Automatic BI-RADS classification of mammograms

Abstract

Mammograms provide a significant amount of information, which allows the classification of breast tissue into one of four breast density categories. The higher the category score, the greater the amount of dense (fibroglandular) tissue in the breast. These categories were proposed to give an indication of the sensitivity of mammography, but it is also widely acknowledged that breast density is associated with the risk of developing cancer. Thus, accurate and reproducible measures of classifying breast density are important for breast cancer screening and risk assessment. We present our VolparaTM algorithm to automatically estimate the volumetric breast density (VBD) from mammograms. VBD is the percentage of fibroglandular tissue in the breast and is a physiological measure of the breast composition. Volpara uses a physics model together with image information derived from a mammogram to report the breast density. In this paper, we compare Volpara’s VBD with various statistical texture measures across 1179 mammograms. This comparison shows that Volpara has the best performance in categorising breast density with respect to radiologist’s readings.