Characterization of shapes for use in classification of starch grains images

Chong Sze TONG*, Siu Kai Choy, Sung Nok CHIU, Zhongzhen ZHAO, Zhitao LIANG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

As tradition Chinese herbal medicine becomes increasingly popular, there is an urgent need for efficient and accurate methods for the authentication of the Chinese Materia Medica (CMM) used in the herbal medicine. In this work, we present a denoising filter and introduce the use of chord length distribution (CLD) for the classification of starch grains in microscopic images of Chinese Materia Medica. Our simple denoising filter is adaptive to the background and is shown to be effective to remove noise, which appears in CMM microscopic starch grains images. The CLD is extracted by considering the frequency of the chord length in the binarized starch grains image, and we shall show that the CLD is an efficient and effective characterization of the starch grains. Experimental results on 240 starch grains images of 24 classes show that our method outperforms benchmark result using the current state-of-the-art method based on circular size distribution extracted by morphological operators at much higher computational cost.

Original languageEnglish
Pages (from-to)651-658
Number of pages8
JournalMicroscopy Research and Technique
Volume71
Issue number9
DOIs
Publication statusPublished - Sep 2008

Scopus Subject Areas

  • Anatomy
  • Histology
  • Instrumentation
  • Medical Laboratory Technology

User-Defined Keywords

  • Chinese materia medica
  • Chord length distribution
  • Granulometry
  • Microscopic classification
  • Noise filtering
  • Similarity measurement
  • Starch grains

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