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Proceedings Paper

Automatic identification of clue cells in microscopic leucorrhea images based on texture features and combination of kernel functions of SVM
Author(s): Ruqian Hao; Lin Liu; Xiangzhou Wang; Jing Zhang; Xiaohui Du; Yong Liu
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Paper Abstract

Automatic identification of clue cells in microscopic leucorrhea images provides important information for evaluating gynecological diseases. Traditional manual microscopic examination of Gram-stained vaginal smears is adopted by most hospitals for identifying clue cells; however, it is both complex and time-consuming. In order to solve these problems, an automatic identification of clue cells in microscopic leucorrhea images based on machine learning is proposed in this paper. First, the Otsu threshold method is used to segment regions of interest (ROI) in image preprocessing according to the morphological features of clue cells. Then, Gabor, HOG and GLCM texture features are extracted to describe irregular edges and rough surfaces of clue cells. Finally, a SVM classifier using a hybrid kernel function by linearly weighted RBF and polynomial kernels is trained to identify clue cells rapidly and conveniently. In experiments, the method using GLCM texture features and a hybrid kernel function of SVM achieved 94.64% accuracy and 94.92% recall rate, which was better than methods using Gabor or HOG texture features and a single kernel function of SVM.

Paper Details

Date Published: 8 February 2019
PDF: 7 pages
Proc. SPIE 10843, 9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging, 108430P (8 February 2019); doi: 10.1117/12.2506329
Show Author Affiliations
Ruqian Hao, Univ. of Electronic Science and Technology of China (China)
Lin Liu, Univ. of Electronic Science and Technology of China (China)
Xiangzhou Wang, Univ. of Electronic Science and Technology of China (China)
Jing Zhang, Univ. of Electronic Science and Technology of China (China)
Xiaohui Du, Univ. of Electronic Science and Technology of China (China)
Yong Liu, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10843:
9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging
Yadong Jiang; Xiaoliang Ma; Xiong Li; Mingbo Pu; Xue Feng; Bernard Kippelen, Editor(s)

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