Improving the automatic diagnosis of benign and malignant masses in breast ultrasound images using an optimal segmentation method

Breast cancer is the most common type of cancer in the female population of the world. Early diagnosis and effective treatment with the aim of reducing mortality from this disease is done through screening methods.Ultrasound imaging is one of the most important and effective methods for the detection and diagnosis of this disease due to its non-invasive nature and its advantages over other diagnostic methods.Computer aided diagnosis systems were introduced to improve diagnostic performance. In this study, a fully automated breast cancer detection and diagnosis system is presented, which consists of four main steps: Image preprocessing in two steps to highlight the low echo areas (Hypo echo) and with the aim of selecting the seed point and the region of interest. As well as noise removal using non-local means filter, segmentation using image spatial and frequency information and improving it using genetic optimization algorithm, extracting a set containing 21 features based on shape and boundary Finally, the classification using the support vector machine classifier in order to separate the masses into benign and malignant groups. The results of evaluations performed on images of different datasets showed an accuracy of more than 95.5%.