Computer-assisted lip diagnosis on Traditional Chinese Medicine using multi-class support vector machines

BMC Complementary and Alternative Medicine (Impact Factor: 2.02). 08/2012; 12(1):127. DOI: 10.1186/1472-6882-12-127
Source: PubMed


In Traditional Chinese Medicine (TCM), the lip diagnosis is an important diagnostic method which has a long history and is applied widely. The lip color of a person is considered as a symptom to reflect the physical conditions of organs in the body. However, the traditional diagnostic approach is mainly based on observation by doctor’s nude eyes, which is non-quantitative and subjective. The non-quantitative approach largely depends on the doctor’s experience and influences accurate the diagnosis and treatment in TCM. Developing new quantification methods to identify the exact syndrome based on the lip diagnosis of TCM becomes urgent and important. In this paper, we design a computer-assisted classification model to provide an automatic and quantitative approach for the diagnosis of TCM based on the lip images.

A computer-assisted classification method is designed and applied for syndrome diagnosis based on the lip images. Our purpose is to classify the lip images into four groups: deep-red, red, purple and pale. The proposed scheme consists of four steps including the lip image preprocessing, image feature extraction, feature selection and classification. The extracted 84 features contain the lip color space component, texture and moment features. Feature subset selection is performed by using SVM-RFE (Support Vector Machine with recursive feature elimination), mRMR (minimum Redundancy Maximum Relevance) and IG (information gain). Classification model is constructed based on the collected lip image features using multi-class SVM and Weighted multi-class SVM (WSVM). In addition, we compare SVM with k-nearest neighbor (kNN) algorithm, Multiple Asymmetric Partial Least Squares Classifier (MAPLSC) and Naïve Bayes for the diagnosis performance comparison. All displayed faces image have obtained consent from the participants.

A total of 257 lip images are collected for the modeling of lip diagnosis in TCM. The feature selection method SVM-RFE selects 9 important features which are composed of 5 color component features, 3 texture features and 1 moment feature. SVM, MAPLSC, Naïve Bayes, kNN showed better classification results based on the 9 selected features than the results obtained from all the 84 features. The total classification accuracy of the five methods is 84%, 81%, 79% and 81%, 77%, respectively. So SVM achieves the best classification accuracy. The classification accuracy of SVM is 81%, 71%, 89% and 86% on Deep-red, Pale Purple, Red and lip image models, respectively. While with the feature selection algorithm mRMR and IG, the total classification accuracy of WSVM achieves the best classification accuracy. Therefore, the results show that the system can achieve best classification accuracy combined with SVM classifiers and SVM-REF feature selection algorithm.

A diagnostic system is proposed, which firstly segments the lip from the original facial image based on the Chan-Vese level set model and Otsu method, then extracts three kinds of features (color space features, Haralick co-occurrence features and Zernike moment features) on the lip image. Meanwhile, SVM-REF is adopted to select the optimal features. Finally, SVM is applied to classify the four classes. Besides, we also compare different feature selection algorithms and classifiers to verify our system. So the developed automatic and quantitative diagnosis system of TCM is effective to distinguish four lip image classes: Deep-red, Purple, Red and Pale. This study puts forward a new method and idea for the quantitative examination on lip diagnosis of TCM, as well as provides a template for objective diagnosis in TCM.

Download full-text


Available from: Guo-Zheng Li, Jan 20, 2014
51 Reads
  • Source
    • "Other than the above researches, Zheng et al. [42] employed SVM to classify different lip color categories with histogram statistical features on various color spaces. Li et al. [43] established an automatic lip color recognition system on a well-designed acquisition device. Different supervised learning algorithms and feature selection algorithms are extensively compared on their database. "
    [Show abstract] [Hide abstract]
    ABSTRACT: As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.
    Evidence-based Complementary and Alternative Medicine 08/2015; 2015:376716. DOI:10.1155/2015/376716 · 1.88 Impact Factor
    • "This study made a ranking of the variables most correlated with the output of the training process monitored by means of a Wilcoxon test. Li (2012) "
    [Show abstract] [Hide abstract]
    ABSTRACT: The interpretation of the results in a classification problem can be enhanced, specially in image texture analysis problems, by feature selection techniques, knowing which features contribute more to the classification performance. This paper presents an evaluation of a number of feature selection techniques for classification in a biomedical image texture dataset (2-DE gel images), with the aim of studying their performance and the stability in the selection of the features. We analyse three different techniques: subgroup-based multiple kernel learning (MKL), which can perform a feature selection by down-weighting or eliminating subsets of features which shares similar characteristic, and two different conventional feature selection techniques such as recursive feature elimination (RFE), with different classifiers (naive Bayes, support vector machines, bagged trees, random forest and linear discriminant analysis), and a genetic algorithm-based approach with an SVM as decision function. The different classifiers were compared using a ten times tenfold cross-validation model, and the best technique found is SVM-RFE, with an AUROC score of ( \(95.88 \pm 0.39\,\%\) ). However, this method is not significantly better than RFE-TREE, RFE-RF and grouped MKL, whilst MKL uses lower number of features, increasing the interpretability of the results. MKL selects always the same features, related to wavelet-based textures, while RFE methods focuses specially co-occurrence matrix-based features, but with high instability in the number of features selected.
    Soft Computing 01/2015; DOI:10.1007/s00500-014-1573-5 · 1.27 Impact Factor
  • Source
    • "Future studies in Oriental medicine may use this device when controlling for exterior-and interior-related syndromes , which are indicated by a floating and a sinking pulse, respectively. The recently introduced computerassisted lip diagnostic model, which analyzes lip color (deep red, red, purple, and pale), may offer future researchers the ability to control for disorders caused by heat, cold, excess, or deficiency, which are determined by differences in lip color [19]. A combination of pulse, inspection of the complexion, and other diagnostic techniques may therefore substantially contribute to the integrity of future trials in Oriental medicine. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Although Oriental medicine, by nature, may be considered an etiology-based approach to healing, its role in modern research is primarily empirical. The absolute dependence on symptomatic presentation to establish acupuncture point selection goes against the grain of traditional Oriental methods, which emphasize pulse, tongue, and other diagnostic tools to determine the overall biological and psychological conditions of the patient. Recently introduced diagnostic methods in Oriental medical research indicate a potential shift from empirically to etiologically centered designs. This article reviews current mainstream approaches to efficacy trial designs and proceeds with the analysis of newer research models, such as a constitutional approach spearheaded in Korea by the field of four-constitutional medicine. Copyright © 2015. Published by Elsevier B.V.
    Journal of Acupuncture and Meridian Studies 11/2014; 8(2). DOI:10.1016/j.jams.2014.11.007
Show more