DE-Net: Dilated Encoder Network for Automated Tongue Segmentation

In the intelligently processing of the tongue image, one of the most important tasks is to accurately segment the tongue body from a whole tongue image, and the good quality of tongue body edge processing is of great significance for the relevant tongue feature extraction. To improve the performance of the segmentation model for tongue images, we propose an efficient tongue segmentation model based on U-Net. Three important studies are launched, including optimizing the model’s main network, innovating a new network to specially handle tongue edge cutting and proposing a weighted binary cross-entropy loss function. The purpose of optimizing the tongue image main segmentation network is to make the model recognize the foreground and background features for the tongue image as well as possible. A novel tongue edge segmentation network is used to focus on handling the tongue edge because the edge of the tongue contains a number of important information. Furthermore, the advantageous loss function proposed is to be adopted to enhance the pixel supervision corresponding to tongue images. Moreover, thanks to a lack of tongue image resources on Traditional Chinese Medicine (TCM), some special measures are adopted to augment training samples. Various comparing experiments on two datasets were conducted to verify the performance of the segmentation model. The experimental results indicate that the loss rate of our model converges faster than the others. It is proved that our model has better stability and robustness of segmentation for tongue image from poor environment. The experimental results also indicate that our model outperforms the state-of-the-art ones in aspects of the two most important tongue image segmentation indexes: IoU and Dice. Moreover, experimental results on augmentation samples demonstrate our model have better performances.

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

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3317 ◽

2021 ◽

Vol 11 (3) ◽

pp. 688-696

Author(s):

Xiaojuan Hu ◽

Zhaobang Liu ◽

Xiaodong Yang ◽

Jiatuo Xu ◽

Liping Tu ◽

...

Keyword(s):

Classification Performance ◽

Contour Detection ◽

Segmentation Method ◽

Contour Map ◽

Hierarchical Tree ◽

Mean And Variance ◽

Detection Capabilities ◽

The Mean ◽

Tongue Segmentation ◽

Better Than

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.

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