Abstract
Knee issues are very frequent among people of all ages, and osteoarthritis is one of the most common reasons behind them. The primary feature in observing extremity and advancement of osteoarthritis is joint space narrowing (cartilage loss) which is manually computed on knee x-rays by a radiologist. Such manual inspections require an expert radiologist to analyze the x-ray image; moreover, it is a tedious and time-consuming task. In this paper, we present a computer-vision-based system that can assist the radiologists by analyzing the radiological symptoms in knee x-rays for osteoarthritis. Different image processing techniques have been applied on knee radiographs to enhance their quality. The knee region is extracted automatically using template matching. The knee joint space width is calculated, and the radiographs are classified based on the comparison with the standard normal knee joint space width. The experimental evaluation performed on a large knee x-ray dataset shows that our method is able to efficiently detect osteoarthritis, achieving more than 97% detection accuracy.
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