Automated recognition of the pericardium contour on processed CT images using genetic algorithms
Copyright © 2017 Elsevier Ltd. All rights reserved..
This work proposes the use of Genetic Algorithms (GA) in tracing and recognizing the pericardium contour of the human heart using Computed Tomography (CT) images. We assume that each slice of the pericardium can be modelled by an ellipse, the parameters of which need to be optimally determined. An optimal ellipse would be one that closely follows the pericardium contour and, consequently, separates appropriately the epicardial and mediastinal fats of the human heart. Tracing and automatically identifying the pericardium contour aids in medical diagnosis. Usually, this process is done manually or not done at all due to the effort required. Besides, detecting the pericardium may improve previously proposed automated methodologies that separate the two types of fat associated to the human heart. Quantification of these fats provides important health risk marker information, as they are associated with the development of certain cardiovascular pathologies. Finally, we conclude that GA offers satisfiable solutions in a feasible amount of processing time.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2017 |
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| Erschienen: |
2017 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:87 |
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| Enthalten in: |
Computers in biology and medicine - 87(2017) vom: 01. Aug., Seite 38-45 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Rodrigues, É O [VerfasserIn] |
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| Links: |
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| Themen: |
Adipose tissue |
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| Anmerkungen: |
Date Completed 02.05.2018 Date Revised 12.06.2018 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compbiomed.2017.05.013 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM272303771 |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2017 Elsevier Ltd. All rights reserved. | ||
| 520 | |a This work proposes the use of Genetic Algorithms (GA) in tracing and recognizing the pericardium contour of the human heart using Computed Tomography (CT) images. We assume that each slice of the pericardium can be modelled by an ellipse, the parameters of which need to be optimally determined. An optimal ellipse would be one that closely follows the pericardium contour and, consequently, separates appropriately the epicardial and mediastinal fats of the human heart. Tracing and automatically identifying the pericardium contour aids in medical diagnosis. Usually, this process is done manually or not done at all due to the effort required. Besides, detecting the pericardium may improve previously proposed automated methodologies that separate the two types of fat associated to the human heart. Quantification of these fats provides important health risk marker information, as they are associated with the development of certain cardiovascular pathologies. Finally, we conclude that GA offers satisfiable solutions in a feasible amount of processing time | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Adipose tissue | |
| 650 | 4 | |a Computed tomography | |
| 650 | 4 | |a Ellipse tracing | |
| 650 | 4 | |a Epicardial | |
| 650 | 4 | |a Epicardium | |
| 650 | 4 | |a Genetic algorithm | |
| 650 | 4 | |a Human heart | |
| 650 | 4 | |a Image segmentation | |
| 650 | 4 | |a Metaheuristic | |
| 650 | 4 | |a Pericardium | |
| 700 | 1 | |a Rodrigues, L O |e verfasserin |4 aut | |
| 700 | 1 | |a Oliveira, L S N |e verfasserin |4 aut | |
| 700 | 1 | |a Conci, A |e verfasserin |4 aut | |
| 700 | 1 | |a Liatsis, P |e verfasserin |4 aut | |
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