Computer-aided Tumor Diagnosis for 3-D Breast Elastography

Breast cancer is the leading cause of cancer death for women. The mortality rate of breast cancer can be greatly reduced if a proper treatment is adopted after an early detection. Recently, many studies have shown that adding elastography examination can improve the diagnostic performance comparing to using only conventional ultrasound. Elastography can estimate the tissue stiffness by calculating the tissue displacement under a certain force. The stiffness of benign and malignant tumors can be used to be features for classifying tumors. Therefore, this study proposed a computer-aided detection (CAD) system using 3-D B-mode ultrasound and elastographic breast images. The CAD system proposed in this study using morphology, texture, and elastography features extracted from the segmented B-mode tumor area. Combining these feature sets in a binary regression model generated the malignancy estimation model.The diagnostic performance was validated by using 110 benign and 49 malignant breast lesions. The performance of combinating gray-level co-occurrence matrix (GLCM), ranklet textures, shape and elastography features achieved an accuracy of 81% (129/159), a sensitivity of 76% (38/49), a specificity of 83% (80/110), and an Az value of 0.8512. Summarily, the combination of B-mode and elastography features is effective to the classification of breast tumor.