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9 March 2010 Computer aided breast calcification auto-detection in cone beam breast CT
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In Cone Beam Breast CT (CBBCT), breast calcifications have higher intensities than the surrounding tissues. Without the superposition of breast structures, the three-dimensional distribution of the calcifications can be revealed. In this research, based on the fact that calcifications have higher contrast, a local thresholding and a histogram thresholding were used to select candidate calcification areas. Six features were extracted from each candidate calcification: average foreground CT number value, foreground CT number standard deviation, average background CT number value, background CT number standard deviation, foreground-background contrast, and average edge gradient. To reduce the false positive candidate calcifications, a feed-forward back propagation artificial neural network was designed. The artificial neural network was trained with the radiologists confirmed calcifications and used as classifier in the calcification auto-detection task. In the preliminary experiments, 90% of the calcifications in the testing data sets were detected correctly with an average of 10 false positives per data set.
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Xiaohua Zhang, Ruola Ning, and Jiangkun Liu "Computer aided breast calcification auto-detection in cone beam breast CT", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76242M (9 March 2010);

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