Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions

作     者：Barrett, HH Abbey, CK Clarkson, E 

作者机构：Univ Arizona Dept Radiol Ctr Opt Sci Tucson AZ 85724 USA Univ Arizona Program Appl Math Tucson AZ 85724 USA 

出 版 物：《JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION》 (美国光学会志，A辑：光学、图象科学与视觉)

年 卷 期：1998年第15卷第6期

页      面：1520-1535页

核心收录：

学科分类：1002[医学-临床医学] 100212[医学-眼科学] 0702[理学-物理学] 10[医学] 

基　　金：NCI NIH HHS [R01 CA52643] Funding Source: Medline 

主　　题：Noise properties Em algorithm 

摘      要：We continue the theme of previous papers [J. Opt. Sec. Am. A 7, 1266 (1990);12, 834 (1995)] on objective (task-based) assessment of image quality. We concentrate on signal-detection tasks and figures of merit related to the ROC (receiver operating characteristic) curve. Many different expressions for the area under an ROC curve (AUC) are derived for an arbitrary discriminant function, with different assumptions on what information about the discriminant function is available. In particular, it is shown that AUC can be expressed by a principal-value integral that involves the characteristic functions of the discriminant. Then the discussion is specialized to the ideal observer, defined as one who uses the likelihood ratio (or some monotonic transformation of it, such as its logarithm) as the discriminant function. The properties of the ideal observer are examined from first principles. Several strong constraints on the moments of the likelihood ratio or the log likelihood are derived, and it is shown that the probability density functions for these test statistics are intimately related. In particular, some surprising results are presented for the case in which the log likelihood is normally distributed under one hypothesis. To unify these considerations, a new quantity called the likelihood-generating function is defined. It is shown that all moments of both the likelihood and the log likelihood under both hypotheses can be derived from this one function. Moreover, the AUC can be expressed, to an excellent approximation, in terms of the likelihood-generating function evaluated at the origin. This expression is the leading term in an asymptotic expansion of the AUG;it is exact whenever the likelihood-generating function behaves linearly near the origin. It is also shown that the likelihood-generating function at the origin sets a lower bound on the AUC in all cases. (C) 1998 Optical Society of America.