Proceedings of the KOSOMBE Conference (대한의용생체공학회:학술대회논문집)
- Volume 1997 Issue 11
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- Pages.361-364
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- 1997
Convergence of MAP-EM Algorithms with Nonquadratic Smoothing Priors
Abstract
Bayesian MAP-EM approaches have been quite useful or tomographic reconstruction in that they can stabilize the instability of well-known ML-EM approaches, and can incorporate a priori information on the underlying emission object. However, MAP reconstruction algorithms with expressive priors often suffer from the optimization problem when their objective unctions are nonquadratic. In our previous work [1], we showed that the use of deterministic annealing method greatly reduces computational burden or optimization and provides a good solution or nonquadratic objective unctions. Here, we further investigate the convergence of the deterministic annealing algorithm; our experimental results show that, while the solutions obtained by a simple quenching algorithm depend on the initial conditions, the estimates converged via deterministic annealing algorithm are consistent under various initial conditions.
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