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Image texture in dental panoramic radiographs as a potential biomarker of osteoporosis

机译：在牙齿全景射线照片的图象纹理作为骨质疏松症的潜在的生物标志物

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Previous studies have shown an association between osteoporosis and automatic measurements of mandibular cortical width on dental panoramic radiographs (DPRs). In this study, we show that additional image texture features increase this association and propose the combined features as a potential biomarker for osteoporosis. We used an existing dataset of 663 DPRs of female patients with bone mineral density (BMD) measurements. The mandibular cortex was located using a previously described computer algorithm. Texture features, based on co-occurrence matrices and fractal dimension, were measured in the bone within the cortex and also in the superior basal bone above the cortex. These, augmented by cortical width measurements, were used by a random forest classifier to identify osteoporosis at femoral neck, total hip, and lumbar spine. Classification performance was assessed by ROC analysis. Area-under-curve (AUC) values for identifying osteoporosis at femoral neck were 0.830, 0.824, and 0.872 using, respectively, cortical width alone, cortical texture (co-occurrence matrix features) alone, and combined width and texture. At 80% sensitivity, these classifiers produced specificity values of 74.4%, 73.6%, and 80.0%, respectively. Fractal dimension was a less effective texture feature. Prediction of osteoporosis at the lumbar spine was poorer, but a combined width and superior basal bone texture classifier gave a significant improvement in AUC at p <0.05 over the use of width alone. © 1964-2012 IEEE.
机译：先前的研究表明，骨质疏松症与牙科全景X光片（DPR）的下颌皮层宽度自动测量之间存在关联。在这项研究中，我们显示了其他图像纹理特征增加了这种关联，并提出了组合特征作为骨质疏松症的潜在生物标记。我们使用现有的663个女性患者DPR的数据集进行了骨矿物质密度（BMD）测量。使用先前描述的计算机算法定位下颌皮层。在共生矩阵和分形维数的基础上，在皮质内的骨以及皮质上方的上基底骨中测量了纹理特征。这些通过皮层宽度测量得到增强，被随机森林分类器用来识别股骨颈，全髋和腰椎的骨质疏松症。通过ROC分析评估分类性能。单独使用皮质宽度，单独使用皮质纹理，并同时使用宽度和纹理来识别股骨颈骨质疏松的曲线下面积（AUC）值分别为0.830、0.824和0.872。在80％的灵敏度下，这些分类器产生的特异性值分别为74.4％，73.6％和80.0％。分形维数是较不有效的纹理特征。腰椎骨质疏松症的预测较差，但与单独使用宽度相比，宽度和优越的基础骨纹理分类器的组合在p <0.05时可显着改善AUC。 ©1964-2012 IEEE。

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