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Proceedings Paper

Computer aided breast calcification auto-detection in cone beam breast CT
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Paper Abstract

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.

Paper Details

Date Published: 9 March 2010
PDF: 8 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76242M (9 March 2010); doi: 10.1117/12.844362
Show Author Affiliations
Xiaohua Zhang, Univ. of Rochester (United States)
Ruola Ning, Univ. of Rochester (United States)
Jiangkun Liu, Univ. of Rochester (United States)


Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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