Lecture Notes in Computer Science Volume 7361, 2012, pp 181-188

Characterizing Breast Phenotype with a Novel Measure of Fibroglandular Structure

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Understanding, and accurately being able to predict, breast cancer risk would greatly enhance the early detection, and hence treatment, of the disease. In this paper we describe a new metric for mammographic structure, “orientated mammographic entropy”, via a comprehensive classification of image pixels into one of seven basic image feature (BIF) classes. These classes are flat (zero order), slope-like (first order), and maximum, minimum, light-lines, dark-lines and saddles (second order). By computing a reference breast orientation with respect to breast shape and nipple location, these classes are further subdivided into 23 orientated BIF classes. For a given mammogram a histogram is constructed from the proportion of pixels in each of the 23 classes, and the orientated mammographic entropy, H om , computed from this histogram. H om , shows good correlation between left and right breasts (r 2 = 0.76, N=478), and is independent of both mammographic breast area, a surrogate for breast size (r 2 = 0.07, N=974), and breast density, as estimated using Volpara TM software (r 2 = 0.11, N=385). We illustrate this metric by examining its relationship to familial breast cancer risk, for 118 subjects, using the BOADICEA genetic susceptibility to breast and ovarian cancer model.