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dc.contributor.authorEr, Füsun
dc.contributor.authorYalman, Yıldıray
dc.date.accessioned2021-06-05T19:56:55Z
dc.date.available2021-06-05T19:56:55Z
dc.date.issued2019
dc.identifier.isbn978-1-7281-3975-3
dc.identifier.issn2154-512X
dc.identifier.urihttps://hdl.handle.net/20.500.12960/399
dc.descriptionWOS:000529320000022en_US
dc.description.abstractThe aim of this study is to propose a histogram shifting-based reversible data hiding scheme for anatomical magnetic resonance images (MRI) of human brain. In this study, a human brain MRI dataset was employed for experimental purposes, which were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) open dataset. In experimental design, only healthy subjects are included in order to avoid any bias due to morphological brain abnormalities. The dataset contained three-dimensional 16-bit-depth T1-weighted brain MRIs of 25 healthy people. The proposed data hiding scheme for an MRI volume started with an atlas-based partitioning procedure to segment the volume into several region-of-interests, called as chunks in this paper. Then, the well-known histogram-shifting method was applied with some modifications to each chunk, separately. Finally, the total capacity of chunks in bit-per-voxel (BPV) and the stego-image quality in peak signal-to-noise ratio (PSNR) were calculated. The average PSNR and BPV values of the cohort were reported in several experimental conditions including different segmentation regions and several numbers of peak-zero bin-pairs per chunk. The main contribution of this study is the modifications on the traditional histogram-shifting-based reversible data hiding scheme that are considering the characteristics of the T1-weighted MRI data. Furthermore, the distinguishing feature of this study is that, on contrary to similar studies in the literature, this study employed three-dimensional MRI scans of real cohort. The experimental results of this study revealed the applicability of data hiding schemas on neuroimaging data.en_US
dc.description.sponsorshipEURASIP, IEEE, Yeditepe Univ, IEEE Turkey Sect, Univ Paris Saclay, IEEE France Sect, IEEE Yeditepe, KEKAMen_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartof2019 Ninth International Conference on Image Processing Theory, Tools and Applications (Ipta)en_US
dc.relation.ispartof9th International Conference on Image Processing Theory, Tools and Applications (IPTA) -- NOV 06-09, 2019 -- Istanbul, TURKEYen_US
dc.relation.ispartofseriesInternational Conference on Image Processing Theory Tools and Applications
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectReversible Data Hidingen_US
dc.subjectMedical İmagingen_US
dc.subjectHistogram Shiftingen_US
dc.subjectTelemedicineen_US
dc.titleAdaptive Multiple Peak and Histogram based Reversible Data Hidingen_US
dc.typeconferenceObjecten_US
dc.departmentİktisadi ve İdari Bilimler Fakültesi, Yönetim Bilişim Sistemleri Bölümüen_US
dc.department-temp[Er, Fitsun] Istanbul Okan Univ, Dept Software Engn, Istanbul, Turkey; [Yalman, Yildiray] Piri Reis Univ, Dept Informat Syst Engn, Istanbul, Turkeyen_US
dc.contributor.institutionauthorYalman, Yıldıray
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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