Three-dimensional active contour model for characterization of solid breast masses on three-dimensional ultrasound images

Abstract

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.

Multimodality CAD: combination of computerized classification techniques based on mammograms and 3D ultrasound volumes for improved accuracy in breast mass characterization

“…Our results indicated that the use of this term increased the accuracy of 3D segmentation. 16 Figure 5 shows the result of the 3D segmentation algorithm for five consecutive slices of a mass.…”

Automatic breast ultrasound image segmentation: A survey

“…In [54,55], Madabhushi et al proposed a fully automatic approach for BUS image segmentation using PDM which is initialized by utilizing the boundary points produced in tumor localization step. In [56,57], Sahiner applied PDM for 3D BUS tumor segmentation, the external forces had two terms: the first term was defined on image gradient by using 3×3 Sobel filters, and the second term is the balloon force. In [58,59],…”