Comparison of Local Binary Pattern and Local Ternary Pattern for Classification of Arthritis in Knee X-Ray Images Using Euclidean Distance Method
V. Rajini · 2013
Arthritis is the most common inflammation in bone- joints and this progressive disease often leads to early disability and joint deformities. By the early diagnosis and treatment of the Arthritis, the damage to the joins can be reduced. A number of therapeutic approaches are now widely available for the diagnosis of this disease. Imaging of the affected joints plays a vital role in the diagnosis. In this paper, a novel classification system for the classification of OA in knee x-ray images based on Local Binary Pattern (LBP) and Local ternary pattern(LTP) is presented. The classification is achieved by extracting the histograms of LBP and LTP of the knee x-ray image. Then classifier system based on K Nearest Neighbor (KNN) is constructed. This system classifies the knee x-ray images into normal or abnormal, and the abnormal severity into medium or worst cases. 50 knee x-ray images are used to evaluate the proposed system. The classification rate achieved is very satisfied.