Invariant Features for Discriminating Cysts from Solid Lesions in Mammography

Abstract

Feature extraction is an integral of all Computer Aided Diagnosis (CAD) systems. Due to the presence of fibroglandular tissue however, measurements are perturbed by unwanted influences and therefore, the same descriptor will yield different values for different amounts of occluding structures. To aid the statistical learning used for classification, we need to design features that are invariant to unwanted influences. In this paper, we propose a simple model of the tumour and its surrounding tissue and show how this model can be used to derive descriptors that are invariant to obscuring tissue, rather than heuristically defining a set of descriptors, which is common practice in many CAD papers. We tailor the descriptors to optimally discriminate between tumours and cysts, by assuming a parametric form of the lesions. Results show a significant discriminative improvement over simple, more commonly used contrast features and we obtained an AUC of 0.77 using both CC and MLO images.