A novel retinal vessel extraction algorithm based on matched filtering and gradient vector flow

2013 ◽  
Author(s):  
Lei Yu ◽  
Mingliang Xia ◽  
Li Xuan
Keyword(s):  
Retinal Vessel ◽  
Matched Filtering ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Extraction Algorithm ◽  
Vessel Extraction

Automatic Retinal Vessel Extraction Algorithm

2016 ◽  
Author(s):  
Toufique A. Soomro ◽  
Mohammad A. U. Khan ◽  
Junbin Gao ◽  
Tariq M. Khan ◽  
Manoranjan Paul ◽  
...  
Keyword(s):  
Retinal Vessel ◽  
Extraction Algorithm ◽  
Vessel Extraction

Automatic retinal vessel extraction algorithm based on contrast-sensitive schemes

2016 ◽  
Author(s):  
Mohammad A. U. Khan ◽  
Toufique A Soomro ◽  
Tariq M. Khan ◽  
Donald G. Bailey ◽  
Junbin Gao ◽  
...  
Keyword(s):  
Retinal Vessel ◽  
Extraction Algorithm ◽  
Vessel Extraction

Improvement of thin retinal vessel extraction using mean matting method

2021 ◽  
Author(s):  
V. Sathananthavathi ◽  
G. Indumathi ◽  
Rita Mahiya ◽  
S. Priyadarshini
Keyword(s):  
Retinal Vessel ◽  
Vessel Extraction

scholarly journals Extraction of Blood Vessels in Retinal Images Using Four Different Techniques

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Asloob Ahmad Mudassar ◽  
Saira Butt
Keyword(s):  
Blood Vessel ◽  
Cross Sections ◽  
Retinal Images ◽  
Matched Filtering ◽  
Edge Enhancement ◽  
Promising Technique ◽  
Filtering Technique ◽  
Continuation Algorithm ◽  
Line Cross ◽  
Vessel Extraction

A variety of blood vessel extraction (BVE) techniques exist in the literature, but they do not always lead to acceptable solutions especially in the presence of anomalies where the reported work is limited. Four techniques are presented for BVE: (1) BVE using Image Line Cross-Sections (ILCS), (2) BVE using Edge Enhancement and Edge Detection (EEED), (3) BVE using Modified Matched Filtering (MMF), and (4) BVE using Continuation Algorithm (CA). These four techniques have been designed especially for abnormal retinal images containing low vessel contrasts, drusen, exudates, and other artifacts. The four techniques were applied to 30 abnormal retinal images, and the success rate was found to be (95 to 99%) for CA, (88–91%) for EEED, (80–85%) for MMF, and (74–78%) for ILCS. Application of these four techniques to 105 normal retinal images gave improved results: (99-100%) for CA, (96–98%) for EEED, (94-95%) for MMF, and (88–93%) for ILCS. Investigations revealed that the four techniques in the order of increasing performance could be arranged as ILCS, MMF, EEED, and CA. Here we demonstrate these four techniques for abnormal retinal images only. ILCS, EEED, and CA are novel additions whereas MMF is an improved and modified version of an existing matched filtering technique. CA is a promising technique.

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