title: Learning a channelized observer for image quality assessment.
abstract: It is now widely accepted that image quality should be evaluated using task-based criteria, such as human-observer performance in a lesion-detection task. The channelized Hotelling observer (CHO) has been widely used as a surrogate for human observers in evaluating lesion detectability. In this paper, we propose that the problem of developing a numerical observer can be viewed as a system-identification or supervised-learning problem, in which the goal is to identify the unknown system of the human observer. Following this approach, we explore the possibility of replacing the Hotelling detector within the CHO with an algorithm that learns the relationship between measured channel features and human observer scores. Specifically, we develop a channelized support vector machine (CSVM) which we compare to the CHO in terms of its ability to predict human-observer performance. In the examples studied, we find that the CSVM is better able to generalize to unseen images than the CHO, and therefore may represent a useful improvement on the CHO methodology, while retaining its essential features.
authors.affiliation: Department of Electrical and Computer Engineering,Illinois Institute of Technology, Chicago, IL 60616 USA. brankov@iit.edu
authors.forename: Jovan G
authors.initials: JG
authors.lastname: Brankov
authors.forename: Yongyi
authors.initials: Y
authors.lastname: Yang
authors.forename: Liyang
authors.initials: L
authors.lastname: Wei
authors.forename: Issam
authors.initials: I
authors.lastname: El Naqa
authors.forename: Miles N
authors.initials: MN
authors.lastname: Wernick
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003952:Diagnostic Imaging / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009010:Monte Carlo Method; D019370:Observation