Early Detection of Osteoarthritis based on Cartilage Thickness in Knee X-ray Images

Abstract

Arthritis is a joint disorder featuring inflammation. There are numerous forms of Arthritis. Arthritis essentially causes joint dis-functioning which may further tend to cause deformity and disability. Osteoarthritis (OA) is one form of arthritis which is mostly seen in old age group. A patient suffering from OA needs to visit medical experts where clinical and radiographic examination is carried out. Analysis of bone structures in initial stage is bit complex. So any vague conclusion drawn from the radiographic images may make the treatment faulty and troublesome. Thus to overcome this we have developed an algorithm that computes the cartilage area/thickness using various shape descriptors. The computed descriptors obtained the accuracy of 99.81% for K-nearest neighbour classifier and 95.09% for decision tree classifier. The estimated cartilage thickness is validated by radiographic experts as per KL grading framework which will be helpful to the doctors for quick and appropriate analysis of ailment in the early stage. The results are competitive and promising as reported in the literature.

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

“…Additionally, a novel pixel density based approach of automatically identifying the region of interest was developed that demonstrated a classification rate of 97.86% [13]. Lastly, in [14], cartilage thickness was calculated using various shape descriptors and the accuracy of 99.81% was attained, that was validated by medical experts. The majority of the earlier works are concentrated on the early prediction of OA using machine learning/computer aided methods, but very little study has been carried out for OA classification as per KL grading system.…”

“…The region in the image that is denser (or thicker), results in high density value. Lastly, extract the region with high density value and enhance it using sine adaptive filter [14]. The steps used by sine adaptive filter are given in equations (3) to (6) Where, CF is a filter co-efficient and FW is Filter Width, which are calculated based on the X-ray reconstructed image characteristics.…”

Automatic Detection and Classification of Knee Osteoarthritis Using Hu's Invariant Moments

“…The portion of the knee is dynamically segmented using 3 × 3 masks (Caselles et al, 1995 ). The object boundaries are further located iteratively by using this mask (Gornale et al, 2016b , c ; Gornale et al, 2017 , 2019b ).…”

“…Gornale et al ( 2016b , c , 2017 , 2019a , b , c , 2020a , b ) have used the semi-automated active contour method for the extraction of the cartilage region and experimented with their own database of 500 Knee X-ray images. The different statistical features, geometric features and Zernike moments are computed and classified obtaining the accuracy of 87.92% for random forest classifier and 88.88% for K-NN classifier (Gornale et al, 2016b , c ).…”

“…The results demonstrated an accuracy of 97.86 and 97.16% with respect to Medical Expert-I and Medical Expert-II opinions. In (Gornale et al, 2019b ), with an expanded dataset of 1,650 knee X-ray images, an algorithm that calculates the cartilage thickness/area has been developed. The calculated thickness demonstrated an accuracy of 99.81% using a K-NN classifier, which is evaluated by radiographic expert incompliance with KL grading framework.…”