A new method for skull stripping in brain MRI using multistable cellular neural networks

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dc.contributor.authorYilmaz, Burak
dc.contributor.authorDurdu, Akif
dc.contributor.authorEmlik, Ganime Dilek
dc.date.accessioned2020-03-26T19:52:43Z
dc.date.available2020-03-26T19:52:43Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis study proposes a new method on "detecting brain region in MRI data". This task is generally named as "skull stripping" in the literature. The algorithm is developed by using the cellular neural networks (CNNs) and multistable CNN structures. It also includes a contrast enhancement and noise reduction algorithm. The algorithm is named as multistable cellular neural network on MRI for skull stripping (mCNN-MRI-SS). Three different case studies are performed for measuring the success of the algorithm. Also a fourth case study is performed to evaluate the supporting algorithm, the CEULICA. First two evaluations are performed by using well-known MIDAS-NAMIC and Brainweb databases, which are properly organized Talairach-compatible databases. The third database was obtained from the research and application hospital of Necmettin Erbakan University Meram Faculty of Medicine. These MRI data were not Talairach-compatible and less sampled. The algorithm achieved 0.595 Jaccard, 0.744 Dice, 0.0344 TPF and 0.383 TNF mean values with the Brainweb T1-weighted images and 0.837 Jaccard, 0.898 Dice, 0.0124 TPF and 0.1511 TNF mean values with the MIDAS-NAMIC T2-weighted images. The algorithm achieved 0.8297 Jaccard, 0.9012 Dice, 0.0951 TPF and 0.1225 TNF mean values and achieved with the obtained data the best values among the other algorithms. As a result, it can be claimed that algorithm performs best with the non-Talairach-compatible MRI data due to its nature of performing at cellular level.en_US
dc.identifier.doi10.1007/s00521-016-2834-2en_US
dc.identifier.endpage95en_US
dc.identifier.issn0941-0643en_US
dc.identifier.issn1433-3058en_US
dc.identifier.issue8en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage79en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-016-2834-2
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36262
dc.identifier.volume29en_US
dc.identifier.wosWOS:000427799900007en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER LONDON LTDen_US
dc.relation.ispartofNEURAL COMPUTING & APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectSkull strippingen_US
dc.subjectBrain MRIen_US
dc.subjectCNNen_US
dc.subjectMultistable CNNen_US
dc.subjectContrast enhancementen_US
dc.subjectArtificial bee colony algorithmen_US
dc.subjectMRIen_US
dc.subjectBiomedical image processingen_US
dc.titleA new method for skull stripping in brain MRI using multistable cellular neural networksen_US
dc.typeArticleen_US

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