Automatic classification of mammographic parenchymal patterns: a statistical approach

Abstract

Breast parenchymal density has been found to be a strong indicator for breast cancer risk, however, to date, measures of breast density are qualitative and require the judgement of the radiologist. Objective, quantitative measures of breast density are crucial tools for assessing the association between the risk of breast cancer and mammographic density as well as for quantification of density changes to the breast. Various schemes have been proposed for classifying breast density patterns, though again each requires the judgement of the clinician to assign a particular region of tissue to its class and so it is time-consuming and prone to inter- and intra-radiologist disagreement. Motivated by recent results in texture classification, we present a new approach to breast parenchymal pattern classification. The proposed scheme uses texture models to capture the mammographic appearance within the breast area: parenchymal density patterns are modelled as a statistical distribution of clustered, rotationally invariant filter responses in a low dimensional space. This robust representation can accommodate large variations in intra-class mammogram appearance and can be trained in a straight-forward manner. Key to the approach is that parenchymal patterns can occupy disconnected regions in feature space. Objective descriptors of breast density based on the digital mammogram, are developed and validated.