Approximate maximum likelihood estimation of scanning observer templates

Craig K. Abbey; Frank W. Samuelson; Adam Wunderlich; Lucretiu M. Popescu; Miguel P. Eckstein; John M. Boone

doi:10.1117/12.2082874

17 March 2015 Approximate maximum likelihood estimation of scanning observer templates

Craig K. Abbey, Frank W. Samuelson, Adam Wunderlich, Lucretiu M. Popescu, Miguel P. Eckstein, John M. Boone

Proceedings Volume 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment; 94160O (2015) https://doi.org/10.1117/12.2082874
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

In localization tasks, an observer is asked to give the location of some target or feature of interest in an image. Scanning linear observer models incorporate the search implicit in this task through convolution of an observer template with the image being evaluated. Such models are becoming increasingly popular as predictors of human performance for validating medical imaging methodology. In addition to convolution, scanning models may utilize internal noise components to model inconsistencies in human observer responses. In this work, we build a probabilistic mathematical model of this process and show how it can, in principle, be used to obtain estimates of the observer template using maximum likelihood methods. The main difficulty of this approach is that a closed form probability distribution for a maximal location response is not generally available in the presence of internal noise. However, for a given image we can generate an empirical distribution of maximal locations using Monte-Carlo sampling. We show that this probability is well approximated by applying an exponential function to the scanning template output. We also evaluate log-likelihood functions on the basis of this approximate distribution. Using 1,000 trials of simulated data as a validation test set, we find that a plot of the approximate log-likelihood function along a single parameter related to the template profile achieves its maximum value near the true value used in the simulation. This finding holds regardless of whether the trials are correctly localized or not. In a second validation study evaluating a parameter related to the relative magnitude of internal noise, only the incorrect localization images produces a maximum in the approximate log-likelihood function that is near the true value of the parameter.

Citation Download Citation

Craig K. Abbey, Frank W. Samuelson, Adam Wunderlich, Lucretiu M. Popescu, Miguel P. Eckstein, and John M. Boone "Approximate maximum likelihood estimation of scanning observer templates", Proc. SPIE 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, 94160O (17 March 2015); https://doi.org/10.1117/12.2082874

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available