Retinal image analysis: concepts, applications and potential.

By N. Patton, Tariq Mehmood Aslam, T. MacGillivray, Ian J. Deary, Baljean Dhillon, Robert H. Eikelboom, K. Yogesan, I. Constable. Progress in retinal and eye research, 2006

709 citations recorded by the source corpus.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/017ab71c579d2d85d8147104ff001dbd313da916

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In Ophthalmology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research (2018, background): “The recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities to the biomedical engineers and computer scientists to meet the requirements of clinical practice [16,17].”
Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement (2012, background): “From initial seed points along vessel centrelines – which may be identified manually [2] or automatically [32] – a pair of vessel edge points and an orientation can be identified.”
Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques (2010, background): “The benefits that a system for automaticly detect early signs of this disease would provide have been widely studied and assessed positively by experts [4], [5].”
Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment (2011, neutral): “It is a fundamental task for automatic processing of retinal images such as image sequence registration, and automatic measurements for treatment evaluation or for diabetic retinopathy diagnosis [27].”
Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores (2016, background): “fractal dimension (complexity of the vasculature) [33] and detection of neovascularization (formation of new microvasculature) [34].”
Robust Vessel Segmentation in Fundus Images (2013, methodology): “A more detailed review of the segmentation and other retinal image processing algorithms can be found in the articles published by Kirbas and Quek [2] and Patton et al. [3].”
IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, background): “Recent scientiﬁc advances in computing capacity and machine learning approaches provide an avenue to biomedical scientists to meet desideratum of clinical practice ( Shortliffe and Blois, 2006; Patton et al., 2006 ).”
Transforms and Operators for Directional Bioimage Analysis: A Survey. (2016, background): “2005) and vessels in retinal fundus images (Lam et al. 2010; Patton et al. 2006), bioimaging (Honnorat et al.”