TY - GEN
T1 - Classification of tongue images based on doublet and color space dictionary
AU - Cao, Guitao
AU - Ding, Jie
AU - Duan, Ye
AU - Tu, Liping
AU - Xu, Jiatuo
AU - Xu, Dong
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/17
Y1 - 2017/1/17
N2 - Recently, pathological diagnosis plays a crucial role in many areas of medicine, and some researchers have proposed many models and algorithms for improving classification accuracy by extracting excellent feature or modifying the classifier. They have also achieved excellent results on pathological diagnosis using tongue images. However, pixel values can't express intuitive features of tongue images and different classifiers for training samples have different adaptability. Accordingly, this paper presents a robust approach to infer the pathological characteristics by observing tongue images. Our proposed method makes full use of the local information and similarity of tongue images. Firstly, tongue images in RGB color space are converted to Lab. Then, we compute tongue statistics information. In the calculation process, Lab space dictionary is created at first, through it, we compute statistic value for each dictionary value. After that, a method based on Doublets is taken for feature optimization. At last, we use XGBOOST classifier to predict the categories of tongue images. We achieve classification accuracy of 95.39% using statistics feature and the improved classifier, which is helpful for TCM (Traditional Chinese Medicine) diagnosis.
AB - Recently, pathological diagnosis plays a crucial role in many areas of medicine, and some researchers have proposed many models and algorithms for improving classification accuracy by extracting excellent feature or modifying the classifier. They have also achieved excellent results on pathological diagnosis using tongue images. However, pixel values can't express intuitive features of tongue images and different classifiers for training samples have different adaptability. Accordingly, this paper presents a robust approach to infer the pathological characteristics by observing tongue images. Our proposed method makes full use of the local information and similarity of tongue images. Firstly, tongue images in RGB color space are converted to Lab. Then, we compute tongue statistics information. In the calculation process, Lab space dictionary is created at first, through it, we compute statistic value for each dictionary value. After that, a method based on Doublets is taken for feature optimization. At last, we use XGBOOST classifier to predict the categories of tongue images. We achieve classification accuracy of 95.39% using statistics feature and the improved classifier, which is helpful for TCM (Traditional Chinese Medicine) diagnosis.
KW - Feature optimization
KW - Image processing
KW - Pattern recognition
KW - TCM (Traditional Chinese Medicine)
KW - Tongue images
UR - https://www.scopus.com/pages/publications/85013293344
U2 - 10.1109/BIBM.2016.7822686
DO - 10.1109/BIBM.2016.7822686
M3 - 会议稿件
AN - SCOPUS:85013293344
T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
SP - 1170
EP - 1175
BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
A2 - Burrage, Kevin
A2 - Zhu, Qian
A2 - Liu, Yunlong
A2 - Tian, Tianhai
A2 - Wang, Yadong
A2 - Hu, Xiaohua Tony
A2 - Jiang, Qinghua
A2 - Song, Jiangning
A2 - Morishita, Shinichi
A2 - Burrage, Kevin
A2 - Wang, Guohua
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Y2 - 15 December 2016 through 18 December 2016
ER -