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dc.contributor.authorGholami, Neda-
dc.contributor.authorDehshibi, Mohammad Mahdi-
dc.contributor.authorAdamatzky, Andrew-
dc.contributor.authorRueda Toicen, Antonio-
dc.contributor.authorZenil, Hector-
dc.contributor.authorFazlali, Mahmood-
dc.contributor.authorMasip Rodó, David-
dc.contributor.otherPattern Research Center-
dc.contributor.otherUniversity of the West of England-
dc.contributor.otherAlgorithmic Nature Group-
dc.contributor.otherKarolinska Institute-
dc.contributor.otherOxford University Innovation-
dc.contributor.otherShahid Beheshti University-
dc.contributor.otherUniversitat Oberta de Catalunya (UOC)-
dc.date.accessioned2020-12-09T09:37:30Z-
dc.date.available2020-12-09T09:37:30Z-
dc.date.issued2020-02-
dc.identifier.citationGholami, N., Dehshibi, M.M., Adamatzky, A., Rueda-Toicen, A., Zenil, H., Fazlali, M. & Masip, D. (2020). A novel method for reconstructing CT images in GATE/GEANT4 with application in medical imaging: A complexity analysis approach. Journal of Information Processing, 28, 161-168. doi: 10.2197/ipsjjip.28.161-
dc.identifier.issn1882-6652MIAR
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dc.identifier.urihttp://hdl.handle.net/10609/125327-
dc.description.abstractFor reconstructing CT images in the clinical setting, "effective energy" is usually used instead of the total X-ray spectrum. This approximation causes an accuracy decline. We proposed to quantize the total X-ray spectrum into irregular intervals to preserve accuracy. A phantom consisting of the skull, rib bone, and lung tissues was irradiated with CT configuration in GATE/GEANT4. We applied inverse Radon transform to the obtained Sinogram to construct a Pixel-based Attenuation Matrix (PAM). PAM was then used to weight the calculated Hounsfield unit scale (HU) of each interval's representative energy. Finally, we multiplied the associated normalized photon flux of each interval to the calculated HUs. The performance of the proposed method was evaluated in the course of Complexity and Visual analysis. Entropy measurements, Kolmogorov complexity, and morphological richness were calculated to evaluate the complexity. Quantitative visual criteria (i.e., PSNR, FSIM, SSIM, and MSE) were reported to show the effectiveness of the fuzzy C-means approach in the segmenting task.en
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherJournal of Information Processing-
dc.relation.ispartofJournal of Information Processing, 2020, 28-
dc.relation.urihttps://doi.org/10.2197/ipsjjip.28.161-
dc.rights(c) Journal-
dc.subjectcomplexityen
dc.subjectCT imageen
dc.subjectFCMen
dc.subjectGATE/Geant4en
dc.subjectHounsfield uniten
dc.subjectpixel-based attenuation matrixen
dc.subjectcomplejidades
dc.subjectimagen del TACes
dc.subjectFCMes
dc.subjectGATE/Geant4es
dc.subjectunidad Hounsfieldes
dc.subjectmatriz de atenuación basada en píxeleses
dc.subjectcomplexitatca
dc.subjectimatge del TACca
dc.subjectFCMca
dc.subjectGATE/Geant4ca
dc.subjectunitat Hounsfieldca
dc.subjectmatriu d'atenuació basada en píxelsca
dc.subject.lcshBioinformaticsen
dc.titleA novel method for reconstructing CT images in GATE/GEANT4 with application in medical imaging: a complexity analysis approachen
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.subject.lemacBioinformàticaca
dc.subject.lcshesBioinformáticaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
dc.identifier.doi10.2197/ipsjjip.28.161-
dc.relation.projectIDinfo:eu-repo/grantAgreement/EP/P016677/1-
dc.relation.projectIDinfo:eu-repo/grantAgreement/TIN2015-66951-C2-2-R-
dc.relation.projectIDinfo:eu-repo/grantAgreement/RTI2018-095232-B-C22-
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