Breast cancer is the second leading cause of death after lung cancer in women all over the world. The survival rate of breast cancer patients depends on the stage of diagnosis; patients with stage 0 are more likely to reach cancer free state. Therefore, early detection of breast cancer is the key to patient survival. In order to enhance diagnostic accuracy of breast cancer, computer aided diagnosis (CAD) systems have been built. Ultrasound is one of the most frequently used methods for early detection of breast cancer. Currently, the accuracy of CAD systems based on ultrasound images is about 90% and needs further enhancement in order to save lives of the undetected. A meaningful approach to do this is to explore new and meaningful features with discriminating ability and incorporate them into CAD systems. Recently, from a thorough investigation of the images, we extracted a new geometric feature related to the mass shape in ultrasound images called Central Regularity Degree (CRD). The CRD reflects the degree of regularity of the middle part of the mass. To demonstrate the effect of CRD on differentiating malignant from benign masses and the potential improvement to the diagnostic accuracy of breast cancer using ultrasound, this study evaluated the diagnostic accuracy of different classifiers when the CRD was added to five powerful mass features obtained from previous studies including one geometric feature: Depth-Width ratio (DW); two morphological features: shape and margin; blood flow and age. Artificial Neural Networks (ANN), K Nearest Neighbour (KNN), Nearest Centroid, Linear Discriminant Analysis (LDA), and Receiver Operating Characteristic (ROC) analysis were employed for classification and evaluation. Ninety nine breast sonograms-46 malignant and 53 benign-were evaluated. The results reveal that CRD is an effective feature discriminating between malignant and benign cases leading to improved accuracy of diagnosis of breast cancer. The best results were obtained by ANN where the area under ROC curve (Az) for training and testing using all features except CRD was 100% and 81.8%, respectively, and 100% and 95.45% using all features. Therefore, the overall improvement by adding CRD was about 14%, a significant improvement.
