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01.12.2014 | Research | Ausgabe 1/2014 Open Access

Chinese Medicine 1/2014

Computerized tongue image segmentation via the double geo-vector flow

Chinese Medicine > Ausgabe 1/2014
Miao-Jing Shi, Guo-Zheng Li, Fu-Feng Li, Chao Xu
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1749-8546-9-7) contains supplementary material, which is available to authorized users.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

MJS and GZL conceived and designed the study. MJS and FFL performed the experiments. MJS, GZL, and CX revised the paper. All authors read and approved the final manuscript.



Visual 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.


Experiments 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.


The 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.


By 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.
Additional file 1: Source codes of the DGF in MATLAB language. Please refer to readme in the zip files. (ZIP 1 MB)
Additional file 2: The collection dataset of tongue images and segmentation benchmarks. Please refer to the subsection entitled Dataset evaluation and error measurements. (ZIP 6 MB)
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