imaging 21, 1421–1439 (2002)
Kadir, T., Brady, M.: Saliency, scale and image description. International Journal of Compter Vision 45(2), 83–105 (2001)
Huang, X., Sun, Y., Metaxas, D., Sauer, F., Xu, C.: Hybrid image registration based on configural matching of scale-invariant salient region features. In: IEEE Workshop on Image and Video Registration, Washington D.C (July 2004)
van Ginneken, B., Frangi, A.F., Staal, J.J., ter Romeny, B.M.H., Viergever, M.A.: Active Shape Model Segmentation With Optimal Features. IEEE Trans. on Medical Imaging 21, 924–933 (2002)
Li, S., Zhu, L., Jiang, T.: Active Shape Model Segmentation Using Local Edge Structures and AdaBoost. In: Yang, G.-Z., Jiang, T.-Z. (eds.) MIAR 2004. LNCS, vol. 3150, pp. 121–128. Springer, Heidelberg (2004)
Jenkinson, M., Bannister, P.R., Brady, J.M., Smith, S.M.: Improved optimisation for the robust and accurate linear registration and motion correction of brain images. Neuro-Image 17, 825–841 (2002)
Davatzikos, C.: Spatial transformation and registration of brain images using elastically deformable models. Comp. Vision and Image Understanding 66, 207–222 (1997)
Mallat, S.: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. on PAMI 11, 674–693 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, G., Qi, F., Shen, D. (2005). Learning Best Features for Deformable Registration of MR Brains. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3750. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566489_23
Download citation
DOI: https://doi.org/10.1007/11566489_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29326-2
Online ISBN: 978-3-540-32095-1
eBook Packages: Computer ScienceComputer Science (R0)