Breast tissue density quantification via digitized mammograms

Studies reported in the literature indicate that breast cancer risk is asso ciated with mammographic densities. An objective, repeatable, and a quantit ative measure of risk derived from mammographic densities will be of consid erable use in recommending alternative screening paradigms and/or preventiv e measures. However, image processing efforts toward this goal seem to be s parse in the literature, and automatic and efficient methods do not seem to exist. In this paper, we describe and validate an automatic and reproducib le method to segment dense tissue regions from fat within breasts from digi tized mammograms using scale-based fuzzy connectivity methods. Different me asures for characterizing mammographic density are computed from the segmen ted regions and their robustness in terms of their linear correlation acros s two different projections-cranio-caudal and medio-lateral-oblique-are stu died. The accuracy of the method is studied by computing the area of mismat ch of segmented dense regions using the proposed method and using manual ou tlining. A comparison between the mammographic density parameter taking int o account the original intensities and that just considering the segmented area indicates that the former may have some advantages over the latter.