Hybrid Ant Colony Optimization-Based Method for Focal of a Disease Segmentation in Lung CT Images

The detection of chest CT scan images of the lung play a key role in clinical decision making for some lung disease, such as tumors, pulmonary tuberculosis, solitary pulmonary nodule, lung masses and so on. In this paper, a novel automated CT scan image segmentation algorithm based on hybrid Ant Col...

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Detalles Bibliográficos
Autores principales: Lu, Mingli, Xu, Benlian, Qin, Weijian, Shi, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354834/
http://dx.doi.org/10.1007/978-3-030-53956-6_19
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author Lu, Mingli
Xu, Benlian
Qin, Weijian
Shi, Jian
author_facet Lu, Mingli
Xu, Benlian
Qin, Weijian
Shi, Jian
author_sort Lu, Mingli
collection PubMed
description The detection of chest CT scan images of the lung play a key role in clinical decision making for some lung disease, such as tumors, pulmonary tuberculosis, solitary pulmonary nodule, lung masses and so on. In this paper, a novel automated CT scan image segmentation algorithm based on hybrid Ant Colony algorithm and snake algorithm is proposed. Firstly, traditional snake algorithm is used to detect the possible edge points of focal of a disease. Then Ant Colony Optimization (ACO) algorithm is applied to search the possible edge points of focal of a disease repeatedly. Finally, real edges can be extracted according to the intensity of pheromones. Simulation experiment results demonstrate that the proposed algorithm is more efficient and effective than the methods we compared it to.
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spelling pubmed-73548342020-07-13 Hybrid Ant Colony Optimization-Based Method for Focal of a Disease Segmentation in Lung CT Images Lu, Mingli Xu, Benlian Qin, Weijian Shi, Jian Advances in Swarm Intelligence Article The detection of chest CT scan images of the lung play a key role in clinical decision making for some lung disease, such as tumors, pulmonary tuberculosis, solitary pulmonary nodule, lung masses and so on. In this paper, a novel automated CT scan image segmentation algorithm based on hybrid Ant Colony algorithm and snake algorithm is proposed. Firstly, traditional snake algorithm is used to detect the possible edge points of focal of a disease. Then Ant Colony Optimization (ACO) algorithm is applied to search the possible edge points of focal of a disease repeatedly. Finally, real edges can be extracted according to the intensity of pheromones. Simulation experiment results demonstrate that the proposed algorithm is more efficient and effective than the methods we compared it to. 2020-06-22 /pmc/articles/PMC7354834/ http://dx.doi.org/10.1007/978-3-030-53956-6_19 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Lu, Mingli
Xu, Benlian
Qin, Weijian
Shi, Jian
Hybrid Ant Colony Optimization-Based Method for Focal of a Disease Segmentation in Lung CT Images
title Hybrid Ant Colony Optimization-Based Method for Focal of a Disease Segmentation in Lung CT Images
title_full Hybrid Ant Colony Optimization-Based Method for Focal of a Disease Segmentation in Lung CT Images
title_fullStr Hybrid Ant Colony Optimization-Based Method for Focal of a Disease Segmentation in Lung CT Images
title_full_unstemmed Hybrid Ant Colony Optimization-Based Method for Focal of a Disease Segmentation in Lung CT Images
title_short Hybrid Ant Colony Optimization-Based Method for Focal of a Disease Segmentation in Lung CT Images
title_sort hybrid ant colony optimization-based method for focal of a disease segmentation in lung ct images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354834/
http://dx.doi.org/10.1007/978-3-030-53956-6_19
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AT qinweijian hybridantcolonyoptimizationbasedmethodforfocalofadiseasesegmentationinlungctimages
AT shijian hybridantcolonyoptimizationbasedmethodforfocalofadiseasesegmentationinlungctimages

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