Simplifying vein detection for intravenous procedures: A comparative assessment through near‐infrared imaging system

Author:

Saeed Atiqa1,Chaudhry Muhammad Rehan12,Khan Muhammad Umair Ahmad1,Saeed Muhammad Ahsan3,Ghfar Ayman A.4,Yasir Muhammad Naveed5,Ajmal Hafiz Muhammad Salman12ORCID

Affiliation:

1. Department of Biomedical Engineering University of Engineering and Technology, Narowal Campus Lahore Pakistan

2. Department of Electrical Engineering University of Engineering and Technology, Narowal Campus Lahore Pakistan

3. School of Electrical Engineering, Korea University Seoul Republic of Korea

4. Department of Chemistry College of Science, King Saud University Riyadh Saudi Arabia

5. Department of Computer Science University of Narowal Narowal Pakistan

Abstract

AbstractThe intravenous (IV) injection procedure can be a challenging task, especially for individuals with thin veins, obesity, or patients with damaged and pigmented skin. Therefore, the IV procedure necessitates a portable medical device that can be used for academic demonstrations to train medical students or by health care professionals to perform venipuncture. Vein visualization with a vein detector is principally based on the interaction of blood components with wavelengths of the electromagnetic spectrum (EMS). In this paper, we first present the process of image formation in the spectral band of the near‐infrared region (NIR) of EMS. Then, we introduce the image acquisition system with image processing to extract the veins as a noninvasive vein detection method. A Raspberry Pi (Model 4B), along with a night vision camera, serves as an image acquisition tool to capture skin area illuminated by NIR. Following this, the data is transferred to the laptop where it can be filtered and processed using Python image processing tools before being viewed on the monitor. The results achieved through the device are quite encouraging, as the image recognition between veins and adjacent tissues from the skin sample can be clearly marked. The functionality, accuracy, and simplicity associated with this vein detection system make it a potential device for IV placement and the morphological study of disease detection.

Publisher

Wiley

Reference34 articles.

1. An optimal method for identification of finger vein using supervised learning

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3. Challenges in preanalytical phase of laboratory medicine: rate of blood sample nonconformity in a tertiary care hospital;Alavi N;EJIFCC,2020

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