Computer-aided diagnosis of breast elastography

Ultrasonography has been an important imaging technique for detecting breast tumors. As opposed to the conventional B-mode image, the real-time tissue elastography by ultrasound is a new technique for imaging the elasticity and applied to detect the stiffness of tissues. The red region of color elastography indicates the soft tissue and the blue one indicates the hard tissue. The harder tissue usually is classified as malignancy. In this paper, the authors proposed a computer-aided diagnosis (CAD) system on elastography to measure whether this system is effective and accurate to classify the tumor into benign and malignant. According to the features of elasticity, the color elastography was transferred to hue, saturation, and value (HSV) color space and extracted meaningful features from hue images. Then the neural network was utilized in multiple features to distinguish tumors. In this experiment, there are 180 pathology-proven cases including 113 benign and 67 malignant cases used to examine the classification. The results of the proposed system showed an accuracy of 83.89%, a sensitivity of 82.09% and a specificity of 84.96%. Compared with the physician's diagnosis, an accuracy of 78.33%, a sensitivity of 53.73% and a specificity of 92.92%, the proposed CAD system had better performance. Moreover, the agreement of the proposed CAD system and the physician's diagnosis was calculated by kappa statistics, the kappa 0.64 indicated there is a fair agreement of observers.