Chen, L.; Lv, M.; Cai, J.; Guo, Z.; Li, Z. U-Net-Embedded Gabor Kernel and Coaxial Correction Methods to Dorsal Hand Vein Image Projection System. Appl. Sci.2023, 13, 11222.
Chen, L.; Lv, M.; Cai, J.; Guo, Z.; Li, Z. U-Net-Embedded Gabor Kernel and Coaxial Correction Methods to Dorsal Hand Vein Image Projection System. Appl. Sci. 2023, 13, 11222.
Chen, L.; Lv, M.; Cai, J.; Guo, Z.; Li, Z. U-Net-Embedded Gabor Kernel and Coaxial Correction Methods to Dorsal Hand Vein Image Projection System. Appl. Sci.2023, 13, 11222.
Chen, L.; Lv, M.; Cai, J.; Guo, Z.; Li, Z. U-Net-Embedded Gabor Kernel and Coaxial Correction Methods to Dorsal Hand Vein Image Projection System. Appl. Sci. 2023, 13, 11222.
Abstract
Vein segmentation and projection correction constitute the core algorithms of auxiliary venipuncture device, responding to the accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting vein and a coaxial correction for image alignment in the self-build vein projection system. The proposed U-Net is embedded by Gabor convolution kernels in the shallow layers to enhance segmentation accuracy. Additionally, to mitigate the semantic information loss caused by channel reduction, the network model is lightweighted by the mean of replacing conventional convolutions with inverted residual blocks. During the visualization process, a method that combining coaxial correction and homography matrix is proposed to address the non-planarity of the dorsal hand in this paper. First, use a hot mirror to adjust the light paths of both projector and camera to be coaxial, and then align the projected image with dorsal hand using a homography matrix. Using this approach, the device requires only a single calibration before use. With the implementation of the improved segmentation method, an accuracy rate of 95.12% is achieved by the dataset. The intersection over union ratio between the segmented and original images is reached at 90.07%. The entire segmentation process is completed in a 0.09 second, and the largest distance error of vein projection onto dorsal hand is 0.53mm. Experiments show that the device has reached practical accuracy and has the value of research and application.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
