Abstract

Osteoarthritis (OA) is the most common form of arthritis seen in aged or older populations. It is caused because of a degeneration of articular cartilage, which functions as shock absorption cushion in knee joint. OA also leads sliding of bones together, cause swelling, pain, eventually and loss of motion. Nowadays, magnetic resonance imaging (MRI) technique is widely used in the progression of osteoarthritis diagnosis due to the ability to display the contrast between bone and cartilage. Usually, analysis of MRI image is done manually by a physician which is very unpredictable, subjective and time consuming. Hence, there is need to develop automated system to reduce the processing time. In this paper, a new automatic knee OA detection system based on feature extraction and artificial neural network is developed. The different features viz GLCM texture, statistical, shape etc. is extracted by using different image processing algorithms. This detection system consists of 4 stages, which are pre-processing with ROI cropping, segmentation, feature extraction, and classification by neural network. This technique results 98.5% of classification accuracy at training stage and 92% at testing stage.

Authors and Affiliations