Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/122067 |
Resumo: | Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images. |
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Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modalityCiências Tecnológicas, Ciências médicas e da saúdeTechnological sciences, Medical and Health sciencesBrain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images.2019-072019-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfimage/jpeghttps://hdl.handle.net/10216/122067eng0208-521610.1016/j.bbe.2019.07.005Nilanjan DeyV. RajinikanthFuqian ShiJoão Manuel R. S. TavaresLuminita MoraruK. Arvind KarthikHong LinK. KamalanandC. Emmanuelinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T14:23:38Zoai:repositorio-aberto.up.pt:10216/122067Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:00:21.354275Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality |
title |
Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality |
spellingShingle |
Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality Nilanjan Dey Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
title_short |
Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality |
title_full |
Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality |
title_fullStr |
Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality |
title_full_unstemmed |
Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality |
title_sort |
Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality |
author |
Nilanjan Dey |
author_facet |
Nilanjan Dey V. Rajinikanth Fuqian Shi João Manuel R. S. Tavares Luminita Moraru K. Arvind Karthik Hong Lin K. Kamalanand C. Emmanuel |
author_role |
author |
author2 |
V. Rajinikanth Fuqian Shi João Manuel R. S. Tavares Luminita Moraru K. Arvind Karthik Hong Lin K. Kamalanand C. Emmanuel |
author2_role |
author author author author author author author author |
dc.contributor.author.fl_str_mv |
Nilanjan Dey V. Rajinikanth Fuqian Shi João Manuel R. S. Tavares Luminita Moraru K. Arvind Karthik Hong Lin K. Kamalanand C. Emmanuel |
dc.subject.por.fl_str_mv |
Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
topic |
Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
description |
Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-07 2019-07-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/122067 |
url |
https://hdl.handle.net/10216/122067 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0208-5216 10.1016/j.bbe.2019.07.005 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf image/jpeg |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799135927590715392 |