A novel automatic retinal vessel extraction using maximum entropy based EM algorithm

2020 ◽  
Vol 79 (31-32) ◽  
pp. 22337-22353 ◽  
Author(s):  
G. R. Jainish ◽  
G. Wiselin Jiji ◽  
P. Alwin Infant
Keyword(s):  
Em Algorithm ◽  
Maximum Entropy ◽  
Retinal Vessel ◽  
Vessel Extraction

Improvement of thin retinal vessel extraction using mean matting method

2021 ◽  
Author(s):  
V. Sathananthavathi ◽  
G. Indumathi ◽  
Rita Mahiya ◽  
S. Priyadarshini
Keyword(s):  
Retinal Vessel ◽  
Vessel Extraction

On the Automatic 2D Retinal Vessel Extraction

2005 ◽  
pp. 165-173 ◽  
Author(s):  
C. Alonso-Montes ◽  
D. L. Vilariño ◽  
M. G. Penedo
Keyword(s):  
Retinal Vessel ◽  
Vessel Extraction

scholarly journals Deep Att-ResGAN: A Retinal Vessel Segmentation Network for Color Fundus Images

2021 ◽  
Vol 38 (5) ◽  
pp. 1309-1317
Author(s):  
Jie Zhao ◽  
Qianjin Feng
Keyword(s):  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Original Image ◽  
Generative Adversarial Network ◽  
Low Contrast ◽  
Adversarial Network ◽  
Vessel Structure ◽  
Retinal Vessel Segmentation ◽  
Vessel Extraction ◽  
Public Datasets

Retinal vessel segmentation plays a significant role in the diagnosis and treatment of ophthalmological diseases. Recent studies have proved that deep learning can effectively segment the retinal vessel structure. However, the existing methods have difficulty in segmenting thin vessels, especially when the original image contains lesions. Based on generative adversarial network (GAN), this paper proposes a deep network with residual module and attention module (Deep Att-ResGAN). The network consists of four identical subnetworks. The output of each subnetwork is imported to the next subnetwork as contextual features that guide the segmentation. Firstly, the problems of the original image, namely, low contrast, uneven illumination, and data insufficiency, were solved through image enhancement and preprocessing. Next, an improved U-Net was adopted to serve as the generator, which stacks the residual and attention modules. These modules optimize the weight of the generator, and enhance the generalizability of the network. Further, the segmentation was refined iteratively by the discriminator, which contributes to the performance of vessel segmentation. Finally, comparative experiments were carried out on two public datasets: Digital Retinal Images for Vessel Extraction (DRIVE) and Structured Analysis of the Retina (STARE). The experimental results show that Deep Att-ResGAN outperformed the equivalent models like U-Net and GAN in most metrics. Our network achieved accuracy of 0.9565 and F1 of 0.829 on DRIVE, and accuracy of 0.9690 and F1 of 0.841 on STARE.

DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images

2020 ◽  
Vol 134 ◽  
pp. 109426 ◽  
Author(s):  
Ümit Budak ◽  
Zafer Cömert ◽  
Musa Çıbuk ◽  
Abdulkadir Şengür
Keyword(s):  
Retinal Vessel ◽  
Fundus Images ◽  
Vessel Extraction

scholarly journals Thin Vessel Detection and Thick Vessel Edge Enhancement to Boost Performance of Retinal Vessel Extraction Methods

2019 ◽  
Vol 163 ◽  
pp. 618-638
Author(s):  
Mohammad A.U Khan ◽  
Nighat Mir ◽  
Akila Sarirete ◽  
James N. Carmichael
Keyword(s):  
Retinal Vessel ◽  
Extraction Methods ◽  
Edge Enhancement ◽  
Vessel Detection ◽  
Vessel Extraction
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