Detection of Glaucoma using image processing techniques: A review

Kumar, B. Naveen; Chauhan, R. P.; Dahiya, Nidhi

doi:10.1109/microcom.2016.7522515

Cited by 36 publications

(9 citation statements)

References 8 publications

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“…If the blockage occurs beyond or somewhere inside the drainage canals, it will lead to open-angle glaucoma. Figure 3 [7] shows the complete architecture of open-angle glaucoma.…”

Section: Open Angle Glaucomamentioning

confidence: 99%

A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma

Veena¹,

Muruganandham

Kumaran³

2020

SN Appl. Sci.

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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.

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“…If the blockage occurs beyond or somewhere inside the drainage canals, it will lead to open-angle glaucoma. Figure 3 [7] shows the complete architecture of open-angle glaucoma.…”

Section: Open Angle Glaucomamentioning

confidence: 99%

A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma

Veena¹,

Muruganandham

Kumaran³

2020

SN Appl. Sci.

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“…Ellipse fitting is performed to find the best fit for the boundary of the OC and OD. It is a well-known technique used to smooth the boundary 12,14,19 . It is usually based on a least square fitting algorithm which assumes that the best-fit curve of a given type is the curve that minimizes the algebraic distance over the set of N data points in the least squares sense.…”

Section: Image Processingmentioning

confidence: 99%

“…The dimension of the features can be reduced by techniques such as principal component analysis and linear stepwise regression. For the classification part, the classifiers that are normally used include K-mean, fuzzy Cmeans clustering 8,12 , Nave Bayesian technique 13 , neural network (NN) 8,14 , support vector machine (SVM) 5,[14][15][16] . Among these, NN and SVM provide high performance and robustness for higher dimensional classification.…”

Section: Introductionmentioning

confidence: 99%

An automatic screening method for primary open-angle glaucoma assessment using binary and multi-class support vector machines

Vejjanugraha¹,

Kongprawechnon²,

Kondo³

et al. 2017

ScienceAsia

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Glaucoma is a chronic progressive eye condition leading to permanent visual loss. An automatic screening system is necessary to detect primary open-angle glaucoma because it is an insidious disease appearing without symptoms or early warning signs. This work introduces an automatic screening technique to diagnose glaucoma using a support vector machine (SVM). Two case studies are investigated: binary-stage and multi-stage classification of glaucoma. First, there is a comparison of the performance of the hard threshold-based approach to the supervised learning approach using an SVM. Image segmentation techniques are performed to detect important features: the actual sizes of the optic cup and optic disc in vertical and horizontal directions. SVMs with a linear kernel function are used to generate the classifier model, and the results show that using threshold-based classification is inadequate to screen glaucoma. In a second case study, an SVM is applied to develop the classification algorithm focused more on the detection of the glaucoma suspect stage, which is an intermediate stage between the healthy and glaucoma stages. A polynomial kernel function is used to implement the classification model. The unbalanced decision tree (UDT) and one-versus-the-rest (OVR) techniques are combined in the models in order to overcome the limitations of an SVM. Finally, the combination of an SVM with both UDT and OVR techniques yields a reliable result with respect to belonging classes at 99.4%.

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“…The term glaucoma refers to a condition caused by a group of diseases that leads to the degeneration of retinal ganglion cells (RGCs). The death of RGCs leads to (i) structural changes to the optic nerve head and the nerve fiber layer and (ii) simultaneous functional constriction of the visual field [4][5][6]. These two effects of glaucoma cause peripheral vision loss and, if left untreated, can eventually lead to blindness.…”

Section: Introductionmentioning

confidence: 99%

Utilizing a responsive web portal for studying disc tracing agreement in retinal images

et al. 2021

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Glaucoma is a leading cause of blindness worldwide whose detection is based on multiple factors, including measuring the cup to disc ratio, retinal nerve fiber layer and visual field defects. Advances in image processing and machine learning have allowed the development of automated approached for segmenting objects from fundus images. However, to build a robust system, a reliable ground truth dataset is required for proper training and validation of the model. In this study, we investigate the level of agreement in properly detecting the retinal disc in fundus images using an online portal built for such purposes. Two Doctors of Optometry independently traced the discs for 159 fundus images obtained from publicly available datasets using a purpose-built online portal. Additionally, we studied the effectiveness of ellipse fitting in handling misalignments in tracing. We measured tracing precision, interobserver variability, and average boundary distance between the results provided by ophthalmologists, and optometrist tracing. We also studied whether ellipse fitting has a positive or negative impact on properly detecting disc boundaries. The overall agreement between the optometrists in terms of locating the disc region in these images was 0.87. However, we found that there was a fair agreement on the disc border with kappa = 0.21. Disagreements were mainly in fundus images obtained from glaucomatous patients. The resulting dataset was deemed to be an acceptable ground truth dataset for training a validation of models for automatic detection of objects in fundus images.

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Detection of Glaucoma using image processing techniques: A review

Cited by 36 publications

References 8 publications

A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma

A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma

An automatic screening method for primary open-angle glaucoma assessment using binary and multi-class support vector machines

Utilizing a responsive web portal for studying disc tracing agreement in retinal images

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