Abstract
In this article, we report on a performance comparison study of inferences on graphical models for model-to-image registration. Both Markov chain Monte Carlo (MCMC) and nonparametric belief propagation (NBP) are widely used for inferring marginal posterior distributions of random variables on graphical models. It is known that the accuracy of the inferred distributions changes according to the methods used for the inference and to the structures of graphical models. In this article, we focus on a model-to-image registration method, which registers a surface model to given 3D images based on the inference on a graphical model. We applied MCMC and NBP for the inference and compared the accuracy of the registration on different structures of graphical models. Then, MCMC outperformed NBP significantly in the accuracy.
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References
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Hontani, H., Watanabe, W.: Point-Based Non-Rigid Surface Registration with Accuracy Estimation. In: Computer Vision and Pattern Recognition, pp. 446–452 (2010)
Sudderth, E.B., Ihler, A.T., Isard, M., Freeman, W.T., Willsky, A.S.: Nonparametric belief propagation. Communication of the ACM 53, 95–103 (2010)
Simonson, K.M., Drescher, S.M., Tanner, F.R.: A statistics-based approach to binary image registration with uncertainty analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 112–125 (2007)
Murphy, K., Weiss, Y., Jordan, M.I.: Loopy Belief Propagation for Approximate Inference: An Empirical Stydy. In: Proceedings of Uncertainty in AI, pp. 467–475 (1999)
Han, T.X., Ning, H., Huang, T.S.: Efficient Nonparametric Belief Propagation with Application to Articulated Body Tracking. In: Computer Vision and Pattern Recognition, pp. 214–221 (2006)
Cates, J.E., Fletcher, P.T., Styner, M.A., Shenton, M.E., Whitaker, R.T.: Shape Modeling and Analysis with Entropy-Based Particle Systems. Information Processing in Medical Imaging, 333–345 (2007)
Bickel, P.J., Levina, E.: Covariance regularization by thresholding. Ann. Statist. 36, 2577–2604 (2008)
Meinshausen, N.: A Note on the Lasso for Gaussian Graphical Model Selection. Statistics and Probability Letters 78, 880–884 (2008)
Donner, R., Reiter, M., Langs, G., Peloschek, P., Bischof, H.: Fast active appearance model search using canonical correlation analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence 28, 1690–1694 (2006)
Book, S., Gelman, A.: Inference and Monitoring Convergence (chapter for Gilks, Richardson, and Spiegelhalter book), vol.10 (2007)
Weiss, Y., Freeman, W.T.: Correctness of belief propagation in graphical models with arbitrary topology. Neural Computation 13, 2173–2200 (2001)
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© 2011 Springer-Verlag Berlin Heidelberg
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Sawada, Y., Hontani, H. (2011). 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) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_32
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DOI: https://doi.org/10.1007/978-3-642-24319-6_32
Publisher Name: Springer, Berlin, Heidelberg
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