Higher Peak Signal To Noise Ratio Research Articles

Imperceptible Image Steganography Using Symmetry-Adapted Deep Learning Techniques

Digital image steganography is the process of embedding information within a cover image in a secure, imperceptible, and recoverable way. This research extends a symmetry-adapted deep-learning approach to identify hidden patterns of images using two-dimensional convolutional neural networks (CNN). The proposed method (SteGuz) uses three CNNs to implement the different phases of the steganography process on digital image covers. SteGuz introduced a gain function, based on several image similarity metrics, to maximize the imperceptibility of the hiding process. Using 10 different pairs of cover-secret images, the performance of the proposed method was measured in terms of standard metrics such as peak signal to noise ratio (PSNR) and structured similarity index measurement (SSIM). The results showed that the proposed methodology outperformed the original method in terms of both imperceptibility and recoverability. In addition, when compared with some existing methods, SteGuz proved the outstanding performance of achieving a very high PSNR value while maintaining high accuracy of the extracted image.

Pseudo low-energy monochromatic imaging of head and neck cancers: Deep learning image reconstruction with dual-energy CT.

Low-energy virtual monochromatic images (VMIs) derived from dual-energy computed tomography (DECT) systems improve lesion conspicuity of head and neck cancer over single-energy CT (SECT). However, DECT systems are installed in a limited number of facilities; thus, only a few facilities benefit from VMIs. In this work, we present a deep learning (DL) architecture suitable for generating pseudo low-energy VMIs of head and neck cancers for facilities that employ SECT imaging. We retrospectively analyzed 115 patients with head and neck cancers who underwent contrast enhanced DECT. VMIs at 70 and 50keV were used as the input and ground truth (GT), respectively. We divided them into two datasets: for DL (104 patients) and for inference with SECT (11 patients). We compared four DL architectures: U-Net, DenseNet-based, and two ResNet-based models. Pseudo VMIs at 50keV (pVMI50keV) were compared with the GT in terms of the mean absolute error (MAE) of Hounsfield unit (HU) values, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). The HU values for tumors, vessels, parotid glands, muscle, fat, and bone were evaluated. pVMI50keV were generated from actual SECT images and the HU values were evaluated. U-Net produced the lowest MAE (13.32 ± 2.20 HU) and highest PSNR (47.03 ± 2.33dB) and SSIM (0.9965 ± 0.0009), with statistically significant differences (P < 0.001). The HU evaluation showed good agreement between the GT and U-Net. U-Net produced the smallest absolute HU difference for the tumor, at < 5.0 HU. Quantitative comparisons of physical parameters demonstrated that the proposed U-Net could generate high accuracy pVMI50keV in a shorter time compared with the established DL architectures. Although further evaluation on diagnostic accuracy is required, our method can help obtain low-energy VMI from SECT images without DECT systems.

Design and Analysis of Reversible Data Hiding Using Hybrid Cryptographic and Steganographic approaches for Multiple Images

Data concealing is the process of including some helpful information on images. The majority of sensitive applications, such sending authentication data, benefit from data hiding. Reversible data hiding (RDH), also known as invertible or lossless data hiding in the field of signal processing, has been the subject of a lot of study. A piece of data that may be recovered from an image to disclose the original image is inserted into the image during the RDH process to generate a watermarked image. Lossless data hiding is being investigated as a strong and popular way to protect copyright in many sensitive applications, such as law enforcement, medical diagnostics, and remote sensing. Visible and invisible watermarking are the two types of watermarking algorithms. The watermark must be bold and clearly apparent in order to be visible. To be utilized for invisible watermarking, the watermark must be robust and visibly transparent. Reversible data hiding (RDH) creates a marked signal by encoding a piece of data into the host signal. Once the embedded data has been recovered, the original signal may be accurately retrieved. For photos shot in poor illumination, visual quality is more important than a high PSNR number. The DH method increases the contrast of the host picture while maintaining a high PSNR value. Histogram equalization may also be done concurrently by repeating the embedding process in order to relocate the top two bins in the input image's histogram for data embedding. It's critical to assess the images after data concealment to see how much the contrast has increased. Common picture quality assessments include peak signal to noise ratio (PSNR), relative structural similarity (RSS), relative mean brightness error (RMBE), relative entropy error (REE), relative contrast error (RCE), and global contrast factor (GCF). The main objective of this paper is to investigate the various quantitative metrics for evaluating contrast enhancement. The results show that the visual quality may be preserved by including a sufficient number of message bits in the input photographs.

Learn Less, Infer More: Learning in the Fourier Domain for Quantitative Susceptibility Mapping.

Quantitative susceptibility mapping (QSM) aims to evaluate the distribution of magnetic susceptibility from magnetic resonance phase measurements by solving the ill-conditioned dipole inversion problem. Removing the artifacts and preserving the anisotropy of tissue susceptibility simultaneously is still a challenge in QSM. To deal with this issue, a novel k-QSM network is proposed to resolve dipole inversion issues in QSM reconstruction. The k-QSM network converts the results obtained by truncated k-space division (TKD) into the Fourier domain as inputs. After passing through several convolutional and residual blocks, the ill-posed signals of TKD are corrected by making the network output close to the calculation of susceptibility through multiple orientation sampling (COSMOS)-labeled QSM. To evaluate the superiority of k-QSM, comparisons with several state-of-the-art methods are performed in terms of QSM artifacts removing, anisotropy preserving, generalization ability, and clinical applications. Compared to existing methods, the k-QSM achieves a 22.31% lower normalized root mean square error, 10.30% higher peak signal-to-noise ratio (PSNR), 33.10% lower high-frequency error norm, and 1.06% higher structural similarity. In addition, the orientation-dependent susceptibility variation obtained by k-QSM is significant, verifying that k-QSM has the ability to preserve susceptibility anisotropy. When the trained models are tested on the dataset from different centers, our k-QSM shows a strong generalization ability with the highest PSNR. Moreover, by comparing the susceptibility maps between healthy controls and drug addicts with different methods, we found the proposed k-QSM is more sensitive to the susceptibility abnormality in the patients. The proposed k-QSM method learns less—only to fix the ill-posed signals of TKD, but infers more—both COSMOS-like and anisotropy-preserving QSM results. Its generalization ability and great sensitivity to susceptibility changes can make it a potential method for distinguishing some diseases.

Reversible Watermarking Method with Low Distortion for the Secure Transmission of Medical Images

In telemedicine, the realization of reversible watermarking through information security is an emerging research field. However, adding watermarks hinders the distribution of pixels in the cover image because it creates distortions (which lead to an increase in the detection probability). In this article, we introduce a reversible watermarking method that can transmit medical images with minimal distortion and high security. The proposed method selects two adjacent gray pixels whose least significant bit (LSB) is different from the relevant message bit and then calculates the distortion degree. We use the LSB pairing method to embed the secret matrix of patient record into the cover image and exchange pixel values. Experimental results show that the designed method is robust to different attacks and has a high PSNR (peak signal-to-noise ratio) value. The MRI image quality and imperceptibility are verified by embedding a secret matrix of up to 262,688 bits to achieve an average PSNR of 51.657 dB. In addition, the proposed algorithm is tested against the latest technology on standard images, and it is found that the average PSNR of our proposed reversible watermarking technology is higher (i.e., 51.71 dB). Numerical results show that the algorithm can be extended to normal images and medical images.

A Rician Noise Prediction and Removal Model for MRI Head Scans using wavelet based Non-Local Median Filter

In the medical imaging field, anatomical structure preservation is a difficult task during the denoising process. Magnetic resonance imaging (MRI) scanner corrupts the images by the Rician noise during the acquisition process. Rician noise affects the diagnosis and treatment planning for the subjects. Regenerating noise-free images is a time-consuming process with limited MR scanner resources available in developing countries. Therefore, the medical industry utilizes the advancement of computer-aided automatic denoising methods. This article presents a novel denoising method for Rician noise using a wavelet-based non-local median filtering (WNLMed) technique. The work contains three phases: noise estimation, wavelet thresholding using a lifting scheme, and non-local median filter (NLMed). Materials used for this experiment are collected from the Brainweb repositories and tested with validation metrics such as normalized absolute error (NAE), peak signal to noise ratio (PSNR), structure similarity index measure (SSIM), the figure of merit (FOM), and compared with the state-of-the-art methods. The method yields high PSNR value than other methods.

Morphological Based Medical Image Processing on Cervical Cytology Cancer Images Using Connected Component Techniques.

Cervical cancer is the fourth most common cancer in women. In 2018, it was estimated 570000 women were diagnosed with cervical cancer worldwide and about 311000 women died from the disease. An efficient technique is essential for solving the complication in the diagnosis of cervical cancer images. In this research, a new method is developed for cervical cancer image segmentation. First, the RGB image is converted into an HSI color model. The thresholding is applied to the saturation and intensity components to get binary images. These binary images are combined to get a new mask. Using the connected component concept nucleus and cytoplasm are segmented accurately. For the performance evaluation, peak signal-to-noise ratio (PSNR), mean square error (MSE), structural similarity index (SSIM), image quality index (IQI), structural content (SC), normalized cross-correlation (NK), precision (PR), recall (RC), the Average Difference (AD) and image fidelity (IF) are taken. The proposed techniques' highest PSNR values are 44.2341, 46.7953, 60.5925, and 61.4862, respectively. The proposed segmentation technique can attain a high PSNR (>40db) value at a threshold value equal to 0.1. Also proposed approach attains good precision, recall, and SSIM values. The lowest MSE values using proposed segmentation techniques are 0.0454, 0.0351, 0.0924, and 0.0271 individually. The AD, NK, SC, NAE, and LMSE values for the implemented approach are low, showing the segmented image's quality is very good. Thus, the proposed model performed better compared to other methods.

Deep learning\u2013based denoising of low-dose SPECT myocardial perfusion images: quantitative assessment and clinical performance

PurposeThis work was set out to investigate the feasibility of dose reduction in SPECT myocardial perfusion imaging (MPI) without sacrificing diagnostic accuracy. A deep learning approach was proposed to synthesize full-dose images from the corresponding low-dose images at different dose reduction levels in the projection space.MethodsClinical SPECT-MPI images of 345 patients acquired on a dedicated cardiac SPECT camera in list-mode format were retrospectively employed to predict standard-dose from low-dose images at half-, quarter-, and one-eighth-dose levels. To simulate realistic low-dose projections, 50%, 25%, and 12.5% of the events were randomly selected from the list-mode data through applying binomial subsampling. A generative adversarial network was implemented to predict non-gated standard-dose SPECT images in the projection space at the different dose reduction levels. Well-established metrics, including peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and structural similarity index metrics (SSIM) in addition to Pearson correlation coefficient analysis and clinical parameters derived from Cedars-Sinai software were used to quantitatively assess the predicted standard-dose images. For clinical evaluation, the quality of the predicted standard-dose images was evaluated by a nuclear medicine specialist using a seven-point (− 3 to + 3) grading scheme.ResultsThe highest PSNR (42.49 ± 2.37) and SSIM (0.99 ± 0.01) and the lowest RMSE (1.99 ± 0.63) were achieved at a half-dose level. Pearson correlation coefficients were 0.997 ± 0.001, 0.994 ± 0.003, and 0.987 ± 0.004 for the predicted standard-dose images at half-, quarter-, and one-eighth-dose levels, respectively. Using the standard-dose images as reference, the Bland–Altman plots sketched for the Cedars-Sinai selected parameters exhibited remarkably less bias and variance in the predicted standard-dose images compared with the low-dose images at all reduced dose levels. Overall, considering the clinical assessment performed by a nuclear medicine specialist, 100%, 80%, and 11% of the predicted standard-dose images were clinically acceptable at half-, quarter-, and one-eighth-dose levels, respectively.ConclusionThe noise was effectively suppressed by the proposed network, and the predicted standard-dose images were comparable to reference standard-dose images at half- and quarter-dose levels. However, recovery of the underlying signals/information in low-dose images beyond a quarter of the standard dose would not be feasible (due to very poor signal-to-noise ratio) which will adversely affect the clinical interpretation of the resulting images.

A Comparative Analysis of LSB, MSB and PVD Based Image Steganography

Steganography is the art and science of hiding information by embedding data into cover media. Numerous techniques are designed to provide the security for the communication of data over the Internet. A good steganographic algorithm is recognized by the performance of the techniques measured with the support of the performance metrics among which are PSNR, MSE, SSIM, robustness and capacity to hide the information in the cover image. In this paper a comparative analysis of Least Significant Bit (LSB), Most Significant Bit (MSB) and Pixel Value Differencing (PVD) image steganography in grayscale and colored images was performed. Three different cover images was used to hide secret message. A comparative performance analysis of LSB, MSB and PVD methods used in image steganography was performed using peak signal to noise ratio (PSNR), Mean square error (MSE) and Structural Similarity index (SSIM) as performance metrics. LSB technique gives higher PSNR and SSIM values than MSB and PVD method with lower MSE than the other two techniques. Future research can be geared towards investigating the embedding capacity, security, and computational complexity of each technique. Keywords: Least Significant Bit (LSB), Most Significant Bit (MSB), Pixel value differencing (PVD), PSNR, SSIM and MSE,

Calculation of Apparent Diffusion Coefficients in Prostate Cancer Using Deep Learning Algorithms: A Pilot Study.

BackgroundApparent diffusion coefficients (ADCs) obtained with diffusion-weighted imaging (DWI) are highly valuable for the detection and staging of prostate cancer and for assessing the response to treatment. However, DWI suffers from significant anatomic distortions and susceptibility artifacts, resulting in reduced accuracy and reproducibility of the ADC calculations. The current methods for improving the DWI quality are heavily dependent on software, hardware, and additional scan time. Therefore, their clinical application is limited. An accelerated ADC generation method that maintains calculation accuracy and repeatability without heavy dependence on magnetic resonance imaging scanners is of great clinical value.ObjectivesWe aimed to establish and evaluate a supervised learning framework for synthesizing ADC images using generative adversarial networks.MethodsThis prospective study included 200 patients with suspected prostate cancer (training set: 150 patients; test set #1: 50 patients) and 10 healthy volunteers (test set #2) who underwent both full field-of-view (FOV) diffusion-weighted imaging (f-DWI) and zoomed-FOV DWI (z-DWI) with b-values of 50, 1,000, and 1,500 s/mm2. ADC values based on f-DWI and z-DWI (f-ADC and z-ADC) were calculated. Herein we propose an ADC synthesis method based on generative adversarial networks that uses f-DWI with a single b-value to generate synthesized ADC (s-ADC) values using z-ADC as a reference. The image quality of the s-ADC sets was evaluated using the peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity (SSIM), and feature similarity (FSIM). The distortions of each ADC set were evaluated using the T2-weighted image reference. The calculation reproducibility of the different ADC sets was compared using the intraclass correlation coefficient. The tumor detection and classification abilities of each ADC set were evaluated using a receiver operating characteristic curve analysis and a Spearman correlation coefficient.ResultsThe s-ADCb1000 had a significantly lower RMSE score and higher PSNR, SSIM, and FSIM scores than the s-ADCb50 and s-ADCb1500 (all P < 0.001). Both z-ADC and s-ADCb1000 had less distortion and better quantitative ADC value reproducibility for all the evaluated tissues, and they demonstrated better tumor detection and classification performance than f-ADC.ConclusionThe deep learning algorithm might be a feasible method for generating ADC maps, as an alternative to z-ADC maps, without depending on hardware systems and additional scan time requirements.

SECURITY TECHNIQUE BASED ON WATERMARKING AND CHAOTIC ENCRYPTION FOR DIGITAL IMAGE

With the widespread usage of the Internet, security takes precedence above anything else when transmitting data. This research proposed a hybrid encoding approach with watermark embedding that provides excellent security. Our novel strategy is based on cryptography, which allows it to recognize higher secrecy and efficiency. In this research, a hybrid method is used to support visual watermarking and cryptography to embed vital data. Our effort aims to improve the security of hidden facts embedded in the cover picture. The embedding and encryption of the watermark image into the cover image document were performed in two steps. To begin, two keys and an XOR bit operation were used to generate a large number of distinct keys for encryption. Second, a modified approach of the least significant bit (LSB) technique was adopted to hide a high-resolution watermark picture in the cover picture. The suggested second stage involved encrypting the cover image using the asymmetric key cryptography method (RSA), which provides additional secrecy during picture transmission. The original picture, the watermark photo, can be recovered and decoded using the permitted techniques. As a result, encoding and watermarking may be combined, giving the term "joint encoding and watermarking" legitimacy. Peak Signal to Noise Ratio (PSNR) and relationship aspects are greater with this method. For the implementation of hiding and encoding for the watermark and encoding the digital cover picture, MATLAB-GUI software was utilized. Experimental results demonstrated a good performance with a good correlation for all encrypted images and very high PSNR of the Stego images.

An efficient trapezoidal compression algorithm using wavelet transformation for medical image

Image compression processes moderate the number of bits essential to signify an image, which can improve the performance of systems during storage and transmission without compromising image quality. They are classified into lossy compression and lossless compression. In this work, a new algorithm called trapezoidal algorithm has been proposed for image compression. The encoding part of proposed algorithm is like trapezoid shape so it is named as trapezoidal algorithm. Two transformation techniques DWT, IWT with three wavelets such as Haar, Sym4 and Coif1 have been combined for image compression to confer good characteristics of these methods. In this each pixel coordinates are encoded using the logic from SPECK algorithm. In SPECK, the set S is encoded when the pixel level is reached whereas in proposed algorithm (trapezoidal) the set S is encoded when the size of set S is reached 4x4. The approximation of image is named as set S. set S can be formed into three subset termed as s1, s2, s3. S is grouped into many subsets each set can be defined based on a pattern of proposed algorithm. Magnetic Resonance Imaging (MRI) of brain and Computer Tomography (CT) of lung images are used for analyzing compression. The proposed algorithm gives high PSNR compared to existing algorithms EZW, SPIHT and SPECK. The performance metrics such as Peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM), Mean square error (MSE), Bits Per Pixel (BPP), Compression Ratio (CR) and Compression Time (CT) are measured for lung and brain images. The dataset has been collected from various scan centers.

Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction

Quantitative susceptibility mapping (QSM) and R2* mapping are MRI post-processing methods that quantify tissue magnetic susceptibility and transverse relaxation rate distributions. However, QSM and R2* acquisitions are relatively slow, even with parallel imaging. Incoherent undersampling and compressed sensing reconstruction techniques have been used to accelerate traditional magnitude-based MRI acquisitions; however, most do not recover the full phase signal, as required by QSM, due to its non-convex nature. In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM and R2* acquisition. Magnitude, phase, R2*, and QSM results from DCRNet were compared with two iterative and one deep learning methods on retrospectively undersampled acquisitions from six healthy volunteers, one intracranial hemorrhage and one multiple sclerosis patients, as well as one prospectively undersampled healthy subject using a 7T scanner. Peak signal to noise ratio (PSNR), structural similarity (SSIM), root-mean-squared error (RMSE), and region-of-interest susceptibility and R2* measurements are reported for numerical comparisons. The proposed DCRNet method substantially reduced artifacts and blurring compared to the other methods and resulted in the highest PSNR, SSIM, and RMSE on the magnitude, R2*, local field, and susceptibility maps. Compared to two iterative and one deep learning methods, the DCRNet method demonstrated a 3.2% to 9.1% accuracy improvement in deep grey matter susceptibility when accelerated by a factor of four. The DCRNet also dramatically shortened the reconstruction time of single 2D brain images from 36-140 seconds using conventional approaches to only 15-70 milliseconds.

COMBINING SUPER-RESOLUTION ALGORITHM (GAUSSIAN DENOISING AND KERNEL BLURRING) AND COMPARING WITH CAMERA SUPER- RESOLUTION

A good Super Resolution (SR) algorithm is one of the key successes to filter frequency that creates noise to a picture. Previous research that has published was concluded the Camera SR is the best algorithm to filter this frequency based on their Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) results. However, the current approach to achieving high resolution have not yielded enough signal to filter unwanted pixel. Hence, there is a need to find a better approach to those leads to higher resolution through lower noise reduction. To fulfill this need, this thesis proposed to utilize two proven SR algorithms; Gaussian Denoising and Kernel Blurring. This thesis will not only be obtaining these two existing algorithms in a stand-alone form but hence the combination of them (two combinations) will also be obtained as the new possible algorithms that can be utilized to filter frequency that create noise to a picture. To reach the research objective, the method that will be used is by training a total of four algorithms one by one to a public data set that contains 200 pictures and gets the PSNR and MSE results of each algorithm. Comprehensive experimental results show that all those four SR algorithms outperform previous SR algorithms in commonly used data set with variously higher PSNR by 21% and lower MSE by 5%.

Application of machine-learning models to improve the image quality of photon-counting CT images

X-ray computed tomography (CT) has been widely used in medical diagnostic imaging. However, conventional, energy-integrated CT requires a high radiation dose and can only provide monochromatic images that cannot eliminate various artifacts. In contrast, photon-counting CT (PC-CT) provides low-dose multicolor CT imaging, which enables the identification of multiple contrast agents. However, in the PC-CT system, the lack of photon statistics, which is also caused by image reconstruction in the limited energy band, severely affects the image quality. In this study, we applied three types of machine-learning (ML) techniques to improved the image quality of PC-CT, that is, dictionary learning, U-Net, and Noise2Noise. These ML models were trained using low- and high-dose image pairs created in simple steps. The trained ML models were applied to simulated data, and experimental PC-CT images of contrast agents used in clinical practice. Consequently, in the simulated data, the peak signal-to-noise ratio (PSNR) value improved from 21.3 for the input to 26.6, 33.3, and 30.1 for dictionary learning, U-Net, and Noise2Noise, respectively. Furthermore, in the actual PC-CT images, we successfully reproduced PC-CT images with high PSNR, which enabled simultaneous imaging of multiple contrast agents with improved accuracy of concentration estimation. As a future perspective, we will develop a processing technique that can be applied to in vivo CT images.

An optimal weighted HEVC coding for video compression

In recent years, the applications of multimedia are rising in greedy mode and hence the amount of video transactions are also increasing exponentially. It has been shouted a great demands for researchers to develop an effective models for video encoding & decoding, compression, bandwidth allocation, transmission channel and congestion control. This research work mainly focusses on to develop an efficient video compression standard algorithm named as High Efficiency Video Coding (HEVC). It proposes a new improved Iterative based propagation update in water wave Optimization Algorithm (IPU-WWO) that optimize the weights in adopted HEVC encoding. The proposed HEVC produces double the ratio of data compression at the similar level of quality of the video and considerably enhanced video quality with same bit rate compared with Advanced Video Coding (AVC). The performance of proposed IPU-WWO is compared with other conventional methods like Artificial Bee Colony (ABC), Firefly (FF), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) with respect to Peak Signal to Noise Ratio (PSNR). The experimental results show that proposed method produces better compression rate of 3.03%, 4.98%, 6.82%, and 8.38% compared with PSO, GA, FF, and ABC, respectively with high PSNR for the block size of 4. Moreover, the proposed method outperforms the state-of-the-art fast encoding algorithms for in terms of compression and performance.

Approximate Full Adders for Energy Efficient Image Processing Applications

This paper proposes six novel approximate 1-bit full adders (AFAs) for inexact computing. The six novel AFAs namely AFA1, AFA2, AFA3, AFA4, AFA5, and AFA6 are derived from state-of-the-art exact 1-bit full adder (EFA) architectures. The performance of these AFAs is compared with reported AFAs (RAAs) in terms of design metrics (DMs) and peak-signal-to-noise-ratio (PSNR). The DMs under consideration are power, delay, power-delay-product (PDP), energy-delay-product (EDP), and area. For a fair comparison, the EFAs and proposed AFAs along with RAAs are described in Verilog, simulated, and synthesized using Cadences’ RC tool, using generic 180 nm standard cell library. The unconstrained synthesis results show that: among all the proposed AFAs, the AFA1 and AFA2 are found to be energy-efficient adders with high PSNR. The AFA1 has a total [Formula: see text][Formula: see text][Formula: see text]W, [Formula: see text][Formula: see text]ps, [Formula: see text][Formula: see text]fJ, [Formula: see text][Formula: see text]Js, [Formula: see text][Formula: see text][Formula: see text]m2, and [Formula: see text][Formula: see text]dB. And the AFA2 has the total [Formula: see text][Formula: see text][Formula: see text]W, [Formula: see text][Formula: see text]ps, [Formula: see text][Formula: see text]fJ, [Formula: see text][Formula: see text]Js, [Formula: see text][Formula: see text][Formula: see text]m2, and [Formula: see text][Formula: see text]dB.

An Improved Method for Combine (LSB and MSB) Based on Color Image RGB

Image steganography is the art of hiding data into an image by using the secret key. This paper presents two techniques that combine the most significant bit (MSB) as well as the least significant bit (LSB) based on a color image (24bit for RGB). The presented study proposes a novel method to combine (LSB and MSB) bits based on check MSB values and replace bits from LSB with a secret message. The result of this proposed method that made not affect quality stego -image based on the resulting histogram that shows a match between the cover image and stego- image and more secure because not hidden in all image. The factors were used Mean Square Error (MSE), Compute Payload, in addition to Peak Signal to Noise Ratio (PSNR). The PSNR’s rate is high and MSE is less. The result of this paper when applying on the different image gives high PSNR of 87.141 and less MSE of 0.00012 when inserting message 80 bits and reduction value PSNR of 72.023 and MSE of 0.0040 when inserting message 1200 bits and measure entropy is the same value for cover image and stego –image then this method is more security for the attacker.

A blind and high-capacity data hiding of DICOM medical images based on fuzzification concepts

Data hiding technique using steganography prospered widely due to its high un-detectability, flexibility to the size of hidden data, and robustness against compression and other image processes. Accordingly, this paper aims to use a steganographic technique based on DICOM medical images, where one medical image is used as a cover image, and the other one is used as the secret message image. This method entails three main parts: preprocessing, data embedding based on the discrete cosine transform (DCT), and an extraction process. The performances of the algorithm were evaluated using Magnetic resonance imaging (MRI) dataset, the metrics of the Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), the Structural Similarity Index (SSIM), the Universal Quality Index (UQI), and the correlation coefficient (R) values. The experimental results scored a high PSNR after the embedding process and high capacity of the hidden data by embedding a DICOM image into another DICOM image of the same size.

An efficient trapezoidal compression algorithm using wavelet transformation for medical image

Image compression processes moderate the number of bits essential to signify an image, which can improve the performance of systems during storage and transmission without compromising image quality. They are classified into lossy compression and lossless compression. In this work, a new algorithm called trapezoidal algorithm has been proposed for image compression. The encoding part of proposed algorithm is like trapezoid shape so it is named as trapezoidal algorithm. Two transformation techniques DWT, IWT with three wavelets such as Haar, Sym4 and Coif1 have been combined for image compression to confer good characteristics of these methods. In this each pixel coordinates are encoded using the logic from SPECK algorithm. In SPECK, the set S is encoded when the pixel level is reached whereas in proposed algorithm (trapezoidal) the set S is encoded when the size of set S is reached 4x4. The approximation of image is named as set S. set S can be formed into three subset termed as s1, s2, s3. S is grouped into many subsets each set can be defined based on a pattern of proposed algorithm. Magnetic Resonance Imaging (MRI) of brain and Computer Tomography (CT) of lung images are used for analyzing compression. The proposed algorithm gives high PSNR compared to existing algorithms EZW, SPIHT and SPECK. The performance metrics such as Peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM), Mean square error (MSE), Bits Per Pixel (BPP), Compression Ratio (CR) and Compression Time (CT) are measured for lung and brain images. The dataset has been collected from various scan centers.