  Objective: Breast tissue is mainly a mixture of adipose and fibro-glandular
tissue. Cancer risk and risk of undetected breast cancer increases with the
amount of glandular tissue in the breast. Therefore, radiologists must report
the total volume glandular fraction or a BI-RADS classification in screening
and diagnostic mammography. A Maximum Likelihood algorithm is shown to estimate
the pixel-wise glandular fraction from mammographic images. The pixel-wise
glandular fraction provides information that helps localize dense tissue. The
total volume glandular fraction can be calculated from pixel-wise glandular
fraction. The algorithm was implemented for images acquired with an
anti-scatter grid, and without using the anti-scatter grid but followed by
software scatter removal. The work also studied if presenting the pixel-wise
glandular fraction image alongside the usual mammographic image has the
potential to improve the contrast-to-noise ratio on micro-calcifications in the
breast. Results: For the TOPAS simulated images, the glandular fraction was
estimated with a root mean squared error of 3.2% and 2.5% for the without and
with anti-scatter grid cases. Average absolute errors were (3.7 +/- 2.4)% and
(3.6 +/- 0.9)%, respectively. Results from DICOM clinical images (where the
true glandular fraction is unknown) show that the algorithm gives a glandular
fraction within the average range expected from the literature. For
microcalcification detection, the contrast-to-noise ratio improved by 17.5-548%
in DICOM images and 5.1-88% in TOPAS images. Conclusion: We show a method of
accurate estimation of pixel-wise glandular fraction image, providing
localization information of breast density. The glandular fraction images also
showed an improvement in contrast to noise ratio for detecting
microcalcifications, a risk factor in breast cancer.
