Agreement among ophthalmologists in marking the optic disc and optic cup in fundus images

2016 ◽  
Vol 37 (3) ◽  
pp. 701-717 ◽  
Author(s):  
Ahmed Almazroa ◽  
Sami Alodhayb ◽  
Essameldin Osman ◽  
Eslam Ramadan ◽  
Mohammed Hummadi ◽  
...  
Keyword(s):  
Optic Disc ◽  
Fundus Images ◽  
Optic Cup

scholarly journals Region Focus Network for Joint Optic Disc and Cup Segmentation

2020 ◽  
Vol 34 (01) ◽  
pp. 751-758
Author(s):  
Ge Li ◽  
Changsheng Li ◽  
Chan Zeng ◽  
Peng Gao ◽  
Guotong Xie
Keyword(s):  
Optic Disc ◽  
Feature Representation ◽  
Fundus Images ◽  
Feature Pooling ◽  
The World ◽  
Optic Cup ◽  
Disc Region ◽  
Class Segmentation ◽  
Fusion Feature ◽  
Screening And Diagnosis

Glaucoma is one of the three leading causes of blindness in the world and is predicted to affect around 80 million people by 2020. The optic cup (OC) to optic disc (OD) ratio (CDR) in fundus images plays a pivotal role in the screening and diagnosis of glaucoma. Existing methods usually crop the optic disc region first, and subsequently perform segmentation in this region. However, these approaches come up with high complexities due to the separate operations. To remedy this issue, we propose a Region Focus Network (RF-Net) that innovatively integrates detection and multi-class segmentation into a unified architecture for end-to-end joint optic disc and cup segmentation with global optimization. The key idea of our method is designing a novel multi-class mask branch which generates a high-quality segmentation in the detected region for both disc and cup. To bridge the connection between the backbone and multi-class mask branch, a Fusion Feature Pooling (FFP) structure is presented to extract features from each level of the pyramid network and fuse them into a final feature representation for segmentation. Extensive experimental results on the REFUGE-2018 challenge dataset and the Drishti-GS dataset show that the proposed method achieves the best performance, compared with competitive approaches reported in the literature and the official leaderboard. Our code will be released soon.

scholarly journals Automated Segmentation of Optic Disc and Cup in Color Fundus Images

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Vijay M Mane
Keyword(s):  
Optic Disc ◽  
Disease Diagnosis ◽  
Eye Disease ◽  
Automated Segmentation ◽  
Fundus Images ◽  
Average Sensitivity ◽  
Watershed Transformation ◽  
Glaucoma Diagnosis ◽  
Optic Cup ◽  
Color Fundus Images

An automatic Optic disc and Optic cup detection technique which is an important step in developing systems for computer-aided eye disease diagnosis is presented in this paper. This paper presents an algorithm for localization and segmentation of optic disc from digital retinal images. OD localization is achieved by circular Hough transform using morphological preprocessing and segmentation is achieved by watershed transformation. Optic cup segmentation is achieved by marker controlled watershed transformation. The optic disc to cup ratio (CDR) is calculated which is an important parameter for glaucoma diagnosis. The presented algorithm is evaluated against publically available DRIVE dataset. The presented methodology achieved 88% average sensitivity and 80% average overlap. The average CDR detected is 0.1983.

scholarly journals Retinal Fundus image processing and ensemble learning: optic disc and optic cup detection

2021 ◽  
Author(s):  
Shima Mohammadali Pishnamaz
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Optic Disc ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fundus Images ◽  
Fundus Image ◽  
Extreme Gradient Boosting ◽  
Optic Cup ◽  
Retinal Fundus

Ophthalmologists have widely used retinal fundus imaging systems to examine the health of the optic nerve, vitreous, macula, retina and their blood vessels. Many critical diseases, such as glaucoma and diabetic retinopathy, can be diagnosed by analyzing retinal fundus images. Retinal image-based glaucoma detection is a comprehensive diagnostic approach that examines the head cup-to-disc ratio (CDR) as an important indicator for detecting the presence and the extent of glaucoma in a patient. The accurate segmentations of the optic disc (OD) and optic cup (OC) are critical for the calculation of CDR. Machine learning based algorithms can be very helpful to efficiently exploit the vast amounts of retinal fundus data. In this thesis project, the main goal is to develop image processing and machine learning algorithms to automatically detect OD and OC from fundus images. This goal has been achieved by developing and applying several image enhancement techniques. First, an algorithm is proposed and tested on several fundus images to detect OD. The proposed algorithm is based on a combination of Contrast Limited Adaptive Histogram Equalization (CLAHE), Alternating Sequential Filters (ASF), thresholding, and Circular Hough Transform (CHT) methods. The results section highlights that the proposed algorithm is highly efficient in segmentation of OD from other parts of the fundus image. Several classification and modeling methods are studied in order to classify detected OD into OC and non-OC regions. In this thesis project three main ensemble modeling algorithms are studied to segment OC. The studied ensemble models are Random Forest, Gradient Boosting Machines (GBM), and Extreme Gradient Boosting Machines (XGBoost). The comparison between these models shows that they have more accurate results than conventional classification methods such as Logistic Regression (LR) or Support Vector Machines (SVM). This study shows that XGBoost is the fastest and most accurate approach to segment optic cup within the optic disc region.

scholarly journals Detection of Glaucoma in Retinal Fundus Images using Fast Fuzzy C Mean Clustering

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Optic Disc ◽  
Vision Loss ◽  
Fluid Pressure ◽  
Fundus Images ◽  
New Approach ◽  
Optic Cup ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Glaucoma is one of the major causes of vision loss in today’s world. Glaucoma is a disease in the eye where fluid pressure in the eye increases; if it is not timely cured, the patient may lose their vision. Glaucoma can be detected by examining boundary of optics cup and optics disc acquired from fundus images. The proposed method suggest automatic detect the boundary of optics cup and optics disc with processing of fundus images. This paper explores the new approach fast fuzzy C-mean technique for segmenting the optic disc and optic cup in fundus images. Results evaluated by fast fuzzy C mean a technique is faster than fuzzy C-mean method. The proposed method reported results to 91.91%, 90.49% and 90.17% when tested on DRIONS, DRIVE and STARE on publicly available databases of fundus images.

scholarly journals Retinal Fundus image processing and ensemble learning: optic disc and optic cup detection

2021 ◽  
Author(s):  
Shima Mohammadali Pishnamaz
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Optic Disc ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fundus Images ◽  
Fundus Image ◽  
Extreme Gradient Boosting ◽  
Optic Cup ◽  
Retinal Fundus

Ophthalmologists have widely used retinal fundus imaging systems to examine the health of the optic nerve, vitreous, macula, retina and their blood vessels. Many critical diseases, such as glaucoma and diabetic retinopathy, can be diagnosed by analyzing retinal fundus images. Retinal image-based glaucoma detection is a comprehensive diagnostic approach that examines the head cup-to-disc ratio (CDR) as an important indicator for detecting the presence and the extent of glaucoma in a patient. The accurate segmentations of the optic disc (OD) and optic cup (OC) are critical for the calculation of CDR. Machine learning based algorithms can be very helpful to efficiently exploit the vast amounts of retinal fundus data. In this thesis project, the main goal is to develop image processing and machine learning algorithms to automatically detect OD and OC from fundus images. This goal has been achieved by developing and applying several image enhancement techniques. First, an algorithm is proposed and tested on several fundus images to detect OD. The proposed algorithm is based on a combination of Contrast Limited Adaptive Histogram Equalization (CLAHE), Alternating Sequential Filters (ASF), thresholding, and Circular Hough Transform (CHT) methods. The results section highlights that the proposed algorithm is highly efficient in segmentation of OD from other parts of the fundus image. Several classification and modeling methods are studied in order to classify detected OD into OC and non-OC regions. In this thesis project three main ensemble modeling algorithms are studied to segment OC. The studied ensemble models are Random Forest, Gradient Boosting Machines (GBM), and Extreme Gradient Boosting Machines (XGBoost). The comparison between these models shows that they have more accurate results than conventional classification methods such as Logistic Regression (LR) or Support Vector Machines (SVM). This study shows that XGBoost is the fastest and most accurate approach to segment optic cup within the optic disc region.

scholarly journals Deep learning based approach for optic disc and optic cup semantic segmentation for glaucoma analysis in retinal fundus images

2020 ◽  
Vol 11 (2) ◽  
pp. 111-120
Author(s):  
Dunja Božić-Štulić ◽  
Maja Braović ◽  
Darko Stipaničev
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Contextual Information ◽  
Automatic Detection ◽  
Semantic Segmentation ◽  
Fundus Images ◽  
Glaucoma Detection ◽  
Average Accuracy ◽  
Optic Cup ◽  
Retinal Fundus

Optic disc and optic cup are one of the most recognized retinal landmarks, and there are numerous methods for their automatic detection. Segmented optic disc and optic cup are useful in providing the contextual information about the retinal image that can aid in the detection of other retinal features, but it is also useful in the automatic detection and monitoring of glaucoma. This paper proposes a deep learning based approach for the automatic optic disc and optic cup semantic segmentation, but also the new model for possible glaucoma detection. The proposed method was trained on DRIVE and DIARETDB1 image datasets and evaluated on MESSIDOR dataset, where it achieved the average accuracy of 97.3% of optic disc and 88.1% of optic cup. Detection rate of glaucoma diesis is 96.75%

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