Computer Aided Lesion Diagnosis in Automated 3D Breast Ultrasound Using Coronal Spiculation

A computer-aided diagnosis (CAD) system for the classification of lesions as malignant or benign in automated 3D breast ultrasound (ABUS) images, is presented. Lesions are automatically segmented when a seed point is provided, using dynamic programming in combination with a spiral scanning technique. A novel aspect of ABUS imaging is the presence of spiculation patterns in coronal planes perpendicular to the transducer. Spiculation patterns are characteristic for malignant lesions. Therefore, we compute spiculation features and combine them with features related to echotexture, echogenicity, shape, posterior acoustic behavior and margins. Classification experiments were performed using a support vector machine (SVM) classifier and evaluation was done with leave-one-patientout cross-validation. Receiver Operator Characteristic (ROC) analysis was used to determine performance of the system on a dataset of 201 lesions. We found that spiculation was among the most discriminative features. Using all features, the area under the ROC curve (Az) was 0.93, which was significantly higher than the performance without spiculation features (Az=0.90, p=0.02). On a subset of 88 cases, classification performance of CAD (Az=0.90) was comparable to the average performance of 10 readers (Az=0.87).