A breast density index for digital mammograms based on radiologists' ranking

J Digit Imaging. 1998 Aug;11(3):101-15. doi: 10.1007/BF03168733.


The purpose of this study was to develop and evaluate a computerized method of calculating a breast density index (BDI) from digitized mammograms that was designed specifically to model radiologists' perception of breast density. A set of 153 pairs of digitized mammograms (cranio-caudal, CC, and mediolateral oblique, MLO, views) were acquired and preprocessed to reduce detector biases. The sets of mammograms were ordered on an ordinal scale (a scale based only on relative rank-ordering) by two radiologists, and a cardinal (an absolute numerical score) BDI value was calculated from the ordinal ranks. The images were also assigned cardinal BDI values by the radiologists in a subsequent session. Six mathematical features (including fractal dimension and others) were calculated from the digital mammograms, and were used in conjunction with single value decomposition and multiple linear regression to calculate a computerized BDI. The linear correlation coefficient between different ordinal ranking sessions were as follows: intraradiologist intraprojection (CC/CC): r = 0.978; intraradiologist interprojection (CC/MLO): r = 0.960; and interradiologist intraprojection (CC/CC): r = 0.968. A separate breast density index was derived from three separate ordinal rankings by one radiologist (two with CC views, one with the MLO view). The computer derived BDI had a correlation coefficient (r) of 0.907 with the radiologists' ordinal BDI. A comparison between radiologists using a cardinal scoring system (which is closest to how radiologists actually evaluate breast density) showed r = 0.914. A breast density index calculated by a computer but modeled after radiologist perception of breast density may be valuable in objectively measuring breast density. Such a metric may prove valuable in numerous areas, including breast cancer risk assessment and in evaluating screening techniques specifically designed to improve imaging of the dense breast.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Breast / pathology
  • Breast Neoplasms / diagnostic imaging
  • Breast Neoplasms / epidemiology
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted*
  • Image Processing, Computer-Assisted
  • Linear Models
  • Mammography / methods*
  • Mammography / statistics & numerical data
  • Observer Variation
  • Radiographic Image Enhancement
  • Risk Assessment