Automatic Optic Disc Detection From Retinal Images by a Line Operator

2011 ◽  
Vol 58 (1) ◽  
pp. 88-94 ◽  
Author(s):  
Shijian Lu ◽  
Joo Hwee Lim
Keyword(s):  
Optic Disc ◽  
Retinal Images ◽  
Line Operator

Automatic optic disc localization and segmentation in retinal images by a line operator and level sets

2016 ◽  
Vol 24 (s2) ◽  
pp. S767-S776 ◽  
Author(s):  
Fulong Ren ◽  
Wei Li ◽  
Jinzhu Yang ◽  
Huan Geng ◽  
Dazhe Zhao
Keyword(s):  
Optic Disc ◽  
Level Sets ◽  
Retinal Images ◽  
Optic Disc Localization ◽  
Line Operator

Optic Disc Recognition Method for Retinal Images

2015 ◽  
pp. 875-889 ◽  
Author(s):  
Florin Rotaru ◽  
Silviu Ioan Bejinariu ◽  
Cristina Diana Niţă ◽  
Ramona Luca ◽  
Mihaela Luca ◽  
...  
Keyword(s):  
Optic Disc ◽  
Retinal Images ◽  
Recognition Method

scholarly journals Correlation Coefficient Based Automatic Optic Disc Detection from Retinal Images

2020 ◽  
Author(s):  
S. Anand ◽  
R. Prabhadevi ◽  
D. Rini
Keyword(s):  
Optic Disc ◽  
Correlation Coefficient ◽  
Retinal Image ◽  
Retinal Images ◽  
Detection Accuracy ◽  
Maximum Correlation ◽  
Exact Location ◽  
Peak Intensity ◽  
The Given

ABSTRACTIn this paper an algorithm to detect the optic disc (OD) automatically is described. The proposed method is based on the circular brightness of the OD and its correlation coefficient. At first the peak intensity points are taken, a mask is generated for the given image which gives the circular bright regions of the image. To locate the OD accurately, a pattern is generated which is similar to the OD. By correlating the retinal image with the pattern generated, the maximum correlation of the pattern with the OD is obtained. On locating the coordinates of maximum correlation, the exact location of the OD is detected. The proposed algorithm has been tested with DRIVE database images and an average OD detection accuracy of 95% was obtained for healthy and pathological retinas respectively.

Examination of Optic Disc Sections of Fundus Retinal Images—A Study with Rim-One Database

2020 ◽  
pp. 711-719
Author(s):  
S. Fuzail Ahmed Razeen ◽  
Emmanuel ◽  
V. Rajinikanth ◽  
P. Tamizharasi ◽  
B. Parvatha Varthini
Keyword(s):  
Optic Disc ◽  
Retinal Images

scholarly journals Deep Learning for Optic Disc Segmentation and Glaucoma Diagnosis on Retinal Images

2020 ◽  
Vol 10 (14) ◽  
pp. 4916
Author(s):  
Syna Sreng ◽  
Noppadol Maneerat ◽  
Kazuhiko Hamamoto ◽  
Khin Yadanar Win
Keyword(s):  
Neural Networks ◽  
Optic Disc ◽  
Convolutional Neural Networks ◽  
Support Vector ◽  
Retinal Images ◽  
Subjective Time ◽  
Deep Convolutional Neural Networks ◽  
Glaucoma Screening ◽  
Glaucoma Diagnosis ◽  
Eye Screening

Glaucoma is a major global cause of blindness. As the symptoms of glaucoma appear, when the disease reaches an advanced stage, proper screening of glaucoma in the early stages is challenging. Therefore, regular glaucoma screening is essential and recommended. However, eye screening is currently subjective, time-consuming and labor-intensive and there are insufficient eye specialists available. We present an automatic two-stage glaucoma screening system to reduce the workload of ophthalmologists. The system first segmented the optic disc region using a DeepLabv3+ architecture but substituted the encoder module with multiple deep convolutional neural networks. For the classification stage, we used pretrained deep convolutional neural networks for three proposals (1) transfer learning and (2) learning the feature descriptors using support vector machine and (3) building ensemble of methods in (1) and (2). We evaluated our methods on five available datasets containing 2787 retinal images and found that the best option for optic disc segmentation is a combination of DeepLabv3+ and MobileNet. For glaucoma classification, an ensemble of methods performed better than the conventional methods for RIM-ONE, ORIGA, DRISHTI-GS1 and ACRIMA datasets with the accuracy of 97.37%, 90.00%, 86.84% and 99.53% and Area Under Curve (AUC) of 100%, 92.06%, 91.67% and 99.98%, respectively, and performed comparably with CUHKMED, the top team in REFUGE challenge, using REFUGE dataset with an accuracy of 95.59% and AUC of 95.10%.

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