Abstract
In this article, the authors demonstrate that you can improve the performance of the registration of a point distribution model (PDM) by accurately estimating the structure of an undirected graphical model that represents the statistical shape model (SSM) of a target surface. Many existing methods for constructing SSMs determine the structure of the graphical model without analyzing the conditional dependencies among the points in PDM, though an edge in the PDM should link two nodes if and only if they are conditionally dependent. In this study, the authors employed four popular methods for estimating the structure of graphical model and obtained four different SSMs from an identical set of training surfaces. The registration performances of the SSMs were experimentally compared, and the results showed that the graphical lasso, which could estimate more accurate structure of the graphical model by avoiding the overfitting to the training data, outperformed the other methods.
Chapter PDF
Similar content being viewed by others
References
Heimann, T., Meinzer, H.P.: Statistical shape models for 3D medical image segmentation: A review. Medical Image Analysis 13, 543–563 (2009)
Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D.N., Zhou, X.S.: Deformable Segmentation via Sparse Shape Representation. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 451–458. Springer, Heidelberg (2011)
Sawada, Y., Hontani, H.: A Comparison Study of Inferences on Graphical Model for Registering Surface Model to 3D Image. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds.) MLMI 2011. LNCS, vol. 7009, pp. 257–264. Springer, Heidelberg (2011)
Hontani, H., Watanabe, W.: Point-Based Non-Rigid Surface Registration with Accuracy Estimation. In: Computer Vision and Pattern Recognition, pp. 446–452 (2010)
Cootes, T.F., Taylor, C.J., Graham, J.: Active Shape Models-Their Training and Application. Computer Vision and Image Understanding 61, 38–59 (1995)
Allassoniere, S., Jolivet, P., Giraud, C.: Detecting Long Distance Conditional Correlations Between Anatomical Regions Using Gaussian Graphical Models. In: 3rd MICCAI Workshop on Mathematical Foundations of Computational Anatomy, pp. 111–122 (2011)
Zhang, P., Adeshina, S.A., Cootes, T.F.: Automatic Learning Sparse Correspondences for Initialising Groupwise Registration. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 635–642. Springer, Heidelberg (2010)
Friedman, J., Hastie, T., Tibshirari, R.: Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9, 432–441 (2008)
Bickel, P.J., Levina, E.: Covariance regularization by thresholding. The Annals of Statistics 36, 2577–2604 (2008)
Cates, J.E., Fletcher, P.T., Styner, M.A., Shenton, M.E., Whitaker, R.T.: Shape Modeling and Analysis with Entropy-Based Particle Systems. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 333–345. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sawada, Y., Hontani, H. (2012). A Study on Graphical Model Structure for Representing Statistical Shape Model of Point Distribution Model. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33418-4_58
Download citation
DOI: https://doi.org/10.1007/978-3-642-33418-4_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33417-7
Online ISBN: 978-3-642-33418-4
eBook Packages: Computer ScienceComputer Science (R0)