ArticleJournal of medical systems2016
Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 44 citations in OpenAlex.
- Developing and validating an explainable clinlabomics-based machine-learning model for screening primary angle-closure glaucoma in the context of PPPM.The EPMA journal · 2025Article
- Artificial intelligence in the anterior segment of eye diseases.International journal of ophthalmology · 2024Review
- Multi-stage glaucoma classification using pre-trained convolutional neural networks and voting-based classifier fusion.Frontiers in physiology · 2023Article
- Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images.Translational vision science & technology · 2021Article
- Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method.PloS one · 2021Article
- Automated glaucoma screening method based on image segmentation and feature extraction.Medical & biological engineering & computing · 2020Article
- Practice Guidelines for Ocular Telehealth-Diabetic Retinopathy, Third Edition.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2020Article
- Telemedicine for Glaucoma: Guidelines and Recommendations.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2020Article
- Current status and future trends of clinical diagnoses via image-based deep learning.Theranostics · 2019Review
- Applications of Artificial Intelligence in Ophthalmology: General Overview.Journal of ophthalmology · 2018Review
- A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis.Journal of medical systems · 2017Article
- A New Method for Computed Tomography Angiography (CTA) Imaging via Wavelet Decomposition-Dependented Edge Matching Interpolation.Journal of medical systems · 2016Article
- Macular Optical Coherence Tomography Imaging in Glaucoma.Journal of ophthalmic & vision researchArticle
- Macular imaging with optical coherence tomography in glaucoma.Survey of ophthalmologyReview
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
Abstract
Glaucoma is one of the leading causes of blindness worldwide. There is no cure for glaucoma but detection at its earliest stage and subsequent treatment can aid patients to prevent blindness. Currently, optic disc and retinal imaging facilitates glaucoma detection but this method requires manual post-imaging modifications that are time-consuming and subjective to image assessment by human observers. Therefore, it is necessary to automate this process. In this work, we have first proposed a novel computer aided approach for automatic glaucoma detection based on Regional Image Features Model (RIFM) which can automatically perform classification between normal and glaucoma images on the basis of regional information. Different from all the existing methods, our approach can extract both geometric (e.g. morphometric properties) and non-geometric based properties (e.g. pixel appearance/intensity values, texture) from images and significantly increase the classification performance. Our proposed approach consists of three new major contributions including automatic localisation of optic disc, automatic segmentation of disc, and classification between normal and glaucoma based on geometric and non-geometric properties of different regions of an image. We have compared our method with existing approaches and tested it on both fundus and Scanning laser ophthalmoscopy (SLO) images. The experimental results show that our proposed approach outperforms the state-of-the-art approaches using either geometric or non-geometric properties. The overall glaucoma classification accuracy for fundus images is 94.4% and accuracy of detection of suspicion of glaucoma in SLO images is 93.9 %.
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Registered trials
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.