OBJECTIVES: To develop and evaluate a computer-aided diagnosis (CAD) system with automatic contouring and morphological analysis to aid in the classification of breast tumors using ultrasound. METHODS: We evaluated 118 breast lesions (34 malignant and 84 benign tumors). Each tumor contour was automatically extracted from the digitized ultrasound image. Nineteen practical morphological features from the extracted contour were calculated and principal component analysis (PCA) was applied to find independent features. A support vector machine (SVM) classifier utilized the selected principal vectors to identify the breast tumor as benign or malignant. In this study, all the cases were sampled with k-fold cross-validation (k = 10) to evaluate the performance by receiver-operating characteristics (ROC) curve analysis. RESULTS: The areas under the ROC curves for the proposed CAD systems using all morphological features and the lower-dimensional principal vector were 0.91 and 0.90, respectively. The classification ability for breast tumors using morphological information was good. CONCLUSIONS: This system differentiates benign from malignant breast tumors well and therefore provides a clinically useful second opinion. Moreover, the morphological features are nearly setting-independent and thus available to various ultrasound machines.
OBJECTIVES: To develop and evaluate a computer-aided diagnosis (CAD) system with automatic contouring and morphological analysis to aid in the classification of breast tumors using ultrasound. METHODS: We evaluated 118 breast lesions (34 malignant and 84 benign tumors). Each tumor contour was automatically extracted from the digitized ultrasound image. Nineteen practical morphological features from the extracted contour were calculated and principal component analysis (PCA) was applied to find independent features. A support vector machine (SVM) classifier utilized the selected principal vectors to identify the breast tumor as benign or malignant. In this study, all the cases were sampled with k-fold cross-validation (k = 10) to evaluate the performance by receiver-operating characteristics (ROC) curve analysis. RESULTS: The areas under the ROC curves for the proposed CAD systems using all morphological features and the lower-dimensional principal vector were 0.91 and 0.90, respectively. The classification ability for breast tumors using morphological information was good. CONCLUSIONS: This system differentiates benign from malignant breast tumors well and therefore provides a clinically useful second opinion. Moreover, the morphological features are nearly setting-independent and thus available to various ultrasound machines.