A Novel Digital Watermarking Algorithm for Medical Color Image

In this paper, we propose a novel medical color image digital watermarking algorithm, which can realize watermark capacity, transparency and robustness effective compromise. We encrypt the watermark image by Arnold Transform at first and then take it into a random sequence. The principal component of medical image is divided into some sub-blocks with 4×4 and each block is transformed by DCT. One bit of watermark is embedded by comparing the size of specific DCT coefficients in the adjacent sub-blocks. Watermark can be extracted without the original medical image. The experimental results show that the digital watermarking algorithm has a high transparency and big capacity as well as certain robustness.

Medical image copyright protection is becoming increasingly relevant as medical images are used more frequently in medical networks and institutions. The traditional embedded watermarking system is inappropriate for medical images since it degrades the original images’ quality. Furthermore, medical-colored image watermarking options are constrained since most medical watermarking systems are built for gray-scale images. This paper proposes a zero-watermarking scheme for medical color image copyright protection based on a chaotic system and Resnet50, which is a convolutional neural network method. The network Resnet50 is used to extract features from the color medical image, and then a logistic Gaussian map is used to scramble these features and scramble the binary image. Finally, an exclusive OR operation is performed (scrambled binary image, scrambled features for the medical color image) to form a zero watermarking. The experimental result proves that our scheme is effective and robust to geometric and common image processing attacks. The BER values of the extracted watermarks are below 0.0039, and the NCC values are above 0.9942, while the average PSNR values of the attacked images are 29.0056 dB. Also, it is superior to other zero-watermark schemes for medical images in terms of robustness to conventional image processing and geometric attacks. Furthermore, the experimental results show that the Resnet50 model outperforms other models in terms of reducing the mean squared errors of the features between the attacked and original image.

Recently, diagnosing diseases using medical images became crucial. As these images are transmitted through the network, they need a high level of protection. If the data in these images are liable for unauthorized usage, this may lead to severe problems. There are different methods for securing images. One of the most efficient techniques for securing medical images is encryption. Confusion and diffusion are the two main steps used in encryption algorithms. This paper presents a new encryption algorithm for encrypting both grey and color medical images. A new image splitting technique based on image blocks introduced. Then, the image blocks scrambled using a zigzag pattern, rotation, and random permutation. Then, a chaotic logistic map generates a key to diffuse the scrambled image. The efficiency of our proposed method in encrypting medical images is evaluated using security analysis and time complexity. The security is tested in entropy, histogram differential attacks, correlation coefficient, PSNR, keyspace, and sensitivity. The achieved results show a high-performance security level reached by successful encryption of both grey and color medical images. A comparison with various encryption methods is performed. The proposed encryption algorithm outperformed the recent existing encryption methods in encrypting medical images.

Recently, compressive sensing (CS) schemes have been studied as a new compression modality that exploits the sensing matrix in the measurement scheme and the reconstruction scheme to recover the compressed signal. In addition, CS is exploited in medical imaging (MI) to support efficient sampling, compression, transmission, and storage of a large amount of MI. Although CS of MI has been extensively investigated, the effect of color space in CS of MI has not yet been studied in the literature. To fulfill these requirements, this article proposes a novel CS of MI based on hue-saturation value (HSV), using spread spectrum Fourier sampling (SSFS) and sparsity averaging with reweighted analysis (SARA). An HSV loop that performs SSFS is proposed to obtain a compressed signal. Next, HSV–SARA is proposed to reconstruct MI from the compressed signal. A set of color MIs is investigated, such as colonoscopy, magnetic resonance imaging of the brain and eye, and wireless capsule endoscopy images. Experiments were performed to show the superiority of HSV–SARA over benchmark methods in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) index, and measurement rate (MR). The experiments showed that a color MI, with a resolution of pixels, could be compressed by the proposed CS at MR of 0.1, and could be improved in terms of SNR being 15.17% and SSIM being 2.53%. The proposed HSV–SARA can be a solution for color medical image compression and sampling to improve the image acquisition of medical devices.

Robust watermarking is a valuable methodology used in protecting the copyright and securing digital images. In this paper, new fractional-order multi-channel orthogonal exponent moments (MFrEMs) and their invariants to geometric transformations are derived for the first time. We utilized these highly accurate moments to construct a new robust watermarking algorithm for color images. This algorithm consists of three phases. First, the bits of the binary watermark scrambled by using a 1D Sine chaotic map. Second, the fractional-order MFrEMs are calculated from the host color image. Finally, a quantization process is performed, where the scrambled bits of the binary watermark embedded into the host color image. Various experiments were conducted to test the proposed watermarking algorithm and compare it with the existing robust watermarking algorithms for color images. The obtained results ensure the proposed robust watermarking algorithm's superiority over existing algorithms regarding the visual imperceptibility and robustness against various attacks.

In recent years most of researcher’s has done tremendous work in the field of medical image applications such as Magnetic Resonance Imaging (MRI), Ultra Sound, CT scan but still there are many research and experiments in medical imaging field and diagnosing of human health by Health Care Institutes. There is a growing interest for medical imaging de-noising as a hot area of research and also imaging equipment as a device. It is used for better image processing and highlighting the important features. These images are affected with random noise during acquisition, analyzing and Transmission process. This results in blurry image visible in low contrast. Wavelet transforms have effective method to separate the noise from the original medical image by using threshold techniques without affecting the important data of an image. Wavelet transform enables us to use the forward wavelet transform to represent sub-band of the original image in decomposition process then reconstructing this sub band coefficients to original image using inverse wavelet transform. In this work, the quality of medical image has been evaluated using filter assessment parameters like Variance, standard deviation, the squared difference error between original medical image & de-noised image (MSE) and the ratio between original image & noisy image. From numerical results, we can see that the algorithm is efficient de-noising of noisy medical image. When, investigating with Baye’s threshold techniques it achieved the Best value of peak signal to noise ratio (PSNR). For best medical image de-noising, the wavelet based de-noising algorithm has been investigated and results of Baye’s techniques and hard & soft threshold methods have been compared.

Introduction: Studies on the security and interoperability of Internet of medical things (IoMT)in health are still scarce. The most important effects of the IoMT on health care are the ability to exchange information and reduce hospitalization and health care costs. The main challenges of the IoMT in healthcare are security and protection of privacy, among which encryption of images is very important in the field of communication and security. The main purpose of this study was to design a secure channel for encrypting and sending medical data. Methods: This study presents a new method for encrypting medical images to preserve patient information by synchronizing two chaotic systems. The use of chaotic signals as carriers of medical images greatly enhances security and greatly reduces the possibility of detection. The idea of synchronization of chaotic systems was presented in multi states with fixed time delay. In this idea, with the help of Lyapunov’s theory of stability, a controller was designed to establish the stability of the closed-loop system. Then, according to the synchronization idea and its error, a chaotic masking method was proposed to encrypt patient images. The simulation and accuracy of the results of the presented method were investigated in MATLAB software version 2020.Results: The simulations were performed on medical color images and CT, MRI, and X-ray images. Encrypted and recovered images were obtained by the proposed system. To evaluate the performance of the proposed method, various criteria including image histogram, signal-to-noise rate, correlation, and entropy of information were evaluated. The results showed the excellent image encrypting efficiency of the proposed method.Conclusion: Using the proposed method, medical images were encrypted based on a secure protocol, and then with satisfactory quality, images were recovered after transmission.

This research has an objective to implement histogram equalization technique in the evaluation of characteristics medical images, especially for statistical characterization of medical images. Implementation starting with the pre-processing of the image by converting RGB medical images into grayscale images, followed by determination of the Region of Interest (ROI) and cropping methods. The next process was to increase the quality of medical images by implementing histogram equalization technique. The medical image characteristic was evaluated using characteristic statistical methods with their parameters are mean value, standard deviation, entropy, skewness, and kurtosis. The Medical image was used in this research is a medical image of advanced breast cancer. This research compared the characteristics statistical characterization of medical images obtained with the original medical image which has enhanced use histogram equalization technique. The results of implementation histogram equalization obtain medical image feature extraction results for the value of mean, entropy, skewness, and kurtosis become smaller and standard deviation value was increase.

In this paper a robust watermarking scheme based on the combination of two transforms, the stationary wavelet transform (SWT) and the Arnold transform is presented. It is able to increase the robustness of the watermarking scheme of medical images while preserving the quality of the image and its confidentiality. The proposed method is part of the hybrid, robust, and blind methods. It initially divides the original medical image into two regions: the region of interest (ROI) and the region of non-interest (RONI). The watermark is inserted in the RONI region because a small distortion of the ROI can lead to undesirable treatment for the patient. So to secure the medical images by applying the watermark, the ROI must be preserved and the watermark will be inserted in the remaining part of the image which is RONI. The results demonstrated the effectiveness of our method in terms of invisibility and robustness against various attacks.

In this paper, a robust algorithm of digital image watermarking based on discrete wavelet transform is introduced It uses blind watermarking technique. Digital image watermarking is one such technology that has been developed to protect digital images from illegal manipulations. In particular, digital image watermarking algorithms which are based on the discrete wavelet transform have been widely recognized to be more prevalent than others. This is due to the wavelets' excellent spatial localization, frequency spread, and multi-resolution characteristics, which are similar to the theoretical models of the human visual system.

Techno media is playing an important role on secure information due to rapid growth of the media. In telemedicine while transferring medical images tampers may be happened. As a part of protecting the information from the attacker, steganography is the art of hiding the information inside the cover medium with different carrier formats. In this paper, we propose a novel squint pixel based medical image steganographic technique to avoid easily distortion by an attacker. In this method, Original medical image itself acts as carrier image. A Medical image segmented into three sets of pixels, ROI and RONI and Border Pixels. The authentication data of ROI embedded in Squint Pixels of Border Pixels and information of ROI of medical image embedded in 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.

In this paper, a novel medical image watermarking (MIW) technique for tele ‐ medicine applications is proposed. In this approach homomorphic transform (HT), redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD) transforms are successfully utilized for developing an effective watermarking technique. Reflectance component of medical host image is obtained using HT, on which RDWT decomposition and SVD are applied. Similarly medical watermark image is processed with RDWT and SVD. Singular values (Svs) were used for embedding the watermark image into host image. 2‐D chaotic Arnold transform (AT) is used for providing extra security to the watermark image by encrypting the watermarked image. Moreover, proposed technique is also tested for several wavelet families. Robustness and imperceptibility of the proposed technique is enhanced as depicted in experimental results under various attacks. Furthermore, better robustness is attained from proposed technique on comparing it with other formerly reported schemes.

The paper presents an imperceptible and robust digital watermarking algorithm using a combination of the DWT-DCT , which improves the robustness via using new embedding strategy. Apply 4-level DWT to the host image. Then, DCT transform of each directional middle frequencies DWT subband is computed; A binary watermarked logo is coded by Run-Length Code(RLC);The watermark bits are embedded in the coefficients of the DCT block based on the energy of each corresponding to coefficient in the highest level selected using the value of RLC. Performance evaluation show that the proposed scheme is invisible and robust against common image manipulations like JPEG compression, noise adding, sharpening, filtering.

This paper presents an algorithm by embedding a digital watermark into an original color image. Considering the characteristics of HVS, YUV color space is employed, this algorithm presents a new adaptive digital audio watermarking based on discrete cosine transform. The result of the experiment shows that the proposed algorithm has good robustness against various image processing and attacks, good invisibility and stability.

Digital watermarking has been widely applied to solve copyright problems of digital media relating to illegal use or distribution. In the past few years, several gray-level image watermarking algorithms have been proposed, but few of them can be applied to color image. A digital watermarking algorithm for a color watermark embedded into a color host image, based on color space transform and IWT (Integer Wavelet Transform), is proposed. According to the Human Visual System peculiarity and quantizing the wavelet coefficient, Encrypted watermark is embedded adaptively into the luminance Y of the YIQ mode in IWT domain. The proposed algorithm allows extracting the watermark without the help of the original watermark and host image. Experimental results show that the embedded watermark is invisible and robust against common image processing.