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20 March 2015 Knee osteoarthritis image registration: data from the Osteoarthritis Initiative
Jorge I. Galván-Tejada, José M. Celaya-Padilla, Victor Treviño, José G. Tamez-Peña
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Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94143C (2015) https://doi.org/10.1117/12.2082426
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States
Abstract
Knee osteoarthritis is a very common disease, in early stages, changes in joint structures are shown, some of the most common symptoms are; formation of osteophytes, cartilage degradation and joint space reduction, among others. Based on a joint space reduction measurement, Kellgren-Lawrence grading scale, is a very extensive used tool to asses radiological OA knee x-ray images, based on information obtained from these assessments, the objective of this work is to correlate the Kellgren-Lawrence score to the bilateral asymmetry between knees. Using public data from the Osteoarthritis initiative (OAI), a set of images with different Kellgren-Lawrencescores were used to determine a relationship of Kellgren-Lawrence score and the bilateral asymmetry, in order to measure the asymmetry between the knees, the right knee was registered to match the left knee, then a series of similarity metrics, mutual information, correlation, and mean squared error where computed to correlate the deformation (mismatch) of the knees to the Kellgren-Lawrence score. Radiological information was evaluated and scored by OAI radiologist groups. The results of the study suggest an association between Radiological Kellgren-Lawrence score and image registration metrics, mutual information and correlation is higher in the early stages, and mean squared error is higher in advanced stages. This association can be helpful to develop a computer aided grading tool.
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Jorge I. Galván-Tejada, José M. Celaya-Padilla, Victor Treviño, and José G. Tamez-Peña "Knee osteoarthritis image registration: data from the Osteoarthritis Initiative", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94143C (20 March 2015); https://doi.org/10.1117/12.2082426
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KEYWORDS
Image registration
Image segmentation
X-ray imaging
X-rays
Image processing
Imaging systems
Image enhancement
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