A novel vessels feature extraction method in traditional Chinese medicine (TCM）

Abstract

In Chinese medicine, eye diagnosis is essential for diagnosis and treatment. However, most current image-processing techniques focus on tongue diagnosis, and most foreign studies on ocular diagnosis focus on segmenting fundus vascular images. Moreover, most of the foreign studies on scleral vessels are focused on identification rather than on TCM discernment. Scleral vessels can significantly characterize the pathological features of the human body’s five internal and six internal organs. Scleral vessels are essential for the objective study of TCM visual diagnosis. However, due to the small size and complex structure of scleral vessels, it is difficult to extract them by existing methods effectively. To achieve more accurate scleral blood vessel extraction, we introduce the residual connection structure and CA-Module attention mechanism in the U2Net1 network to avoid the incompatibility between high-level and low-level features and enhance the information extraction by input fusion and feature extraction of RSU blocks. The experimental results show that Miou achieves an accuracy of 83.3%. The F1-score reaches 91.7%, which is more effective than the existing SOTA fundus vascular segmentation network FR-UNet2 for the experiments. According to the experimental results, Res-U2Net can segment sclerar vessels accurately. In future experiments, we will improve the vessel feature extraction network to increase its accuracy and gradually achieve better results.