Breast density has become an established risk indicator for developing breast cancer. Current clinical practice
reflects this by grading mammograms patient-wise as entirely fat, scattered fibroglandular, heterogeneously dense,
or extremely dense based on visual perception. Existing (semi-) automated methods work on a per-image basis
and mimic clinical practice by calculating an area fraction of fibroglandular tissue (mammographic percent density).
We suggest a method that follows clinical practice more strictly by segmenting the fibroglandular tissue portion
directly from the joint data of all four available mammographic views (cranio-caudal and medio-lateral oblique,
left and right), and by subsequently calculating a consistently patient-based mammographic percent density estimate.
In particular, each mammographic view is first processed separately to determine a region of interest (ROI) for
segmentation into fibroglandular and adipose tissue. ROI determination includes breast outline detection via
edge-based methods, peripheral tissue suppression via geometric breast height modeling, and - for medio-lateral
oblique views only - pectoral muscle outline detection based on optimizing a three-parameter analytic curve with
respect to local appearance. Intensity harmonization based on separately acquired calibration data is performed
with respect to compression height and tube voltage to facilitate joint segmentation of available mammographic
views. A Gaussian mixture model (GMM) on the joint histogram data with a posteriori calibration guided
plausibility correction is finally employed for tissue separation.
The proposed method was tested on patient data from 82 subjects. Results show excellent correlation (r = 0.86)
to radiologist's grading with deviations ranging between -28%, (q = 0.025) and +16%, (q = 0.975).