Segmentation of retinal blood vessels using artificial neural networks for early detection of diabetic retinopathy

Mann, Kulwinder Singh; Kaur, Sukhpreet

doi:10.1063/1.4981966

Cited by 13 publications

(7 citation statements)

References 27 publications

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“…Kulwinder et al presented a novel method based on neural networks to segmentation of blood vessel in color fundus images. The method has been tested on several wellknown databases and the results were compared with Human Observer that were very close to each other (61).…”

Section: Segmentation Of Vascular and Neovascularizationmentioning

confidence: 86%

The computer based method to diabetic retinopathy assessment in retinal images: a review

et al. 2019

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Diabetic retinopathy accounts for a considerable amount of the blindness especially among the patients who are between 20 to 60 years. The early detection of this disease plays an important role in preventing vision damages and appropriate follow-up care of diabetic eye. Manual investigation of color fundus images to check morphological changes in dark and bright lesions is tedious work and very time-consuming that can be made easily with the help of computer-aided diagnosis system. Many techniques were proposed for early detection of the abnormalities in the retinal images help the ophthalmologists recognize the retinopathy earlier. This paper presents a review of various automated algorithms that have been used for the detection of diabetic retinopathy.

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Section: Segmentation Of Vascular and Neovascularizationmentioning

confidence: 86%

The computer based method to diabetic retinopathy assessment in retinal images: a review

et al. 2019

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“…• Proliferative-final stages [21][22][23][24][25][26][27][28][29][30]. In the initial stage of DR, which is mild or non-proliferate, there would be inflation in the structure of a tiny bubble in some of the blood capillaries or veins present around the retina [31][32][33][34][35].…”

Section: E Figure 1 Human Eye Structurementioning

confidence: 99%

A Study on Diabetic Retinopathy Detection Using Image Processing

Manimegalai¹,

Jayalakshmi²

2021

J Comput Sci Intell Technol

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This study provides an overview of the Diabetic Retinopathy (DR) detection in human eyes utilizing various preprocessing and segmentation approaches. There are various ways for segmenting the blood veins in the retina. It is possible to determine whether or not the eyes have DR if the retinal nerve fibers have been segmented. This detection is determined by the location of the Retinal nerve fiber layer (RNFL). DR affects nerve fibers with a small total area. If the nerve fiber is large, the eyes are not damaged by DR and thus it is healthy. It is a prominent truth that diabetes has a significant impact on human health and damage every organ, including the eyes. Because the optical nerve is linked to the brain, this DR will influence vision loss in humans. Images of the retinal fundus are frequently utilized to detect and analyze diseases on infected images. Machine learning algorithms struggle to analyze raw retinal fundus images. As a result, a survey is being conducted in this area. In this way, the most current works by diverse authors from across the world are presented in this context. DR is one of the severe infections that may occur in the eyes. According to the WHO – United Nations study, this disease is the second most common among humans. As a result, eye care must be given top priority. This disease is caused by a decrease in the nerve layer of the retina. If the RNFL shrinks, the optic nerve that connects to the brain gets damaged, resulting in vision loss. This document just provides a basic overview of diabetic retinopathy.

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“…Numerous researches have employed machine learning of different types and methods to segment blood vessels in retinal fundus images. Machine learning methods such as artificial neural network (ANN) [3,8], support vector machine (SVM) [14], and recently convolutional neural network (CNN) [7,19] has shown to be a reliable method to provide an accurate segmentation towards the blood vessels in retinal fundus images. Nevertheless, employing machine learning requires massive amount of training datasets to allow the algorithm to learn different pathological features in the retinal fundus images.…”

Section: Machine Learningmentioning

confidence: 99%

Retinal Fundus Image Blood Vessels Segmentation via Object-Oriented Metadata Structures

Fadzil

Ahmad

Khalid³

et al. 2018

IJET

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Retinal fundus image is a crucial tool for ophthalmologists to diagnose eye-related diseases. These images provide visual information of the interior layer of the retina structures such as optic disc, optic cup, blood vessels and macula that can assist ophthalmologist in determining the health of an eye. Segmentation of blood vessels in fundus images is one of the most fundamental phase in detecting diseases such as diabetic retinopathy. However, the ambiguity of the retina structures in the retinal fundus images presents a challenge for researcher to segment the blood vessels. Extensive pre-processing and training of the images is necessary for precise segmentation, which is very intricate and laborious. This paper proposes the implementation of object-oriented-based metadata (OOM) structures of each pixel in the retinal fundus images. These structures comprise of additional metadata towards the conventional red, green, and blue data for each pixel within the images. The segmentation of the blood vessels in the retinal fundus images are performed by considering these additional metadata that enunciates the location, color spaces, and neighboring pixels of each individual pixel. From the results, it is shown that accurate segmentation of retinal fundus blood vessels can be achieved by purely employing straightforward thresholding method via the OOM structures without extensive pre-processing image processing technique or data training.

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Segmentation of retinal blood vessels using artificial neural networks for early detection of diabetic retinopathy

Cited by 13 publications

References 27 publications

The computer based method to diabetic retinopathy assessment in retinal images: a review

The computer based method to diabetic retinopathy assessment in retinal images: a review

A Study on Diabetic Retinopathy Detection Using Image Processing

Retinal Fundus Image Blood Vessels Segmentation via Object-Oriented Metadata Structures

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