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.