Evaluation of segmented brain regions for medical image steganography

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dc.authoridGURKAHRAMAN, KALI/0000-0002-0697-125X
dc.authoridKarakis, Rukiye/0000-0002-1797-3461
dc.contributor.authorKarakis, Rukiye
dc.contributor.authorGurkahraman, Kali
dc.contributor.authorCigdem, Burhanettin
dc.contributor.authorOztoprak, Ibrahim
dc.contributor.authorTopaktas, A. Suat
dc.date.accessioned2024-10-26T18:00:28Z
dc.date.available2024-10-26T18:00:28Z
dc.date.issued2021
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractIn medical image steganography, diagnosis and treatment of a disease can be affected as a result of the distortion caused by the embedding data in the images. For this reason, data is embedded in the region of non-interest determined by basic techniques such as manual or thresholding, and none of these methods involve the segmentation of brain tissues such as tumours. The present study aims to hide the data used in the diagnosis and treatment of a disease without affecting the medical information in the images with a segmentation-based steganography method by combining them into one file format. Magnetic Resonance (MR) images of epilepsy patients were segmented as background, gray matter, white matter, and tumour by discrete wavelet transform (DWT) and k-means clustering-based segmentation method. The hidden data includes confidential patient information, doctor's comment, selected Electroencephalogram (EEG) signals, and EEG health reports. The high-capacity message, which encoded by DNA encryption using chaotic and hash functions, and then compressed, is hidden in the least significant bits of non-tumour pixels of images. In the study, the difference between the cover and the stego images was measured by the peak signal-to-noise ratio, the structural similarity measure, the universal quality index, and the correlation coefficient. These values were obtained as 64.0334 decibels (dB), 0.9979, 0.9971, 0.9993, respectively. A comparison of the results indicates that the proposed method combines the high capacity data of the patients in a single file format and increases both the security and recording space of medical data.
dc.description.sponsorshipSivas Cumhuriyet University Scientific and Research Project Fund [TEKNO-017]
dc.description.sponsorshipThis work has been supported by Sivas Cumhuriyet University Scientific and Research Project Fund (Projects No: TEKNO-017).
dc.identifier.doi10.17341/gazimmfd.753989
dc.identifier.endpage2314
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85117796023
dc.identifier.scopusqualityQ2
dc.identifier.startpage2301
dc.identifier.trdizinid494861
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.753989
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/494861
dc.identifier.urihttps://hdl.handle.net/20.500.12418/27685
dc.identifier.volume36
dc.identifier.wosWOS:000692521900038
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isotr
dc.publisherGazi Univ, Fac Engineering Architecture
dc.relation.ispartofJournal of the Faculty of Engineering and Architecture of Gazi University
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMedical image steganography
dc.subjectdiscrete wavelet transform
dc.subjectk-means clustering
dc.subjectmedical image segmentation
dc.titleEvaluation of segmented brain regions for medical image steganography
dc.typeArticle

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