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Proceedings Paper

How does lesion location affect detection performance in digital mammography?
Author(s): Walter Huda; Kent M. Ogden; Ernest M. Scalzetti; Joon Park; Roger Hunt; David R. Dance
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Paper Abstract

We investigated how the thickness of a mass lesion at the observer detection threshold varied with lesion location in the breast. A digital mammography system was used to acquire radiographs of an anthropomorphic breast phantom. Mammograms were acquired with and without mass lesions, thereby permitting a difference image to be generated corresponding to the lesion alone. This isolated lesion was added at a reduced intensity to a non-lesion digital mammogram during a 4-Alternate Forced-Choice (4-AFC) experiment. The lesion intensity that corresponded to a 92% correct performance level in the 4-AFC experiments was determined (I92%). Values of I92% were obtained at different locations in the anthropomorphic phantom, thereby permitting the importance of breast thickness and structured background on lesion detection to be investigated. Lesion detection (I92%) was found to be best in high signal intensity regions (black) and ~25% lower in the low signal regions (white). Lesion detection also appeared to depend on the characteristics of the structured background. The experimental results showed a good correlation with a computation that used a convolution of the lesion and the local background region in the mammogram.

Paper Details

Date Published: 22 May 2003
PDF: 11 pages
Proc. SPIE 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, (22 May 2003); doi: 10.1117/12.480085
Show Author Affiliations
Walter Huda, SUNY Upstate Medical Univ. (United States)
Kent M. Ogden, SUNY Upstate Medical Univ. (United States)
Ernest M. Scalzetti, SUNY Upstate Medical Univ. (United States)
Joon Park, SUNY Upstate Medical Univ. (United States)
Roger Hunt, The Royal Marsden Hospital (United Kingdom)
David R. Dance, The Royal Marsden Hospital (United Kingdom)


Published in SPIE Proceedings Vol. 5034:
Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment
Dev P. Chakraborty; Elizabeth A. Krupinski, Editor(s)

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