Study on TCM Tongue Image Segmentation Model Based on Convolutional Neural Network Fused with Superpixel

Tongue image segmentation is a base work of TCM tongue processing. Nowadays, deep learning methods are widely used on tongue segmentation, which has better performance than conventional methods. However, when the tongue color is close to the color of the adjoining area, the contour of tongue segmentation by deep learning may be coarse which could influence the subsequent analysis. Here a novel tongue image segmentation model based on a convolutional neural network fused with superpixel was proposed to solve the problem. Methods. On the basis of a convolutional neural network fused with superpixel, the novel tongue image segmentation model SpurNet was proposed in this study. The residual structure of ResNet18 was introduced as the feature extraction layer on the encoding path, to construct the first stage processing module UrNet of SpurNet. The superpixel segmentation was fused with UrNet to form the second stage process of SpurNet. To verify the effect of SpurNet. The models before and after fusion with superpixel, classical image segmentation models FCN and DeepLab were compared with SpurNet on the dataset of 367 manually labeled tongue images. Results. The SpurNet model performance test with 10-fold cross-validation showed PA of 0.9145 +/- 0.0043, MPA of 0.9168 +/- 0.0048, MIoU of 0.8417 +/- 0.0072 and FWIoU of 0.8454 +/- 0.0072. Relative to FCN, DeepLab and their superpixel fused models, the SpurNet model was superior in tongue image segmentation and could increase PA by 1.91%-3.17%, MPA by 1.38%-2.61%, MIoU by 3.09%-5.07%, and FWIoU by 3.11%-5.08%. Compared to UrNet, the first stage processing module, the SpurNet model also increased the PA, MPA, MIoU and FWIoU by 0.15%, 0.09%, 0.24% and 0.24%, respectively. Conclusion. The SpurNet model, after fusing with superpixel image segmentation, can better accomplish the task of tongue image segmentation, more accurately process the margins of tongue and resolve the over-segmentation and under-segmentation. The thought of this study is a new exploration in the field of tongue image segmentation, which could provide a reference for the modern research on TCM tongue images.