Design and implementation of a vein detection system for improved accuracy in blood sampling

Blood sampling is a routine procedure in medical diagnostics, yet precise vein visualization methods remain limited. This project introduces a system designed to improve vein detection during blood collection. It relies on Near-Infrared (NIR) light, which interacts with the skin and highlights veins by taking advantage of hemoglobin’s infrared absorption properties. Using a Raspberry Pi and an infrared camera, image acquisition and processing are handled through MATLAB and Python algorithms, which allow real-time visualization of veins. The system has been tested on a database of infrared images of hands and arms, effectively enhancing vein contrast in real time. The display is connected to the Raspberry Pi, giving medical staff a visual guide. This technology aims to streamline procedures for healthcare professionals, including doctors, nurses, and medical students, particularly in high-volume settings like labs and blood transfusion centers where vein visualization is critical to patient care.