9 March 2010 Computer aided breast calcification auto-detection in cone beam breast CT
Author Affiliations +
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.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaohua Zhang, Xiaohua Zhang, Ruola Ning, Ruola Ning, Jiangkun Liu, 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); doi: 10.1117/12.844362; https://doi.org/10.1117/12.844362
PROCEEDINGS
8 PAGES


SHARE
Back to Top