Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images

2020 ◽  
Vol 186 ◽  
pp. 105201
Author(s):  
Álvaro S. Hervella ◽  
José Rouco ◽  
Jorge Novo ◽  
Manuel G. Penedo ◽  
Marcos Ortega
Keyword(s):  
Retinal Vessel ◽  
Fundus Images

scholarly journals Measurement of Retinal Vessel Widths From Fundus Images Based on 2-D Modeling

2004 ◽  
Vol 23 (10) ◽  
pp. 1196-1204 ◽  
Author(s):  
J. Lowell ◽  
A. Hunter ◽  
D. Steel ◽  
A. Basu ◽  
R. Ryder ◽  
...  
Keyword(s):  
Retinal Vessel ◽  
Fundus Images

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

Identifying retinal vessel networks in ocular fundus images

1993 ◽  
Author(s):  
Shirley H. Lee ◽  
Gregory W. Donohoe ◽  
Michael J. Wilcox
Keyword(s):  
Retinal Vessel ◽  
Fundus Images ◽  
Ocular Fundus ◽  
Vessel Networks

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.

scholarly journals Information Extraction from Retinal Images with Agent-Based Technology

Processes ◽  
2018 ◽  
Vol 6 (12) ◽  
pp. 254 ◽  
Author(s):  
Pablo Chamoso ◽  
Sara Rodríguez ◽  
Luis García-Ortiz ◽  
Juan Corchado
Keyword(s):  
Retinal Vessel ◽  
Virtual Organizations ◽  
Retinal Vessels ◽  
Fundus Images ◽  
Agent Based ◽  
Medical Software ◽  
Wide Range ◽  
Clinical Tools ◽  
Effective Diagnosis

The study of retinal vessels can provide information on a wide range of illnesses in the human body. Numerous works have already focused on this new field of research and several medical software programs have been proposed to facilitate the close examination of retinal vessels. Some allow for the automatic extraction of information and can be combined with other clinical tools for effective diagnosis and further medical studies. This article proposes an Agent-based Virtual Organizations (VO) System which applies a novel methodology for taking measurements from fundus images and extracting information on the retinal vessel caliber. A case study was conducted to evaluate the performance of the developed system, and the fundus images of different patients were used to extract information. Its performance was compared with that of similar tools.

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