A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina

2002 ◽  
Vol 21 (10) ◽  
pp. 1236-1243 ◽  
Author(s):  
T. Walter ◽  
J. Klein ◽  
P. Massin ◽  
A. Erginay
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Fundus Images ◽  
Human Retina ◽  
Color Fundus Images

Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Fuzzy Image Processing

2015 ◽  
pp. 379-388 ◽  
Author(s):  
Sarni Suhaila Rahim ◽  
Vasile Palade ◽  
Chrisina Jayne ◽  
Andreas Holzinger ◽  
James Shuttleworth
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Fundus Images ◽  
Fuzzy Image

An Approach for Automatic Detection and Grading of Macular Edema

2017 ◽  
pp. 1677-1702
Author(s):  
Jyoti Prakash Medhi
Keyword(s):  
Image Processing ◽  
Decision Making ◽  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Macular Edema ◽  
Automatic Detection ◽  
Automatic Analysis ◽  
Fundus Images

Prolonged Diabetes causes massive destruction to the retina, known as Diabetic Retinopathy (DR) leading to blindness. The blindness due to DR may consequence from several factors such as Blood vessel (BV) leakage, new BV formation on retina. The effects become more threatening when abnormalities involves the macular region. Here automatic analysis of fundus images becomes important. This system checks for any abnormality and help ophthalmologists in decision making and to analyze more number of cases. The main objective of this chapter is to explore image processing tools for automatic detection and grading macular edema in fundus images.

A method to assist in the diagnosis of early diabetic retinopathy: Image processing applied to detection of microaneurysms in fundus images

2015 ◽  
Vol 44 ◽  
pp. 41-53 ◽  
Author(s):  
Roberto Rosas-Romero ◽  
Jorge Martínez-Carballido ◽  
Jonathan Hernández-Capistrán ◽  
Laura J. Uribe-Valencia
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Fundus Images

scholarly journals Developments for Diabetic Retinopathy Detection and Classification: A Review

2018 ◽  
Vol 7 (4.5) ◽  
pp. 134
Author(s):  
Pooja M. Pawar ◽  
Avinash J. Agrawal
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Eye Disease ◽  
Retinal Images ◽  
Screening Methods ◽  
Fundus Images ◽  
Insulin Deficiency ◽  
Vascular Disorder ◽  
Impaired Metabolism ◽  
Color Fundus Images

Diabetes is characterized by impaired metabolism of glucose caused by insulin deficiency. Diabetic retinopathy is the eye disease, is caused by retinal damage which is generally formed as a result of diabetes mellitus. It is a serious vascular disorder for which early detection and the treatment are required to inhibit the intense vision loss. Also, the diagnosis entails skilled professionals for detection because non-automatic screening methods are very time consuming and are not that efficient for a large number of retinal images. This paper provides a broad review of various techniques and methodologies used by the authors for diabetic retinopathy detection and classification. Furthermore, most recent work and developments are studied in this paper. We are proposing an advanced deep learning CNN approach for automatic diagnosis of DR from color fundus images.  

Decision Support System for Diagnosing Diabetic Retinopathy from Color Fundus Images

2020 ◽  
Vol 64 (2) ◽  
pp. 20502-1-20502-10
Author(s):  
Duygu Çelik Ertuğrul ◽  
Yıltan Bitirim ◽  
Basmah Yakoub Anber
Keyword(s):  
Diabetic Retinopathy ◽  
State Of The Art ◽  
Medical Condition ◽  
Measurement Techniques ◽  
Automated System ◽  
Eye Disease ◽  
Data Sets ◽  
Fundus Images ◽  
Diabetic Eye Disease ◽  
Color Fundus Images

Abstract Diabetic Retinopathy (DR) is a medical condition, also known as diabetic eye disease, which is vision-threatening damage to the retina of the eye caused by diabetes. As the technology advances, researchers are becoming more interested in intelligent medical diagnosis systems to assist screening of DR in earlier stages. In this study, variety of state-of-the-art procedures are used to extract the anatomic segments and lesions from the color fundus images. In addition, an automated system is proposed for the detection of anatomic segments and lesions by grading approach to help clinical diagnosis of the DR analysis. Four publicly available databases of color fundus images and various appropriate measurement techniques are used to compare quantitatively the performance of the proposed system. The experiments conducted on DIARETDB0, DIARETDB1, STARE, and HRF data sets have proved that accuracy, sensitivity, and specificity of the proposed system are comparable or superior to state-of-the-art methods.

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