Abstract
Graph matching is a robust correspondence detection approach which considers potential correspondences as graph nodes and uses graph links to measure the pairwise agreement between potential correspondences. In this paper, we propose a novel graph matching method to augment its power in establishing anatomical correspondences in medical images, especially for the cases with large inter-subject variations. Our contributions have twofold. First, we propose a robust measurement to characterize the pairwise agreement of appearance information on each graph link. In this way, our method is more robust to ambiguous matches than the conventional graph matching methods that generally consider only the simple geometric information. Second, although multiple correspondences are allowed for robust correspondence, we further introduce the sparsity constraint upon the possibilities of correspondences to suppress the distraction from misleading matches, which is very important for achieving accurate one-to-one correspondences in the end of the matching procedure. We finally incorporate these two improvements into a new objective function and solve it by quadratic programming. The proposed graph matching method has been evaluated in the public hand X-ray images with comparison to a conventional graph matching method. In all experiments, our method achieves the best matching performance in terms of matching accuracy and robustness.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chui, H., Rangarajan, A.: A New Point Matching Algorithm for Non-Rigid Registration. Comput. Vis. Image Underst. 89(2-3), 114–141 (2003)
Castillo, E., Castillo, R., Martinez, J., et al.: Four-Dimensional Deformable Image Registration Using Trajectory Modeling. Phys. Med. Biol. 55(1), 305–327 (2010)
Maciel, J., Costeira, J.P.: A Global Solution to Sparse Correspondence Problems. IEEE Trans. on Pattern Anal. Mach. Intell. 25(2), 187–199 (2003)
Cour, T., Srinivasan, P., Shi, J.: Balanced Graph Matching. In: Advances in Neural Information Processing Systems 19, pp. 313–320. MIT Press (2006)
Leordeanu, M., Hebert, M.: A Spectral Technique for Correspondence Problems Using Pairwise Constraints. In: International Conference of Computer Vision, vol. 2, pp. 1482–1489. IEEE Computer Society (2005)
Tibshirani, R.: Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society Series B 58(1), 267–288 (1996)
Wright, J., Yang, A.Y., Ganesh, A., et al.: Robust Face Recognition Via Sparse Representation. IEEE Trans. on Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)
Nocedal, J., Wright, S.J.: Numerical Optimization, 2nd edn. Springer, New York (2006)
Cao, F., Huang, H.K., Pietka, E., et al.: An Image Database for Digital Hand Atlas. In: Proceedings of SPIE Medical Imaging: PACS and Integrated Medical Information Systems: Design and Evaluation, vol. 5033, pp. 461–470 (2003)
Zhang, P., Cootes, T.: Automatic Construction of Parts+Geometry Models for Initialising Groupwise Registration. IEEE Transactions on Medical Imaging 31(2), 341–358 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, Y., Wu, G., Dai, Y., Jiang, J., Shen, D. (2013). Robust Anatomical Correspondence Detection by Graph Matching with Sparsity Constraint. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2012. Lecture Notes in Computer Science, vol 7766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36620-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-36620-8_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36619-2
Online ISBN: 978-3-642-36620-8
eBook Packages: Computer ScienceComputer Science (R0)