Evaluation of Bayes risk weighted vector quantization with posterior estimation in the detection of lesions in digitized mammograms

The automated detection of suspicious tissue in digital mammograms can provide a useful aid to diagnosis by permitting a radiologist to see all regions deemed suspicious by the computer. The authors apply to digital mammography a method that combines aspects of data compression techniques based on clustering and decision trees together with algorithms for classification and regression. The idea is to use a distortion measure in a clustering algorithm that includes both squared error for general appearance and average Bayes risk for classification accuracy. The algorithm structure is that of a vector quantization compression system that incorporates Bayes risk into the optimization algorithm.<>