Machine learning classification on texture analyzed T2 maps of osteoarthritic cartilage: oulu knee osteoarthritis study.
Objective: To introduce local binary pattern (LBP) texture analysis to cartilage osteoarthritis (OA) research and compare the performance of different classification systems in discrimination of OA subjects from healthy controls using gray-level co-occurrence matrix (GLCM) and LBP texture data. Clas...
| Publicado en: | Osteoarthritis & Cartilage Vol. 29; no. 6; pp. 859 - 870 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150446893&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150446893 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10634584 NZV jtl: Osteoarthritis & Cartilage issn: 10634584 maglogo: N pubinfo: dt: Jun2021 vid: 29 iid: 6 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 150446893 150446893 NLM33631317 150446893 10.1016/j.joca.2021.02.561 NLM33631317 150446893 ppf: 859 ppct: 11 formats: tig: atl: Machine learning classification on texture analyzed T2 maps of osteoarthritic cartilage: oulu knee osteoarthritis study. aug: au: Peuna, A. Thevenot, J. Saarakkala, S. Nieminen, M.T. Lammentausta, E. Peuna, Arttu Thevenot, Jérome Saarakkala, Simo Nieminen, Miika T Lammentausta, Eveliina affil: Department of Medical Imaging, Central Finland Central Hospital, Jyväskylä, Finland sug: subj: Osteoarthritis, Knee Pathology Osteoarthritis, Knee Classification Human Aged Cross Sectional Studies Male Female Middle Age Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales Aged: 65+ years Middle Aged: 45-64 years Male Female ab: Objective: To introduce local binary pattern (LBP) texture analysis to cartilage osteoarthritis (OA) research and compare the performance of different classification systems in discrimination of OA subjects from healthy controls using gray-level co-occurrence matrix (GLCM) and LBP texture data. Classification algorithms were used to reduce the dimensionality of texture data into a likelihood of subject belonging to the reference class.Method: T2 relaxation time mapping with multi-slice multi-echo spin echo sequence was performed for eighty symptomatic OA patients and 63 asymptomatic controls on a 3T clinical MRI scanner. Relaxation time maps were subjected to GLCM and LBP texture analysis, and classification algorithms were deployed with an in-house developed software. Implemented algorithms were K nearest neighbors, support vector machine, and neural network classifier.Results: LBP and GLCM discerned OA patients from controls with a significant difference in all studied regions. Classification models comprising GLCM and LBP showed high accuracy in classing OA patients and controls. The best performance was obtained with a multilayer perceptron type classifier with an overall accuracy of 90.2 %.Conclusion: LBP texture analysis complements prior results with GLCM, and together LBP and GLCM serve as significant input data for classification algorithms trained for OA assessment. Presented algorithms are adaptable to versatile OA evaluations also for future gradational or predictive approaches. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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