Moment Invariants with Data Augmentation for Tongue Image Segmentation

Tongue diagnosis plays an essential role in diagnosing the syndrome types, pathological types, lesion location and clinical stages of cancers in Traditional Chinese Medicine (TCM). The quality of the tongue image datasets is crucial to tongue image segmentation in modern tongue diagnosis. However, the tongue image dataset maintains a challenging problem because of the lack of datasets for the sublingual image, the complexity and scarcity of the tongue images. In this paper, we propose an effective segmentation framework for tongue images, called Moment Invariants with Data Augmentation (DAMI), which can be flexibly applied to different segmentation models. To overcome over-fitting caused by excessive data augmentation, a data augmentation module with random transformations is designed to achieve appropriate data augmentation. Meanwhile, we develop a new moment invariants module to optimize data augmentation in image segmentation. In addition, a novel tongue image dataset, Lingual-Sublingual Image Dataset (LSID), has been established for the classification and segmentation of tongue or sublingual veins. Experimental results confirm that the models using DAMI can remarkably outperform the existing methods on LID, SID and BioHit for the tongue image segmentation.