Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10655
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dc.contributor.authorFıçıcı, Cansel-
dc.contributor.authorEroğul, Osman-
dc.contributor.authorTelatar, Ziya-
dc.contributor.authorKoçak, Onur-
dc.date.accessioned2023-10-24T06:59:01Z-
dc.date.available2023-10-24T06:59:01Z-
dc.date.issued2023-
dc.identifier.citationFicici, C., Erogul, O., Telatar, Z., & Kocak, O. (2023). Automatic Brain Tumor Detection and Volume Estimation in Multimodal MRI Scans via a Symmetry Analysis. Symmetry, 15(8), 1586.-
dc.identifier.issn2073-8994-
dc.identifier.urihttps://doi.org/10.3390/sym15081586-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/10655-
dc.description.abstractIn this study, an automated medical decision support system is presented to assist physicians with accurate and immediate brain tumor detection, segmentation, and volume estimation from MRI which is very important in the success of surgical operations and treatment of brain tumor patients. In the proposed approach, first, tumor regions on MR images are labeled by an expert radiologist. Then, an automated medical decision support system is developed to extract brain tumor boundaries and to calculate their volumes by using multimodal MR images. One advantage of this study is that it provides an automated brain tumor detection and volume estimation algorithm that does not require user interactions by determining threshold values adaptively. Another advantage is that, because of the unsupervised approach, the proposed study realized tumor detection, segmentation, and volume estimation without using very large labeled training data. A brain tumor detection and segmentation algorithm is introduced that is based on the fact that the brain consists of two symmetrical hemispheres. Two main analyses, i.e., histogram and symmetry, were performed to automatically estimate tumor volume. The threshold values used for skull stripping were computed adaptively by examining the histogram distances between T1- and T1C-weighted brain MR images. Then, a symmetry analysis between the left and right brain lobes on FLAIR images was performed for whole tumor detection. The experiments were conducted on two brain MRI datasets, i.e., TCIA and BRATS. The experimental results were compared with the labeled expert results, which is known as the gold standard, to demonstrate the efficacy of the presented method. The performance evaluation results achieved accuracy values of 89.7% and 99.0%, and a Dice similarity coefficient value of 93.0% for whole tumor detection, active core detection, and volume estimation, respectively.en_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofSymmetry-Baselen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjecthistogram analysisen_US
dc.subjectadaptive thresholdingen_US
dc.subjectskull strippingen_US
dc.subjectsymmetry analysisen_US
dc.subjectfuzzy c-means clusteringen_US
dc.subjectSegmentationen_US
dc.subjectGliomaen_US
dc.subjectInformationen_US
dc.subjectAlgorithmen_US
dc.subjectFrameworken_US
dc.subjectImagesen_US
dc.titleAutomatic Brain Tumor Detection and Volume Estimation in Multimodal Mri Scans Via a Symmetry Analysisen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Biomedical Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi,Biyomedikal Mühendisliği Bölümü-
dc.identifier.volume15en_US
dc.identifier.issue8en_US
dc.authoridEROGUL, Osman/0000-0002-4640-6570-
dc.authoridKocak, Onur/0000-0002-8240-4046-
dc.identifier.wosWOS:001055757300001en_US
dc.identifier.scopus2-s2.0-85168882834en_US
dc.institutionauthor-
dc.identifier.doi10.3390/sym15081586-
dc.authorscopusid57215433394-
dc.authorscopusid56247443100-
dc.authorscopusid6603237932-
dc.authorscopusid55953641400-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ2-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.2. Department of Biomedical Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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