Tongue diagnosis method for extraction of effective region and classification of tongue coating

Kim, K.H.; Do, Jun-Hyeong; Ryu, Hyunhee; Kim, J.-Y.

doi:10.1109/ipta.2008.4743772

Cited by 20 publications

(12 citation statements)

References 10 publications

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“…Owing to the special characteristics of the tongue color, different color channels have been embedded into these segmentation methods, such as region growing [20], fuzzy C-means [21], and shortest path [22]. …”

Section: Introductionmentioning

confidence: 99%

Computerized tongue image segmentation via the double geo-vector flow

Shi

et al. 2014

Chin Med

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BackgroundVisual inspection for tongue analysis is a diagnostic method in traditional Chinese medicine (TCM). Owing to the variations in tongue features, such as color, texture, coating, and shape, it is difficult to precisely extract the tongue region in images. This study aims to quantitatively evaluate tongue diagnosis via automatic tongue segmentation.MethodsExperiments were conducted using a clinical image dataset provided by the Laboratory of Traditional Medical Syndromes, Shanghai University of TCM. First, a clinical tongue image was refined by a saliency window. Second, we initialized the tongue area as the upper binary part and lower level set matrix. Third, a double geo-vector flow (DGF) was proposed to detect the tongue edge and segment the tongue region in the image, such that the geodesic flow was evaluated in the lower part, and the geo-gradient vector flow was evaluated in the upper part.ResultsThe performance of the DGF was evaluated using 100 images. The DGF exhibited better results compared with other representative studies, with its true-positive volume fraction reaching 98.5%, its false-positive volume fraction being 1.51%, and its false-negative volume fraction being 1.42%. The errors between the proposed automatic segmentation results and manual contours were 0.29 and 1.43% in terms of the standard boundary error metrics of Hausdorff distance and mean distance, respectively.ConclusionsBy analyzing the time complexity of the DGF and evaluating its performance via standard boundary and area error metrics, we have shown both efficiency and effectiveness of the DGF for automatic tongue image segmentation.

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Section: Introductionmentioning

confidence: 99%

Computerized tongue image segmentation via the double geo-vector flow

Shi

et al. 2014

Chin Med

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“…Ridge edge and slope edge can be processed properly by the algorithm above. A disk structure element with size 363 is dilated to form other bigger structure elements, and then all these structure elements are used to detect edges according to equation (2). Finally, mean value of all scales is calculated to get a multi-scale gradient value.…”

Section: Multi-scales Morphological Gradient Algorithmmentioning

confidence: 99%

Tongue-coating image segmentation based on combination of morphological gradient and watershed algorithms

Zhang

Qin

Zhang

2011

The Imaging Science Journal

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The segmentation of tongue-coating images is very important to computer-aided traditional Chinese medicine (TCM) clinic diagnosis system. By now, algorithms for this segmentation are in low-accuracy or high-complexity. Upon the edge-gradient feature of tongue-coating images, a new segmenting method is presented in this paper, which combines edge and region processing technologies. With the new method, the multi-scale morphological gradient images of the original images are taken firstly, and then watershed algorithm is used to segment them so that tongue-coating images are separated. The following experimental results show this method's effectiveness and efficiency.

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“…However, with the development of computer techniques, application of digital image processing techniques in tongue diagnosis has been widely studied, such as segmentation of the tongue image [1–3], separation of tongue substance and tongue coating [4, 5], and analysis of tongue image [6]. …”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

BioMed Research International

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Background and Goal. The application of digital image processing techniques and machine learning methods in tongue image classification in Traditional Chinese Medicine (TCM) has been widely studied nowadays. However, it is difficult for the outcomes to generalize because of lack of color reproducibility and image standardization. Our study aims at the exploration of tongue colors classification with a standardized tongue image acquisition process and color correction. Methods. Three traditional Chinese medical experts are chosen to identify the selected tongue pictures taken by the TDA-1 tongue imaging device in TIFF format through ICC profile correction. Then we compare the mean value of L * a * b * of different tongue colors and evaluate the effect of the tongue color classification by machine learning methods. Results. The L * a * b * values of the five tongue colors are statistically different. Random forest method has a better performance than SVM in classification. SMOTE algorithm can increase classification accuracy by solving the imbalance of the varied color samples. Conclusions. At the premise of standardized tongue acquisition and color reproduction, preliminary objectification of tongue color classification in Traditional Chinese Medicine (TCM) is feasible.

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Tongue diagnosis method for extraction of effective region and classification of tongue coating

Cited by 20 publications

References 10 publications

Computerized tongue image segmentation via the double geo-vector flow

Computerized tongue image segmentation via the double geo-vector flow

Tongue-coating image segmentation based on combination of morphological gradient and watershed algorithms

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

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