Retinal vessel extraction using dynamic multi-scale matched filtering and dynamic threshold processing based on histogram fitting

2018 ◽  
Vol 29 (4) ◽  
pp. 655-666 ◽  
Author(s):  
Duoduo Gou ◽  
Ying Wei ◽  
Hong Fu ◽  
Ning Yan
Keyword(s):  
Retinal Vessel ◽  
Matched Filtering ◽  
Dynamic Threshold ◽  
Multi Scale ◽  
Vessel Extraction ◽  
Threshold Processing

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.

MD-Net: A multi-scale dense network for retinal vessel segmentation

2021 ◽  
Vol 70 ◽  
pp. 102977
Author(s):  
Zhengjin Shi ◽  
Tianyu Wang ◽  
Zheng Huang ◽  
Feng Xie ◽  
Zihong Liu ◽  
...  
Keyword(s):  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Dense Network ◽  
Multi Scale ◽  
Retinal Vessel Segmentation

On the Automatic 2D Retinal Vessel Extraction

2005 ◽  
pp. 165-173 ◽  
Author(s):  
C. Alonso-Montes ◽  
D. L. Vilariño ◽  
M. G. Penedo
Keyword(s):  
Retinal Vessel ◽  
Vessel Extraction
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