| Citation: | ZHANG Xinfeng, GUO Yutong, CAI Yiheng, et al. Tongue image segmentation algorithm based on deep convolutional neural network and fully conditional random fields[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(12): 2364-2374. doi: 10.13700/j.bh.1001-5965.2019.0370(in Chinese)
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The disadvantage of tongue image segmentation in traditional Chinese medicine are low accuracy, slow segmentation speed and manual calibration of candidate regions.To solve these problems, we propose an end-to-end tongue image segmentation algorithm. Compared with the traditional tongue segmentation algorithm, more accurate segmentation results can be obtained by the proposed method which does not need any manual operation. Firstly, the atrous convolution algorithm is used to increase the feature map of the network without increasing the parameters. Secondly, the atrous spatial pyramid pooling (ASPP) module is used to enable the network to learn the multi-scale feature of the tongue image through different receptive fields. Finally, the deep convolutional neural networks (DCNN) are combined with fully connected conditional random fields (CRF) to refine the edge of the segmented tongue image. The experimental results show that the proposed method outperforms traditional tongue image segmentation algorithm and popular DCNN with higher segmentation accuracy, and the mean intersection over union reaches 95.41%.
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