Gabor wavelet and unsupervised fuzzy C-means clustering for edge detection of medical images

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dc.contributor.authorErgen, Burhan
dc.contributor.authorÇinar, Ahmet
dc.contributor.authorAydin, Galip
dc.date.accessioned2026-08-12T16:10:01Z
dc.date.issued2012
dc.departmentFırat Üniversitesi
dc.descriptionInternational Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2012 -- 2 July 2012 through 4 July 2012 -- Trabzon -- 92831
dc.description.abstractIt is well known that the Gabor wavelet transform (GWT) provides directional information for the analysis of an image. In this paper, we proposed an approach based on the GWT by combining unsupervised Fuzzy c-means (FCM) clustering which provides plays an important role in recognition as a classifier. After enhancing the edge of the input image using GWT, the binary image showing the edge is obtained using FCM clustering and morphological skeletonization. When compared to the Canny method and other conventional method, the proposed method has showed a better performance in terms of detection accuracy for noisy medical images. © 2012 IEEE.
dc.identifier.doi10.1109/INISTA.2012.6246972
dc.identifier.isbn978-146731446-6
dc.identifier.scopus2-s2.0-84866612210
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/INISTA.2012.6246972
dc.identifier.urihttps://hdl.handle.net/11508/41716
dc.indekslendigikaynakScopus
dc.language.isoen
dc.relation.ispartofINISTA 2012 - International Symposium on INnovations in Intelligent SysTems and Applications
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260511
dc.subjectEdge detection; Gabor Wavelet Transform and Fuzzy c-mean clustering
dc.titleGabor wavelet and unsupervised fuzzy C-means clustering for edge detection of medical images
dc.typeConference Object

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