Tongue diagnosis is an important part of the Traditional Chinese Medicine (TCM) diagnosis. In Chinese Medicine, tongue body reflects the most sensitive indicators of the physiological function and pathological changes, which has important application value in the process of diagnosis and treatment of the TCM diagnosis. The accurate separation of tongue body from tongue image is the premise of recognition and diagnosis. Most of the proposed tongue segmentation algorithms are based on the improvement of traditional approaches. These algorithms can improve the segmentation accuracy of tongue image to some extent, but they are less robust. To address above problems, a method of fast tongue image segmentation algorithm using convolutional neural network is proposed in this paper. The network is inspired from ShuffleNet which provided an efficient classification and detection network. The running time of our structure is about 0.16s, and the average segmentation precision is about 90.5%, which makes it of great potential for real-time applications. As opposed to the two common traditional segmentation methods (Kmeans++, GrabCut), the proposed method performs better than the above algorithms.
Tongue diagnosis is an extremely effective and non-invasive auxiliary diagnostic technique. Tongue segmentation is a vital procedure of computer-aided tongue diagnosis system, and its accuracy will directly influence the results of tongue diagnosis. By revising the edge indicator function, in this paper, an improved distance preserving level set evolution (DPLSE) method is proposed for tongue image segmentation. And before segmentation, the grey-level integral projection algorithm, Otus method as well as morphological processing will be utilized to handle tongue images, which to obtain the initial contour curve of this method. The improved algorithm has been experimented on 20 tongue images. Compared with other methods, it shows the competitive performances on tongue images segmentation.
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