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Fracture Detection in Traumatic Pelvic CT Images

Fracture detection in pelvic bones is vital for patient diagnostic decisions and treatment planning in traumatic pelvic injuries. Manual detection of bone fracture from computed tomography (CT) images is very challenging due to low resolution of the images and the complex pelvic structures. Automate...

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Autores principales: Wu, Jie, Davuluri, Pavani, Ward, Kevin R., Cockrell, Charles, Hobson, Rosalyn, Najarian, Kayvan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3263603/
https://www.ncbi.nlm.nih.gov/pubmed/22287952
http://dx.doi.org/10.1155/2012/327198
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author Wu, Jie
Davuluri, Pavani
Ward, Kevin R.
Cockrell, Charles
Hobson, Rosalyn
Najarian, Kayvan
author_facet Wu, Jie
Davuluri, Pavani
Ward, Kevin R.
Cockrell, Charles
Hobson, Rosalyn
Najarian, Kayvan
author_sort Wu, Jie
collection PubMed
description Fracture detection in pelvic bones is vital for patient diagnostic decisions and treatment planning in traumatic pelvic injuries. Manual detection of bone fracture from computed tomography (CT) images is very challenging due to low resolution of the images and the complex pelvic structures. Automated fracture detection from segmented bones can significantly help physicians analyze pelvic CT images and detect the severity of injuries in a very short period. This paper presents an automated hierarchical algorithm for bone fracture detection in pelvic CT scans using adaptive windowing, boundary tracing, and wavelet transform while incorporating anatomical information. Fracture detection is performed on the basis of the results of prior pelvic bone segmentation via our registered active shape model (RASM). The results are promising and show that the method is capable of detecting fractures accurately.
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spelling pubmed-32636032012-01-27 Fracture Detection in Traumatic Pelvic CT Images Wu, Jie Davuluri, Pavani Ward, Kevin R. Cockrell, Charles Hobson, Rosalyn Najarian, Kayvan Int J Biomed Imaging Research Article Fracture detection in pelvic bones is vital for patient diagnostic decisions and treatment planning in traumatic pelvic injuries. Manual detection of bone fracture from computed tomography (CT) images is very challenging due to low resolution of the images and the complex pelvic structures. Automated fracture detection from segmented bones can significantly help physicians analyze pelvic CT images and detect the severity of injuries in a very short period. This paper presents an automated hierarchical algorithm for bone fracture detection in pelvic CT scans using adaptive windowing, boundary tracing, and wavelet transform while incorporating anatomical information. Fracture detection is performed on the basis of the results of prior pelvic bone segmentation via our registered active shape model (RASM). The results are promising and show that the method is capable of detecting fractures accurately. Hindawi Publishing Corporation 2012 2012-01-04 /pmc/articles/PMC3263603/ /pubmed/22287952 http://dx.doi.org/10.1155/2012/327198 Text en Copyright © 2012 Jie Wu et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wu, Jie
Davuluri, Pavani
Ward, Kevin R.
Cockrell, Charles
Hobson, Rosalyn
Najarian, Kayvan
Fracture Detection in Traumatic Pelvic CT Images
title Fracture Detection in Traumatic Pelvic CT Images
title_full Fracture Detection in Traumatic Pelvic CT Images
title_fullStr Fracture Detection in Traumatic Pelvic CT Images
title_full_unstemmed Fracture Detection in Traumatic Pelvic CT Images
title_short Fracture Detection in Traumatic Pelvic CT Images
title_sort fracture detection in traumatic pelvic ct images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3263603/
https://www.ncbi.nlm.nih.gov/pubmed/22287952
http://dx.doi.org/10.1155/2012/327198
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AT hobsonrosalyn fracturedetectionintraumaticpelvicctimages
AT najariankayvan fracturedetectionintraumaticpelvicctimages