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Proceedings Paper

Automated estimation of breast density on mammogram using combined information of histogram statistics and boundary gradients
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Paper Abstract

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.

Paper Details

Date Published: 9 March 2010
PDF: 8 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76242F (9 March 2010); doi: 10.1117/12.844083
Show Author Affiliations
Youngwoo Kim, Seoul National Univ. College of Medicine (Korea, Republic of)
Changwon Kim, Seoul National Univ. College of Medicine (Korea, Republic of)
Jong-Hyo Kim, Seoul National Univ. College of Medicine (Korea, Republic of)


Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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