A Single-Resolution Fully Convolutional Network for Retinal Vessel Segmentation in Raw Fundus Images

2019 ◽  
pp. 59-69
Author(s):  
Ricardo J. Araújo ◽  
Jaime S. Cardoso ◽  
Hélder P. Oliveira
Keyword(s):  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Fundus Images ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Retinal Vessel Segmentation

scholarly journals Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0188939 ◽  
Author(s):  
Nogol Memari ◽  
Abd Rahman Ramli ◽  
M. Iqbal Bin Saripan ◽  
Syamsiah Mashohor ◽  
Mehrdad Moghbel
Keyword(s):  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Matched Filtering ◽  
Fundus Images ◽  
Retinal Vessel Segmentation ◽  
Color Fundus Images ◽  
Adaboost Classifier

scholarly journals Retinal Vessel Segmentation Algorithm Based on Residual Convolution Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Shuang Xu ◽  
Zhiqiang Chen ◽  
Weiyi Cao ◽  
Feng Zhang ◽  
Bo Tao
Keyword(s):  
Neural Network ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Segmentation Algorithm ◽  
Convolution Neural Network ◽  
Superior Performance ◽  
Retinal Vessels ◽  
Fundus Images ◽  
Data Set ◽  
Retinal Vessel Segmentation

Retinal vessels are the only deep micro vessels that can be observed in human body, the accurate identification of which has great significance on the diagnosis of hypertension, diabetes and other diseases. To this end, a retinal vessel segmentation algorithm based on residual convolution neural network is proposed according to the characteristics of the retinal vessels on fundus images. Improved residual attention module and deep supervision module are utilized, in which the low-level and high-level feature graphs are joined to construct the encoder-decoder network structure, and atrous convolution is introduced to the pyramid pooling. The experiments result on the fundus image data set DRIVE and STARE show that this algorithm can obtain complete retinal vessel segmentation as well as connected vessel stems and terminals. The average accuracy on DRIVE and STARE reaches 95.90 and 96.88%, and the average specificity is 98.85 and 97.85%, which shows superior performance compared to other methods. This algorithm is verified feasible and effective for retinal vessel segmentation of fundus images and has the ability to detect more capillaries.

An Ensemble Retinal Vessel Segmentation Based on Supervised Learning in Fundus Images

2016 ◽  
Vol 25 (3) ◽  
pp. 503-511 ◽  
Author(s):  
Chengzhang Zhu ◽  
Beiji Zou ◽  
Jinkai Cui ◽  
Yao Xiang ◽  
Hui Wu
Keyword(s):  
Supervised Learning ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Fundus Images ◽  
Retinal Vessel Segmentation

Retinal vessel segmentation in colour fundus images using Extreme Learning Machine

2017 ◽  
Vol 55 ◽  
pp. 68-77 ◽  
Author(s):  
Chengzhang Zhu ◽  
Beiji Zou ◽  
Rongchang Zhao ◽  
Jinkai Cui ◽  
Xuanchu Duan ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Fundus Images ◽  
Retinal Vessel Segmentation ◽  
Learning Machine
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