HPA-UNet: A Hybrid Post-Processing Attention U-Net for Tongue Segmentation.

Abstract

Tongue diagnosis is the kernel method of Traditional Chinese Medicine (TCM), and it has been proved that the condition of the tongue can serve as an indicator of a person's health status. To automatically recognize a person's latent diseases by computer vision technology, getting the tongue segmentation from a picture with high precision has significant importance. However, the precision of tongue segmentation images in most prior methods is not satisfactory, which will inevitably result in misjudging. In this paper, an effective method is proposed for highly precise tongue segmentation, which is combined with an improved U-shaped neural network and an edge refinement post-processing method. The contributions are three-fold. First, a carefully designed data augmentation strategy is imported to prevent the network from over-fitting. Second, an updated U-shaped neural network is designed to segment tongue images with high precision. Third, a post-processing method is imported to refine the edge of the tongue segmentation further. The proposed method achieves competitive performance in almost all experiments on two datasets. Furthermore, the proposed post-processing method can effectively improve all classic neural networks in tongue segmentation, which strongly proves the flexibility and generalization of the proposed method.