Retinal blood vessel segmentation in fundus images based on morphological operators within entropy information

2021 ◽  
Author(s):  
Amal Chouchene ◽  
Walid Barhoumi ◽  
Amer Ahmad
Keyword(s):  
Blood Vessel ◽  
Vessel Segmentation ◽  
Fundus Images ◽  
Retinal Blood Vessel ◽  
Morphological Operators ◽  
Blood Vessel Segmentation ◽  
Entropy Information

Flexible architectures for retinal blood vessel segmentation in high-resolution fundus images

2016 ◽  
Vol 15 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Hamza Bendaoudi ◽  
Farida Cheriet ◽  
Ashley Manraj ◽  
Houssem Ben Tahar ◽  
J. M. Pierre Langlois
Keyword(s):  
High Resolution ◽  
Blood Vessel ◽  
Vessel Segmentation ◽  
Fundus Images ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Flexible Architectures

scholarly journals Retinal blood vessel segmentation based on heuristic image analysis

2019 ◽  
Vol 16 (1) ◽  
pp. 227-245 ◽  
Author(s):  
Maja Braovic ◽  
Darko Stipanicev ◽  
Ljiljana Seric
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Vessel Segmentation ◽  
Optic Disk ◽  
Fundus Images ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Automatic analysis of retinal fundus images is becoming increasingly present today, and diseases such as diabetic retinopathy and age-related macular degeneration are getting a higher chance of being discovered in the early stages of their development. In order to focus on discovering those diseases, researchers commonly preprocess retinal fundus images in order to detect the retinal landmarks - blood vessels, fovea and the optic disk. A large number of methods for the automatic detection of retinal blood vessels from retinal fundus images already exists, but many of them are using unnecessarily complicated approaches. In this paper we demonstrate that a reliable retinal blood vessel segmentation can be achieved with a cascade of very simple image processing methods. The proposed method puts higher emphasis on high specificity (i.e. high probability that the segmented pixels actually belong to retinal blood vessels and are not false positive detections) rather than on high sensitivity. The proposed method is based on heuristically determined parametric edge detection and shape analysis, and is evaluated on the publicly available DRIVE and STARE datasets on which it achieved the average accuracy of 96.33% and 96.10%, respectively.

An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering

2021 ◽  
Vol 201 ◽  
pp. 105949
Author(s):  
Oscar Ramos-Soto ◽  
Erick Rodríguez-Esparza ◽  
Sandra E. Balderas-Mata ◽  
Diego Oliva ◽  
Aboul Ella Hassanien ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Vessel Segmentation ◽  
Fundus Images ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Homomorphic Filtering

scholarly journals Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image

2020 ◽  
Vol 10 (11) ◽  
pp. 3777 ◽  
Author(s):  
Yun Jiang ◽  
Falin Wang ◽  
Jing Gao ◽  
Simin Cao
Keyword(s):  
Blood Vessel ◽  
Optic Disc ◽  
Vessel Segmentation ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Optic Cup ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetes can induce diseases including diabetic retinopathy, cataracts, glaucoma, etc. The blindness caused by these diseases is irreversible. Early analysis of retinal fundus images, including optic disc and optic cup detection and retinal blood vessel segmentation, can effectively identify these diseases. The existing methods lack sufficient discrimination power for the fundus image and are easily affected by pathological regions. This paper proposes a novel multi-path recurrent U-Net architecture to achieve the segmentation of retinal fundus images. The effectiveness of the proposed network structure was proved by two segmentation tasks: optic disc and optic cup segmentation and retinal vessel segmentation. Our method achieved state-of-the-art results in the segmentation of the Drishti-GS1 dataset. Regarding optic disc segmentation, the accuracy and Dice values reached 0.9967 and 0.9817, respectively; as regards optic cup segmentation, the accuracy and Dice values reached 0.9950 and 0.8921, respectively. Our proposed method was also verified on the retinal blood vessel segmentation dataset DRIVE and achieved a good accuracy rate.

scholarly journals Unsupervised multiscale retinal blood vessel segmentation using fundus images

2020 ◽  
Vol 14 (11) ◽  
pp. 2616-2625 ◽  
Author(s):  
Kamini Upadhyay ◽  
Monika Agrawal ◽  
Praveen Vashist
Keyword(s):  
Blood Vessel ◽  
Vessel Segmentation ◽  
Fundus Images ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation

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.

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