@InProceedings{RodriguesLoGiBaViGuSi:2019:CASyBr,
               author = "Rodrigues, Paulo Sergio Silva and Lopes, Guilherme A. Wachs and 
                         Giraldi, Gilson A. and Barcelos, Celia A. Z. and Vieira, Luciana 
                         and Guliato, Denise and Singh, Bikesh Kumar",
          affiliation = "Computer Science Department, Centro Universit{\'a}rio FEI and 
                         Computer Science Department, Centro Universit{\'a}rio FEI and 
                         {National Laboratory for Scientific Computing} and {Federal 
                         University of Uberlandia} and {Federal University of Uberlandia} 
                         and {Federal University of Uberlandia} and Department of 
                         Biomedical Engineering, National Institute of Technology Raipur",
                title = "CAD System for Breast US Images with Speckle Noise Reduction and 
                         Bio-inspired Segmentation",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Oliveira, Luciano Rebou{\c{c}}as de and Sarder, Pinaki and Lage, 
                         Marcos and Sadlo, Filip",
         organization = "Conference on Graphics, Patterns and Images, 32. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "CAD System, Ultrasound images, Speckle Noise Reduction, 
                         Bio-inspired Segmentation.",
             abstract = "Ultrasound (US) images are highly susceptible to speckle-like 
                         noise which makes imperative to use specific techniques for image 
                         smoothing. However, this process can lead to undesirable side 
                         effects such as the degradation of the real contour of the region 
                         of interest (ROI). In such context, this paper presents a new 
                         methodology for computer aided diagnosis (CAD) systems whose heart 
                         is the combination of a method for speckle noise reduction, with 
                         histogram equalization and a technique for image segmentation that 
                         uses the bio-inspired firefly algorithm and Bayesian model. The 
                         segmentation approach and the equalization are applied in two 
                         distinct stages: globally and locally. The global application 
                         produces an initial coarse estimate of the ROI, and the local 
                         application defines this region more precisely. In the 
                         classification step we carried out experiments which show that the 
                         combination of features computed both within and below the lesion 
                         strongly influences the final accuracy. We show that the 
                         gray-scale distribution and statistical moments within the lesion 
                         together with gray-scale distribution and contrast of the region 
                         below the lesion is the combination that produces the better 
                         classification results. Experiments in a database of 250 US images 
                         of breast anomalies (100 benign and 150 malignant) show that the 
                         proposed methodology reaches performance of 95%.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00018",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00018",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U2KGNE",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U2KGNE",
           targetfile = "PID6125907.pdf",
        urlaccessdate = "2024, Apr. 27"
}