Proposal for computer-aided diagnosis based on ultrasound images of the kidney: is it possible to compare shades of gray among such images?
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Radiologia Brasileira (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-39842021000100007 |
Resumo: | Abstract Objective: To compare ultrasound images of the kidney obtained, randomly or in a controlled manner (standardizing the physical aspects of the ultrasound system), by various professionals and with different devices. Materials and Methods: We evaluated a total of 919 images of kidneys, obtained by five professionals using two types of ultrasound systems, in 24 patients. The images were categorized into four types, by how they were acquired and processed. We compared the gray-scale median and different gray-scale ranges representative of virtual histological tissues. Results: There were statistically significant differences among the five professionals, regardless of the type of ultrasound system employed, in terms of the gray-scale medians for the images obtained (p < 2.2e-16). Analyzing the four categories of images-a totally random image (without any standardization); a standardized image (with fixed values for gain, time gain control, and dynamic range); a normalized version of the random image; and a normalized version of the standardized image-we determined that the random image, even after normalization, differed quite significantly among the professionals (p = 0.006098). The analysis of the normalized version of the standardized image did not differ significantly among the professionals (p = 0.7319). Conclusion: Our findings indicate that a gray-scale analysis of ultrasound images of the kidney performs better when the image acquisition process is standardized and the images undergo a process of normalization. |
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Radiologia Brasileira (Online) |
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Proposal for computer-aided diagnosis based on ultrasound images of the kidney: is it possible to compare shades of gray among such images?Diagnosis, computer-assistedKidney/diagnostic imagingUltrasonography/methodsUltrasonography, interventional/methodsImage processing, computer-assisted/methodsAbstract Objective: To compare ultrasound images of the kidney obtained, randomly or in a controlled manner (standardizing the physical aspects of the ultrasound system), by various professionals and with different devices. Materials and Methods: We evaluated a total of 919 images of kidneys, obtained by five professionals using two types of ultrasound systems, in 24 patients. The images were categorized into four types, by how they were acquired and processed. We compared the gray-scale median and different gray-scale ranges representative of virtual histological tissues. Results: There were statistically significant differences among the five professionals, regardless of the type of ultrasound system employed, in terms of the gray-scale medians for the images obtained (p < 2.2e-16). Analyzing the four categories of images-a totally random image (without any standardization); a standardized image (with fixed values for gain, time gain control, and dynamic range); a normalized version of the random image; and a normalized version of the standardized image-we determined that the random image, even after normalization, differed quite significantly among the professionals (p = 0.006098). The analysis of the normalized version of the standardized image did not differ significantly among the professionals (p = 0.7319). Conclusion: Our findings indicate that a gray-scale analysis of ultrasound images of the kidney performs better when the image acquisition process is standardized and the images undergo a process of normalization.Publicação do Colégio Brasileiro de Radiologia e Diagnóstico por Imagem2021-02-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-39842021000100007Radiologia Brasileira v.54 n.1 2021reponame:Radiologia Brasileira (Online)instname:Colégio Brasileiro de Radiologia e Diagnóstico por Imagem (CBR)instacron:CBR10.1590/0100-3984.2019.0138info:eu-repo/semantics/openAccessSiqueira,Gustavo Lopes Gomes deSousa,Robson Pequeno deOlinda,Ricardo Alves deEngelhorn,Carlos AlbertoSilva,André Luiz Siqueira daAlmeida,Juliana Gonçalveseng2021-01-29T00:00:00Zoai:scielo:S0100-39842021000100007Revistahttps://www.scielo.br/j/rb/https://old.scielo.br/oai/scielo-oai.phpradiologiabrasileira@cbr.org.br1678-70990100-3984opendoar:2021-01-29T00:00Radiologia Brasileira (Online) - Colégio Brasileiro de Radiologia e Diagnóstico por Imagem (CBR)false |
dc.title.none.fl_str_mv |
Proposal for computer-aided diagnosis based on ultrasound images of the kidney: is it possible to compare shades of gray among such images? |
title |
Proposal for computer-aided diagnosis based on ultrasound images of the kidney: is it possible to compare shades of gray among such images? |
spellingShingle |
Proposal for computer-aided diagnosis based on ultrasound images of the kidney: is it possible to compare shades of gray among such images? Siqueira,Gustavo Lopes Gomes de Diagnosis, computer-assisted Kidney/diagnostic imaging Ultrasonography/methods Ultrasonography, interventional/methods Image processing, computer-assisted/methods |
title_short |
Proposal for computer-aided diagnosis based on ultrasound images of the kidney: is it possible to compare shades of gray among such images? |
title_full |
Proposal for computer-aided diagnosis based on ultrasound images of the kidney: is it possible to compare shades of gray among such images? |
title_fullStr |
Proposal for computer-aided diagnosis based on ultrasound images of the kidney: is it possible to compare shades of gray among such images? |
title_full_unstemmed |
Proposal for computer-aided diagnosis based on ultrasound images of the kidney: is it possible to compare shades of gray among such images? |
title_sort |
Proposal for computer-aided diagnosis based on ultrasound images of the kidney: is it possible to compare shades of gray among such images? |
author |
Siqueira,Gustavo Lopes Gomes de |
author_facet |
Siqueira,Gustavo Lopes Gomes de Sousa,Robson Pequeno de Olinda,Ricardo Alves de Engelhorn,Carlos Alberto Silva,André Luiz Siqueira da Almeida,Juliana Gonçalves |
author_role |
author |
author2 |
Sousa,Robson Pequeno de Olinda,Ricardo Alves de Engelhorn,Carlos Alberto Silva,André Luiz Siqueira da Almeida,Juliana Gonçalves |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Siqueira,Gustavo Lopes Gomes de Sousa,Robson Pequeno de Olinda,Ricardo Alves de Engelhorn,Carlos Alberto Silva,André Luiz Siqueira da Almeida,Juliana Gonçalves |
dc.subject.por.fl_str_mv |
Diagnosis, computer-assisted Kidney/diagnostic imaging Ultrasonography/methods Ultrasonography, interventional/methods Image processing, computer-assisted/methods |
topic |
Diagnosis, computer-assisted Kidney/diagnostic imaging Ultrasonography/methods Ultrasonography, interventional/methods Image processing, computer-assisted/methods |
description |
Abstract Objective: To compare ultrasound images of the kidney obtained, randomly or in a controlled manner (standardizing the physical aspects of the ultrasound system), by various professionals and with different devices. Materials and Methods: We evaluated a total of 919 images of kidneys, obtained by five professionals using two types of ultrasound systems, in 24 patients. The images were categorized into four types, by how they were acquired and processed. We compared the gray-scale median and different gray-scale ranges representative of virtual histological tissues. Results: There were statistically significant differences among the five professionals, regardless of the type of ultrasound system employed, in terms of the gray-scale medians for the images obtained (p < 2.2e-16). Analyzing the four categories of images-a totally random image (without any standardization); a standardized image (with fixed values for gain, time gain control, and dynamic range); a normalized version of the random image; and a normalized version of the standardized image-we determined that the random image, even after normalization, differed quite significantly among the professionals (p = 0.006098). The analysis of the normalized version of the standardized image did not differ significantly among the professionals (p = 0.7319). Conclusion: Our findings indicate that a gray-scale analysis of ultrasound images of the kidney performs better when the image acquisition process is standardized and the images undergo a process of normalization. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-02-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-39842021000100007 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-39842021000100007 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0100-3984.2019.0138 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Publicação do Colégio Brasileiro de Radiologia e Diagnóstico por Imagem |
publisher.none.fl_str_mv |
Publicação do Colégio Brasileiro de Radiologia e Diagnóstico por Imagem |
dc.source.none.fl_str_mv |
Radiologia Brasileira v.54 n.1 2021 reponame:Radiologia Brasileira (Online) instname:Colégio Brasileiro de Radiologia e Diagnóstico por Imagem (CBR) instacron:CBR |
instname_str |
Colégio Brasileiro de Radiologia e Diagnóstico por Imagem (CBR) |
instacron_str |
CBR |
institution |
CBR |
reponame_str |
Radiologia Brasileira (Online) |
collection |
Radiologia Brasileira (Online) |
repository.name.fl_str_mv |
Radiologia Brasileira (Online) - Colégio Brasileiro de Radiologia e Diagnóstico por Imagem (CBR) |
repository.mail.fl_str_mv |
radiologiabrasileira@cbr.org.br |
_version_ |
1754208941004292096 |