Color Active Contour Models Based Tongue Segmentation in Traditional Chinese Medicine

Automated tongue image segmentation in tongue diagnosis system of traditional Chinese medicine is difficult due to two factors: There are lots of pathological details on the surface of tongue, and the shapes of tongue bodies are quite different. By adequately considering color information of tongue images, knowledge-based initial tongue body boundary detection and a color gradient are introduced into the gradient vector flow snake. The roughly detected tongue body boundary is employed to lead the usage of color gradient and the convergence of snake. The experiments results show robustness and accuracy of the algorithm. This work establishes a solid foundation for feature analysis of tongue diagnosis.

[1]  Chuang-Chien Chiu,et al.  A novel approach based on computerized image analysis for traditional Chinese medical diagnosis of the tongue , 2000, Comput. Methods Programs Biomed..

[2]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[3]  Arnold W. M. Smeulders,et al.  Color Invariant Snakes , 1998, BMVC.

[4]  Jae-Hyung Jang,et al.  Development of the digital tongue inspection system with image analysis , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[5]  Peter Meer,et al.  Unsupervised segmentation based on robust estimation and color active contour models , 2005, IEEE Transactions on Information Technology in Biomedicine.

[6]  David Zhang,et al.  On automated tongue image segmentation in Chinese medicine , 2002, Object recognition supported by user interaction for service robots.

[7]  Junaed Sattar Snakes , Shapes and Gradient Vector Flow , 2022 .

[8]  Guillermo Sapiro,et al.  Anisotropic diffusion of color images , 1996, Electronic Imaging.

[9]  Shen Lan Image Analysis for Tongue Characterization , 2001 .

[10]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[11]  Silvano Di Zenzo,et al.  A note on the gradient of a multi-image , 1986, Comput. Vis. Graph. Image Process..