A blind and high-capacity data hiding of DICOM medical images based on fuzzification concepts
Data hiding technique using steganography prospered widely due to its high un-detectability, flexibility to the size of hidden data, and robustness against compression and other image processes. Accordingly, this paper aims to use a steganographic technique based on DICOM medical images, where one m...
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doaj-202d1420b95c45af8c301f368a33f7022021-06-02T17:17:35ZengElsevierAlexandria Engineering Journal1110-01682021-04-0160224712482A blind and high-capacity data hiding of DICOM medical images based on fuzzification conceptsAhmed Elhadad0A Ghareeb1Safia Abbas2Department of Computer Science, Faculty of Computers and Information, South Valley University, Egypt; Corresponding author.Al-Baha University, Faculty of Science, Department of Mathematics, Saudi Arabia; Department of Mathematics and Computer Science, Faculty of Science, South Valley University, EgyptComputer Science Department, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Saudi Arabia; Computer Science Department, College of Computer and Information Sciences, Ain Shams University, Cairo, EgyptData hiding technique using steganography prospered widely due to its high un-detectability, flexibility to the size of hidden data, and robustness against compression and other image processes. Accordingly, this paper aims to use a steganographic technique based on DICOM medical images, where one medical image is used as a cover image, and the other one is used as the secret message image. This method entails three main parts: preprocessing, data embedding based on the discrete cosine transform (DCT), and an extraction process. The performances of the algorithm were evaluated using Magnetic resonance imaging (MRI) dataset, the metrics of the Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), the Structural Similarity Index (SSIM), the Universal Quality Index (UQI), and the correlation coefficient (R) values. The experimental results scored a high PSNR after the embedding process and high capacity of the hidden data by embedding a DICOM image into another DICOM image of the same size.http://www.sciencedirect.com/science/article/pii/S1110016820306992DICOMImage fuzzificationDCTData HidingCompression |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ahmed Elhadad A Ghareeb Safia Abbas |
spellingShingle |
Ahmed Elhadad A Ghareeb Safia Abbas A blind and high-capacity data hiding of DICOM medical images based on fuzzification concepts Alexandria Engineering Journal DICOM Image fuzzification DCT Data Hiding Compression |
author_facet |
Ahmed Elhadad A Ghareeb Safia Abbas |
author_sort |
Ahmed Elhadad |
title |
A blind and high-capacity data hiding of DICOM medical images based on fuzzification concepts |
title_short |
A blind and high-capacity data hiding of DICOM medical images based on fuzzification concepts |
title_full |
A blind and high-capacity data hiding of DICOM medical images based on fuzzification concepts |
title_fullStr |
A blind and high-capacity data hiding of DICOM medical images based on fuzzification concepts |
title_full_unstemmed |
A blind and high-capacity data hiding of DICOM medical images based on fuzzification concepts |
title_sort |
blind and high-capacity data hiding of dicom medical images based on fuzzification concepts |
publisher |
Elsevier |
series |
Alexandria Engineering Journal |
issn |
1110-0168 |
publishDate |
2021-04-01 |
description |
Data hiding technique using steganography prospered widely due to its high un-detectability, flexibility to the size of hidden data, and robustness against compression and other image processes. Accordingly, this paper aims to use a steganographic technique based on DICOM medical images, where one medical image is used as a cover image, and the other one is used as the secret message image. This method entails three main parts: preprocessing, data embedding based on the discrete cosine transform (DCT), and an extraction process. The performances of the algorithm were evaluated using Magnetic resonance imaging (MRI) dataset, the metrics of the Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), the Structural Similarity Index (SSIM), the Universal Quality Index (UQI), and the correlation coefficient (R) values. The experimental results scored a high PSNR after the embedding process and high capacity of the hidden data by embedding a DICOM image into another DICOM image of the same size. |
topic |
DICOM Image fuzzification DCT Data Hiding Compression |
url |
http://www.sciencedirect.com/science/article/pii/S1110016820306992 |
work_keys_str_mv |
AT ahmedelhadad ablindandhighcapacitydatahidingofdicommedicalimagesbasedonfuzzificationconcepts AT aghareeb ablindandhighcapacitydatahidingofdicommedicalimagesbasedonfuzzificationconcepts AT safiaabbas ablindandhighcapacitydatahidingofdicommedicalimagesbasedonfuzzificationconcepts AT ahmedelhadad blindandhighcapacitydatahidingofdicommedicalimagesbasedonfuzzificationconcepts AT aghareeb blindandhighcapacitydatahidingofdicommedicalimagesbasedonfuzzificationconcepts AT safiaabbas blindandhighcapacitydatahidingofdicommedicalimagesbasedonfuzzificationconcepts |
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