TY - GEN
T1 - Markov-chain Monte Carlo for the performance of a channelized-ideal observer in detection tasks with non-Gaussian lumpy backgrounds
AU - Park, Subok
AU - Clarkson, Eric
PY - 2008
Y1 - 2008
N2 - The Bayesian ideal observer is optimal among all observers and sets an upper bound for observer performance in binary detection tasks.1 This observer provides a quantitative measure of diagnostic performance of an imaging system, summarized by the area under the receiver operating characteristic curve (AUC),1 and thus should be used for image quality assessment whenever possible. However, computation of ideal-observer performance is difficult because this observer requires the full description of the statistical properties of the signal-absent and signal-present data, which are often unknown in tasks involving complex backgrounds. Furthermore, the dimension of the integrals that need to be calculated for the observer is huge. To estimate ideal-observer performance in detection tasks with non-Gaussian lumpy backgrounds, Kupinski et al.2 developed a Markovchain Monte Carlo (MCMC) method, but this method has a disadvantage of long computation times. In an attempt to reduce the computation load and still approximate ideal-observer performance, Park et al.3,4 investigated a channelized-ideal observer (CIO) in similar tasks and found that the CIO with singular vectors of the imaging system approximated the performance of the ideal observer. But. in that work, an extension of the Kupinski MCMC was used for calculating the performance of the CIO and it did not reduce the computational burden. In the current work, we propose a new MCMC method, which we call a CIO-MCMC, to speed up the computation of the CIO. We use singular vectors of the imaging system as efficient channels for the ideal observer. Our results show that the CIO-MCMC has the potential to speed up the computation of ideal observer performance with a large number of channels.
AB - The Bayesian ideal observer is optimal among all observers and sets an upper bound for observer performance in binary detection tasks.1 This observer provides a quantitative measure of diagnostic performance of an imaging system, summarized by the area under the receiver operating characteristic curve (AUC),1 and thus should be used for image quality assessment whenever possible. However, computation of ideal-observer performance is difficult because this observer requires the full description of the statistical properties of the signal-absent and signal-present data, which are often unknown in tasks involving complex backgrounds. Furthermore, the dimension of the integrals that need to be calculated for the observer is huge. To estimate ideal-observer performance in detection tasks with non-Gaussian lumpy backgrounds, Kupinski et al.2 developed a Markovchain Monte Carlo (MCMC) method, but this method has a disadvantage of long computation times. In an attempt to reduce the computation load and still approximate ideal-observer performance, Park et al.3,4 investigated a channelized-ideal observer (CIO) in similar tasks and found that the CIO with singular vectors of the imaging system approximated the performance of the ideal observer. But. in that work, an extension of the Kupinski MCMC was used for calculating the performance of the CIO and it did not reduce the computational burden. In the current work, we propose a new MCMC method, which we call a CIO-MCMC, to speed up the computation of the CIO. We use singular vectors of the imaging system as efficient channels for the ideal observer. Our results show that the CIO-MCMC has the potential to speed up the computation of ideal observer performance with a large number of channels.
KW - Bayesian ideal observer
KW - Channelized-ideal observer
KW - Efficient channels
KW - Markov-chain Monte Carlo
KW - Non-Gaussian lumpy backgrounds
UR - http://www.scopus.com/inward/record.url?scp=44949093599&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=44949093599&partnerID=8YFLogxK
U2 - 10.1117/12.771704
DO - 10.1117/12.771704
M3 - Conference contribution
AN - SCOPUS:44949093599
SN - 9780819471017
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2008 - Image Perception, Observer Performance, and Technology Assessment
T2 - Medical Imaging 2008 - Image Perception, Observer Performance, and Technology Assessment
Y2 - 20 February 2008 through 21 February 2008
ER -