Xinjiang Uygur Medicine Image Feature Extraction and Discriminant Analysis Based on Color and Textural Features / 中国中医药信息杂志

Objective To extract Xinjiang Uyghur medicine image features and analyze the features; To investigate the image classification effect of the researched features; To find the suitable features for Xinjiang Uyghur medicine image classification; To lay the foundation for content-based medical image retrieval system of Xinjiang Uyghur medicine images.Methods The flowers and leaves of Xinjiang Uyghur medicine were treated as the research objects. First, images were under preprocessing. Then color and textural features were extracted as original features and statistics method was used to analyze the features. Maximum classification distance was used to analyze the main features obtained from image classification. At last, the classification ability of features was evaluated by Bayes discriminant analysis.Results Color and textural features were selected and classified. The correct classification rate of flower images was 85% and the correct classification rate of leaf images was 62%. The classification effect of flower images used by selected features was better than classification effect of original feature.Conclusion Compared with the classification of original features, the classification accuracy of flower medicine is higher through selected features. This research can lay a certain foundation for the further researches on Xinjiang Uyghur medicine images and the improvement of feature extraction methods.

Extraction of Texture Feature Parameter of Transverse Section in Chinese Herbal Medicine by Gray-Level Co-occurrence Matrix / 世界科学技术-中医药现代化

Digitalization is an important method for the objectification and quantification of quality control on Chi-nese herbal medicine. To solve the problem of texture online visualization of Chinese herbal medicine and the estab-lishment of automatic identification method based on the texture, 12 Chinese herbal medicines were selected to cap-ture the images of their transverse sections. A total of 11 features were extracted from images using Gray-level Co-occurrence Matrix (GLCM) method. Through analyzing the influence of distances and angles between pixels on identi-fication results, it was concluded that when the distance was d = 3 and the angle was ? = 0o, the features extracted were suitable for expressing the texture of the transverse sections. The results indicated the feasibility of establishing the automatic identification method on Chinese herbal medicine based on image of transverse section. It will provide new technologies for the objectification and quantification of identification on Chinese herbal medicine.