Automatic segmentation algorithm of breast ultrasound image based on improved level set algorithm

Abstract

Breast cancer is one of the leading causes of death in women worldwide. Therefore, ultrasound examination has become an important method of detecting breast tumors. However, given the special features of ultrasonic imaging, lesion segmentation is a challenging task in computer-aided diagnosis systems. In this study, we proposed a complex and automated approach to segment breast ultrasound images. In the preliminary contour selection, an efficient method was performed by preprocessing of breast ultrasound images, selecting the iterative threshold, filtrating candidate areas, and ranking remaining areas to confirm the region of interest (ROI). After the selection of the ROI, a seed point could be determined. Then, region growing started from the selected seed to obtain a preliminary contour that will serve as the intermediate result. Finally a novel and improved level set algorithm was proposed to confirm the final contour, combined with global statistics, local statistics, and region-based energy constraint. The proposed algorithm was tested on a database of 44 breast ultrasound images, and the experimental results proved high accuracy. Compared with the classic Chan-Vese model, the proposed method increases the similarity rate and reduces the error rate.