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C2G2FSnake: automatic tongue image segmentation utilizing prior knowledge

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Abstract

Extraction of the tongue body from digital images is essential for automated tongue diagnoses in traditional Chinese medicine. This paper presents a fully automated active contour initial method that utilizes prior knowledge of the tongue shape and its location in tongue images. Then colorspace information is introduced to control curve evolution. Combining the geometrical Snake model with the parameterized GVFSnake model, a novel approach for automatic tongue segmentation: C2G2FSnake (color control-geometric & gradient flow Snake) is proposed. This method increases the curve velocity but decreases the complexity. C2G2FSnake greatly extends practical usage to tongue segmentation, at the same time increasing the precision. Compared with other state-of-the-art works using different images of tongue color, C2G2FSnake realizes automatic tongue segmentation with greatly improved accuracy.

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Authors and Affiliations

  1. The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China

    MiaoJing Shi & GuoZheng Li

  2. Laboratory of Information Access and Synthesis of TCM Four Diagnosis, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China

    FuFeng Li

Authors
  1. MiaoJing Shi
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  2. GuoZheng Li
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  3. FuFeng Li
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Correspondence to GuoZheng Li.

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Shi, M., Li, G. & Li, F. C2G2FSnake: automatic tongue image segmentation utilizing prior knowledge. Sci. China Inf. Sci. 56, 1–14 (2013). https://doi.org/10.1007/s11432-011-4428-z

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  • DOI: https://doi.org/10.1007/s11432-011-4428-z

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