scholarly journals Fast and efficient retinal blood vessel segmentation method based on deep learning network

2021 ◽  
pp. 101902
Author(s):  
Henda Boudegga ◽  
Yaroub Elloumi ◽  
Mohamed Akil ◽  
Mohamed Hedi Bedoui ◽  
Rostom Kachouri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Vessel ◽  
Vessel Segmentation ◽  
Segmentation Method ◽  
Retinal Blood Vessel ◽  
Learning Network ◽  
Blood Vessel Segmentation ◽  
Deep Learning Network

scholarly journals Semi-Supervised Learning Method of U-Net Deep Learning Network for Blood Vessel Segmentation in Retinal Images

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1067
Author(s):  
Dali Chen ◽  
Yingying Ao ◽  
Shixin Liu
Keyword(s):  
Deep Learning ◽  
Blood Vessel ◽  
Supervised Learning ◽  
Vessel Segmentation ◽  
Training Dataset ◽  
Retinal Images ◽  
Learning Method ◽  
Learning Network ◽  
Blood Vessel Segmentation ◽  
Deep Learning Network

Blood vessel segmentation methods based on deep neural networks have achieved satisfactory results. However, these methods are usually supervised learning methods, which require large numbers of retinal images with high quality pixel-level ground-truth labels. In practice, the task of labeling these retinal images is very costly, financially and in human effort. To deal with these problems, we propose a semi-supervised learning method which can be used in blood vessel segmentation with limited labeled data. In this method, we use the improved U-Net deep learning network to segment the blood vessel tree. On this basis, we implement the U-Net network-based training dataset updating strategy. A large number of experiments are presented to analyze the segmentation performance of the proposed semi-supervised learning method. The experiment results demonstrate that the proposed methodology is able to avoid the problems of insufficient hand-labels, and achieve satisfactory performance.

scholarly journals Morphological and Otsu’s Thresholding-Based Retinal Blood Vessel Segmentation for Detection of Retinopathy

2018 ◽  
Vol 7 (3.18) ◽  
pp. 16
Author(s):  
Kuryati Kipli ◽  
Cripen Jiris ◽  
Siti Kudnie Sahari ◽  
Rohan Sapawi ◽  
Nazreen Junaidi ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Image Database ◽  
Vessel Segmentation ◽  
Fundus Image ◽  
Segmentation Method ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
Automated Method ◽  
Arteriolar Narrowing ◽  
Retinal Fundus

Retinal blood vessel segmentation is crucial as it is the earliest process in measuring various indicators of retinopathy sign such as arterial-venous nicking, and focal arteriolar and generalized arteriolar narrowing. The segmentation can be clinically used if its accuracy is close to 100%. In this study, a new method of segmentation is developed for extraction of retinal blood vessel. In this paper, we present a new automated method to extract blood vessels in retinal fundus images. The proposed method comprises of two main parts and a few subcomponents which include pre-processing and segmentation. The main focus for the segmentation part is two morphological reconstructions which are the morphological reconstructions followed by the morphological top-hat transform. Then the technique to classify the vessel pixels and background pixels is Otsu’s Thresholding. The image database used in this study is the High Resolution Fundus Image Database (HRFID). The developed segmentation method accuracies are 95.17%, 92.06% and 94.71% when tested on dataset of healthy, diabetic retinopathy (DR) and glaucoma patients respectively. Overall, the performance of the proposed method is comparable with existing methods with overall accuracies were more than 90 % for all three different categories: healthy, DR and glaucoma. 

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 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>

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