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In the era of 5 G network advancements, the potential for extremely robust, less-latency, and huge-capacity communication opens up new perspective for multimedia. Steganography enables embedding of sensitive data within multimedia files, making it unreadable to unauthorized third parties. Notably, when using videos as cover, the capacity for data embedding is substantially increased. Recent developments in steganography have largely revolved around modified versions of transform domain techniques. Due to this repetitiveness, it becomes easier for steganalytic tools in detecting concealed data. Addressing this issue, our paper introduces an innovative data embedding approach MARVIS based on the Mellin transform. The superiority of the proposed approach is exhibited using the metrics, MSE, PSNR, and SSIM. MARVIS has achieved PSNR of 50-60 dB and SSIM of 0.9998 for embedding 4 bits of secret data, outperforming other methods that achieve 40 dB for 1 bit. By quadrupling stego capacity, we can embed more secret data per pixel without compromising the integrity of the cover object.•MARVIS utilizes phase modulation for data embedding, offering advantages beyond traditional frequency domain techniques which use frequency domain for data embedding.•The effectiveness of the proposed data embedding approach is validated through Y-Net, a deep learning-based steganalysis tool.

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The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients' information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients' information in an electrocardiogram (ECG). In this system, we first consider a non-linear model with a hiding state switch to enhance the quality of the hidden ECG signals. Next, we utilize particle swarm optimization (PSO) to solve the non-linear model so as to have a good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative root mean square error (rRMSE). Accordingly, the distortion of the shape in each ECG signal is tiny, while the hidden information can fulfill the needs of physiological diagnostics. The extraction of hidden information is reversely similar to a hiding procedure without primary ECG signals. Preliminary outcomes confirm the effectiveness of our proposed method, especially an Amplitude Similarity of almost 1, an Interval RMSE of almost 0, and SNRs all above 30.

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PURPOSE: Dynamic PET is an essential tool in oncology due to its ability to visualize and quantify radiotracer uptake, which has the potential to improve imaging quality. However, image noise caused by a low photon count in dynamic PET is more significant than in static PET. This study aims to develop a novel denoising method, namely the Guided Block Matching and 4-D Transform Domain Filter (GBM4D) projection, to enhance dynamic PET image reconstruction. METHODS: The sinogram was first transformed using the Anscombe method, then denoised using a combination of hard thresholding and Wiener filtering. Each denoising step involved guided block matching and grouping, collaborative filtering, and weighted averaging. The guided block matching was performed on accumulated PET sinograms to prevent mismatching due to low photon counts. The performance of the proposed denoising method (GBM4D) was compared to other methods such as wavelet, total variation, non-local means, and BM3D using computer simulations on the Shepp-Logan and digital brain phantoms. The denoising methods were also applied to real patient data for evaluation. RESULTS: In all phantom studies, GBM4D outperformed other denoising methods in all time frames based on the structural similarity and peak signal-to-noise ratio. Moreover, GBM4D yielded the lowest root mean square error in the time-activity curve of all tissues and produced the highest image quality when applied to real patient data. CONCLUSION: GBM4D demonstrates excellent denoising and edge-preserving capabilities, as validated through qualitative and quantitative assessments of both temporal and spatial denoising performance.

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Recently, stereoscopic image quality assessment has attracted a lot attention. However, compared with 2D image quality assessment, it is much more difficult to assess the quality of stereoscopic images due to the lack of understanding of 3D visual perception. This paper proposes a novel no-reference quality assessment metric for stereoscopic images using natural scene statistics with consideration of both the quality of the cyclopean image and 3D visual perceptual information (binocular fusion and binocular rivalry). In the proposed method, not only is the quality of the cyclopean image considered, but binocular rivalry and other 3D visual intrinsic properties are also exploited. Specifically, in order to improve the objective quality of the cyclopean image, features of the cyclopean images in both the spatial domain and transformed domain are extracted based on the natural scene statistics (NSS) model. Furthermore, to better comprehend intrinsic properties of the stereoscopic image, in our method, the binocular rivalry effect and other 3D visual properties are also considered in the process of feature extraction. Following adaptive feature pruning using principle component analysis, improved metric accuracy can be found in our proposed method. The experimental results show that the proposed metric can achieve a good and consistent alignment with subjective assessment of stereoscopic images in comparison with existing methods, with the highest SROCC (0.952) and PLCC (0.962) scores being acquired on the LIVE 3D database Phase I.

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Multi-Focus image fusion is of great importance in order to cope with the limited Depth-of-Field of optical lenses. Since input images contain noise, multi-focus image fusion methods that support denoising are important. Transform-domain methods have been applied to image fusion, however, they are likely to produce artifacts. In order to cope with these issues, we introduce the Conditional Random Field (CRF) CRF-Guided fusion method. A novel Edge Aware Centering method is proposed and employed to extract the low and high frequencies of the input images. The Independent Component Analysis-ICA transform is applied to high-frequency components and a Conditional Random Field (CRF) model is created from the low frequency and the transform coefficients. The CRF model is solved efficiently with the α-expansion method. The estimated labels are used to guide the fusion of the low-frequency components and the transform coefficients. Inverse ICA is then applied to the fused transform coefficients. Finally, the fused image is the addition of the fused low frequency and the fused high frequency. CRF-Guided fusion does not introduce artifacts during fusion and supports image denoising during fusion by applying transform domain coefficient shrinkage. Quantitative and qualitative evaluation demonstrate the superior performance of CRF-Guided fusion compared to state-of-the-art multi-focus image fusion methods.

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With the advent of the aging era, healthcare and elderly care have become the focus of medical care, especially the care of the elderly with dementia. Patients' confidential data hiding is a useful technology for healthcare and patient information privacy. In this study, we implement an intelligent healthcare system using the multiple-coefficient quantization technology in transform domain to hide patients' confidential data into electrocardiogram (ECG) signals obtained by ECG sensor module. In embedding patients' confidential data, we first consider a non-linear model for optimizing the quality of the embedded ECG signals. Next, we apply simulated annealing (SA) to solve the non-linear model so as to have good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative RMSE (rRMSE). Accordingly, the distortion of the PQRST complexes and the ECG amplitude is very small so that the embedded confidential data can satisfy the requirements of physiological diagnostics. In end devices, one can receive the ECG signals with the embedded confidential data and without the original ECG signals. Experimental results confirm the effectiveness of our method, which remains high quality for each ECG signal with the embedded confidential data no matter how the quantization size Q is increased.

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BACKGROUND: Obtaining the medical history from a patient is a tedious task for doctors as it depends on a lot of factors which are difficult to keep track from a patient's perspective. Doctors have to rely upon technological tools to make a swift and accurate judgment about the patient's health. INTRODUCTION: Out of many such tools, there are two special imaging modalities known as X-ray - Computed Tomography (CT) and Magnetic Resonance imaging (MRI) which are of significant importance in the medical world assisting the diagnosis process. METHODS: The advancement in signal processing theory and analysis has led to the design and implementation of a large number of image processing and fusion algorithms. Each of these methods has evolved in the terms of their computational efficiency and visual results over the years. RESULTS: Various researches have revealed their properties in terms of their efficiency and outreach and it has been concluded that image fusion can be a very suitable process that can help to compensate for the drawbacks. CONCLUSION: In this manuscript, recent state-of-the-art techniques have been used to fuse these image modalities and established its need and importance in a more intuitive way with the help of a wide range of assessment parameters.

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The Internet of Things (IoT) leads the era of interconnection, where numerous sensors and devices are being introduced and interconnected. To support such an amount of data traffic, wireless communication technologies have to overcome available spectrum shortage and complex fading channels. The transform domain communication system (TDCS) is a cognitive anti-interference communication system with a low probability of detection and dynamic spectrum sensing and accessing. However, the non-continuous and asymmetric spectrum brings new challenges to the traditional TDCS block-type pilot, which uses a series of discrete symbols in the time domain as pilots. Low efficiency and poor adaptability in fast-varying channels are the main drawbacks for the block-type pilot in TDCS. In this study, a frequency domain non-uniform pilot design method was proposed with intersecting, skewing, and edging of three typical non-uniform pilots. Some numerical examples are also presented with multipath model COST207RAx4 to verify the proposed methods in the bit error ratio and the mean square error. Compared with traditional block-type pilot, the proposed method can adapt to the fast-varying channels, as well as the non-continuous and asymmetric spectrum conditions with much higher efficiency.

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Image fusion is a process that integrates similar types of images collected from heterogeneous sources into one image in which the information is more definite and certain. Hence, the resultant image is anticipated as more explanatory and enlightening both for human and machine perception. Different image combination methods have been presented to consolidate significant data from a collection of images into one image. As a result of its applications and advantages in variety of fields such as remote sensing, surveillance, and medical imaging, it is significant to comprehend image fusion algorithms and have a comparative study on them. This paper presents a review of the present state-of-the-art and well-known image fusion techniques. The performance of each algorithm is assessed qualitatively and quantitatively on two benchmark multi-focus image datasets. We also produce a multi-focus image fusion dataset by collecting the widely used test images in different studies. The quantitative evaluation of fusion results is performed using a set of image fusion quality assessment metrics. The performance is also evaluated using different statistical measures. Another contribution of this paper is the proposal of a multi-focus image fusion library, to the best of our knowledge, no such library exists so far. The library provides implementation of numerous state-of-the-art image fusion algorithms and is made available publicly at project website.

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Towards the era of mobile Internet and the Internet of Things (IoT), numerous sensors and devices are being introduced and interconnected. To support such an amount of data traffic, traditional wireless communication technologies are facing challenges both in terms of the increasing shortage of spectrum resources and massive multiple access. The transform-domain communication system (TDCS) is considered as an alternative multiple access system, where 5G and mobile IoT are mainly focused. However, previous studies about TDCS are under the assumption that the transceiver has the global spectrum information, without the consideration of spectrum sensing mismatch (SSM). In this paper, we present the deterministic analysis of TDCS systems under arbitrary given spectrum sensing scenarios, especially the influence of the SSM pattern to the signal to noise ratio (SNR) performance. Simulation results show that arbitrary SSM pattern can lead to inferior bit error rate (BER) performance.

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Using a large set of ultrasound features does not necessarily ensure improved quantitative classification of breast tumors; rather, it often degrades the performance of a classifier. In this paper, we propose an effective feature reduction approach in the transform domain for improved multi-class classification of breast tumors. Feature transformation methods, such as empirical mode decomposition (EMD) and discrete wavelet transform (DWT), followed by a filter- or wrapper-based subset selection scheme are used to extract a set of non-redundant and more potential transform domain features through decorrelation of an optimally ordered sequence of N ultrasonic bi-modal (i.e., quantitative ultrasound and elastography) features. The proposed transform domain bi-modal reduced feature set with different conventional classifiers will classify 201 breast tumors into benign-malignant as well as BI-RADS⩽3, 4, and 5 categories. For the latter case, an inadmissible error probability is defined for the subset selection using a wrapper/filter. The classifiers use train truth from histopathology/cytology for binary (i.e., benign-malignant) separation of tumors and then bi-modal BI-RADS scores from the radiologists for separating malignant tumors into BI-RADS category 4 and 5. A comparative performance analysis of several widely used conventional classifiers is also presented to assess their efficacy for the proposed transform domain reduced feature set for classification of breast tumors. The results show that our transform domain bimodal reduced feature set achieves improvement of 5.35%, 3.45%, and 3.98%, respectively, in sensitivity, specificity, and accuracy as compared to that of the original domain optimal feature set for benign-malignant classification of breast tumors. In quantitative classification of breast tumors into BI-RADS categories⩽3, 4, and 5, the proposed transform domain reduced feature set attains improvement of 3.49%, 9.07%, and 3.06%, respectively, in likelihood of malignancy and 4.48% in inadmissible error probability compared to that of the original domain optimal subset. In summary, the construction of a transform domain reduced feature set by extracting complementary information from a large set of available bi-modal features and use of qualitative bi-modal BI-RADS can contribute to improved quantitative classification of breast tumors and thereby help reduce the number of unnecessary biopsies, securing a nearly minimum chance of a life-endangering diagnosis.

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The aim of this paper is to study the ultrasonic response to a transient source that radiates ultrasonic waves in a 3D embedded multilayered anisotropic and dissipative plate. The source can be inside the plate or outside, in a fluid loading the plate for example. In the context of Non-Destructive Testing applied to composite materials, our goal is to create a robust algorithm to calculate ultrasonic field, irrespective of the source and receiver positions. The principle of the method described in this paper is well-established. This method is based on time analysis using the Laplace transform. In the present work, it has been customized for computing ultrasonic source interactions with multilayered dissipative anisotropic plates. The fields are transformed in the 2D Fourier wave-vector domain for the space variables related to the plate surface, and they are expressed in the partial-wave basis. Surprisingly, this method has been very little used in the ultrasonic community, while it is a useful tool which complements the much used technique based on generalized Lamb wave decomposition. By avoiding mode analysis - which can be problematic in some cases - exact numerical calculations (i.e., approximations by truncating infinite series that may be poorly convergent are not needed) can be made in a relatively short time for immersed plates and viscoelastic layers. Even for 3D cases, numerical costs are relatively low. Special attention is given to separate up- and down-going waves, which is a simple matter when using the Laplace transform. Numerical results show the effectiveness of this method. Three examples are presented here to investigate the quality of the model and the robustness of the algorithm: first, a comparison of experiment and simulation for a monolayer carbon-epoxy plate, where the diffracted field is due to a source located on the first free surface of the sample, for both dissipative and non-dissipative cases; second, the basic configuration of an aluminum plate immersed in water has been chosen to study wave propagation in ZGV (Zero Group Velocity) conditions; finally, a 2D plate consisting of 8 stacked carbon-epoxy layers immersed in water is treated, with a source located inside the plate, distributed in depth and extending over four layers.

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Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian-Wolf and Wyner-Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs.

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Periodicity is an important property of speech signals. It is the basis of the signal's fundamental frequency and the pitch of voice, which is crucial to speech communication. This paper presents a novel framework of periodicity enhancement for noisy speech. The enhancement is applied to the linear prediction residual of speech. The residual signal goes through a constant-pitch time warping process and two sequential lapped-frequency transforms, by which the periodic component is concentrated in certain transform coefficients. By emphasizing the respective transform coefficients, periodicity enhancement of noisy residual signal is achieved. The enhanced residual signal and estimated linear prediction filter parameters are used to synthesize the output speech. An adaptive algorithm is proposed for adjusting the weights for the periodic and aperiodic components. Effectiveness of the proposed approach is demonstrated via experimental evaluation. It is observed that harmonic structure of the original speech could be properly restored to improve the perceptual quality of enhanced speech.

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The Electrocardiogram (ECG) is the P-QRS-T wave depicting the cardiac activity of the heart. The subtle changes in the electric potential patterns of repolarization and depolarization are indicative of the disease afflicting the patient. These clinical time domain features of the ECG waveform can be used in cardiac health diagnosis. Due to the presence of noise and minute morphological parameter values, it is very difficult to identify the ECG classes accurately by the naked eye. Various computer aided cardiac diagnosis (CACD) systems, analysis methods, challenges addressed and the future of cardiovascular disease screening are reviewed in this paper. Methods developed for time domain, frequency transform domain, and time-frequency domain analysis, such as the wavelet transform, cannot by themselves represent the inherent distinguishing features accurately. Hence, nonlinear methods which can capture the small variations in the ECG signal and provide improved accuracy in the presence of noise are discussed in greater detail in this review. A CACD system exploiting these nonlinear features can help clinicians to diagnose cardiovascular disease more accurately.

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A feature extraction scheme based on discrete cosine transform (DCT) of electromyography (EMG) signals is proposed for the classification of normal event and a neuromuscular disease, namely the amyotrophic lateral sclerosis. Instead of employing DCT directly on EMG data, it is employed on the motor unit action potentials (MUAPs) extracted from the EMG signal via a template matching-based decomposition technique. Unlike conventional MUAP-based methods, only one MUAP with maximum dynamic range is selected for DCT-based feature extraction. Magnitude and frequency values of a few high-energy DCT coefficients corresponding to the selected MUAP are used as the desired feature which not only reduces computational burden, but also offers better feature quality with high within-class compactness and between-class separation. For the purpose of classification, the K-nearest neighbourhood classifier is employed. Extensive analysis is performed on clinical EMG database and it is found that the proposed method provides a very satisfactory performance in terms of specificity, sensitivity and overall classification accuracy.