Detection of glaucoma using retinal fundus images

Khan, Fauzia; Khan, Shoaib A.; Yasin, Ubaid Ullah; Haq, Ihtisham Ul; Qamar, Usman

doi:10.1109/bmeicon.2013.6687674

Cited by 52 publications

(25 citation statements)

References 7 publications

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“…To determine the performance of our approach, 90 retinal images were processed and their CDR and ISNT quadrants were computed. The method achieves an average accuracy of 94% to 96% which is better than the previous approach mentioned in [2] as firstly the classification is done manually and the accuracy mentioned in there is 94%. Whereas here the accuracy achieved is 96.7%.…”

Section: Resultsmentioning

confidence: 84%

“…Fauzia Khan et al [2] used Morphological techniques to extract two major features for detection of glaucoma i.e. CDR,…”

Section: Literature Reviewmentioning

confidence: 99%

“…In next step, blood vessels are removed from the image. For this morphological operation such as the dilation, erosion, is performed as defined in equation (1) and (2). Dilation causes objects to grow in size by adding pixels to the boundaries of the object in the input image.…”

Section: Extraction Of Optic Disc and Cupmentioning

confidence: 99%

“…In healthy eyes, there is normal balance between the fluids, one that is produced in the eye, and the second that leaves the eye through eye's drainage system [2]. This balance of fluids keeps Inter Ocular Pressure (IOP) within the eye constant but in glaucoma, the balance of fluids produced within the eye is not maintained properly which in turn causes an increase in IOP, resulting in the damage of optic nerve.…”

Section: Introductionmentioning

confidence: 99%

“…Due to increase in IOP, the cup size begins to increase which consequently increases the CDR. As the cup size increases it also affects the Neuroretinal Rim (NRR) [2]. NRR is the region located between the edge of the disc and the physiological cup.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

ANN Glaucoma Detection using Cup-to-Disk Ratio and Neuroretinal Rim

Choudhary¹,

Tiwari²

2015

IJCA

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Glaucoma is a disease in which the intraocular pressure is very high, causing the optic disc to become cupped with eventual everlasting impairment of vision. It is the second leading cause of permanent blindness. It cannot be cured, but its progression can be slowed down by treatment in early stage. Therefore, detecting glaucoma in time is crucial. In this paper glaucoma is classified by extracting two features using retinal fundus images. (i) Cup to Disc Ratio (CDR). (ii) Ratio of Neuroretinal Rim in inferior, superior, temporal and nasal quadrants that is to say ISNT quadrants. Glaucoma frequently damages superior and inferior fibers before temporal and nasal optic nerve fibers and which start decreasing the superior and inferior rims areas and change the order of ISNT rule. Hence, the detection of rim areas in four directions can assist the correct verification of ISNT rule and then improve the correct diagnosis of glaucoma at early stages. In the end, feed forward back propagation neural network is used for classification based on the above two features. The tool used to accomplish the objective is MATLAB R2013a. The average accuracy of the system is around 96%. The method does not rely on trained glaucoma specialists or specialized and costly OCT/HRT machines. Several fundus retinal images containing normal and glaucoma were applied to the proposed method for demonstration.

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Section: Resultsmentioning

confidence: 84%

“…Fauzia Khan et al [2] used Morphological techniques to extract two major features for detection of glaucoma i.e. CDR,…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Extraction Of Optic Disc and Cupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

ANN Glaucoma Detection using Cup-to-Disk Ratio and Neuroretinal Rim

Choudhary¹,

Tiwari²

2015

IJCA

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Improved automated detection of glaucoma by correlating fundus and SD‐OCT image analysis

Shehryar

Akram

Khalid

et al. 2020

Int J Imaging Syst Tech

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Glaucoma is a multifactorial ocular disease. Ophthalmologists mostly use fundus or optical coherence tomography (OCT) for diagnosis of glaucoma. In this study, a hybrid computer‐aided‐diagnosis (H‐CAD) system has been proposed that integrates both fundus and OCT imaging technologies for reliable diagnosis of glaucoma. Fundus module inspects the outer layer of eye's posterior part. It considers a variety of structural and textural features and makes a decision using support vector machine (SVM). In OCT module, the cup to disc ratio (CDR) has been computed by examining the internal layers of the retina. The cup contour has been extracted from inner‐limiting‐membrane (ILM) layer using a set of novel techniques for the calculation of cup diameter. Similarly, in the disc diameter calculation the retinal‐pigment‐epithelium (RPE) layer termination points have been identified by a number of innovative strategies to locate disc margin. Furthermore, a new criterion based on the mean value of RPE‐layer end points has been proposed for the determination of cup edges. A local‐dataset annotated by four ophthalmologists has been used for evaluation of proposed H‐CAD system. The evaluations and results have shown that the final result of H_CAD system is more trustable than its contemporary automated models.

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A study to identify limitations of existing automated systems to detect glaucoma at initial and curable stage

Khalil

Akram

Khalid

et al. 2021

Int J Imaging Syst Tech

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Glaucoma ocular disease is the second topmost reason for irreversible visual impairment around the world. This malady can be cured and permanent blindness can be prevented by timely diagnosis and treatment. This study is an attempt to analyse the current status of automated glaucoma diagnosis systems.Existing systems have been analysed on the base of ophthalmic imaging technology, capability to detect glaucoma at initial or latter stages, detection strategy and performance. Analysis revealed several research gaps mainly in automated glaucoma detection based on Fundus and Optical Coherence Tomography (OCT) ophthalmic imaging technologies. More accurate diagnosis of glaucoma at early and curable stages is possible by bridging the identified gaps.

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Detection of glaucoma using retinal fundus images

Cited by 52 publications

References 7 publications

ANN Glaucoma Detection using Cup-to-Disk Ratio and Neuroretinal Rim

ANN Glaucoma Detection using Cup-to-Disk Ratio and Neuroretinal Rim

Improved automated detection of glaucoma by correlating fundus and SD‐OCT image analysis

A study to identify limitations of existing automated systems to detect glaucoma at initial and curable stage

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