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@InProceedings{SeixasSaadSouzSant:2007:AuSeCo,
               author = "Seixas, Flavio Luiz and Saade, D{\'e}bora C. Muchaluat and Souza, 
                         Andrea Silveira de and Santos, Alair Augusto Sarmet M. D. dos",
          affiliation = "{UFF - Engenharia de Telecomunica{\c{c}}{\~o}es} and {UFF - 
                         Engenharia de Telecomunica{\c{c}}{\~o}es} and {LABS - Rede D'Or} 
                         and {UFF - Faculdade de Medicina}",
                title = "Automated Segmentation of the Corpus Callosum Midsagittal Surface 
                         Area",
            booktitle = "Proceedings...",
                 year = "2007",
               editor = "Falc{\~a}o, Alexandre Xavier and Lopes, H{\'e}lio C{\^o}rtes 
                         Vieira",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 20. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Magnetic Resonance, Segmentation, Image Processing, Brain, Corpus 
                         Callosum, Neuroanatomical Structures.",
             abstract = "The non-invasive in vivo nature of magnetic resonance imaging 
                         (MRI) makes it the modality of choice of many neuroanatomical 
                         imaging studies. This paper discusses automatic brain structure 
                         segmentation based on previous knowledge on statistical models. 
                         The method is validated by an experiment involving magnetic 
                         resonance images acquired from 20 healthy adult individuals (10 
                         men and 10 women). The results provide normative data of the 
                         midsagittal surface area of the corpus callosum from a 46-55 years 
                         old range group, splitting results by gender. Our results were 
                         also compared with data obtained from other authors, validating 
                         the correlation between brain volume and the area of this 
                         structure. The final goal of this work is computer-aided diagnosis 
                         for brain diseases.",
  conference-location = "Belo Horizonte",
      conference-year = "Oct. 7-10, 2007",
             language = "en",
           targetfile = "Seixas-AutomatedSegmentationCorpusCallosum.pdf",
        urlaccessdate = "2020, Oct. 22"
}


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