sis 18: e14

Research Article

Combining GLCM with LBP features for knee osteoarthritis prediction: Data from the Osteoarthritis initiative

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  • @ARTICLE{10.4108/eai.20-10-2021.171550,
        author={Khaled Harrar and Khadidja Messaoudene and Mohammed Ammar},
        title={Combining GLCM with LBP features for knee osteoarthritis prediction: Data from the Osteoarthritis initiative},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={10},
        keywords={Knee osteoarthritis, X-ray images, DWT, GLCM, LBP, LogitBoost},
        doi={10.4108/eai.20-10-2021.171550}
    }
    
  • Khaled Harrar
    Khadidja Messaoudene
    Mohammed Ammar
    Year: 2021
    Combining GLCM with LBP features for knee osteoarthritis prediction: Data from the Osteoarthritis initiative
    SIS
    EAI
    DOI: 10.4108/eai.20-10-2021.171550
Khaled Harrar1,*, Khadidja Messaoudene2, Mohammed Ammar1
  • 1: LIST Laboratory, University M’Hamed Bougara of Boumerdes, Algeria
  • 2: LIMOSE Laboratory, University M’Hamed Bougara of Boumerdes, Algeria
*Contact email: khaled.harrar@univ-boumerdes.dz

Abstract

INTRODUCTION: Knee osteoarthritis is a chronic disease that can make a person more susceptible to develop health complications. It is a significant cause of disability among adults. In advanced stages, people can die from these complications.

OBJECTIVES: This paper introduces a quick and effective approach to classify knee X-ray images using LogitBoost and wavelet-based Gray Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP) to increase image classification accuracy and minimize training and testing time.

METHODS: The proposed technique involves image enhancement followed by Haar wavelet transformation. GLCM and LBP were extracted from the transformed image and these attributes were used to differentiate the radiographs into two groups of patients composed of 100 normal subjects (KL 0) and 100 pathological cases with osteoarthritis (KL 2). The validation of the classification was carried out using the K-fold cross-validation technique with k = 10.

RESULTS: The results revealed that the GLCM provided an accuracy of 77 % and the LBP approach achieved an accuracy of 82.5 %. Moreover, the combination of the two techniques LBP-GLCM improved the accuracy of the prediction with the LogitBoost model (91.16 %). Compared to other classifiers (SVM, logistic regression, and decision tree), the LogitBoost provided a low root mean square error (RMSE) of 27.5 %.

CONCLUSION: In addition, the proposed method was compared to the state-of-the-art and revealed the highest accuracy in the prediction of KOA, outperforming the methods existing in the literature.