A Robust Pre-processing Framework for ROI Extraction in Knee Osteoarthritis X-Ray Analysis

Abstract

Medical image processing depends much on pre-processing, particularly in cases of precise categorisation and prediction—like those involving the diagnosis of osteoarthritis (OA) in the knee. This work presents a rapid and simple approach to get the area of interest (ROI) from knee X-ray images such that the Kellgren and Lawrence (KL) technique may be used for accurate grading. By means of a planned approach including Gaussian blurring, thresholding, and sophisticated statistical and wavelet-based algorithms for finetuning characteristics, the system addresses issues like noise, poor contrast, and uneven illumination. The recommended approach divides the knee joint, eliminates regions not required, and enhances the view of crucial diagnostic elements like the gap between the upper and lower knee bones. It makes separating ROIs for all five KL grades—from healthy knees to severe OA cases—simple. With more than 8,000 X-ray images in the Kaggle knee OA dataset, the research ensures system dependability and applicability in various contexts. The method significantly increases the precision of ROI segmentation, thereby enhancing the feature extraction for next classification projects. While eliminating uncertainty, the separated ROIs maintain crucial characteristics and help to distinctly differentiate KL grades. Though it might function even better with future developments like adding more sophisticated brightness control and noise reduction techniques, the framework performs effectively in many different image settings. The foundation for automated OA detection is laid by this study. It closes the distance between the limitations of imaging and clinical requirements. It also guides the direction of medical image processing forward.