Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

Retinal blood vessels have been presented to contribute confirmation with regard to tortuosity, branching angles, or change in diameter as a result of ophthalmic disease. Although many enhancement filters are extensively utilized, the Jerman filter responds quite effectively at vessels, edges, and bifurcations and improves the visualization of structures. In contrast, curvelet transform is specifically designed to associate scale with orientation and can be used to recover from noisy data by curvelet shrinkage. This paper describes a method to improve the performance of curvelet transform further. A distinctive fusion of curvelet transform and the Jerman filter is presented for retinal blood vessel segmentation. Mean-C thresholding is employed for the segmentation purpose. The suggested method achieves average accuracies of 0.9600 and 0.9559 for DRIVE and CHASE_DB1, respectively. Simulation results establish a better performance and faster implementation of the suggested scheme in comparison with similar approaches seen in the literature.

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Retinal Blood Vessel Segmentation and Identification of Glaucoma Using Convolutional Neural Network

SSRN Electronic Journal ◽

10.2139/ssrn.3643870 ◽

2020 ◽

Author(s):

G Roopa Krishna Chandra ◽

NV Kranthi ◽

K Kavya

Keyword(s):

Neural Network ◽

Blood Vessel ◽

Convolutional Neural Network ◽

Vessel Segmentation ◽

Retinal Blood Vessel ◽

Blood Vessel Segmentation

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Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

Computational Intelligence and Neuroscience ◽

10.1155/2020/8822407 ◽

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.

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