Parameter estimation in deformable models using Markov chain Monte Carlo

Vikram Chalana; David R. Haynor; Paul D. Sampson; Yongmin Kim

doi:10.1117/12.274096

25 April 1997 Parameter estimation in deformable models using Markov chain Monte Carlo

Vikram Chalana, David R. Haynor, Paul D. Sampson, Yongmin Kim

Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997) https://doi.org/10.1117/12.274096
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

Deformable models have gained much popularity recently for many applications in medical imaging, such as image segmentation, image reconstruction, and image registration. Such models are very powerful because various kinds of information can be integrated together in an elegant statistical framework. Each such piece of information is typically associated with a user-defined parameter. The values of these parameters can have a significant effect on the results generated using these models. Despite the popularity of deformable models for various applications, not much attention has been paid to the estimation of these parameters. In this paper we describe systematic methods for the automatic estimation of these deformable model parameters. These methods are derived by posing the deformable models as a Bayesian inference problem. Our parameter estimation methods use Markov chain Monte Carlo methods for generating samples from highly complex probability distributions.

Citation Download Citation

Vikram Chalana, David R. Haynor, Paul D. Sampson, and Yongmin Kim "Parameter estimation in deformable models using Markov chain Monte Carlo", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274096

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