摘要
目的:建立基于切面图像纹理特征参数的辨识模型,探讨中药饮片自动识别的可行性。方法:基于中药饮片切面图像的灰度共生矩阵和灰度梯度共生矩阵,选取18种中药材不同样本图像的26个纹理特征参数,分别建立训练集和测试集。利用最小协方差行列式MCD方法对训练集进行离群值剔除处理。采用朴素贝叶斯及BP神经网络2种建模方法和十折交叉验证,建立18种中药材的判别模型。结果:在提取的26个纹理特征参数的基础上,利用MCD方法剔除训练集的离群值后,用BP神经网络建立的判别模型判正率达到90%,说明效能良好。结论:将建立的辨识模型用于中药饮片的自动识别具有可行性,为中药直观鉴别的定量化、科学化以及客观化提供了一套新的技术手段。
This study was aimed to establish the classification method of Chinese herbal medicine based on feature parameters extracted from images of herbal transverse section, in order to explore the feasibility of automatic identi-fication method of herbal medicine. The extracted 26 parameters of 18 herbal medicine images by gray-level co-oc-currence matrix and grayscale gradient matrix were used as the basic data set. And the minimum covariance determi-nant （MCD） was used to delete the outliers. A total of 18 identification models were established using the native Bayes method and BP neural network methods. The results showed that the average correct rates of models were 90%. It was concluded the feasibility of using these models in the establishment of the automatic identification method of herbal medicines. It provided new technologies for the quantitative, scientific and objective identification of Chinese herbal medicine.
出处
《世界科学技术-中医药现代化》
北大核心
2014年第12期2558-2562,共5页
Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology
基金
国家自然科学基金委青年基金项目(81102800):基于辩状论质思想的中药质量性状评价数学模型研究
负责人:陶欧
教育部新世纪优秀人才支持项目(NCET-11-0605):中药信息融合与利用
负责人:王耘
关键词
中药饮片
纹理特征参数
分类模型
最小协方差行列式
BP神经网络
Chinese herbal medicine
parameters of texture feature
classification model
minimum covariance determinant
BP neural network
