Feature Extraction for Early Detection of Diabetic Retinopathy

2010 ◽  
Author(s):  
V. Vijaya Kumari ◽  
N. Suriyaharayananm ◽  
C. Thanka Saranya
Keyword(s):  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Early Detection

Feature extraction from retinal fundus image for early detection of diabetic retinopathy

2013 ◽  
Author(s):  
Syna Sreng ◽  
Jun-Ichi Takada ◽  
Noppadol Maneerat ◽  
Don Isarakorn ◽  
Ruttikorn Varakulsiripunth ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus

scholarly journals ANALISIS TEKSTUR PADA CITRA IRIS MATA MENGGUNAKAN ALGORITMA GRAY LEVEL CO-OCCURENCY MATRIX

2019 ◽  
Vol 15 (2) ◽  
pp. 183-188
Author(s):  
Asti Herliana ◽  
Toni Arifin
Keyword(s):  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Correlation Energy ◽  
Process Analysis ◽  
Eye Disease ◽  
Gray Level ◽  
Disease Effect ◽  
Preprocessing Technique ◽  
Process Detection

According to data from the ministry of health, with the high intensity of use the gadget nowadays, therefore the number of people with eye disease is increasing. To overcome increase suffers of eye disease, it takes need early detection for who suffers potentially eye disease so that handling and prevention of blindness from eye disease effect can be immediately. The process detection of eye disease can be see in iris, there are several disease can be seen in iris among there are diabetic retinopathy and glaucoma. This research present texture analysis for iris images, the method is used GLCM (Gray Level Co-occurency Matrix) which is implemented using Matlab, and using 5 parameters namely contrast, correlation, energy, homogeneity and entropy. Process analysis texture is developed with preprocessing technique, the result of texture in images data iris can be recognized and produce the dataset of result from feature extraction with GLCM (Gray Level Co-occurency Matrix).

INTEGRATING FUZZY C-MEANS AND MAHALANOBIS METRIC CLASSIFICATION FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGING

2015 ◽  
Vol 15 (05) ◽  
pp. 1550085 ◽  
Author(s):  
MADHURI TASGAONKAR ◽  
MADHURI KHAMBETE
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Ground Truth ◽  
Vital Role ◽  
Detection Accuracy ◽  
Fuzzy C Means ◽  
Fundus Imaging ◽  
Imaging Detection ◽  
Retinal Structure

Diabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. Detection of exudates is significant in diabetic retinopathy (DR) as they are earlier signs and can cause blindness. Finding the exact location as well as correct number of exudates play vital role in the overall treatment of a patient. This paper presents an algorithm for automatic detection of exudates for DR. The algorithm combines the advantages of supervised and unsupervised techniques. It uses fuzzy-C means (FCM) segmentation on coarse level and mahalanobis metric for finer classification of segmented pixels. Mahalanobis criterion gives significance to most relevant features and thus proves a better classifier. The results are validated using DIARETDB0 and DIARETDB1 databases and the ground truth provided with it. This evaluation provided 95.77% detection accuracy.

Early detection of diabetic retinopathy from big data in hadoop framework

Displays ◽  
2021 ◽  
Vol 70 ◽  
pp. 102061
Author(s):  
Amartya Hatua ◽  
Badri Narayan Subudhi ◽  
Veerakumar T. ◽  
Ashish Ghosh
Keyword(s):  
Big Data ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Hadoop Framework

scholarly journals Diabetic Vitrectomy

2020 ◽  
Author(s):  
Ogugua N. Okonkwo
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Visual Impairment ◽  
Surgical Techniques ◽  
Surgical Removal ◽  
Disease Monitoring ◽  
Advanced Stage ◽  
Improved Outcomes ◽  
Diabetic Vitrectomy ◽  
Proliferative Disease

Diabetic retinopathy (DR) in its advanced stage is a leading cause of blindness and visual impairment. Despite efforts at early detection of DR, disease monitoring, and medical therapy, significant proportions of people living with diabetes still progress to develop the advanced proliferative disease, which is characterized by neovascularization, actively proliferating fibrovascular membranes, and retinal traction. The surgical removal of this proliferating tissue and the treatment of the retinal ischemic drive can be very rewarding, providing significant stability of the retina and in several cases improved retinal anatomy and vision. Diabetic vitrectomy comprises a broad range of surgical techniques and maneuvers, which offer the surgeon and patient opportunity to reverse deranged vitreoretinal anatomy and improve or stabilizes vision. Advances in vitreoretinal technology have contributed greatly to more recent improved outcomes; it is expected that future advances will offer even more benefit.

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