Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey

Mary, M. Caroline Viola Stella; Rajsingh, Elijah Blessing; Naik, Ganesh R.

doi:10.1109/access.2016.2596761

Cited by 85 publications

(27 citation statements)

References 118 publications

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“…It was reported that by 2030, people with diabetes will be 82 million in developing countries and 48 million in developed countries. Similarly, if untreated, DR, which exists in diabetic patients, could lead to blindness in elderly people all over the world, whereas glaucoma, a chronic disease that affects the optic nerve, is the second major cause of blindness leading to loss in the visual field that eventually leads to permanent blindness [2,3]. There are serious risk factors associated with glaucoma, such as raised intraocular pressure that destroys the blood vessels and optic nerve.…”

Section: Introductionmentioning

confidence: 99%

“…The fluid leaves the chamber at the open angle where the cornea and iris meet. When the fluid reaches the angle, it flows through a spongy meshwork which acts as a drain and leaves the eye-opening [3]. A normal fundus image in comparison to glaucoma is shown in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

“…In a recent study conducted by the National Eye Institute (NEI) [7], the statistical prediction for the number of people who will be affected by glaucoma is staggering in comparison to previous years. Hence, identifying these eye diseases at an early stage is essential; however, a large number of patients remains uninformed of the illness until it has progressed to a much more advanced stage [3]. Considering a significant rise in patients suffering from glaucoma and DR, it is the need of the hour that an efficient automated eye disease detection system is developed in both software and hardware that are able to detect glaucoma and DR at early stages so that early treatment could prevent permanent blindness.…”

Section: Introductionmentioning

confidence: 99%

“…Retinal fundus images show a difference between a normal retina (a) and diabetic retinopathy (b) where blood vessels are swollen and leak[3].…”

mentioning

confidence: 99%

“…Raw fundus images: (a) normal retinal image; (b) glaucoma with poor vascular supply and a cupped optic nerve[3].…”

mentioning

confidence: 99%

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Accelerating Retinal Fundus Image Classification Using Artificial Neural Networks (ANNs) and Reconfigurable Hardware (FPGA)

Ghani

See

Sudhakaran³

et al. 2019

Electronics

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Diabetic retinopathy (DR) and glaucoma are common eye diseases that affect a blood vessel in the retina and are two of the leading causes of vision loss around the world. Glaucoma is a common eye condition where the optic nerve that connects the eye to the brain becomes damaged, whereas DR is a complication of diabetes caused by high blood sugar levels damaging the back of the eye. In order to produce an accurate and early diagnosis, an extremely high number of retinal images needs to be processed. Given the required computational complexity of image processing algorithms and the need for high-performance architectures, this paper proposes and demonstrates the use of fully parallel field programmable gate arrays (FPGAs) to overcome the burden of real-time computing in conventional software architectures. The experimental results achieved through software implementation were validated on an FPGA device. The results showed a remarkable improvement in terms of computational speed and power consumption. This paper presents various preprocessing methods to analyse fundus images, which can serve as a diagnostic tool for detection of glaucoma and diabetic retinopathy. In the proposed adaptive thresholding-based preprocessing method, features were selected by calculating the area of the segmented optic disk, which was further classified using a feedforward neural network (NN). The analysis was carried out using feature extraction through existing methodologies such as adaptive thresholding, histogram and wavelet transform. Results obtained through these methods were quantified to obtain optimum performance in terms of classification accuracy. The proposed hardware implementation outperforms existing methods and offers a significant improvement in terms of computational speed and power consumption.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Retinal fundus images show a difference between a normal retina (a) and diabetic retinopathy (b) where blood vessels are swollen and leak[3].…”

mentioning

confidence: 99%

“…Raw fundus images: (a) normal retinal image; (b) glaucoma with poor vascular supply and a cupped optic nerve[3].…”

mentioning

confidence: 99%

See 3 more Smart Citations

Accelerating Retinal Fundus Image Classification Using Artificial Neural Networks (ANNs) and Reconfigurable Hardware (FPGA)

Ghani

See

Sudhakaran³

et al. 2019

Electronics

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show abstract

Robust segmentation of optic disc and optic cup using statistical Kurtosis test

Biswal¹,

Eadara²,

Bebarta³

et al. 2019

Int J Imaging Syst Tech

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Glaucoma is a chronic and irreversible eye disease that leads to the loss of vision. Evaluation of the Cup to Disc Ratio (CDR) plays a prominent role in the early detection of glaucoma. This paper presents a novel algorithm to compute the CDR for the fundus images. In order to calculate the CDR, the vertical diameter of Optic Disc (OD) and the vertical diameter of the Optic Cup (OC) are calculated from the segmented OD and segmented OC, respectively. This study presents OD and OC segmentation algorithms based on a new statistics‐based kurtosis test. A unique OC boundary segmentation method is presented, which is the combination of partial cup extraction and the cup boundary under the Central Retinal Blood vessels (CRBV). A novel preprocessing technique is introduced to extract the CRBV from the automated Region of Interest (ROI). The experimental results confirm that the proposed algorithm outperforms the state‐of‐art OD and OC segmentation on the three publicly available datasets: ORIGA, DIARETDB0, and DIARETDB1. The proposed OD and OC segmentation achieve accuracies of up to 0.99 and 0.97, respectively. In addition, the proposed model achieves excellent CDR evaluation with an average error percentage reduced to up to 9.6496 for the considered datasets.

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TWEEC: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

Abdel-Hamid

2021

Int J Imaging Syst Tech

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A novel two-branched deep convolutional (TWEEC) network is developed for computer-aided glaucoma diagnosis. The TWEEC network is designed to simultaneously extract anatomical information related to the optic disc and surrounding blood vessels which are the retinal structures most affected by glaucoma progression. The spatial retinal images and wavelet approximation subbands are compared as inputs to the proposed network. TWEEC's performance is compared to three implemented convolutional networks, one of which employs transfer learning. Experiments showed that the introduced TWEEC network achieved accuracies of 98.78% and 96.34% for the spatial and wavelet inputs, respectively, by that outperforming the other three deep networks by 8-15%. This work paves the way for the development of efficient deep learning based computer-aided glaucoma diagnosis tools. Moreover, the present study illustrates that considering specific wavelet subbands for the training of convolutional networks can result in reliable performance with the advantage of reduced overall network training time.

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Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey

Cited by 85 publications

References 118 publications

Accelerating Retinal Fundus Image Classification Using Artificial Neural Networks (ANNs) and Reconfigurable Hardware (FPGA)

Accelerating Retinal Fundus Image Classification Using Artificial Neural Networks (ANNs) and Reconfigurable Hardware (FPGA)

Robust segmentation of optic disc and optic cup using statistical Kurtosis test

TWEEC: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

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