Statistical Analysis of Tongue Images for Feature Extraction and Diagnostics

Wang, Xingzheng; Zhang, Bob; Yang, Ziqiang; Wang, Haoqian; Zhang, David

doi:10.1109/tip.2013.2284070

Cited by 94 publications

(32 citation statements)

References 42 publications

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“…Figure 4 illustrates the tongue color gamut (green area) enclosed by the black boundary. As a reference from our previous work, 12 the predominate color of the Diseased samples is "Red" with a RGB value of [189 99 91], while for Healthy samples it is "Light Red" with a RGB value of [227 150 147].…”

Section: Statistical Tongue Color Gamutmentioning

confidence: 99%

“…This is still the most popular way to perform tongue diagnosis. As more research is done in this area using modern techniques, either computerized TCM [5][6][7][8][9] or using the tongue as a physiological characteristic in medical biometrics, [10][11][12][13][14][15] its capture and represent on in digital form is vital. The quality of a digital tongue image acquired by a camera can cause enormous influences to its diagnostic result.…”

Section: Introductionmentioning

confidence: 99%

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A novel Color Rendition Chart for digital tongue image calibration

Zhang

Nie

Zhao

2018

Color Research & Application

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Digital tongue images are usually acquired by a camera under specific illumination environments. In order to guarantee better color representation of the tongue body, we propose a novel tongue Color Rendition Chart acting as a color reference to be used in color calibration algorithms to standardize the captured tongue images. First, based on a large tongue image database captured with our digital tongue image acquisition system, we establish a statistical tongue color gamut. Then, from the first step, different quantities of colors in the Color Rendition Chart are determined via experimentation.

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Section: Statistical Tongue Color Gamutmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel Color Rendition Chart for digital tongue image calibration

Zhang

Nie

Zhao

2018

Color Research & Application

Self Cite

View full text Add to dashboard Cite

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“…There are several researches that utilized neural network in categorizing the tongue features [18, 22–24]. Nonetheless, the two most applied classifiers in tongue diagnosis field are Bayesian network classifier [10, 25] and SVM-based classifier [3, 11–13, 26, 27]. Even though there are sufficient training examples from the textural and chromatic properties of a tongue used in the classifier, the accuracy in some reported works needs to be improved [10, 28].…”

Section: Previous Workmentioning

confidence: 99%

“…In 2010 and 2013, a tongue colour gamut descriptor has been proposed by several researchers using one class SVM [3, 4]. This proposed work suggested that the tongue colour gamut is very narrow and comprises of different types of identical colour; thus, there are many overlapping and similar pixel values that exist.…”

Section: Introductionmentioning

confidence: 99%

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

Kamarudin

Ooi²,

Kawanabe

et al. 2017

Journal of Healthcare Engineering

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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.

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“…Spectral Angle Mapper (SAM) [6] can recognize and classify tongue colors using their spectral signatures rather than their color values in RGB color space. In [7] the authors aimed to build a mathematically described tongue color space for diagnostic feature extraction based on the statistical distribution of tongue color. In [8], partition patients' state (either healthy or diseased) was quantitatively analyzed using geometry tongue shape features with the computerized methods.…”

Section: Introductionmentioning

confidence: 99%

Tongue Images Classification Based on Constrained High Dispersal Network

Meng

Cao

Duan

et al. 2017

Evidence-Based Complementary and Alternative Medicine

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Computer aided tongue diagnosis has a great potential to play important roles in traditional Chinese medicine (TCM). However, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by deep convolutional neural network (CNN), we propose a novel feature extraction framework called constrained high dispersal neural networks (CHDNet) to extract unbiased features and reduce human labor for tongue diagnosis in TCM. Previous CNN models have mostly focused on learning convolutional filters and adapting weights between them, but these models have two major issues: redundancy and insufficient capability in handling unbalanced sample distribution. We introduce high dispersal and local response normalization operation to address the issue of redundancy. We also add multiscale feature analysis to avoid the problem of sensitivity to deformation. Our proposed CHDNet learns high-level features and provides more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed method on a set of 267 gastritis patients and a control group of 48 healthy volunteers. Test results show that CHDNet is a promising method in tongue image classification for the TCM study.

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Statistical Analysis of Tongue Images for Feature Extraction and Diagnostics

Cited by 94 publications

References 42 publications

A novel Color Rendition Chart for digital tongue image calibration

A novel Color Rendition Chart for digital tongue image calibration

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

Tongue Images Classification Based on Constrained High Dispersal Network

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