skip to main content
Visitante
Meu Espaço
Minha Conta
Sair
Identificação
This feature requires javascript
Tags
Revistas Eletrônicas (eJournals)
Livros Eletrônicos (eBooks)
Bases de Dados
Bibliotecas USP
Ajuda
Ajuda
Idioma:
Inglês
Espanhol
Português
This feature required javascript
This feature requires javascript
Primo Search
Busca Geral
Busca Geral
Acervo Físico
Acervo Físico
Produção Intelectual da USP
Produção USP
Search For:
Clear Search Box
Search in:
Busca Geral
Or hit Enter to replace search target
Or select another collection:
Search in:
Busca Geral
Busca Avançada
Busca por Índices
This feature requires javascript
Tipo de recurso
criteria input
qualquer lugar do registro
no título
como autor
no assunto
Data de publicação
lsr01
lsr02
lsr03
lsr04
Orientador
Show Results with:
no título
Show Results with:
qualquer lugar do registro
no título
como autor
no assunto
Data de publicação
lsr01
lsr02
lsr03
lsr04
Orientador
Mostra resultados com:
criteria input
que contêm minhas palavras de busca
com a frase exata
começa com
Mostra resultados com:
Índice
criteria input
E
OU
NÃO
This feature requires javascript
ANALYSIS OF DUAL-ENERGY X-RAY ABSORPTIOMETRY IMAGES USING COMPUTER VISION METHODS
10.15547/tjs.2020.s.01.020
Trakia journal of sciences, 2020-12, Vol.18 (Suppl. 1), p.114-117
[Periódico revisado por pares]
Trakia University
Texto completo disponível
Citações
Citado por
Exibir Online
Detalhes
Resenhas & Tags
Mais Opções
Nº de Citações
This feature requires javascript
Enviar para
Adicionar ao Meu Espaço
Remover do Meu Espaço
E-mail (máximo 30 registros por vez)
Imprimir
Link permanente
Referência
EasyBib
EndNote
RefWorks
del.icio.us
Exportar RIS
Exportar BibTeX
This feature requires javascript
Título:
ANALYSIS OF DUAL-ENERGY X-RAY ABSORPTIOMETRY IMAGES USING COMPUTER VISION METHODS
Autor:
10.15547/tjs.2020.s.01.020
Assuntos:
computer vision
;
convolutional neuronal networks
;
diagnostic analysis
;
dual-energy x-ray absorptiometry (dxa)
É parte de:
Trakia journal of sciences, 2020-12, Vol.18 (Suppl. 1), p.114-117
Descrição:
PURPOSE: Dual-energy x-ray absorptiometry (DXA) is the “golden standard” for diagnosing osteoporosis. Its analyzing algorithm (software) makes it possible to distinguish the bone from the soft tissue. Until now there are only attempts to process and acquire images using automatic segmentation with convolutional neural networks (CNN). Machine reconstruction and precise specific models of anatomic structures from medical images could be accomplished using computer vision. The objective of the current work is to introduce the potential of the two computer methods and their application in the diagnostic DXA analysis. METHODS: DXA generates a report in the DICOM format which includes patient data (age, gender, height, weight, bone mineral density, T-score and Z-score) and an image of the scanned spine as well as the region of interest (ROI). The CNN methods are based mainly on intermediate analysis. The learning of the segmentation of CNN by generating segmentation labels using simple heuristic is done using computer vision. The functions of the loss and the architecture of the CNN is then determined. In that manner the right analysis of the existing medical image is made possible. RESULTS: The computer library OpenCV is the way to realize a model for the assessment of a DXA analysis. The library is available for Python programming language. The library has functions for the extraction of colour objects, image smoothing, Canny’s edge detector, Hough transform and methods for work with contours. CONCLUSIONS: The detection and extraction of images is fundamental for the analysis of DXA which is a step forward in the precision of the in-vivo diagnostic of the bone.
Editor:
Trakia University
Idioma:
Inglês
This feature requires javascript
This feature requires javascript
Voltar para lista de resultados
This feature requires javascript
This feature requires javascript
Buscando em bases de dados remotas. Favor aguardar.
Buscando por
em
scope:(USP_VIDEOS),scope:("PRIMO"),scope:(USP_FISICO),scope:(USP_EREVISTAS),scope:(USP),scope:(USP_EBOOKS),scope:(USP_PRODUCAO),primo_central_multiple_fe
Mostrar o que foi encontrado até o momento
This feature requires javascript
This feature requires javascript