Dilated Deep Neural Network for Segmentation of Retinal Blood Vessels in Fundus Images

2019 ◽  
Vol 44 (1) ◽  
pp. 505-518 ◽  
Author(s):  
Raj Biswas ◽  
Ashwin Vasan ◽  
Sanjiban Sekhar Roy
Keyword(s):  
Neural Network ◽  
Blood Vessels ◽  
Deep Neural Network ◽  
Fundus Images ◽  
Retinal Blood Vessels

scholarly journals Retinal blood vessels segmentation using classical edge detection filters and the neural network

2021 ◽  
Vol 23 ◽  
pp. 100521
Author(s):  
Beaudelaire Saha Tchinda ◽  
Daniel Tchiotsop ◽  
Michel Noubom ◽  
Valerie Louis-Dorr ◽  
Didier Wolf
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Blood Vessels ◽  
The Neural Network ◽  
Retinal Blood Vessels

scholarly journals A Machine Learning based Approach for Segmenting Retinal Nerve Images using Artificial Neural Networks

2020 ◽  
Vol 10 (4) ◽  
pp. 5986-5991
Author(s):  
A. N. Saeed
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Back Propagation ◽  
Texture Feature ◽  
Texture Features ◽  
Classification Performance ◽  
Fundus Images ◽  
Retinal Blood Vessels

Artificial Intelligence (AI) based Machine Learning (ML) is gaining more attention from researchers. In ophthalmology, ML has been applied to fundus photographs, achieving robust classification performance in the detection of diseases such as diabetic retinopathy, retinopathy of prematurity, etc. The detection and extraction of blood vessels in the retina is an essential part of various diagnosing problems associated with eyes, such as diabetic retinopathy. This paper proposes a novel machine learning approach to segment the retinal blood vessels from eye fundus images using a combination of color features, texture features, and Back Propagation Neural Networks (BPNN). The proposed method comprises of two steps, namely the color texture feature extraction and training the BPNN to get the segmented retinal nerves. Magenta color and correlation-texture features are given as input to the BPNN. The system was trained and tested in retinal fundus images taken from two distinct databases. The average sensitivity, specificity, and accuracy obtained for the segmentation of retinal blood vessels were 0.470%, 0.914%, and 0.903% respectively. Results obtained reveal that the proposed methodology is excellent in automated segmentation retinal nerves. The proposed segmentation methodology was able to obtain comparable accuracy with other methods.

scholarly journals FGS-HDNN: Fractional Gravitational Search based Hybrid Deep Neural Network for Glaucoma Detection using Fundus Images

2021 ◽  
Author(s):  
M. Madhumalini ◽  
T. Meera Devi
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Early Stage ◽  
Retinal Disease ◽  
Accurate Diagnosis ◽  
Fundus Images ◽  
Irreversible Loss ◽  
Glaucoma Detection ◽  
Gravitational Search

Abstract Glaucoma is a retinal disease that damages the eye's optic nerve, frequently causing an irreversible loss of vision. However, the accurate diagnosis of this disease is difficult but early-stage diagnosis may cure this retinal disease. The objective of this research is to diagnose glaucoma disease in the top of the eye's optical nerve. The proposed approach detects glaucoma via four major steps namely Data enhancement phase, segmentation phase, feature extraction phase, and classification phase by the fractional gravitational search-based hybrid deep neural network (FGSA-HDNN) classifier. The proposed classifier is used for the exact classification of glaucoma infected images and normal images. Here, the proposed approach utilizes the statistical, textural, and vessel features from the segmented output. Also, the proposed FGSO algorithm is used for testing the deep neural network. From the experimental results, it is observed that the proposed glaucoma detection has obtained a sensitivity of 99.64%, a specificity of 97.84%, and an accuracy of 98.75% that outperforms other state-of-art methods.

Non-Compactness Attribute Filtering to Extract Retinal Blood Vessels in Fundus Images

2010 ◽  
Vol 1 (3) ◽  
pp. 16-27 ◽  
Author(s):  
I. K. E. Purnama ◽  
K. Y. E. Aryanto ◽  
M. H. F. Wilkinson
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Human Vision ◽  
Retinal Images ◽  
Retinal Vessels ◽  
Fundus Images ◽  
Pathological Changes ◽  
Retinal Blood Vessels ◽  
Average Accuracy ◽  
Filtering Approach

Retinal blood vessels can give information about abnormalities or disease by examining its pathological changes. One abnormality is diabetic retinopathy, characterized by a disorder of retinal blood vessels resulting from diabetes mellitus. Currently, diabetic retinopathy is one of the major causes of human vision abnormalities and blindness. Hence, early detection can lead to proper treatment, and segmentation of the abnormality provides a map of retinal vessels that can facilitate the assessment of the characteristics of these vessels. In this paper, the authors propose a new method, consisting of a sequence of procedures, to segment blood vessels in a retinal image. In the method, attribute filtering with a so-called Max-Tree is used to represent the image based on its gray value. The filtering process is done using the branches filtering approach in which the tree branches are selected based on the non-compactness of the nodes. The selection is started from the leaves. This experiment was performed on 40 retinal images, and utilized the manual segmentation created by an observer to validate the results. The proposed method can deliver an average accuracy of 94.21%.

scholarly journals Hybrid UNet Architecture based on Residual Learning of Fundus Images for Retinal Vessel Segmentation

2021 ◽  
Vol 2070 (1) ◽  
pp. 012104
Author(s):  
Sushma Nagdeote ◽  
Sapna Prabhu
Keyword(s):  
Blood Vessels ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Intensity Difference ◽  
Fundus Images ◽  
The Past ◽  
Retinal Blood Vessels ◽  
Retinal Fundus Image ◽  
Retinal Vessel Segmentation ◽  
Retinal Fundus

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.

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