Increasing lesion specificity with fusion of manually and automatically segmented liver MR images [Manuel ve otomatik bölütlenmiş karaci?er MR görüntülerinin füzyonu ile lezyon belirginli?inin artirilmasi]

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dc.contributor.authorErvural S.
dc.contributor.authorCeylan M.
dc.date.accessioned2020-03-26T20:11:42Z
dc.date.available2020-03-26T20:11:42Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesien_US
dc.descriptionAselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netasen_US
dc.description26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- 137780en_US
dc.description.abstractIn this study, it is aimed to analyze the magnetic resonance (MR) images used in the diagnosis of liver focal lesions using image fusion methods and to help diagnosis by adding automatic segmentation results to the manual segmentation process preferred by experts. For this aim fusions of liver MR images, segmented by a fuzzy method and segmented manually. 120 T1-weighted dynamic contrast-enhanced liver MR images of pre-contrast phase, arterial phase, portal vein phase and late venous phase, taken from 30 different patients, were used. Each phase image is also fused with images segmented by the fuzzy c-means algorithm in the same phase, so that the lesion surfaces and contours are displayed on the segmented image manually. Thus, the significance of the lesion was increased before the information in the MR image in which the liver function information was displayed was lost. The resulting new image contains more useful information for automatic decision systems. The results obtained were evaluated using structural similarity index, peak signal-to-noise ratio and fusion factor quality metrics. © 2018 IEEE.en_US
dc.identifier.doi10.1109/SIU.2018.8404559en_US
dc.identifier.endpage4en_US
dc.identifier.isbn9.78154E+12
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://dx.doi.org/10.1109/SIU.2018.8404559
dc.identifier.urihttps://hdl.handle.net/20.500.12395/37153
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof26th IEEE Signal Processing and Communications Applications Conference, SIU 2018en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectDiscrete wavelet transformen_US
dc.subjectFuzzy c-meansen_US
dc.subjectImage fusionen_US
dc.subjectSegmentationen_US
dc.titleIncreasing lesion specificity with fusion of manually and automatically segmented liver MR images [Manuel ve otomatik bölütlenmiş karaci?er MR görüntülerinin füzyonu ile lezyon belirginli?inin artirilmasi]en_US
dc.typeConference Objecten_US

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