ArticlePloS one2016
Comprehensive Retinal Image Analysis for Aggressive Posterior Retinopathy of Prematurity.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 23 citations in OpenAlex.
- A novel methodology based on Contrast-Adaptive Threshold Model for accurate optic disc detection in retinal images of premature infants.Physical and engineering sciences in medicine · 2025Article
- Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population.Frontiers in pediatrics · 2023Article
- Imaging the pediatric retina: An overview.Indian journal of ophthalmology · 2021Review
- Retinal immaturity at first screening and retinopathy of prematurity: Image-based validation of 1202 eyes of premature infants to predict disease progression.Indian journal of ophthalmology · 2019Observational
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer aided analysis plays a nontrivial role in assisting the diagnosis of various eye pathologies. In this paper, we propose a framework to help diagnose the presence of Aggressive Posterior Retinopathy Of Prematurity (APROP), a pathology that is characterised by rapid onset and increased tortuosity of blood vessels close to the optic disc (OD). We quantify vessel characteristics that are of clinical relevance to APROP such as tortuosity and the extent of branching i.e., vessel segment count in the defined diagnostic region. We have adapted three vessel segmentation techniques: matched filter response, scale space theory and morphology with local entropy based thresholding. The proposed feature set equips us to build a linear discriminant classifier to discriminate APROP images from clinically healthy images. We have studied 36 images from 21 APROP subjects against a control group of 15 clinically healthy age matched infants. All subjects are age matched ranging from 33-40 weeks of post menstrual age. Experimental results show that we attain 100% recall and 95.45% precision, when the vessel network obtained from morphology is used for feature extraction.
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Registered trials
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