Hybrid textural feature set based automated diagnosis system for Age Related Macular Degeneration using fundus images

2017 ◽  
Author(s):  
Samina Khalid ◽  
M. Usman Akram ◽  
Tehmina Khalil
Keyword(s):  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Fundus Images ◽  
Textural Feature ◽  
Automated Diagnosis ◽  
Diagnosis System ◽  
Age Related

scholarly journals Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

2014 ◽  
Vol 53 ◽  
pp. 55-64 ◽  
Author(s):  
Muthu Rama Krishnan Mookiah ◽  
U. Rajendra Acharya ◽  
Joel E.W. Koh ◽  
Vinod Chandran ◽  
Chua Kuang Chua ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Fundus Images ◽  
Automated Diagnosis ◽  
Age Related

scholarly journals Automatic Drusen Quantification and Risk Assessment of Age-Related Macular Degeneration on Color Fundus Images

2013 ◽  
Vol 54 (4) ◽  
pp. 3019 ◽  
Author(s):  
Mark J. J. P. van Grinsven ◽  
Yara T. E. Lechanteur ◽  
Johannes P. H. van de Ven ◽  
Bram van Ginneken ◽  
Carel B. Hoyng ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Fundus Images ◽  
Age Related ◽  
Color Fundus Images

scholarly journals Analyzing Age-Related Macular Degeneration Progression in Patients with Geographic Atrophy Using Joint Autoencoders for Unsupervised Change Detection

2020 ◽  
Vol 6 (7) ◽  
pp. 57
Author(s):  
Guillaume Dupont ◽  
Ekaterina Kalinicheva ◽  
Jérémie Sublime ◽  
Florence Rossant ◽  
Michel Pâques
Keyword(s):  
Change Detection ◽  
Macular Degeneration ◽  
Geographic Atrophy ◽  
Research Field ◽  
Age Related Macular Degeneration ◽  
Detection Methods ◽  
Fundus Images ◽  
Manual Annotation ◽  
Current Standard ◽  
Age Related

Age-Related Macular Degeneration (ARMD) is a progressive eye disease that slowly causes patients to go blind. For several years now, it has been an important research field to try to understand how the disease progresses and find effective medical treatments. Researchers have been mostly interested in studying the evolution of the lesions using different techniques ranging from manual annotation to mathematical models of the disease. However, artificial intelligence for ARMD image analysis has become one of the main research focuses to study the progression of the disease, as accurate manual annotation of its evolution has proved difficult using traditional methods even for experienced practicians. In this paper, we propose a deep learning architecture that can detect changes in the eye fundus images and assess the progression of the disease. Our method is based on joint autoencoders and is fully unsupervised. Our algorithm has been applied to pairs of images from different eye fundus images time series of 24 ARMD patients. Our method has been shown to be quite effective when compared with other methods from the literature, including non-neural network based algorithms that still are the current standard to follow the disease progression and change detection methods from other fields.

Sign in / Sign up

Export Citation Format

Share Document