Ideal-Observer Performance under Signal and Background Uncertainty

We use the performance of the Bayesian ideal observer as a figure of merit for hardware optimization because this observer makes optimal use of signal-detection information. Due to the high dimensionality of certain integrals that need to be evaluated, it is difficult to compute the ideal observer test statistic, the likelihood ratio, when background variability is taken into account. Methods have been developed in our laboratory for performing this computation for fixed signals in random backgrounds. In this work, we extend these computational methods to compute the likelihood ratio in the case where both the backgrounds and the signals are random with known statistical properties. We are able to write the likelihood ratio as an integral over possible backgrounds and signals, and we have developed Markov-chain Monte Carlo (MCMC) techniques to estimate these high-dimensional integrals. We can use these results to quantify the degradation of the ideal-observer performance when signal ...

• Type: Conference
• Year: 2003
• Where: IPMI
• Authors: Subok Park, Matthew A. Kupinski, Eric Clarkson, Harrison H. Barrett
• Keywords: Bayesian Ideal Observer, Ideal Observer, IPMI 2003, Likelihood Ratio, Medical Imaging