Recent statistics show that breast cancer is a major cause of death among women in all of the world. Hence, early diagnostic with Computer Aided Diagnosis (CAD) systems is a very important tool. This task is not easy due to poor ultrasound resolution and large amount of patient data size. Then, initial image segmentation is one of the most important and challenging task. Among several methods for medical image segmentation, the use of entropy for maximization the information between the foreground and background is a well known and applied technique. But, the traditional Shannon entropy fails to describe some physical systems with characteristics such as long-range and longtime interactions. Then, a new kind of entropy, called nonextensive entropy, has been proposed in the literature for generalizing the Shannon entropy. In this paper, we propose the use of non-extensive entropy, also called q-entropy, applied in a CAD system for breast cancer classification in ultrasound of mammographic exams. Our proposal combines the non-extensive entropy, a level set formulation and a Support Vector Machine framework to achieve better performance than the current literature offers. In order to validate our proposal, we have tested our automatic protocol in a data base of 250 breast ultrasound images (100 benign and 150 malignant). With a cross-validation protocol, we demonstrate system?s accuracy, sensitivity, specificity, positive predictive value and negative predictive value as: 95%, 97%, 94%, 92% and 98%, respectively, in terms of ROC (Receiver Operating Characteristic) curves and Az areas.