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Accurate segmentation of optic disc (OD) and optic cup (OC) is a fundamental task for fundus image analysis. Most existing methods focus on segmenting OD and OC inside the optic nerve head (ONH) area but paying little attention to accurate ONH localization. In this paper, we propose a Mask-RCNN based paradigm to localize ONH and jointly segment OD...
In the recent years the usage of mobile phone is increased and it is the major reason for cause of vision loss in several people. The continuous usage increases pressure inside optic nerve head and it leads to glaucoma disease. Also, there are lot of other reasons which leads to the cause of glaucoma. The purpose of this paper is to determine the i...
Public databases for glaucoma studies contain color images of the retina, emphasizing the optic papilla. These databases are intended for research and standardized automated methodologies such as those using deep learning techniques. These techniques are used to solve complex problems in medical imaging, particularly in the automated screening of g...
Glaucoma is one of the prevalent causes of blindness in the modern world. It is a salient chronic eye disease that leads to irreversible vision loss. The impediments of glaucoma can be restricted if it is identified at primary stages. In this paper, a novel two-phase Optic Disk localization and Glaucoma Diagnosis Network (ODGNet) has been proposed....
Glaucoma is a leading cause of irreversible vision impairment globally and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention which can prevent further visual field loss. To detect glaucoma, examination of the optic nerve head via fundus imaging can be performed, at the centre of which is the assessmen...
Citations
... Further work by Dutta and coworkers (31) proposed the use of the ACDR independently. The authors stated that the parameter of the ACDR is more appropriate than the vCDR parameter for glaucoma classification. ...
... The point is that there are many different approaches to segmentation with differing degrees of success. In the segmentation of the ONH, it is well-known that the optic cup is much more challenging to segment than the optic disc due to the low contrast between the optic cup and the neighboring disc region (31). As such, there are very few papers focused on developing optic cup segmentation methods. ...
Glaucoma is a leading cause of irreversible vision impairment globally, and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention that can prevent further visual field loss. To detect glaucoma, examination of the optic nerve head via fundus imaging can be performed, at the center of which is the assessment of the optic cup and disc boundaries. Fundus imaging is non-invasive and low-cost; however, the image examination relies on subjective, time-consuming, and costly expert assessments.
A timely question to ask is: “Can artificial intelligence mimic glaucoma assessments made by experts?”. Specifically, can artificial intelligence automatically find the boundaries of the optic cup and disc (providing a so-called segmented fundus image) and then use the segmented image to identify glaucoma with high accuracy?
We conducted a comprehensive review on artificial intelligence-enabled glaucoma detection frameworks that produce and use segmented fundus images and summarized the advantages and disadvantages of such frameworks. We identified 36 relevant papers from 2011-2021 and 2 main approaches: 1) logical rule-based frameworks, based on a set of rules; and 2) machine learning/statistical modelling based frameworks. We critically evaluated the state-of-art of the 2 approaches, identified gaps in the literature and pointed at areas for future research.
... The regular eye check-up is the only precautionary measure to avoid glaucoma. Mainly glaucoma occurs due to the Intraocular Pressure (IOP) [5,6] on the eye. Intraocular pressure is the high pressure on the eye; due to this, more fluid is produced on the eye, leading to the blockage on the eye's channel. ...
Glaucoma is one of the leading severe retinal disease which damages the optic nerve head on the retinal part of the eye irreversibly. Once the person is diagnosed with glaucoma, it cannot be treated entirely, but it can be controlled. If glaucoma is not diagnosed in time, it will lead to vision loss by damaging the Optic Nerve Head. The glaucoma detection is performed based on the optic disc and optic cup parameters on the retinal part of the eye. In the existing system, many image processing and machine learning techniques used for the segmentation and classification of optic disc and optic cup. To improve the precision of diagnosis, the existing techniques used need an improvement. This article helps readers with more information about the existing methods applied for the diagnosis of glaucoma, it also lists the research gaps and technical challenges to improve the accuracy of segmentation and classification methods.
... The regular eye check-up is the only precautionary measure to avoid glaucoma. Mainly glaucoma occurs due to the Intraocular Pressure (IOP) [5,6] on the eye. Intraocular pressure is the high pressure on the eye; due to this, more fluid is produced on the eye, leading to the blockage on the eye's channel. ...
... 3) CDR measurement and prediction: For estimating the diameter of the segmented objects, an ellipse fitting based approach [17] is followed. The best fit ellipse for optic disc and optic cup is found and the length of its major axis is taken as the diameter of the respective object [18]. For predicting ...
... The segmentation of Optic Disc and Cup is the primary requirement in extracting the different parameters from the fundus images. The segmentation process has been elaborately discussed in our previous work [6]. The ISNT rule is used to determine the presence and direction of the focal notch from the width of the neuroretinal rim. ...
Automatic evaluation of the retinal fundus image is regarded as one of the most important future tools for early detection and treatment of progressive eye diseases like glaucoma. Glaucoma leads to progressive degeneration of vision which is characterized by shape deformation of the optic cup associated with focal notching, wherein the degeneration of the blood vessels results in the formation of a notch along the neuroretinal rim. In this study, we have developed a methodology for automated prediction of glaucoma based on feature analysis of the focal notching along the neuroretinal rim and cup to disc ratio values. This procedure has three phases: the first phase segments the optic disc and cup by suppressing the blood vessels with dynamic thresholding; the second phase computes the neuroretinal rim width to detect the presence and direction of notching by the conventional ISNT rule apart from calculating the cup-to-disc ratio from the color fundus image (CFI); the third phase uses linear support vector based machine learning algorithm by integrating extracted parameters as features for classification of CFIs into glaucomatous or normal. The algorithm outputs have been evaluated on a freely available database of 101 images, each marked with decision of five glaucoma expert ophthalmologists, thereby returning an accuracy rate of 87.128%.
Retinal cell neurodegeneration relates to glaucoma, diabetic retinopathy, age-related macular degeneration, and retinitis pigmentosa. Early stages of such disease are preceded by retinal neurodegeneration or angiogenesis in retinal vessels. It is a well established fact that total or partial vision loss is caused by abnormal retinal degeneration of the photo receptors or the inner retinal neurons, as well as unusual growth of cells forming extra retinal venules and arterioles. However the reasons and mechanisms behind the abnormal retinal cell deaths or unusual cell growths remains unknown and is considered an area of active research. This paper reviews contemporary research on retinal disease citing reasons for a typical retinal vessel cell growth, unusual death of retinal cells, and ways to repair the retinal damaged cells under various degrees of impairment. Knowledge about the mechanism behind the sudden change in cell pattern behavior will help to track and better understand the reasons behind early start and rate of progression for such retinal diseases, and how to harness an quick control over disease progression, with help to prognosis and complete recovery. Neurodegeneration of RGC (retinal ganglion cell) is due to Glutamate excitoxicity and a few other factors. Neovascularization occurs from increased VEGF presence in vitreous of eye. Precise cellular repair is done on retina using derived information.
To diagnose diseases, various healthcare systems use content-predicted picture analysis and computer vision techniques. Fundus images acquired with a fundus camera are used to detect anomalies in a human ocular perceiver. Glaucoma is the second most common ocular perceiver disease that can lead to neurodegeneration. The main cause of this condition is said to be an insufficient intraocular pressure within the human ocular perceiver. Glaucoma has no symptoms in its early stages and can lead to ocular incapacitation if it is not treated. Glaucoma can be detected early enough to prevent representative vision loss. Manual evaluation of the human ocular perceiver is a valid technique, but it is prone to human error. To identify glaucoma, we will need image processing, artificial intelligence, and computer vision techniques.
