Measuring shape complexity of breast lesions on ultrasound images

The shapes of malignant breast tumors are more complex than the benign lesions due to their nature of infiltration into surrounding tissues. We investigated the efficacy of shape features and presented a method using polygon shape complexity to improve the discrimination of benign and malignant breast lesions on ultrasound. First, 63 lesions (32 benign and 31 malignant) were segmented by K-way normalized cut with the priori rules on the ultrasound images. Then, the shape measures were computed from the automatically extracted lesion contours. A polygon shape complexity measure (SCM) was introduced to characterize the complexity of breast lesion contour, which was calculated from the polygonal model of lesion contour. Three new statistical parameters were derived from the local integral invariant signatures to quantify the local property of the lesion contour. Receiver operating characteristic (ROC) analysis was carried on to evaluate the performance of each individual shape feature. SCM outperformed the other shape measures, the area under ROC curve (AUC) of SCM was 0.91, and the sensitivity of SCM could reach 0.97 with the specificity 0.66. The measures of shape feature and margin feature were combined in a linear discriminant classifier. The resubstitution and leave-one-out AUC of the linear discriminant classifier were 0.94 and 0.92, respectively. The distinguishing ability of SCM showed that it could be a useful index for the clinical diagnosis and computer-aided diagnosis to reduce the number of unnecessary biopsies.