Identification of starch grains in microscopic images based on granulometric operations

Chong Sze TONG*, Siu Kai Choy, Zhongzhen ZHAO, Zhitao LIANG, Hubiao CHEN

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

12 Citations (Scopus)


This study demonstrates the feasibility of feature extraction and similarity measurement for the identification of starch grains, which are often found in microscopic images of Chinese Materia Medica (CMM) and as such is an important feature for use in the authentication of CMM with abundance of starch grains and otherwise without other special microscopic characteristics. Our proposed image signature, namely, the size distribution, indicates that it is effective to characterize starch grains in microscopic images of those CMM. Experimental results in a small scale study show that the recognition rate for the proposed signature using different measures of discrepancy is around 90%, which is considered very good while the benchmark wavelet-based energy signature has only about 60% recognition. Moreover, the extraction of the size distribution only requires moderate level of computational complexity. In this study, our proposed image signature provides greater accuracy and flexibility in capturing starch grains information for the authentication of CMM, compared with other existing preliminary identification approach in which manual image segmentation is necessary.

Original languageEnglish
Pages (from-to)724-732
Number of pages9
JournalMicroscopy Research and Technique
Issue number8
Publication statusPublished - Aug 2007

Scopus Subject Areas

  • Anatomy
  • Histology
  • Instrumentation
  • Medical Laboratory Technology

User-Defined Keywords

  • Chinese materia medica
  • Granulometry
  • Microscopic identification
  • Similarity measurement
  • Starch grain


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