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dc.contributor.authorFernández Martínez, Roberto ORCID
dc.contributor.authorOcariz Larrea, Ana María ORCID
dc.contributor.authorIturrondobeitia Ellacuria, Maider
dc.contributor.authorIbarretxe Uriguen, Julen
dc.date.accessioned2024-04-29T17:58:58Z
dc.date.available2024-04-29T17:58:58Z
dc.date.issued2023-10
dc.identifier.citationMicroscopy Research and Technique 86(10) : 1237-1248 (2023)es_ES
dc.identifier.issn1097-0029
dc.identifier.issn1059-910X
dc.identifier.urihttp://hdl.handle.net/10810/66929
dc.description.abstractA method for optimizing an automatic selection of values for parameters that feed segmentation algorithms is proposed. Evolutionary optimization techniques in combination with a fitness function based on a mutual information parameter have been used to find the optimal parameter values of region growing, fuzzy c-means and graph cut segmentation algorithms. To validate the method, the segmentation of two transmission electron microscopy tomography reconstructed volumes of a carbon black-reinforced rubber and a polylactic acid and clay nanocomposite is carried out (i) using evolutionary optimization techniques and (ii) manually by experts. The results confirm that the use of evolutionary optimization techniques, such as genetic algorithms, reduces the computational operation cost needed for a total grid search of segmentation parameters, reducing the probability of reaching a false optimum, and improving the segmentation quality.es_ES
dc.description.sponsorshipThe authors would like to thank PhD Saad Ullah Akram for the script of the GC-based segmentation algorithm and the Basque Government for financial support through the KK-2019-00033-METALCRO2 project. The volume representations of the reconstructions were performed with the UCSF Chimera package (Pettersen et al., 2004). Chimera is developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, United States, San Francisco (supported by NIGMS P41-GM103311).es_ES
dc.language.isoenges_ES
dc.publisherWileyes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectcharacterizationes_ES
dc.subjectgenetic algorithmses_ES
dc.subjectmicroscopyes_ES
dc.subjectmutual informationes_ES
dc.subjectoptimization techniqueses_ES
dc.subjectsegmentationes_ES
dc.subjectsegmentation algorithmses_ES
dc.subjectTEM tomographyes_ES
dc.titleThe determination of optimum segmentation parameters using genetic algorithms: Application to different segmentation algorithms and transmission electron microscopy tomography reconstructed volumeses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2023 The Authors. Microscopy Research and Technique published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.es_ES
dc.rights.holderAtribución-NoComercial-SinDerivadas 3.0 España*
dc.relation.publisherversionhttps://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/jemt.24318es_ES
dc.identifier.doi10.1002/jemt.24318
dc.departamentoesExpresión grafica y proyectos de ingenieríaes_ES
dc.departamentoesFísica aplicada Ies_ES
dc.departamentoeuAdierazpen grafikoa eta ingeniaritzako proiektuakes_ES
dc.departamentoeuFisika aplikatua Ies_ES


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© 2023 The Authors. Microscopy Research and Technique published by Wiley Periodicals LLC.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Except where otherwise noted, this item's license is described as © 2023 The Authors. Microscopy Research and Technique published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.