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The development of osteoporosis detective analysis method in trabecular condylus of menopause women using panoramic radiograph

Abstract

Osteoporosis incidence in Indonesia increase each year, 1 of 3 menopause women suspected have osteoporosis, then earlier detection is needed. Factors that influence the successful analysis is the choice of region of interset (ROI) and extract feature method. The purpose of this research is to determine the best method to define the bone quality based on trabecular of condylus analysis. Data were obtained from Dentistry Hospital, Padjadjaran University Bandung. Research were conducted cross-section to 79 samples which measured in dual energy X-ray absorbsimetry (DEXA) as a base standard then taken its panoramic radiograph. Trabecular analysis was conducted in ROI of condylus using panoramic radiograph then cursor was clicked in cortical endorsal following the condylus head shape. To reduce the noise, we conducted pre-processing by compensational method, it is a finding of the lowest means of variant number around condylus as a reduce factor then affect the radiograph of condylus become darker. Background sets in zero (0) meanwhile trabecular stay at gray scale. Feature extraction applied 3 analytical methods, they are: gray level co accurance matrix (GLCM), histogram and fraction. Statistical analysis shows T-score DEXA correlation with 3 methods, proofed that fraction method performed the best correlation which r value is 0.377and GLCM (contrast r=0.233, correlation =0.342, energy -0.147, homogenity= r =-0.107), meanwhile histogram (max histogram r=0.253, range histogram r=0.06). As a conclusion, fraction method with ROI of condylus head shape is the best method to determine osteoporosis in post menopause women.
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How to Cite

Azhari, A., Sitam, S., Hidajat, N. N., Arifin, A. Z., & Suprijanto, S. (2016). The development of osteoporosis detective analysis method in trabecular condylus of menopause women using panoramic radiograph. Journal of Dentomaxillofacial Science, 1(2), 84–88. https://doi.org/10.15562/jdmfs.v1i2.2

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