Muramatsu Chisako, Horiba Kazuki, Hayashi Tatsuro, Fukui Tatsumasa, Hara Takeshi, Katsumata Akitoshi, Fujita Hiroshi
Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1194, Japan.
Media Co., Ltd, 3-26-6 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
Int J Comput Assist Radiol Surg. 2016 Nov;11(11):2021-2032. doi: 10.1007/s11548-016-1438-8. Epub 2016 Jun 11.
Studies reported that the mandibular cortical width (MCW) measured on dental panoramic radiographs (DPRs) was significantly correlated with bone mineral density. However, MCW is not a perfect index by itself, and studies suggest the added utility of mandibular cortical index (MCI). In this study, we propose a method for computerized estimation of mandibular cortical degree (MCD), a new continuous measure of MCI, for osteoporotic risk assessment.
The mandibular contour was automatically segmented using an active contour model. The regions of interest near mental foramen were extracted for MCW and MCD determination. The MCW was measured on the basis of residue-line detection results and pixel profiles. Image features including texture features based on gray-level co-occurrence matrices were determined. The MCD were estimated using support vector regression (SVR). The SVR was trained using previously collected 99 DPRs, including 26 osteoporotic cases, by a computed radiography system. The proposed scheme was tested using 99 DPRs obtained by a photon-counting system with data of bone mineral density at distal forearm. The number of osteoporotic, osteopenic, and control cases were 12, 18, and 69 cases, respectively. The subjective MCD by a dental radiologist was employed for training and evaluation.
The correlation coefficients with the subjective MCD were -0.549 for MCW alone, 0.609 for the MCD by the features without MCW, and 0.617 for the MCD by the features and MCW. The correlation coefficients with the BMD were 0.619, -0.608, and -0.670, respectively. The areas under the receiver operating characteristic curves for detecting osteoporotic cases were 0.830, 0.884, and 0.901, respectively, whereas those for detecting high-risk cases were 0.835, 0.833, and 0.880, respectively.
In conclusion, our scheme may have a potential to identify asymptomatic osteoporotic and osteopenic patients through dental examinations.
研究报告称,在牙科全景X线片(DPR)上测量的下颌骨皮质宽度(MCW)与骨密度显著相关。然而,MCW本身并不是一个完美的指标,研究表明下颌骨皮质指数(MCI)具有额外的效用。在本研究中,我们提出了一种用于计算机化估计下颌骨皮质程度(MCD)的方法,MCD是一种新的连续MCI测量指标,用于骨质疏松风险评估。
使用主动轮廓模型自动分割下颌轮廓。提取颏孔附近的感兴趣区域用于MCW和MCD的测定。基于残留线检测结果和像素轮廓测量MCW。确定包括基于灰度共生矩阵的纹理特征在内的图像特征。使用支持向量回归(SVR)估计MCD。通过计算机X线摄影系统,使用先前收集的99张DPR(包括26例骨质疏松病例)对SVR进行训练。使用光子计数系统获得的99张DPR以及前臂远端骨密度数据对所提出的方案进行测试。骨质疏松、骨量减少和对照病例的数量分别为12例、18例和69例。由牙科放射科医生进行的主观MCD用于训练和评估。
单独MCW与主观MCD的相关系数为-0.549,不包括MCW的特征的MCD与主观MCD的相关系数为0.609,包括特征和MCW的MCD与主观MCD的相关系数为0.617。与骨密度的相关系数分别为0.619、-0.608和-0.670。检测骨质疏松病例的受试者工作特征曲线下面积分别为0.830、0.884和0.901,而检测高危病例的受试者工作特征曲线下面积分别为0.835、0.833和0.880。
总之,我们的方案可能有潜力通过牙科检查识别无症状的骨质疏松和骨量减少患者。