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Laser & Optoelectronics Progress, Volume. 59, Issue 24, 2415001(2022)

Location Decision of Needle Entry Point Based on Improved Pruning Algorithm

Xiaonan Gao1, Guangyuan Zhang1、*, Fengyü Zhou2, and Dexin Yu3
Author Affiliations
  • 1School of Information Science and Electrical Engineering, Shan Dong Jiao Tong University, Jinan 250375, Shandong, China
  • 2School of Control Science and Engineering, Shandong University, Jinan 250000, Shandong, China
  • 3Department of Radiology, Qilu Hospital of Shandong University, Jinan 250000, Shandong, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (28)
    Equations (9)
    References (29)
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    Paper Information
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    Figures & Tables(28)
    Vein detection algorithm flow of semi-automatic blood collection

    Fig. 1. Vein detection algorithm flow of semi-automatic blood collection

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    Algorithm flow of automatic detection and annotation of dorsal hand vein injection image

    Fig. 2. Algorithm flow of automatic detection and annotation of dorsal hand vein injection image

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    Structure of AT-U-NET model

    Fig. 3. Structure of AT-U-NET model

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    Non-Local structure

    Fig. 4. Non-Local structure

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    Structure of U-Netup module

    Fig. 5. Structure of U-Netup module

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    Strengthened feature extraction network

    Fig. 6. Strengthened feature extraction network

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    Original map and segmentation map of dorsal hand vein. (a) Original map; (b) segmentation map

    Fig. 7. Original map and segmentation map of dorsal hand vein. (a) Original map; (b) segmentation map

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    PT-Pruning flowchart

    Fig. 8. PT-Pruning flowchart

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    Cannibalization stage

    Fig. 9. Cannibalization stage

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    Vein segmentation figure and main line of vascular skeleton. (a) Segmentation; (b) main line

    Fig. 10. Vein segmentation figure and main line of vascular skeleton. (a) Segmentation; (b) main line

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    Decision experiment of needle entry point position

    Fig. 11. Decision experiment of needle entry point position

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    Dorsal hand vein imaging acquisition equipment

    Fig. 12. Dorsal hand vein imaging acquisition equipment

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    Original dorsal hand vein images

    Fig. 13. Original dorsal hand vein images

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    Original pictures and label images

    Fig. 14. Original pictures and label images

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    Original pictures and label images

    Fig. 15. Original pictures and label images

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    Detection and segmentation effect of at-u-net dorsal hand vein

    Fig. 16. Detection and segmentation effect of at-u-net dorsal hand vein

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    Original back of hand

    Fig. 17. Original back of hand

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    Image after homomorphic filtering

    Fig. 18. Image after homomorphic filtering

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    Image after CLAHE processing

    Fig. 19. Image after CLAHE processing

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    Image after adaptive threshold segmentation

    Fig. 20. Image after adaptive threshold segmentation

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    Image after morphological processing

    Fig. 21. Image after morphological processing

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    Image after closed operation

    Fig. 22. Image after closed operation

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    Different semantic segmentation model processing effects

    Fig. 23. Different semantic segmentation model processing effects

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    Effect of PT-Pruning needle entry point position decision

    Fig. 24. Effect of PT-Pruning needle entry point position decision

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    Optimal needle entry point position decision

    Fig. 25. Optimal needle entry point position decision

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    • Table 1. Performance indexes of different semantic segmentation models for segmentation of dorsal hand vein

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      Table 1. Performance indexes of different semantic segmentation models for segmentation of dorsal hand vein

      ModelMIOUMPAF1-score
      PSPNet61.4968.3569.8
      U-Net67.9773.8477.6
      SegNet67.3271.5375.1
      RefineNet66.6370.6174.9
      DeepLabv366.4169.4672.8
      AT-U-Net79.5284.9893.6

    • Table 2. Accuracy of needle entry point recognition in effective area

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      Table 2. Accuracy of needle entry point recognition in effective area

      MethodDetection success timesDetection failure timesp1 /%
      Automatic labeling and recognition of dorsal hand vein image238715993.75
      PT-Pruning24638396.73

    • Table 3. Recognition accuracy of optimal needle entry point

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      Table 3. Recognition accuracy of optimal needle entry point

      MethodDetection success timesDetection failure timesp2
      PT-Pruning24578996.5%
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    Xiaonan Gao, Guangyuan Zhang, Fengyü Zhou, Dexin Yu. Location Decision of Needle Entry Point Based on Improved Pruning Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(24): 2415001

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    Paper Information

    Category: Machine Vision

    Received: Aug. 31, 2021

    Accepted: Oct. 27, 2021

    Published Online: Jan. 6, 2023

    The Author Email: Guangyuan Zhang (sdjtzhangguangyuan@163.com)

    DOI:10.3788/LOP202259.2415001

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology