Analysis of Segmentation Algorithms in Colour Fundus and OCT Images for Glaucoma Detection

Nithya, R.; Venkateswaran, N.

doi:10.17485/ijst/2015/v8i24/80151

Cited by 16 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The cup diameter has been calculated at a distance of 150 μm from the optic disc edges on ILM layer. In another research, both the fundus and OCT images have been involved in the diagnosis of glaucoma 16 . In fundus images, CDR was calculated using different segmentations algorithms for both cup and disc segmentation.…”

Section: Related Workmentioning

confidence: 99%

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

Shehryar

Akram

Khalid

et al. 2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Shehryar

Akram

Khalid

et al. 2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…is will contain different features like hybrid features, optic disk, and classification to classify glaucoma [31].…”

Section: Databasesmentioning

confidence: 99%

A Comprehensive Convolutional Neural Network Survey to Detect Glaucoma Disease

Zafar

Aamir

Nawi

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

Due to recent advances in computer technology and the accessibility of large datasets, deep learning has become at the forefront of artificial intelligence and on various tasks, especially those related to image classification and modelling, its performance is often equal to or even better than human appreciation. Ophthalmology has always been in an ideal position to employ one of CNN’s most popular deep learning algorithms to evaluate vast volumes of data from these tests since it is one of the health practices that focuses extensively on imaging. Glaucoma is one among the situations in which CNN can benefit from the enormous quantity of data collected by tests that assess the anatomy as well as function of the optic nerve and macula. We discussed the recommended use of CNN algorithm for specific glaucoma scenarios, such as fundus photography screening and diagnosis and detection of the course of glaucoma through OCT imaging modality. The purpose of this article is not only to critically examine and discuss the latest applications of CNN models in glaucoma but also to focus on the challenges associated with developing such models for screening, diagnosis, and progress detection. After a brief overview of the clinical practices and their comparison with conventional clinical methods, we discussed training and validation of CNN algorithm and how it was developed and why it is particularly suitable for glaucoma. The following features make our contribution worthwhile and unique among the reviews of similar kind: (i) our review classifies the existing literature with respect to detection of the glaucoma disease using conventional and nonconventional approaches; (ii) it covers a very different outlook of the glaucoma disease by providing in-depth discussions of the existing works at different granularity levels, that is, from primary to mediatory to the severe level; (iii) this state-of-the-art review covers each article in the following dimensions: the computer-based approach to tomographic model; analysis of different datasets; and summarizing the literature review in a disciplined way by mentioning the research gap concluded with discussion on future work.

show abstract

“…Similarly, around the same time Ganesh Babu et al, 72 have included both fundus and the OCT images in the computerized glaucoma detection. Two basic highlights, CDR and ISNT proportion have been utilized for the fundus picture characterization utilizing back-probation-neu-ral_network (BPN) and SVM in the OCT images arrangement.…”

Section: Oct Imaging Technologymentioning

confidence: 99%

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

View full text Add to dashboard Cite

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.

show abstract

Analysis of Segmentation Algorithms in Colour Fundus and OCT Images for Glaucoma Detection

Cited by 16 publications

References 7 publications

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

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

A Comprehensive Convolutional Neural Network Survey to Detect Glaucoma Disease

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

Contact Info

Product

Resources

About