Four Texture Algorithms for Recognizing Early Signs of Osteoarthritis (DAEV)

This master thesis aims to provide an in-depth comparison of four texture algorithms in their capacity of discriminating patients with osteoarthritis (OA) from the ones without, recognizing early signs of Osteoarthritis and tracking disease progression from 2D radiographs of the knee trabecular bone (TB). Given the fractal properties of the trabecular bone (TB), two fractal-based algorithms (Bone Variance Value (BVV) and Bone Score Value (BSV)) that try to characterize the complexity of the underlying 3D structure of the bone are presented. The third algorithm (Bone Entropy Value (BEV), based on Shannon’s Entropy) stems from the information theory and aims to describe the bone structure in terms of information complexity. The last algorithm (Bone Coocurrence Value (BCV)) is based on the co-occurrence matrix of an image and describes the image texture in terms of certain Haralick features. If successful, such algorithms posses a great potential to lower the costs (financial, time) associated with the diagnosis of osteoarthritis (OA) through automation of the procedure, and with the treatment. The earlier treatments and risk reduction measures are less costly than the procedures involved due to a more advanced stage of the disease (surgery, implants, etc.).