Paper Info
Title
Retinal Blood Vessel Segmentation by Multi-channel Deep Convolutional Autoencoder.
Abstract
The evaluation and diagnosis of retina pathologies are usually made by the analysis of different image modalities that allows to explore its structure. The most popular retina image method is the retinography, a technique to show the retina and other structures in the fundus of the eye. This paper deals with an important stage of the retina image processing for a diagnosis tool which aims to show the blood vessel structure. Our proposal is based on a deep convolutional neural network, that avoids any preprocessing stage such as gray scale conversion, histogram equalization, and other image transformations that determine the final result. Thus, we obtain the blood vessel segmentation directly from the original RGB color retinography image. The results obtained with our method are comparable to the state-of-the art methods but using a smaller network with less memory and computation requirements. Our approach has been assessed using the DRIVE database.
Year
DOI
Venue
2018
10.1007/978-3-319-94120-2_4
INTERNATIONAL JOINT CONFERENCE SOCO'18-CISIS'18- ICEUTE'18
Field
DocType
Volume
Computer vision,Autoencoder,Computer science,Convolutional neural network,Image processing,Retinography,Fundus (eye),RGB color model,Artificial intelligence,Histogram equalization,Machine learning,Grayscale
Conference
771
ISSN
Citations 
PageRank 
2194-5357
0
0.34
References 
Authors
12
6
Name
Order
Citations
PageRank
Andrés Ortiz119525.64
Javier Ramírez265668.23
Ricardo Cruz-Arándiga300.34
María J. García-Tarifa400.68
Francisco Jesús Martínez-Murcia57417.08
Juan Manuel Górriz Sáez628935.14