Assessment of Joint Space in Knee Osteoarthritis using Particle Swarm Optimization Technique
Vijayakumari G1, Ganga Holi2

1Vijayakumari G, Department of Computer Science & Engineering, SLN College of Engineering, Raichur, Karnataka, India. 
2Ganga Holi, Department of Information Science & Engineering, Global Academy of Technology, Bangalore, Karnataka, India. 
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 5818-5823 | Volume-8 Issue-12, October 2019. | Retrieval Number: C4246098319/2019©BEIESP | DOI: 10.35940/ijitee.C4246.1081219
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Abstract: Osteoarthritis (OA) is one of the most common joint disorder which is debility seen in elderly & overweight people which affects the cartilage of bone joints like knee, feet, hip, and spine. In OA usually, cartilage is ruptured due to the kneading of bones with each other which will end up causing severe pain. In this condition, it is necessary to analyze the severity of OA which involves various medical imaging and clinical examination techniques. In this paper, automated analysis and detection of OA are proposed by calculating the thickness of cartilage which also helps to effectively detect and analyze the abnormalities in bone structures. Where we have considered various knee X-ray images. Initially, preprocessing and noise removal is performed. Further by implementing Particle Swarm Optimization (PSO) segmentation and thresholding, the specified knee region is cropped and analyzed to calculate the thickness of cartilage to detect the presence of OA.
Keywords: Osteoarthritis (OA), Articular Cartilage, X-ray images, Segmentation, Particle Swarm Optimization.
Scope of the Article: Particle Swarm Optimization