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		<citationkey>AlvesFerrLima:2018:MéHíFu</citationkey>
		<title>Um método híbrido fuzzy-swarm-clustering para segmentação de MRI</title>
		<format>On-line</format>
		<year>2018</year>
		<date>Oct. 29 - Nov. 1, 2018</date>
		<numberoffiles>1</numberoffiles>
		<size>294 KiB</size>
		<author>Alves, Emilly Pereira,</author>
		<author>Ferreira, Felipe Alberto Barbosa Simão,</author>
		<author>Lima, Márcio José de Carvalho,</author>
		<affiliation>Universidade de Pernambuco</affiliation>
		<affiliation>Universidade Federal de Pernambuco</affiliation>
		<affiliation>Universidade de Pernambuco</affiliation>
		<editor>Ross, Arun,</editor>
		<editor>Gastal, Eduardo S. L.,</editor>
		<editor>Jorge, Joaquim A.,</editor>
		<editor>Queiroz, Ricardo L. de,</editor>
		<editor>Minetto, Rodrigo,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Oliveira, Manuel M.,</editor>
		<editor>Arbeláez, Pablo,</editor>
		<editor>Mery, Domingo,</editor>
		<editor>Oliveira, Maria Cristina Ferreira de,</editor>
		<editor>Spina, Thiago Vallin,</editor>
		<editor>Mendes, Caroline Mazetto,</editor>
		<editor>Costa, Henrique Sérgio Gutierrez,</editor>
		<editor>Mejail, Marta Estela,</editor>
		<editor>Geus, Klaus de,</editor>
		<editor>Scheer, Sergio,</editor>
		<e-mailaddress>epa@poli.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)</conferencename>
		<conferencelocation>Foz do Iguaçu, PR, Brazil</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<documentstage>not transferred</documentstage>
		<transferableflag>1</transferableflag>
		<tertiarytype>Undergraduate Work</tertiarytype>
		<keywords>segmentação de imagens, lógica fuzzy, inteligência de enxames, MRI.</keywords>
		<abstract>The segmentation process in Magnetic Resonance Imaging (MRI) stands out when it acts in the detection of different regions of the brain. Among the used techniques, clustering segmentation methods have been commonly used in the literature. In order to optimize the already existing techniques, this paper proposes a hybrid technique with Fuzzy C-Means and Particle Swarm Optimization algorithms. With the purpose of evaluating the algorithms performance, synthetic images and brain simulated MRI were used. The performance was measured in terms of Peak Signal-to-noise Ratio (PSNR), Segmentation Accuracy (SA) and Mean Squared Error (MSE).</abstract>
		<language>pt</language>
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