AI Article Synopsis
- The increasing popularity of traditional Chinese herbal medicine highlights the need for accurate methods to authenticate Chinese Materia Medica (CMM).
- This study introduces a new denoising filter and chord length distribution (CLD) technique to classify starch grains in microscopic images.
- Results from analyzing 240 starch grains across 24 classes demonstrate that this method is more effective and efficient than existing approaches, which rely on circular size distribution and require greater computational resources.
Article 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.
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| http://dx.doi.org/10.1002/jemt.20606 | DOI Listing |
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