scholarly journals Retinal Blood Vessel Segmentation Based on Multi-Scale Deep Learning

2018 ◽  
Author(s):  
Ming Li ◽  
Qingbo Yin ◽  
Mingyu Lu
Keyword(s):  
Deep Learning ◽  
Blood Vessel ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Multi Scale

An effective retinal blood vessel segmentation method using multi-scale line detection

2013 ◽  
Vol 46 (3) ◽  
pp. 703-715 ◽  
Author(s):  
Uyen T.V. Nguyen ◽  
Alauddin Bhuiyan ◽  
Laurence A.F. Park ◽  
Kotagiri Ramamohanarao
Keyword(s):  
Blood Vessel ◽  
Vessel Segmentation ◽  
Line Detection ◽  
Segmentation Method ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Multi Scale ◽  
Scale Line

scholarly journals Retinal blood vessel segmentation using a deep learning method based on modified U-NET model

2021 ◽  
Author(s):  
Sanjeewani NA ◽  
arun kumar yadav ◽  
Mohd Akbar ◽  
mohit kumar ◽  
Divakar Yadav
Keyword(s):  
Deep Learning ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Automatic Segmentation ◽  
Vessel Segmentation ◽  
Great Success ◽  
Retinal Blood Vessel ◽  
Data Set ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels

<div>Automatic retinal blood vessel segmentation is very crucial to ophthalmology. It plays a vital role in the early detection of several retinal diseases such as Diabetic Retinopathy, hypertension, etc. In recent times, deep learning based methods have attained great success in automatic segmentation of retinal blood vessels from images. In this paper, a U-NET based architecture is proposed to segment the retinal blood vessels from fundus images of the eye. Furthermore, 3 pre-processing algorithms are also proposed to enhance the performance of the system. The proposed architecture has provided significant results. On the basis of experimental evaluation on the publicly available DRIVE data set, it has been observed that the average accuracy (Acc) is .9577, sensitivity (Se) is .7436, specificity (Sp) is .9838 and F1-score is .7931. The proposed system outperforms all recent state of art approaches mentioned in the literature.</div>

scholarly journals Multiple Multi-Scale Neural Networks Knowledge Transfer and Integration for Accurate Pixel-Level Retinal Blood Vessel Segmentation

2021 ◽  
Vol 11 (24) ◽  
pp. 11907
Author(s):  
Chen Ding ◽  
Runze Li ◽  
Zhouyi Zheng ◽  
Youfa Chen ◽  
Dushi Wen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Knowledge Transfer ◽  
Blood Vessel ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Low Contrast ◽  
Blood Vessel Segmentation ◽  
Multi Scale ◽  
Network Training ◽  
Average Accuracy

Retinal blood vessel segmentation plays an important role for analysis of retinal diseases, such as diabetic retinopathy and glaucoma. However, retinal blood vessel segmentation remains a challenging task due to the low contrast between some vessels and background, the different presenting conditions caused by uneven illumination and the artificial segmentation results are influenced by human experience, which seriously affects the classification accuracy. To address this problem, we propose a multiple multi-scale neural networks knowledge transfer and integration method in order to accurately segment for retinal blood vessel image. With the integration of multi-scale networks and multi-scale input patches, the blood vessel segmentation performance is obviously improved. In addition, applying knowledge transfer to the network training process, the pre-trained network reduces the number of network training iterations. The experimental results on the DRIVE dataset and the CHASE_DB1 dataset show the effectiveness of the method, whose average accuracy on the two datasets are 96.74% and 97.38%, respectively.

Retinal Blood Vessel Segmentation Based on Multi-Scale Wavelet Transform Fusion

2021 ◽  
Vol 41 (4) ◽  
pp. 0410001
Author(s):  
田丰 Tian Feng ◽  
李莹 Li Ying ◽  
王静 Wang Jing
Keyword(s):  
Wavelet Transform ◽  
Blood Vessel ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Multi Scale
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