Stabilized estimates of Hotelling-observer detection performance in patient-structured noise

This paper addresses the question of how to determine the performance of the optimum linear or Hotelling observer when only sample images are available. This observer is specified by a template from which a scalar test statistic is computed for each image. It is argued that estimation of the Hotelling template is analogous to problems in image reconstruction, where many difficulties can be avoided through judicious use of prior information. In the present problem, prior information is enforced by choice of the representation used for the template. We consider specifically a representation based on Laguerre-Gauss functions, and we discuss ways of estimating the coefficients in this expansion from sample images for the problem of detection of a known signal. The method is illustrated by two experiments, one based on simulated nonuniform fields called lumpy backgrounds, the other on real coronary angiograms.