Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Active shape models—their training and application
Computer Vision and Image Understanding
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Comparison of Multiple View Strategies to Reduce False Positives in Breast Imaging
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
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Computer-aided detection (CAD) has become increasingly common in recent years as a tool in catching breast cancer in its early, more treatable stages. More and more breast centers are using CAD as studies continue to demonstrate its effectiveness. As the technology behind CAD improves, so do its results and its impact on society. In trying to improve the sensitivity and specificity of CAD algorithms, a good deal of work has been done on feature extraction, the generation of mathematical representations of mammographic features which can help distinguish true cancerous lesions from false ones. One feature that is not currently seen in the literature that physicians rely on in making their decisions is location within the breast. This is a difficult feature to calculate as it requires a good deal of prior knowledge as well as some way of accounting for the tremendous variability present in breast shapes. In this paper, we present a method for the generation and implementation of a probabilistic breast cancer atlas. We then validate this method on data from the Digital Database for Screening Mammography (DDSM).