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Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis

Traditionally, for diagnosing patellar dislocation, clinicians make manual geometric measurements on computerized tomography (CT) images taken in the knee area, which is often complex and error-prone. Therefore, we develop a prototype CAD system for automatic measurement and diagnosis. We firstly se...

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Detalles Bibliográficos
Autores principales: Sun, Limin, Kong, Qi, Huang, Yan, Yang, Jiushan, Wang, Shaoshan, Zou, Ruiqi, Yin, Yilong, Peng, Jingliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7212331/
https://www.ncbi.nlm.nih.gov/pubmed/32454878
http://dx.doi.org/10.1155/2020/1782531
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author Sun, Limin
Kong, Qi
Huang, Yan
Yang, Jiushan
Wang, Shaoshan
Zou, Ruiqi
Yin, Yilong
Peng, Jingliang
author_facet Sun, Limin
Kong, Qi
Huang, Yan
Yang, Jiushan
Wang, Shaoshan
Zou, Ruiqi
Yin, Yilong
Peng, Jingliang
author_sort Sun, Limin
collection PubMed
description Traditionally, for diagnosing patellar dislocation, clinicians make manual geometric measurements on computerized tomography (CT) images taken in the knee area, which is often complex and error-prone. Therefore, we develop a prototype CAD system for automatic measurement and diagnosis. We firstly segment the patella and the femur regions on the CT images and then measure two geometric quantities, patellar tilt angle (PTA), and patellar lateral shift (PLS) automatically on the segmentation results, which are finally used to assist in diagnoses. The proposed quantities are proved valid and the proposed algorithms are proved effective by experiments.
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spelling pubmed-72123312020-05-23 Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis Sun, Limin Kong, Qi Huang, Yan Yang, Jiushan Wang, Shaoshan Zou, Ruiqi Yin, Yilong Peng, Jingliang Comput Math Methods Med Research Article Traditionally, for diagnosing patellar dislocation, clinicians make manual geometric measurements on computerized tomography (CT) images taken in the knee area, which is often complex and error-prone. Therefore, we develop a prototype CAD system for automatic measurement and diagnosis. We firstly segment the patella and the femur regions on the CT images and then measure two geometric quantities, patellar tilt angle (PTA), and patellar lateral shift (PLS) automatically on the segmentation results, which are finally used to assist in diagnoses. The proposed quantities are proved valid and the proposed algorithms are proved effective by experiments. Hindawi 2020-01-28 /pmc/articles/PMC7212331/ /pubmed/32454878 http://dx.doi.org/10.1155/2020/1782531 Text en Copyright © 2020 Limin Sun 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
Sun, Limin
Kong, Qi
Huang, Yan
Yang, Jiushan
Wang, Shaoshan
Zou, Ruiqi
Yin, Yilong
Peng, Jingliang
Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis
title Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis
title_full Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis
title_fullStr Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis
title_full_unstemmed Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis
title_short Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis
title_sort automatic segmentation and measurement on knee computerized tomography images for patellar dislocation diagnosis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7212331/
https://www.ncbi.nlm.nih.gov/pubmed/32454878
http://dx.doi.org/10.1155/2020/1782531
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