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Characterization of shapes for use in classification of starch grains images
Author(s) -
Tong ChongSze,
Choy SiuKai,
Chiu SungNok,
Zhao ZhongZhen,
Liang ZhiTao
Publication year - 2008
Publication title -
microscopy research and technique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 118
eISSN - 1097-0029
pISSN - 1059-910X
DOI - 10.1002/jemt.20606
Subject(s) - starch , noise reduction , computer science , artificial intelligence , pattern recognition (psychology) , noise (video) , filter (signal processing) , benchmark (surveying) , chord (peer to peer) , materials science , mathematics , image (mathematics) , computer vision , biology , geography , distributed computing , biochemistry , geodesy
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. cost. Microsc. Res. Tech., 2008. © 2008 Wiley‐Liss, Inc.