A hybrid LBP-HOG model and naive Bayes classifier for knee osteoarthritis detection: data from the osteoarthritis initiative

Abstract

Knee OsteoArthritis (KOA) is a disease characterized by a degeneration of cartilage and the underlying bone. It does not evolve uniformly; it can stay silent for a long time and can quickly intensify for several months or weeks. For this reason, it is necessary to develop an automatic system for diagnosis and reduce the subjectivity in the detection of the disease. In this paper, we present a method for detecting knee osteoarthritis based on the combination of histograms of oriented gradient (HOG) and local binary pattern (LBP). Four classifiers including KNN, SVM, Adaboost, and Naïve Bayes were tested and compared for the prediction of the illness. A total of 620 X-Ray images were analyzed, composed of 310 images from healthy subjects (Grade 0), and 310 images from pathological patients (Grade 2). The results obtained reveal that Naïve Bayes achieved the highest performance in terms of accuracy (ACC = 91%) on the Osteoarthritis Initiative (OAI) dataset. The fusion of HOG and LBP features in KOA classification outperforms the use of either feature alone and the existing methods in the literature.