Texture analysis of mandibular cortical bone on digital dental panoramic radiographs for the diagnosis of osteoporosis in Korean women.

Objective: To determine whether individual measurements or a combination of textural features and mandibular cortical width (MCW) derived from digital dental panoramic radiographs (DPRs) are more useful in assessment of osteoporosis.

Study Design: Textural features were obtained by using fractal dimension (FD) and gray-level co-occurrence matrix (GLCM). Digital DPRs and bone mineral densities (BMDs) of the lumbar spine and the femoral neck were obtained from 141 female patients. A naïve Bayes classifier, a k-nearest neighbor (k-NN) algorithm, and a support vector machine were assessed for classifying osteoporosis.

Results: The combinations of FD plus MCW (95.3%, 92.1%, 96.8%) and GLCM plus MCW (93.7%, 89.5%, 94.2%) for femoral neck BMD showed the highest diagnostic accuracy with the use of the naïve Bayes, k-NN, and support vector machine classifiers, respectively.

Conclusions: The combination of textural features and MCW contributed a better assessment of osteoporosis compared with the use of only individual measurements.