Breast ultrasound images are an important diagnostic factor for breast cancer detection. However, ultrasound imaging is intrinsically degraded by noise, resulting in a difficult detection of masses or nodules, and, most importantly, the evaluation of their size and shape. Computer-aided diagnosis figures as a major help factor, when it comes to analyzing this type of medical imaging. A fully automated and computationally efficient method for breast ultrasound segmentation is proposed. The algorithm classifies the images, with Support Vector Machines and Discriminant Analysis classifiers, based on a pixel descriptor formed with the information from anisotropic diffusion, band-pass filtering and scale-space curvature. The final segmentation results after the application of a set of heuristic rules for the selection of the classifiers' result, based on the ultrasound image characteristics. The final segmentation results yielded good overall accuracy, precision and also recall rates.