Simple fractal dimensions have been proposed for use in the analysis of the characteristics of digitized tongue pictures and tongue coating texture, which could further the establishment of objectified classification criteria under the conditions of expanding sample size. However, detailed descriptions on simple fractal dimensions have been limited. Therefore, BP (back propagation) neural network model classifiers could be designed by further calculation of the multiple fractal spectrum characteristics of digitized tongue pictures in order to classify and recognize the thin/thick or greasy characteristics of tongue coating.The fractal dimensions of sample data of 587 digitized tongue pictures were collected in a standard environment. A statistical analysis was conducted on the calculation results of the sample data, and the sensitivity of the fractal dimensions to the thin/thick and greasy characteristics of digitized tongue pictures was observed. As the overlap region resulted from a range of values of a single parameter, another eight characteristic parameters of the multiple fractal spectra of the digitized tongue pictures were further proposed as the elements in the input layer of the three-layers BP neural network. Automatic recognition classifiers were designed and trained for the characteristics of digitized tongue pictures and tongue coating textures.The simple fractal dimension was sensitive to the thin/thick and greasy characteristics of digitized tongue pictures and could better judge the characteristics of the thickness of the tongue coating. A classifier with characteristic parameters of multiple fractal spectra as the input vectors identified by the BP neural network models could effectively increase the accuracy rate judged by the characteristics of the tongue coating texture.

中文翻译：

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基于分形理论的中医数字化舌象和舌苔纹理特征分析与识别。

已经提出了简单的分形维数用于分析数字化舌头图像和舌头涂层纹理的特征，这可以在扩大样本量的条件下进一步建立客观的分类标准。但是，关于简单的分形维数的详细描述受到限制。因此，可以通过进一步计算数字化舌头图像的多重分形谱特征来设计BP（反向传播）神经网络模型分类器，以对舌苔的薄/厚或油腻特性进行分类和识别。在标准环境中收集了587张数字化舌头图片。对样本数据的计算结果进行了统计分析，观察到分形维数对数字化舌头图像的薄/厚，油腻特性的敏感性。由于重叠区域是由单个参数值的范围引起的，因此进一步提出了数字化舌图像的多重分形谱的另外八个特征参数作为三层BP神经网络输入层中的元素。设计并训练了针对数字化舌图像和舌苔纹理特征的自动识别分类器，简单的分形维数对数字化舌图像的薄/厚，油腻特性敏感，可以更好地判断舌苔厚度的特征。