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A study on image processing of vein extraction images according to development of vein detector.

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Title: A study on image processing of vein extraction images according to development of vein detector.
Authors: Bang, So-Hyeon1 (AUTHOR), Kim, Seung-Hun2 (AUTHOR), Jeong, Jin-Hyoung3 (AUTHOR) wlsgud0201@cku.ac.kr
Source: Technology & Health Care. Mar2026, Vol. 34 Issue 2, p167-178. 12p.
Subjects: Image enhancement (Imaging systems), Raspberry Pi, Veins, Medical equipment, Venography, Infrared imaging, Diagnostic imaging, Image processing
Abstract: Background: Intravenous infusion often faces difficulties in patients with obesity, aging, or dark skin. Low-cost vein detection using near-infrared (NIR) light is gaining attention to improve vascular access. Previous studies focused mainly on high-end devices or single algorithm performance. Objective: This study aimed to develop a low-cost vein detection system using 850 nm NIR LEDs and Raspberry Pi 4. It also sought to evaluate and compare multiple image enhancement algorithms. Performance was assessed using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM) metrics. Methods: The device consisted of an NIR LED module, IR-sensitive camera, and Raspberry Pi 4. Algorithms used were Contrast Limited Adaptive Histogram Equalization (CLAHE), Unsharp Masking, Median Filter, and Fuzzy Adaptive Gamma. Images from 13 subjects were enhanced and evaluated using three quantitative metrics. Results: Unsharp Masking achieved the lowest MSE (36.17) and highest PSNR (32.98), showing strong contrast enhancement. Median Filtering produced the highest SSIM (0.926), effectively preserving structural consistency. Combining CLAHE + Unsharp Masking + Median Filter yielded the best overall performance. However, this combination led to a slight SSIM decrease due to over-enhancement and edge distortion. Hardware limitations (low resolution and processing speed of Raspberry Pi 4) also impacted image quality and SSIM. Conclusion: The proposed low-cost vein detection system effectively enhanced vascular images using selected algorithms. Unsharp Masking and Median Filtering were particularly effective in improving contrast and maintaining structure. Future work should focus on real-time optimization and hardware upgrades to improve clinical applicability. [ABSTRACT FROM AUTHOR]
Copyright of Technology & Health Care is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: A study on image processing of vein extraction images according to development of vein detector.
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  Data: <searchLink fieldCode="AR" term="%22Bang%2C+So-Hyeon%22">Bang, So-Hyeon</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Seung-Hun%22">Kim, Seung-Hun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jeong%2C+Jin-Hyoung%22">Jeong, Jin-Hyoung</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> wlsgud0201@cku.ac.kr</i>
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  Data: <searchLink fieldCode="JN" term="%22Technology+%26+Health+Care%22">Technology & Health Care</searchLink>. Mar2026, Vol. 34 Issue 2, p167-178. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Raspberry+Pi%22">Raspberry Pi</searchLink><br /><searchLink fieldCode="DE" term="%22Veins%22">Veins</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+equipment%22">Medical equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Venography%22">Venography</searchLink><br /><searchLink fieldCode="DE" term="%22Infrared+imaging%22">Infrared imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Intravenous infusion often faces difficulties in patients with obesity, aging, or dark skin. Low-cost vein detection using near-infrared (NIR) light is gaining attention to improve vascular access. Previous studies focused mainly on high-end devices or single algorithm performance. Objective: This study aimed to develop a low-cost vein detection system using 850 nm NIR LEDs and Raspberry Pi 4. It also sought to evaluate and compare multiple image enhancement algorithms. Performance was assessed using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM) metrics. Methods: The device consisted of an NIR LED module, IR-sensitive camera, and Raspberry Pi 4. Algorithms used were Contrast Limited Adaptive Histogram Equalization (CLAHE), Unsharp Masking, Median Filter, and Fuzzy Adaptive Gamma. Images from 13 subjects were enhanced and evaluated using three quantitative metrics. Results: Unsharp Masking achieved the lowest MSE (36.17) and highest PSNR (32.98), showing strong contrast enhancement. Median Filtering produced the highest SSIM (0.926), effectively preserving structural consistency. Combining CLAHE + Unsharp Masking + Median Filter yielded the best overall performance. However, this combination led to a slight SSIM decrease due to over-enhancement and edge distortion. Hardware limitations (low resolution and processing speed of Raspberry Pi 4) also impacted image quality and SSIM. Conclusion: The proposed low-cost vein detection system effectively enhanced vascular images using selected algorithms. Unsharp Masking and Median Filtering were particularly effective in improving contrast and maintaining structure. Future work should focus on real-time optimization and hardware upgrades to improve clinical applicability. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Technology & Health Care is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/09287329251389493
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        Text: English
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        Type: general
      – SubjectFull: Raspberry Pi
        Type: general
      – SubjectFull: Veins
        Type: general
      – SubjectFull: Medical equipment
        Type: general
      – SubjectFull: Venography
        Type: general
      – SubjectFull: Infrared imaging
        Type: general
      – SubjectFull: Diagnostic imaging
        Type: general
      – SubjectFull: Image processing
        Type: general
    Titles:
      – TitleFull: A study on image processing of vein extraction images according to development of vein detector.
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            NameFull: Bang, So-Hyeon
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            NameFull: Kim, Seung-Hun
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            NameFull: Jeong, Jin-Hyoung
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            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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