Evidence map›Paper›PMID 37370657›Full record

ArticleBioengineering (Basel, Switzerland)2023

Near-Infrared Blood Vessel Image Segmentation Using Background Subtraction and Improved Mathematical Morphology.

Ling Li, Haoting Liu, Qing Li, Zhen Tian, Yajie Li, Wenjia Geng, Song Wang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.8field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 8 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Ling LiBeijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Haoting LiuBeijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.ORCID 0000-0003-2537-6138
Qing LiBeijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.ORCID 0000-0002-6361-5008
Zhen TianBeijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Yajie LiBeijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Wenjia GengDepartment of Traditional Chinese Medicine, Peking University People's Hospital, Beijing 100044, China.
Song WangDepartment of Nephrology, Peking University Third Hospital, Beijing 100191, China.
University of Science and Technology Beijing · CNPeking University · CN

Funding

Fundamental Research Fund for the China Central Universities of USTB FRF-BD-19-002AFund of Science and Technology on Near-Surface Detection Laboratory 6142414221403Fund of State Key Laboratory of Intense Pulsed Radiation Simulation and Effect SKLIPR2024National Natural Science Foundation of China 61975011Natural Science Foundation of Guangdong Province 2023A1515010275
6 · The paper itself

Abstract

The precise display of blood vessel information for doctors is crucial. This is not only true for facilitating intravenous injections, but also for the diagnosis and analysis of diseases. Currently, infrared cameras can be used to capture images of superficial blood vessels. However, their imaging quality always has the problems of noises, breaks, and uneven vascular information. In order to overcome these problems, this paper proposes an image segmentation algorithm based on the background subtraction and improved mathematical morphology. The algorithm regards the image as a superposition of blood vessels into the background, removes the noise by calculating the size of connected domains, achieves uniform blood vessel width, and smooths edges that reflect the actual blood vessel state. The algorithm is evaluated subjectively and objectively in this paper to provide a basis for vascular image quality assessment. Extensive experimental results demonstrate that the proposed method can effectively extract accurate and clear vascular information.

Indexed as

blood vessel segmentationimage qualitymathematical morphologynear-infrared imagenoise reduction

Identifiers

PMID37370657
PMCPMC10295426
OpenAlexW4380990089

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.