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		<citationkey>SeixasSaadSouzSant:2007:AuSeCo</citationkey>
		<title>Automated Segmentation of the Corpus Callosum Midsagittal Surface Area</title>
		<format>Printed, On-line.</format>
		<year>2007</year>
		<date>Oct. 7-10, 2007</date>
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		<author>Seixas, Flavio Luiz,</author>
		<author>Saade, Débora C. Muchaluat,</author>
		<author>Souza, Andrea Silveira de,</author>
		<author>Santos, Alair Augusto Sarmet M. D. dos,</author>
		<affiliation>UFF - Engenharia de Telecomunicações</affiliation>
		<affiliation>UFF -  Engenharia de Telecomunicações</affiliation>
		<affiliation>LABS - Rede D'Or</affiliation>
		<affiliation>UFF - Faculdade de Medicina</affiliation>
		<editor>Falcão, Alexandre Xavier,</editor>
		<editor>Lopes, Hélio Côrtes Vieira,</editor>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)</conferencename>
		<conferencelocation>Belo Horizonte</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<keywords>Magnetic Resonance, Segmentation, Image Processing, Brain, Corpus Callosum, Neuroanatomical Structures.</keywords>
		<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.</abstract>
		<language>en</language>
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