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Proceedings Paper

Image discrimination models predict signal detection in natural medical image backgrounds
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Paper Abstract

The ability of a human observer to locate a lesion in natural medical image backgrounds (extracted from patients x-ray coronary angiograms) is degraded by two major factors: (1) the noisy variations in the background, (2) the presence of a high contrast complex background (through pattern masking effects). The purpose of this paper is to isolate and model the effect of a deterministic complex background on visual signal detection in natural medical image backgrounds. We perform image discrimination experiments where the observers have to discriminate an image containing the background plus signal from an image containing the background only. Five different samples of medical image backgrounds were extracted from patients' digital x-ray coronary angiograms. On each trial, two images were shown sequentially, one image with the simulated contrast target and the other without. The observer's task was to select the image with the target. An adaptive staircase method was used to determine the sequence of signal contrasts presented and the signal's energy thresholds were determined by maximum likelihood estimation. We tested the ability of single channel and multiple channel image discrimination models with a variety of contrast gain control mechanisms to predict the variation of the signal energy threshold in the different background samples. Human signal energy thresholds were best predicted by a multiple channel model with wide band masking.

Paper Details

Date Published: 3 June 1997
PDF: 13 pages
Proc. SPIE 3016, Human Vision and Electronic Imaging II, (3 June 1997); doi: 10.1117/12.274531
Show Author Affiliations
Miguel P. Eckstein, Cedars-Sinai Medical Ctr. and NASA Ames Research Ctr. (United States)
Albert J. Ahumada, NASA Ames Research Ctr. (United States)
Andrew B. Watson, NASA Ames Research Ctr. (United States)


Published in SPIE Proceedings Vol. 3016:
Human Vision and Electronic Imaging II
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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