scholarly journals Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area

Computers ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 152
Author(s):  
Ching-Yu Yang ◽  
Ja-Ling Wu
Keyword(s):  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Medical Images ◽  
High Capacity ◽  
Personal Privacy ◽  
Hiding Capacity ◽  
X Ray ◽  
Magnetic Resonance Imaging Mri ◽  
Hidden Data

During medical treatment, personal privacy is involved and must be protected. Healthcare institutions have to keep medical images or health information secret unless they have permission from the data owner to disclose them. Reversible data hiding (RDH) is a technique that embeds metadata into an image and can be recovered without any distortion after the hidden data have been extracted. This work aims to develop a fully reversible two-bit embedding RDH algorithm with a large hiding capacity for medical images. Medical images can be partitioned into regions of interest (ROI) and regions of noninterest (RONI). ROI is informative with semantic meanings essential for clinical applications and diagnosis and cannot tolerate subtle changes. Therefore, we utilize histogram shifting and prediction error to embed metadata into RONI. In addition, our embedding algorithm minimizes the side effect to ROI as much as possible. To verify the effectiveness of the proposed approach, we benchmarked three types of medical images in DICOM format, namely X-ray photography (X-ray), computer tomography (CT), and magnetic resonance imaging (MRI). Experimental results show that most of the hidden data have been embedded in RONI, and the performance achieves high capacity and leaves less visible distortion to ROIs.

scholarly journals High-Capacity Reversible Data Hiding in Encrypted Images Based on Hierarchical Quad-Tree Coding and Multi-MSB Prediction

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 664
Author(s):  
Ya Liu ◽  
Guangdong Feng ◽  
Chuan Qin ◽  
Haining Lu ◽  
Chin-Chen Chang
Keyword(s):  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Cloud Storage ◽  
Additional Data ◽  
State Of The Art ◽  
Data Extraction ◽  
High Capacity ◽  
Quad Tree ◽  
Encrypted Images

Nowadays, more and more researchers are interested in reversible data hiding in encrypted images (RDHEI), which can be applied in privacy protection and cloud storage. In this paper, a new RDHEI method on the basis of hierarchical quad-tree coding and multi-MSB (most significant bit) prediction is proposed. The content owner performs pixel prediction to obtain a prediction error image and explores the maximum embedding capacity of the prediction error image by hierarchical quad-tree coding before image encryption. According to the marked bits of vacated room capacity, the data hider can embed additional data into the room-vacated image without knowing the content of original image. Through the data hiding key and the encryption key, the legal receiver is able to conduct data extraction and image recovery separately. Experimental results show that the average embedding rates of the proposed method can separately reach 3.504 bpp (bits per pixel), 3.394 bpp, and 2.746 bpp on three well-known databases, BOSSBase, BOWS-2, and UCID, which are higher than some state-of-the-art methods.

scholarly journals Authenticated Medical Image Transmission using Enhanced Reversible Data Hiding (RDH) with NNP2 Algorithm and Rhombus Prediction

2019 ◽  
Vol 8 (4) ◽  
pp. 12188-12192
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Medical Information ◽  
Medical Images ◽  
Chinese Remainder Theorem ◽  
Image Transmission ◽  
Hiding Capacity ◽  
Distortion Level ◽  
Home Monitor ◽  
Nearest Neighborhood

During the last few years, the medical information of concerned patient is transferred from one doctor to another doctor via internet for better diagnosis and studies. Transferring medical information over a transmission medium is known as telemedicine. Telemedicine has been used to overcome distance barriers and to improve access to medical service. The telemedicine application includes emergency treatment, home monitor, military applications, and medical education. These medical images are corrupted by hackers when it is transferred through internet. Hence security of medical images is necessary. Watermarking is used for providing security while transferring medical images. Reversible Data Hiding (RDH) is one of the efficient methods for secure transmission of medical images. In this method, data hiding capacity is very small and the distortion level of recovers images is very large. To avoid these drawbacks, Nearest Neighborhood Pixel Prediction (NNP2 ) algorithm based on Chinese Remainder Theorem (CRT) is proposed and Rhombus Prediction is applied in NNP2 to increase data hiding capacity. The distortion level is reduced by Histogram Shifting. The performance of proposed method is evaluated using PSNR for number of medical images. The results shows that the proposed method gives good results when compared with traditional methods.

A high capacity reversible data hiding scheme using orthogonal projection and prediction error modification

2010 ◽  
Vol 90 (11) ◽  
pp. 2911-2922 ◽  
Author(s):  
Wien Hong ◽  
Tung-Shou Chen ◽  
Yu-Ping Chang ◽  
Chih-Wei Shiu
Keyword(s):  
Data Hiding ◽  
Orthogonal Projection ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
High Capacity

A Novel Method for High Capacity Reversible Data Hiding Scheme Using Difference Expansion

2019 ◽  
Vol 8 (4) ◽  
pp. 13-27
Author(s):  
Subhadip Mukherjee ◽  
Biswapati Jana
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Data Extraction ◽  
High Capacity ◽  
Correlation Coefficients ◽  
Extraction Process ◽  
Research Area ◽  
Difference Expansion ◽  
Secret Data ◽  
Hiding Capacity

Data hiding techniques are very significant in the research area of information security. In this article, the authors propose a new reversible data hiding (RDH) scheme using difference expansion. At first, the original image is partitioned into 3 × 3 pixel blocks, then marked Type-one and Type-two pixels based on their coordinate values. After that, the authors find correlated pixels by computing correlation coefficients and the median of Type-one pixels. Next, secret data bits are embedded within Type-two pixels based on correlated pixels and Type-one pixels based on the stego Type-two pixels. The data extraction process successfully extracts secret data as well as recovers the cover image. The authors observed the effects of the proposed method by performing experiments on some standard cover images and found significantly better result in terms of data hiding capacity compared with existing data hiding schemes.

Sign in / Sign up

Export Citation Format

Share Document