Microscopic Image Recognition of Chinese Medicinal Materials Based on Gray-level Matching Template / 中国实验方剂学杂志

ABSTRACT

Objective:To build a gray-level matching template by using the gray level information of the microscopic image of the transverse section of Chinese medicinal materials,in order to realize the automatic recognition of the images of Chinese medicinal materials independent of scale and orientation. Method:By using the embedding method of polyethylene glycol (PEG),the transverse slices of 19 kinds of common rhizomatous medicinal materials were obtained. The images of the slices were taken by digital microscopic imaging technology,and the mosaic grayscale images were obtained by image registration,noise removal and boundary location. The center of the structure of the materials in the images was selected to establish the polar coordinate system, so as to divide grids from the radial and angular directions. By counting the gray information in each grid,the gray information digital matrix that can characterize the microscopic identification characteristics of the materials was obtained. Images in an appropriate sample size was used to train the matrix for generalization of the matrix. The covariance coefficients between the matrix of positive or negative verification sample and the template matrix were calculated to set the best identification parameters. For each medicinal material,80 fan-shaped images were prepared,including 70% of training samples,15% of validation samples and 15% of test samples,and single template and template set were tested with test samples. Result:In the test of 240 images including non-template-set medicinal materials,the correct recognition rate of single-template test was 90.1%,and that of template-set test was 92.5%. Conclusion:This method can well characterize the microscopic identification characteristics of Chinese medicinal materials, with a strong anti-interference ability and less subjective-errors, acquire sample images easily, and provide technical support for the digitization of morphological quality control of Chinese medicinal materials.