Diabetic Retinal Fundus Images: Preprocessing and Feature Extraction For Early Detection of Diabetic Retinopathy

Sisodia, Dilip Singh; Nair, Shruti; Khobragade, Pooja

doi:10.13005/bpj/1148

Cited by 92 publications

(42 citation statements)

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“…Gabor filter is used to enhance blood vessels [5]. [7] but DR image has less than this area because of contraction. The Morphological operation followed by thresholding is applied to detect [2,12] blood vessels.…”

Section: Methodsmentioning

confidence: 99%

“…To detect EXs, we need to detect a portion of the optic disc in retinal fundus image because Optic disc and EXs both have same intensity [7][8]. Optic disc mark was created using brightest pixels in an image and its coordinates are a center of the marks [2,10].…”

Section: ) Optic Disc Detectionmentioning

confidence: 99%

“…Figure 1 shows both of the symptoms. EXs are nothing but some fluid, oily by nature [7] and appearing as yellowish regions [6]. This all symptoms are detectable using the fundus image of the retina.…”

Section: Introductionmentioning

confidence: 97%

“…Sharath et al [6] detect optic disc, blood vessels and white/yellow lesions using different image processing methods and for detection of red lesions, they removed all detected features like an optic disc, blood vessels and white/yellow lesion from the pre-processed. Dilip et al [7] applied pre-processing, extract features like blood vessels area, optic disc area, EXs area, HMs area, MAs area, Shannon entropy for classification and quantitative analysis. MAs and EXs both are extracted using Selective Binary and Gaussian Filtering regularized Level Set (SBGFRLS) on pre-processed fundus images and matched filter with the first-order derivative of Gaussian (MF-FDOG) is used to detect blood vessels [8].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Diabetic Retinopathy: A Survey

Panchal¹

2018

IJRASET

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Diabetic Retinopathy (DR) is a severe condition for the diabetic patient in their eye ailment. This issue can be detected from the retinal fundus image. Microaneurysms (MAs), Hemorrhages (HMs) and Exudates (EXs) are three features of nonproliferative diabetic retinopathy. Microaneurysms are the earlier stage of DR. There are many methods or techniques proposed till date for detection of the disease in the eye from the retinal fundus image. This paper aims to survey some techniques to detect blood vessels, optic disc exudates (EXs), microaneurysms (MAs) and Hemorrhages (HMs) and then classified using different classifier.

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Section: Methodsmentioning

confidence: 99%

Section: ) Optic Disc Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Detection of Diabetic Retinopathy: A Survey

Panchal¹

2018

IJRASET

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“…Fourteen features are also extracted from pre-processed images for quantitative analysis. The experiments are performed using Kaggle Diabetic Retinopathy dataset, and the results are evaluated by considering the mean value and standard deviation for extracted features [4]. Digital retinal imaging is a stimulating screening method for which effective, robust and cost effective methods are still to be developed.…”

Section: Introductionmentioning

confidence: 99%

Fundus Blood Vessels Extraction Using Digital Image Processing Techniques

Gaikwad¹,

Manza²

2017

IJERT

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Abstract-Diabetic retinopathy is major eye complications produced by the diabetes mellitus, which causes other problems such as stroke, cardiovascular disease, diabetic nephropathy and diabetic neuropathy. Diabetic retinopathy consequences in visual conflicts and can lead to permanent blindness. Therefore, a regular diabetic retinopathy screening is essential for early treatment, along with an effective risk factor management to overcome the diabetic complications and reduce morbidity and mortality influence. In this paper we perform the digital image processing techniques to extract the retinal blood vessels. This algorithm is tested on STARE database.

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Diabetic Retinopathy Detection and Classification Using Deep Reinforcement Learning

Manjunatha,

Sathish

2024

Deep Reinforcement Learning and Its Industrial Use Cases

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Diabetic Retinal Fundus Images: Preprocessing and Feature Extraction For Early Detection of Diabetic Retinopathy

Cited by 92 publications

References 0 publications

Detection of Diabetic Retinopathy: A Survey

Detection of Diabetic Retinopathy: A Survey

Fundus Blood Vessels Extraction Using Digital Image Processing Techniques

Diabetic Retinopathy Detection and Classification Using Deep Reinforcement Learning

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