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Reference TypeConference Paper (Conference Proceedings)
Last Update2018:
Metadata Last Update2020: administrator
Citation KeyAlvesFerrLima:2018:MéHíFu
TitleUm método híbrido fuzzy-swarm-clustering para segmentação de MRI
DateOct. 29 - Nov. 1, 2018
Access Date2021, Jan. 19
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
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeUndergraduate Work
History2018-10-16 00:18:51 :: -> administrator ::
2020-02-20 22:06:49 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
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).
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Next Higher Units8JMKD3MGPAW/3RPADUS
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