scholarly journals A Review on Recent Developments for Detection of Diabetic Retinopathy

Scientifica ◽  
2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Javeria Amin ◽  
Muhammad Sharif ◽  
Mussarat Yasmin
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Morphological Changes ◽  
Fundus Images ◽  
Ongoing Research ◽  
Detection Techniques ◽  
Nonproliferative Diabetic Retinopathy ◽  
Manual Inspection ◽  
Computer Aided ◽  
Recent Developments

Diabetic retinopathy is caused by the retinal micro vasculature which may be formed as a result of diabetes mellitus. Blindness may appear as a result of unchecked and severe cases of diabetic retinopathy. Manual inspection of fundus images to check morphological changes in microaneurysms, exudates, blood vessels, hemorrhages, and macula is a very time-consuming and tedious work. It can be made easily with the help of computer-aided system and intervariability for the observer. In this paper, several techniques for detecting microaneurysms, hemorrhages, and exudates are discussed for ultimate detection of nonproliferative diabetic retinopathy. Blood vessels detection techniques are also discussed for the diagnosis of proliferative diabetic retinopathy. Furthermore, the paper elaborates a discussion on the experiments accessed by authors for the detection of diabetic retinopathy. This work will be helpful for the researchers and technical persons who want to utilize the ongoing research in this area.

Construction of benchmark retinal image database for diabetic retinopathy analysis

2020 ◽  
Vol 234 (9) ◽  
pp. 1036-1048
Author(s):  
Jaskirat Kaur ◽  
Deepti Mittal
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Medical Condition ◽  
Diagnostic Technique ◽  
Performance Comparison ◽  
Fundus Images ◽  
Benchmark Database ◽  
Computer Aided ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetic retinopathy, a symptomless medical condition of diabetes, is one of the significant reasons of vision impairment all over the world. The prior detection and diagnosis can decrease the occurrence of acute vision loss and enhance efficiency of treatment. Fundus imaging, a non-invasive diagnostic technique, is the most frequently used mode for analyzing retinal abnormalities related to diabetic retinopathy. Computer-aided methods based on retinal fundus images support quick diagnosis, impart an additional perspective during decision-making, and behave as an efficient means to assess response of treatment on retinal abnormalities. However, in order to evaluate computer-aided systems, a benchmark database of clinical retinal fundus images is required. Therefore, a representative database comprising of 2942 clinical retinal fundus images is developed and presented in this work. This clinical database, having varying attributes such as position, dimensions, shapes, and color, is formed to evaluate the generalization capability of computer-aided systems for diabetic retinopathy diagnosis. A framework for the development of benchmark retinal fundus images database is also proposed. The developed database comprises of medical image annotations for each image from expert ophthalmologists corresponding to anatomical structures, retinal lesions and stage of diabetic retinopathy. In addition, the substantial performance comparison capability of the proposed database aids in analyzing candidature of different methods, and subsequently its usage in medical practice for real-time applications.

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.

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%.

Computer Aided Systems for Diabetic Retinopathy Detection Using Digital Fundus Images: A Survey

2016 ◽  
Vol 12 (4) ◽  
pp. 234-241 ◽  
Author(s):  
Imran Qureshi ◽  
Muhammad Sharif ◽  
Mussarat Yasmin ◽  
Mudassar Raza ◽  
Muhammad Javed
Keyword(s):  
Diabetic Retinopathy ◽  
Fundus Images ◽  
Computer Aided

Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images

2014 ◽  
Vol 52 (8) ◽  
pp. 663-672 ◽  
Author(s):  
Karthikeyan Ganesan ◽  
Roshan Joy Martis ◽  
U. Rajendra Acharya ◽  
Chua Kuang Chua ◽  
Lim Choo Min ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Fundus Images ◽  
Computer Aided
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