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Automatic Segmentation of Ulna and Radius in Forearm Radiographs

Automatic segmentation of ulna and radius (UR) in forearm radiographs is a necessary step for single X-ray absorptiometry bone mineral density measurement and diagnosis of osteoporosis. Accurate and robust segmentation of UR is difficult, given the variation in forearms between patients and the nonu...

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Detalles Bibliográficos
Autores principales: Gou, Xiaofang, Rao, Yuming, Feng, Xiuxia, Yun, Zhaoqiang, Yang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374800/
https://www.ncbi.nlm.nih.gov/pubmed/30838049
http://dx.doi.org/10.1155/2019/6490161
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author Gou, Xiaofang
Rao, Yuming
Feng, Xiuxia
Yun, Zhaoqiang
Yang, Wei
author_facet Gou, Xiaofang
Rao, Yuming
Feng, Xiuxia
Yun, Zhaoqiang
Yang, Wei
author_sort Gou, Xiaofang
collection PubMed
description Automatic segmentation of ulna and radius (UR) in forearm radiographs is a necessary step for single X-ray absorptiometry bone mineral density measurement and diagnosis of osteoporosis. Accurate and robust segmentation of UR is difficult, given the variation in forearms between patients and the nonuniformity intensity in forearm radiographs. In this work, we proposed a practical automatic UR segmentation method through the dynamic programming (DP) algorithm to trace UR contours. Four seed points along four UR diaphysis edges are automatically located in the preprocessed radiographs. Then, the minimum cost paths in a cost map are traced from the seed points through the DP algorithm as UR edges and are merged as the UR contours. The proposed method is quantitatively evaluated using 37 forearm radiographs with manual segmentation results, including 22 normal-exposure and 15 low-exposure radiographs. The average Dice similarity coefficient of our method reached 0.945. The average mean absolute distance between the contours extracted by our method and a radiologist is only 5.04 pixels. The segmentation performance of our method between the normal- and low-exposure radiographs was insignificantly different. Our method was also validated on 105 forearm radiographs acquired under various imaging conditions from several hospitals. The results demonstrated that our method was fairly robust for forearm radiographs of various qualities.
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spelling pubmed-63748002019-03-05 Automatic Segmentation of Ulna and Radius in Forearm Radiographs Gou, Xiaofang Rao, Yuming Feng, Xiuxia Yun, Zhaoqiang Yang, Wei Comput Math Methods Med Research Article Automatic segmentation of ulna and radius (UR) in forearm radiographs is a necessary step for single X-ray absorptiometry bone mineral density measurement and diagnosis of osteoporosis. Accurate and robust segmentation of UR is difficult, given the variation in forearms between patients and the nonuniformity intensity in forearm radiographs. In this work, we proposed a practical automatic UR segmentation method through the dynamic programming (DP) algorithm to trace UR contours. Four seed points along four UR diaphysis edges are automatically located in the preprocessed radiographs. Then, the minimum cost paths in a cost map are traced from the seed points through the DP algorithm as UR edges and are merged as the UR contours. The proposed method is quantitatively evaluated using 37 forearm radiographs with manual segmentation results, including 22 normal-exposure and 15 low-exposure radiographs. The average Dice similarity coefficient of our method reached 0.945. The average mean absolute distance between the contours extracted by our method and a radiologist is only 5.04 pixels. The segmentation performance of our method between the normal- and low-exposure radiographs was insignificantly different. Our method was also validated on 105 forearm radiographs acquired under various imaging conditions from several hospitals. The results demonstrated that our method was fairly robust for forearm radiographs of various qualities. Hindawi 2019-01-29 /pmc/articles/PMC6374800/ /pubmed/30838049 http://dx.doi.org/10.1155/2019/6490161 Text en Copyright © 2019 Xiaofang Gou et al. http://creativecommons.org/licenses/by/4.0/ 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
Gou, Xiaofang
Rao, Yuming
Feng, Xiuxia
Yun, Zhaoqiang
Yang, Wei
Automatic Segmentation of Ulna and Radius in Forearm Radiographs
title Automatic Segmentation of Ulna and Radius in Forearm Radiographs
title_full Automatic Segmentation of Ulna and Radius in Forearm Radiographs
title_fullStr Automatic Segmentation of Ulna and Radius in Forearm Radiographs
title_full_unstemmed Automatic Segmentation of Ulna and Radius in Forearm Radiographs
title_short Automatic Segmentation of Ulna and Radius in Forearm Radiographs
title_sort automatic segmentation of ulna and radius in forearm radiographs
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374800/
https://www.ncbi.nlm.nih.gov/pubmed/30838049
http://dx.doi.org/10.1155/2019/6490161
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