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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Ribas, Lucas C. [1, 2] ; Riad, Rabia [3] ; Jennane, Rachid [4] ; Bruno, Odemir M. [1, 2]
Total Authors: 4
Affiliation:
[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
Total Affiliations: 4
Document type: Journal article
Source: Biomedical Signal Processing and Control; v. 71, n. A JAN 2022.
Web of Science Citations: 0
Abstract

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)

FAPESP's process: 18/22214-6 - Towards a convergence of technologies: from sensing and biosensing to information visualization and machine learning for data analysis in clinical diagnosis
Grantee:Osvaldo Novais de Oliveira Junior
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 16/23763-8 - Modeling and analysis of complex networks for computer vision
Grantee:Lucas Correia Ribas
Support Opportunities: Scholarships in Brazil - Doctorate