A Tongue Feature Extraction Method Based on a Sublingual Vein Segmentation

Sublingual vein features including swelling, varicose and cyanosis are essential for the symptoms differentiation and treatment selection in Traditional Chinese Medicine (TCM) tongue diagnosis, especially reflecting the state of human blood circulation. However, automatic and accurate extraction of sublingual vein features remains a great challenge, limited by both the lack of datasets for sublingual images and the influence of noise from non-tongue and non-sublingual vein components. In this paper, we propose a novel tongue features extraction method based on segmenting the sublingual vein instead of the whole tongue bottom, in which a sublingual vein segmentation framework based on a Polyp-PVT network is developed to eliminate the noise from the surrounding part of the sublingual vein. Meanwhile, we first adopt a transformer-based method such as Swin-Transformer network to extract sublingual vein features by virtue of the awesome capability of the transformer network. In addition, we construct a large dataset including 4018 sublingual vein images for the segmentation and classification of sublingual veins. Experimental results have shown that the tongue feature extraction method combined with a sublingual vein segmentation can greatly outperform the existing tongue feature extracting methods.