Deep convolutional neural network-based traditional Chinese medicine tongue image automatic segmentation method

Abstract

The invention relates to a deep convolutional neural network-based traditional Chinese medicine tongue image automatic segmentation method and belongs to the computer vision field and traditional Chinese medicine tongue diagnosis field. According to the method of the invention, a convolutional neural network structure is designed; collected sample data are adopted to train the network, so that a network model can be obtained; and the model is adopted to automatically segment a traditional Chinese medicine tongue image. The method includes an offline training phase and an online segmentation phase. The method can be applied to both closed type and open tongue image acquisition environments and can effectively improve the accuracy and robustness of the automatic segmentation of the traditional Chinese medicine tongue image. The method of the present invention specifically relates to deep learning, semantic segmentation, image processing and other technologies.

Description

technical field [0001] The present invention introduces deep learning technology into the objective research of TCM tongue diagnosis, and proposes a TCM tongue image automatic segmentation method based on deep convolutional neural network, which can be applied to both closed and open tongue image acquisition environments, It can effectively improve the accuracy and robustness of automatic tongue image segmentation in traditional Chinese medicine. The invention belongs to the field of computer vision and the field of tongue diagnosis of traditional Chinese medicine, and specifically relates to deep learning, semantic segmentation, image processing and other technologies. Background technique [0002] Tongue diagnosis in traditional Chinese medicine is an important part of inspection in traditional Chinese medicine to understand the physiological functions and pathological changes of the human body by observing the changes of the tongue. Since the 1980s, with the development ...

AI Technical Summary

Problems solved by technology

[0003] Although some progress has been made in the research of automatic tongue image segmentation, there is still no such method due to a series of objective factors such as image quality, differences in patient tongue images, tongue color and lip color similarity, and illumination changes. The automatic tongue image segmentation method can achieve ideal segmentation results for all kinds of tongue images

In addition, most of the existing automatic segmentation algorithms are suitable for closed acquisition environments, and often cannot meet the tongue image segmentation requirements of open acquisition environments

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