TY - GEN
T1 - A novel hybrid of S2DPCA and SVM for knee osteoarthritis classification
AU - Wahyuningrum, Rima Tri
AU - Anifah, Lilik
AU - Purnama, I. Ketut Eddy
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/27
Y1 - 2016/7/27
N2 - A computer-based system was designed for grading and quantifying knee osteoarthritis (OA) severity. This paper presents a novel approach to knee osteoarthritis classification. The knee X-ray image data sets were obtained from the Osteoarthritis Initiative (OAI) in 2011. The classification was based on the Kellgren-Lawrence (KL) grades, which related to the various stages of OA solidity. The classifier was constructed using manual knee X-rays image classification, indicating the first four KL grades (normal, doubtful, minimal and moderate). Computer-based image analysis was conducted by employing Machine Learning involving various stages-first, preprocessing using Contrast Limited Adaptive Histogram Equalization (CLAHE) and cropping images manually to 400 × 100 dimension; second, feature extraction by using Structural 2 Dimensional Principal Component Analysis (S2DPCA); and the last stage, classifying the images using Support Vector Machine (SVM). The experimental results showed that KL grade 0 could be differentiated from the other grades with accuracy up to 94.33% on Gaussian kernel.
AB - A computer-based system was designed for grading and quantifying knee osteoarthritis (OA) severity. This paper presents a novel approach to knee osteoarthritis classification. The knee X-ray image data sets were obtained from the Osteoarthritis Initiative (OAI) in 2011. The classification was based on the Kellgren-Lawrence (KL) grades, which related to the various stages of OA solidity. The classifier was constructed using manual knee X-rays image classification, indicating the first four KL grades (normal, doubtful, minimal and moderate). Computer-based image analysis was conducted by employing Machine Learning involving various stages-first, preprocessing using Contrast Limited Adaptive Histogram Equalization (CLAHE) and cropping images manually to 400 × 100 dimension; second, feature extraction by using Structural 2 Dimensional Principal Component Analysis (S2DPCA); and the last stage, classifying the images using Support Vector Machine (SVM). The experimental results showed that KL grade 0 could be differentiated from the other grades with accuracy up to 94.33% on Gaussian kernel.
KW - CLAHE
KW - KL grades
KW - Knee OAI
KW - S2DPCA
KW - SVM
UR - https://www.scopus.com/pages/publications/84984660090
U2 - 10.1109/CIVEMSA.2016.7524317
DO - 10.1109/CIVEMSA.2016.7524317
M3 - Conference contribution
AN - SCOPUS:84984660090
T3 - 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2016 - Proceedings
BT - 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2016
Y2 - 27 July 2016 through 29 July 2016
ER -