scholarly journals Comparative Study of Retinal Vessel Segmentation Based on Global Thresholding Techniques

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Temitope Mapayi ◽  
Serestina Viriri ◽  
Jules-Raymond Tapamo

Due to noise from uneven contrast and illumination during acquisition process of retinal fundus images, the use of efficient preprocessing techniques is highly desirable to produce good retinal vessel segmentation results. This paper develops and compares the performance of different vessel segmentation techniques based on global thresholding using phase congruency and contrast limited adaptive histogram equalization (CLAHE) for the preprocessing of the retinal images. The results obtained show that the combination of preprocessing technique, global thresholding, and postprocessing techniques must be carefully chosen to achieve a good segmentation performance.

2021 ◽  
Vol 2070 (1) ◽  
pp. 012104
Author(s):  
Sushma Nagdeote ◽  
Sapna Prabhu

Abstract This paper deals with the new segmentation techniques for retinal blood vessels on fundus images. This technique aims at extracting thin vessels to reduce the intensity difference between thick and thin vessels. This paper proposes the modified UNet model by incorporating ResNet blocks into it which includes structured prediction. In this work we generate the visualization of blood vessels from retinal fundus image for two loss functions namely cross entropy loss and Dice loss where the network classifies several pixels simultaneously. The results shows higher accuracy by considering a much more expressive UNet algorithm and outperforms the past algorithms for Retinal Vessel Segmentation. The benefits of this approach will be demonstrated empirically.


PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0188939 ◽  
Author(s):  
Nogol Memari ◽  
Abd Rahman Ramli ◽  
M. Iqbal Bin Saripan ◽  
Syamsiah Mashohor ◽  
Mehrdad Moghbel

Author(s):  
Shuang Xu ◽  
Zhiqiang Chen ◽  
Weiyi Cao ◽  
Feng Zhang ◽  
Bo Tao

Retinal vessels are the only deep micro vessels that can be observed in human body, the accurate identification of which has great significance on the diagnosis of hypertension, diabetes and other diseases. To this end, a retinal vessel segmentation algorithm based on residual convolution neural network is proposed according to the characteristics of the retinal vessels on fundus images. Improved residual attention module and deep supervision module are utilized, in which the low-level and high-level feature graphs are joined to construct the encoder-decoder network structure, and atrous convolution is introduced to the pyramid pooling. The experiments result on the fundus image data set DRIVE and STARE show that this algorithm can obtain complete retinal vessel segmentation as well as connected vessel stems and terminals. The average accuracy on DRIVE and STARE reaches 95.90 and 96.88%, and the average specificity is 98.85 and 97.85%, which shows superior performance compared to other methods. This algorithm is verified feasible and effective for retinal vessel segmentation of fundus images and has the ability to detect more capillaries.


2016 ◽  
Vol 25 (3) ◽  
pp. 503-511 ◽  
Author(s):  
Chengzhang Zhu ◽  
Beiji Zou ◽  
Jinkai Cui ◽  
Yao Xiang ◽  
Hui Wu

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