Pattern Recognition Of Blood Vessel Networks In Ocular Fundus Images

1982 ◽  
Author(s):  
K. Akita ◽  
H. Kuga
Keyword(s):  
Pattern Recognition ◽  
Blood Vessel ◽  
Fundus Images ◽  
Ocular Fundus ◽  
Vessel Networks

Identifying retinal vessel networks in ocular fundus images

1993 ◽  
Author(s):  
Shirley H. Lee ◽  
Gregory W. Donohoe ◽  
Michael J. Wilcox
Keyword(s):  
Retinal Vessel ◽  
Fundus Images ◽  
Ocular Fundus ◽  
Vessel Networks

scholarly journals Modified Curvature-based Trigonometric Identities for Retinal Blood Vessel Tortuosity Measurement in Diabetic Retinopathy Fundus Images

2018 ◽  
Vol 7 (4.11) ◽  
pp. 133
Author(s):  
N. Badariah A. Mustafa ◽  
W. Mimi Diyana W. Zaki ◽  
Aini Hussain ◽  
Jemaima Che Hamzah
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
False Negative ◽  
False Negative Rate ◽  
Fundus Images ◽  
Current Clinical Practice ◽  
Clinical Experiences ◽  
Retinal Blood Vessel ◽  
Specific Standard ◽  
Vessel Curvature

In current clinical practice, there is no specific standard and grading system that can be used to measure the behaviour of the retinal blood vessel curvature. The retinal blood vessel curvature is measured based on clinical experiences. It is very subjective and inconsistent to describe the presence of tortuosity in fundus images. Thus, this paper aims to measure the tortuosity of retinal blood vessel using curvature-based method and investigate its relationship with diabetic retinopathy (DR) disease. The proposed tortuosity measures have been tested on 43 fundus images belonging to patients who have been diagnosed with DR disease and validated by two clinical experts from our collaborative hospital. On average, the proposed algorithm achieved 90.7% (accuracy), 98.72% (sensitivity) and 9.3% (false negative rate), that shows significant tortuosity presence in diabetic retinopathy fundus images. 

scholarly journals Synthesis of neural network structure for the analysis of complex structured ocular fundus images

2021 ◽  
pp. 1-12
Author(s):  
Aslan Tatarkanov ◽  
Islam Alexandrov ◽  
Rasul Glashev
Keyword(s):  
Neural Network ◽  
Human Factor ◽  
Negative Impact ◽  
Medical Decision ◽  
Fundus Images ◽  
Ocular Fundus ◽  
Neural Network Structure ◽  
Ocular Diagnostics ◽  
Low Entropy ◽  
Domestic Medical

This paper proposes an algorithm for synthesizing a neural network (NN) structure to analyze complex structured, low entropy, ocular fundus images, characterized by iterative tuning of the adaptive model’s solver modules. This algorithm will assist in synthesizing models of NNs that meet the predetermined characteristics of the classification quality. The relevance of automating the process of ocular diagnostics of fundus pathologies is due to the need to develop domestic medical decision-making systems. Because of using the developed algorithm, the NN structure is synthesized, which will include two solver modules, and is intended to classify the dual-alternative information. Automated hybrid NN structures for intelligent segmentation of complex structured, low entropy, retinal images should provide increased efficiency of ocular diagnostics of fundus pathologies, reduce the burden on specialists, and decrease the negative impact of the human factor in diagnosis.

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