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@InProceedings{AlvesFerrLima:2018:MéHíFu,
               author = "Alves, Emilly Pereira and Ferreira, Felipe Alberto Barbosa 
                         Sim{\~a}o and Lima, M{\'a}rcio Jos{\'e} de Carvalho",
          affiliation = "{Universidade de Pernambuco} and {Universidade Federal de 
                         Pernambuco} and {Universidade de Pernambuco}",
                title = "Um m{\'e}todo h{\'{\i}}brido fuzzy-swarm-clustering para 
                         segmenta{\c{c}}{\~a}o de MRI",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "segmenta{\c{c}}{\~a}o de imagens, l{\'o}gica fuzzy, 
                         intelig{\^e}ncia de enxames, MRI.",
             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).",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "pt",
                  ibi = "8JMKD3MGPAW/3S36EU2",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3S36EU2",
           targetfile = "wuw_paper_20_camera_ready.pdf",
        urlaccessdate = "2020, Dec. 04"
}


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