Reversible information hiding scheme based on interpolation and histogram shift for medical images

A new method for compact storage and transmission of medical images with concealed patient information in noisy environment is evinced. Digital watermarking is the technique adapted here for interleaving patient information with medical images. The patient information, which comprises of text data and signal graph, is encrypted to prevent unauthorized access of data. The latest encryption algorithm (Rijndael) is used for encrypting the text information. Signal graphs (ECG, EEG EMG etc.) are compressed using DPCM technique. To enhance the robustness of the embedded information, the patient information is coded by error correcting codes (ECC) such as (7,4) Hamming, Bose-Chaudhuri-Hocquenghem (BCH) and Reed Solomon (RS) codes .The noisy scenario is simulated by adding salt and pepper (S&P) noise to the embedded image. For different signal to noise ratio (SNR) of the image, bit error rate (BER) and number of character altered (NOCA) for text data and percentage distortion (PDIST) for the signal graph are evaluated. The performance comparison based on the above parameters is conducted for three types of ECC. It is elicited that coded systems can perform better than the uncoded systems.

The modern healthcare systems consider the secure transmission and storage of medical images as vital because of the sensitivity of patient data and the strict privacy regulations. This paper presents a secure and reversible RGB medical image scrambling and encryption scheme that aims to defend diagnostic images from unauthorized access, while, guaranteeing the integrity of the data. A 512- bit master key is generated through the memory-hard Argon2i algorithm which makes the system extremely resistant to brute-force and dictionary attacks. Domain-separated subkeys are generated using master key via AES in counter (CTR) mode. These subkeys are utilized at confusion and diffusion stages of the encryption and decryption process. The image confusion is performed with the use of a Fisher-Yates permutation which is controlled by cryptographically secure keystreams thereby disrupting spatial pixel relationships. The diffusion is done at both single colour channel and total colour channel levels utilizing a feedback-based mechanism that combines XOR operation, modular addition, and bit-level permutation. Both confusion and diffusion process ensures a strong avalanche effect across the entire image. Performance analysis shows that the proposed method provides an effective balance between security and computational efficiency, making it suitable for secure storage and transmission of medical images in telemedicine and healthcare information systems.

The ever-growing numbers of medical digital images and the need to share them among specialists and hospitals for better and more accurate diagnosis require that patients' privacy be protected. During the transmission of medical images between hospitals or specialists through the network, the main priority is to protect a patient's documents against any act of tampering by unauthorised individuals. Because of this, there is a need for medical image authentication scheme to enable proper diagnosis on patient. In addition, medical images are also susceptible to salt and pepper impulse noise through the transmission in communication channels. This noise may also be intentionally used by the invaders to corrupt the embedded watermarks inside the medical images. A common drawback of existing watermarking methods is their weakness against salt and pepper noise. The research carried out in this work addresses the issue of designing a new watermarking method that can withstand high density of salt and pepper noise for brain MRI images. For this purpose, combination of a spatial domain watermarking method, channel coding and noise filtering schemes are used. The region of non-interest (RONI) of MRI images from five different databases are used as embedding area and electronic patient record (EPR) is considered as embedded data. The quality of watermarked image is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the accuracy of the extracted watermark is assessed in terms of Bit Error Rate (BER).

Transfer of digital medical images between multiple parties requires the assurance of image identity and integrity, which can be achieved through image watermarking. This raises concerns for loss in viewer performance due to degradation of image quality. This paper presents Analysis and assessment of robust watermarking scheme suited for medical images and the techniques involved in minimizing perceptual degradation of medical images that result due to payload embedding. The efficient and reliable watermarking scheme is one of the important tasks in storage and transmission of medical images and patient data, multiple watermarking methods are available for embedding and extraction of payload in medical images of different modalities. Discrete Cosine Transform (DCT) technique has been widely known to serve as powerful tool to ensure that impact on the image quality is well below the threshold of visual perceptibility. The principle on which this approach rests are the choice of a suitably light payload, distribution of payload on least significant area and the use of robust watermarking method. We provide examples of this approach applied to MR and CT images

Purpose: The purpose of this article is to offer an overview of the recent applications of cloud computing in medical imaging and their advances in the field. It reviews existing scientific and academic literature on cloud computing-based platforms and solutions for medical image analysis and storage of medical imaging data. Materials and methods: This article uses available scientific literature on the applications of cloud computing in medical imaging from PubMed, Google Scholar and ScienceDirect. Results: The review shows that interest and research in cloud computing applications in medical imaging has increased in recent years. This has led to new and more effective platforms and solutions for medical image analysis and storage of medical imaging data. Innovative applications of cloud computing in medical imaging try to address ethical and security concerns using authentication, encryption, anonymization and access controls. Conclusions: Cloud computing offers promising applications in medical imaging. Notwithstanding, further research is necessary to demonstrate their effectiveness, safety and security in a medical setting.

A novel image scaling based reversible watermarking scheme for secure medical image transmission

Medical records generated in hospitals are treasures for academic research and future references. Medical Image Retrieval (MIR) Systems contribute significantly to locating the relevant records required for a particular diagnosis, analysis, and treatment. An efficient classifier and effective indexing technique are required for the storage and retrieval of medical images. In this paper, a retrieval framework is formulated by adopting a modified Local Binary Pattern feature (AvN-LBP) for indexing and an optimized Fuzzy Art Map (FAM) for classifying and searching medical images. The proposed indexing method extracts LBP considering information from neighborhood pixels and is robust to background noise. The FAM network is optimized using the Differential Evaluation (DE) algorithm (DEFAMNet) with a modified mutation operation to minimize the size of the network without compromising the classification accuracy. The performance of the proposed DEFAMNet is compared with that of other classifiers and descriptors; the classification accuracy of the proposed AvN-LBP operator with DEFAMNet is higher. The experimental results on three benchmark medical image datasets provide evidence that the proposed framework classifies the medical images faster and more efficiently with lesser computational cost.

The security and confidentiality of medical images are of utmost importance due to frequent issues such as leakage, theft, and tampering during transmission and storage, which seriously impact patient privacy. Traditional encryption techniques applied to entire images have proven to be ineffective in guaranteeing timely encryption and preserving the privacy of organ regions separated from the background. In response, this study proposes a specialized and efficient local image encryption algorithm for the medical field. The proposed encryption algorithm focuses on the regions of interest (ROI) within massive medical images. Initially, the Laplacian of Gaussian operator and the outer boundary tracking algorithm are employed to extract the binary image and achieve ROI edge extraction. Subsequently, the image is divided into ROI and ROB (regions outside ROI). The ROI is transformed into a row vector and rearranged using the Lorenz hyperchaotic system. The rearranged sequence is XOR with the random sequence generated by the Henon chaotic map. Next, the encrypted sequence is arranged according to the location of the ROI region and recombined with the unencrypted ROB to obtain the complete encrypted image. Finally, the least significant bit algorithm controlled by the key is used to embed binary image into the encrypted image to ensure lossless decryption of the medical images. Experimental verification demonstrates that the proposed selective encryption algorithm for massive medical images offers relatively ideal security and higher encryption efficiency. This algorithm addresses the privacy concerns and challenges faced in the medical field and contributes to the secure transmission and storage of massive medical images.

Medical image transmission and storage must be secure due to the rapid growth of digital healthcare systems and telemedicine. Medical images have high data volumes and strong pixel correlations, making conventional encryption algorithms inefficient or computationally expensive. Chaos-based encryption methods are popular due to their sensitivity to initial conditions and strong randomness, but finite precision limits key spaces and short periodicity in chaotic maps. A novel hybrid image encryption scheme is proposed that integrates long short-term memory (LSTM) networks with classical chaotic maps to generate adaptive keystreams using a hash float-seeded chaotic map and statistical features. The chaotic maps provide nonlinearity and high key sensitivity, while the LSTM extends the randomness, eliminates periodicity, and expands the key space to test its robustness. Three variants of the proposed model were experimented with: LSTM + Logistic, LSTM + sine map, and LSTM + Chebyshev map. Results demonstrate that all 3 models achieve strong encryption performance, but LSTM + Logistic map outperforms the other two in terms of randomness quality, noise, and crop attacks. The proposed model achieves 99.62% of NPCR, 39.59% of UACI, and 7.975 of entropy. Compared with conventional chaos methods and GAN encryption schemes, the proposed method provides high security, lightweight computation, and practical deployment.

In this paper, based on the principle of Code Division Multiple Access (CDMA), a novel reversible data hiding scheme is presented. The to-be-embedded data are represented by different orthogonal spreading sequences and embedded into a cover image while degrading the image quality slightly. According to the feature of orthogonality, different spreading sequences are repeatedly embedded into the image without disturbing each other, and most elements of different spreading sequences are mutually cancelled in the process of multilevel data embedding. Thus, it keeps the distortion of the embedded image at a relatively low level even with a high embedding capacity. Moreover, the location-map of the proposed scheme can be highly compressed and thus the size is quite small; it further helps to obtain high net embedding capacity. Experimental results have demonstrated that the CDMA based reversible data hiding scheme can achieve higher image quality at the moderate-to-high embedding capacity than other state-of-the-art reversible data hiding works.

A drastic change in communication is happening with digitization. Technological advancements will escalate its pace further. The human health care systems have improved with technology, remodeling the traditional way of treatments. There has been a peak increase in the rate of telehealth and e-health care services during the coronavirus disease 2019 (COVID-19) pandemic. These implications make reversible data hiding (RDH) a hot topic in research, especially for medical image transmission. Recovering the transmitted medical image (MI) at the receiver side is challenging, as an incorrect MI can lead to the wrong diagnosis. Hence, in this paper, we propose a MSB prediction error-based RDH scheme in an encrypted image with high embedding capacity, which recovers the original image with a peak signal-to-noise ratio (PSNR) of infty dB and structural similarity index (SSIM) value of 1. We scan the MI from the first pixel on the top left corner using the snake scan approach in dual modes: i) performing a rightward direction scan and ii) performing a downward direction scan to identify the best optimal embedding rate for an image. Banking upon the prediction error strategy, multiple MSBs are utilized for embedding the encrypted PHR data. The experimental studies on test images project a high embedding rate with more than 3 bpp for 16-bit high-quality DICOM images and more than 1 bpp for most natural images. The outcomes are much more promising compared to other similar state-of-the-art RDH methods.

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.

An encryption frame of medical image with watermark based on hyperchaotic system is proposed in this paper. Medical information, such as the patients’ private information, data needed for diagnosis, and information for authentication or protection of medical files, is embedded into the regions of interest (ROI) in medical images with a high capacity difference-histogram-based reversible data-hiding scheme. After that, the watermarked medical images are encrypted with hyperchaotic systems. In the receiving end, the receiver with encryption key can decrypt the image to get similar images for diagnosis. If the receiver has the key for data hiding at the same time, he/she can extract the embedded private information and reversibly recover the original medical image. Experiments and analyses demonstrate that high embedding capacity and low distortion have been achieved in the process of data hiding, and, at the same time, high security has been acquired in the encryption phase.

Medical images have been used as one of the objects to diagnose the patient. A digitally formatted medical image is easier to be stored and distributed but also easier to be modified for illegal purposes. Digital image watermarking offers a solution to protect digital medical images. By embedding fragile authentication watermark, the watermarking system can detect and localize the tampered area of medical images. Moreover, by embedding the feature extraction in the form of average intensities of the image, an original image can be recovered from the tampered image. This paper will study and test a watermarking scheme using LSB Modification to perform tamper detection and recovery in the ROI. To make this watermarking scheme reversible, RLE is used to embed the original LSBs in the RONI to get higher embedding capacity. The experimental results show that this watermarking system can detect and localize tamper with up to 100% accuracy and perform image recovery up to 100% recovery rate until 20% of tempered area in ROI.

Compression serves as a significant feature for efficient storage and transmission of medical, satellite, and natural images. Transmission speed is a key challenge in transmitting a large amount of data especially for magnetic resonance imaging and computed tomography scan images. Compressive sensing is an optimization-based option to acquire sparse signal using sub-Nyquist criteria exploiting only the signal of interest. This chapter explores compressive sensing for correct sensing, acquisition, and reconstruction of clinical images. In this chapter, distinctive overall performance metrics like peak signal to noise ratio, root mean square error, structural similarity index, compression ratio, etc. are assessed for medical image evaluation by utilizing best three reconstruction algorithms: basic pursuit, least square, and orthogonal matching pursuit. Basic pursuit establishes a well-renowned reconstruction method among the examined recovery techniques. At distinct measurement samples, on increasing the number of measurement samples, PSNR increases significantly and RMSE decreases.