Automated estimation of breast density on mammogram using combined information of histogram statistics and boundary gradients
Youngwoo Kim, Chang-Won Kim, Jong Hyo Kim · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
This paper presents an automated scheme for breast density estimation on mammogram using statistical and boundary information. Breast density is regarded as a meaningful indicator for breast cancer risk, but measurement of breast density still relies on the qualitative judgment of radiologists. Therefore, we attempted to develop an automated system achieving objective and quantitative measurement. For preprocessing, we first segmented the breast region, performed contrast stretching, and applied median filtering. Then, two features were extracted: statistical information including standard deviation of fat and dense regions in breast area and boundary information which is the edge magnitude of a set of pixels with the same intensity. These features were calculated for each intensity level. By combining these features, the optimal threshold was determined which best divided the fat and dense regions. For evaluation purpose, 80 cases of Full-Field Digital Mammography (FFDM) taken in our institution were utilized. Two observers conducted the performance evaluation. The correlation coefficients of the threshold and percentage between human observer and automated estimation were 0.9580 and 0.9869 on average, respectively. These results suggest that the combination of statistic and boundary information is a promising method for automated breast density estimation.