Machine learning classification on texture analyzed T2 maps of osteoarthritic cartilage: oulu knee osteoarthritis study.

Peuna, A; Thevenot, J; Saarakkala, S; Nieminen, M T; Lammentausta, E

Peuna, A; Thevenot, J; Saarakkala, S; Nieminen, M T; Lammentausta, E.

Afiliación

Peuna A; Department of Medical Imaging, Central Finland Central Hospital, Jyväskylä, Finland; Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland. Electronic address: arttu.peuna@k
Thevenot J; Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland.
Saarakkala S; Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland.
Nieminen MT; Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland; Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland.
Lammentausta E; Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland; Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland.

Osteoarthritis Cartilage ; 29(6): 859-869, 2021 06.

Article en En | MEDLINE | ID: mdl-33631317

RESUMEN

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.

Asunto(s)

Aprendizaje Automático; Osteoartritis de la Rodilla/clasificación; Osteoartritis de la Rodilla/patología; Anciano; Estudios Transversales; Femenino; Humanos; Masculino; Persona de Mediana Edad

Palabras clave

GLCM; LBP; Osteoarthritis; Pattern recognition and data classification; T2 relaxation time; Texture analysis

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Osteoartritis de la Rodilla / Aprendizaje Automático Tipo de estudio: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Osteoarthritis Cartilage Asunto de la revista: ORTOPEDIA / REUMATOLOGIA Año: 2021 Tipo del documento: Article Pais de publicación: Reino Unido

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