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Medical images modality classification using discrete Bayesian networks

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dc.contributor.authorArias, Jacinto
dc.contributor.authorMartínez-Gómez, Jesús
dc.contributor.authorGámez, Jose A.
dc.contributor.authorGarcía Seco de Herrera, Alba
dc.contributor.authorMüller, Henning
dc.date.accessioned2025-03-27T07:02:45Z
dc.date.available2025-03-27T07:02:45Z
dc.date.issued2016-10
dc.descriptionEsta es la versión aceptada del artículo. La versión registrada fue publicada por primera vez en Computer Vision and Image Understanding 151 (2016): 61-71, está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/J.CVIU.2016.04.002. This is the accepted version of the article. The registered version was first published in Computer Vision and Image Understanding 151 (2016): 61-71, is available online at the publisher's website: Elsevier, https://doi.org/10.1016/J.CVIU.2016.04.002.
dc.description.abstractIn this paper we propose a complete pipeline for medical image modality classification focused on the application of discrete Bayesian network classifiers. Modality refers to the categorization of biomedical images from the literature according to a previously defined set of image types, such as X-ray, graph or gene sequence. We describe an extensive pipeline starting with feature extraction from images, data combination, pre-processing and a range of different classification techniques and models. We study the expressive power of several image descriptors along with supervised discretization and feature selection to show the performance of discrete Bayesian networks compared to the usual deterministic classifiers used in image classification. We perform an exhaustive experimentation by using the ImageCLEFmed 2013 collection. This problem presents a high number of classes so we propose several hierarchical approaches. In a first set of experiments we evaluate a wide range of parameters for our pipeline along with several classification models. Finally, we perform a comparison by setting up the competition environment between our selected approaches and the best ones of the original competition. Results show that the Bayesian Network classifiers obtain very competitive results. Furthermore, the proposed approach is stable and it can be applied to other problems that present inherent hierarchical structures of classes.en
dc.description.versionversión final
dc.identifier.citationJacinto Arias, Jesus Martínez-Gómez, Jose A. Gámez, Alba García Seco de Herrera, y Henning Müller. «Medical images modality classification using discrete Bayesian networks». Computer Vision and Image Understanding 151 (2016): 61-71. https://doi.org/10.1016/J.CVIU.2016.04.002
dc.identifier.doihttps://doi.org/10.1016/J.CVIU.2016.04.002
dc.identifier.issn1077-3142; eISSN: 1090-235X
dc.identifier.urihttps://hdl.handle.net/20.500.14468/26368
dc.journal.titleComputer Vision and Image Understanding
dc.journal.volume151
dc.language.isoen
dc.page.final71
dc.page.initial61
dc.publisherElsevier
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywordsMedical image analysisen
dc.subject.keywordsVisual features extractionen
dc.subject.keywordsBayesian networksen
dc.subject.keywordsHierarchical classificationen
dc.titleMedical images modality classification using discrete Bayesian networksen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication33e1cf81-6a46-4cc6-828f-1c0f2a7e7497
relation.isAuthorOfPublication.latestForDiscovery33e1cf81-6a46-4cc6-828f-1c0f2a7e7497
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