scholarly journals Automated, real time extraction of fundus images from slit lamp fundus biomicroscope video image sequences

2000 ◽  
Vol 84 (6) ◽  
pp. 645-647 ◽  
Author(s):  
B. D Madjarov
Keyword(s):  
Real Time ◽  
Image Sequences ◽  
Video Image ◽  
Fundus Images ◽  
Slit Lamp

Direct estimation of articulatory kinematics from real-time magnetic resonance image sequences

2011 ◽  
Author(s):  
Michael Proctor ◽  
Adam Lammert ◽  
Athanasios Katsamanis ◽  
Louis Goldstein ◽  
Christina Hagedorn ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Real Time ◽  
Magnetic Resonance Image ◽  
Image Sequences ◽  
Direct Estimation

A single-chip 16-bit 25-ns real-time video/image signal processor

1989 ◽  
Vol 24 (6) ◽  
pp. 1662-1667 ◽  
Author(s):  
K. Kikuchi ◽  
Y. Nukada ◽  
Y. Aoki ◽  
T. Kanou ◽  
Y. Endo ◽  
...  
Keyword(s):  
Real Time ◽  
Video Image ◽  
Single Chip ◽  
Image Signal Processor ◽  
Signal Processor

scholarly journals Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ozer Can Devecioglu ◽  
Junaid Malik ◽  
Turker Ince ◽  
Serkan Kiranyaz ◽  
Eray Atalay ◽  
...  
Keyword(s):  
Real Time ◽  
Fundus Images ◽  
Glaucoma Detection

scholarly journals Detection of Multi-Class Retinal Diseases Using Artificial Intelligence: An Expeditious Learning Using Deep CNN with Minimal Data

2019 ◽  
Vol 12 (3) ◽  
pp. 1577-1586 ◽  
Author(s):  
Karthikeyan S. ◽  
Sanjay Kumar P. ◽  
R J Madhusudan Madhusudan ◽  
S K Sundaramoorthy Sundaramoorthy ◽  
P K Krishnan Namboori3
Keyword(s):  
Real Time ◽  
Retinal Vein Occlusion ◽  
Central Retinal Vein Occlusion ◽  
Research Work ◽  
Diagnostic Strategy ◽  
Fundus Images ◽  
Vein Occlusion ◽  
Deep Cnn ◽  
The Right ◽  
Central Retinal Vein

The health-related complications such as diabetes, macular degeneration, inflammatory conditions, ageing and fungal infections may cause damages to the retina and the macula of the eye, leading to permanent vision loss. The major diseases associated with retina are Arteriosclerotic retinopathy (AR), Central retinal vein occlusion (CRVO), Branch retinal artery occlusion (BRAO), Coat's disease (CD) and Hemi-Central Retinal Vein Occlusion (HRVO). The symptomatic variations among these disorders are relatively confusing so that a systematic diagnostic strategy is difficult to set in. Therefore, an early detection device is required that is capable of differentiating the various ophthalmic complications and thereby helping in providing the right treatment to the patient at the right time. In this research work, 'Deep Convolution Neural Networks (Deep CNN) based machine learning approach has been used for the detection of the twelve major retinal complications from the minimal set of fundus images. The model was further cross-validated with real-time fundus images. The model is found to be superior in its efficiency, specificity and ability to minimize the misclassification. The “multi-class retinal disease” model on further cross-validation with real-time fundus image of the gave an accuracy of 95.63 %, validation accuracy of 92.99 % and F1 score of 91.96 %. The multi-class model is found to be a theranostic clinical support system for the ophthalmologist for diagnosing different kinds of retinal problems, especially BRAO, BRVO, CRAO, CD, DR, HRVO, HP, HR, and CN.

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