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The shape language in application to the diagnosis of cervical vertebrae pathology

In this paper the possibility of classification of X-ray images of the cervical vertebrae is studied. The images should be classified into one of the following classes—the images of healthy vertebrae and the images of vertebrae with syndesmophytes. The vertebra contours, described unambiguously by u...

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Detalles Bibliográficos
Autores principales: Bielecka, Marzena, Obuchowicz, Rafał, Korkosz, Mariusz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6181296/
https://www.ncbi.nlm.nih.gov/pubmed/30307944
http://dx.doi.org/10.1371/journal.pone.0204546
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author Bielecka, Marzena
Obuchowicz, Rafał
Korkosz, Mariusz
author_facet Bielecka, Marzena
Obuchowicz, Rafał
Korkosz, Mariusz
author_sort Bielecka, Marzena
collection PubMed
description In this paper the possibility of classification of X-ray images of the cervical vertebrae is studied. The images should be classified into one of the following classes—the images of healthy vertebrae and the images of vertebrae with syndesmophytes. The vertebra contours, described unambiguously by using the generalized shape language, are the basis of the analysis. As a result, the contour is represented as a chain of sinquads that determine switches. The found switches are the characteristic points of the analyzed contour. In these points additional features of the contour are determined. On the basis of these features two aforementioned classes of images are defined as fuzzy sets. Such an approach allows us to create a hierarchical algorithm of classification based on the syntactic and fuzzy description of the contour.
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spelling pubmed-61812962018-10-26 The shape language in application to the diagnosis of cervical vertebrae pathology Bielecka, Marzena Obuchowicz, Rafał Korkosz, Mariusz PLoS One Research Article In this paper the possibility of classification of X-ray images of the cervical vertebrae is studied. The images should be classified into one of the following classes—the images of healthy vertebrae and the images of vertebrae with syndesmophytes. The vertebra contours, described unambiguously by using the generalized shape language, are the basis of the analysis. As a result, the contour is represented as a chain of sinquads that determine switches. The found switches are the characteristic points of the analyzed contour. In these points additional features of the contour are determined. On the basis of these features two aforementioned classes of images are defined as fuzzy sets. Such an approach allows us to create a hierarchical algorithm of classification based on the syntactic and fuzzy description of the contour. Public Library of Science 2018-10-11 /pmc/articles/PMC6181296/ /pubmed/30307944 http://dx.doi.org/10.1371/journal.pone.0204546 Text en © 2018 Bielecka et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Bielecka, Marzena
Obuchowicz, Rafał
Korkosz, Mariusz
The shape language in application to the diagnosis of cervical vertebrae pathology
title The shape language in application to the diagnosis of cervical vertebrae pathology
title_full The shape language in application to the diagnosis of cervical vertebrae pathology
title_fullStr The shape language in application to the diagnosis of cervical vertebrae pathology
title_full_unstemmed The shape language in application to the diagnosis of cervical vertebrae pathology
title_short The shape language in application to the diagnosis of cervical vertebrae pathology
title_sort shape language in application to the diagnosis of cervical vertebrae pathology
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6181296/
https://www.ncbi.nlm.nih.gov/pubmed/30307944
http://dx.doi.org/10.1371/journal.pone.0204546
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