Evidence map›Paper›PMID 39953248›Full record

ArticleScientific reports2025

Diabetic retinopathy detection via exudates and hemorrhages segmentation using iterative NICK thresholding, watershed, and Chi

Patsaphon Chandhakanond, Pakinee Aimmanee

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

  1. Article
  2. Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Review
  3. Review
  4. Article
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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

2 authors.

Patsaphon ChandhakanondDepartment of Information Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, 131 Moo 5, Tivanont Rd, Bangkadi, Meung, 12000, Patumthani, Thailand.
Pakinee AimmaneeDepartment of Information Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, 131 Moo 5, Tivanont Rd, Bangkadi, Meung, 12000, Patumthani, Thailand. pakinee@siit.tu.ac.th.

Funding

Thailand Science Research and Innovation TUFF 48/2568
6 · The paper itself

Abstract

Diabetic retinopathy (DR) is a common eye condition that affects one-third of patients with diabetes, leading to vision loss in both working-age and elderly populations. Early detection and intervention can improve patient outcomes and reduce the burden on healthcare. By developing robust computational techniques, we can advance automated systems for screening and managing diabetic retinopathy. Our specific goal is to detect and segment exudates and hemorrhages in fundus images. In this study, we used the iterative NICK thresholding region growing (INRG) method as a basis. To further improve our results in different applications, we incorporated the watershed separation algorithm (WS) and the Chi

Indexed as

Diabetic RetinopathyExudates and TransudatesHemorrhageImage Processing, Computer-AssistedRetinal HemorrhageAlgorithmsHumansDR detectionExudate segmentationHemorrhage segmentationIterative nick thresholding region growing (INRG)Watershed separation (WS)

Identifiers

PMID39953248
PMCPMC11829032

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Registered trials

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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.