A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation

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dc.contributor.authorAshour, Amira S.
dc.contributor.authorGuo, Yanhui
dc.contributor.authorKüçükkülahlı, Enver
dc.contributor.authorErdoğmuş, Pakize
dc.contributor.authorPolat, Kemal
dc.date.accessioned2020-04-30T22:38:40Z
dc.date.available2020-04-30T22:38:40Z
dc.date.issued2018
dc.departmentDÜ, Düzce Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümüen_US
dc.descriptionPolat, Kemal/0000-0003-1840-9958; Ashour, Amira/0000-0003-3217-6185; Guo, Yanhui/0000-0003-1814-9682en_US
dc.descriptionWOS: 000438775200024en_US
dc.description.abstractIn this work, a novel skin lesion detection approach, called HBCENCM, is proposed using histogram-based clustering estimation (HBCE) algorithm to determine the required number of clusters in the neutrosophic c-means clustering (NCM) method. Initially, the dermoscopic images are mapped into the neutrosophic domain over three memberships, namely true, indeterminate, and false subsets. Then, an NCM algorithm is employed to group the pixels in the dermoscopy images, where the number of clusters in the dermoscopy images is determined using the HBCE algorithm. Lastly, the skin lesion is detected based on its intensity and morphological features. The public dataset (ISIC 2016) of 900 images for training and 379 images for testing are used in the present work. A comparative study of the original NCM clustering method is conducted on the same dataset. The results showed the superiority of the proposed approach to detect the lesion with 96.3% average accuracy compared to the average accuracy of 94.6% using the original NCM without HBCE algorithm. (C) 2018 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2018.05.003en_US
dc.identifier.endpage434en_US
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage426en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2018.05.003
dc.identifier.urihttps://hdl.handle.net/20.500.12684/2363
dc.identifier.volume69en_US
dc.identifier.wosWOS:000438775200024en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDermoscopy imagesen_US
dc.subjectSkin lesionen_US
dc.subjectNeutrosophic clusteringen_US
dc.subjectImage histogramen_US
dc.subjectImage segmentationen_US
dc.subjectNeutrosophic c-means clusteringen_US
dc.subjectHistogram based cluster estimation (HBCE)en_US
dc.titleA hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimationen_US
dc.typeArticleen_US

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