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

Research output: Contribution to conference › Paper › peer-review

22 Citations (Scopus)

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.

Original language	English
Pages	61-64
Number of pages	4
Publication status	Published - 2005
Externally published	Yes
Event	9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA - Hsinchu, Taiwan, Province of China Duration: 28 May 2005 → 30 May 2005

Conference

Conference	9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA
Country/Territory	Taiwan, Province of China
City	Hsinchu
Period	28/05/05 → 30/05/05

Cite this

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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.",

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year = "2005",

language = "English",

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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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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

Abstract

Conference

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