Results 31 to 40 of about 238,742 (294)

Ensemble Learning Approach to Retinal Thickness Assessment in Optical Coherence Tomography

IEEE Access, 2021
Manual assessment of the retinal thickness in optical coherence tomography images is a time-consuming task, prone to error and inter-observer variability.
Alex Cazanas-Gordon, Esther Parra-Mora, Luis A. Da Silva Cruz   +2 more
doaj   +1 more source

A Smartphone-Based Tool for Rapid, Portable, and Automated Wide-Field Retinal Imaging. [PDF]

, 2018
Purpose:High-quality, wide-field retinal imaging is a valuable method for screening preventable, vision-threatening diseases of the retina. Smartphone-based retinal cameras hold promise for increasing access to retinal imaging, but variable image quality
Davila, Jose R, Echanique, Chris, Fletcher, Daniel A, Keenan, Jeremy, Kim, Tyson N, Li, Patrick, Loumou, Panagiota, Loury, PJ, Maamari, Robi N, Margolis, Todd, Myers, Frank, Paulus, Yannis M, Reber, Clay, Switz, Neil A, Webster, Doug, Woodward, Maria A   +15 more
core   +3 more sources

Microscopic Inner Retinal Hyper-reflective Phenotypes in Retinal and Neurologic Disease [PDF]

, 2014
Purpose. We surveyed inner retinal microscopic features in retinal and neurologic disease using a reflectance confocal adaptive optics scanning light ophthalmoscope (AOSLO). Methods. Inner retinal images from 101 subjects affected by one of 38 retinal or
Carroll, Joseph, Cooper, Robert F., Dubis, Adam M., Dubra, Alfredo, Higgins, Brian P., Kim, Judy E., Scoles, Drew, Stepien, Kimberly E., Summerfelt, Phyllis, Weinberg, David V.   +9 more
core   +2 more sources

Deep Retinal Image Understanding [PDF]

, 2016
MICCAI 2016 Camera ...
Maninis, Kevis-Kokitsi, Pont-Tuset, Jordi, Arbeláez, Pablo, Van Gool, Luc   +3 more
openaire   +3 more sources

Spatial and spatio-temporal statistical analyses of retinal images: a review of methods and applications

BMJ Open Ophthalmology, 2020
Background Clinical research and management of retinal diseases greatly depend on the interpretation of retinal images and often longitudinally collected images. Retinal images provide context for spatial data, namely the location of specific pathologies
Simon Harding, Yalin Zheng, Gabriela Czanner, Ruwanthi Kolamunnage-Dona, Wenyue Zhu   +4 more
doaj   +1 more source

Automated Fovea Detection Based on Unsupervised Retinal Vessel Segmentation Method [PDF]

, 2017
The Computer Assisted Diagnosis systems could save workloads and give objective diagnostic to ophthalmologists. At first level of automated screening of systems feature extraction is the fundamental step. One of these retinal features is the fovea.
Kalantari, Faraz, Kelley, Patrick, Nazar, Mahdieh, Tavakoli, Meysam   +3 more
core   +1 more source

A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform

IJAIN (International Journal of Advances in Intelligent Informatics), 2022
The eye-fundus photograph is widely used for eye examinations. Accurate identification of retinal blood vessels could reveal information that is helpful for clinical diagnoses of many health disorders.
Kartika Firdausy, Oyas Wahyunggoro, Hanung Adi Nugroho, Muhammad Bayu Sasongko   +3 more
doaj   +1 more source

Automated Detection of Retinopathy of Prematurity Using Quantum Machine Learning and Deep Learning Techniques

IEEE Access, 2023
Retinopathy of prematurity (ROP) is a vasoproliferative retinal disease that affects premature infants and causes permanent blindness if left untreated.
V. M. Raja Sankari, U. Umapathy, Sultan Alasmari, Shabnam Mohamed Aslam   +3 more
doaj   +1 more source

Observation of cone and rod photoreceptors in normal subjects and patients using a new generation adaptive optics scanning laser ophthalmoscope. [PDF]

, 2011
We demonstrate the capability of a new generation adaptive optics scanning laser ophthalmoscope (AOSLO) to resolve cones and rods in normal subjects, and confirm our findings by comparing cone and rod spacing with published histology measurements.
Duncan, Jacque L, Merino, David, Roorda, Austin, Tiruveedhula, Pavan   +3 more
core   +3 more sources

OCT-based deep-learning models for the identification of retinal key signs

Scientific Reports, 2023
A new system based on binary Deep Learning (DL) convolutional neural networks has been developed to recognize specific retinal abnormality signs on Optical Coherence Tomography (OCT) images useful for clinical practice.
Inferrera Leandro, Borsatti Lorenzo, Miladinovic Aleksandar, Marangoni Dario, Giglio Rosa, Accardo Agostino, Tognetto Daniele   +6 more
doaj   +1 more source