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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Main Authors: Ahmed Elhadad, A Ghareeb, Safia Abbas
Format: Article
Language:English
Published: Elsevier 2021-04-01
Series:Alexandria Engineering Journal
Subjects:
DCT
Online Access:http://www.sciencedirect.com/science/article/pii/S1110016820306992
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spelling 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
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