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		<identifier>8JMKD3MGPBW34M/3D85S8P</identifier>
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		<isbn>978-85-7669-271-3</isbn>
		<citationkey>PessoaSilv:1993:MeDiAu</citationkey>
		<title>Uma metodologia para diagnóstico automático da filariose utilizando imagens microscópicas digitalizadas</title>
		<format>Impresso, On-line.</format>
		<year>1993</year>
		<numberoffiles>1</numberoffiles>
		<size>7227 KiB</size>
		<author>Pessoa, Lúcio Flávio Cavalcanti,</author>
		<author>Silva, Ascendino Flávio Dias e,</author>
		<affiliation>Departamento de Eletrônica e Sistemas do Centro de Tecnologia da Universidade Federal de Pernambuco (UFPE)</affiliation>
		<affiliation>Departamento de Eletrônica e Sistemas do Centro de Tecnologia da Universidade Federal de Pernambuco (UFPE)</affiliation>
		<editor>Figueiredo, Luiz Henrique de,</editor>
		<editor>Gomes, Jonas de Miranda,</editor>
		<e-mailaddress>cintiagraziele.silva@gmail.com</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Computação Gráfica e Processamento de Imagens, 6 (SIBGRAPI)</conferencename>
		<conferencelocation>Recife</conferencelocation>
		<date>19 - 22 out. 1993</date>
		<volume>1</volume>
		<pages>333-342</pages>
		<booktitle>Anais</booktitle>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<tertiarytype>Artigo</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>morfologia matemática, reconhecimento de padrões, análise de imagens, imagens médicas, diagnóstico automático da filariose.</keywords>
		<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.</abstract>
		<type>Imagens Médicas</type>
		<language>pt</language>
		<targetfile>39 Uma metodologia para diagnostico automatico.pdf</targetfile>
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		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2012/12.17.15.44</url>
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