Analysis and classification of lung tissue in ultrasound images for pneumonia detection
Pneumonia is an infection of the lungs caused by virus, bacteria or fungi. It affects mainly children under five and can be life-threatening. Diagnosis of pneumonia is usually performed using imaging techniques such as chest radiography, ultrasound, and CT. Several studies have shown that ultrasound...
- Autores:
-
Valdes-Burgos, L.
Contreras Ojeda, Sara
Domínguez Jiménez, Juan Antonio
López-Bueno J.
Contreras Ortiz, Sonia Helena
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9517
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9517
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11330/1133003/Analysis-and-classification-of-lung-tissue-in-ultrasound-images-for/10.1117/12.2542615.short
- Palabra clave:
- Pneumonia
Structure of parenchyma of lung
Principal Component Analysis
Plain chest X-ray
Imaging Techniques
Accidental Falls
Cross Infection
Radiographic imaging procedure
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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dc.title.spa.fl_str_mv |
Analysis and classification of lung tissue in ultrasound images for pneumonia detection |
title |
Analysis and classification of lung tissue in ultrasound images for pneumonia detection |
spellingShingle |
Analysis and classification of lung tissue in ultrasound images for pneumonia detection Pneumonia Structure of parenchyma of lung Principal Component Analysis Plain chest X-ray Imaging Techniques Accidental Falls Cross Infection Radiographic imaging procedure |
title_short |
Analysis and classification of lung tissue in ultrasound images for pneumonia detection |
title_full |
Analysis and classification of lung tissue in ultrasound images for pneumonia detection |
title_fullStr |
Analysis and classification of lung tissue in ultrasound images for pneumonia detection |
title_full_unstemmed |
Analysis and classification of lung tissue in ultrasound images for pneumonia detection |
title_sort |
Analysis and classification of lung tissue in ultrasound images for pneumonia detection |
dc.creator.fl_str_mv |
Valdes-Burgos, L. Contreras Ojeda, Sara Domínguez Jiménez, Juan Antonio López-Bueno J. Contreras Ortiz, Sonia Helena |
dc.contributor.author.none.fl_str_mv |
Valdes-Burgos, L. Contreras Ojeda, Sara Domínguez Jiménez, Juan Antonio López-Bueno J. Contreras Ortiz, Sonia Helena |
dc.subject.keywords.spa.fl_str_mv |
Pneumonia Structure of parenchyma of lung Principal Component Analysis Plain chest X-ray Imaging Techniques Accidental Falls Cross Infection Radiographic imaging procedure |
topic |
Pneumonia Structure of parenchyma of lung Principal Component Analysis Plain chest X-ray Imaging Techniques Accidental Falls Cross Infection Radiographic imaging procedure |
description |
Pneumonia is an infection of the lungs caused by virus, bacteria or fungi. It affects mainly children under five and can be life-threatening. Diagnosis of pneumonia is usually performed using imaging techniques such as chest radiography, ultrasound, and CT. Several studies have shown that ultrasound is an effective, safe and cost-efficient technique for pneumonia detection. However, due to the low signal-to-noise ratio of the images, this technique is highly dependent on the experience of the practitioner. This paper proposes an approach for pneumonia detection from image texture features. We used empirical mode decomposition for feature extraction, principal component analysis for dimensionality reduction and supervised learning methods for classification. Results show that features of the first mode present large differences between healthy and pneumonia patients according to the Cohen’s d index. Pneumonia detection was possible with a rotation forest model with a mean accuracy of 83.33%. |
publishDate |
2020 |
dc.date.accessioned.none.fl_str_mv |
2020-10-30T16:30:36Z |
dc.date.available.none.fl_str_mv |
2020-10-30T16:30:36Z |
dc.date.issued.none.fl_str_mv |
2020-01-03 |
dc.date.submitted.none.fl_str_mv |
2020-10-28 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_8544 |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/lecture |
dc.type.hasversion.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.spa.spa.fl_str_mv |
Artículo |
status_str |
publishedVersion |
dc.identifier.citation.spa.fl_str_mv |
L. Valdes-Burgos, S. L. Contreras-Ojeda, J. A. Domínguez-Jiménez, J. Lopez-Bueno, and S. H. Contreras-Ortiz "Analysis and classification of lung tissue in ultrasound images for pneumonia detection", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 1133003 (3 January 2020); https://doi.org/10.1117/12.2542615 |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/9517 |
dc.identifier.url.none.fl_str_mv |
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11330/1133003/Analysis-and-classification-of-lung-tissue-in-ultrasound-images-for/10.1117/12.2542615.short |
dc.identifier.doi.none.fl_str_mv |
10.1117/12.2542615 |
dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
identifier_str_mv |
L. Valdes-Burgos, S. L. Contreras-Ojeda, J. A. Domínguez-Jiménez, J. Lopez-Bueno, and S. H. Contreras-Ortiz "Analysis and classification of lung tissue in ultrasound images for pneumonia detection", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 1133003 (3 January 2020); https://doi.org/10.1117/12.2542615 10.1117/12.2542615 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
url |
https://hdl.handle.net/20.500.12585/9517 https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11330/1133003/Analysis-and-classification-of-lung-tissue-in-ultrasound-images-for/10.1117/12.2542615.short |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_14cb |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/closedAccess |
eu_rights_str_mv |
closedAccess |
rights_invalid_str_mv |
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dc.format.mimetype.spa.fl_str_mv |
application/pdf |
dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
dc.source.spa.fl_str_mv |
Proceedings Volume 11330, 15th International Symposium on Medical Information Processing and Analysis; 1133003 (2020) |
institution |
Universidad Tecnológica de Bolívar |
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Valdes-Burgos, L.898f2ee7-bc48-48db-8e19-aaaf71b2428eContreras Ojeda, Sara2f78dd6b-33ca-46d8-a5a0-bc083e4cd7e2Domínguez Jiménez, Juan Antoniod2ff9be0-9c22-42f6-a0eb-ce74b44b6ab2López-Bueno J.5b81ac56-074b-4769-a4fb-97ff02492ec6Contreras Ortiz, Sonia Helena690f7c84-d6e0-464a-b059-47146b2f92f52020-10-30T16:30:36Z2020-10-30T16:30:36Z2020-01-032020-10-28L. Valdes-Burgos, S. L. Contreras-Ojeda, J. A. Domínguez-Jiménez, J. Lopez-Bueno, and S. H. Contreras-Ortiz "Analysis and classification of lung tissue in ultrasound images for pneumonia detection", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 1133003 (3 January 2020); https://doi.org/10.1117/12.2542615https://hdl.handle.net/20.500.12585/9517https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11330/1133003/Analysis-and-classification-of-lung-tissue-in-ultrasound-images-for/10.1117/12.2542615.short10.1117/12.2542615Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarPneumonia is an infection of the lungs caused by virus, bacteria or fungi. It affects mainly children under five and can be life-threatening. Diagnosis of pneumonia is usually performed using imaging techniques such as chest radiography, ultrasound, and CT. Several studies have shown that ultrasound is an effective, safe and cost-efficient technique for pneumonia detection. However, due to the low signal-to-noise ratio of the images, this technique is highly dependent on the experience of the practitioner. This paper proposes an approach for pneumonia detection from image texture features. We used empirical mode decomposition for feature extraction, principal component analysis for dimensionality reduction and supervised learning methods for classification. Results show that features of the first mode present large differences between healthy and pneumonia patients according to the Cohen’s d index. 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