DocumentCode :
2560284
Title :
CNN-based automatic retinal vascular tree extraction
Author :
Alonso-Montes, C. ; Vilariño, D.L. ; Penedo, M.G.
Author_Institution :
Dept. of Comput. Sci., Coruna Univ., Spain
fYear :
2005
fDate :
28-30 May 2005
Firstpage :
61
Lastpage :
64
Abstract :
The retinal vascular tree has become an important task of medical image processing in different scientific areas. Many studies have focused on developing an automatic algorithm, however little attention has been paid to improve computational processing time of these algorithms. In this paper, an automatic methodology for retinal vascular tree extraction using cellular neural networks (CNNs) is proposed. The aim of using CNNs is to improve computational time in order to achieve real-time requirements.
Keywords :
blood vessels; cellular neural nets; eye; medical image processing; CNN-based automatic retinal vascular tree extraction; cellular neural networks; medical image processing; Active contours; Angiography; Biomedical image processing; Biomedical imaging; Cellular neural networks; Computer architecture; Computer science; Histograms; Image segmentation; Retina;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cellular Neural Networks and Their Applications, 2005 9th International Workshop on
Print_ISBN :
0-7803-9185-3
Type :
conf
DOI :
10.1109/CNNA.2005.1543161
Filename :
1543161
Link To Document :
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