Near-Infrared Forearm Vascular Width Calculation Using Radius Estimation of Tangent Circle

Qianru Ji; Haoting Liu; Zhen Tian; Song Wang; Qing Li; Dewei Yi

doi:10.3390/bioengineering11080801

Near-Infrared Forearm Vascular Width Calculation Using Radius Estimation of Tangent Circle

Qianru Ji, Haoting Liu^* (Corresponding Author), Zhen Tian, Song Wang^* (Corresponding Author), Qing Li, Dewei Yi

^*Corresponding author for this work

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Abstract

In response to the analysis of the functional status of forearm blood vessels, this paper fully considers the orientation of the vascular skeleton and the geometric characteristics of blood vessels and proposes a blood vessel width calculation algorithm based on the radius estimation of the tangent circle (RETC) in forearm near-infrared images. First, the initial infrared image obtained by the infrared camera is preprocessed by image cropping, contrast stretching, denoising, enhancement, and initial segmentation. Second, the Zhang–Suen refinement algorithm is used to extract the vascular skeleton. Third, the Canny edge detection method is used to perform vascular edge detection. Finally, a RETC algorithm is developed to calculate the vessel width. This paper evaluates the accuracy of the proposed RETC algorithm, and experimental results show that the mean absolute error between the vessel width obtained by our algorithm and the reference vessel width is as low as 0.36, with a variance of only 0.10, which can be significantly reduced compared to traditional calculation measurements.

Original language	English
Article number	801
Number of pages	27
Journal	Bioengineering
Volume	11
Issue number	8
Early online date	7 Aug 2024
DOIs	https://doi.org/10.3390/bioengineering11080801
Publication status	Published - 7 Aug 2024

Data Availability Statement

The data presented in this study are available on request from the corresponding author, Haoting Liu.

Funding

This work was funded by the National Natural Science Foundation of China under Grant 62373042, the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515010275, the Foundation of National Key Laboratory of Human Factors Engineering under Grant No. HFNKL2023WW11, the Fund of Science and Technology on Near-Surface Detection Laboratory under Grant 6142414221403, and the Fundamental Research Fund for the China Central Universities of USTB under Grant FRF-BD-19-002A.

Funders	Funder number
National Natural Science Foundation of China	62373042
Guangdong Basic and Applied Basic Research Foundation	2023A1515010275
Foundation of National Key Laboratory of Human Factors Engineering	HFNKL2023WW11

Keywords

near-infrared image
vascular width
image enhancement
skeleton extraction
edge detection
geometric characteristics
APSLSE image segmentation
radius estimation of tangent circle (RETC)

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Ji_etal_B_Near_Infrared_Forearm_VoR
Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Final published version, 5.2 MBLicence: CC BY

Cite this

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title = "Near-Infrared Forearm Vascular Width Calculation Using Radius Estimation of Tangent Circle",

abstract = "In response to the analysis of the functional status of forearm blood vessels, this paper fully considers the orientation of the vascular skeleton and the geometric characteristics of blood vessels and proposes a blood vessel width calculation algorithm based on the radius estimation of the tangent circle (RETC) in forearm near-infrared images. First, the initial infrared image obtained by the infrared camera is preprocessed by image cropping, contrast stretching, denoising, enhancement, and initial segmentation. Second, the Zhang–Suen refinement algorithm is used to extract the vascular skeleton. Third, the Canny edge detection method is used to perform vascular edge detection. Finally, a RETC algorithm is developed to calculate the vessel width. This paper evaluates the accuracy of the proposed RETC algorithm, and experimental results show that the mean absolute error between the vessel width obtained by our algorithm and the reference vessel width is as low as 0.36, with a variance of only 0.10, which can be significantly reduced compared to traditional calculation measurements.",

keywords = "near-infrared image, vascular width, image enhancement, skeleton extraction, edge detection, geometric characteristics, APSLSE image segmentation, radius estimation of tangent circle (RETC)",

author = "Qianru Ji and Haoting Liu and Zhen Tian and Song Wang and Qing Li and Dewei Yi",

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T1 - Near-Infrared Forearm Vascular Width Calculation Using Radius Estimation of Tangent Circle

AU - Ji, Qianru

AU - Liu, Haoting

AU - Tian, Zhen

AU - Wang, Song

AU - Li, Qing

AU - Yi, Dewei

PY - 2024/8/7

Y1 - 2024/8/7

N2 - In response to the analysis of the functional status of forearm blood vessels, this paper fully considers the orientation of the vascular skeleton and the geometric characteristics of blood vessels and proposes a blood vessel width calculation algorithm based on the radius estimation of the tangent circle (RETC) in forearm near-infrared images. First, the initial infrared image obtained by the infrared camera is preprocessed by image cropping, contrast stretching, denoising, enhancement, and initial segmentation. Second, the Zhang–Suen refinement algorithm is used to extract the vascular skeleton. Third, the Canny edge detection method is used to perform vascular edge detection. Finally, a RETC algorithm is developed to calculate the vessel width. This paper evaluates the accuracy of the proposed RETC algorithm, and experimental results show that the mean absolute error between the vessel width obtained by our algorithm and the reference vessel width is as low as 0.36, with a variance of only 0.10, which can be significantly reduced compared to traditional calculation measurements.

AB - In response to the analysis of the functional status of forearm blood vessels, this paper fully considers the orientation of the vascular skeleton and the geometric characteristics of blood vessels and proposes a blood vessel width calculation algorithm based on the radius estimation of the tangent circle (RETC) in forearm near-infrared images. First, the initial infrared image obtained by the infrared camera is preprocessed by image cropping, contrast stretching, denoising, enhancement, and initial segmentation. Second, the Zhang–Suen refinement algorithm is used to extract the vascular skeleton. Third, the Canny edge detection method is used to perform vascular edge detection. Finally, a RETC algorithm is developed to calculate the vessel width. This paper evaluates the accuracy of the proposed RETC algorithm, and experimental results show that the mean absolute error between the vessel width obtained by our algorithm and the reference vessel width is as low as 0.36, with a variance of only 0.10, which can be significantly reduced compared to traditional calculation measurements.

KW - near-infrared image

KW - vascular width

KW - image enhancement

KW - skeleton extraction

KW - edge detection

KW - geometric characteristics

KW - APSLSE image segmentation

KW - radius estimation of tangent circle (RETC)

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JF - Bioengineering

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ER -

Near-Infrared Forearm Vascular Width Calculation Using Radius Estimation of Tangent Circle

Abstract

Data Availability Statement

Funding

Keywords

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