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Proceedings Paper

Mass detection on real and synthetic mammograms: human observer templates and local statistics
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Paper Abstract

In this study we estimated human observer templates associated with the detection of a realistic mass signal superimposed on real and simulated but realistic synthetic mammographic backgrounds. Five trained naïve observers participated in two-alternative forced-choice (2-AFC) experiments in which they were asked to detect a spherical mass signal extracted from a mammographic phantom. This signal was superimposed on statistically stationary clustered lumpy backgrounds (CLB) in one instance, and on nonstationary real mammographic backgrounds in another. Human observer linear templates were estimated using a genetic algorithm. An additional 2-AFC experiment was conducted with twin noise in order to determine which local statistical properties of the real backgrounds influenced the ability of the human observers to detect the signal. Results show that the estimated linear templates are not significantly different for stationary and nonstationary backgrounds. The estimated performance of the linear template compared with the human observer is within 5% in terms of percent correct (Pc) for the 2-AFC task. Detection efficiency is significantly higher on nonstationary real backgrounds than on globally stationary synthetic CLB. Using the twin-noise experiment and a new method to relate image features to observers trial to trial decisions, we found that the local statistical properties preventing or making the detection task easier were the standard deviation and three features derived from the neighborhood gray-tone difference matrix: coarseness, contrast and strength. These statistical features showed a dependency with the human performance only when they are estimated within an area sufficiently small around the searched location. These findings emphasize that nonstationary backgrounds need to be described by their local statistics and not by global ones like the noise Wiener spectrum.

Paper Details

Date Published: 8 March 2007
PDF: 12 pages
Proc. SPIE 6515, Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment, 65150U (8 March 2007); doi: 10.1117/12.708492
Show Author Affiliations
Cyril Castella, Institut Univ. de Radiophysique Appliquée (Switzerland)
Karen Kinkel, Clinique des Grangettes (Switzerland)
Francis R. Verdun, Institut Univ. de Radiophysique Appliquée (Switzerland)
Miguel P. Eckstein, Univ. of California/Santa Barbara (United States)
Craig K. Abbey, Univ. of California/Santa Barbara (United States)
François O. Bochud, Institut Univ. de Radiophysique Appliquée (Switzerland)


Published in SPIE Proceedings Vol. 6515:
Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment
Yulei Jiang; Berkman Sahiner, Editor(s)

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