International Journal of Engineering and Techniques - Volume 1 Issue 3, May - June 2015 ISSN: 2395-1303 http://www.ijetjournal.org Page 134 Effective Feature Extraction Based Automatic Knee Osteoarthritis Detection and Classification using Neural Network Dipali D. Deokar 1 , Chandrasekhar G. Patil 2 1(Department of Electronics and Tele-communication,Sinhgad Academy of Engineering Pune, India) 2 (Department of Electronics and Tele-communication, Sinhgad Academy of Engineering Pune, India) I. INTRODUCTION The knee joint is the largest and most complex joint of the human body. It is a major weight bearing joint which made up of condyles of femur, condyles of tibia and posterior surface of patella (knee cap). Articular cartilage covers ends of the femur and tibia bone. Cartilage is ultra-slippery thin layer of high- quality hyaline material between the femur and tibia bones and helps in smooth movement of knee joint [1]. Osteoarthritis (OA) is degenerative joint disease and occurs when cartilage becomes soft and gets damaged due to continuous wear and tear movements and with ageing. This reduces the ability of the cartilage to work as a shock absorber. It is growing common among women, obese and older people. OA situation also arises due to previous knee injury, repetitive stress on the knee, and obesity problem [2]. There are few methods that can be used for diagnosis of osteoarthritis but the most common diagnosis method which used newly is through magnetic resonance imaging (MRI). As compared with other methods, MRI gain popularity because it is able to produce high quality images of the anatomical structures of knee by influences contrast of different tissue types [3]. MRI image is invasive and repetitive. It is most widely used because it is hazardless as well as noise free as compared to X- Rays and computer tomography (CT) images. Usually, analysis of MRI images is done manually by physicians, which are very subjective, time consuming and inconsistent. The situation may become worst if patients exceed certain limit. Thus, there is a demand of automated knee osteoarthritis classifier, which able to reduce the time consumption for analysis as well as avoid the inconsistent of the interpretation. In digital image analysis, feature selection/ extraction are used to extract or retain the optimum salient characteristics for proper analyse and classify the image [4]. This feature extraction process also able to reduce the dimensionality of the measurement space, thus minimize the timeconsumption of image processing. So in this paper,initially input MRI images are pre- processed using contrast enhancement, histogram equalization, thresholding, and canny edge detection etc. RESEARCH ARTICLE OPEN ACCESS 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. Keywords — Artificial Neural Network (ANN), Gray Level Co-occurrence Matrix (GLCM),Knee Joint, Magnetic Resonance Imaging (MRI), Osteoarthritis(OA).