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@InProceedings{ParolinHerzJung:2010:SeDiMe,
               author = "Parolin, Alessandro and Herzer, Eduardo and Jung, Claudio R.",
          affiliation = "Unisinos and Unisinos and UFRGS",
                title = "Semi-Automated Diagnosis of Melanoma Through the Analysis of 
                         Dermatological Images",
            booktitle = "Proceedings...",
                 year = "2010",
               editor = "Bellon, Olga and Esperan{\c{c}}a, Claudio",
         organization = "Conference on Graphics, Patterns and Images, 23. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "image processing, medical imaging, classification, MDA-FKT.",
             abstract = "Melanoma is the deadliest kind of skin cancer, but it can be 100% 
                         cured if recognized early in advance. This paper proposes a 
                         non-invasive automated skin lesion classifier based on digitized 
                         dermatological images. In the proposed approach, the lesion is 
                         initially segmented using snakes guided by an edge map based on 
                         the Wavelet Transform (WT) computed at different resolutions. A 
                         set of features is extracted from lesion pixels, and a 
                         probabilistic classifier is used to identify melanoma lesions. The 
                         detection rate of the proposed system can be adjusted to control 
                         the tradeoff between false positives and false negatives, and 
                         experimental results indicated that a false negative rate of 1.89% 
                         can be achieved, in a total accuracy rate of 82.55%.",
  conference-location = "Gramado",
      conference-year = "Aug. 30 - Sep. 3, 2010",
             language = "en",
           targetfile = "sib10_dermato_camera_ready.pdf",
        urlaccessdate = "2020, Nov. 25"
}


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