A robust multi-environment tongue image segmentation method for computer-aided tongue diagnosis

The tongue is an important organ in the oral cavity. It can provide information about the oral cavity and physical conditions, and it is also one of the references for traditional Chinese medicine diagnosis. The segmentation of the tongue is a crucial stage in computer-assisted tongue diagnostic systems. Existing methods for segmenting images of the tongue are based on standard dataset, cannot be generalized without a large number of training data from different sources, making it difficult to adapt to mobile devices. A new method for automatically segmenting tongue images by combining traditional image processing and small sample deep learning is proposed. In a complicated context, the Yolo-V5 target detection module is employed to acquire the tongue area. A unified Gaussian distribution is utilized to adjust the color of this region to minimize the negative impact of varied colors on segmentation. Then, for precise segmentation, an enhanced Unet with RFB and attention mechanism is input. The potential noise is then eliminated using a morphological combining process. This technique enhances the segmentation performance of non-standard tongue photos taken by mobile devices by 8% to 10% compared to a single segmentation network, and the average DSC and IoU on non-standard dataset are 95.62% and 91.70%, respectively. It is anticipated that the suggested technique would be applied in stationary and mobile computer-assisted tongue diagnostic equipment due to its improved multi-environment robustness.