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Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach

Image segmentation has attracted a lot of attention due to its potential biomedical applications. Based on these, in the current research, an attempt has been made to explore object enhancement and segmentation for CT images of lungs infected with COVID-19. By implementing Pythagorean fuzzy entropy,...

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Detalles Bibliográficos
Autores principales: Premalatha, R., Dhanalakshmi, P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer London 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889401/
https://www.ncbi.nlm.nih.gov/pubmed/35250182
http://dx.doi.org/10.1007/s00521-022-07043-5
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author Premalatha, R.
Dhanalakshmi, P.
author_facet Premalatha, R.
Dhanalakshmi, P.
author_sort Premalatha, R.
collection PubMed
description Image segmentation has attracted a lot of attention due to its potential biomedical applications. Based on these, in the current research, an attempt has been made to explore object enhancement and segmentation for CT images of lungs infected with COVID-19. By implementing Pythagorean fuzzy entropy, the considered images were enhanced. Further, by constructing Pythagorean fuzzy measures and utilizing the thresholding technique, the required values of thresholds for the segmentation of the proposed scheme are assessed. The object extraction ability of the five segmentation algorithms including current sophisticated, and proposed schemes are evaluated by applying the quality measurement factors. Ultimately, the proposed scheme has the best effect on object separation as well as the quality measurement values.
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spelling pubmed-88894012022-03-02 Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach Premalatha, R. Dhanalakshmi, P. Neural Comput Appl Original Article Image segmentation has attracted a lot of attention due to its potential biomedical applications. Based on these, in the current research, an attempt has been made to explore object enhancement and segmentation for CT images of lungs infected with COVID-19. By implementing Pythagorean fuzzy entropy, the considered images were enhanced. Further, by constructing Pythagorean fuzzy measures and utilizing the thresholding technique, the required values of thresholds for the segmentation of the proposed scheme are assessed. The object extraction ability of the five segmentation algorithms including current sophisticated, and proposed schemes are evaluated by applying the quality measurement factors. Ultimately, the proposed scheme has the best effect on object separation as well as the quality measurement values. Springer London 2022-03-02 2022 /pmc/articles/PMC8889401/ /pubmed/35250182 http://dx.doi.org/10.1007/s00521-022-07043-5 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 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 Original Article
Premalatha, R.
Dhanalakshmi, P.
Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach
title Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach
title_full Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach
title_fullStr Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach
title_full_unstemmed Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach
title_short Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach
title_sort enhancement and segmentation of medical images through pythagorean fuzzy sets-an innovative approach
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889401/
https://www.ncbi.nlm.nih.gov/pubmed/35250182
http://dx.doi.org/10.1007/s00521-022-07043-5
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