ELM for Retinal Vessel Classification

Authors: Iñigo Barandiaran, Odei Maiz, Ion Marqués, Jurgi Ugarte, Manuel Graña

Date: 15.10.2013


PDF

Abstract

Robust image segmentation can be achieved by pixel classification based on features extracted from the image. Retinal vessel quantification is an important component of retinal disease screening protocols. Some vessel parameters are potential biomarkers for the diagnosis of several diseases. Specifically, the arterio-venular ratio (AVR) has been proposed as a biomarker for Diabetic retinopathy and other diseases. Classification of retinal vessel pixels into arteries or veins is required for computing AVR. This paper compares Extreme Learning Machines (ELM) with other state-of-the-art classifier building approaches for this tasks, finding that ELM approaches improve over most of them in classification accuracy and computational time load. Experiments are performed on a well known benchmark dataset of retinal images.

BIB_text

@Article {
author = {Iñigo Barandiaran, Odei Maiz, Ion Marqués, Jurgi Ugarte, Manuel Graña},
title = {ELM for Retinal Vessel Classification},
pages = {135-145},
volume = {16},
keywds = {

fundus, retina, ELM, classification


}
abstract = {

Robust image segmentation can be achieved by pixel classification based on features extracted from the image. Retinal vessel quantification is an important component of retinal disease screening protocols. Some vessel parameters are potential biomarkers for the diagnosis of several diseases. Specifically, the arterio-venular ratio (AVR) has been proposed as a biomarker for Diabetic retinopathy and other diseases. Classification of retinal vessel pixels into arteries or veins is required for computing AVR. This paper compares Extreme Learning Machines (ELM) with other state-of-the-art classifier building approaches for this tasks, finding that ELM approaches improve over most of them in classification accuracy and computational time load. Experiments are performed on a well known benchmark dataset of retinal images.


}
isbn = {978-3-319-04741-6},
date = {2013-10-15},
year = {2013},
}
Vicomtech

Parque Científico y Tecnológico de Gipuzkoa,
Paseo Mikeletegi 57,
20009 Donostia / San Sebastián (Spain)

+(34) 943 309 230

close overlay