An Artery/Vein Classification Method Based on Color and Vascular Structure Information

Fu, Dongmei; Liu, Yang; Ma, Haosen

doi:10.1007/978-3-319-71589-6_42

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…SVM Akbar et al (2018); Vijayakumar et al (2016); Hu et al (2015); Vapnik et al (1995) has been reported with width, orientation, Gabor, intensity and morphological features, including feature selection using random forest (RF), and graph-theoretic frameworks with vessel tree network topology Estrada et al (2015a). kNN was tried with multi-scale, color, texture, and adaptive LBP features Xu et al (2017); Zhu et al (2017); Joshi et al (2014); Zou et al (2017); Yin et al (2020), and k-means clustering with color features to classify artery/vein in specic fundus image quadrantsFu et al (2017) to compute AVRRelan et al (2013Relan et al ( , 2019. Another classier tried was JointBoost with vessel network topologyYan et al (2017).…”

mentioning

confidence: 99%

A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification

Mookiah

Hogg

MacGillivray

et al. 2021

Medical Image Analysis

118

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confidence: 99%

A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification

Mookiah

Hogg

MacGillivray

et al. 2021

Medical Image Analysis

118

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“…Additionally, k-means clustering with color features has been applied to classify arteries and veins within specific sections of fundus images. To compute the Arteriole-to-Venule Ratio (AVR), Fu, et al, [105] and Relan, et al, [106,107] have used these methods.…”

Section: Bayes Classifiers and Graph Cut Techniques Have Been Used Bymentioning

confidence: 99%

A Comprehensive Overview of Deep Learning Techniques for Retinal Vessel Segmentation

2023

AJBSR

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The application of new techniques in eye imaging has revolutionized the process by increasing accuracy and efficiency levels considerably. An essential aspect of analyzing ocular images is the correct segmentation of retinal vessels. Deep learning algorithms represent an effective solution giving high accuracy rates for this task. In this article readers can find an extensive overview of several novel approaches built specifically to address this issue-among them being a groundbreaking deep learning architecture that applies multi-scale/multi-level Convolutional Neural Networks (CNNs) to obtain a comprehensive hierarchical representation of retinal vessels. Furthermore, advanced approaches are also discussed on detecting retinal vessels from Fluorescein Angiography (FA) images -with notable examples being one that combines cross modality transfer with human in the loop learning helping reduce manual labelling efforts significantly while enhancing both accuracy and performance metrics simultaneously. These stateof-the-art methods unravel critical insights into how deep learning methods could improve retinal vessel segmentation. The impact they could have on diagnosing and treating various eye related illnesses is also noteworthy.

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An Artery/Vein Classification Method Based on Color and Vascular Structure Information

Cited by 2 publications

References 16 publications

A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification

A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification

A Comprehensive Overview of Deep Learning Techniques for Retinal Vessel Segmentation

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