A new computer-based approach for fully automated segmentation of knee meniscus 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.accessioned2022-05-11T14:15:49Z
dc.date.available2022-05-11T14:15:49Z
dc.date.issued2017
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractMenisci are tissues that enable mobility and absorb excess loads on the knee. Problems in meniscus can trigger the disorder of osteoarthritis (OA). OA is one of the most common causes of disability, especially among young athlethes and elderly people. Therefore, the early diagnosis and treatment of abnormalities that occur in the meniscus are of significant importance. This study proposes a new computer-based and fully automated approach to support radiologists by: (i) the segmentation of medial menisci, (ii) enabling early diagnosis and treatment, and (iii) reducing the errors caused by MR intra-reader variability. In this study, 88 different MR images provided by the Osteoarthritis Initiative (OAI) are used. The histogram of oriented gradients (HOG) and local binary patterns (LBP) methods are used for feature extraction from these MR images along with the extreme learning machine (ELM) and random forests (RF) methods which are used for model learning (regression). As the first step of the pipeline, the most compact rectangular patches bounding the menisci are located. After this, meniscus boundaries are revealed by the morphological processes. Then, the similarities between these boundaries and the ground truth images are measured and compared with each other. The highest score is acquired with Dice similarity measurement with a success rate of 82%. A successful segmentation is performed on the diseased knee MR images. The proposed approach can be implemented as a decision support system for radiologists, while the segmented menisci can be used in classification of meniscal tear in future studies. (C) 2017 Published by Elsevier B.V. on behalf of Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences.
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; Foundation for the National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA; Turkish Scientific and Technical Research Council-TSBITAK [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 study was supported by Turkish Scientific and Technical Research Council-TSBITAK (Project Number: 116E151). We are very grateful to all who assisted us in our study.
dc.identifier.doi10.1016/j.bbe.2017.04.008
dc.identifier.endpage442
dc.identifier.issn0208-5216
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85020230448
dc.identifier.scopusqualityQ1
dc.identifier.startpage432
dc.identifier.urihttps://doi.org/10.1016/j.bbe.2017.04.008
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6087
dc.identifier.volume37
dc.identifier.wosWOS:000410935300010
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorSaygılı, Ahmet
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofBiocybernetics and Biomedical Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectSegmentation
dc.subjectKnee-joint
dc.subjectMeniscus
dc.subjectRegression
dc.subjectMorphological-operations
dc.subjectMedical-images
dc.subjectCartilage
dc.subjectClassification
dc.subjectTears
dc.subjectRegion
dc.titleA new computer-based approach for fully automated segmentation of knee meniscus from magnetic resonance images
dc.typeArticle

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