Fundus retinal image analyses for screening and diagnosing diabetic retinopathy, macular edema, and glaucoma disorders

Chalakkal, Renoh Johnson; Abdulla, Waleed H.; Hong, Sheng Chiong

doi:10.1016/b978-0-12-817440-1.00003-6

Cited by 37 publications

(29 citation statements)

References 126 publications

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“…8,10,13 Therefore, we extract a value that reflects the density of blood vessels in order to classify the image as healthy or NVD. For this purpose, we calculate the density of all blood vessels D vessel in the OD, which is considered to be the ratio between 10,11 the vessel area and the OD area, as indicated in Equation (6).…”

Section: Blood Vessel Densitymentioning

confidence: 99%

“…5 The neovascularization (NV) can rapidly progress early PDR to advanced PDR that cause bleeding, fibrous proliferation, vitreous hemorrhage and retinal detachment, hence posing a high risk for severe vision loss and blindness. 4,6 The detection of NV in early stage in order to stop or delay its progression leads to avoid vision loss and blindness.…”

Section: Introductionmentioning

confidence: 99%

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Mobile‐aided screening system for proliferative diabetic retinopathy

Boukadida

Elloumi

Akil

et al. 2021

Int J Imaging Syst Tech

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NeoVascularization (NV) occurs in the Proliferative Diabetic Retinopathy (PDR) stage, where the development progress of new vessels presents a high risk for severe vision loss and blindness. Therefore, early NV detection is primordial to preserve patient's vision. Several automated methods have been proposed to detect the NV on retinograph-captured fundus images. However, their employment is constrained by the reduced ophthalmologist-per-person ratio and the expensive equipment for image capturing. This paper presents a novel method for NV detection in smartphone-captured fundus images. The implementation of the method on a smartphone having an optical lens for fundus capturing leads to a Mobiles-Aided-Screening system of PDR (MAS-PDR). The challenge is to ensure accurate and robust detection even with moderate quality of fundus image, on reduced execution time.Within this objective, we identify the major criteria of neovascularized vessels which are tortuosity, width, bifurcation, and density. Our main contribution consists in proposing a sharp feature to reflect each criterion on reduced computational complexity processing. Therefore, the features are provided to a random forest classifier to deduce the PDR stage.A dataset raised from publicly databases is used on a 10-cross validation process where 98.69% accuracy, 97.73% sensitivity, and 99.12% specificity are achieved. To evaluate the robustness, the same experimentation is repeated after applying motion blur filters to the fundus image dataset, where 98.91% accuracy, 96.75% sensitivity, and 100% specificity are deduced. Moreover, NV screening is performed under 3 s when executed in smartphone devices demonstrating the appropriateness of our method to MAS-PDR.

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Section: Blood Vessel Densitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mobile‐aided screening system for proliferative diabetic retinopathy

Boukadida

Elloumi

Akil

et al. 2021

Int J Imaging Syst Tech

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“…Anatomy of Human Eye and Clinical Features of DR: Human eye structure comprises of various cellular structure tissues which help for maintaining and functioning of a vision [10]. Retina assumes a significant part in vision system, the properties and the functioning of the retina helps to find out the changes occurred due to the eye diseases and fundus retinal imaging is generally utilized imaging method for capturing the auxiliary features of retina [11]. The central area within the retina is known as macula consisting of fovea, rods & cones responsible for photographic vision [10].…”

Section: Diabetes Mellitusmentioning

confidence: 99%

“…This review paper mainly focuses on the recent 2-year research work done in the domain of Diabetic Retinopathy and Diabetic Macular Edema detection or grading Fig. 1 Healthy Retinal fundus image of human eye showing the key landmarks such as retinal veins, fovea, macula, and optic disc (OD) considered in detection of DR [11] Fig. 2 a Normal vision b vision affected due to DR c vision affected due to DME [11] Table 1 Classification or Stages of DR [11,13]…”

Section: Diabetes Mellitusmentioning

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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“…Retinal fundus imaging is the preferred technique for a first patient screening due to its simplicity, world-wide availability and low cost compared to OCT, which is more costly 10 . Prior studies on automated glaucoma detection from retinal fundus images can be divided into two different categories: 1) Those based on retinal clinical indicators and 2) those using image texture analysis techniques.…”

Section: Introductionmentioning

confidence: 99%

Glaucoma patient screening from online retinal fundus images via Artificial Intelligence

Abad

Coronado-Gutierrez

Lopez

et al. 2021

Preprint

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Objectives: To design and evaluate a novel automated glaucoma classifier from retinal fundus images. <BR> Methods: We designed a novel Artificial Intelligence (AI) automated tool to detect glaucoma from retinal fundus images. We then downloaded publicly available retinal fundus image datasets containing healthy patients and images with verified glaucoma labels. Two thirds of the images were used to train the classifier. The remaining third of the images was used to create several cross-validation evaluation sets with a realistic glaucoma prevalence, to evaluate the classifier's performance in a screening scenario. <BR> Results: 10,658 retinal fundus images from seven different sources were found and downloaded. They were randomly divided into 7,106 for training and 3,551 for validation. Glaucoma prevalence was 24%. Using the validation set, we created 50 random sets of 1,000 images with a 5% glaucoma prevalence. On these sets, the classifier reached a detection rate of 84.1% (CI 95=+-1.1%) and 95.8% (CI 95=+-0.2%) specificity. <BR> Conclusions: A novel glaucoma classifier from retinal fundus images showed promising results as screening tool on a large cohort of patients. A large clinical study is needed to confirm these results.

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Fundus retinal image analyses for screening and diagnosing diabetic retinopathy, macular edema, and glaucoma disorders

Cited by 37 publications

References 126 publications

Mobile‐aided screening system for proliferative diabetic retinopathy

Mobile‐aided screening system for proliferative diabetic retinopathy

A Survey on Automatic Diabetic Retinopathy Screening

Glaucoma patient screening from online retinal fundus images via Artificial Intelligence

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