Abstract
This paper exhibits an automatic determination framework for knee osteoarthritis. The pre-handling stage incorporates three primary steps. The initial step is visualizing DICOM in three perspectives then distinguishes the three main knee parts, trailed by applying the second step which is an adaptive threshold and the third step is applying adjusted region growing methods to isolate the knee from the encompassing bones and tissues. At that point, the second stage is applied to divide each part in the knee utilizing a K-means clustering algorithm. At long last, the third stage happened by three dimensions (3D) recreation for fragmented parts of the knee to help orthopedics in the diagnosis procedure. The 3D reconstruction detection framework was tried on seven datasets obtained from the Cancer Imaging Archive (TCIA). The test results emphatically exhibit that the expected framework is useful in the detection of knee osteoarthritis.
Kadry Ali Ezzat, Lamia Nabil Mahdy, Aboul Ella Hassanien and Ashraf Darwish: Scientific Research Group in Egypt (SRGE), http://www.egyptscience.net.
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Ezzat, K.A., Mahdy, L.N., Hassanien, A.E., Darwish, A., Vaclav, S., Gupta, D. (2020). Automatic 3D Reconstruction Detection System for Knee Osteoarthritis Based on K-Means Algorithm. In: Khanna, A., Gupta, D., Bhattacharyya, S., Snasel, V., Platos, J., Hassanien, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1087. Springer, Singapore. https://doi.org/10.1007/978-981-15-1286-5_72
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