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An Improved Retinal Blood Vessel Detection System Using an Extreme Learning Machine

An Improved Retinal Blood Vessel Detection System Using an Extreme Learning Machine

Lucas S. Sousa (Programa de Pós-Graduação em Ciências da Computação do IFCE, Fortaleza, Brazil), Pedro P Rebouças Filho (Programa de Pós-Graduação em Ciências da Computação do IFCE, Fortaleza, Brazil), Francisco Nivando Bezerra (Programa de Pós-Graduação em Ciências da Computação do IFCE, Fortaleza, Brazil), Ajalmar R Rocha Neto (Instituto Federal do Ceará, Fortaleza, Brazil), and Saulo A. F. Oliveira (Programa de Pós-Graduação em Engenharia de Telecomunicações do IFCE, Fortaleza, Brazil)
Copyright: © 2019 |Pages: 17
DOI: 10.4018/IJEHMC.2019070103
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Abstract

Retinal images are commonly used to diagnose various diseases, such as diabetic retinopathy, glaucoma, and hypertension. An important step in the analysis of such images is the detection of blood vessels, which is usually done manually and is time consuming. The main proposal in this work is a fast method for retinal blood vessel detection using Extreme Learning Machine (ELM). ELM requires only one iteration to complete its training and it is a robust and fast network in all aspects. The proposal is a compact and efficient representation of retinal images in which the authors achieved a reduction up to 39% of the initial data volume, while still keeping representativeness. To achieve such a reduction whilst maintaining the representativeness, three features (local tophat, local average, and local variance) were used. According to the simulations carried out, this proposal achieved an accuracy of about 95% for most results, outperforming most of the state-of-art methods. Furthermore, this proposal has greater sensitivity, meaning that more vessel pixels are detected correctly.
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Background

In this section the ELM is described in detail and some of the more recent methods for retinal vessel segmentation, which were also used for performance comparison, are discussed in order to facilitate a full understanding of the proposed approach.

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