Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2007: administrator
Metadata Last Update2020: administrator
Citation KeySeixasSaadSouzSant:2007:AuSeCo
TitleAutomated Segmentation of the Corpus Callosum Midsagittal Surface Area
FormatPrinted, On-line.
DateOct. 7-10, 2007
Access Date2021, Jan. 16
Number of Files1
Size333 KiB
Context area
Author1 Seixas, Flavio Luiz
2 Saade, Débora C. Muchaluat
3 Souza, Andrea Silveira de
4 Santos, Alair Augusto Sarmet M. D. dos
Affiliation1 UFF - Engenharia de Telecomunicações
2 UFF - Engenharia de Telecomunicações
3 LABS - Rede D'Or
4 UFF - Faculdade de Medicina
EditorFalcão, Alexandre Xavier
Lopes, Hélio Côrtes Vieira
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)
Conference LocationBelo Horizonte
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2007-07-31 22:30:55 :: -> administrator ::
2007-08-02 21:17:47 :: administrator -> ::
2008-07-17 14:09:43 :: -> administrator ::
2009-08-13 20:38:29 :: administrator -> banon ::
2010-08-28 20:02:29 :: banon -> administrator ::
2020-02-19 03:06:19 :: administrator -> :: 2007
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsMagnetic Resonance, Segmentation, Image Processing, Brain, Corpus Callosum, Neuroanatomical Structures.
AbstractThe 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.
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Target FileSeixas-AutomatedSegmentationCorpusCallosum.pdf
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