We describe a probit regression approach for maximum-likelihood (ML) estimation of a linear observer template from human-observer data in two-alternative forced-choice experiments. Like a previous approach to ML estimation in this problem [Abbey & Eckstein, Proc. SPIE, Vol. 4324, 2001], our approach does not make any assumptions about the distribution of the images. The previous approach utilized a regularizing prior distribution to control the degrees of freedom in the problem. In this work, we constrain the observer template to be represented by a limited number of linear features. Standard methods of probit regression are described for estimating the feature weights, and hence the observer templates. We have used this probit regression method to estimate human-observer templates for the detection of a small (5mm diameter) round simulated mass embedded in digitized mammograms. Our estimated templates for detecting the mass contain a band of heavily weighted spatial frequencies from 0.08 to 0.3 cycles/mm. We show comparisons between the human-observer template data, and the templates of a number of linear model observers that have been investigated as perceptual models of the human.

Date Published: 12 April 2002
PDF: 12 pages
Proc. SPIE 4686, Medical Imaging 2002: Image Perception, Observer Performance, and Technology Assessment, (12 April 2002); doi: 10.1117/12.462683

Published in SPIE Proceedings Vol. 4686:
Medical Imaging 2002: Image Perception, Observer Performance, and Technology Assessment
Dev Prasad Chakraborty; Elizabeth A. Krupinski, Editor(s)