title: Efficient estimation of ideal-observer performance in classification tasks involving high-dimensional complex backgrounds.
abstract: The Bayesian ideal observer is optimal among all observers and sets an absolute upper bound for the performance of any observer in classification tasks [Van Trees, Detection, Estimation, and Modulation Theory, Part I (Academic, 1968).]. Therefore, the ideal observer should be used for objective image quality assessment whenever possible. However, computation of ideal-observer performance is difficult in practice because this observer requires the full description of unknown, statistical properties of high-dimensional, complex data arising in real life problems. Previously, Markov-chain Monte Carlo (MCMC) methods were developed by Kupinski et al. [J. Opt. Soc. Am. A 20, 430(2003) ] and by Park et al. [J. Opt. Soc. Am. A 24, B136 (2007) and IEEE Trans. Med. Imaging 28, 657 (2009) ] to estimate the performance of the ideal observer and the channelized ideal observer (CIO), respectively, in classification tasks involving non-Gaussian random backgrounds. However, both algorithms had the disadvantage of long computation times. We propose a fast MCMC for real-time estimation of the likelihood ratio for the CIO. Our simulation results show that our method has the potential to speed up ideal-observer performance in tasks involving complex data when efficient channels are used for the CIO.
authors.affiliation: NIBIB/CDRH Laboratory for the Assessment of Medical Imaging Systems, DIAM, CDRH, FDA, Silver Spring, Maryland 20993, USA. subok.park@fda.hhs.gov
authors.forename: Subok
authors.initials: S
authors.lastname: Park
authors.forename: Eric
authors.initials: E
authors.lastname: Clarkson
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016011:Normal Distribution; D055095:Optics and Photonics; D010363:Pattern Recognition, Automated / Q000379:methods; D011336:Probability; D015203:Reproducibility of Results; D014785:Vision, Ocular