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.
Get full access to this article
View all access options for this article.