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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, Pedro P Rebouças Filho, Francisco Nivando Bezerra, Ajalmar R Rocha Neto, Saulo A. F. Oliveira
Copyright: © 2019 |Volume: 10 |Issue: 3 |Pages: 17
ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781522566663|DOI: 10.4018/IJEHMC.2019070103
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MLA

Sousa, Lucas S., et al. "An Improved Retinal Blood Vessel Detection System Using an Extreme Learning Machine." IJEHMC vol.10, no.3 2019: pp.39-55. http://doi.org/10.4018/IJEHMC.2019070103

APA

Sousa, L. S., Filho, P. P., Bezerra, F. N., Neto, A. R., & Oliveira, S. A. (2019). An Improved Retinal Blood Vessel Detection System Using an Extreme Learning Machine. International Journal of E-Health and Medical Communications (IJEHMC), 10(3), 39-55. http://doi.org/10.4018/IJEHMC.2019070103

Chicago

Sousa, Lucas S., et al. "An Improved Retinal Blood Vessel Detection System Using an Extreme Learning Machine," International Journal of E-Health and Medical Communications (IJEHMC) 10, no.3: 39-55. http://doi.org/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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