An efficient and fast computer-aided method for fully automated diagnosis of meniscal tears from magnetic resonance images

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dc.authorid0000-0001-8625-4842
dc.authorid0000-0002-1786-6869
dc.authorscopusid55807379700
dc.authorscopusid16309030500
dc.authorwosidSAYGILI, AHMET/AAG-4161-2019
dc.authorwosidVarlı, Songül/AAZ-4672-2020
dc.contributor.authorSaygılı, Ahmet
dc.contributor.authorAlbayrak, Songül
dc.date.accessioned2022-05-11T14:15:55Z
dc.date.available2022-05-11T14:15:55Z
dc.date.issued2019
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractMenisci are structures that directly affect movement, so early detection of meniscus tears also helps to prevent progressive knee disorders such as osteoarthritis. Manual segmentation of the menisci and diagnosis of the meniscal tear is a costly process in terms of time and effort for a radiologist. The aim of this study is to automatically determine the location and the type of meniscal tears that are important in the diagnosis and effective treatment of this problem. For this purpose, 29 different MR images, which were provided by Osteoarthritis Initiative (OAI), were used in the study. This study proposes a novel three-stage (preprocessing, segmentation and classification) method for fully automated classification from MR images, and shows the performance of each stage separately. At the preprocessing step, the most compact rectangular windows for the menisci were obtained from MR slices. At the segmentation step, the menisci were segmented using fuzzy clustering methods. In order to classify the segmented images and to determine meniscus tears, three different classifiers were used. The method first decides whether there are tears on menisci; if this is the case then, determines the place and type of the tears. There are no studies that classify the meniscus tears according to their types up to now in the literature. The experimental results indicate that the automated process can be completed within a time range of 3 to 4 min with a high classification performance. Hence, the suggested computer-aided diagnosis (CAD) system can be used as a decision support system for the diagnosis of meniscal tears by radiologists.
dc.description.sponsorshipNational Institutes of Health, a branch of the Department of Health and Human ServicesUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [N01-AR-2-2258, N01-AR-2-2259, N01-AR-2-2260, N01-AR-2- 2261, N01-AR-2-2262]; Merck Research LaboratoriesMerck & Company; Novartis Pharmaceuticals CorporationNovartis; GlaxoSmithKlineGlaxoSmithKline; Pfizer, Inc.Pfizer; Turkish Scientific and Technical Research Council-TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [116E151]
dc.description.sponsorshipThe OAI is a public-private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2- 2261; N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. This manuscript was prepared using an OAI public use data set and does not necessarily reflect the opinions or views of the OAI investigators, the NIH, or the private funding partners.; This work was supported by the Turkish Scientific and Technical Research Council-TUBITAK (Project Number: 116E151).
dc.identifier.doi10.1016/j.artmed.2018.11.008
dc.identifier.endpage130
dc.identifier.issn0933-3657
dc.identifier.issn1873-2860
dc.identifier.pmid30527276
dc.identifier.scopus2-s2.0-85057824307
dc.identifier.scopusqualityQ1
dc.identifier.startpage118
dc.identifier.urihttps://doi.org/10.1016/j.artmed.2018.11.008
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6118
dc.identifier.volume97
dc.identifier.wosWOS:000474326600013
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorSaygılı, Ahmet
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofArtificial Intelligence in Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectComputer-aided diagnosis (CAD)
dc.subjectKnee joint
dc.subjectMeniscus tear
dc.subjectMagnetic resonance imaging (MRI)
dc.subjectClassification
dc.subjectMedical image processing
dc.subjectExtreme Learning-Machine
dc.subjectKnee Meniscus
dc.subjectSegmentation
dc.subjectSystem
dc.subjectT2
dc.titleAn efficient and fast computer-aided method for fully automated diagnosis of meniscal tears from magnetic resonance images
dc.typeArticle

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