scholarly journals High Capacity and Secure Watermarking for Medical Images Using Tchebichef Moments

2020 ◽  
Vol 29 (4) ◽  
pp. 636-643
Author(s):  
A. Bastani ◽  
F. Ahouz
Keyword(s):  
Medical Images ◽  
High Capacity ◽  
Tchebichef Moments

scholarly journals Reversible and high-capacity data hiding in medical images

2011 ◽  
Vol 5 (2) ◽  
pp. 190 ◽  
Author(s):  
M. Fallahpour ◽  
D. Megias ◽  
M. Ghanbari
Keyword(s):  
Data Hiding ◽  
Medical Images ◽  
High Capacity ◽  
Capacity Data

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.

A High-Capacity Reversible Watermarking Scheme Based on Shape Decomposition for Medical Images

2018 ◽  
Vol 33 (01) ◽  
pp. 1950001 ◽  
Author(s):  
Xin Zhong ◽  
Frank Y. Shih
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Saliency Detection ◽  
High Capacity ◽  
Detection Algorithm ◽  
Cross Sectional ◽  
Shape Decomposition ◽  
Image Fidelity ◽  
Embedded Image ◽  
Watermarking Scheme

We present a high-capacity reversible, fragile, and blind watermarking scheme for medical images in this paper. A bottom-up saliency detection algorithm is applied to automatically locate the multiple arbitrarily-shaped regions of interest (ROIs). The iterative square-production algorithm is developed to generate different sizes of squares for shape decomposition on the regions of noninterest (RONIs). This scheme of combining the frequency-domain watermarking and arbitrarily-shaped ROI methods can significantly increase the watermarking capacity, whereas the embedded image fidelity is preserved. Extensive experiments were carried out on the OASIS medical image dataset, which consists of a cross-sectional collection of 416 subjects, aged from 18 to 96 years old. The results show that the proposed scheme outperforms six existing state-of-the-art schemes in terms of watermarking capacity and embedded image fidelity.

scholarly journals Medical Image Blind Integrity Verification with Krawtchouk Moments

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xu Zhang ◽  
Xilin Liu ◽  
Yang Chen ◽  
Huazhong Shu
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Image Features ◽  
Image Feature ◽  
Verification Method ◽  
Integrity Verification ◽  
Krawtchouk Moments ◽  
Tchebichef Moments ◽  
Block Based ◽  
Image Integrity

A new blind integrity verification method for medical image is proposed in this paper. It is based on a new kind of image features, known as Krawtchouk moments, which we use to distinguish the original images from the modified ones. Basically, with our scheme, image integrity verification is accomplished by classifying images into the original and modified categories. Experiments conducted on medical images issued from different modalities verified the validity of the proposed method and demonstrated that it can be used to detect and discriminate image modifications of different types with high accuracy. We also compared the performance of our scheme with a state-of-the-art solution suggested for medical images—solution that is based on histogram statistical properties of reorganized block-based Tchebichef moments. Conducted tests proved the better behavior of our image feature set.

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