Detection of changes in color fundus images due to diabetic retinopathy

2012 ◽  
Author(s):  
Malaya Kumar Nath ◽  
Samarendra Dandapat
Keyword(s):  
Diabetic Retinopathy ◽  
Fundus Images ◽  
Color 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.

scholarly journals Diabetic Retinopathy Grading by Digital Curvelet Transform

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Shirin Hajeb Mohammad Alipour ◽  
Hossein Rabbani ◽  
Mohammad Reza Akhlaghi
Keyword(s):  
Diabetic Retinopathy ◽  
Region Of Interest ◽  
Curvelet Transform ◽  
Optic Disk ◽  
Foveal Avascular Zone ◽  
Support Vector ◽  
Fundus Images ◽  
Fundus Fluorescein Angiography ◽  
Avascular Zone ◽  
Color Fundus Images

One of the major complications of diabetes is diabetic retinopathy. As manual analysis and diagnosis of large amount of images are time consuming, automatic detection and grading of diabetic retinopathy are desired. In this paper, we use fundus fluorescein angiography and color fundus images simultaneously, extract 6 features employing curvelet transform, and feed them to support vector machine in order to determine diabetic retinopathy severity stages. These features are area of blood vessels, area, regularity of foveal avascular zone, and the number of micro-aneurisms therein, total number of micro-aneurisms, and area of exudates. In order to extract exudates and vessels, we respectively modify curvelet coefficients of color fundus images and angiograms. The end points of extracted vessels in predefined region of interest based on optic disk are connected together to segment foveal avascular zone region. To extract micro-aneurisms from angiogram, first extracted vessels are subtracted from original image, and after removing detected background by morphological operators and enhancing bright small pixels, micro-aneurisms are detected. 70 patients were involved in this study to classify diabetic retinopathy into 3 groups, that is, (1) no diabetic retinopathy, (2) mild/moderate nonproliferative diabetic retinopathy, (3) severe nonproliferative/proliferative diabetic retinopathy, and our simulations show that the proposed system has sensitivity and specificity of 100% for grading.

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