Close
Metadata

Identity statement area
Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3S36EU2
Repositorysid.inpe.br/sibgrapi/2018/10.16.00.18
Last Update2018:10.16.00.18.51 epa@poli.br
Metadatasid.inpe.br/sibgrapi/2018/10.16.00.18.51
Metadata Last Update2020:02.20.22.06.49 administrator
Citation KeyAlvesFerrLima:2018:MéHíFu
TitleUm método híbrido fuzzy-swarm-clustering para segmentação de MRI
FormatOn-line
Year2018
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size294 KiB
Context area
Author1 Alves, Emilly Pereira
2 Ferreira, Felipe Alberto Barbosa Simão
3 Lima, Márcio José de Carvalho
Affiliation1 Universidade de Pernambuco
2 Universidade Federal de Pernambuco
3 Universidade de Pernambuco
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addressepa@poli.br
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
History2018-10-16 00:18:51 :: epa@poli.br -> administrator ::
2020-02-20 22:06:49 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Transferable1
Tertiary TypeUndergraduate Work
Keywordssegmentação de imagens, lógica fuzzy, inteligência de enxames, MRI.
AbstractThe 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).
source Directory Contentthere are no files
agreement Directory Content
agreement.html 15/10/2018 21:18 1.2 KiB 
Conditions of access and use area
Languagept
Target Filewuw_paper_20_camera_ready.pdf
User Groupepa@poli.br
Visibilityshown
Allied materials area
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Notes area
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume

Close