author = "Rodrigues, Paulo S{\'e}rgio Silva",
          affiliation = "{National Laboratory for Scientific Computing}",
                title = "Non-Extensive Entropy for CAD Systems of Breast Cancer Images",
            booktitle = "Proceedings...",
                 year = "2006",
               editor = "Oliveira Neto, Manuel Menezes de and Carceroni, Rodrigo Lima",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 19. 
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "CAD Tsallis Entropy Medical Image Analysis Breast Tumor.",
             abstract = "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 systems 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 
  conference-location = "Manaus",
      conference-year = "8-11 Oct. 2006",
             language = "en",
           targetfile = "rodriguesr-CADSystems.pdf",
        urlaccessdate = "2020, Nov. 25"