Level set method based tongue segmentation in traditional chinese medicine

被引:0
|
作者
Zou, Fengmei [1 ]
Yang, Dasheng [1 ]
Li, Shaozi [1 ]
Xu, Jiatuo [1 ]
Zhou, Changle [1 ]
机构
[1] Xiamen Univ, Inst Artificial Intelligence, Xiamen 361005, Peoples R China
关键词
Traditional Chinese medicine; tongue segmentation; level set method; geodesic active contour model;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The correct segmentation of the tongue is the precondition to the quantification of tongue diagnosis in Traditional Chinese Medicine. The main method at present is by Snake model which uses the gradient of a tongue image as external energy to make the initial contour converge to the edge of the tongue body. Although this method has strong adaptive capability, it has a defect: for those images including the lower lip, the final contour often converges to the edge of the lip. To address the problem, a geometric model is proposed to correct the edges influenced by large concavity or sharp angle such as those with the lower lip. With this model, the initial contour does not include the whole lip. Then the initial contour is evolved toward the edge of the tongue body by Geodesic Active Contour Model after the Signed Distance Function is constructed. From the experiment results, the method is demonstrated to have good segmentation accuracy.
引用
收藏
页码:37 / +
页数:2
相关论文
共 50 条
  • [1] Traditional Chinese Medicine Tongue Image Segmentation Based on Lever set Model
    Tang, Suxiang
    Wang, Renhuang
    Zhang, Lei
    Liu, Hongjiang
    Liu, Xiaofeng
    Ming, Junfeng
    [J]. 2010 THE 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION (PACIIA2010), VOL VII, 2010, : 198 - 201
  • [2] Traditional Chinese Medicine Tongue Image Segmentation Based on Lever set Model
    Tang, Suxiang
    Wang, Renhuang
    Zhang, Lei
    Liu, Hongjiang
    Liu, Xiaofeng
    Ming, Junfeng
    [J]. 2011 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION AND INDUSTRIAL APPLICATION (ICIA2011), VOL II, 2011, : 197 - 200
  • [3] Tongue segmentation algorithm for traditional Chinese medicine based on convolutional neural network
    Sun, Pengzhao
    Yang, XiaoPing
    Ban, Yuhong
    [J]. AOPC 2019: OPTICAL SENSING AND IMAGING TECHNOLOGY, 2019, 11338
  • [4] TONGUE INSPECTION - A DIAGNOSTIC METHOD IN TRADITIONAL CHINESE MEDICINE
    CHEN, TL
    CHEN, MF
    [J]. CHINAS MEDICINE, 1966, (01): : 69 - &
  • [5] Comparison of Edge Segmentation Methods to Tongue Diagnosis in Traditional Chinese Medicine
    Wang, Chieh-Hsuan
    Wei, Ching-Chuan
    Li, Che-Hao
    [J]. COMPUTATIONAL COLLECTIVE INTELLIGENCE: TECHNOLOGIES AND APPLICATIONS, PT II, 2010, 6422 : 232 - 238
  • [6] Semi-supervised tongue image segmentation method for traditional chinese medicine based on mutual learning with dual models
    Li, Fangxu
    Xu, Wangming
    Xu, Xue
    Jia, Yun
    [J]. CHINESE JOURNAL OF LIQUID CRYSTALS AND DISPLAYS, 2024, 39 (08) : 1014 - 1023
  • [7] An Assessment Method of Tongue Image Quality in Traditional Chinese Medicine
    Zhang, Xiang
    Zhang, Xinfeng
    Wang, Bo Chao
    Hu, Guangqin
    [J]. 2016 9TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2016), 2016, : 640 - 644
  • [8] An Assessment Method of Tongue Image Quality Based on Random Forest in Traditional Chinese Medicine
    Zhang, Xinfeng
    Wang, Yazhen
    Hu, Guangqin
    Zhang, Jing
    [J]. ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, ICIC 2015, PT III, 2015, 9227 : 730 - 737
  • [9] A Novel Automatic Tongue Coating Extraction Method in Tongue Diagnose of Traditional Chinese Medicine
    Bai, Linda Yunlu
    Shi, Yundi
    Wu, Jia
    Zhang, Yonghong
    weng, Weiliang
    Wu, Yu
    Bai, Jing
    [J]. WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2006, VOL 14, PTS 1-6, 2007, 14 : 2624 - +
  • [10] The Segmentation of the Body of Tongue Based on the Improved Level Set in TCM
    Li, Wenshu
    Yao, Jianfu
    Yuan, Linlin
    Zhou, Qinian
    [J]. LIFE SYSTEM MODELING AND INTELLIGENT COMPUTING, 2010, 6330 : 220 - 229