Level Set-Based Core Segmentation of Mammographic Masses Facilitating Three Stage (Core, Periphery, Spiculation) Analysis

We present mammographic mass core segmentation, based on the Chan-Vese level set method. The proposed method is analyzed via resulting feature efficacies. Additionally, the core segmentation method is used to investigate the idea of a three stage segmentation approach, i.e. segment the mass core, periphery, and spiculations (if any exist) and use features from these three segmentations to classify the mass as either benign or malignant. The proposed core segmentation method and a proposed end-to-end computer aided detection (CAD) system using a three stage segmentation are implemented and experimentally tested with a set of 60 mammographic images from the digital database of screening mammography. Receiver operating characteristic (ROC) curve A_z values for morphological and texture features extracted from the core segmentation are shown to be on par, or better, than those extracted from a periphery segmentation. The efficacy of the core segmentation features when combined with the periphery and spiculation segmentation features are shown to be feature set dependent. The proposed end-to-end system uses stepwise linear discriminant analysis for feature selection and a maximum likelihood classifier. Using all three stages (core + periphery + spiculations) results in an overall accuracy (OA) of 90% with 2 false negatives (FN). Since many CAD systems only perform a periphery analysis, adding core features could be a benefit to potentially increase OA and reduce FN cases.