Osteoarthritis estimated as the eighth-leading nonfatal burden of disease in the world is the one important reason why it is investigated. The status of osteoarthritis is important because it is used as a basis for determining treatment for patients. The aim of this research is analyzing the texture feature of Junction Space Area (JSA) and design system based texture feature in order to predict the severity of osteoarthritis in the knee using a linear vector quantization. Textures extracted in this study are first order, second order, and gray level run length matrix. Several stages involved as the research procedures covering image processing, feature extraction, learning process, and testing process. The result of feature extraction obtained several FO, GLCM and GLRLM features for each cluster with overlapping conditions, making it difficult to classify using linear methods, so learning used linear vector quantization (LVQ). Feature extraction was carried out for both training data and testing data. The training process, which was divided into several stages namely first order learning, GLCM learning data, GLRLM learning data, and combined learning data features. learning process for the aforementioned features of combined learning data used learning parameter rate of 0.5 with epoch values of 1000, 5000, 10000, and 15000. The best results obtained when using a system using LVQ based on GLCM features. But the disadvantage of this system is that it cannot recognize grade 2 well where recognizing grade 2.
|Number of pages
|Journal of Theoretical and Applied Information Technology
|Published - 2019