Results 11 to 20 of about 220,384 (141)
Machine Learning Method for Functional Assessment of Retinal Models [PDF]
, 2022 Challenges in the field of retinal prostheses motivate the development of retinal models to accurately simulate Retinal Ganglion Cells (RGCs) responses. The goal of retinal prostheses is to enable blind individuals to solve complex, reallife visual tasks.
arxiv +1 more source
Deep Learning Methods for Retinal Blood Vessel Segmentation: Evaluation on Images with Retinopathy of Prematurity [PDF]
Proceedings of 18th International Symposium on Intelligent Systems
and Informatics (SISY), IEEE, 2020, pp. 131-136, 2023 Automatic blood vessel segmentation from retinal images plays an important role in the diagnosis of many systemic and eye diseases, including retinopathy of prematurity. Current state-of-the-art research in blood vessel segmentation from retinal images is based on convolutional neural networks.
arxiv +1 more source
Advances in Retinal Optical Imaging [PDF]
Photonics, 2018 Retinal imaging has undergone a revolution in the past 50 years to allow for better understanding of the eye in health and disease. Significant improvements have occurred both in hardware such as lasers and optics in addition to software image analysis.
Yanxiu Li, Xiaobo Xia, Yannis M. Paulus
openaire +4 more sources
Frontiers in Retinal Image Processing
Journal of Imaging, 2022 Visual impairment is considered as a primary global challenge in the present era [...]
Vasudevan Lakshminarayanan, P. Jidesh
openaire +3 more sources
OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods [PDF]
Scientific Data 11.1 (2024): 365, 2023 Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases.
arxiv +1 more source
Segmentation of Retinal Blood Vessels Using Deep Learning [PDF]
arXiv, 2023 The morphology of retinal blood vessels can indicate various diseases in the human body, and researchers have been working on automatic scanning and segmentation of retinal images to aid diagnosis. This project compares the performance of four neural network architectures in segmenting retinal images, using a combined dataset from different databases ...
arxiv
A novel approach for glaucoma classification by wavelet neural networks using graph-based, statisitcal features of qualitatively improved images [PDF]
arXiv, 2022 In this paper, we have proposed a new glaucoma classification approach that employs a wavelet neural network (WNN) on optimally enhanced retinal images features. To avoid tedious and error prone manual analysis of retinal images by ophthalmologists, computer aided diagnosis (CAD) substantially aids in robust diagnosis.
arxiv
Automatic Segmentation of Retinal Vasculature [PDF]
IEEE International Conference on Acoustics Speech and Signal
Processing (ICASSP), page: 886-890, 2017, 2017 Segmentation of retinal vessels from retinal fundus images is the key step in the automatic retinal image analysis. In this paper, we propose a new unsupervised automatic method to segment the retinal vessels from retinal fundus images. Contrast enhancement and illumination correction are carried out through a series of image processing steps followed ...
arxiv +1 more source
Retinal Image Restoration and Vessel Segmentation using Modified Cycle-CBAM and CBAM-UNet [PDF]
arXiv, 2022 Clinical screening with low-quality fundus images is challenging and significantly leads to misdiagnosis. This paper addresses the issue of improving the retinal image quality and vessel segmentation through retinal image restoration. More specifically, a cycle-consistent generative adversarial network (CycleGAN) with a convolution block attention ...
arxiv