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

In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

[1]  Nur Diyana Kamarudin,et al.  Quantification of tongue colour using machine learning in Kampo medicine , 2016 .

[2]  Bo Peng,et al.  An SA-GA-BP neural network-based color correction algorithm for TCM tongue images , 2014, Neurocomputing.

[3]  Jitendra Malik,et al.  SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[4]  David Zhang,et al.  Classification of hyperspectral medical tongue images for tongue diagnosis , 2007, Comput. Medical Imaging Graph..

[5]  Kostas Karpouzis,et al.  Emerging Artificial Intelligence Applications in Computer Engineering - Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies , 2007, Emerging Artificial Intelligence Applications in Computer Engineering.

[6]  Carlo Sansone,et al.  Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review. , 2013, Journal of healthcare engineering.

[7]  David Zhang,et al.  Tongue image analysis for appendicitis diagnosis , 2005, Inf. Sci..

[8]  David Zhang,et al.  Detecting Diabetes Mellitus and Nonproliferative Diabetic Retinopathy Using Tongue Color, Texture, and Geometry Features , 2014, IEEE Transactions on Biomedical Engineering.

[9]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[10]  Jiawei Han,et al.  Clustered Support Vector Machines , 2013, AISTATS.

[11]  Jae-Hyung Jang,et al.  Development of the digital tongue inspection system with image analysis , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[12]  Liang Xu,et al.  Sublingual vein extraction algorithm based on hyperspectral tongue imaging technology , 2011, Comput. Medical Imaging Graph..

[13]  Yanjun Zhang,et al.  Automatic sleep staging using multi-dimensional feature extraction and multi-kernel fuzzy support vector machine. , 2014, Journal of healthcare engineering.

[14]  Zhang Hengxiang.  Research on the Tongue Color Classification with Automatic Tongue Analysis in Traditional Chinese Medicine , 2006 .

[15]  Xindong Wu,et al.  Support vector machines based on K-means clustering for real-time business intelligence systems , 2005, Int. J. Bus. Intell. Data Min..

[16]  D. Zhang,et al.  Computer Aided Tongue Diagnosis System , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[17]  David Zhang,et al.  Computerized tongue diagnosis based on Bayesian networks , 2004, IEEE Transactions on Biomedical Engineering.

[18]  David Zhang,et al.  Statistical Analysis of Tongue Images for Feature Extraction and Diagnostics , 2013, IEEE Transactions on Image Processing.

[19]  David Zhang,et al.  An Optimized Tongue Image Color Correction Scheme , 2010, IEEE Transactions on Information Technology in Biomedicine.

[20]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[21]  Xiaozhao Xu,et al.  Tongue Image Classification Based on the TSVM , 2009, 2009 2nd International Congress on Image and Signal Processing.

[22]  K. Purnama,et al.  Tongue Image Analysis for Diabetes Mellitus Diagnosis Based on SOM Kohonen , 2011 .

[23]  Si Wu,et al.  Improving support vector machine classifiers by modifying kernel functions , 1999, Neural Networks.

[24]  Rameswar Debnath,et al.  A decision based one-against-one method for multi-class support vector machine , 2004, Pattern Analysis and Applications.

[25]  Li Zhuo,et al.  Tongue image classification based on Universum SVM , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.

[26]  Ye Duan,et al.  Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine , 2012, Evidence-based complementary and alternative medicine : eCAM.

[27]  Lai-Man Po,et al.  A Novel Computerized Method Based on Support Vector Machine for Tongue Diagnosis , 2007, 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System.

[28]  A. Lu,et al.  Current situation of International Organization for Standardization/Technical Committee 249 international standards of traditional Chinese medicine , 2017, Chinese Journal of Integrative Medicine.

[29]  Sang Won Yoon,et al.  Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms , 2014, Expert Syst. Appl..

[30]  Yang Liu,et al.  K-SVM: An Effective SVM Algorithm Based on K-means Clustering , 2013, J. Comput..

[31]  Jane You,et al.  Tongue Color Analysis for Medical Application , 2013, Evidence-based complementary and alternative medicine : eCAM.

[32]  Zhi Liu,et al.  Tongue color analysis and discrimination based on hyperspectral images , 2009, Comput. Medical Imaging Graph..

[33]  Siu Cheung Hui,et al.  Machine learning for tongue diagnosis , 2007, 2007 6th International Conference on Information, Communications & Signal Processing.

[34]  Patrick Haffner,et al.  Support vector machines for histogram-based image classification , 1999, IEEE Trans. Neural Networks.

[35]  Chia Yee Ooi,et al.  Tongue's substance and coating recognition analysis using HSV color threshold in tongue diagnosis , 2016, International Workshop on Pattern Recognition.

[36]  Guanming Lu,et al.  A Novel Computerized System for Tongue Diagnosis , 2008, 2008 International Seminar on Future Information Technology and Management Engineering.

[37]  Gao Dunyue.  A Method for Tongue Coat Classification Based on Neural Network Integration , 2003 .

[38]  Hyeran Byun,et al.  Applications of Support Vector Machines for Pattern Recognition: A Survey , 2002, SVM.