Filtering of the skin portion on lung ultrasound digital images to facilitate automatic diagnostics of pneumonia

Franklin Barrientos; Avid Roman-Gonzalez; Ronald Barrientos; Leonardo Solis; Alicia Alva; Malena Correa; Monica Pajuelo; Cynthia Anticona; Roberto Lavarello; Benjamin Castaneda; Richard Oberhelman; Robert H. Gilman; Mirko Zimic

doi:10.1109/CONCAPAN.2016.7942376

Filtering of the skin portion on lung ultrasound digital images to facilitate automatic diagnostics of pneumonia

Franklin Barrientos
, Avid Roman-Gonzalez
, Ronald Barrientos
, Leonardo Solis
, Alicia Alva
, Malena Correa
, Monica Pajuelo
, Cynthia Anticona
, Roberto Lavarello
, Benjamin Castaneda
, Richard Oberhelman
, Robert H. Gilman
, Mirko Zimic

School of Science and Engineering

Universidad Peruana Cayetano Heredia
Pontificia Universidad Católica del Perú
Tulane University School of Public Health and Tropical Medicine
Johns Hopkins University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Pneumonia is one of the major causes of child mortality, but it is curable if one can achieves early diagnostics. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. The use of ultrasound to detect pneumonia is limited by the image analysis for interpretation, which is carried by human experts. Pattern recognition and image analysis is a potential tool to facilitate recognition of pneumonia consolidates in absence of medical experts for automatic diagnostics. To perform an automatic analysis of lung ultrasound images for pneumonia detection, the noise introduced by the image portion of the skin, notably complicates the processing and interpretation. This paper presents a methodology to recognize and eliminate the portion of the skin in lung ultrasound images.

Original language	English
Title of host publication	2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467395786
DOIs	https://doi.org/10.1109/CONCAPAN.2016.7942376
State	Published - 2 Jul 2016
Event	36th IEEE Central American and Panama Convention, CONCAPAN 2016 - San Jose, Costa Rica Duration: 9 Nov 2016 → 11 Nov 2016

Publication series

Name	2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016

Conference

Conference	36th IEEE Central American and Panama Convention, CONCAPAN 2016
Country/Territory	Costa Rica
City	San Jose
Period	9/11/16 → 11/11/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Ultrasound images
echography
image processing
remote diagnostics

Access to Document

10.1109/CONCAPAN.2016.7942376

Cite this

Barrientos, F., Roman-Gonzalez, A., Barrientos, R., Solis, L., Alva, A., Correa, M., Pajuelo, M., Anticona, C., Lavarello, R., Castaneda, B., Oberhelman, R., Gilman, R. H., & Zimic, M. (2016). Filtering of the skin portion on lung ultrasound digital images to facilitate automatic diagnostics of pneumonia. In 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016 Article 7942376 (2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CONCAPAN.2016.7942376

Barrientos, Franklin ; Roman-Gonzalez, Avid ; Barrientos, Ronald et al. / Filtering of the skin portion on lung ultrasound digital images to facilitate automatic diagnostics of pneumonia. 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016. Institute of Electrical and Electronics Engineers Inc., 2016. (2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016).

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title = "Filtering of the skin portion on lung ultrasound digital images to facilitate automatic diagnostics of pneumonia",

abstract = "Pneumonia is one of the major causes of child mortality, but it is curable if one can achieves early diagnostics. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. The use of ultrasound to detect pneumonia is limited by the image analysis for interpretation, which is carried by human experts. Pattern recognition and image analysis is a potential tool to facilitate recognition of pneumonia consolidates in absence of medical experts for automatic diagnostics. To perform an automatic analysis of lung ultrasound images for pneumonia detection, the noise introduced by the image portion of the skin, notably complicates the processing and interpretation. This paper presents a methodology to recognize and eliminate the portion of the skin in lung ultrasound images.",

keywords = "Ultrasound images, echography, image processing, remote diagnostics",

author = "Franklin Barrientos and Avid Roman-Gonzalez and Ronald Barrientos and Leonardo Solis and Alicia Alva and Malena Correa and Monica Pajuelo and Cynthia Anticona and Roberto Lavarello and Benjamin Castaneda and Richard Oberhelman and Gilman, \{Robert H.\} and Mirko Zimic",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 36th IEEE Central American and Panama Convention, CONCAPAN 2016 ; Conference date: 09-11-2016 Through 11-11-2016",

year = "2016",

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doi = "10.1109/CONCAPAN.2016.7942376",

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Barrientos, F, Roman-Gonzalez, A, Barrientos, R, Solis, L, Alva, A, Correa, M, Pajuelo, M, Anticona, C, Lavarello, R, Castaneda, B, Oberhelman, R, Gilman, RH & Zimic, M 2016, Filtering of the skin portion on lung ultrasound digital images to facilitate automatic diagnostics of pneumonia. in 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016., 7942376, 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016, Institute of Electrical and Electronics Engineers Inc., 36th IEEE Central American and Panama Convention, CONCAPAN 2016, San Jose, Costa Rica, 9/11/16. https://doi.org/10.1109/CONCAPAN.2016.7942376

Filtering of the skin portion on lung ultrasound digital images to facilitate automatic diagnostics of pneumonia. / Barrientos, Franklin; Roman-Gonzalez, Avid; Barrientos, Ronald et al.
2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7942376 (2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Roman-Gonzalez, Avid

AU - Barrientos, Ronald

AU - Solis, Leonardo

AU - Alva, Alicia

AU - Correa, Malena

AU - Pajuelo, Monica

AU - Anticona, Cynthia

AU - Lavarello, Roberto

AU - Castaneda, Benjamin

AU - Oberhelman, Richard

AU - Gilman, Robert H.

AU - Zimic, Mirko

PY - 2016/7/2

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N2 - Pneumonia is one of the major causes of child mortality, but it is curable if one can achieves early diagnostics. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. The use of ultrasound to detect pneumonia is limited by the image analysis for interpretation, which is carried by human experts. Pattern recognition and image analysis is a potential tool to facilitate recognition of pneumonia consolidates in absence of medical experts for automatic diagnostics. To perform an automatic analysis of lung ultrasound images for pneumonia detection, the noise introduced by the image portion of the skin, notably complicates the processing and interpretation. This paper presents a methodology to recognize and eliminate the portion of the skin in lung ultrasound images.

AB - Pneumonia is one of the major causes of child mortality, but it is curable if one can achieves early diagnostics. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. The use of ultrasound to detect pneumonia is limited by the image analysis for interpretation, which is carried by human experts. Pattern recognition and image analysis is a potential tool to facilitate recognition of pneumonia consolidates in absence of medical experts for automatic diagnostics. To perform an automatic analysis of lung ultrasound images for pneumonia detection, the noise introduced by the image portion of the skin, notably complicates the processing and interpretation. This paper presents a methodology to recognize and eliminate the portion of the skin in lung ultrasound images.

KW - Ultrasound images

KW - echography

KW - image processing

KW - remote diagnostics

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DO - 10.1109/CONCAPAN.2016.7942376

M3 - Contribución a la conferencia

AN - SCOPUS:85021395494

T3 - 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016

BT - 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 36th IEEE Central American and Panama Convention, CONCAPAN 2016

Y2 - 9 November 2016 through 11 November 2016

ER -

Barrientos F, Roman-Gonzalez A, Barrientos R, Solis L, Alva A, Correa M et al. Filtering of the skin portion on lung ultrasound digital images to facilitate automatic diagnostics of pneumonia. In 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7942376. (2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016). doi: 10.1109/CONCAPAN.2016.7942376

Filtering of the skin portion on lung ultrasound digital images to facilitate automatic diagnostics of pneumonia

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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