Optimal joint detection and estimation that maximizes ROC-type curves

Published

September 1, 2016

Author(s)

Adam J. Wunderlich, Bart Goossens, Craig K. Abbey

Abstract

Combined detection-estimation tasks are frequently encountered in medical imaging. Hence, optimal methods for joint detection and estimation are of interest because they provide upper bounds on observer performance and can motivate practical image analysis algorithms. We present a unified Bayesian framework for decision rules that maximize receiver operating characteristic (ROC)-type summary curves, including ROC, localization ROC (LROC), estimation ROC (EROC), free-response ROC (FROC), alternative free-response ROC (AFROC), and exponentially- transformed FROC (EFROC) curves, succinctly summarizing previous results. The approach relies on an interpretation of ROC-type summary curves as plots of an expected utility versus an expected disutility (or penalty) for signal-present decisions. We propose a general utility structure that is flexible enough to encompass many ROC variants and yet sufficiently constrained to allow derivation of a linear expected utility equation that is similar to that for simple binary detection. We illustrate our theory with an example comparing decision strategies for joint detection-estimation of a known signal with unknown amplitude. In addition, building on insights from our utility framework, we propose new ROC-type summary curves and associated optimal decision rules for joint detection-estimation tasks with an unknown number of signals in each observation.

Citation

IEEE Transactions on Medical Imaging

Volume

35

Issue

9

Pub Type

Journals

Download Paper

https://doi.org/10.1109/TMI.2016.2553001

Keywords

Signal detection theory, utility theory, ROC curve

Citation

Wunderlich, A. , Goossens, B. and Abbey, C. (2016), Optimal joint detection and estimation that maximizes ROC-type curves, IEEE Transactions on Medical Imaging, [online], https://doi.org/10.1109/TMI.2016.2553001 (Accessed April 25, 2024)