A Novel Retinal Blood Vessel Segmentation Method Based on Line Operator and Edge Detector

2009 ◽  
Author(s):  
Chin-Chen Chang ◽  
Chia-Chen Lin ◽  
Pei-Yan Pai ◽  
Yen-Chang Chen
Keyword(s):  
Blood Vessel ◽  
Vessel Segmentation ◽  
Segmentation Method ◽  
Edge Detector ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation ◽  
On Line ◽  
Line Operator

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 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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