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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A Digital Shade-Matching Device for Dental Color Determination Using the Support Vector Machine Algorithm
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A Digital Shade-Matching Device for Dental Color Determination Using the Support Vector Machine Algorithm

机译：基于支持向量机算法的数字式色彩匹配阴影匹配装置

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摘要

In this study, we developed a digital shade-matching device for dental color determination using the support vector machine (SVM) algorithm. Shade-matching was performed using shade tabs. For the hardware, the typically used intraoral camera was modified to apply the cross-polarization scheme and block the light from outside, which can lead to shade-matching errors. For reliable experiments, a precise robot arm with ±0.1 mm position repeatability and a specially designed jig to fix the position of the VITA 3D-master (3D) shade tabs were used. For consistent color performance, color calibration was performed with five standard colors having color values as the mean color values of the five shade tabs of the 3D. By using the SVM algorithm, hyperplanes and support vectors for 3D shade tabs were obtained with a database organized using five developed devices. Subsequently, shade matching was performed by measuring 3D shade tabs, as opposed to real teeth, with three additional devices. On average, more than 90% matching accuracy and a less than 1% failure rate were achieved with all devices for 10 measurements. In addition, we compared the classification algorithm with other classification algorithms, such as logistic regression, random forest, and k-nearest neighbors, using the leave-pair-out cross-validation method to verify the classification performance of the SVM algorithm. Our proposed scheme can be an optimum solution for the quantitative measurement of tooth color with high accuracy.
机译：在这项研究中，我们开发了一种使用支持​​向量机（SVM）算法确定牙齿颜色的数字阴影匹配设备。使用阴影选项卡执行阴影匹配。对于硬件，通常使用的口腔内窥镜经过了修改，以应用交叉偏振方案并阻挡来自外部的光线，这可能导致阴影匹配错误。为了进行可靠的实验，使用了具有±0.1 mm位置可重复性的精密机械臂和专门设计的夹具来固定VITA 3D-master（3D）遮光片的位置。为了获得一致的颜色性能，使用五种标准颜色执行颜色校准，这些标准颜色的颜色值作为3D五个阴影选项卡的平均颜色值。通过使用SVM算法，通过使用五个开发的设备组织的数据库，获得了3D阴影选项卡的超平面和支持向量。随后，通过使用三个附加设备测量3D阴影选项卡（与真实牙齿相对）来执行阴影匹配。平均而言，使用所有设备进行10次测量，匹配精度均达到90％以上，故障率不到1％。此外，我们使用离开配对交叉验证方法将分类算法与其他分类算法（例如逻辑回归，随机森林和k近邻）进行了比较，以验证SVM算法的分类性能。我们提出的方案可以成为高精度定量测量牙齿颜色的最佳解决方案。

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