A complex network based approach for knee Osteoarthritis detection: Data from the Osteoarthritis initiative

Autor(es)

Ribas, Lucas C. ^{[1, 2]} ; Riad, Rabia ^[3] ; Jennane, Rachid ^[4] ; Bruno, Odemir M. ^{[1, 2]}

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Sao Carlos Inst Phys, POB 369, BR-13560970 Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, Inst Math & Comp Sci, Ave Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP - Brazil

^[3] Ibn Zohr Univ, ERMAM FPO, Ouarzazate 45000 - Morocco

^[4] Univ Orleans, CNRS, UMR 7013, IDP Lab, Orleans 45067 - France

• Tipo de documento: Artigo Científico
• Fonte: Biomedical Signal Processing and Control; v. 71, n. A JAN 2022.
• Citações Web of Science: 0

Resumo

OsteoArthritis (OA) is a joint disease caused by cartilage loss in the joint and bone changes. Early knee OA prediction based on bone texture analysis is a difficult task in medical image analysis. This paper presents a new approach based on concepts of complex network theory to extract texture features related to OA from radiographic knee X-ray images. An X-ray image is modeled into a complex network mapping each pixel into a node and connecting two nodes based on a given Euclidean distance. Then, a set of thresholds is applied to remove some edges and reveal texture properties. Our proposed model employs a specific strategy to automatically select the set of thresholds. A new set of statistical measures extracted from the network are used to compute a feature vector evaluated in a classification experiment using knee X-ray images from the OsteoArthritis Initiative (OAI) database. Our proposed approach is compared to state-of-the-art learning models (AlexNet, VGG, GoogleNet, InceptionV3, ResNet, DenseNet and EfficientNet) as well as to different traditional texture descriptors. Results show that the proposed method is competitive and is potentially promising for early knee OA detection. (AU)

• Processo FAPESP: 18/22214-6 - Rumo à convergência de tecnologias: de sensores e biossensores à visualização de informação e aprendizado de máquina para análise de dados em diagnóstico clínico
• Beneficiário: Osvaldo Novais de Oliveira Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 16/23763-8 - Modelagem e análise de redes complexas para visão computacional
• Beneficiário: Lucas Correia Ribas
• Modalidade de apoio: Bolsas no Brasil - Doutorado