An Efficient Pre-Processing Framework for Segmenting Region of Interest from Knee Osteoarthritis X-Ray Images
Keywords:
Pre-processing, image processing, ambiguous, osteoarthritis, region of interest, Kellgren and Lawrence grade, Kaggle dataset.Abstract
Pre-processing is primary and essential element of most of the image processing applications especially in classification and prediction problems. Especially in medical applications where images are prone to different noises and poor contrast, even a good descriptor would make the classifier ambiguous. The article presents an efficient pre-processing approach to eliminate the soft tissues from the hard tissues and provide a distinguishable knee gap to identify the grade of Osteoarthritis. Also, the simple statistical approach provide a generalized solution to extract the region of interest required for Kellgren and Lawrence grade classification of the Kaggle dataset images with 5 levels of grade. The proposed framework has the ability to obtain segmented region of interest with better accuracy required for classification.
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