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Abstract #3362

An Automatic Template-Based Breast Segmentation Method for Fat-Suppressed MRI

Xiaoyong Wang1, Muqing Lin1, Peter T. Fwu1, Ling-Chuan Chang2, Yi-Ting Wu2, Chin-Yu Chang2, Jeon-Hor Chen3, Min-Ying Su1

1Center for Functional Onco-Imaging, University of California, Irvine, CA, United States; 2Department of Radiology, China Medical University Hospital, Taichung, Taiwan; 3Center for Functional Onco-Imaging, University of California Irvine, Irvine, CA, United States

The purpose is to develop an automatic breast segmentation method for fat-suppressed MRI. The algorithm is based on template matching of chest region using nonrigid registration. The body landmarks defined on the template can be transformed to the subjects space to determine the posterior-lateral boundary of the breasts. The results show that this chest template-based method is robust for different body and breast shapes, with a mean error of 5.4% for breast, and 2.7% for fibroglandular tissue segmentation. This tool can help developing computer-aided-diagnosis method for breast cancer detection, as well as quantitative analysis of breast density for risk management.