Observer models for statistically-defined backgrounds

Arthur E. Burgess

doi:10.1117/12.171739

Translator Disclaimer

1 April 1994 Observer models for statistically-defined backgrounds

Arthur E. Burgess

Author Affiliations +

Proceedings Volume 2166, Medical Imaging 1994: Image Perception; (1994) https://doi.org/10.1117/12.171739
Event: Medical Imaging 1994, 1994, Newport Beach, CA, United States

Abstract

Investigation of human signal-detection performance for noise- limited tasks with statistically defined signal or image parameters represents a step towards clinical realism. However, the ideal observer procedure is then usually nonlinear, and analysis becomes mathematically intractable. Two linear but suboptimal observer models, the Hotelling observer and the non- prewhitening (NPW) matched filter, have been proposed for mathematical convenience. Experiments by Rolland and Barrett involving detection of signals in white noise superimposed on statistically defined backgrounds showed that the Hotelling model gave a good fit while the simple NPW matched filter gave a poor fit. It will be shown that the NPW model can be modified to fit their data by adding a spatial frequency filter of shape similar to the human contrast sensitivity function. The best fit is obtained using an eye filter model, E(f) equals f^1.3 exp(-cf²) with c selected to give a peak at 4 cycles per degree.

Citation Download Citation

Arthur E. Burgess "Observer models for statistically-defined backgrounds", Proc. SPIE 2166, Medical Imaging 1994: Image Perception, (1 April 1994); https://doi.org/10.1117/12.171739

ACCESS THE FULL ARTICLE