Effective management of medical information through ROI-lossless fragile image watermarking technique

Advances in medical image watermarking: a state of the art review

Watermarking has been considered to be a potent and persuasive gizmo for its application in healthcare setups that work online, especially in the current COVID-19 scenario. The security and protection of medical image data from various manipulations that take place over the internet is a topic of concern that needs to be addressed. A detailed review of security and privacy protection using watermarking has been presented in this paper. Watermarking of medical images helps in the protection of image content, authentication of Electronic Patient Record (EPR), and integrity verification. At first, we discuss the various prerequisites of medical image watermarking systems, followed by the classification of Medical Image Watermarking Techniques (MIWT) that include state-of-the-art. We have classified MIWT's into four broader classes for providing better understanding of medical image watermarking. The existing schemes have been presented along with their cons so that the reader may be able to grasp the shortcomings of the technique in order to develop novel techniques proving the inevitability of the presented review. Further, various evaluation parameters along with potential challenges pertaining to medical image watermarking systems have been discussed to provide a deep insight into this research area.

Machine learning based multipurpose medical image watermarking

Digital data security has become an exigent area of research due to a huge amount of data availability at present time. Some of the fields like medical imaging and medical data sharing over communication platforms require high security against counterfeit access, manipulation and other processing operations. It is essential because the changed/manipulated data may lead to erroneous judgment by medical experts and can negatively influence the human's heath. This work offers a blind and robust medical image watermarking framework using deep neural network to provide effective security solutions for medical images. During watermarking, the region of interest (ROI) data of the original image is preserved by employing the LZW (Lampel-Ziv-Welch) compression algorithm. Subsequently the robust watermark is inserted into the original image using IWT (integer wavelet transform) based embedding approach. Next, the SHA-256 algorithm-based hash keys are generated for ROI and RONI (region of non-interest) regions. The fragile watermark is then prepared by ROI recovery data and the hash keys. Further, the LSB replacement-based insertion mechanism is utilized to embed the fragile watermark into RONI embedding region of robust watermarked image. A deep neural network-based framework is used to perform robust watermark extraction for efficient results with less computational time. Simulation results verify that the scheme has significant imperceptibility, efficient robust watermark extraction, correct authentication and completely reversible nature for ROI recovery. The relative investigation with existing schemes confirms the dominance of the proposed work over already existing work.

An Encryption Algorithm for Region of Interest in Medical DICOM Based on One-Dimensional eλ-cos-cot Map

Today, with the rapid development of the Internet, improving image security becomes more and more important. To improve image encryption efficiency, a novel region of interest (ROI) encryption algorithm based on a chaotic system was proposed. First, a new 1D eλ-cos-cot (1D-ECC) with better chaotic performance than the traditional chaotic system is proposed. Second, the chaotic system is used to generate a plaintext-relate keystream based on the label information of a medical image DICOM (Digital Imaging and Communications in Medicine) file, the medical image is segmented using an adaptive threshold, and the segmented region of interest is encrypted. The encryption process is divided into two stages: scrambling and diffusion. In the scrambling stage, helical scanning and index scrambling are combined to scramble. In the diffusion stage, two-dimensional bi-directional diffusion is adopted, that is, the image is bi-directionally diffused row by column to make image security better. The algorithm offers good encryption speed and security performance, according to simulation results and security analysis.

COMPUTATIONAL 2D and 3D MEDICAL IMAGE DATA COMPRESSION MODELS

In this world of big data, the development and exploitation of medical technology is vastly increasing and especially in big biomedical imaging modalities available across medicine. At the same instant, acquisition, processing, storing and transmission of such huge medical data requires efficient and robust data compression models. Over the last two decades, numerous compression mechanisms, techniques and algorithms were proposed by many researchers. This work provides a detailed status of these existing computational compression methods for medical imaging data. Appropriate classification, performance metrics, practical issues and challenges in enhancing the two dimensional (2D) and three dimensional (3D) medical image compression arena are reviewed in detail.

Optimal Medical Image Size Reduction Model Creation Using Recurrent Neural Network and GenPSOWVQ

Medical diagnosis is always a time and a sensitive approach to proper medical treatment. Automation systems have been developed to improve these issues. In the process of automation, images are processed and sent to the remote brain for processing and decision making. It is noted that the image is written for compaction to reduce processing and computational costs. Images require large storage and transmission resources to perform their operations. A good strategy for pictures compression can help minimize these requirements. The question of compressing data on accuracy is always a challenge. Therefore, to optimize imaging, it is necessary to reduce inconsistencies in medical imaging. So this document introduces a new image compression scheme called the GenPSOWVQ method that uses a recurrent neural network with wavelet VQ. The codebook is built using a combination of fragments and genetic algorithms. The newly developed image compression model attains precise compression while maintaining image accuracy with lower computational costs when encoding clinical images. The proposed method was tested using real-time medical imaging using PSNR, MSE, SSIM, NMSE, SNR, and CR indicators. Experimental results show that the proposed GenPSOWVQ method yields higher PSNR SSIMM values for a given compression ratio than the existing methods. In addition, the proposed GenPSOWVQ method yields lower values of MSE, RMSE, and SNR for a given compression ratio than the existing methods.

Multipurpose medical image watermarking for effective security solutions

Digital medical images contain important information regarding patient's health and very useful for diagnosis. Even a small change in medical images (especially in the region of interest (ROI)) can mislead the doctors/practitioners for deciding further treatment. Therefore, the protection of the images against intentional/unintentional tampering, forgery, filtering, compression and other common signal processing attacks are mandatory. This manuscript presents a multipurpose medical image watermarking scheme to offer copyright/ownership protection, tamper detection/localization (for ROI (region of interest) and different segments of RONI (region of non-interest)), and self-recovery of the ROI with 100% reversibility. Initially, the recovery information of the host image's ROI is compressed using LZW (Lempel-Ziv-Welch) algorithm. Afterwards, the robust watermark is embedded into the host image using a transform domain based embedding mechanism. Further, the 256-bit hash keys are generated using SHA-256 algorithm for the ROI and eight RONI regions (i.e. RONI-1 to RONI-8) of the robust watermarked image. The compressed recovery data and hash keys are combined and then embedded into the segmented RONI region of the robust watermarked image using an LSB replacement based fragile watermarking approach. Experimental results show high imperceptibility, high robustness, perfect tamper detection, significant tamper localization, and perfect recovery of the ROI (100% reversibility). The scheme doesn't need original host or watermark information for the extraction process due to the blind nature. The relative analysis demonstrates the superiority of the proposed scheme over existing schemes.

ROI-based medical image watermarking for accurate tamper detection, localisation and recovery

Smart healthcare systems play a vital role in the current era of Internet of Things (IoT) and Cyber-Physical Systems (CPS); i.e. Industry 4.0. Medical data security has become the integral part of smart hospital applications to ensure data privacy and patient data security. Usually, patient medical reports and diagnostic images are transferred to the specialist physician in other hospitals for effective diagnostics. Therefore, the transmission of medical data over the internet has attained significant interest among many researchers. The three main challenges associated with the e-healthcare systems are the following: (1) ensuring authentication of medical information; (2) transmission of medical image and patient health record (PHR) should not cause data mismatch/detachment; and (3) medical image should not be modified accidentally or intentionally as they are transmitted over the insecure medium. Thus, it is highly essential to ensure the integrity of the medical image, especially the region of interest (ROI) before taking any diagnostic decisions. Watermarking is a well-known technique used to overcome these challenges. The current research work has developed a watermarking algorithm to ensure integrity and authentication of the medical data and image. In this paper, a novel watermarking algorithm is designed based on Integer Wavelet Transform (IWT), combined chaotic map, recovery bit generation and SHA-256 to address the objective as mentioned earlier. The paper's significant contribution is divided into four phases, namely, watermark generation and data embedding phase, authentication phase, tamper detection phase and localisation and lossless recovery phase. Experiments are carried out to prove that the developed IWT-based data embedding scheme offers high robustness to the data embedded in region of non-interest (RONI), detects and localises the tampered blocks inside ROI with 100% accuracy and recovers the tampered segments of ROI with zero MSE. Further, a comparison is made with the state-of-art schemes to verify the sternness of the developed system.

Secure Exchange of Medical Data Using a Novel Real-Time Biometric-Based Protection and Recognition Method

Security and privacy are essential requirements, and their fulfillment is considered one of the most challenging tasks for healthcare organizations to manage patient data using electronic health records. Electronic health records (clinical notes, images, and documents) become more vulnerable to breaching patients’ privacy when shared with an external organization in the current arena of the internet of medical things (IoMT). Various watermarking techniques were introduced in the medical field to secure patients’ data. Most of the existing techniques focus on an image or document’s imperceptibility without considering the watermark(logo). In this research, a novel technique of watermarking is introduced, which supersedes the shortcomings of existing approaches. It guarantees the imperceptibility of the image/document and takes care of watermark(biometric), which is further passed through a process of recognition for claiming ownership. It extracts suitable frequencies from the transform domain using specialized filters to increase the robustness level. The extracted frequencies are modified by adding the biomedical information while considering the strength factor according to the human visual system. The watermarked frequencies are further decomposed through a singular value decomposition technique to increase payload capacity up to (256 × 256). Experimental results over a variety of medical and official images demonstrate the average peak signal-to-noise ratio (PSNR 54.43), and the normal correlation (N.C.) value is 1. PSNR and N.C. of the watermark were calculated after attacks. The proposed technique is working in real-time for embedding, extraction, and recognition of biometrics over the internet, and its uses can be realized in various platforms of IoMT technologies.

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

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.

A Multiple Criteria-Based Cost Function Using Wavelet and Edge Transformation for Medical Image Steganography

With the ever-increasing need for concealing messages within cover media like image, video, and audio, numerous attempts have been developed for steganography. Most of the steganographic techniques perform their embedding operation on the cover image without selecting a better location. The right selection of location for embedding the information can lead to high imperceptibility and robustness. Accordingly, in this paper, we develop a new cost function for estimating the cost of every pixel to identify the good location to embed the message data. The proposed cost estimation procedure utilizes multiple parameters like wavelet coefficient, edge transformation, and pixel intensity. The proposed cost matrix is then utilized to embed the message data into the cover media using an embedding integer. The proposed steganographic technique is experimented with two magnetic resonance brain images, and the results are analyzed with the peak-to-peak signal-to-noise ratio (PSNR) and mean square error. The robustness analysis ensured that the proposed steganographic technique outperforms the existing methods by reaching the maximum PSNR of 72.74 dB.

A New Medical Image Watermarking Technique with Finer Tamper Localization

Medical imaging and information management systems require transmission of medical images over the Internet. Many image watermarking techniques have been proposed in recent years to ensure the integrity and authenticity of medical images transferred over insecure networks. In this work, we propose a new medical image watermarking technique to detect tampered regions on medical images with finer accuracy by authenticating 4 × 4 blocks and without restricting region of interest (ROI) size. The proposed method can mark a 4 × 4 pixel block if it has even one tampered pixel, while similar methods (which have no ROI size restriction) mark 8 × 8, 16 × 16, and 40 × 40 pixel blocks. Modified difference expansion (MDE) and least significant bit (LSB) embedding techniques are used together first in the literature by the method to embed authentication bits into corresponding blocks. The method uses a small 4 × 4 window to mark the tampered region. Experimental results show that the proposed method detects tampered regions on medical images with high accuracy and can be used by all medical image modalities. The results also indicate that the method has finer accuracy and no ROI size restriction compared to similar works reported in the literature.

Medical Image Tamper Detection Based on Passive Image Authentication

Telemedicine has gained popularity in recent years. Medical images can be transferred over the Internet to enable the telediagnosis between medical staffs and to make the patient's history accessible to medical staff from anywhere. Therefore, integrity protection of the medical image is a serious concern due to the broadcast nature of the Internet. Some watermarking techniques are proposed to control the integrity of medical images. However, they require embedding of extra information (watermark) into image before transmission. It decreases visual quality of the medical image and can cause false diagnosis. The proposed method uses passive image authentication mechanism to detect the tampered regions on medical images. Structural texture information is obtained from the medical image by using local binary pattern rotation invariant (LBPROT) to make the keypoint extraction techniques more successful. Keypoints on the texture image are obtained with scale invariant feature transform (SIFT). Tampered regions are detected by the method by matching the keypoints. The method improves the keypoint-based passive image authentication mechanism (they do not detect tampering when the smooth region is used for covering an object) by using LBPROT before keypoint extraction because smooth regions also have texture information. Experimental results show that the method detects tampered regions on the medical images even if the forged image has undergone some attacks (Gaussian blurring/additive white Gaussian noise) or the forged regions are scaled/rotated before pasting.

A Reversible Data Hiding Scheme for Efficient Management of Tele-Ophthalmological Data

Advancements in medical sciences and induction of advanced technologies have led to increased role of medical images in tele-diagnosis. This paper proposes a technique for easy, efficient and accurate management of distributed medical databases and alleviates the risk of any distortion in images during transmission. It also provides remedy of issues like tampering, accidentally or intentionally, authentication and reliability without affecting the perceptual properties of the image. The technique is blind and completely reversible. Values of PSNR and BER imply that the changes made to original images are imperceptible to the Human Visual System. Performance of the technique has been evaluated for fundus images and the results are extremely encouraging. The technique is lossless and conforms to the firm necessities of medical data management by maintaining perceptual quality and diagnostic significance of the images, therefore is very practical to be used in health care centers.

A Reversible Data Hiding Scheme for Efficient Management of Tele-Ophthalmological Data

Advancements in medical sciences and induction of advanced technologies have led to increased role of medical images in tele-diagnosis. This paper proposes a technique for easy, efficient and accurate management of distributed medical databases and alleviates the risk of any distortion in images during transmission. It also provides remedy of issues like tampering, accidentally or intentionally, authentication and reliability without affecting the perceptual properties of the image. The technique is blind and completely reversible. Values of PSNR and BER imply that the changes made to original images are imperceptible to the Human Visual System. Performance of the technique has been evaluated for fundus images and the results are extremely encouraging. The technique is lossless and conforms to the firm necessities of medical data management by maintaining perceptual quality and diagnostic significance of the images, therefore is very practical to be used in health care centers.

Watermark Compression in Medical Image Watermarking Using Lempel-Ziv-Welch (LZW) Lossless Compression Technique

In teleradiology, image contents may be altered due to noisy communication channels and hacker manipulation. Medical image data is very sensitive and can not tolerate any illegal change. Illegally changed image-based analysis could result in wrong medical decision. Digital watermarking technique can be used to authenticate images and detect as well as recover illegal changes made to teleradiology images. Watermarking of medical images with heavy payload watermarks causes image perceptual degradation. The image perceptual degradation directly affects medical diagnosis. To maintain the image perceptual and diagnostic qualities standard during watermarking, the watermark should be lossless compressed. This paper focuses on watermarking of ultrasound medical images with Lempel-Ziv-Welch (LZW) lossless-compressed watermarks. The watermark lossless compression reduces watermark payload without data loss. In this research work, watermark is the combination of defined region of interest (ROI) and image watermarking secret key. The performance of the LZW compression technique was compared with other conventional compression methods based on compression ratio. LZW was found better and used for watermark lossless compression in ultrasound medical images watermarking. Tabulated results show the watermark bits reduction, image watermarking with effective tamper detection and lossless recovery.

Protection of Health Imagery by Region Based Lossless Reversible Watermarking Scheme

Providing authentication and integrity in medical images is a problem and this work proposes a new blind fragile region based lossless reversible watermarking technique to improve trustworthiness of medical images. The proposed technique embeds the watermark using a reversible least significant bit embedding scheme. The scheme combines hashing, compression, and digital signature techniques to create a content dependent watermark making use of compressed region of interest (ROI) for recovery of ROI as reported in literature. The experiments were carried out to prove the performance of the scheme and its assessment reveals that ROI is extracted in an intact manner and PSNR values obtained lead to realization that the presented scheme offers greater protection for health imageries.

Quality optimized medical image information hiding algorithm that employs edge detection and data coding

OBJECTIVES

The present work has the goal of developing a secure medical imaging information system based on a combined steganography and cryptography technique. It attempts to securely embed patient's confidential information into his/her medical images.

METHODS

The proposed information security scheme conceals coded Electronic Patient Records (EPRs) into medical images in order to protect the EPRs' confidentiality without affecting the image quality and particularly the Region of Interest (ROI), which is essential for diagnosis. The secret EPR data is converted into ciphertext using private symmetric encryption method. Since the Human Visual System (HVS) is less sensitive to alterations in sharp regions compared to uniform regions, a simple edge detection method has been introduced to identify and embed in edge pixels, which will lead to an improved stego image quality. In order to increase the embedding capacity, the algorithm embeds variable number of bits (up to 3) in edge pixels based on the strength of edges. Moreover, to increase the efficiency, two message coding mechanisms have been utilized to enhance the ±1 steganography. The first one, which is based on Hamming code, is simple and fast, while the other which is known as the Syndrome Trellis Code (STC), is more sophisticated as it attempts to find a stego image that is close to the cover image through minimizing the embedding impact. The proposed steganography algorithm embeds the secret data bits into the Region of Non Interest (RONI), where due to its importance; the ROI is preserved from modifications.

RESULTS

The experimental results demonstrate that the proposed method can embed large amount of secret data without leaving a noticeable distortion in the output image. The effectiveness of the proposed algorithm is also proven using one of the efficient steganalysis techniques.

CONCLUSION

The proposed medical imaging information system proved to be capable of concealing EPR data and producing imperceptible stego images with minimal embedding distortions compared to other existing methods. In order to refrain from introducing any modifications to the ROI, the proposed system only utilizes the Region of Non Interest (RONI) in embedding the EPR data.

Medical data sheet in safe havens - A tri-layer cryptic solution

Secured sharing of the diagnostic reports and scan images of patients among doctors with complementary expertise for collaborative treatment will help to provide maximum care through faster and decisive decisions. In this context, a tri-layer cryptic solution has been proposed and implemented on Digital Imaging and Communications in Medicine (DICOM) images to establish a secured communication for effective referrals among peers without compromising the privacy of patients. In this approach, a blend of three cryptic schemes, namely Latin square image cipher (LSIC), discrete Gould transform (DGT) and Rubik׳s encryption, has been adopted. Among them, LSIC provides better substitution, confusion and shuffling of the image blocks; DGT incorporates tamper proofing with authentication; and Rubik renders a permutation of DICOM image pixels. The developed algorithm has been successfully implemented and tested in both the software (MATLAB 7) and hardware Universal Software Radio Peripheral (USRP) environments. Specifically, the encrypted data were tested by transmitting them through an additive white Gaussian noise (AWGN) channel model. Furthermore, the sternness of the implemented algorithm was validated by employing standard metrics such as the unified average changing intensity (UACI), number of pixels change rate (NPCR), correlation values and histograms. The estimated metrics have also been compared with the existing methods and dominate in terms of large key space to defy brute force attack, cropping attack, strong key sensitivity and uniform pixel value distribution on encryption.

A HIPAA-compliant key management scheme with revocation of authorization

Patient control over electronic protected health information (ePHI) is one of the major concerns in the Health Insurance and Accountability Act (HIPAA). In this paper, a new key management scheme is proposed to facilitate control by providing two functionalities. First, a patient can authorize more than one healthcare institute within a designated time period to access his or her ePHIs. Second, a patient can revoke authorization and add new authorized institutes at any time as necessary. In the design, it is not required to re-encrypt ePHIs for adding and revoking authorizations, and the implementation is time- and cost-efficient. Consent exception is also considered by the proposed scheme.