Meniscus Tear Classification Using Histogram of Oriented Gradients in Knee MR Images

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dc.authorid0000-0001-8625-4842
dc.authorid0000-0002-1786-6869
dc.authorwosidSAYGILI, AHMET/AAG-4161-2019
dc.authorwosidVarlı, Songül/AAZ-4672-2020
dc.contributor.authorSaygılı, Ahmet
dc.contributor.authorAlbayrak, Songül
dc.date.accessioned2022-05-11T14:15:53Z
dc.date.available2022-05-11T14:15:53Z
dc.date.issued2018
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEY
dc.description.abstractAutomatic segmentation and classification studies in medical images have been intensely studied in recent years. The results obtained will support the decisions of medical experts. In this study, features were obtained by applying histogram of oriented gradients (HOG) method to segmented knee MR images with fuzzy clustering approaches and these features were trained with different classifiers to perform automatic meniscus tear detection. For this automatic detection, 28 different MR images provided by the Osteoarthritis Initiative were used. In particular, the effects of HOG have been studied in detail. Support vector machines, extreme learning machines, and k-nearest neighbor classifiers have been used in the classification stage. The support vector machines became the most successful classifier with a success rate of 88.78%. It is aimed to increase the success of the system with different feature extraction and segmentation methods in the following studies.
dc.description.sponsorshipIEEE, Huawei, Aselsan, NETAS, IEEE Turkey Sect, IEEE Signal Proc Soc, IEEE Commun Soc, ViSRATEK, Adresgezgini, Rohde & Schwarz, Integrated Syst & Syst Design, Atilim Univ, Havelsan, Izmir Katip Celebi Univ
dc.identifier.isbn978-1-5386-1501-0
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85050791523
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6110
dc.identifier.wosWOS:000511448500228
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorSaygılı, Ahmet
dc.language.isotr
dc.publisherIEEE
dc.relation.ispartof2018 26th Signal Processing and Communications Applications Conference (Siu)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMeniscus tears
dc.subjectSegmentation
dc.subjectClassification
dc.subjectMedical Image Processing
dc.subjectHistogram of Oriented Gradients
dc.subjectSegmentation
dc.titleMeniscus Tear Classification Using Histogram of Oriented Gradients in Knee MR Images
dc.typeConference Object

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