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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Jeremy Yik Xian Sia
Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering

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K-means Clustering In Knee Cartilage Classification: Data from the OAI Joyce Sin Yin Sia; Tian Swee Tan; Matthias Foh Thye Tiong; Kah Meng Leong; Kelvin Chia Hiik Ling; Sameen Ahmed Malik; Jeremy Yik Xian Sia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 2: June 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v8i2.1649

Abstract

Knee osteoarthritis is a degenerative joint disease which affects people mostly from elderly population. Knee cartilage segmentation is still a driving force in managing early symptoms of knee pain and its consequences of physical disability. However, manual delineation of the tissue of interest by single trained operator is very time consuming. This project utilized a fully-automated segmentation that combined a series of image processing methods to process sagittal knee images. MRI scans undergo Bi-Bezier curve contrast enhancement which increase the distinctiveness of cartilage tissue. Bone-cartilage complex is extracted with dilation of mask resulted from region growing at distal femoral bone. Later, the processed image is clustered with k = 2, into two groups, including coarse cartilage group and background. The thin layer of cartilage is successfully clustered with satisfactory accuracy of 0.987±0.004, sensitivity 0.685±0.065 of and specificity of 0.994±0.004. The results obtained are promising and potentially replace the manual labelling process of training set in convolutional neural network model.
Co-Authors Joyce Sin Yin Sia Kah Meng Leong Kelvin Chia Hiik Ling Matthias Foh Thye Tiong Sameen Ahmed Malik Tian Swee Tan