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Proceedings Paper

Computer-aided detection of breast masses on mammograms: performance improvement using a dual system
Author(s): Jun Wei; Berkman Sahiner; Lubomir M. Hadjiiski; Heang-Ping Chan; Mark A. Helvie; Marilyn A. Roubidoux; Nicholas Petrick; Jun Ge; Chuan Zhou
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Paper Abstract

We have developed a computer-aided detection (CAD) system for breast masses on mammograms. In this study, our purpose was to improve the performance of our mass detection system by using a new dual system approach which combines a CAD system optimized with "average" masses with another CAD system optimized with subtle masses. The latter system is trained to provide high sensitivity in detecting subtle masses. For an unknown mammogram, the two systems are used in parallel to detect suspicious objects. A feed-forward backpropagation neural network trained to merge the scores of the two linear discriminant analysis (LDA) classifiers from the two systems makes the final decision in differentiation of true masses from normal tissue. A data set of 86 patients containing 172 mammograms with biopsy-proven masses was partitioned into a training set and an independent test set. This data set is referred to as the average data set. A second data set of 214 prior mammograms was used for training the second CAD system for detection of subtle masses. When the single CAD system trained on the average data set was applied to the test set, the Az for false positive (FP) classification was 0.81 and the FP rates were 2.1, 1.5 and 1.3 FPs/image at the case-based sensitivities of 95%, 90% and 85%, respectively. With the dual CAD system, the Az was 0.85 and the FP rates were improved to 1.7, 1.2 and 0.8 FPs/image at the same case-based sensitivities. Our results indicate that the dual CAD system can improve the performance of mass detection on mammograms.

Paper Details

Date Published: 29 April 2005
PDF: 7 pages
Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); doi: 10.1117/12.595745
Show Author Affiliations
Jun Wei, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Mark A. Helvie, Univ. of Michigan (United States)
Marilyn A. Roubidoux, Univ. of Michigan (United States)
Nicholas Petrick, U.S. Food and Drug Administration (United States)
Jun Ge, Univ. of Michigan (United States)
Chuan Zhou, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 5747:
Medical Imaging 2005: Image Processing
J. Michael Fitzpatrick; Joseph M. Reinhardt, Editor(s)

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