Arteriolar-to-venular diameter ratio estimation: A pixel-parallel approach

C. Alonso-Montes; M. G. Penedo; D. L. Vilariño

doi:10.1109/CNNA.2008.4588655

Arteriolar-to-venular diameter ratio estimation: A pixel-parallel approach

C. Alonso-Montes^*
, M. G. Penedo
, D. L. Vilariño

^*Corresponding author for this work

University of A Coruna
University of Santiago de Compostela

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

2 Citations (Scopus)

Abstract

The study of blood vessel features plays an important role in order to characterise markers used in early disease diagnosis. The arteriolar-to-venular (AVR) diameter ratio is an earlier marker related with cardiovascular risk, hypertension and diabetes. The extraction of the retinal vessel tree is not only the main task related with those medical applications intended to compute the AVR ratio, but it also implies a high computation effort. From the image processing point of view, many strategies and algorithms have been developed to deal with the extraction of this retinal vessel tree but specially regarding on the accuracy, but the execution time is still an open problem. In this paper, a methodology to extract the retinal vessel tree, tested in a fine-grain pixel-parallel processor array, is integrated into an application for the estimation of the AVR ratio in angiographies.

Original language	English
Title of host publication	2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures
Pages	86-91
Number of pages	6
DOIs	https://doi.org/10.1109/CNNA.2008.4588655
Publication status	Published - 2008
Externally published	Yes
Event	2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures - Santiago de Compostela, Spain Duration: 14 Jul 2008 → 16 Jul 2008

Publication series

Name	Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications

Conference

Conference	2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures
Country/Territory	Spain
City	Santiago de Compostela
Period	14/07/08 → 16/07/08

UN SDGs

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

SDG 3 Good Health and Well-being

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10.1109/CNNA.2008.4588655

Cite this

Alonso-Montes, C., Penedo, M. G., & Vilariño, D. L. (2008). Arteriolar-to-venular diameter ratio estimation: A pixel-parallel approach. In 2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures (pp. 86-91). Article 4588655 (Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications). https://doi.org/10.1109/CNNA.2008.4588655

Alonso-Montes, C. ; Penedo, M. G. ; Vilariño, D. L. / Arteriolar-to-venular diameter ratio estimation : A pixel-parallel approach. 2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures. 2008. pp. 86-91 (Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications).

@inproceedings{fc6b9093edc14deaad4750dc9a8fe9fc,

title = "Arteriolar-to-venular diameter ratio estimation: A pixel-parallel approach",

abstract = "The study of blood vessel features plays an important role in order to characterise markers used in early disease diagnosis. The arteriolar-to-venular (AVR) diameter ratio is an earlier marker related with cardiovascular risk, hypertension and diabetes. The extraction of the retinal vessel tree is not only the main task related with those medical applications intended to compute the AVR ratio, but it also implies a high computation effort. From the image processing point of view, many strategies and algorithms have been developed to deal with the extraction of this retinal vessel tree but specially regarding on the accuracy, but the execution time is still an open problem. In this paper, a methodology to extract the retinal vessel tree, tested in a fine-grain pixel-parallel processor array, is integrated into an application for the estimation of the AVR ratio in angiographies.",

author = "C. Alonso-Montes and Penedo, \{M. G.\} and Vilari{\~n}o, \{D. L.\}",

year = "2008",

doi = "10.1109/CNNA.2008.4588655",

language = "English",

isbn = "9781424420902",

series = "Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications",

pages = "86--91",

booktitle = "2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures",

note = "2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures ; Conference date: 14-07-2008 Through 16-07-2008",

}

Alonso-Montes, C, Penedo, MG & Vilariño, DL 2008, Arteriolar-to-venular diameter ratio estimation: A pixel-parallel approach. in 2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures., 4588655, Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications, pp. 86-91, 2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures, Santiago de Compostela, Spain, 14/07/08. https://doi.org/10.1109/CNNA.2008.4588655

Arteriolar-to-venular diameter ratio estimation: A pixel-parallel approach. / Alonso-Montes, C.; Penedo, M. G.; Vilariño, D. L.
2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures. 2008. p. 86-91 4588655 (Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications).

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

TY - GEN

T1 - Arteriolar-to-venular diameter ratio estimation

T2 - 2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures

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AB - The study of blood vessel features plays an important role in order to characterise markers used in early disease diagnosis. The arteriolar-to-venular (AVR) diameter ratio is an earlier marker related with cardiovascular risk, hypertension and diabetes. The extraction of the retinal vessel tree is not only the main task related with those medical applications intended to compute the AVR ratio, but it also implies a high computation effort. From the image processing point of view, many strategies and algorithms have been developed to deal with the extraction of this retinal vessel tree but specially regarding on the accuracy, but the execution time is still an open problem. In this paper, a methodology to extract the retinal vessel tree, tested in a fine-grain pixel-parallel processor array, is integrated into an application for the estimation of the AVR ratio in angiographies.

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Alonso-Montes C, Penedo MG, Vilariño DL. Arteriolar-to-venular diameter ratio estimation: A pixel-parallel approach. In 2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures. 2008. p. 86-91. 4588655. (Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications). doi: 10.1109/CNNA.2008.4588655

Arteriolar-to-venular diameter ratio estimation: A pixel-parallel approach

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