Characterization of shapes for use in classification of starch grains images.
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.
Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China. cstong@hkbu.edu.hk
Chong-Sze
CS
Tong
Siu-Kai
SK
Choy
Sung-Nok
SN
Chiu
Zhong-Zhen
ZZ
Zhao
Zhi-Tao
ZT
Liang
D004365:Drugs, Chinese Herbal / Q000737:chemistry* / Q000592:standards; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010316:Particle Size; D010944:Plants; D013213:Starch / Q000737:chemistry / Q000145:classification* / Q000648:ultrastructure*
D004365:Drugs, Chinese Herbal; D013213:Starch