UBC Theses and Dissertations
A fully automated breast density computation and classification algorithm McAvoy, Steven M.
Breast cancer is the most common cancer in Canadian women and early detection dramatically increases a woman's chance of survival. Until recently, womens' 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. Finally, a modification to dramatically improve the running time is shown to have minimal effect on the overall accuracy of the algorithm.
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