Abstract
The major cause for the occurrence of osteoarthritis (OA) is due to wear and tear of protectivetissue at the ends of cartilage. It occurs in the joints of hands, neck, lower back, knees, or hips. We propose a method to identify the existence of osteoarthritis in knee joint using X-ray images. The proposed methodology involves image enhancement using contrast-limited adaptive histogram equalization followed by identifying the location of center part of synovial cavity region, which exists between upper and lower knee bones. Four texture features namely, contrast, correlation, energy and homogeneity were extracted from synovial cavity of enhanced image and these features are used to classify the images into two classes: normal or affected with OA. The proposed method used cubic SVM and KNN as a classifier which gives robust results as compared to others.
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Acknowledgements
We would like to thank Chidgupkar Hospital Pvt. Ltd., a multispeciality hospital in Solapur, India, for providing us the X-ray images and giving permission to use these images for our experimentation.
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Hegadi, R.S., Chavan, U.P., Navale, D.I. (2019). Identification of Knee Osteoarthritis Using Texture Analysis. In: Nagabhushan, P., Guru, D., Shekar, B., Kumar, Y. (eds) Data Analytics and Learning. Lecture Notes in Networks and Systems, vol 43. Springer, Singapore. https://doi.org/10.1007/978-981-13-2514-4_11
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DOI: https://doi.org/10.1007/978-981-13-2514-4_11
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