Statistical Appearance Models of Mammographic Masses

We present a method for building generative statistical appearance models of mammographic masses. We address several key issues that limited the performance of previous methods. In particular, we use MDL optimization to generate more compact shape correspondences; we describe a technique for the accurate estimation of the background tissue on which a mass is superimposed; and we highlight the importance of choosing suitable weighting between shape, texture and scale components in the final combined model. Improvements in the ability of the model to characterize a set of 101 mammographic masses are quantified using leave-one-out testing, showing a reduction in mean square error per pixel from 3.109 using a previous method to 1.262 using the new appearance model.