Survey on retinal vessel segmentation

The primary causes of vision loss are disorders of the retina, such as diabetic retinopathy (DR). The ability to segment blood vessels in retinal images is crucial for the early detection of eye disorders. The geometric characteristics of the ocular arteries can be used to distinguish between variou...

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Vydáno v:Multimedia tools and applications Ročník 84; číslo 11; s. 8361 - 8411
Hlavní autoři: Godishala, Arunakranthi, Raj, Veena, Lai, Daphne Teck Ching, Yassin, Hayati
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.03.2025
Springer Nature B.V
Témata:
Algorithms
Blood vessels
Clustering
Complexity
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Disorders
Machine learning
Multimedia Information Systems
Retina
Retinal images
Special Purpose and Application-Based Systems
Trends
Retinal vessel segmentation
Performance metrics
Survey
Segmentation algorithms
ISSN:1573-7721, 1380-7501, 1573-7721
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Shrnutí:The primary causes of vision loss are disorders of the retina, such as diabetic retinopathy (DR). The ability to segment blood vessels in retinal images is crucial for the early detection of eye disorders. The geometric characteristics of the ocular arteries can be used to distinguish between various ocular illnesses symptoms. However, segmenting the retina image is a difficult task because of the intricate structure of the blood vessels and their varying thicknesses. Many segmentation techniques have aided in the identification of retinal disorders. These segmentation approaches vary in complexity, from simple clustering algorithms to more complex machine-learning models. This research is essential to understand the contemporary segmentation methods and the challenges that still lay ahead. It also provides a useful overview of the current trends in retinal vascular segmentation, allowing researchers to identify potential areas for further development. By understanding the current trends, researchers can develop more effective segmentation techniques that are tailored to specific applications. Additionally, they can improve existing algorithms and methods, making them more accurate and efficient. By comparing different methods and algorithms on freely accessible databases, researchers can gain a greater comprehension of the strengths and weaknesses of each method. In order to provide a systematic review, articles from 2004 to 2023 are considered, with an emphasis on techniques based on segmenting and classifying retinal vessels utilizing fundus camera pictures.
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ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-024-19075-1