Segmentation and Characterization of Saposhnikoviae Radix Micrograph Based on Edge Detection

LIANG Li-jin; LU Wen-biao; WANG Feng-mei

doi:10.13422/j.cnki.syfjx.20180608

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Segmentation and Characterization of Saposhnikoviae Radix Micrograph Based on Edge Detection

更新时间：2024-01-04

- Segmentation and Characterization of Saposhnikoviae Radix Micrograph Based on Edge Detection
- Chinese Journal of Experimental Traditional Medical Formulae Vol. 24, Issue 6, Pages: 37-41(2018)
- 作者机构：
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- DOI：10.13422/j.cnki.syfjx.20180608
  CLC： R282.5
- Published：2018
- 稿件说明：
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LIANG Li-jin, LU Wen-biao, WANG Feng-mei. Segmentation and Characterization of Saposhnikoviae Radix Micrograph Based on Edge Detection[J]. Chinese journal of experimental traditional medical formulae, 2018, 24(6): 37-41.
DOI：

LIANG Li-jin, LU Wen-biao, WANG Feng-mei. Segmentation and Characterization of Saposhnikoviae Radix Micrograph Based on Edge Detection[J]. Chinese journal of experimental traditional medical formulae, 2018, 24(6): 37-41. DOI： 10.13422/j.cnki.syfjx.20180608.

摘要

目的：通过编程实现对防风药材中多种组织结构的量化表征，为定量描述中药材显微鉴定特征提供技术支持。方法：根据药材显微图像中主要形态结构的特征，利用数字图像处理技术和采用MATLAB R2013a软件编程的方法，进行防风药材横切面各视野显微图像的摄取和编码，采用模板匹配法对各视野灰度图像进行配准并拼接，用Canny算子对拼接图像进行边缘检测，通过形态学基本操作对拼接图像进行图像二值化以及分割各种显微特征目标物和分析其大小、形态、数量，并采用系统聚类法对油管和导管的分布进行表征；将提取的4个产地防风药材的数据进行判别分析。结果：从样品图像中分割出了防风药材的木栓层、韧皮部、木质部、油管、导管等横切面中的主要显微特征；从分割的图像中各提取了20个参数，经逐步判别分析筛选出12个参数，以此对4个产地防风药材样品在组织形态方面的差异进行判别分析，总正确率为91.4%。结论：该方法能较准确地描述4个产地防风药材横切面组织结构的细微差别，这对不同产地防风药材的显微鉴别具有一定的参考意义。

Abstract

Objective: To realize the quantitative characterization of varied morphological structures in Saposhnikoviae Radix by programming

in order to provide technical support for the quantitative description of the microscopic identification characteristics of the Chinese herbal medicines.Method: According to the characteristics of the main structures in micrograph of the materials

digital image processing technology and MATLAB R2013a software programming were used for taking and encoding cross sectional micrographs of the materials; registration and stitching for each gray image were completed by template matching; edge detection was conducted for mosaic images using Canny operator; binarization and segmentation were made for microscopic characteristics to analyze the size

shape and number by basic morphological operation; distribution characterization was made for oil tubes and vessels using by hierarchical clustering; and discriminant analysis was performed for the data of the samples from four origins.Result: The main microscopic characteristics in the transverse section images of the materials

such as phellem

phloem

xylem

oil tubes and vessels

were respectively segmented into sub images; 20 parameters were extracted from the sub images

and 12 parameters of them were screened out by stepwise discriminant analysis. Through the discriminant analysis of the morphological parameters

the total accuracy of the sample discrimination was 91.4%.Conclusion: The method can accurately describe the fine differences of the transverse section tissues of the samples

which has certain reference significance for the microscopic identification of the medicinal materials from different origins.

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