Real-Time Retinal Vessel Segmentation on High-Resolution Fundus Images Using Laplacian Pyramids

2019 ◽  
pp. 337-350 ◽  
Author(s):  
Robert Dachsel ◽  
Annika Jöster ◽  
Michael Breuß
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Fundus Images ◽  
Retinal Vessel Segmentation

scholarly journals Improved retinal vessel segmentation using the enhanced pre-processing method for high resolution fundus images

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1222
Author(s):  
Aziah Ali ◽  
Aini Hussain ◽  
Wan Mimi Diyana Wan Zaki ◽  
Wan Haslina Wan Abdul Halim ◽  
Wan Noorshahida Mohd Isa ◽  
...  
Keyword(s):  
High Resolution ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Age Related Macular Degeneration ◽  
Qualitative Assessment ◽  
Fundus Images ◽  
Fundus Image ◽  
Age Related ◽  
Noisy Pixel ◽  
Retinal Vessel Segmentation

Background: By diagnosing using fundus images, ophthalmologists can possibly detect symptoms of retinal diseases such as diabetic retinopathy, age-related macular degeneration, and retinal detachment. A number of studies have also found some links between fundus image analysis data and other underlying systemic diseases such as cardiovascular diseases, including hypertension and kidney dysfunction. Now that imaging technology is advancing further, more fundus cameras are currently equipped with the capability to produce high resolution fundus images. One of the public databases for high-resolution fundus images called High-Resolution Fundus (HRF) is consistently used for validating vessel segmentation algorithms. However, it is noticed that the segmentation outputs from the HRF database normally include noisy pixels near the upper and lower edges of the image. In this study, we propose an enhanced method of pre-processing the images so that these noisy pixels can be eliminated, and thus the overall segmentation performance can be increased. Without eliminating the noisy pixels, the visual segmentation output shows a large number of false positive pixels near the top and bottom edges. Methods: The proposed method involves adding additional padding to the image before the segmentation procedure is applied. In this study, the Bar-Combination Of Shifted FIlter REsponses (B-COSFIRE) filter is used for retinal vessel segmentation. Results: Qualitative assessment of the segmentation results when using the proposed method showed improvement in terms of noisy pixel removal from near the edges. Quantitatively, the additional padding step improves all considered metrics for vessel segmentation, namely Sensitivity (73.76%), Specificity (97.53%), and Matthew’s Correlation Coefficient (MCC) value (71.57%) for the HRF database. Conclusions: Findings from this study indicate improvement in the overall segmentation performance when using the proposed double-padding method of pre-processing the fundus image prior to segmentation. In the future, more databases with various resolutions and modalities can be included for further validation.

A high resolution representation network with multi-path scale for retinal vessel segmentation

2021 ◽  
pp. 106206
Author(s):  
Zefang Lin ◽  
Jianping Huang ◽  
Yingyin Chen ◽  
Xiao Zhang ◽  
Wei Zhao ◽  
...  
Keyword(s):  
High Resolution ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Retinal Vessel Segmentation

scholarly journals Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0188939 ◽  
Author(s):  
Nogol Memari ◽  
Abd Rahman Ramli ◽  
M. Iqbal Bin Saripan ◽  
Syamsiah Mashohor ◽  
Mehrdad Moghbel
Keyword(s):  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Matched Filtering ◽  
Fundus Images ◽  
Retinal Vessel Segmentation ◽  
Color Fundus Images ◽  
Adaboost Classifier

scholarly journals Retinal Vessel Segmentation Algorithm Based on Residual Convolution Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Shuang Xu ◽  
Zhiqiang Chen ◽  
Weiyi Cao ◽  
Feng Zhang ◽  
Bo Tao
Keyword(s):  
Neural Network ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Segmentation Algorithm ◽  
Convolution Neural Network ◽  
Superior Performance ◽  
Retinal Vessels ◽  
Fundus Images ◽  
Data Set ◽  
Retinal Vessel Segmentation

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.

An Ensemble Retinal Vessel Segmentation Based on Supervised Learning in Fundus Images

2016 ◽  
Vol 25 (3) ◽  
pp. 503-511 ◽  
Author(s):  
Chengzhang Zhu ◽  
Beiji Zou ◽  
Jinkai Cui ◽  
Yao Xiang ◽  
Hui Wu
Keyword(s):  
Supervised Learning ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Fundus Images ◽  
Retinal Vessel Segmentation
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