We are developing computer vision techniques for the characterization of breast masses as malignant or benign on radiologic examinations. In this study, we investigated the computerized characterization of breast masses on three-dimensional (3-D) ultrasound (US) volumetric images. We developed 2-D and 3-D active contour models for automated segmentation of the mass volumes. The effect of the initialization method of the active contour on the robustness of the iterative segmentation method was studied by varying the contour used for its initialization. For a given segmentation, texture and morphological features were automatically extracted from the segmented masses and their margins. Stepwise discriminant analysis with the leave-one-out method was used to select effective features for the classification task and to combine these features into a malignancy score. The classification accuracy was evaluated using the area Az under the receiver operating characteristic (ROC) curve, as well as the partial area index Az(0.9), defined as the relative area under the ROC curve above a sensitivity threshold of 0.9. For the purpose of comparison with the computer classifier, four experienced breast radiologists provided malignancy ratings for the 3-D US masses. Our dataset consisted of 3-D US volumes of 102 biopsied masses (46 benign, 56 malignant). The classifiers based on 2-D and 3-D segmentation methods achieved test Az values of 0.87+/-0.03 and 0.92+/-0.03, respectively. The difference in the Az values of the two computer classifiers did not achieve statistical significance. The Az values of the four radiologists ranged between 0.84 and 0.92. The difference between the computer's Az value and that of any of the four radiologists did not achieve statistical significance either. However, the computer's Az(0.9) value was significantly higher than that of three of the four radiologists. Our results indicate that an automated and effective computer classifier can be designed for differentiating malignant and benign breast masses on 3-D US volumes. The accuracy of the classifier designed in this study was similar to that of experienced breast radiologists.

The streaming detection technique improved differentiation of cysts from solid masses in indeterminate lesions and has potential for reducing the number of recommended cyst aspirations for the diagnosis of indeterminate breast masses.

The accuracy of discrimination between malignant and benign solid breast masses on ultrasound images may be improved by using computer-aided diagnosis and 3-D information. The purpose of this study was to develop automated 3-D segmentation and classification methods for 3-D ultrasound images, and to compare the classification accuracy based on 2-D and 3-D segmentation techniques. The 3-D volumes were recorded by translating the transducer across the lesion in the z-direction while conventional 2-D images were acquired in the x-y plane. 2-D and 3-D segmentation methods based on active contour models were developed to delineate the mass boundaries. Features were automatically extracted based on the segmented mass shapes, and were merged into a malignancy score using a linear classifier. 3-D volumes containing biopsy-proven solid breast masses were collected from 102 patients (44 benign and 58 malignant). A leave-one-out method was used for feature selection and classifier design. The area A z under the test receiver operating characteristic curves for the classifiers using the 3-D and 2-D active contour boundaries were 0.88 and 0.84, respectively. More than 45% of the benign masses could be correctly identified using the 3-D features without missing a malignancy. Our results indicate that an accurate computer classifier can be designed for differentiation of malignant and benign solid breast masses on 3-D sonograms.