scholarly journals Comparison of Unsupervised Segmentation of Retinal Blood Vessels in Gray Level Image with PCA and Green Channel Image

2017 ◽  
Vol 4 (5) ◽  
pp. 163-167 ◽  
Author(s):  
Esra Kaya ◽  
Ismail Saritas
Keyword(s):  
Blood Vessels ◽  
Gray Level ◽  
Unsupervised Segmentation ◽  
Green Channel ◽  
Retinal Blood Vessels ◽  
Gray Level Image ◽  
Channel Image

Integrated Features Based on Gray-Level and Hu Moment-Invariants with Ant Colony System for Retinal Blood Vessels Segmentation

2012 ◽  
Vol 1 (4) ◽  
pp. 60-73 ◽  
Author(s):  
Ahmed H. Asad ◽  
Ahmad Taher Azar ◽  
Aboul Ella Hassanien
Keyword(s):  
Blood Vessels ◽  
Real Life ◽  
Ant Colony ◽  
Moment Invariants ◽  
Ant Colony System ◽  
Gray Level ◽  
Retinal Blood Vessels ◽  
Positive Rate ◽  
Hu Moment ◽  
Hu Moment Invariants

Abnormality detection plays an important role in many real-life applications. Retinal vessel segmentation algorithms are the critical components of circulatory blood vessel Analysis systems for detecting the various abnormalities in retinal images. Traditionally, the vascular network is mapped by hand in a time-consuming process that requires both training and skill. Automating the process allows consistency, and most importantly, frees up the time that a skilled technician or doctor would normally use for manual screening. Several studies were carried out on the segmentation of blood vessels in general; however, only a small number of them were associated to retinal blood vessels. In this paper, an approach for segmenting retinal blood vessels is proposed using only ant colony system. Eight features are selected for the developed system; four are based on gray-level and the other features on Hu moment-invariants. The features are directly computed from values of image pixels, so they take about 90 seconds in computation. The performance of the proposed structure is evaluated in terms of accuracy, true positive rate (TPR) and false positive rate (FPR). The results showed that the overall accuracy and sensitivity of the presented approach achieved 90.28% and 74%, respectively.

scholarly journals Retinal blood vessels segmentation using classical edge detection filters and the neural network

2021 ◽  
Vol 23 ◽  
pp. 100521
Author(s):  
Beaudelaire Saha Tchinda ◽  
Daniel Tchiotsop ◽  
Michel Noubom ◽  
Valerie Louis-Dorr ◽  
Didier Wolf
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Blood Vessels ◽  
The Neural Network ◽  
Retinal Blood Vessels

scholarly journals Investigation of Bilateral Similarity in Central Retinal Blood Vessels

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sangeeta Biswas ◽  
Johan Rohdin ◽  
Andrii Kavetskyi ◽  
Gabriel Saraiva ◽  
Angkan Biswas ◽  
...  
Keyword(s):  
Blood Vessels ◽  
Retinal Blood Vessels

Insulin Receptors in Calf and Human Retinal Blood Vessels

1983 ◽  
Vol 15 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Edward Cotlier ◽  
Charles Davidson
Keyword(s):  
Blood Vessels ◽  
Insulin Receptors ◽  
Retinal Blood Vessels

scholarly journals Hyperglycemia Accelerates Impairment of Vasodilator Responses to Acetylcholine of Retinal Blood Vessels in Rats

2009 ◽  
Vol 110 (2) ◽  
pp. 160-168 ◽  
Author(s):  
Asami Mori ◽  
Orie Saigo ◽  
Masayuki Hanada ◽  
Tsutomu Nakahara ◽  
Kunio Ishii
Keyword(s):  
Blood Vessels ◽  
Retinal Blood Vessels

Retinal blood vessels are damaged in a rat model of NMDA-induced retinal degeneration

2010 ◽  
Vol 485 (1) ◽  
pp. 55-59 ◽  
Author(s):  
Kaori Ueda ◽  
Tsutomu Nakahara ◽  
Maya Hoshino ◽  
Asami Mori ◽  
Kenji Sakamoto ◽  
...  
Keyword(s):  
Blood Vessels ◽  
Retinal Degeneration ◽  
Rat Model ◽  
Retinal Blood Vessels
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