Automated determination of cup-to-disc ratio for classification of glaucomatous and normal eyes on stereo retinal fundus images

2011 ◽  
Vol 16 (9) ◽  
pp. 096009 ◽  
Author(s):  
Chisako Muramatsu ◽  
Toshiaki Nakagawa ◽  
Akira Sawada ◽  
Yuji Hatanaka ◽  
Tetsuya Yamamoto ◽  
...  
Keyword(s):  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Automated Determination ◽  
Cup To Disc Ratio

scholarly journals Automatic Determination of Vertical Cup-to-Disc Ratio in Retinal Fundus Images for Glaucoma Screening

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 8527-8541 ◽  
Author(s):  
Jiapan Guo ◽  
George Azzopardi ◽  
Chenyu Shi ◽  
Nomdo M. Jansonius ◽  
Nicolai Petkov
Keyword(s):  
Fundus Images ◽  
Automatic Determination ◽  
Glaucoma Screening ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Cup To Disc Ratio

scholarly journals Automatic detection and classification of diabetic retinopathy from retinal fundus images

2021 ◽  
Author(s):  
Abdullah Biran
Keyword(s):  
Diabetic Retinopathy ◽  
Gabor Filter ◽  
Automatic Detection ◽  
Histogram Equalization ◽  
Support Vector ◽  
Svm Classifier ◽  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Automatic Detection and Classification of Diabetic Retinopathy from Retinal Fundus Images by Abdullah Biran, Master of Applied Science, lectrical and computer engineering Department, Ryerson University, 2017. Diabetic Retinopathy (DR) is an eye disease that leads to blindness when it progresses to proliferative level. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness. In this thesis, an automatic algorithm for detecting diabetic retinopathy is presented. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. In addition, Support Vector Machine (SVM) classifier is used to classify retinal images into normal or abnormal cases of DR including non-proliferative (NPDR) or proliferative diabetic retinopathy (PDR). The proposed method has been tested on fundus images from Standard Diabetic Retinopathy Database (DIARETDB). The implementation of the presented methodology was done in MATLAB. The methodology is tested for sensitivity and accuracy.

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