Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma [PDF]
Clinical Ophthalmology, 2020 Miguel Angel Zapata,1 Dídac Royo-Fibla,1 Octavi Font,1 José Ignacio Vela,2,3 Ivanna Marcantonio,2,3 Eduardo Ulises Moya-Sánchez,4,5 Abraham Sánchez-Pérez,5 Darío Garcia-Gasulla,4 Ulises Cortés,4,6 Eduard ...
Zapata MA +10 more
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Structure analysis and lesion detection from retinal fundus images [PDF]
, 2011 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Ocular pathology is one of the main health problems worldwide.
Salazar Gonzalez, Ana
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Vessel identification in diabetic retinopathy [PDF]
Diabetic retinopathy is the single largest cause of sight loss and blindness in 18 to 65 year olds. Screening programs for the estimated one to six per- cent of the diabetic population have been demonstrated to be cost and sight saving, howeverthere are ...
Teng, Thomas Bart
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Retinal Vessel Extraction with the Image Ray Transform [PDF]
, 2010 Extraction of blood vessels within the retina is an important task that can help in detecting a number of diseases, including diabetic retinopathy. Current techniques achieve good, but not perfect performance and this suggests that improved preprocessing
Nixon, Mark, Cummings, Alastair
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Automated image curation in diabetic retinopathy screening using deep learning
Scientific Reports, 2022 Diabetic retinopathy (DR) screening images are heterogeneous and contain undesirable non-retinal, incorrect field and ungradable samples which require curation, a laborious task to perform manually.
Paul Nderitu +8 more
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Life, 2022 Background: The aim of this study was to assess the performance of regional graders and artificial intelligence algorithms across retinal cameras with different specifications in classifying an image as gradable and ungradable.
Ramyaa Srinivasan +4 more
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Segmenting the eye fundus images for identification of blood vessels
Mathematical Modelling and Analysis, 2012 Retinal (eye fundus) images are widely used for diagnostic purposes by ophthalmologists. The normal features of eye fundus images include the optic nerve disc, fovea and blood vessels.
Gediminas Balkys, Gintautas Dzemyda
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Automatic production of synthetic labelled OCT images using an active shape model
IET Image Processing, 2020 Limited labelled data is a challenge in the field of medical imaging and the need for a large number of them is paramount for the training of machine learning algorithms, as well as measuring the performance of image processing algorithms. The purpose of
Hajar Danesh +3 more
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Trainable COSFIRE filters for vessel delineation with application to retinal images [PDF]
, 2015 Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to ...
Petkov, Nicolai, Azzopardi, George
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Quantitative Analysis of Retinal Vascular Leakage in Retinal Vasculitis Using Machine Learning
Applied Sciences, 2022 Retinal vascular leakage is known to be an important biomarker to monitor the disease activity of uveitis. Although fluorescein angiography (FA) is a gold standard for the diagnosis and assessment of the disease activity of uveitis, the evaluation of FA ...
Hiroshi Keino +3 more
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