Abstract: To solve the issue of inaccurate segmentation accuracy of human dorsal hand veins (HDHV), we propose a segmentation method based on the global residual U-Net (GR-Unet) model. Initially, a visual acquisition device for dorsal hand vein imaging was designed utilizing near-infrared technology, resulting in the creation of a dataset comprising 864 images of HDHV. Subsequently, a Bottleneck from the deep residual network-50 (ResNet50) was integrated into the U-Net model to enhance its depth and alleviate the problem of vanishing gradients. Furthermore, a global attention mechanism (GAM) was introduced at the junction to improve the acquisition of global feature information. Additionally, a weighted loss function that combines cross-entropy loss and Dice loss was employed to address the imbalance between positive and negative samples. The experimental results indicate that the GR-Unet model achieved accuracies of 78.82%, 88.03%, 93.92%, and 97.5% in terms of intersection over union, mean intersection over union, mean pixel accuracy, and overall accuracy, respectively.

Zhike Zhao, Wen Zeng, Kunkun Wu and Xiaocan Cui, “Human Dorsal Hand Vein Segmentation Method Based on GR-UNet Model” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151057

@article{Zhao2024,
title = {Human Dorsal Hand Vein Segmentation Method Based on GR-UNet Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151057},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151057},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Zhike Zhao and Wen Zeng and Kunkun Wu and Xiaocan Cui}
}