Results 251 to 260 of about 188,294 (288)

Neural Network Based Retinal Image Analysis

2008 Congress on Image and Signal Processing, 2008
Diabetic-retinopathy contributes to serious health problem in many parts of the world. With the motivation of the needs of the medical community system for early screening of diabetics and other diseases a computer aided diagnosis system is proposed. This work is aimed to develop an automated system to analyze the retinal images for important features ...
J. David, Rekha Krishnan, Sukesh Kumar A.   +2 more
openaire   +1 more source

Electronic Image Analysis in Retinal Fluoroangiography

Ophthalmologica, 1979
The autors describe an original method of fluororetinographic densitometry based on electronic image analysis.
Cardillo Piccolino, M. Zingirian, G.C. Parodi   +2 more
openaire   +1 more source

Studying disagreements among retinal experts through image analysis

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
In recent years, many image analysis algorithms have been presented to assist Diabetic Retinopathy (DR) screening. The goal was usually to detect healthy examination records automatically, in order to reduce the number of records that should be analyzed by retinal experts. In this paper, a novel application is presented: these algorithms are used to 1)
Gwénolé, Quellec, Mathieu, Lamard, Béatrice, Cochener, Zakarya, Droueche, Bruno, Lay, Agnès, Chabouis, Christian, Roux, Guy, Cazuguel   +7 more
openaire   +2 more sources

Image Analysis for Exudate Detection in Retinal Images

2010
Diabetic retinopathy is recognised as one of the most common causes of blindness. Early diagnosis is important and is based on detection of features such as exudates during eye fundus image screening. In this chapter it is shown how areas corresponding to exudates can be automatically detected using a neural network that, following contrast enhancement
Gerald Schaefer, Albert Clos
openaire   +1 more source

Spatiotemporal Independent Component Analysis for Retinal Images

2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006
This paper presents a new Independent Component Analysis algorithm (ICA-P) for modeling and measuring physiological responses to optical stimulation from a new, non-invasive optical imaging device. The ICA-P algorithm uses prior information on the visual stimulus to provide improved detection performance.
Eduardo S. Barriga, Marios S. Pattichis, M.D. Abramoff, D. Ts'o, R. Kardon, Y. Kwon, P. Soliz   +6 more
openaire   +1 more source

Textural feature extraction for retinal image analysis

2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET), 2012
Diabetic Retinopathy (DR) is a complication of diabetes which leads to vision deterioration and causes total blindness in diabetic patients. Exudates are one of the most prevalent earliest clinical signs of retinopathy. Thus, earlier identification and classification of exudates from retinal images is clinically important which facilitates the ...
V.K. Jestin, J. Anitha, D. Jude Hemanth
openaire   +1 more source

Machine learning algorithm for retinal image analysis

2016 IEEE Region 10 Conference (TENCON), 2016
Diabetic retinopathy is the most general diabetes complication that affects eyes and results in blindness. It's due to impairment of the arteries a veins located in the fundus of eye (retina) that are composed of light sensitive tissues. The aim of this research work is to design an efficient and sensitive tool for Diabetic Retinopathy using the images
null Santhakumar R, Megha Tandur, E R Rajkumar, null Geetha K S, Girish Haritz, Kumar Thirunellai Rajamani   +5 more
openaire   +1 more source

Artificial neural networks in retinal image analysis

2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN), 2015
Glaucoma disease detection from retinal images using classifiers like Least Square-Support Vector Machine classifier, Random forest, Dual Sequential Minimal Optimization classifier, Naive bayes classifier and Artificial neural networks. The textual features obtained from retinal images are used for this classification. Energy distributions over wavelet
null Gayathri Devi T.M., null Sudha S., null Suraj P.   +2 more
openaire   +1 more source

Size-Analysis of Retinal Images by Orientation Detectors

Perceptual and Motor Skills, 1980
A model for the determination of retinal-image size is presented. The size-analysis is based upon the range of orientation detectors activated by a stimulus. The model is applied to size aftereffects and is also used to predict changes in perceived size in configurations which may be expected to affect the range of orientation detectors activated. The
openaire   +2 more sources

RFMiD: Retinal Image Analysis for multi-Disease Detection challenge

Medical Image Analysis
In the last decades, many publicly available large fundus image datasets have been collected for diabetic retinopathy, glaucoma, and age-related macular degeneration, and a few other frequent pathologies. These publicly available datasets were used to develop a computer-aided disease diagnosis system by training deep learning models to detect these ...
Samiksha Pachade, Prasanna Porwal, Manesh Kokare, Girish Deshmukh, Vivek Sahasrabuddhe, Zhengbo Luo, Feng Han, Zitang Sun, Li Qihan, Sei-ichiro Kamata, Edward Ho, Edward Wang, Asaanth Sivajohan, Saerom Youn, Kevin Lane, Jin Chun, Xinliang Wang, Yunchao Gu, Sixu Lu, Young-tack Oh, Hyunjin Park, Chia-Yen Lee, Hung Yeh, Kai-Wen Cheng, Haoyu Wang, Jin Ye, Junjun He, Lixu Gu, Dominik Müller, Iñaki Soto-Rey, Frank Kramer, Hidehisa Arai, Yuma Ochi, Takami Okada, Luca Giancardo, Gwenolé Quellec, Fabrice Mériaudeau   +36 more
openaire   +3 more sources