Automatic Diagnosis of Elbow Arthritis Based on Edge Algorithm

Abstract

Osteoarthritis is an age-related degenerative joint disease; it is mainly because the cartilage tissue between bones is worn and thinned, which leads to the damage of the periosteum and bone including the surrounding ligaments. Clinically, its manifestations are mainly joint pain, swelling, stiffness, and even partial loss of function, which seriously affects the quality of life of patients. The main clinical manifestations are elbow joint pain and limited movement. Elbow articular cartilage degenerates and falls off, and the more serious manifestation is subchondral hyperosteogeny and sclerosis, which leads to unsmooth articular surface and narrow joint space. Finally, elbow joint pain is severe with different degrees of mobility disorder, elbow joint extension and flexion range is getting smaller and smaller, and elbow joint pain is getting more and more serious. In this paper, the segmentation of left and right elbow images is completed based on gray projection through the analysis of image gray distribution. After obtaining the region of interest of elbow joint, the extraction algorithm of elbow joint hard bone edge is studied. Firstly, the extraction of elbow joint hard bone contour edge is completed based on active shape model algorithm combined with image characteristics. Finally, according to the extraction results of hard bone contour edge, this paper realizes the automatic diagnosis of multiple elbow arthritis indexes and compares with the results given by the image set, which proves that the whole algorithm has good adaptability and accuracy.