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
AU - Alonso-Montes, C.
AU - Penedo, M. G.
AU - Vilariño, D. L.
PY - 2008
Y1 - 2008
N2 - 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.
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.
UR - https://www.scopus.com/pages/publications/51949114085
U2 - 10.1109/CNNA.2008.4588655
DO - 10.1109/CNNA.2008.4588655
M3 - Conference contribution
AN - SCOPUS:51949114085
SN - 9781424420902
T3 - Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications
SP - 86
EP - 91
BT - 2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures
Y2 - 14 July 2008 through 16 July 2008
ER -
