Imaging Science in Dentistry

  • 제43권3호
  • /
  • Pages.153-161
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  • 2013
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  • 2233-7822(pISSN)
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  • 2233-7830(eISSN)
대한영상치의학회 (Korean Academy of Oral and Maxillofacial Radiology)
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The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis

  • Kavitha, Muthu Subash (Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University) ;
  • Asano, Akira (Graduate School of Engineering, Hiroshima University) ;
  • Taguchi, Akira (Department of Oral and Maxillofacial Radiology, Matsumoto Dental University) ;
  • Heo, Min-Suk (Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University)
  • 투고 : 2013.04.07
  • 심사 : 2013.05.07
  • 발행 : 2013.09.30
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초록

Purpose: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. Materials and Methods: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. Results: The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Conclusion: Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

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