Literature DB >> 23849699

Automated characterization of blood vessels as arteries and veins in retinal images.

Qazaleh Mirsharif1, Farshad Tajeripour, Hamidreza Pourreza.   

Abstract

In recent years researchers have found that alternations in arterial or venular tree of the retinal vasculature are associated with several public health problems such as diabetic retinopathy which is also the leading cause of blindness in the world. A prerequisite for automated assessment of subtle changes in arteries and veins, is to accurately separate those vessels from each other. This is a difficult task due to high similarity between arteries and veins in addition to variation of color and non-uniform illumination inter and intra retinal images. In this paper a novel structural and automated method is presented for artery/vein classification of blood vessels in retinal images. The proposed method consists of three main steps. In the first step, several image enhancement techniques are employed to improve the images. Then a specific feature extraction process is applied to separate major arteries from veins. Indeed, vessels are divided to smaller segments and feature extraction and vessel classification are applied to each small vessel segment instead of each vessel point. Finally, a post processing step is added to improve the results obtained from the previous step using structural characteristics of the retinal vascular network. In the last stage, vessel features at intersection and bifurcation points are processed for detection of arterial and venular sub trees. Ultimately vessel labels are revised by publishing the dominant label through each identified connected tree of arteries or veins. Evaluation of the proposed approach against two different datasets of retinal images including DRIVE database demonstrates the good performance and robustness of the method. The proposed method may be used for determination of arteriolar to venular diameter ratio in retinal images. Also the proposed method potentially allows for further investigation of labels of thinner arteries and veins which might be found by tracing them back to the major vessels.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Artery/vein classification; Blood vessels; Feature extraction; Retinal images; Retinex image enhancement; Structural features

Mesh:

Year:  2013        PMID: 23849699     DOI: 10.1016/j.compmedimag.2013.06.003

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  13 in total

1.  Mapping the 3D Connectivity of the Rat Inner Retinal Vascular Network Using OCT Angiography.

Authors:  Conor Leahy; Harsha Radhakrishnan; Geoffrey Weiner; Jeffrey L Goldberg; Vivek J Srinivasan
Journal:  Invest Ophthalmol Vis Sci       Date:  2015-09       Impact factor: 4.799

2.  Retinal Artery-Vein Classification via Topology Estimation.

Authors:  Rolando Estrada; Michael J Allingham; Priyatham S Mettu; Scott W Cousins; Carlo Tomasi; Sina Farsiu
Journal:  IEEE Trans Med Imaging       Date:  2015-06-10       Impact factor: 10.048

3.  Artery/vein classification of retinal vessels using classifiers fusion.

Authors:  Samra Irshad; Xiao-Xia Yin; Yanchun Zhang
Journal:  Health Inf Sci Syst       Date:  2019-11-08

4.  MF-AV-Net: an open-source deep learning network with multimodal fusion options for artery-vein segmentation in OCT angiography.

Authors:  Mansour Abtahi; David Le; Jennifer I Lim; Xincheng Yao
Journal:  Biomed Opt Express       Date:  2022-08-22       Impact factor: 3.562

Review 5.  Application of artificial intelligence in ophthalmology.

Authors:  Xue-Li Du; Wen-Bo Li; Bo-Jie Hu
Journal:  Int J Ophthalmol       Date:  2018-09-18       Impact factor: 1.779

Review 6.  A Comprehensive Study of Retinal Vessel Classification Methods in Fundus Images.

Authors:  Maliheh Miri; Zahra Amini; Hossein Rabbani; Raheleh Kafieh
Journal:  J Med Signals Sens       Date:  2017 Apr-Jun

7.  Automatic Artery/Vein Classification Using a Vessel-Constraint Network for Multicenter Fundus Images.

Authors:  Jingfei Hu; Hua Wang; Zhaohui Cao; Guang Wu; Jost B Jonas; Ya Xing Wang; Jicong Zhang
Journal:  Front Cell Dev Biol       Date:  2021-06-11

8.  Changes in retinal microvascular diameter in patients with diabetes.

Authors:  Andréa Vasconcellos Batista da Silva; Sonia Alves Gouvea; Aurélio Paulo Batista da Silva; Saulo Bortolon; Anabel Nunes Rodrigues; Glaucia Rodrigues Abreu; Fernando Luiz Herkenhoff
Journal:  Int J Gen Med       Date:  2015-08-25

9.  An easy method to differentiate retinal arteries from veins by spectral domain optical coherence tomography: retrospective, observational case series.

Authors:  Yanling Ouyang; Qing Shao; Dirk Scharf; Antonia M Joussen; Florian M Heussen
Journal:  BMC Ophthalmol       Date:  2014-05-15       Impact factor: 2.209

10.  Evidence of altered brain network centrality in patients with diabetic nephropathy and retinopathy: an fMRI study using a voxel-wise degree centrality approach.

Authors:  Yu Wang; Lei Jiang; Xiao-Yu Wang; Weizhe Chen; Yi Shao; Qin-Kai Chen; Jin-Lei Lv
Journal:  Ther Adv Endocrinol Metab       Date:  2019-07-27       Impact factor: 3.565

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