Comparison of blood vessel detection techniques in low quality and pathological retinal images

2016 ◽  
Author(s):  
Manish Kumar Aggarwal ◽  
Vijay Khare
Keyword(s):  
Blood Vessel ◽  
Retinal Images ◽  
Detection Techniques ◽  
Blood Vessel Detection ◽  
Vessel Detection

A Comparative Study of Different Blood Vessel Detection on Retinal Images

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Wan Azani Mustafa ◽  
◽  
Ahmad Syauqi Mahmud ◽  
Muhammad Zaid Aihsan ◽  
M. Saifizi ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Comparative Study ◽  
Retinal Images ◽  
Blood Vessel Detection ◽  
Vessel Detection

Ridge-branch-based blood vessel detection algorithm for multimodal retinal images

2009 ◽  
Author(s):  
Y. Li ◽  
N. Hutchings ◽  
R. W. Knighton ◽  
G. Gregori ◽  
B. J. Lujan ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Detection Algorithm ◽  
Retinal Images ◽  
Blood Vessel Detection ◽  
Vessel Detection

An Improved Retinal Blood Vessel Detection System Using an Extreme Learning Machine

2019 ◽  
Vol 10 (3) ◽  
pp. 39-55
Author(s):  
Lucas S Sousa ◽  
Pedro P Rebouças Filho ◽  
Francisco Nivando Bezerra ◽  
Ajalmar R Rocha Neto ◽  
Saulo A. F. Oliveira
Keyword(s):  
Blood Vessel ◽  
Extreme Learning Machine ◽  
Detection System ◽  
Retinal Images ◽  
Fast Method ◽  
Retinal Blood Vessel ◽  
Local Variance ◽  
Blood Vessel Detection ◽  
Vessel Detection ◽  
Learning Machine

Retinal images are commonly used to diagnose various diseases, such as diabetic retinopathy, glaucoma, and hypertension. An important step in the analysis of such images is the detection of blood vessels, which is usually done manually and is time consuming. The main proposal in this work is a fast method for retinal blood vessel detection using Extreme Learning Machine (ELM). ELM requires only one iteration to complete its training and it is a robust and fast network in all aspects. The proposal is a compact and efficient representation of retinal images in which the authors achieved a reduction up to 39% of the initial data volume, while still keeping representativeness. To achieve such a reduction whilst maintaining the representativeness, three features (local tophat, local average, and local variance) were used. According to the simulations carried out, this proposal achieved an accuracy of about 95% for most results, outperforming most of the state-of-art methods. Furthermore, this proposal has greater sensitivity, meaning that more vessel pixels are detected correctly.

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