Automatic gender determination from 3D digital maxillary tooth plaster models based on the random forest algorithm and discrete cosine transform

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dc.contributor.authorAkkoç, Betül
dc.contributor.authorArslan, Ahmet
dc.contributor.authorKök, Hatice
dc.date.accessioned2020-03-26T19:34:02Z
dc.date.available2020-03-26T19:34:02Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractBackground and Objective: One of the first stages in the identification of an individual is gender determination. Through gender determination, the search spectrum can be reduced. In disasters such as accidents or fires, which can render identification somewhat difficult, durable teeth are an important source for identification. This study proposes a smart system that can automatically determine gender using 3D digital maxillary tooth plaster models. Methods: The study group was composed of 40 Turkish individuals (20 female, 20 male) between the ages of 21 and 24. Using the iterative closest point (ICP) algorithm, tooth models were aligned, and after the segmentation process, models were transformed into depth images. The local discrete cosine transform (DCT) was used in the process of feature extraction, and the random forest (RF) algorithm was used for the process of classification. Results: Classification was performed using 30 different seeds for random generator values and 10 fold cross-validation. A value of 85.166% was obtained for average classification accuracy (CA) and a value of 91.75% for the area under the ROC curve (AUC). Conclusions: A multi-disciplinary study is performed here that includes computer sciences, medicine and dentistry. A smart system is proposed for the determination of gender from 3D digital models of maxillary tooth plaster models. This study has the capacity to extend the field of gender determination from teeth. (C) 2017 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.cmpb.2017.03.001en_US
dc.identifier.endpage65en_US
dc.identifier.issn0169-2607en_US
dc.identifier.issn1872-7565en_US
dc.identifier.pmid28391819en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage59en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.cmpb.2017.03.001
dc.identifier.urihttps://hdl.handle.net/20.500.12395/34829
dc.identifier.volume143en_US
dc.identifier.wosWOS:000400531900007en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherELSEVIER IRELAND LTDen_US
dc.relation.ispartofCOMPUTER METHODS AND PROGRAMS IN BIOMEDICINEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectGender determinationen_US
dc.subjectFeature extractionen_US
dc.subjectImage processingen_US
dc.subjectRandom forest algorithmen_US
dc.titleAutomatic gender determination from 3D digital maxillary tooth plaster models based on the random forest algorithm and discrete cosine transformen_US
dc.typeArticleen_US

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