Similarity evaluation of retinal vascular network based on tree edit distance

Wenjian Li; Guannan Wu; Huiqi Li

doi:10.1109/ICIEA58696.2023.10241828

Similarity evaluation of retinal vascular network based on tree edit distance

Wenjian Li
, Guannan Wu
, Huiqi Li^*

^*Corresponding author for this work

School of Information and Electronics

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The similarity of retinal vascular networks has important applications in the early diagnosis, prediction, and treatment of retinal diseases, such as diabetic retinopathy and age-related macular degeneration (AMD). Unlike direct comparison of vascular parameters or vessel segmentation pixels, a method for calculating the distance between vascular networks based on the tree edit distance is proposed in this paper to represent the similarity between vascular networks. We model the retinal vessel as a tree structure and calculate the tree edit distance between vessel trees which is sensitive to geometrical and topological changes of vessels. Since the retinal vascular network is composed of multiple vessel trees, we weight the distance between vessels and sum them up to represent the distance between networks. The smaller the distance, the higher the network similarity. Finally, experimental results verify that our proposed network distance algorithm can evaluate the similarity between networks objectively.

Original language	English
Title of host publication	Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
Editors	Wenjian Cai, Guilin Yang, Jun Qiu, Tingting Gao, Lijun Jiang, Tianjiang Zheng, Xinli Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	722-727
Number of pages	6
ISBN (Electronic)	9798350312201
DOIs	https://doi.org/10.1109/ICIEA58696.2023.10241828
Publication status	Published - 2023
Event	18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 - Ningbo, China Duration: 18 Aug 2023 → 22 Aug 2023

Publication series

Name	Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

Conference

Conference	18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
Country/Territory	China
City	Ningbo
Period	18/08/23 → 22/08/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

retinal vascular network similarity
retinal vessel tree
tree edit distance

Access to Document

10.1109/ICIEA58696.2023.10241828

Cite this

Li, W., Wu, G., & Li, H. (2023). Similarity evaluation of retinal vascular network based on tree edit distance. In W. Cai, G. Yang, J. Qiu, T. Gao, L. Jiang, T. Zheng, & X. Wang (Eds.), Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 (pp. 722-727). (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIEA58696.2023.10241828

Li, Wenjian ; Wu, Guannan ; Li, Huiqi. / Similarity evaluation of retinal vascular network based on tree edit distance. Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. editor / Wenjian Cai ; Guilin Yang ; Jun Qiu ; Tingting Gao ; Lijun Jiang ; Tianjiang Zheng ; Xinli Wang. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 722-727 (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023).

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title = "Similarity evaluation of retinal vascular network based on tree edit distance",

abstract = "The similarity of retinal vascular networks has important applications in the early diagnosis, prediction, and treatment of retinal diseases, such as diabetic retinopathy and age-related macular degeneration (AMD). Unlike direct comparison of vascular parameters or vessel segmentation pixels, a method for calculating the distance between vascular networks based on the tree edit distance is proposed in this paper to represent the similarity between vascular networks. We model the retinal vessel as a tree structure and calculate the tree edit distance between vessel trees which is sensitive to geometrical and topological changes of vessels. Since the retinal vascular network is composed of multiple vessel trees, we weight the distance between vessels and sum them up to represent the distance between networks. The smaller the distance, the higher the network similarity. Finally, experimental results verify that our proposed network distance algorithm can evaluate the similarity between networks objectively.",

keywords = "retinal vascular network similarity, retinal vessel tree, tree edit distance",

author = "Wenjian Li and Guannan Wu and Huiqi Li",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 ; Conference date: 18-08-2023 Through 22-08-2023",

year = "2023",

doi = "10.1109/ICIEA58696.2023.10241828",

language = "English",

series = "Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "722--727",

editor = "Wenjian Cai and Guilin Yang and Jun Qiu and Tingting Gao and Lijun Jiang and Tianjiang Zheng and Xinli Wang",

booktitle = "Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023",

address = "United States",

}

Li, W, Wu, G & Li, H 2023, Similarity evaluation of retinal vascular network based on tree edit distance. in W Cai, G Yang, J Qiu, T Gao, L Jiang, T Zheng & X Wang (eds), Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023, Institute of Electrical and Electronics Engineers Inc., pp. 722-727, 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023, Ningbo, China, 18/08/23. https://doi.org/10.1109/ICIEA58696.2023.10241828

Similarity evaluation of retinal vascular network based on tree edit distance. / Li, Wenjian; Wu, Guannan; Li, Huiqi.
Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. ed. / Wenjian Cai; Guilin Yang; Jun Qiu; Tingting Gao; Lijun Jiang; Tianjiang Zheng; Xinli Wang. Institute of Electrical and Electronics Engineers Inc., 2023. p. 722-727 (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Similarity evaluation of retinal vascular network based on tree edit distance

AU - Li, Wenjian

AU - Wu, Guannan

AU - Li, Huiqi

PY - 2023

Y1 - 2023

N2 - The similarity of retinal vascular networks has important applications in the early diagnosis, prediction, and treatment of retinal diseases, such as diabetic retinopathy and age-related macular degeneration (AMD). Unlike direct comparison of vascular parameters or vessel segmentation pixels, a method for calculating the distance between vascular networks based on the tree edit distance is proposed in this paper to represent the similarity between vascular networks. We model the retinal vessel as a tree structure and calculate the tree edit distance between vessel trees which is sensitive to geometrical and topological changes of vessels. Since the retinal vascular network is composed of multiple vessel trees, we weight the distance between vessels and sum them up to represent the distance between networks. The smaller the distance, the higher the network similarity. Finally, experimental results verify that our proposed network distance algorithm can evaluate the similarity between networks objectively.

AB - The similarity of retinal vascular networks has important applications in the early diagnosis, prediction, and treatment of retinal diseases, such as diabetic retinopathy and age-related macular degeneration (AMD). Unlike direct comparison of vascular parameters or vessel segmentation pixels, a method for calculating the distance between vascular networks based on the tree edit distance is proposed in this paper to represent the similarity between vascular networks. We model the retinal vessel as a tree structure and calculate the tree edit distance between vessel trees which is sensitive to geometrical and topological changes of vessels. Since the retinal vascular network is composed of multiple vessel trees, we weight the distance between vessels and sum them up to represent the distance between networks. The smaller the distance, the higher the network similarity. Finally, experimental results verify that our proposed network distance algorithm can evaluate the similarity between networks objectively.

KW - retinal vascular network similarity

KW - retinal vessel tree

KW - tree edit distance

UR - https://www.scopus.com/pages/publications/85173608084

U2 - 10.1109/ICIEA58696.2023.10241828

DO - 10.1109/ICIEA58696.2023.10241828

M3 - Conference contribution

AN - SCOPUS:85173608084

T3 - Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

SP - 722

EP - 727

BT - Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

A2 - Cai, Wenjian

A2 - Yang, Guilin

A2 - Qiu, Jun

A2 - Gao, Tingting

A2 - Jiang, Lijun

A2 - Zheng, Tianjiang

A2 - Wang, Xinli

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

Y2 - 18 August 2023 through 22 August 2023

ER -

Li W, Wu G, Li H. Similarity evaluation of retinal vascular network based on tree edit distance. In Cai W, Yang G, Qiu J, Gao T, Jiang L, Zheng T, Wang X, editors, Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 722-727. (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023). doi: 10.1109/ICIEA58696.2023.10241828

Similarity evaluation of retinal vascular network based on tree edit distance

Abstract

Publication series

Conference

UN SDGs

Keywords

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