Early Detection of Osteoarthritis Using Local Binary Patterns: A Study Directed at Human Joint Imagery

Osteoarthritis (OA) is a chronic health condition that causes severe joint pain and stiffness; it is a major cause of disability in older people. The risk of OA increases from age 45 and older. Early diagnosis is typically made using X-ray imagery. In this paper an automated mechanism for OA screening is proposed. The fundamental idea is to generate a classifier that is able to distinguish between OA or non-OA images. The challenge is how bast to translate an X-ray image into a form that serves to both captures key information while remaining compatible with the classification process. It is suggested that image texture is the most desirable feature to be considered. The process is filly described and evaluated. The data used for the evaluation was obtained from the right Tibia of 50 female subjects. Excellent results were obtained, recorded AUC values of 1.0.