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DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...

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Updated: Aug 22, 2025

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

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Design and Implementation of Embedded-Based Vein Image Processing System with Enhanced Denoising Capabilities.

Jongwon Lee¹, Incheol Jeong¹, Kapyol Kim¹

¹Department of Computer Engineering, Gachon University Global Campus, Seongnam 13120, Korea.

Sensors (Basel, Switzerland)

|November 11, 2022

Summary

Accurate vein detection is crucial for medical procedures. This study enhances near-infrared (NIR) vein detection by integrating digital hair removal, improving accuracy and patient outcomes.

Keywords:

digital hair removal embedded system image processing vein detection vein projection

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Published on: December 8, 2010

Area of Science:

Medical Imaging
Biomedical Engineering
Optical Diagnostics

Background:

Accurate visual localization of blood vessels for intravenous injections and surgery is challenging.
Vein detection failures lead to patient discomfort and hospital financial losses.
Near-infrared (NIR)-based vein detection technology offers a promising solution.

Purpose of the Study:

To improve the accuracy of NIR-based vein detection.
To address the challenge of body hair interfering with vein detection.
To enhance the overall performance of vein visualization algorithms.

Main Methods:

Combining digital hair removal with NIR-based vein detection.
Developing an integrated system to eliminate hair as a source of noise.
Evaluating the performance improvement of the combined algorithm.

Main Results:

The integrated algorithm improved overall performance by 10.38% compared to existing systems.
Vein detection performance was 5.04% higher on patients without body hair.
The study verified the effectiveness of the proposed method.

Conclusions:

Integrating digital hair removal significantly enhances NIR vein detection accuracy.
The proposed technology offers a more reliable method for creating vascular maps.
This advancement is expected to improve patient care and reduce healthcare costs.