Osteoarthritis Stages Classification to Human Joint Imagery Using Texture Analysis: A Comparative Study on Ten Texture Descriptors

Abstract

Osteoarthritis (OA) is a disease that commonly affects the joints in humans. It is the major diseases in aging and obesity society. OA has affected millions of people around the world. OA has affected 10% of Thai population in 2014. According to National Statistical Office (NSO) estimates, 31.8% of Thai population will be affected in OA by 2050. It is difficult to bring back to normal when people suffer from OA. X-ray imaging is the basic method to detect OA. There are 5 grades to differentiate the level of OA which starts from grade 0 to grade 4. This proposed work is to classify the stage of OA by applying image analysis, and classification techniques. The first step of the proposed technique is to identify the region of interest (ROI). Which regions can specify of each texture image. There are four ROIs: (i) Lateral Femur (LF), (ii) Medial Femur (MF), (iii) Lateral Tibia (LT), (iv) Medial Tibia (MT). When ROI was identified, next each ROI (sub-image) was applied with texture descriptor for extractin the feature vectors. To reduce the dimension of feature space, Correlation-based Feature Selection (CFS) approach was implemented. Finally, these selected feature vectors were used to generate the desired classifiers. The research challenge is to find which ROI can produce the better classification result. With respect to a collection of 130 images data set, the obtained result found that Medial Tibia (MT) produced the best performance with Area Under the ROC Curve (AUC) value of 0.871.