Paper
17 March 2006 Lesion detection using an a-contrario detector in simulated digital mammograms
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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.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bénédicte Grosjean, Serge Muller, and Henri Souchay "Lesion detection using an a-contrario detector in simulated digital mammograms", Proc. SPIE 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment, 61460S (17 March 2006); https://doi.org/10.1117/12.654140
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Cited by 7 scholarly publications.
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KEYWORDS
Breast

Mammography

Signal detection

Sensors

Image filtering

Mathematical modeling

Digital mammography

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