Abstract
Arthritis is a most common disease in the worldwide population targeting knee, neck, hand, hip, and almost all the joints of the human body. It is a frequently noticed problem in elder people, especially women. The severity of the disease is analyzed using the older KL grading system. Traditionally, the detection of various grades of OA (osteoarthritis) is interpreted by just a visual examination. A traditional modality, X-ray images are considered as the data for the project. The images are segmented using different segmentation techniques to extract the articular cartilage as region of interest. From the literature, eight different segmentation techniques were identified out of which seven are automated and one is semiautomated. By implementing those techniques and evaluating their performance, it is inferred that block-based segmentation, center rectangle segmentation, and the semiautomated seed point selection segmentation performs well and provides sensitivity, positive prediction value and dice Sorenson’s coefficient of 100%, respectively, and specificity of 0%.
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Acknowledgements
The images used for this study were obtained from Manisundaram Medical Mission Hospitals, Vellore under the supervision of Dr. Manivannun K., M.S (Ortho.), Mr. Selva Prakash S., Dip. in X-ray Technology, and Ms. Suganya S B.Sc MSW, Counselor. We duly state that my data collection does not involve patient’s interference and invasive protocol.
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Nagaraj, K., Jeyakumar, V. (2019). A Study on Comparative Analysis of Automated and Semiautomated Segmentation Techniques on Knee Osteoarthritis X-Ray Radiographs. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_153
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DOI: https://doi.org/10.1007/978-3-030-00665-5_153
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