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@InProceedings{PessoaSilv:1993:MeDiAu,
               author = "Pessoa, L{\'u}cio Fl{\'a}vio Cavalcanti and Silva, Ascendino 
                         Fl{\'a}vio Dias e",
          affiliation = "{Departamento de Eletr{\^o}nica e Sistemas do Centro de 
                         Tecnologia da Universidade Federal de Pernambuco (UFPE)} and 
                         {Departamento de Eletr{\^o}nica e Sistemas do Centro de 
                         Tecnologia da Universidade Federal de Pernambuco (UFPE)}",
                title = "Uma metodologia para diagn{\'o}stico autom{\'a}tico da filariose 
                         utilizando imagens microsc{\'o}picas digitalizadas",
            booktitle = "Anais...",
                 year = "1993",
               editor = "Figueiredo, Luiz Henrique de and Gomes, Jonas de Miranda",
                pages = "333--342",
         organization = "Simp{\'o}sio Brasileiro de Computa{\c{c}}{\~a}o Gr{\'a}fica e 
                         Processamento de Imagens, 6. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "morfologia matem{\'a}tica, reconhecimento de padr{\~o}es, 
                         an{\'a}lise de imagens, imagens m{\'e}dicas, diagn{\'o}stico 
                         autom{\'a}tico da filariose.",
             abstract = "This paper describes a methodology for automatic diagnosis of 
                         filariasis, a tropical disease that represents a serious health 
                         problem in the State of Pernambuco, Northeast of Brazil. The 
                         medical diagnosis of filariasis is made scanning blood samples 
                         under an optical microscope and counting the number of a 
                         microscopic warm, commonly known as microfilariae. The methodology 
                         is based on the theories of Mathematical Morphology and Pattern 
                         Recognition and uses digital microscopic images, with resolution 
                         640x480x64, as inputs. Using a training set with 56 patterns, the 
                         automatic recognition of microfilariaes was performed by a Linear 
                         Discriminant Function with 4 features only, and the automation 
                         viability of this diagnosis was finally confirmed through the 
                         excellent classification results.",
  conference-location = "Recife",
      conference-year = "19 - 22 out. 1993",
                 isbn = "978-85-7669-271-3",
             language = "pt",
                  ibi = "8JMKD3MGPBW34M/3D85S8P",
                  url = "http://urlib.net/rep/8JMKD3MGPBW34M/3D85S8P",
           targetfile = "39 Uma metodologia para diagnostico automatico.pdf",
                 type = "Imagens M{\'e}dicas",
               volume = "1",
        urlaccessdate = "2020, May 27"
}


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