A Comparison Study of Inferences on Graphical Model for Registering Surface Model to 3D Image
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.
Keywordsregistration Markov chain Monte Carlo nonparametric belief propagation graphical model
Unable to display preview. Download preview PDF.
- 2.Hontani, H., Watanabe, W.: Point-Based Non-Rigid Surface Registration with Accuracy Estimation. In: Computer Vision and Pattern Recognition, pp. 446–452 (2010)Google Scholar
- 4.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)Google Scholar
- 5.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)Google Scholar
- 6.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)Google Scholar
- 7.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)Google Scholar
- 11.Book, S., Gelman, A.: Inference and Monitoring Convergence (chapter for Gilks, Richardson, and Spiegelhalter book), vol.10 (2007)Google Scholar