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Proceedings Paper

Observer performance detecting signals in globally non-stationary oriented noise
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Paper Abstract

Most of the studies on signal detection task for medical images have used backgrounds that are or assumed to be statistically stationary. However, medical images usually present statistically non-stationary properties. Fewer studies have addressed how humans detect signals in non-stationary backgrounds. In particular, it is unknown whether humans can adapt their strategy to different local statistical properties in non-stationary backgrounds. In this paper, we measured human performance detecting a signal embedded in statistically non-stationary noise and in statistically stationary noise. Test images were designed so that performance of model observers that assumed statistically stationary and made no use of differences in local statistics would be constant across both conditions. In contrast, performance of an ideal model observer that uses local statistics is about 140% higher with the non-stationary backgrounds than the stationary ones. Human performance was 30% higher in the non-stationary backgrounds. We conclude that humans can adapt their strategy to the local statistical properties of non-stationary backgrounds (although suboptimally compared to the ideal observer) and that model observers that derive their templates based on stationary assumptions might be inadequate to predict human performance in some non-stationary backgrounds.

Paper Details

Date Published: 17 March 2006
PDF: 10 pages
Proc. SPIE 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment, 61460Z (17 March 2006); doi: 10.1117/12.655749
Show Author Affiliations
Yani Zhang, Univ. of California, Santa Barbara (United States)
Craig K. Abbey, Univ. of California, Santa Barbara (United States)
Miguel P. Eckstein, Univ. of California, Santa Barbara (United States)


Published in SPIE Proceedings Vol. 6146:
Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment
Yulei Jiang; Miguel P. Eckstein, Editor(s)

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