An Image Watermarking Algorithm for Medical Computerized Tomography Images

We use images in several important areas such as military, health, security, and science. Images can be distorted during the capturing, recording, processing, and storing. Image quality metrics are the techniques to measure the quality and quality accuracy level of the images and videos. Most of the quality measurement algorithms does not affect by small distortions in the image. Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasonic Imaging (UI) are widely used in the health sector. Because of several reasons it might be artifacts in the medical images. Doctor decisions might be affected by these image artifacts. Image quality measurement is an important and challenging area to work on. There are several metrics that have been done in the literature such as mean square error, peak signal-noise ratio, gradient similarity measure, structural similarity index, and universal image quality. Patient information can be an embedded corner of the medical image as a watermark. Watermark can be considered one of the image distortions types. The most common objective evaluation algorithms are simple pixel based which are very unreliable, resulting in poor correlation with the human visual system. In this work, we proposed a new image quality metric which is a Measure of Singular Value Decomposition (M-SVD). Experimental results show that novel M-SVD algorithm gives very promising results against Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), Structural Similarity Index Measures (SSIM), and 3.4. Universal Image Quality (UIQ) assessments in watermarked and distorted images such as histogram equalization, JPEG compression, Gamma Correction, Gaussian Noise, Image Denoising, and Contrast Change.

Telemedicine is well known application where enormous amount of medical data need to be transferred securely over network and manipulate effectively. Security of digital data, especially medical images, becomes important for many reasons such as confidentiality, authentication and integrity. Digital watermarking has emerged as a advanced technology to enhance the security of digital images. The insertion of watermark in medical images can authenticate it and guarantee its integrity. The watermark must be generally hidden does not affect the quality of the medical image. In this paper, we propose blind watermarking based on Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD), we compare the performance of this technique with watermarking based DWT and SVD. The proposed method DWT, DCT and SVD comparatively better than DWT and SVD method.

Distal radius fractures (DRFs) account for approximately 18% of fractures in patients 65 years and older. While plain radiographs are standard, the value of high-resolution computed tomography (CT) for detailed imaging crucial for diagnosis, prognosis, and intervention planning, and increasingly recognized. High-definition 3D reconstructions from CT scans are vital for applications like 3D printing in orthopedics and for the utility of mobile C-arm CT in orthopedic diagnostics. However, concerns over radiation exposure and suboptimal image resolution from some devices necessitate the exploration of advanced computational techniques for refining CT imaging without compromising safety. Therefore, this study aims to utilize conditional Generative Adversarial Networks (cGAN) to improve the resolution of 3 mm CT images (CT enhancement). Following institutional review board approval, 3 mm-1 mm paired CT data from 11 patients with DRFs were collected. cGAN was used to improve the resolution of 3 mm CT images to match that of 1 mm images (CT enhancement). Two distinct methods were employed for training and generating CT images. In Method 1, a 3 mm CT raw image was used as input with the aim of generating a 1 mm CT raw image. Method 2 was designed to emphasize the difference value between the 3 mm and 1 mm images; using a 3 mm CT raw image as input, it produced the difference in image values between the 3 mm and 1 mm CT scans. Both quantitative metrics, such as peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index (SSIM), and qualitative assessments by two orthopedic surgeons were used to evaluate image quality by assessing the grade (1~4, which low number means high quality of resolution). Quantitative evaluations showed that our proposed techniques, particularly emphasizing the difference value in Method 2, consistently outperformed traditional approaches in achieving higher image resolution. In qualitative evaluation by two clinicians, images from method 2 showed better quality of images (grade: method 1, 2.7; method 2, 2.2). And more choice was found in method 2 for similar image with 1 mm slice image (15 vs 7, p = 201). In our study utilizing cGAN for enhancing CT imaging resolution, the authors found that the method, which focuses on the difference value between 3 mm and 1 mm images (Method 2), consistently outperformed.

Watermarking is crucial for protecting medical images, guaranteeing data integrity and authenticity in telemedicine, especially for rural healthcare facilities with limited computational power. This paper introduces a lightweight watermarking scheme that performs a comparative study of Lifting Wavelet Transform (LWT) versus Discrete Wavelet Transform (DWT), both combined with Discrete Cosine Transform (DCT), to achieve optimal imperceptibility, robustness, and computational efficiency. The comparison of LWT and DWT is essential because LWT has lower computational complexity and memory usage, hence more appropriate for resource-limited environments than DWT, and without the use of Singular Value Decomposition (SVD) to further reduce processing requirements and improve compatibility with low-power devices. The suggested approach adopts adaptive embedding against texture complexity, secure synchronization patterns with m-sequences and Barker codes, and geometric correction utilizing ORB for guaranteed watermark extraction under attacks from JPEG compression, Gaussian noise attack, rotation attack, median filter attack, and Gaussian blur. A new error correction code interleaved with majority voting is employed to increase the watermark’s strength. Tested at various embedding strengths, the scheme attains Peak Signal to Noise Ratio (PSNR) values over 40 dB and Structural Similarity Index (SSIM) values above 0.95, signifying extremely little visual degradation. Low Bit Error Rates (BER) and high Normalized Correlation (NC) in various attack scenarios validate its success. This computational-friendly solution is designed to arm remote healthcare organizations with limited computer ability to reliably authenticate and attest to medical images, meeting severe telemedicine demands.

In this paper, a blind image watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed. In this scheme, DWT is applied on ROI (region of interest) of the medical image to get different frequency subbands of its wavelet decomposition. On the low frequency subband LL of the ROI, block-SVD is applied to get different singular matrices. A pair of elements with similar values is identified from the left singular value matrix of these selected blocks. The values of these pairs are modified using certain threshold to embed a bit of watermark content. Appropriate threshold is chosen to achieve the imperceptibility and robustness of medical image and watermark contents respectively. For authentication and identification of original medical image, one watermark image (logo) and other text watermark have been used. The watermark image provides authentication whereas the text data represents electronic patient record (EPR) for identification. At receiving end, blind recovery of both watermark contents is performed by a similar comparison scheme used during the embedding process. The proposed algorithm is applied on various groups of medical images like X-ray, CT scan and mammography. This scheme offers better visibility of watermarked image and recovery of watermark content due to DWT-SVD combination. Moreover, use of Hamming error correcting code (ECC) on EPR text bits reduces the BER and thus provides better recovery of EPR. The performance of proposed algorithm with EPR data coding by Hamming code is compared with the BCH error correcting code and it is found that later one perform better. A result analysis shows that imperceptibility of watermarked image is better as PSNR is above 43 dB and WPSNR is above 52 dB for all set of images. In addition, robustness of the scheme is better than existing scheme for similar set of medical images in terms of normalized correlation coefficient (NCC) and bit-error-rate (BER). An analysis is also carried out to verify the performance of the proposed scheme for different size of watermark contents (image and EPR data). It is observed from analysis that the proposed scheme is also appropriate for watermarking of color image. Using proposed scheme, watermark contents are extracted successfully under various noise attacks like JPEG compression, filtering, Gaussian noise, Salt and pepper noise, cropping, filtering and rotation. Performance comparison of proposed scheme with existing schemes shows proposed scheme has better robustness against different types of attacks. Moreover, the proposed scheme is also robust under set of benchmark attacks known as checkmark attacks.

In this paper, a new robust and secure video watermarking scheme based on discrete Wavelet Transform (DWT), singular value decomposition (SVD), and Laplacian pyramid is proposed for copyright protection applications. The main contributions of this work are: 1) Increasing watermark robustness towards common signal processing attacks by separating the watermark image into a high-frequency image and a low-frequency image using laplacian pyramid. Then, the singular values of each of the low-frequency watermark image and the high-frequency watermark image are hidden in two different DWT subbands of the original video frame. Separating the watermark and hiding its two parts in two different subbands of the DWT of the video frame makes it robust towards noise, filtering, and compression attacks. 2) Improving the quality of the extracted watermarks via correcting the received watermarked videos using SURF points when those videos are geometrically attacked, mainly by rotation, scaling, and shear attacks. 3) Increasing security and avoiding false positive problems (FPP) associated with SVD by using perceptual image hashing in the proposed scheme. Through experimental results and comparisons to other techniques, the proposed scheme has shown very high imperceptibility for the tested watermarked videos in terms of peak signal to noise ratio (PSNR). Also, the proposed scheme has demonstrated high watermark robustness towards various types of attacks, and an improvement in the quality of the extracted watermarks from 4 to 20 dB compared to other techniques.

Due to the development of the latest technologies in the areas of communication and networking, the present businesses are moving to the digital world for accuracy, accessibility, safety and security. Normally, the medical image requires additional care to handle because treatment is mainly focused on the detail received from the medical images. Nowadays, digital watermarking is a blooming methodology for protecting medical data as for as security is concerned. This proposed method gives a new watermarking scheme for a secure transfer of Electronic Patient Record (EPR) using Hybrid Transform (DWT+DCT+SVD). Initially, the medical image is decomposed by Discrete Wavelet Transform then after Discrete Cosine Transform is performed in LH band and then Singular Value Decomposition is performed and a similar process to be done for the watermark image for embedding process and perform an inverse process for obtaining the watermarked image. Similarly, the above process to be done for the extraction process on the watermarked image to exact the EPR data and medical image. The effectiveness and performance of the proposed watermarking scheme being analyzed by measuring Peak Signal to Noise Ratio (PSNR) and Quality Index. The result is discussed with the help of experimental results using MATLAB tools.

Security and authentication of the people information is effectively provided through various watermarking algorithms. Most of the conventional watermarking algorithms have their own drawbacks in maintaining the quality and security of the image against various attacks. In order to overcome the above stated issues, a watermarking technique based on optimization is required for preserving image quality. In this paper, singular value decomposition (SVD) based digital image watermarking (DIW) scheme is proposed in hybrid discrete cosine transform (DCT) and discrete wavelet transform (DWT) using optimization algorithms like particle swarm optimization (PSO) and Jaya algorithm. The watermark image (WI) is embedded into low frequency DWT sub-band of DCT of cover image. PSO and Jaya algorithms are applied to improve the security threats and quality by assessing the fitness function. The work involves in utilizing the feature points in embedding of watermarks and analyzing the impact of various types of attacks with respect to image quality. The performance parameters like peak signal to noise ratio (PSNR), Structural Similarity Index (SSIM) and correlation coefficient of fitness function given by PSO and Jaya algorithm.

The success of internet technology transform the world of technology and fashioned our life such a lot easier. The matter of duplication and unauthorized use of information become a great threat within the field of technology. To beat these problems, techniques like digital watermarking, steganography and cryptography were introduced. The approach of embedding a secret data associated with the digital signal inside the signal itself is digital watermarking. For embedding and detecting the watermark different techniques are used; spatial domain techniques like Least Significant Bit (LSB) and Patch Work Algorithm, then the frequency domain techniques such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT) are some of them. Combination of DCT & DWT technique for digital watermarking is proposed here. The proposed methodology is implemented in MATLAB 2017a simulator and result is analysed using evaluation parameters like Peak Signal to Noise Ratio (PSNR) & Mean Square Error (MSE). PSNR value obtained for the case of watermarking using DWT is 27.46 and MSE value is 17.45. The value of PSNR for watermarking using DCT is 41.4 and for MSE the value is 0.67. The PSNR and MSE values obtained for watermarking using LSB technique is 50.55 and 0.58. The proposed method improves the watermarked image quality and the MSE & PSNR value obtained is 0.52 and 51.017 respectively.

An enhanced hybrid image watermarking scheme for security of medical and non-medical images based on DWT and 2-D SVD

Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. In recent studies, an optimum denoising filter using the wavelet threshold and deep-CNN was used to eliminate Gaussian noise in CT images using the image quality index (IQI) and peak signal-to-noise ratio (PSNR). Although the results were better than those with traditional techniques, the performance resulted in a loss of clarity and fine details’ preservation that rendered the CT images unsuitable. To address these challenges, this paper focuses on eliminating noise in CT scan images corrupted with additive Gaussian blur noise (AGBN) using an ensemble approach that integrates anisotropic Gaussian filter (AGF) and wavelet transform with a deep learning denoising convolutional neural network (DnCNN). First, the noisy image is denoised by AGF and Haar wavelet transform as preprocessing operations to eliminate AGBN. The DnCNN is then combined with AGF and wavelet for post-processing operation to eliminate the rest of the noises. Specifically, we used AGF due to its adaptability to edge orientation and directional information, which prevents blurring along edges for non-uniform noise distribution. Denoised images are evaluated using PSNR, mean squared error (MSE), and the structural similarity index measure (SSIM). Results revealed that the average PSNR value of the proposed ensemble approach is 28.28, and the average computational time is 0.01666 s. The implication is that the MSE between the original and reconstructed images is very low, implying that the image is restored correctly. Since the SSIM values are between 0 and 1.0, 1.0 perfectly matches the reconstructed image with the original image. In addition, the SSIM values at 1.0 or near 1.0 implicitly reveal a remarkable structural similarity between the denoised CT image and the original image. Compared to other techniques, the proposed ensemble approach has demonstrated exceptional performance in maintaining the quality of the image and fine details’ preservation.

Noise Removal in medical images is a requisite pre-processing step for medical image analysis. In this paper, we present three deep learning architectures: Convolutional Auto-encoders, U-Net and a Double U-Net architecture utilizing convolution layers for medical Image De-noising. We use PSNR(Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Metric) to compare the results of the networks.In our experiments, the variant of the U-Net architecture implemented performed better than the two architectures in terms of its SSIM.

The protection of medical images against unauthorized access and tampering is paramount. This paper presents a robust watermarking framework that integrates Discrete Wavelet Transform (DWT), Hessenberg Decomposition (HMD), Singular Value Decomposition (SVD), and Arnold Scrambling to enhance the security of medical images. By applying DWT to decompose the medical image into frequency subbands and embedding the watermark into the most significant subband, the proposed algorithm ensures minimal impact on image quality. HMD simplifies the subband matrix, while SVD extracts and manipulates the essential features of the image. Arnold Scrambling is employed to further secure the watermark image before embedding. Experimental results on various medical image datasets demonstrate the algorithm’s effectiveness in maintaining imperceptibility, with a peak signal-to-noise ratio (PSNR) of up to 49 dB, and robustness against common image processing attacks, such as compression and noise addition. The proposed scheme achieves a balance between imperceptibility and robustness, making it suitable for securing medical images in digital environments. The proposed scheme has been implemented on different medical datasets and the performance is evaluated in terms of its imperceptibility and robustness. The PSNR value achieved by the proposed work is 49 dB which proves that the embedded watermark image is imperceptible while the NC value achieved is higher than 0.9 against most of the attacks, hence proves its robustness against multiple attacks.

This paper presents a new robust hybrid multiple watermarking technique using fusion of discrete wavelet transforms (DWT), discrete cosine transforms (DCT), and singular value decomposition (SVD) instead of applying DWT, DCT and SVD individually or combination of DWT-SVD / DCT-SVD. For identity authentication purposes, multiple watermarks are embedded into the same medical image / multimedia objects simultaneously, which provides extra level of security with acceptable performance in terms of robustness and imperceptibility. In the embedding process, the cover image is decomposed into first level discrete wavelet transforms where the A (approximation/lower frequency sub-band) is transformed by DCT and SVD. The watermark image is also transformed by DWT, DCT and SVD. The S vector of watermark information is embedded in the S component of the cover image. The watermarked image is generated by inverse SVD on modified S vector and original U, V vectors followed by inverse DCT and inverse DWT. The watermark is extracted using an extraction algorithm. Furthermore, the text watermark is embedding at the second level of the D (diagonal sub-band) of the cover image. The security of the text watermark considered as EPR (Electronic Patient Record) data is enhanced by using encryption method before embedding into the cover. The results are obtained by varying the gain factor, size of the text watermark, and cover medical images. The method has been extensively tested and analyzed against known attacks and is found to be giving superior performance for robustness, capacity and reduced storage and bandwidth requirements compared to reported techniques suggested by other authors.

Image denoising is crucial in applications like medical imaging and photography, where restoring high-quality images from noisy data is essential. Traditional techniques often struggle with complex noise patterns, while deep learning-based methods typically rely on clean-noisy image pairs for training, limiting their practicality. Additionally, deep learning approaches face challenges such as the lack of ground truth clean images, sensitivity to specific noise types, and the introduction of artifacts during processing. In this work, we propose two novel self-supervised denoising approaches: a Discrete Wavelet Transform (DWT)-based model and a Non-Local Means (NLM)-based model. The DWT-based approach employs wavelet decomposition to separate image details across multiple frequency scales, selectively suppressing high-frequency noise via soft thresholding while preserving low-frequency components. The resulting wavelet coefficients are used to create pseudo-clean targets for training a U-Net architecture, ensuring effective denoising while maintaining structural integrity. The NLM-based approach leverages redundancy in image patches by applying the NLM algorithm to generate pseudo-clean targets through patch similarity-based averaging. These targets train a U-Net model with a custom loss function that balances Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), optimizing perceptual quality. Both models are trained on 5,000 noisy images from the ImageNet validation set without relying on clean references. Validated on synthetic Gaussian and Poisson noise at varying magnitudes, the DWT-based model achieved Mean PSNR and SSIM values of 31.07 and 0.9279, respectively, while the NLM-based model attained 30.17 and 0.9303. These results demonstrate the robustness and effectiveness of the proposed methods, making them suitable for real-world applications such as medical diagnostics and low-light photography.