抽象的な

An Edge Detection Algorithm for Human Knee Osteoarthritis Images

Prof. Samir K. Bandyopadhyay

Digital image processing comprises varieties of applications, where some of these used in medical image processing include convolution, edge detection as well as contrast enhancement. Efficient edge detection depends on choosing the threshold; the choice of threshold directly determines the results of edge detection. Osteoarthritis (OA) results from a failure of cells within the joint to maintain the balance between synthesis and degradation of the extracellular matrix. OA is a major cause of pain and disability in the elderly yet there is at present no effective treatment for loss of joint function. This is partly because the condition is heterogeneous with obscure pathogenesis but also because there are no specific laboratory tests or screening procedures that provide a specific diagnosis of early OA. There is a clear need to be able to define onset of characteristic pathological changes when intervention would be timely and to monitor the natural history up to the stage of Radiological detected damage. In this paper, edge detection operator and its enhanced algorithm is used to detect edges for human knee osteoarthritis images in different critical situations. It is shown that the algorithm is very effective in case of noisy and blurs images.