Optic disc localization algorithm based on improved corner detection

Gui, Bin; Shuai, Renjun; Chen, Ping

doi:10.1016/j.procs.2018.04.169

Cited by 26 publications

(12 citation statements)

References 7 publications

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“…With small benefits, it is impossible to generate varied solid energy amongst grasshoppers with wide separations amongst others. This issue could be solved by utilizing grasshoppers' isolation and mapping [1,4]. Therefore, it can be illustrated as follows…”

Section: Edge Detection Using Goafed Modelmentioning

confidence: 99%

“…Recent advancements permit fundus images to be done by using smartphones with a traditional handheld indirect ophthalmoscopy lens. Several medical experts, namely orthoptists, ophthalmologists, and optometrists, check for conditions or healthcare problems through retinal fundus images [1]. Additionally, observing the retina change in fundus images is a means to check a person undergoing anti-malarial therapy [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

Alkhaldi¹,

Halawani²

2023

Computers, Materials &Amp; Continua

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Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis. The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease. This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification (GOFED-RBVSC) model. The proposed GOFED-RBVSC model initially employs contrast enhancement process. Besides, GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions. The ORB (Oriented FAST and Rotated BRIEF) feature extractor is exploited to generate feature vectors. Finally, Improved Conditional Variational Auto Encoder (ICAVE) is utilized for retinal image classification, shows the novelty of the work. The performance validation of the GOFED-RBVSC model is tested using benchmark dataset, and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.

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Section: Edge Detection Using Goafed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

Alkhaldi¹,

Halawani²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…The WTA network then moves robotically to the most prominent place to show the facility to successfully shrink [25] the alter times and hunt errors to choose helpful things, regions, and prepared groups, and the capability to elastically choose visual things whether positioned in the fovea field or on the diagram fringe. The finding of the optic disc [26] location and function of the macula also discusses corner detection, the optic disc location convoluted, the corner detection complex and the computational tool for automatic [27] glaucoma detection and thresholding measure cup and segmentation ratio. The dense dilated element removal block into an encoder-decoder [28] configuration to remove accrued attributes at different scales and an optic disc and optic cup segmentation technique using the Graph Convolution Network (GCN) [29] method.…”

Section: Related Workmentioning

confidence: 99%

A Dense sub-graph based approach for Automatic detection of Optic Disc

Jana¹,

Banerjee²,

Thakur³

et al. 2022

Preprint

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Glaucoma is a situation of greater than normal intraocular pressure surrounded by the eyes. This explains the harm to the optic nerves as the limb passes in sequence to the brain. The graph base is used in this paper for automatic localization of the optic disc. This paper proposed and modified a new dense sub - graph approach to locate the affected optic disc by using DRIVE, STAIR, and Drishti -GS1 databases. This model has proved to be more accurate when compared with other standard models representing the concert’s progress. This method provides a new idea for the location of the optic disc with a system accuracy of 93%.

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“…Bin Gui et al [29] proposed OD detection method based on improved corner detection approach. Simplified fast corner detection and improved Harris corner detection algorithms followed by preprocessing of image.…”

Section: Optic Disc (Od) Segmentationmentioning

confidence: 99%

A Survey on Automatic Diabetic Retinopathy Screening

Nage

Shitole

2021

SN COMPUT. SCI.

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Diabetes is a chronic disease caused due to the increase in the sugar level in the blood. Diabetes mainly affects heart, blood vessels, kidneys, eyes and nerves. There are mellitus type 1 and 2 diabetes, in which insulin is either not taken by the body or not created by the body. As per the statistics provided by WHO, about 422 million people worldwide are fighting with diabetes. Primary source of vision loss in the patients suffering from diabetes is diabetic retinopathy where the individual suffers from the damage and growth of abnormal blood veins in the retina. The disease is observed by ophthalmologist through identifying the presence of abnormalities starting from microaneurysms in the non-proliferative stage of DR and if these lesions' presence is ignored and not detected then it leads to the neovascularization in the proliferative stage which leads to unavoidable vision loss. DR can be cured if detected at the beginning stage. Manual method takes lots of time for detection of DR hence it is important to develop computer-based diagnostic system for DR detection using artificial intelligence (AI) and advance image processing to help ophthalmologists for spotting early symptoms of DR in less time. This paper provides a descriptive study about recent trends and technologies used for automatic spotting and grading of DR.

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Optic disc localization algorithm based on improved corner detection

Cited by 26 publications

References 7 publications

Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

A Dense sub-graph based approach for Automatic detection of Optic Disc

A Survey on Automatic Diabetic Retinopathy Screening

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