Performance Evaluation of Dorsal Vein Network of Hand Imaging Using Relative Total Variation-Based Regularization for Smoothing Technique in a Miniaturized Vein Imaging System: A Pilot Study.

Kyuseok Kim,Hyun-Woo Jeong,Youngjin Lee

doi:10.3390/ijerph18041548

Abstract

Vein puncture is commonly used for blood sampling, and accurately locating the blood vessel is an important challenge in the field of diagnostic tests. Imaging systems based on near-infrared (NIR) light are widely used for accurate human vein puncture. In particular, segmentation of a region of interest using the obtained NIR image is an important field, and research for improving the image quality by removing noise and enhancing the image contrast is being widely conducted. In this paper, we propose an effective model in which the relative total variation (RTV) regularization algorithm and contrast-limited adaptive histogram equalization (CLAHE) are combined, whereby some major edge information can be better preserved. In our previous study, we developed a miniaturized NIR imaging system using light with a wavelength of 720–1100 nm. We evaluated the usefulness of the proposed algorithm by applying it to images acquired by the developed NIR imaging system. Compared with the conventional algorithm, when the proposed method was applied to the NIR image, the visual evaluation performance and quantitative evaluation performance were enhanced. In particular, when the proposed algorithm was applied, the coefficient of variation was improved by a factor of 15.77 compared with the basic image. The main advantages of our algorithm are the high noise reduction efficiency, which is beneficial for reducing the amount of undesirable information, and better contrast. In conclusion, the applicability and usefulness of the algorithm combining the RTV approach and CLAHE for NIR images were demonstrated, and the proposed model can achieve a high image quality.

Highlights

In the medical field, venipuncture is one of the critical and fundamental approaches for examination of health conditions and precise treatment monitoring of diseases, and involves blood sampling analysis or intravenous medication administration through intravenous catheterization [1]
We developed a cost-effective, miniaturized vein imaging system using a relative total variation (RTV) regularization [29]-based vein adaptive smoothing approach in a preprocessing framework for accurate vein pattern extraction
Two NIR light-emitting diodes (LEDs) centered at a wavelength of 940 nm were employed as light sources to illuminate target samples, as the veins located at the dorsum of hand and at

Summary

Introduction

Venipuncture is one of the critical and fundamental approaches for examination of health conditions and precise treatment monitoring of diseases, and involves blood sampling analysis or intravenous medication administration through intravenous catheterization [1]. Improper venipuncture can induce medical side effects such as swelling, bleeding, permanent vein damage, and infection, as well as erroneous examination results; this procedure is performed by nurses, medical practitioners, and medical laboratory scientists [2]. If a patient’s vein is not clearly visible, multiple attempts are needed; the patient may experience severe pain several times. It is frequently affected by the patient’s age, patient’s health condition (diabetes and degree of obesity), environmental conditions (brightness and light color), and experience of the clinical practitioners.

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