This paper presents a computer-aided-detection system of osteoporosis. The proposed technique is implemented and applied to 79 proximal femur radiographs. A dual-energy xray absorptiometry (DEXA) scan is used to measure T-score of the images as a justification. Three feature extraction techniques are introduced to describe trabecular pattern changes in proximal femur recorded: wavelet-based hierarchical pyramid, Gabor filter, and intensity gradient map. The selected features were utilized in the design and training of support vector machine (SVM) classifier. The accuracy, sensitivity, and specificity are used to measure the quality of the proposed detection system. The best result and detect femur bone fractures and osteoporosis were obtained efficiently by using wavelet-based hierarchical approach combined with Gabor filter, and intensity gradient map features. The proposed system showed superior performance as compared to other related work.
Khaled, H., Mekky, N., Atwan, A., & Soliman, H. (2019). Osteoporosis Detection Using Combined Texture Features of Proximal Femur Radiographs. Mansoura Journal for Computer and Information Sciences, 15(2), 27-34. doi: 10.21608/mjcis.2019.321065
MLA
Heba Khaled; Nagham Mekky; Ahmed Atwan; Hassen Soliman. "Osteoporosis Detection Using Combined Texture Features of Proximal Femur Radiographs". Mansoura Journal for Computer and Information Sciences, 15, 2, 2019, 27-34. doi: 10.21608/mjcis.2019.321065
HARVARD
Khaled, H., Mekky, N., Atwan, A., Soliman, H. (2019). 'Osteoporosis Detection Using Combined Texture Features of Proximal Femur Radiographs', Mansoura Journal for Computer and Information Sciences, 15(2), pp. 27-34. doi: 10.21608/mjcis.2019.321065
VANCOUVER
Khaled, H., Mekky, N., Atwan, A., Soliman, H. Osteoporosis Detection Using Combined Texture Features of Proximal Femur Radiographs. Mansoura Journal for Computer and Information Sciences, 2019; 15(2): 27-34. doi: 10.21608/mjcis.2019.321065
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