Human Dorsal Hand Vein Segmentation Method Based on GR-UNet Model

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.

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