Intraocular pressure-related pattern of optic disc cupping in adult glaucoma patients
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Optic disc
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Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. Current tests using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Optic nerve head assessment in retinal fundus images is both more promising and superior. This paper proposes optic disc and optic cup segmentation using superpixel classification for glaucoma screening. In optic disc segmentation, histograms, and center surround statistics are used to classify each superpixel as disc or non-disc. A self-assessment reliability score is computed to evaluate the quality of the automated optic disc segmentation. For optic cup segmentation, in addition to the histograms and center surround statistics, the location information is also included into the feature space to boost the performance. The proposed segmentation methods have been evaluated in a database of 650 images with optic disc and optic cup boundaries manually marked by trained professionals. Experimental results show an average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively. The results also show an increase in overlapping error as the reliability score is reduced, which justifies the effectiveness of the self-assessment. The segmented optic disc and optic cup are then used to compute the cup to disc ratio for glaucoma screening. Our proposed method achieves areas under curve of 0.800 and 0.822 in two data sets, which is higher than other methods. The methods can be used for segmentation and glaucoma screening. The self-assessment will be used as an indicator of cases with large errors and enhance the clinical deployment of the automatic segmentation and screening.
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A troublesome disease in which damages of the optic nerve of eye's is nothing but the glaucoma, which causes irretrievable loss of vision. Glaucoma is a disease where if treatment is get late, the person can blind. Normally glaucoma detects when there is an increase in the fluid in the front of eye. When that extra fluid is increased, the pressure in your eye is also getting increased. Accordingly, the size of the optic disc and optic cup is increased as a result diameter also increased. The ratio of the cup and disc diameter is called cup-to-disc ratio (CDR). Threshold type segmentation method is used in this system for localizing the optic disc and optic cup. Another edge detection and ellipse fitting algorithm are also used. The proposed system for optic disc and optic cup localization and CDR calculation is MATLAB GUI software.
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We present a technique for optic cup segmentation and outlining based on Kåsa's circle fit model. The outlining problem is posed as a task of fitting a circle to the sparse set of optic cup boundary points. For automatic localization of the optic disc, we use the matched filtering technique. We clear-off the non-optic disc area by drawing a circle with point of optic disc localization as the coordinates of the center and diameter just above the normal optic disc diameter to overcome the problem of optic cup overestimation due to any retinal pathology. We report validation results on three publicly available fundus image databases, amounting to a total of 1411 fundus images for automatic optic disc localization, and 300 fundus images randomly selected for optic cup segmentation and outlining. The proposed method results in an optic disc localization accuracy of 94.06%, 94.17%, and 95.45%, and an average Dice similarity index of 0.7302, 0.7050, and 0.7120 on DRISHTI-GS, MESSIDOR, and DRIONS-DB fundus image databases, respectively. The average computation times for optic disc localization and optic cup segmentation are 3.83 and 5.33 seconds, respectively.
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Glaucoma is one of the leading cause of blindness. The manual examination of optic cup and disc is a standard procedure used for detecting glaucoma. This paper presents a fully automatic regression based method which accurately segments optic cup and disc in retinal colour fundus image. First, we roughly segment optic disc using circular hough transform. The approximated optic disc is then used to compute the initial optic disc and cup shapes. We propose a robust and efficient cascaded shape regression method which iteratively learns the final shape of the optic cup and disc from a given initial shape. Gradient boosted regression trees are employed to learn each regressor in the cascade. A novel data augmentation approach is proposed to improve the regressors performance by generating synthetic training data. The proposed optic cup and disc segmentation method is applied on an image set of 50 patients and demonstrate high segmentation accuracy for optic cup and disc with dice metric of 0.95 and 0.85 respectively. Comparative study shows that our proposed method outperforms state of the art optic cup and disc segmentation methods.
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Examination of stereoscopic optic disc photographs allowed accurate prediction of glaucomatous and normal fields to be made in 82 and 95% of eyes respectively and for visual field loss to be correctly located in upper and lower half in 83 and 91% of cases respectively. Despite this high correlation the existence of false positive and fale negative predictions means that the total reliance on optic nerve examination without visual field estimation in the evaluation of the glaucoma patient should not be made. Optic disc examination is too insensitive for long-term follow-up of visual function in a glaucoma patient. The high correlation between the state of the visual field and the optic disc means that, in the evaluation of the visual functions of a glaucoma patient, the appearance of the optic disc and the visual field should be in agreement.
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Retinal fundus photographs has always remained the gold standard for evaluating the changes in retina. Here, a novel method for automatic glaucoma detection from digital retinal fundus images is proposed. The methodology makes use of optic disc and cup segmentation. Optic disc is segmented using morphological operations and hybrid level-set methodology. Optic cup is segmented by first detecting blood vessels using SVM classifier and then the bending points on the circum linear vessels. Parameters such as vertical cup-to-disc ratio (CDR), cup-to-disc area ratio are calculated and used for glaucoma detection. A CDR value greater than 0.5 and cup-to-disc area ratio greater than 0.3 indicates the presence of glaucoma. The proposed method is found to produce a mean error as low as 0.021 (CDR) when compared with expert observation.
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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.
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Glaucoma is a condition of increased intraocular pressure within the eyes. Such increase then causes the damage on optic nerves as the organ bringing information to be processed in brain. One of the parameters to detect the glaucoma is the ratio between the optic cup and optic disc that can be identified through an examination towards the retinal fundus image of the patient. The ratio is obtained by firstly calculating the width of the area of the optic cup and the optic disc. This research was aimed to propose a method of the segmentation of the optic cup and optic disc with the adaptive threshold. The value of the adaptive threshold was obtained once calculating the mean value and standard deviation on the retinal fundus image of the patient. Before conducting the segmentation, the red component of the image would firstly be extracted followed by doing the contrast stretching. The last one was to perform the morphological operation such as closing and opening to remove the blood vessel to make the ratio calculation more accurate. This method has been tested in a number of retinal fundus images coming from DRISTHI-GS and RIM-ONE.
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Early diagnosis and treatment of glaucoma can prevent the progression of this disease and the sudden loss of vision. Glaucoma affects the optic disc and optic cup located inside the optic disc. In this paper, first, the optic disc is localized and then segmentation of optic disc and cup is performed to diagnose based on the optic cup to disc ratio (CDR). A Faster Region-based Convolutional Neural Network (Faster-RCNN) with the pre-trained ResNet50 network is used for the optic disc localization step. The segmentation step is performed by the modified U-Net architecture using the SE-ResNet50 network as its encoding layers, and finally CDR is evaluated. The Drishti-GS1 and RIM-ONE v3 databases are used to train and test the proposed method and the MESSIDOR database is only used in the test phase. In addition, for segmentation of optic disc and cup, two approaches are proposed to consider the optic disc and cup annotations in the Drishti-GS1 data set’s ground truth. In the second proposed approach and according to the F1-score criteria, the result of optic cup and disc segmentation for Drishti-GS1 data set is 0.93 and 0.97, respectively, for RIM-ONE v3 data set is 0.79 and 0.95, respectively, and for MESSIDOR data set is 0.84 and 0.93, respectively, which is competitive with other works.
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