Significant Geometry Features in Tongue Image Analysis.
Significant Geometry Features in Tongue Image Analysis.
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DOI：
10.1155/2015/897580
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Zhang H
中科院分区：
文献类型：
--
作者：
Zhang B;Zhang H
本文用计算机方法，从舌的几何特征出发，定量分析了舌的形状及其与病人健康或患病状态的关系。基于测量，距离，面积，和它们的比率的13个几何特征提取舌图像由一个专门设计的设备与颜色校正。利用这些特征，基于中医学定义了5种舌形（矩形、锐角和钝角三角形、正方形和圆形）。形状的分类随后用决策树进行。测试了由672幅图像组成的大型数据集，其中包括130幅健康图像和542幅疾病图像（根据西方医学实践进行标记）。实验结果表明，所提取的几何特征在舌形分类（粗分类）上是有效的。即使多个疾病类别属于同一形状，疾病类别仍然可以通过使用几何特征的组合的精细级别分类进行区分，对于所有形状的平均准确率为76.24%。
The shape of a human tongue and its relation to a patients' state, either healthy or diseased (and if diseased which disease), is quantitatively analyzed using geometry features by means of computerized methods in this paper. Thirteen geometry features based on measurements, distances, areas, and their ratios are extracted from tongue images captured by a specially designed device with color correction. Using the features, 5 tongue shapes (rectangle, acute and obtuse triangles, square, and circle) are defined based on traditional Chinese medicine (TCM). Classification of the shapes is subsequently carried out with a decision tree. A large dataset consisting of 672 images comprising of 130 healthy and 542 disease examples (labeled according to Western medical practices) are tested. Experimental results show that the extracted geometry features are effective at tongue shape classification (coarse level). Even if more than one disease class belongs to the same shape, the disease classes can still be discriminated via fine level classification using a combination of the geometry features, with an average accuracy of 76.24% for all shapes.