Optic Disc Segmentation Research Articles

Associations of Optic Nerve Head and Anterior Segment Parameters in Normotensive Saudi Females

Objectives: To determine the relationship between anterior segment parameters and the optic nerve head (ONH) parameters of females. Methods: Anterior segment, optic nerve head parameters, Intraocular Pressure (IOP) and Axial Length (AL) of 99 participants (aged 20.1±1.3 years) were obtained. Differences between refraction groups and relationship between anterior segment and ONH parameters were assessed as well as linear regression for factors associated with ONH and anterior segment parameters after adjusting for cofounders. Results: SER (-0.12 Acknowledgements: The authors acknowledge the support of the Research Centre, of the Female Scientific and Medical Colleges, Deanship of Scientific Research at King Saud University.0.58D), AL (23.5±0.8 mm), IOP (17.8±2.0 mmHg), anterior segment parameters and optic disc parameters were similar between refraction groups (p>0.20 for all). Age was correlated with anterior chamber volume (r=-0.19), while SER was correlated with AL (r=-0.26), anterior chamber volume (r=-0.25) and angle (r=- 0.19) and with the disc area (r=0.25; p<0.02 for all). Myopes had wider anterior chamber depths than emmetropes (mean difference, 2.06, 95% confidence interval: 0.32/3.80 degs, p=0.02) with smaller disc area (-0.21mm², 95% CI: -0.38/-0.03mm²; p=0.021). After correcting for age and refraction, axial length was associated with corneal refractive power (r=-0.60, p<0.001), and anterior segment parameters (p<0.001 for all). Conclusions: The ONH structure was related to anterior segment morphology including corneal thickness, anterior chamber volume and depth, which is important when interpreting OCT imaging.

Greedy segmentation based diabetic retinopathy identification using curvelet transform and scale invariant features

Diabetic retinopathy (DR) is the major reason of vision loss in the active population. It can usually be prevented by regulating the blood glucose and providing a timely treatment. DR has clinical features recognized by the experts including the blood vessel area, exudates, neovascularization, hemorrhages, and microaneurysm. Because DR has some varieties and complexities due to its geometrical and haemodynamic features, it is hard and time-consuming to detect DR in manual diagnosis. In Computer Aided Diagnosis (CAD) systems, the features of DR fundus images are detected using computer vision techniques. In this paper, a CAD system is proposed, which distinguishes automatically whether the fundus is normal or it suffers from diabetic retinopathy disease. As preprocess morphological operations like filtering, opening, and dilation are applied to the images firstly, then, Optic Disk (OD) segmentation is implemented using Greedy algorithm. Because of the intensity of an OD is similar to some DR intensities, OD regions are removed from the fundus images for an accurate feature extraction. The features extracted with Curvelet Transform (CT) and Scale Invariant Feature Transform (SIFT), respectively, are concatenated to provide a feature set that defines the fundus data optimally. Finally, the feature set is given to the Support Vector Machines (SVM), K-Nearest Neighborhood (KNN), and Naïve–Bayes (NB) classifiers for the DR identification purpose. The proposed method achieves the highest accuracy and sensitivity as 92.8% and 97.6%, respectively, with SVM and specificity as 92.5% with KNN classifier.

A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images

Glaucoma is currently leading retinal disease, which damages the eye because of the Intraocular pressure (IOP) on the eye. If glaucoma is left untreated it will lead to vision loss by damaging the Optic Nerve Head (ONH). The progression of glaucoma is examined on the retinal part of the eye by an experienced ophthalmologist. This approach is very tedious, and it consumes more time to do it manually. Hence this issue is right problem that can be solved by automatically diagnosing glaucoma with the help of the deep learning approaches. Convolutional Neural Networks (CNN's) are appropriate to find the solution for this type of issue as they can extract various levels of data from the input image, and which encourages to differentiate among non-glaucomic and glaucomic images. This proposed paper introduces an efficient glaucoma master framework to segment the optic cup and optic disc to find the Cup-to-Disc-Ratio (CDR). Here the diagnosis of glaucoma is achieved by using deep learning with novel CNN. The proposed system uses two individual CNN architecture to segment the Optic Cup (OC) and Optic Disc (OD) to get more accurate result. This model is trained and tested on DRISHTI – GS database, which is publicly available and an accuracy of 98% for optic disc and 97% for optic cup segmentation is achieved.

A multi-scale convolutional neural network with context for joint segmentation of optic disc and cup

Glaucoma is the leading cause of irreversible blindness. For glaucoma screening, the cup to disc ratio (CDR) is a significant indicator, whose calculation relies on the segmentation of optic disc(OD) and optic cup(OC) in color fundus images. This study proposes a residual multi-scale convolutional neural network with a context semantic extraction module to jointly segment the OD and OC. The proposed method uses a W-shaped backbone network, including image pyramid multi-scale input with the side output layer as an early classifier to generate local prediction output. The proposed method includes a context extraction module that extracts contextual semantic information from multiple level receptive field sizes and adaptively recalibrates channel-wise feature responses. It can effectively extract global information and reduce the semantic gaps in the fusion of deep and shallow semantic information. We validated the proposed method on four datasets, including DRISHTI-GS1, REFUGE, RIM-ONE r3, and a private dataset. The overlap errors are 0.0540, 0.0684, 0.0492, 0.0511 in OC segmentation and 0.2332, 0.1777, 0.2372, 0.2547 in OD segmentation, respectively. Experimental results indicate that the proposed method can estimate the CDR for a large-scale glaucoma screening.

GlaucoNet: Patch-Based Residual Deep Learning Network for Optic Disc and Cup Segmentation Towards Glaucoma Assessment

Glaucoma is a chronic eye condition causing irreversible vision damage and presently stands as the second leading cause of blindness worldwide. Damaged optic disc and optic cup assessment in color fundus image has been shown to be a promising method for glaucoma screening. In this paper, we propose a new accurate and efficient optic disc and cup segmentation methodology using a residual deep learning approach. The proposed patch-based deep network (GlaucoNet) is trained with a large number of preprocessed input image patches, big enough to include the important discriminatory information around each pixel. The proposed architecture gives better segmentation results as extra skip connections are introduced in the framework which explicitly reformulate the layers such that the learning function is dependent on a residual function of the input layer. It addresses the degradation problem of the deep network by improving the information flow. The convex hull transformation is applied on the initial results to obtain the final segmentation output. The proposed method is tested on publicly available DRISHTI-GS, RIM-ONE and ORIGA-light datasets. The experimental results for optic disc segmentation show an overlapping score (OS) of 0.9106, 0.8972 and 0.8835 and the optic cup segmentation achieves an OS of 0.8229, 0.7401 and 0.8106 in DRISHTI-GS, RIM-ONE and ORIGA-light, respectively. Finally, the glaucoma risk index is obtained by computing the cup-to-disc height ratio obtained from the segmented regions. Experimental results showing better or comparable segmentation performance and decrease in cup-to-disc height ratio estimation errors demonstrate the efficacy of the proposed GlaucoNet-based segmentation methodology.

A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images

Retinal disorders are a severe health threat for older adults because they may lead to vision loss and blindness. Diabetic patients are particularly prone to suffer from Diabetic Retinopathy. Identifying relevant structural components in color fundus images like the optic disc (OD) is crucial to diagnose retinal diseases. Automatic OD detection is complex because of its location in an area where blood vessels converge, and color distribution is uneven. Several image processing techniques have been developed for OD detection so far, but vessel segmentation is sometimes required, increasing computational complexity and time. Moreover, precise OD segmentation methods utilize complex algorithms that need special hardware or extensive labeled datasets. We propose an OD detection approach based on the Modern Portfolio Theory of Markowitz to generate an innovative color fusion model. Specifically, the training phase calculates the optimal weights for each color channel. A fusion of weighted color channels is then applied in the testing phase. This approach acts as a powerful and real-time preprocessing stage. We use four heterogeneous datasets to validate the presented methodology. Three out of four datasets are publicly available (i.e., DRIVE, Messidor, and HRF), and the last corresponds to an in–house dataset acquired from Hospital Universitari Sant Joan de Reus (Spain). Two different segmentation methods are presented and compared with state-of-the-art computer vision techniques to analyze the model performance. An outstanding accuracy and overlap above 0.9 and 80%, respectively, and a minimal execution time of 0.05 s are reached. Therefore, our model could be integrated into daily clinical practice to accelerate the diagnosis of Diabetic Retinopathy due to its simplicity, performance, and speed.

Improved optic disc and cup segmentation in Glaucomatic images using deep learning architecture

Glaucoma is an ailment causing permanent vision loss but can be prevented through the early detection. Optic disc to cup ratio is one of the key factors for glaucoma diagnosis. But accurate segmentation of disc and cup is still a challenge. To mitigate this challenge, an effective system for optic disc and cup segmentation using deep learning architecture is presented in this paper. Modified Groundtruth is utilized to train the proposed model. It works as fused segmentation marking by multiple experts that helps in improving the performance of the system. Extensive computer simulations are conducted to test the efficiency of the proposed system. For the implementation three standard benchmark datasets such as DRISHTI-GS, DRIONS-DB and RIM-ONE v3 are used. The performance of the proposed system is validated against the state-of-the-art methods. Results indicate an average overlapping score of 96.62%, 96.15% and 98.42% respectively for optic disc segmentation and an average overlapping score of 94.41% is achieved on DRISHTI-GS which is significant for optic cup segmentation.

Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation.

A common shortfall of supervised deep learning for medical imaging is the lack of labeled data, which is often expensive and time consuming to collect. This article presents a new semisupervised method for medical image segmentation, where the network is optimized by a weighted combination of a common supervised loss only for the labeled inputs and a regularization loss for both the labeled and unlabeled data. To utilize the unlabeled data, our method encourages consistent predictions of the network-in-training for the same input under different perturbations. With the semisupervised segmentation tasks, we introduce a transformation-consistent strategy in the self-ensembling model to enhance the regularization effect for pixel-level predictions. To further improve the regularization effects, we extend the transformation in a more generalized form including scaling and optimize the consistency loss with a teacher model, which is an averaging of the student model weights. We extensively validated the proposed semisupervised method on three typical yet challenging medical image segmentation tasks: 1) skin lesion segmentation from dermoscopy images in the International Skin Imaging Collaboration (ISIC) 2017 data set; 2) optic disk (OD) segmentation from fundus images in the Retinal Fundus Glaucoma Challenge (REFUGE) data set; and 3) liver segmentation from volumetric CT scans in the Liver Tumor Segmentation Challenge (LiTS) data set. Compared with state-of-the-art, our method shows superior performance on the challenging 2-D/3-D medical images, demonstrating the effectiveness of our semisupervised method for medical image segmentation.

Machine Learning for Diabetic Retinopathy Detection using Image Processing

The disorder of Diabetic Retinopathy (DR), a complication of Diabetes that may lead to blindness if not treated at an early stage, is diagnosed by evaluating the retina images of eye. However, the manual grading of images for identifying the seriousness of DR disease requires many resources and it also takes a lot of time. Automated systems give accurate results along with saving time. Ophthalmologists may find it useful in reducing their workload. Proposed work presents the method to correctly identify the lesions and classify DR images efficiently. Blood leaking out of veins form features such as exudates, microaneurysms and haemorrhages, on retina. Image processing techniques assist in DR detection. Median filtering is used on gray scale converted image to reduce noise. The features of the pre-processed images are extracted by textural feature analysis. Optic disc (OD) segmentation methodology is implemented for the removal of OD. Blood vessels are extracted using haar wavelet filters. KNN classifier is applied for classifying retinal image into diseased or healthy .The proposed algorithm is executed in MATLAB software and analyze results with regard to certain parameters such as accuracy, sensitivity, and specificity. The outcomes prove the superiority of the new method with sensitivity of 92.6%, specificity of 87.56% and accuracy of 95% on Diaretdb1 database.

Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network

BackgroundGlaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup.MethodsIn this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider network to improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset.ResultsThe experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7% in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79% on the REFUGE dataset, respectively.ConclusionsThe promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.

CAFR-CNN: coarse-to-fine adaptive faster R-CNN for cross-domain joint optic disc and cup segmentation

Glaucoma is a leading cause of blindness. Accurate and efficient segmentation of the optic disc and cup from fundus images is important for glaucoma screening. However, using off-the-shelf networks against new datasets may lead to degraded performances due to domain shift. To address this issue, in this paper, we propose a coarse-to-fine adaptive Faster R-CNN framework for cross-domain joint optic disc and cup segmentation. The proposed CAFR-CNN consists of the Faster R-CNN detector, a spatial attention-based region alignment module, a pyramid ROI alignment module and a prototype-based semantic alignment module. The Faster R-CNN detector extracts features from fundus images using a VGG16 network as a backbone. The spatial attention-based region alignment module extracts the region of interest through a spatial mechanism and aligns the feature distribution from different domains via multilayer adversarial learning to achieve a coarse-grained adaptation. The pyramid ROI alignment module learns multilevel contextual features to prevent misclassifications due to the similar appearances of the optic disc and cup. The prototype-based semantic alignment module minimizes the distance of global prototypes with the same category between the target domain and source domain to achieve a fine-grained adaptation. We evaluated the proposed CAFR-CNN framework under different scenarios constructed from four public retinal fundus image datasets (REFUGE2, DRISHTI-GS, DRIONS-DB and RIM-ONE-r3). The experimental results show that the proposed method outperforms the current state-of-the-art methods and has good accuracy and robustness: it not only avoids the adverse effects of low contrast and noise interference but also preserves the shape priors and generates more accurate contours.

Optic disc and optic cup segmentation in retinal images

Automated segmentation of the optic disc (OD) and optic cup (OC) is important for retinal image analysis and retinal diabetic retinopathy systems. For OD segmentation, this paper presents a method done in three steps that combines variance and brightness features of the OD to localise leading to increased accuracy in detecting OD rather than using just one feature. As a first step, the image is divided into non-over lapping windows. Then the brightest window with maximum variance in intensity is selected. Subsequently the circular Hough transform (CHT) is applied to get the OD segmentation. OC segmentation is done in two steps: 1) blood vessels inside the OD are eliminated and; 2) restricted region growing performed to get the OC segmentation. The efficacy of the proposed method is demonstrated using the standard benchmark DRIVE and DIARETDB1 databases and by comparing the results of proposed method and some methods in silico.

Research on the segmentation of optic disc and cup based on modified U-Net

Research on the segmentation of optic disc and cup based on modified U-Net

Frequency domain analysis of grey level intensities for extraction of optic disc in retinal images

Revealing and extraction of optic disc (OD) in fundus images is the most important step in automatic screening system of diabetic retinopathy. The algorithm presented in this paper is focused on revealing and extraction of optic disc (OD) in fundus images. This algorithm adopts frequency domain approach to focus the OD and mathematical morphology for extraction of the OD. Large pathological signs, bright lesions limit the OD segmentation performance as brightness of both OD and bright lesions is similar. Bright lesions are extracted before localisation of the OD using DFT and morphological dilation is used for segmentation of the OD. This method was assessed on standard databases namely: DRIVE, DIARETDB1 and DIARETDB0. This algorithm acceptably recognises the OD in 255 out of 259 retinal images (98.45%) in 0.75 seconds.78.9-86.65% and 99.37-99.58% are the OD segmentation sensitivity and specificity achieved on these three databases. The proposed method demonstrates acceptable robustness on normal and pathological signs retinal images. Focused experimental results reveal the superior performance of presented work with respect to the methods demonstrated in literature.

Frequency domain analysis of grey level intensities for extraction of optic disc in retinal images

Revealing and extraction of optic disc (OD) in fundus images is the most important step in automatic screening system of diabetic retinopathy. The algorithm presented in this paper is focused on revealing and extraction of optic disc (OD) in fundus images. This algorithm adopts frequency domain approach to focus the OD and mathematical morphology for extraction of the OD. Large pathological signs, bright lesions limit the OD segmentation performance as brightness of both OD and bright lesions is similar. Bright lesions are extracted before localisation of the OD using DFT and morphological dilation is used for segmentation of the OD. This method was assessed on standard databases namely: DRIVE, DIARETDB1 and DIARETDB0. This algorithm acceptably recognises the OD in 255 out of 259 retinal images (98.45%) in 0.75 seconds.78.9-86.65% and 99.37-99.58% are the OD segmentation sensitivity and specificity achieved on these three databases. The proposed method demonstrates acceptable robustness on normal and pathological signs retinal images. Focused experimental results reveal the superior performance of presented work with respect to the methods demonstrated in literature.

Early detection of glaucoma using optic disc and optic cup segmentation: A survey

Abstract Glaucoma is the subsequent driving reason for visual impairment around the world. Glaucoma happens because of number of issues in eye which brings about continuous visual misfortune. A portion of the glaucoma screenings are either unidentified or untreated. Glaucoma is recognized by additional liquid in the front of eye. A review of automated glaucoma detection techniques is presented in this paper. Two significant classes of glaucoma are open-edge glaucoma and limited point glaucoma. Manifestations acquired by intense edge conclusion glaucoma are foggy vision, radiances around lights, extraordinary eye torment, queasiness and retching. A portion of the hazard elements of glaucoma are diabetes, hypertension or family ancestry of glaucoma. Decrease or disposal of undiscovered glaucoma is made by starting mindfulness and continuous assessments by Ophthalmologist. A detailed survey is performed on early analysis of glaucoma utilizing different techniques such as Machine Learning, Convolution Neural Network, Transfer Learning and Deep Learning.

Optic disc and optic cup segmentation in retinal images

Optic disc and optic cup segmentation in retinal images

Glaucoma detection in retinal fundus images using U-Net and supervised machine learning algorithms

Background and objectiveGlaucoma is a neuro-degenerative eye disease developed due to an increase in the Intra-ocular Pressure inside the retina. Being the second largest cause of blindness worldwide, it can lead the person towards complete blindness if an early diagnosis does not take place. With respect to this underlying issue, there is an immense need of developing a system that can effectively work in the absence of excessive equipments, skilled medical practitioners and also is less time consuming. MethodsThis work proposes an offline Computer-Aided Diagnosis (CAD) system for glaucoma diagnosis using retinal fundus images. This application is developed using image processing, deep learning and machine learning approaches. Le-Net architecture is used for input image validation and Region of Interest (ROI) detection is done using brightest spot algorithm. Further, the optic disc and optic cup segmentation is performed with the help of U-Net architecture and classification is done using SVM, Neural Network and Adaboost classifiers. ResultsThe accuracy of 99% is achieved using Le-Net for input image validation. Considering the application of brightest spot algorithm, an accuracy of 98.67% is achieved for ROI extraction. Further, a dice-coefficient of 0.93 and 0.87 was attained for the segmentation of optic disc and optic cup respectively using U-Net architecture. Using SVM, Neural Network and Adaboost classifiers, the proposed methodology managed to achieve a classification accuracy, recall, specificity and sensitivity of 100%, thus proving the system to be reliable and promising. ConclusionsIn conclusion, the proposed desktop application is easy to use and can play a major role in early glaucoma detection. The modular design of the CAD system is made up of a set of standalone components that can be used for a variety of different tasks for detecting and classifying glaucoma. Due to the model being trained on a variety of different datasets, the system proves to be robust and more accurate.

Optic disc and cup segmentation by combining context and attention

Optic disc and cup segmentation by combining context and attention

Minimizing-Entropy and Fourier Consistency Network for Domain Adaptation on Optic Disc and Cup Segmentation

Automated segmentation of the optic disc (OD) and optic cup (OC) from different datasets plays an important role in the diagnosis of glaucoma and greatly saves human resources in both data annotation and image segmentation. However, the domain shift between different datasets suppresses the generalization ability of the segmentation network, especially damaging the performance of segmentation in the target domain, which is unlabeled. Therefore, using a transfer learning algorithm or domain adaptation method to enhance the migration ability of segmentation models has become an essential step and has attracted the attention of many researchers. In this paper, we propose an unsupervised domain adaptation network, called the Minimizing-entropy and Fourier Domain Adaptation network (MeFDA), to narrow the discrepancy between the source and target domains and prevent the degradation of segmentation performance. First, we perform adversarial optimization on the entropy maps of the predicted segmentation results to alleviate the domain shift. Then, direct entropy-minimization optimization is applied to the unlabeled target domain data to improve the credibility of the prediction segmentation maps. To enhance the prediction consistency of the target domain data, we augment the target domain dataset through the Fourier transform by replacing the low-frequency part in the target images with that of the source images. Then, a semantic consistency constraint is imposed on the raw images and augmented images of the target domain to improve the prediction consistency of the segmentation model, thereby further narrowing the discrepancy between the source and target domains. Experiments on several public retinal fundus image datasets prove the superiority of MeFDA compared with state-of-the-art methods, and the ablation study analyzes the importance of the different proposed components.