Breast cancer is the most common cancer in Canadian women and early detection dramatically increases a woman's chance of survival. Until recently, women's ages were considered the single most influential risk factor for developing breast cancer. Today, the density of fibroglandular tissue within the breast is considered just as important a risk factor as age. Because of this, accuracy and consistency while estimating tissue density is paramount. Currently, radiologists use the BI-RADS classification system to place mammographic images into one of four different categories. However, inter-observer variance has been shown to be as high as 30% and the methodology can be highly subjective. Many computer vision algorithms have been developed to automatically quantify breast density but only a few of these algorithms take advantage of the latest digital mammographic imaging technology. One algorithm, specifically designed to use digital mammography images, is explored in detail. Its ability to quantify and classify fibroglandular breast tissue is demonstrated and its accuracy is shown to be consistent with experienced radiologists.

Two types of images are produced from digital mammography machines: the raw image, acquired from the imaging sensor, and the processed image which contains propriety techniques for visual enhancement. Currently, automated breast density algorithms focus on utilizing the raw image while radiologists use the processed image for visual inspection. The processed image is then stored within a patient’s medical file. Discovering a means to detect breast density from processed images would allow radiologists to assess retroactively the breast cancer risk of any patient who has previously received a digital mammogram using minimal financial and human resources.