Automatic Segmentation of Retinal Images for Glaucoma Screening

2014 ◽  
pp. 233-253
Author(s):  
Jun Cheng ◽  
Fengshou Yin ◽  
Damon Wing Kee Wong ◽  
Jiang Liu
Keyword(s):  
Automatic Segmentation ◽  
Retinal Images ◽  
Glaucoma Screening

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%.

scholarly journals Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening

2021 ◽  
Author(s):  
Guangmin Sun ◽  
Zhongxiang Zhang ◽  
Junjie Zhang ◽  
Meilong Zhu ◽  
Xiao-rong Zhu ◽  
...  
Keyword(s):  
Optic Disc ◽  
Hausdorff Distance ◽  
Automatic Segmentation ◽  
Fundus Images ◽  
Dice Coefficient ◽  
Scale Feature ◽  
Multi Scale ◽  
Glaucoma Screening ◽  
Feature Extractor ◽  
Public Datasets

AbstractAutomatic segmentation of optic disc (OD) and optic cup (OC) is an essential task for analysing colour fundus images. In clinical practice, accurate OD and OC segmentation assist ophthalmologists in diagnosing glaucoma. In this paper, we propose a unified convolutional neural network, named ResFPN-Net, which learns the boundary feature and the inner relation between OD and OC for automatic segmentation. The proposed ResFPN-Net is mainly composed of multi-scale feature extractor, multi-scale segmentation transition and attention pyramid architecture. The multi-scale feature extractor achieved the feature encoding of fundus images and captured the boundary representations. The multi-scale segmentation transition is employed to retain the features of different scales. Moreover, an attention pyramid architecture is proposed to learn rich representations and the mutual connection in the OD and OC. To verify the effectiveness of the proposed method, we conducted extensive experiments on two public datasets. On the Drishti-GS database, we achieved a Dice coefficient of 97.59%, 89.87%, the accuracy of 99.21%, 98.77%, and the Averaged Hausdorff distance of 0.099, 0.882 on the OD and OC segmentation, respectively. We achieved a Dice coefficient of 96.41%, 83.91%, the accuracy of 99.30%, 99.24%, and the Averaged Hausdorff distance of 0.166, 1.210 on the RIM-ONE database for OD and OC segmentation, respectively. Comprehensive results show that the proposed method outperforms other competitive OD and OC segmentation methods and appears more adaptable in cross-dataset scenarios. The introduced multi-scale loss function achieved significantly lower training loss and higher accuracy compared with other loss functions. Furthermore, the proposed method is further validated in OC to OD ratio calculation task and achieved the best MAE of 0.0499 and 0.0630 on the Drishti-GS and RIM-ONE datasets, respectively. Finally, we evaluated the effectiveness of the glaucoma screening on Drishti-GS and RIM-ONE datasets, achieving the AUC of 0.8947 and 0.7964. These results proved that the proposed ResFPN-Net is effective in analysing fundus images for glaucoma screening and can be applied in other relative biomedical image segmentation applications.

Automatic detection of optic disc using distance regularized level-set segmentation for glaucoma screening system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Naganagouda Patil ◽  
Preethi N. Patil ◽  
P.V. Rao
Keyword(s):  
Optic Disc ◽  
Level Set ◽  
Retinal Image ◽  
Retinal Images ◽  
World Population ◽  
Human Beings ◽  
Content Type ◽  
Glaucoma Screening ◽  
Disc Region ◽  
Existing Result

PurposeThe abnormalities of glaucoma have high impact on deciding and representing the causes that effects severity of blindness in human beings. The simulation experimental results would help the ophthalmologist in diagnosing of glaucoma abnormality accurately. The significant effect of glaucoma has a huge impact on the quality of human life, and its growth rate in world population tremendously increases. Glaucoma is considered as second largest cause for the blindness in the world; hence identification of it marks the importance of its detection at the earliest.Design/methodology/approachThe prime objective of the work proposed is to build up a human intervention free image preparing framework for glaucoma screening. The disc calculation is assessed on retinal image dataset called retinal Image for glaucoma Analysis. The proposed method briefs a novel optic disc division calculation depending on applying a level-set strategy on a confined optic disc image. In the instance of low quality image, a twofold level set is designed, in which the principal level set is viewed as restriction for the optic disc. To keep the veins from meddling with the level-set procedure, an inpainting strategy has been applied. Also a significant commitment is to include the varieties in notion adopted by the ophthalmologists in distinguishing the disc localization and diagnosing the glaucoma. Most of the past investigations are prepared and tested depending on just a single feature, which can be thought to be one-sided for the ophthalmologist.FindingsIn continuation, the correctness has been determined depending on the quantity of image that matched with the investigation pattern adopted by the ophthalmologist. The 175 retinal images were utilized to test the results of proposed work with the manual markings of ophthalmologists. The error-free calculation in marking the optic disc region and centroid was 98.95% in comparison with the existing result of 87.34%.Originality/valueIn continuation, the correctness has been determined depending on the quantity of image that matched with the investigation pattern adopted by the ophthalmologist. The 175 retinal images were utilized to test the results of proposed work with the manual markings of ophthalmologists. The error-free calculation in marking the optic disc region and centroid was 98.95% in comparison with the existing result of 87.34%.

A New Heuristic Function of Ant Colony System for Retinal Vessel Segmentation

2017 ◽  
pp. 2063-2081
Author(s):  
Ahmed Hamza Asad ◽  
Ahmad Taher Azar ◽  
Aboul Ella Hassanien
Keyword(s):  
Blood Vessels ◽  
Automatic Segmentation ◽  
Retinal Vessel ◽  
Ant Colony ◽  
Significant Feature ◽  
Retinal Images ◽  
Ant Colony System ◽  
Heuristic Function ◽  
Retinal Vessel Segmentation ◽  
Baseline Approach

The automatic segmentation of blood vessels in retinal images is the crucial stage in any retina diagnosis systems. This article discussed the impact of two improvements to the previous baseline approach for automatic segmentation of retinal blood vessels based on the ant colony system. The first improvement is in features where the length of previous features vector used in segmentation is reduced to the half since four less significant features are replaced by a new more significant feature when applying the correlation-based feature selection heuristic. The second improvement is in ant colony system where a new probability-based heuristic function is applied instead of the previous Euclidean distance based heuristic function. Experimental results showed the improved approach gives better performance than baseline approach when it is tested on DRIVE database of retinal images. Also, the statistical analysis demonstrated that was no statistically significant difference between the baseline and improved approaches in the sensitivity (0.7388± 0.0511 vs. 0.7501±0.0385, respectively; P = 0.4335). On the other hand, statistically significant improvements were found between the baseline and improved approaches for specificity and accuracy (P = 0.0024 and 0.0053, respectively). It was noted that the improved approach showed an increase of 1.1% in the accuracy after applying the new probability-based heuristic function.

A New Heuristic Function of Ant Colony System for Retinal Vessel Segmentation

2014 ◽  
Vol 1 (2) ◽  
pp. 15-30 ◽  
Author(s):  
Ahmed Hamza Asad ◽  
Ahmad Taher Azar ◽  
Aboul Ella Hassanien
Keyword(s):  
Blood Vessels ◽  
Automatic Segmentation ◽  
Ant Colony ◽  
Significant Feature ◽  
Retinal Images ◽  
Ant Colony System ◽  
Heuristic Function ◽  
Significant Difference ◽  
The Impact ◽  
Baseline Approach

The automatic segmentation of blood vessels in retinal images is the crucial stage in any retina diagnosis systems. This article discussed the impact of two improvements to the previous baseline approach for automatic segmentation of retinal blood vessels based on the ant colony system. The first improvement is in features where the length of previous features vector used in segmentation is reduced to the half since four less significant features are replaced by a new more significant feature when applying the correlation-based feature selection heuristic. The second improvement is in ant colony system where a new probability-based heuristic function is applied instead of the previous Euclidean distance based heuristic function. Experimental results showed the improved approach gives better performance than baseline approach when it is tested on DRIVE database of retinal images. Also, the statistical analysis demonstrated that was no statistically significant difference between the baseline and improved approaches in the sensitivity (0.7388± 0.0511 vs. 0.7501±0.0385, respectively; P = 0.4335). On the other hand, statistically significant improvements were found between the baseline and improved approaches for specificity and accuracy (P = 0.0024 and 0.0053, respectively). It was noted that the improved approach showed an increase of 1.1% in the accuracy after applying the new probability-based heuristic function.

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