Evidence map›Paper›PMID 41522504›Full record

ArticlePeerJ2026

An improved tortuosity measurement method combining curvature-based, breadth-first search and Euclidean distance for retinal image analysis.

Nur Asyiqin Amir Hamzah, Wan Mimi Diyana Wan Zaki, Aziah Ali

Abstract read
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In one paragraph

Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Nur Asyiqin Amir HamzahCentre of Advanced Analytics, Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka, Malaysia.
Wan Mimi Diyana Wan ZakiFaculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.ORCID 0000-0001-5808-4348
Aziah AliCenter for Image and Vision Computing, Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Retinal vascular tortuosity is a clinically relevant biomarker linked to systemic and ocular diseases; however, its quantitative assessment particularly the distinction between arteries and veins remains underexplored in both healthy and pathological conditions. This study investigates tortuosity behavior using three publicly available retinal fundus image datasets: Digital Retinal Images for Vessel Extraction (DRIVE), High-Resolution Fundus (HRF), and Labelled Eye fundus Segmentation-Artery Vein (LES-AV). A standardized analytical pipeline combining curvature-based metrics, breadth-first search (BFS) and Euclidean distance was applied following vessel segmentation, artery-vein separation, skeletonization, and optic disc-based tracing. BFS algorithm was utilized for vessel path tracing, chosen for its robustness and suitability in navigating complex vascular structures with high reproducibility. Five comparative analyses were performed: artery

Indexed as

Image Processing, Computer-AssistedRetinal ArteryRetinal VeinRetinal VesselsAlgorithmsDiabetic RetinopathyFundus OculiGlaucomaHumansReproducibility of ResultsArtery-vein separationCurvature-based metricsDiabetic retinopathyEuclidean distanceField of view (FOV)Fundus image analysisGlaucomaRetinal vascular tortuosity

Identifiers

PMID41522504
PMCPMC12786152

What Socratic 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 Socratic graph.