Abstract
In this study, a technique to extract crossectional fat (CSFA), muscle (CSMA), femur (CSFEMA) and bone (CSBA) areas of the thigh in the knee offering osteoarthritis (OA) disease signs is established. These morphometric measures are obtained by using segmentation, based on Fuzzy C-means (FCM) clustering and used as features. 103 subjects which are presenting normal and four levels of severity OA are used. Subjects are allocated into five OA-severity categories, formed in accordance with the Kellgren–Lawrence scale from KL values 0 to 4 as “normal”, “doubtful, “minimal”, “moderate”, and “severe” respectively. A support vector machine (SVM) classifier is used to classify morphometric features to see the relations and detect OA between the KL scores and the morphology of the thigh muscles. Regarding the number of data for each classes and hardness of severity symptoms of OA, to get a better classification accuracy different combinations of groups, such as five individual groups and two groups (KL0-1 as group one, KL3-4 / KL0-1 as group one, KL2, 3 and 4 as group two) are tried to get classification accuracies. The best classification accuracy rate is achieved when the KL scores are grouped into two main classes. The first class represented the less severe cases and belongs to the KL scores of 0 and 1. The second class is composed of cases with KL grades greater than or equal to 2. The SVM classifier accuracy (72%) is a satisfactory result regarding the hardness of the application domain. That is, analysis of the morphometric measures used in this study is not an easy task because of the variability of MRI image morphologies depending on the people. Results demonstrate that the two groups are classified 72% classification accuracy which will provoke new researches for a precise analysis of the OA and hence leading to more accurate prognosis in clinical practice. Key words: Osteoarthritis, fuzzy c-means clustering, support vector machine, segmentation.
Highlights
Osteoarthritis (OA) is a widespread joint disease that causes degenerative alterations in the knee as well as other joints
Since the Kellgren and Lawrence (KL) values of the used data are given only for right leg, the morphometric measurements are computed for the right leg only
The obtained morphometric measures are applied to a support vector machine (SVM) Classifier
Summary
Osteoarthritis (OA) is a widespread joint disease that causes degenerative alterations in the knee as well as other joints. Knee OA disturbs about 30% of those over 65 years old and as frequently associated with disability as chronic lung diseases and heart are characterized by pathological features including joint space lessening, osteophyte formation, and joint angulations (Felson and Zhang, 1998; Yelin and Callahan, 1995). Factors such as being overweight, getting older or injuring a joint may be one of the causes of OA, no direct link could be found with these factors. Osteoarthritis therapies can relieve pain and help patient remain active (Prescott et al, 2009; He et al, 2002)
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