AUTOMATED AND ROBUST ROI LOCALIZATION FRAMEWORK FOR KNEE OSTEOARTHRITIS DETECTION IN RADIOGRAPHIC IMAGES

OA in the knees is a long-term joint disease that gets worse over time and makes it hard to move and enjoy life, especially for older people. Using the Kellgren and Lawrence (KL) scale, x-rays are still the easiest and most common way to diagnose OA and rate its intensity. Noise, low contrast, uneven lighting, and differences in patient anatomy make it hard to accurately and automatically identify the Region of Interest (ROI) from X-ray pictures. This study shows a strong and effective way to prepare X-ray pictures of the knee so that ROIs can be located accurately. This makes it possible to accurately grade OA. To fix problems with picture clarity, the system uses adaptive thresholding, Gaussian blurring, histogram normalisation, and wavelet-based improvement. Additionally, it has a model for object localisation based on deep learning and a cascaded process that combines morphological operations and a convolutional region proposal network (RPN) to improve joint border recognition. It does a good job of separating important structural structures, like the femur, tibia, and joint space, from unimportant areas. This makes ROI segmentation uniform across all five KL grades. The system works well with over 8,000 knee X-rays from the Kaggle Knee OA dataset. It can be scaled up and down easily, and it makes inputs more consistent for later classification models. The suggested method greatly improves feature extraction and grade separation, providing a strong base for fully automatic systems that diagnose OA. This work fixes some important problems with the way medical images are prepared and also helps make clinical decision support tools better for analysing radiographic knee OA.