CNN-based automatic retinal vascular tree extraction

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

CNN-based automatic retinal vascular tree extraction

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

University of A Coruna
University of Santiago de Compostela

Producción científica: Contribución a una conferencia › Artículo › revisión exhaustiva

22 Citas (Scopus)

Resumen

The retinal vascular tree has become an important task of medical image processing in different scientific areas. Many recent 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.

Idioma original	Inglés
Páginas	61-64
Número de páginas	4
Estado	Publicada - 2005
Publicado de forma externa	Sí
Evento	9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA - Hsinchu, Taiwán Duración: 28 may 2005 → 30 may 2005

Conferencia

Conferencia	9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA
País/Territorio	Taiwán
Ciudad	Hsinchu
Período	28/05/05 → 30/05/05

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title = "CNN-based automatic retinal vascular tree extraction",

abstract = "The retinal vascular tree has become an important task of medical image processing in different scientific areas. Many recent 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.",

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

year = "2005",

language = "English",

pages = "61--64",

note = "9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA ; Conference date: 28-05-2005 Through 30-05-2005",

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TY - CONF

T1 - CNN-based automatic retinal vascular tree extraction

AU - Alonso-Montes, C.

AU - Vilariño, D. L.

AU - Penedo, M. G.

PY - 2005

Y1 - 2005

N2 - The retinal vascular tree has become an important task of medical image processing in different scientific areas. Many recent 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.

AB - The retinal vascular tree has become an important task of medical image processing in different scientific areas. Many recent 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.

UR - https://www.scopus.com/pages/publications/27144439015

M3 - Paper

AN - SCOPUS:27144439015

SP - 61

EP - 64

T2 - 9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA

Y2 - 28 May 2005 through 30 May 2005

ER -

CNN-based automatic retinal vascular tree extraction

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