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Proceedings Paper

Two-view information fusion for improvement of computer-aided detection (CAD) of breast masses on mammograms
Author(s): Jun Wei; Berkman Sahiner; Lubomir M. Hadjiiski; Heang-Ping Chan; Mark A. Helvie; Marilyn A. Roubidoux; Chuan Zhou; Jun Ge; Yiheng Zhang
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Paper Abstract

We are developing a two-view information fusion method to improve the performance of our CAD system for mass detection. Mass candidates on each mammogram were first detected with our single-view CAD system. Potential object pairs on the two-view mammograms were then identified by using the distance between the object and the nipple. Morphological features, Hessian feature, correlation coefficients between the two paired objects and texture features were used as input to train a similarity classifier that estimated a similarity scores for each pair. Finally, a linear discriminant analysis (LDA) classifier was used to fuse the score from the single-view CAD system and the similarity score. A data set of 475 patients containing 972 mammograms with 475 biopsy-proven masses was used to train and test the CAD system. All cases contained the CC view and the MLO or LM view. We randomly divided the data set into two independent sets of 243 cases and 232 cases. The training and testing were performed using the 2-fold cross validation method. The detection performance of the CAD system was assessed by free response receiver operating characteristic (FROC) analysis. The average test FROC curve was obtained from averaging the FP rates at the same sensitivity along the two corresponding test FROC curves from the 2-fold cross validation. At the case-based sensitivities of 90%, 85% and 80% on the test set, the single-view CAD system achieved an FP rate of 2.0, 1.5, and 1.2 FPs/image, respectively. With the two-view fusion system, the FP rates were reduced to 1.7, 1.3, and 1.0 FPs/image, respectively, at the corresponding sensitivities. The improvement was found to be statistically significant (p<0.05) by the AFROC method. Our results indicate that the two-view fusion scheme can improve the performance of mass detection on mammograms.

Paper Details

Date Published: 10 March 2006
PDF: 7 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 614424 (10 March 2006); doi: 10.1117/12.654593
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)
Chuan Zhou, Univ. of Michigan (United States)
Jun Ge, Univ. of Michigan (United States)
Yiheng Zhang, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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