Mr. Hong Peng Profile

Hong Peng

at Hainan Univ

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Proceedings Article | 18 July 2023 Paper

Application of image segmentation technology in TCM eye diagnosis

Hong Peng, Ning Niu, Yilin Zhang, Guanjun Wang, Chenyang Xue, Mengxing Huang

Proceedings Volume 12745, 127450D (2023) https://doi.org/10.1117/12.2679810

KEYWORDS: Image segmentation, Eye, Visualization, Eye models, Education and training, Network architectures, Feature extraction, Sclera

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Image segmentation is a critical technology in many fields, such as image processing, pattern recognition, and artificial intelligence. It is also the first and critical step in computer vision technology. Tongue diagnosis combined with deep learning for segmentation and extracting pathological features is relatively mature, but deep learning combined with TCM visualization is sporadic. First, We used the U2Net network¹ for segmentation extraction of the sclera in this study. Where the U2Net¹ network1 (based on PyTorch) relies on the extensive use of data enhancements to use the available annotation samples more efficiently, and compared with the U-Net network, the U2Net network¹ updates an RSU module, each RSU module is a small U-net network,merging multiple U-Net outputs to get the merged Mask target. Finally, we applied classical CNN networks to evaluate the segmentation effect, introducing different evaluation metrics such as Miou, Precision, and Recall. We used the publicly available dataset UBIVIS.V12 for our experiments, where our Miou was as high as 97.3%, and U2Net achieved better results among all the networks, which laid the foundation for our subsequent segmentation and extraction of blood filament features.

Proceedings Article | 18 July 2023 Paper

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

Hong Peng, Yilin Zhang, Ning Niu, Jiahao Wang, Yiming Liu, Guanjun Wang, Chenyang Xue, Mengxing Huang

Proceedings Volume 12745, 127450R (2023) https://doi.org/10.1117/12.2679820

KEYWORDS: Visualization, Feature extraction, Medicine, Image segmentation, Diagnostics, Network architectures, Blood

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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.

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