Computer-Aided Tumor Diagnosis for Automated Breast Ultrasound

In women aged 20-59 years, breast cancer is the highest mortality. Early treatment can effectively reduce the mortality of breast cancer. In recently, breast ultrasound image is often used to diagnose between benign and malignant tumors. For increasing the accuracy, most researches segment the tumor before classification, and the segmented results directly affect the classification between benign and malignant tumors. Therefore, the purpose of this study is to refine segmented tumors using the image matting method for computer-aided diagnosis. First, the volume-of-interest (VOI) of tumor was extracted from the ultrasound image and pre-segmented by a conventional segmentation method. The tri-map including the background, foreground, and unknown region was created with the pre-segmented tumor, and then the image matting method was applied for refining the segmentation according to the unknown region of tri-map. Texture and morphology features were extracted from refined segmentation result and then the support vector machine was applied with extracted features to classify tumor into benign or malignant tumor. This study was validated with 80 cases including 40 benign and 40 malignant breast lesions. According to the experiment results, applying the image matting method had better performance than not applying the image matting method, and the combination of GLCM, ranklet, and ellipsoid fitting feature set had significant (resolution??. The accuracy, sensitivity, specificity, and the area under ROC achieved 85.0% (68/80), 87.5% (35/40), 82.5% (33/40), and 0.8829, respectively. From the experiment results, the image matting method could actually refine tumor segmentation, and more precise classification between benign and malignant tumor results were obtained.