Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
On active contour models and balloons
CVGIP: Image Understanding
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
The fast construction of extension velocities in level set methods
Journal of Computational Physics
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of Polar Edge Detection and Active Contour Model for Automated Tongue Segmentation
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
The segmentation of the body of tongue based on the improved level set in TCM
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
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The correct segmentation of the tongue is the precondition to the quantification of tongue diagnosis in Traditional Chinese Medicine. The main method at present is by Snake model which uses the gradient of a tongue image as external energy to make the initial contour converge to the edge of the tongue body. Although this method has strong adaptive capability, it has a defect: for those images including the lower lip, the final contour often converges to the edge of the lip. To address the problem, a geometric model is proposed to correct the edges influenced by large concavity or sharp angle such as those with the lower lip. With this model, the initial contour does not include the whole lip. Then the initial contour is evolved toward the edge of the tongue body by Geodesic Active Contour Model after the Signed Distance Function is constructed. From the experiment results, the method is demonstrated to have good segmentation accuracy.