Retinal blood vessel segmentation based on the Gaussian matched filter and U-net

2017 ◽  
Author(s):  
Xurong Gao ◽  
Yiheng Cai ◽  
Changyan Qiu ◽  
Yize Cui
Keyword(s):  
Blood Vessel ◽  
Matched Filter ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation

scholarly journals Multichannel Retinal Blood Vessel Segmentation Based on the Combination of Matched Filter and U-Net Network

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yuliang Ma ◽  
Zhenbin Zhu ◽  
Zhekang Dong ◽  
Tao Shen ◽  
Mingxu Sun ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Current Problem ◽  
Matched Filter ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels ◽  
Vessel Enhancement ◽  
Better Than

Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods.

scholarly journals Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuliang Ma ◽  
Xue Li ◽  
Xiaopeng Duan ◽  
Yun Peng ◽  
Yingchun Zhang
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Area Under The Curve ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Superior Performance ◽  
Retinal Blood Vessel ◽  
Low Contrast ◽  
Blood Vessel Segmentation ◽  
Small Vessels

Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

Convexity shape constraints for retinal blood vessel segmentation and foveal avascular zone detection

2020 ◽  
Vol 127 ◽  
pp. 104049
Author(s):  
José Escorcia-Gutierrez ◽  
Jordina Torrents-Barrena ◽  
Margarita Gamarra ◽  
Pedro Romero-Aroca ◽  
Aida Valls ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Vessel Segmentation ◽  
Foveal Avascular Zone ◽  
Retinal Blood Vessel ◽  
Shape Constraints ◽  
Blood Vessel Segmentation ◽  
Avascular Zone
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