An Unsupervised Tongue Segmentation Algorithm Based on Improved gPb-owt-ucm

Abstract

Background and Objective: The modernization of tongue diagnosis is an important research in Traditional Chinese Medicine. Accurate and practical tongue segmentation method is a premise in subsequent analyses. In this paper, an unsupervised tongue segmentation method is proposed based on an improved gPb-owt-ucm algorithm. The gPb-owt-ucm is short for global pixel point, oriented watershed transform and ultrametric contour map. Methods: Improved gPb-owt-ucm algorithm is adopted in this paper because of its powerful contour detection capabilities. The boundary feasibility of each pixel is calculated by the weight of pixel, and the result is converted to multiple closed regions and hierarchical tree. Finally, locating tongue accurate boundary by rectangular slider is taken to perform the final tongue segmentation. Two experiments are designed to evaluate its effectiveness by comparing with the snake method. Results: 300 tongue images were tested (150 images for the diabetes and 150 images for the health) in two experiments. The first one is to validate boundary detection performance (CBDR experiment). The second one is for validation of classification performance (CCE experiment) between diabetic and healthy tongues. In CBDR experiment, the mean and variance of IoU obtained using our improved gPb-owt-ucm method are 0.72±0.19, which are better than the snake method. In CCE experiment, the obtained precision and F1-score using our method are 1.0 and 0.97 over diabetic data respectively, and results of 0.94, 0.97 over health data. Conclusion: The effectiveness of our improved unsupervised gPb-owt-ucm method is validated in comparisons with the snake method. In the future, we plan to combine the proposed method with a supervised method in order to achieve more improvements for the tongue segmentation.