Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/2900
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dc.contributor.authorSelvi, Esref-
dc.contributor.authorSelver, M. Alper-
dc.contributor.authorKavur, Ali Emre-
dc.contributor.authorGuzelis, Cuneyt-
dc.contributor.authorDicle, Oguz-
dc.date.accessioned2023-06-16T14:50:39Z-
dc.date.available2023-06-16T14:50:39Z-
dc.date.issued2015-
dc.identifier.issn1300-1884-
dc.identifier.issn1304-4915-
dc.identifier.urihttps://hdl.handle.net/20.500.14365/2900-
dc.description.abstractMedical imaging modalities can provide very detailed and informative mappings of the anatomy of a subject. These detailed and informative mappings can be processed to extract the information of interest instead of dealing with whole data (segmentation). Since manual segmentation on each slice is time consuming, tedious and operator dependent, automatic tools and techniques are needed. Segmentation of abdominal organs is a very challenging field of application due to overlapping intensity ranges of the organs, variations in human anatomy and pathology and the number of studies is very limited for Magnetic Resonance (MR), which is a relatively newer and rapidly developing imaging modality. Since it is obligatory to analyze and visualize MR images of abdominal organs (i.e. liver, right/left kidneys, spleen, pancreas, gall bladder) for several medical procedures, the main goal of this paper is to design and develop a segmentation system (method+software), which is robust to the challenges mentioned above, adaptive to the properties of the abdominal organs as well as to the interrelationships of these organs.en_US
dc.language.isotren_US
dc.publisherGazi Univ, Fac Engineering Architectureen_US
dc.relation.ispartofJournal of the Faculty of Engıneerıng And Archıtecture of Gazı Unıversıtyen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSegmentationen_US
dc.subjectMRen_US
dc.subjecthierarchical classificationen_US
dc.subjectabdomenen_US
dc.subjectCombining Multiple Classifiersen_US
dc.subjectAutomatic Segmentationen_US
dc.subjectNeural-Networken_US
dc.subjectCten_US
dc.subjectAtlasen_US
dc.subjectRecognitionen_US
dc.titleSegmentation of abdominal organs from MR images using multi-level hierarchical classificationen_US
dc.title.alternativeBatin bölgesi organlarinin mr görüntülerinden çok aşamali hiyerarşik siniflama ile bölütlenmesien_US
dc.typeArticleen_US
dc.identifier.scopus2-s2.0-84942924883en_US
dc.departmentİzmir Ekonomi Üniversitesien_US
dc.authoridKavur, Ali Emre/0000-0002-9328-8140-
dc.authoridSelver, M. Alper/0000-0002-8445-0388-
dc.authorwosidKavur, Ali Emre/B-9569-2016-
dc.authorwosidSelver, M. Alper/V-5118-2019-
dc.authorwosidSelver, Alper/AAN-1987-2021-
dc.authorwosidDicle, oğuz/AFM-0858-2022-
dc.identifier.volume30en_US
dc.identifier.issue3en_US
dc.identifier.startpage533en_US
dc.identifier.endpage546en_US
dc.identifier.wosWOS:000367386500020en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ3-
dc.identifier.wosqualityQ4-
item.grantfulltextreserved-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1tr-
item.cerifentitytypePublications-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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