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Proceedings Paper

Lesion detection using an a-contrario detector in simulated digital mammograms
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Paper Abstract

Burgess showed that lesion detectability does have a non-trivial behavior with textured mammographic backgrounds: the threshold detectability occurs when the log contrast is linearly related to the log size with positive slope. Grosjean et al. proposed the a-contrario detector as an acceptable observer for detection on such backgrounds. In this study, we quantitatively simulated projected breast images containing lesions with a variety of sizes and thicknesses, for a 55 mm thick, 50/50 glandular breast and with different textured background types generated by the power-law filtered noise model proposed by Burgess. The acquisition parameters used in the simulation correspond to the optimal techniques provided by a digital mammography system for that specific breast. Images have been automatically scored by the a-contrario detector in order to find the minimum thickness of the lesion needed to reach the detection threshold. Taking into account the Fourier spectrum properties of the breast texture and using the a-contrario observer as a new metric for the detection task, we found the same detection slopes as described by Burgess. With our quantitative simulation, which includes a realistic image chain of a digital mammography system, and with the implementation of a novel detection process, we found that for the considered lesion sizes, lesions are easier to detect on textures with a high value of power-law exponent.

Paper Details

Date Published: 17 March 2006
PDF: 12 pages
Proc. SPIE 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment, 61460S (17 March 2006); doi: 10.1117/12.654140
Show Author Affiliations
Bénédicte Grosjean, GE Healthcare (France)
Serge Muller, GE Healthcare (France)
Henri Souchay, GE Healthcare (France)

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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