Literature DB >> 17281824

Computer aided tongue diagnosis system.

H Zhang1, K Wang, D Zhang, B Pang, B Huang.   

Abstract

To circumvent the subjective and qualitative problems of traditional tongue diagnosis, we present a novel computer aided tongue diagnosis system (CATDS). In this system, a standard acquisition device as well as a new color correction method is utilized to capture qualified tongue images. The system is constituted by five components: User Interface Module, Acquisition Module, Tongue Image Database, Image Preprocessing Module and Diagnosis Engine. In contrast to existing CATDS, the proposed system aims to establish the relationship between quantitative features and diseases via the Bayesian networks. System tests are carried out on a group of 544 patients affected by 9 common diseases and 56 healthy volunteers. The results show that the system can properly identify six groups: healthy, pulmonary heart disease, appendicitis, gastritis, pancreatitis and bronchitis with accuracy higher than 75%. Moreover, the execution time for the whole diagnosis process including image preprocessing and diagnosis is less than 5 seconds.

Entities:  

Year:  2005        PMID: 17281824     DOI: 10.1109/IEMBS.2005.1616055

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  10 in total

1.  Color Correction Parameter Estimation on the Smartphone and Its Application to Automatic Tongue Diagnosis.

Authors:  Min-Chun Hu; Ming-Hsun Cheng; Kun-Chan Lan
Journal:  J Med Syst       Date:  2015-11-02       Impact factor: 4.460

2.  Proposal for a new noncontact method for measuring tongue moisture to assist in tongue diagnosis and development of the tongue image analyzing system, which can separately record the gloss components of the tongue.

Authors:  Toshiya Nakaguchi; Kanako Takeda; Yuya Ishikawa; Takeshi Oji; Satoshi Yamamoto; Norimichi Tsumura; Keigo Ueda; Koichi Nagamine; Takao Namiki; Yoichi Miyake
Journal:  Biomed Res Int       Date:  2015-01-28       Impact factor: 3.411

3.  Computerized tongue image segmentation via the double geo-vector flow.

Authors:  Miao-Jing Shi; Guo-Zheng Li; Fu-Feng Li; Chao Xu
Journal:  Chin Med       Date:  2014-02-08       Impact factor: 5.455

4.  The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine.

Authors:  Zhen Qi; Li-Ping Tu; Jing-Bo Chen; Xiao-Juan Hu; Jia-Tuo Xu; Zhi-Feng Zhang
Journal:  Biomed Res Int       Date:  2016-12-06       Impact factor: 3.411

5.  A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis.

Authors:  Nur Diyana Kamarudin; Chia Yee Ooi; Tadaaki Kawanabe; Hiroshi Odaguchi; Fuminori Kobayashi
Journal:  J Healthc Eng       Date:  2017-04-20       Impact factor: 2.682

Review 6.  Review on the current trends in tongue diagnosis systems.

Authors:  Chang Jin Jung; Young Ju Jeon; Jong Yeol Kim; Keun Ho Kim
Journal:  Integr Med Res       Date:  2012-10-05

7.  Association between tongue coating thickness and ultraviolet fluorescence in patients with functional dyspepsia: A prospective observational study.

Authors:  Jihye Kim; Jiwon Kim; Inkwon Yeo; Juyeon Kim; Jinsung Kim; Dong-Hyun Nam
Journal:  Medicine (Baltimore)       Date:  2019-07       Impact factor: 1.817

8.  Variations of Tongue Coating Microbiota in Patients with Gastric Cancer.

Authors:  Jie Hu; Shuwen Han; Yan Chen; Zhaoning Ji
Journal:  Biomed Res Int       Date:  2015-09-17       Impact factor: 3.411

Review 9.  Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective.

Authors:  Changbo Zhao; Guo-Zheng Li; Chengjun Wang; Jinling Niu
Journal:  Evid Based Complement Alternat Med       Date:  2015-07-12       Impact factor: 2.629

10.  Study of factors involved in tongue color diagnosis by kampo medical practitioners using the farnsworth-munsell 100 hue test and tongue color images.

Authors:  Takeshi Oji; Takao Namiki; Toshiya Nakaguchi; Keigo Ueda; Kanako Takeda; Michimi Nakamura; Hideki Okamoto; Yoshiro Hirasaki
Journal:  Evid Based Complement Alternat Med       Date:  2014-04-06       Impact factor: 2.629

  10 in total

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