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Fast Parabola Detection Using Estimation of Distribution Algorithms
This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resultin...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5339634/ https://www.ncbi.nlm.nih.gov/pubmed/28321264 http://dx.doi.org/10.1155/2017/6494390 |
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author | Guerrero-Turrubiates, Jose de Jesus Cruz-Aceves, Ivan Ledesma, Sergio Sierra-Hernandez, Juan Manuel Velasco, Jonas Avina-Cervantes, Juan Gabriel Avila-Garcia, Maria Susana Rostro-Gonzalez, Horacio Rojas-Laguna, Roberto |
author_facet | Guerrero-Turrubiates, Jose de Jesus Cruz-Aceves, Ivan Ledesma, Sergio Sierra-Hernandez, Juan Manuel Velasco, Jonas Avina-Cervantes, Juan Gabriel Avila-Garcia, Maria Susana Rostro-Gonzalez, Horacio Rojas-Laguna, Roberto |
author_sort | Guerrero-Turrubiates, Jose de Jesus |
collection | PubMed |
description | This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications. |
format | Online Article Text |
id | pubmed-5339634 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-53396342017-03-20 Fast Parabola Detection Using Estimation of Distribution Algorithms Guerrero-Turrubiates, Jose de Jesus Cruz-Aceves, Ivan Ledesma, Sergio Sierra-Hernandez, Juan Manuel Velasco, Jonas Avina-Cervantes, Juan Gabriel Avila-Garcia, Maria Susana Rostro-Gonzalez, Horacio Rojas-Laguna, Roberto Comput Math Methods Med Research Article This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications. Hindawi Publishing Corporation 2017 2017-02-21 /pmc/articles/PMC5339634/ /pubmed/28321264 http://dx.doi.org/10.1155/2017/6494390 Text en Copyright © 2017 Jose de Jesus Guerrero-Turrubiates et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Guerrero-Turrubiates, Jose de Jesus Cruz-Aceves, Ivan Ledesma, Sergio Sierra-Hernandez, Juan Manuel Velasco, Jonas Avina-Cervantes, Juan Gabriel Avila-Garcia, Maria Susana Rostro-Gonzalez, Horacio Rojas-Laguna, Roberto Fast Parabola Detection Using Estimation of Distribution Algorithms |
title | Fast Parabola Detection Using Estimation of Distribution Algorithms |
title_full | Fast Parabola Detection Using Estimation of Distribution Algorithms |
title_fullStr | Fast Parabola Detection Using Estimation of Distribution Algorithms |
title_full_unstemmed | Fast Parabola Detection Using Estimation of Distribution Algorithms |
title_short | Fast Parabola Detection Using Estimation of Distribution Algorithms |
title_sort | fast parabola detection using estimation of distribution algorithms |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5339634/ https://www.ncbi.nlm.nih.gov/pubmed/28321264 http://dx.doi.org/10.1155/2017/6494390 |
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