Adaptive Segmentation and Feature Quantization of Sublingual Veins of Healthy Humans

Abstract

The Sublingual Vein Diagnosis, one part of Tongue Diagnosis, plays an important role in deciding the healthy condition of humans. This paper focuses on establishing a feature quantization framework for the inspection of sublingual veins of healthy humans, composed of two parts: the segmentation of sublingual veins of healthy humans and the feature quantization of them. Firstly, a novel technique of sublingual vein segmentation is proposed here. Sublingual Vein Color Model, which combines the Bayesian Decision with CIEYxy color space, is established based on a large number of labeled sublingual images. Experiments prove that the proposed method performs well on the segmentation of images from healthy humans with weak color contrast between sublingual vein and tongue proper. And then, a chromatic system in conformity with diagnostic standard of Traditional Chinese Medicine doctors is established to describe the chromatic feature of sublingual veins. Experimental results show that the geometrical and chromatic features quantized by the proposed framework are properly consistent with the diagnostic standard summarized by TCM doctors for healthy humans.