Cellular neural network based algorithms in the early detection of hand osteoarthritis

Cellular neural network (CNN) algorithms have been successfully used in a plethora of image processing applications including the medical imaging domain. Analogic CNN algorithms use CNN templates combined with logic operations to perform operations such as blurring and thresholding for image processing. In this paper we apply CNN based techniques incorporating image enhancement, region segmentation and line detection for detecting the manifestations of osteoarthritis, a metabolic disease afflicting the elderly population caused by wear and tear of cartilage surrounding weight bearing bone joints like the human hand. The two main indicators of osteoarthritis that we examine are the cystic regions, and osteophytes or bony spurs in the vicinity of the joints, produced by the rubbing together of bones due to joint space narrowing.