Osteorthritis Classification Using Kmeas and Contrast Improvement Based on Combining Aiht &Amp; Clahe Techniques

This paper presents the technique of classifying knee OA levels automatically by computer, supporting doctors in diagnosing the extent of arthritis. The proposed method is performed in four steps: image preprocessing, segmentation, exploiting the architecture, and character of image classification. In this system, techniques CLAHE (Adaptive Histogram Equalization Contrast Limited) and AIHT (Adaptive Inverse Hyperbolic Tangent) are used for pre-treatment steps to enhance the contrast map. The process of segmentation is done with Gabor Kernel. algorithm The exploitation of the image structure using algorithm GLCMs (Gray Level Co-occurrence Matrix) exploits the features such as energy, correlation, contrast and Homogeneity of pixels to make the dataset training for classifying levels of arthritis