Squint Pixel Steganography

2016 ◽  
Vol 8 (4) ◽  
pp. 37-47
Author(s):  
Rupa Ch.
Keyword(s):  
Information Security ◽  
Rapid Growth ◽  
Medical Image ◽  
Medical Images ◽  
Region Of Interest ◽  
High Accuracy ◽  
High Quality ◽  
Carrier Image ◽  
Authentic Data ◽  
Least Significant Bits

Technology is playing a major role in the rapid growth of Techno media in relation to information security. Tampers are a major handicap while transferring medical images. In order to circumvent these issues used Steganography to hide the information inside a cover medium with different carrier formats. In this paper, the author proposes a novel squint pixel based medical image steganography technique to avoid distortion by an attacker. In this method, Original medical image itself acts as a carrier image. A Medical image segmented into two sets of pixels, Region of interest (ROI) and squint pixels of region of non-interest (RONI). The authentic data and information of ROI of a medical image embedded in penultimate and least significant bits (PLSB) of squint pixels of RONI. Results of experiments on various medical images show that the proposed method produces high quality stego medical images with high accuracy and recovery of ROI data without loss.

scholarly journals Medical Image Watermarking Technique for Accurate Tamper Detection in ROI and Exact Recovery of ROI

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
R. Eswaraiah ◽  
E. Sreenivasa Reddy
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Image Watermarking ◽  
Region Of Interest ◽  
Tamper Detection ◽  
High Quality ◽  
Medical Image Watermarking ◽  
Block Based ◽  
Embedding Distortion

In telemedicine while transferring medical images tampers may be introduced. Before making any diagnostic decisions, the integrity of region of interest (ROI) of the received medical image must be verified to avoid misdiagnosis. In this paper, we propose a novel fragile block based medical image watermarking technique to avoid embedding distortion inside ROI, verify integrity of ROI, detect accurately the tampered blocks inside ROI, and recover the original ROI with zero loss. In this proposed method, the medical image is segmented into three sets of pixels: ROI pixels, region of noninterest (RONI) pixels, and border pixels. Then, authentication data and information of ROI are embedded in border pixels. Recovery data of ROI is embedded into RONI. Results of experiments conducted on a number of medical images reveal that the proposed method produces high quality watermarked medical images, identifies the presence of tampers inside ROI with 100% accuracy, and recovers the original ROI without any loss.

scholarly journals OFCS: Optimized Framework of Compressive Sensing for Medical Images in Bottleneck Network Condition

2018 ◽  
Vol 8 (5) ◽  
pp. 2829
Author(s):  
Lakshminarayana M ◽  
Mrinal Sarvagya
Keyword(s):  
Compressive Sensing ◽  
Medical Image ◽  
Medical Images ◽  
Region Of Interest ◽  
Lossless Compression ◽  
Cost Effective ◽  
Signal Quality ◽  
Effective Solution ◽  
The Cost

Compressive sensing is one of teh cost effective solution towards performing compression of heavier form of signals. We reviewed the existing research contribution towards compressive sensing to find that existing system doesnt offer any form of optimization for which reason the signal are superiorly compressed but at the cost of enough resources. Therefore, we introduce a framework that optimizes the performance of the compressive sensing by introducing 4 sequential algorithms for performing Random Sampling, Lossless Compression for region-of-interest, Compressive Sensing using transform-based scheme, and optimization. The contribution of proposed paper is a good balance between computational efficiency and quality of reconstructed medical image when transmitted over network with low channel capacity. The study outcome shows that proposed system offers maximum signal quality and lower algorithm processing time in contrast to existing compression techniuqes on medical images.

A FRT - SVD Based Blind Medical Watermarking Technique for Telemedicine Applications

2021 ◽  
pp. 632-653
Author(s):  
Surekah Borra ◽  
Rohit Thanki
Keyword(s):  
Singular Value Decomposition ◽  
Medical Image ◽  
Medical Images ◽  
Image Watermarking ◽  
Region Of Interest ◽  
Singular Value ◽  
Ridgelet Transform ◽  
Simulation Results ◽  
The Individual ◽  
Value Decomposition

In this article, a blind and robust medical image watermarking technique based on Finite Ridgelet Transform (FRT) and Singular Value Decomposition (SVD) is proposed. A host medical image is first transformed into 16 × 16 non-overlapping blocks and then ridgelet transform is applied on the individual blocks to obtain sets of ridgelet coefficients. SVD is then applied on these sets, to obtain the corresponding U, S and V matrix. The watermark information is embedded into the host medical image by modification of the value of the significant elements of U matrix. This proposed technique is tested on various types of medical images such as X-ray and CT scan. The simulation results revealed that this technique provides better imperceptibility, with an average PSNR being 42.95 dB for all test medical images. This technique also overcomes the limitation of the existing technique which is applicable on only the Region of Interest (ROI) of the medical image.

scholarly journals Steganography Technique Based on WPT and ElGamal Encryption with Confusion for Robust Medical Image

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2239-2248
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Wavelet Packet ◽  
Region Of Interest ◽  
Image Data ◽  
Third Party ◽  
Medicine Service ◽  
Health Related ◽  
Elgamal Encryption ◽  
Embedding Rate

For delivering effective health medical images and Electronic Patient Record (EPR) play an important role and these are stored in cloud, remote medical care and tele medicine service. For health care system, all the medical image data are stored in third party a server that is cloud. So, there is more chance to process or change the medical images as well as patient’s records which leads to health-related issues. To prevent the medical details from the hackers, many techniques are proposed and analyzed by the researchers. Anyway, data corruption is done by the attackers till now. In order to improve the security for data, this paper proposes a steganography technique which embed the important details into the medical image by using Wavelet Packet Transform (WPT) without affecting Region of Interest (ROI) which is useful for further diagnosis. Before embedding the patient’s record, these data are encrypted by using ElGamal Encryption technique which provides more security to the data. It is observed from the simulation results that the proposed technique produces better performance in terms of MSE, PSNR and WPSNR values. The PSNR value of the proposed system can increase 8.8%, 6.2%, 12.5%, 9.6%, 6.7% and 6.9% for embedding rate 5%, 10%, 20%, 25%, 30% and 40% respectively from the existing (DWT-ElGamal) technique.

An Imperceptible Watermarking Scheme for Medical Images Tamper Detection

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0
Keyword(s):  
Secure Communication ◽  
Medical Image ◽  
Medical Images ◽  
Image Watermarking ◽  
Chaotic Map ◽  
Region Of Interest ◽  
Digital Image Watermarking ◽  
Watermark Image ◽  
Speed Up ◽  
Watermarking Scheme

One of the important issues in telemedicine field refers to an advanced secure communication. Digital image watermarking is an ideal solution since it protects the electronic patient information’s from unauthorized access. This paper presents a novel blind fragile-based image watermarking scheme in spatial domain that merges Speed Up Robust Features (SURF) descriptor with the well-known Weber Descriptors (WDs) and Arnold algorithm. It provides a good way for enhancing the image quality and time complexity for medical data integrity. Firstly, the watermark image is shuffled using Arnold chaotic map. Secondly, the SURF technique is practiced to Region of Interest (ROI) of the medical image and then the blocks around the SURF points are selected to insert the watermark. Finally, the watermark is encrusted and extracted using WDs. Experimental results show good image fidelity with the shortest execution time to ensure medical images integrity.

scholarly journals An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm

2018 ◽  
Vol 29 (1) ◽  
pp. 1063-1078
Author(s):  
P. Sreenivasulu ◽  
S. Varadarajan
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Medical Image ◽  
Medical Images ◽  
Region Of Interest ◽  
Region Growing ◽  
Set Partitioning ◽  
Discrete Wavelet ◽  
Huffman Encoding ◽  
Encoding Method

Abstract Nowadays, medical imaging and telemedicine are increasingly being utilized on a huge scale. The expanding interest in storing and sending medical images brings a lack of adequate memory spaces and transmission bandwidth. To resolve these issues, compression was introduced. The main aim of lossless image compression is to improve accuracy, reduce the bit rate, and improve the compression efficiency for the storage and transmission of medical images while maintaining an acceptable image quality for diagnosis purposes. In this paper, we propose lossless medical image compression using wavelet transform and encoding method. Basically, the proposed image compression system comprises three modules: (i) segmentation, (ii) image compression, and (iii) image decompression. First, the input medical image is segmented into region of interest (ROI) and non-ROI using a modified region growing algorithm. Subsequently, the ROI is compressed by discrete cosine transform and set partitioning in hierarchical tree encoding method, and the non-ROI is compressed by discrete wavelet transform and merging-based Huffman encoding method. Finally, the compressed image combination of the compressed ROI and non-ROI is obtained. Then, in the decompression stage, the original medical image is extracted using the reverse procedure. The experimentation was carried out using different medical images, and the proposed method obtained better results compared to different other methods.

Medical Image Reliability Verification Using Hash Signatures and Sequential Square Encoding

2018 ◽  
Vol 27 (1) ◽  
pp. 19-30
Author(s):  
J. Jennifer Ranjani ◽  
M. Babu
Keyword(s):  
Data Hiding ◽  
Medical Image ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Random Sequence ◽  
Region Of Interest ◽  
Critical Issue ◽  
Hiding Capacity ◽  
Random Sequence Generation ◽  
Image Integrity

Abstract Increased growth of information technology in healthcare has led to a situation where the security of patient information is more important and is a critical issue. The aim of the proposed algorithm is to provide a framework to verify the integrity of the medical images. In this paper, the integrity of the medical images is verified by embedding hash signatures using the sequential square embedding technique. This technique is as efficient as the diamond encoding technique but with increased payload capability. The medical image is first divided into the region of interest (ROI) block and the signature block. The hash signatures are determined by dividing the ROI into nonoverlapping blocks. During the data hiding stage, the hash signatures are embedded in randomly chosen pixel pairs in the signature block using the sequential square encoding (SSE) technique. In the experimental results, the data hiding capacity of the proposed SSE technique is verified in terms of peak signal-to-noise ratio. Also, the medical image integrity is substantiated by comparing the L2 norm between computed and extracted hash signatures. Modifications such as contrast enhancement, rotation, scaling, and changing the image information result in increased L2 norm; thus, the integrity of the medical images can be verified. The parameters required for embedding, such as the embedding parameter and the seed for random sequence generation, are encrypted and communicated to the receiving end. Hence, the proposed algorithm provides a secure framework for medical image integrity verification.

scholarly journals Fproi-GAN with Fused Regional Features for the Synthesis of High-Quality Paired Medical Images

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jiale Dong ◽  
Caiwei Liu ◽  
Panpan Man ◽  
Guohua Zhao ◽  
Yaping Wu ◽  
...  
Keyword(s):  
Prediction Models ◽  
Medical Images ◽  
A Priori ◽  
Region Of Interest ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
High Quality ◽  
Regional Feature ◽  
Tissue Contrast ◽  
Structural Consistency

The use of medical image synthesis with generative adversarial networks (GAN) is effective for expanding medical samples. The structural consistency between the synthesized and actual image is a key indicator of the quality of the synthesized image, and the region of interest (ROI) of the synthesized image is related to its usability, and these parameters are the two key issues in image synthesis. In this paper, the fusion-ROI patch GAN (Fproi-GAN) model was constructed by incorporating a priori regional feature based on the two-stage cycle consistency mechanism of cycleGAN. This model has improved the tissue contrast of ROI and achieved the pairwise synthesis of high-quality medical images and their corresponding ROIs. The quantitative evaluation results in two publicly available datasets, INbreast and BRATS 2017, show that the synthesized ROI images have a DICE coefficient of 0.981 ± 0.11 and a Hausdorff distance of 4.21 ± 2.84 relative to the original images. The classification experimental results show that the synthesized images can effectively assist in the training of machine learning models, improve the generalization performance of prediction models, and improve the classification accuracy by 4% and sensitivity by 5.3% compared with the cycleGAN method. Hence, the paired medical images synthesized using Fproi-GAN have high quality and structural consistency with real medical images.

Detection and Segmentation of Medical Images Using Generic Algorithms

2021 ◽  
Vol 3 (1) ◽  
pp. 39-46
Author(s):  
Hardev Mukeshbhai Khandhar ◽  
Chintan M. Bhatt ◽  
Simon Fong
Keyword(s):  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Region Of Interest ◽  
Primary Objective ◽  
Detection Methods ◽  
Modern World ◽  
Diagnosis And Prognosis ◽  
Canny Edge ◽  
Image Segementation

Image processing plays an indispensable and significant role in the development of various fields like medical imaging, astronomy, GIS, disaster management, agriculture monitoring, and so on. Medical images which are recorded in digital forms are processed by high-end computers to extract whatever information we desire. In the fast-developing modern world of medical imaging diagnosis and prognosis, where manual photo interpretation is time-consuming, automatic object detection from devices like CT-Scans and MRIs has limited potential to generate the required results. This article addresses the process of identifying Region of Interests in cancer based medical images based on combination of Otsu’s algorithm and Canny edge detection methods. The primary objective of this paper is to derive meaningful and potential information from medical image in different scenarios by applying the image segementation in combination with genetic algorithms in a robust manner to detect region of interest.

A FRT - SVD Based Blind Medical Watermarking Technique for Telemedicine Applications

2019 ◽  
Vol 11 (2) ◽  
pp. 13-33 ◽  
Author(s):  
Surekah Borra ◽  
Rohit Thanki
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Image Watermarking ◽  
Region Of Interest ◽  
Singular Value ◽  
Ridgelet Transform ◽  
X Ray ◽  
Simulation Results ◽  
The Individual ◽  
Value Decomposition

In this article, a blind and robust medical image watermarking technique based on Finite Ridgelet Transform (FRT) and Singular Value Decomposition (SVD) is proposed. A host medical image is first transformed into 16 × 16 non-overlapping blocks and then ridgelet transform is applied on the individual blocks to obtain sets of ridgelet coefficients. SVD is then applied on these sets, to obtain the corresponding U, S and V matrix. The watermark information is embedded into the host medical image by modification of the value of the significant elements of U matrix. This proposed technique is tested on various types of medical images such as X-ray and CT scan. The simulation results revealed that this technique provides better imperceptibility, with an average PSNR being 42.95 dB for all test medical images. This technique also overcomes the limitation of the existing technique which is applicable on only the Region of Interest (ROI) of the medical image.

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