Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image quality.Moreover,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy protocols.Nevertheless,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression alone.In response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking scheme.The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion vectors.In the process of watermark embedding,motion vectors are selected based on their magnitude and phase angle.When embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the images.The embedding of watermark bits is dependent on motion vectors.Although reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking method.The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights.The restoration of original data or images may call into question ownership or other legal claims.The peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image quality.Across all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds 0.92.Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.

Image sequence-based risk behavior detection of power operation inspection personnel

A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper.In this method,the original image sequence data is first separated from the foreground and background.Then,the free anchor frame detection method is used in the foreground image to detect the personnel and correct their direction.Finally,human posture nodes are extracted from each frame of the image sequence,which are then used to identify the abnormal behavior of the human.Simulation experiment results demonstrate that the proposed algorithm has significant advantages in terms of the accuracy of human posture node detection and risk behavior identification.

Center of rotation estimation for rocket nozzle by infrared reflective makers and image sequences

The determination of an accurate center of rotation of rocket motor nozzle or other object to be measured is of great interest across a wide range of applications,such as rocket,missile,robotics,industry,spaceflight,aviation and human motion analysis fields,particularly for clinical gait analysis.A new approach was proposed to estimate the moving objects' instantaneous center of rotation and other motion parameters.The new method assumes that the two segment of object to be measured are rigid body which rotates around a center of rotation between each other relatively.The center of rotation varies with time in the global coordinate system but is fixed in the local coordinate system attached to each segment.The models of rocket motor nozzle and its movement were established.The arbitrary moving object's corresponding to motion equations were deduced,and the least square closed-form solutions of the object's motion parameters were figured out.It is assumed that the two high speed CCD cameras mounted on the 750 nm infrared(IR) filter are synchronized and calibrated in advance.The virtual simulation experiment using 3D coordinates of markers was conducted by synchronized stereo image sequences based on 6-DOF motion platform and the experimental results prove the feasibility of our algorithm.The test results show that the precision of x,y,z component on center of rotation is up to 0.14 mm,0.13 mm,0.15 mm.

Three-dimensional motion parameters estimation from stereo image sequences based on infrared reflective markers

In this paper,an innovative 3D motion parameters estimation method from stereo image sequences based on infrared(IR) reflective markers is presented.It was assumed that two high speed CCD cameras had been calibrated previously.The method consists of the following steps:1) the coordinate of several markers and depth map for each stereo pair was determined from the sequences of stereo images by relations of markers' coordinate the correspondence between markers was established,2) the 3D motion parameters of the target was computed based upon the matched markers' coordinate,and 3) translated 3D motion parameters estimation into the problem of least square according to the movement model of the object to be measured.Without using line,curve or corner correspondence,this method can calculate the depth of these markers feature easily and quickly in contrast to traditional approaches.The two CCD cameras work on 200 f/s,and each processing cost time is about 3 ms.It was found that,by using several markers and a large number of stereo images,this method can improve the computational speed,robustness and numerical accuracy of the motion parameters in comparison with traditional methods.The virtual simulation experiment was conducted using synthesized stereo image sequences based on 6-DOF motion platform and the experimental results proved the validity of our approach and showed that the translation and rotation precision is up to 0.1 mm and 0.1°.

Intelligent extraction of road cracks based on vehicle laser point cloud and panoramic sequence images

In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strategy for extracting road cracks.This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images.The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously.A convolutional neural network is utilized to extract cracks from the panoramic sequence image.The extracted sequence image is then aligned with the laser point cloud,enabling the assignment of RGB information to the vehicle-mounted three dimensional(3D)point cloud and location information to the two dimensional(2D)panoramic image.Additionally,a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud.The three-dimensional data pertaining to the cracks can be acquired.The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks.The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks.This approach exhibits superior accuracy when compared to conventional methods.Moreover,it facilitates rapid and accurate identification and localization of road cracks,thereby playing a crucial role in ensuring road maintenance and traffic safety.Consequently,this technique finds extensive application in the domains of intelligent transportation and urbanization development.The technology exhibits significant promise for use in the domains of intelligent transportation and city development.

Super-Resolution Reconstruction of Image Sequence Using Multiple Motion Estimation Fusion

Super-resolution reconstruction algorithm produces a high-resolution imagefrom a low-resolution image sequence. The accuracy and the stability of the motion estimation (ME)are essential for the whole restoration. In this paper, a new super-resolution reconstructionalgorithm is developed using a robust ME method, which fuses multiple estimated motion vectorswithin the sequence. The new algorithm has two major improvements compared with the previousresearch. First, instead of only two frames, the whole sequence is used to obtain a more accurateand stable estimation of the motion vector of each frame; second, the reliability of the ME isquantitatively measured and introduced into the cost function of the reconstruction algorithm. Thealgorithm is applied to both synthetic and real sequences, and the results are presented in thepaper.

A deep Koopman operator-based modelling approach for long-term prediction of dynamics with pixel-level measurements

Although previous studies have made some clear leap in learning latent dynamics from high-dimensional representations,the performances in terms of accuracy and inference time of long-term model prediction still need to be improved.In this study,a deep convolutional network based on the Koopman operator(CKNet)is proposed to model non-linear systems with pixel-level measurements for long-term prediction.CKNet adopts an autoencoder network architecture,consisting of an encoder to generate latent states and a linear dynamical model(i.e.,the Koopman operator)which evolves in the latent state space spanned by the encoder.The decoder is used to recover images from latent states.According to a multi-step ahead prediction loss function,the system matrices for approximating the Koopman operator are trained synchronously with the autoencoder in a mini-batch manner.In this manner,gradients can be synchronously transmitted to both the system matrices and the autoencoder to help the encoder self-adaptively tune the latent state space in the training process,and the resulting model is time-invariant in the latent space.Therefore,the proposed CKNet has the advantages of less inference time and high accuracy for long-term prediction.Experiments are per-formed on OpenAI Gym and Mujoco environments,including two and four non-linear forced dynamical systems with continuous action spaces.The experimental results show that CKNet has strong long-term prediction capabilities with sufficient precision.

Novel Frame Shift and Integral Technique for Enhancing Low-Light-Level Moving Images

A novel frame shift and integral technique for the enhancement of low light level moving image sequence is introduced. According to the technique, motion parameters of target are measured by algorithm based on difference processing. To obtain spatial relativity, images are shifted according to the motion parameters. As a result, the processing of integral and average can be applied to images that have been shifted. The technique of frame shift and integral that includes the algorithm of motion parameter determination is discussed, experiments with low light level moving image sequences are also described. The experiment results show the effectiveness and the robustness of the parameter determination algorithm, and the improvement in the signal-to-noise ratio （SNR） of low light level moving images.

Comparision of DdQm models in image optical flow analysis based on LBM

The optical flow analysis of the image sequence based on the formal lattice Boltzmann equation, with different DdQm models, is discussed in this paper. The Mgorithm is based on the lattice Boltzmann method （LBM）, which is used in computational fluid dynamics theory for the simulation of fluid dynamics. At first, a generalized approximation to the formal lattice Boltzmann equation is discussed. Then the effects of different DdQm models on the results of the optical flow estimation are compared with each other, while calculating the movement vectors of pixels in the image sequence. The experimental results show that the higher dimension DdQm models, e.g., D3Q15 are more effective than those lower dimension ones.

Dim Moving Small Target Detection by Local and Global Variance Filtering on Temporal Profiles in Infrared Sequences

In this paper, the temporal different characteristics between the target and background pixels are used to detect dim moving targets in the slow-evolving complex background. A local and global variance filter on temporal profiles is presented that addresses the temporal characteristics of the target and background pixels to eliminate the large variation of background temporal profiles. Firstly, the temporal behaviors of different types of image pixels of practical infrared scenes are analyzed.Then, the new local and global variance filter is proposed. The baseline of the fluctuation level of background temporal profiles is obtained by using the local and global variance filter. The height of the target pulse signal is extracted by subtracting the baseline from the original temporal profiles. Finally, a new target detection criterion is designed. The proposed method is applied to detect dim and small targets in practical infrared sequence images. The experimental results show that the proposed algorithm has good detection performance for dim moving small targets in the complex background.

Discontinuity-preserving optical flow algorithm

A modification of Horn and Schunk＇s approach is investigated, which leads to a better preservation of flow discontinuities. It improves Horn-Schunk model in three aspects： （1） It replaces the smooth weight coefficient in the energy equation by the variable weight coefficient. （2） It adopts a novel method to compute the mean velocity. The novel method also reflects the effect of the intensity difference on the image velocity diffusion. （3） It introduces a more efficient iterative method than the Gauss-Seidel method to solve the associated Euler-Lagrange equation. The experiment results validate the better effect of the improved method on preserving discontinuities.

A New Method of Small Moving Target Detection and Its Performance Analysis

This paper describes a new method of small moving target detection and analyzes the performance of this algorithm. The method is based on multi-level threshold decision-making and sliding trajectory confidence testing technology. The parameters of the algorithm are also given. Experiments have been conducted, the results show that the algorithm has advantages of high detection probability, simple structure, and excellent real-time performance.

Fast speedometer identification in dynamic scene based on phase correlation

Speedometer identification has been researched for many years.The common approaches to that problem are usually based on image subtraction,which does not adapt to image offsets caused by camera vibration.To cope with the rapidity,robust and accurate requirements of this kind of work in dynamic scene,a fast speedometer identification algorithm is proposed,it utilizes phase correlation method based on regional entire template translation to estimate the offset between images.In order to effectively reduce unnecessary computation and false detection rate,an improved linear Hough transform method with two optimization strategies is presented for pointer line detection.Based on VC＋＋ 6.0 software platform with OpenCV library,the algorithm performance under experiments has shown that it celerity and precision.

基于单目序列图像的非合作目标相对导航

Along with the increase of the number of failed satellites,plus space debris,year by year,it will take considerable manpower and resources if we rely just on ground surveillance and early warning.An alternative effective way would be to use autonomous long-range non-cooperative target relative navigation to solve this problem.For longrange non-cooperative targets,the stereo cameras or lidars that are commonly used would not be applicable.This paper studies a relative navigation method for long-range relative motion estimation of non-cooperative targets using only a monocular camera.Firstly,the paper provides the nonlinear relative orbit dynamics equations and then derives the discrete recursive form of the dynamics equations.An EKF filter is then designed to implement the relative navigation estimation.After that,the relative"locally weakly observability"theory for nonlinear systems is used to analyze the observability of monocular sequence images.The analysis results show that by relying only on monocular sequence images it has the possibility of deducing the relative navigation for long-range non-cooperative targets.Finally,numerical simulations show that the method given in this paper can achieve a complete estimation of the relative motion of longrange non-cooperative targets without conducting orbital maneuvers.

Optimization of magnetic resonance sequences in lymph node staging of nasopharyngeal carcinoma

Background Detection rate of retropharyngeal lymph node metastasis in patients with nasopharyngeal carcinoma （NPC） needs to be improved. The purpose of this study was to compare three magnetic resonance （MR） sequences for detecting lymph nodes in patients with NPC. Methods Between July 2007 and March 2008, MR staging of pre-treated tumor was conducted on 120 patients with pathologically confirmed NPC. The outcome of three different sequences for MR NPC staging were compared： coronal short T1 inversion recovery （STIR）, axial proton density fat-suppressed （PDWI fs）, and coronal contrast enhanced fast spin echo T1 weighted fat-suppressed （CE FSE TlWl fs）. Nodal classification method （1999） was applied to count the number of retropharyngeal and cervical lymph nodes discovered by each MR sequence. Paired t tests were used for statistical analysis. Results A total of 2575 lymph nodes were found using coronal STIR sequence; 1816 lymph nodes for coronal CE FSE TIWI fs sequence and 2638 lymph nodes for axial PDWl fs sequence. Significant differences existed in the number of lymph nodes detected by axial PDWI fs and coronal CE FSE T1WI fs sequence （paired t test, P 〈0.05）, with the former sequence getting higher numbers. Statistical differences also existed between coronal STIR and coronal CE FSE TlWl fs sequence （paired ttest, P 〈0.05）, with the former sequence getting higher numbers. No significant difference was found between coronal STIR sequence and axial PDWI fs sequence （paired ttest, P 〉0.05）. Conclusions For the detection of retropharyngeal and cervical lymph nodes, coronal STIR sequence and axial PDWI fs sequence have similar performance and both sequences showed better detection than CE FSE TIWI fs sequence. Furthermore, by combining coronal STIR sequence and axial PDWI fs sequence, we can improve the detection of lymph nodes in NPC N-staging before treatment, especially for lymph nodes located in the thoracic entrance.

Parametric Tracking of Legs by Exploiting Intelligent Edge

In this paper the idea of Intelligent Scissors is adopted for contourtracking in dynamic image sequence. Tracking contour of human can therefore be converted to trackingseed points in images by making use of the properties of the optimal path (Intelligent Edge). Themain advantage of the approach is that it can handle correctly occlusions that occur frequently whenhuman is moving. Non-Uniform Rational B-Spline (NURBS) is used to represent parametrically thecontour that one wants to track. In order to track robustly the contour in images, similarity andcompatibility measurements of the edge are computed as the weighting functions of optimal estimator.To reduce dramatically the computational load, an efficient method for extracting the regioninterested is proposed. Experiments show that the approach works robustly for sequences withfrequent occlusions.

A Neural Paradigm forTime-Varying Motion Segmentation

This paper proposes a new neural algorithm to perform the segmentation of an observed scene into regions corresponding to different moving objects byanalyzing a time-varying images sequence. The method consists of a classificationstep, where the motion of small patches is characterized through an optimizationapproach, and a segmentation step merging neighboring patches characterized bythe same motion. Classification of motion is performed without optical flow computation, but considering only the spatial and temporal image gradients into anappropriate energy function minimized with a Hopfield-like neural network givingas output directly the 3D motion parameter estimates. Network convergence is accelerated by integrating the quantitative estimation of motion parameters with aqualitative estimate of dominant motion using the geometric theory of differentialequations.

Rapid estimation of apple phenotypic parameters based on 3D reconstruction

In order to obtain the phenotypic parameters of apple quickly and accurately,which were commonly used as the basis of fruit sorting,a fast estimation method of apple phenotypic parameters based on three-dimensional(3D)reconstruction was proposed in this study.In this study,a three-dimensional model was constructed to estimate the phenotypic parameters of apple,such as volume,height,diameter,and fruit shape index.Firstly,an image acquisition system was built to capture sequence images of fruit with a binocular stereo vision system,and the images were extracted and matched using the Accelerated-KAZE algorithm to create the point cloud data.Secondly,the point cloud data were matched with the algorithm of Iterative Closest Point to establish a whole model of apple,and the surface reconstruction model of fruit was obtained by constructing irregular triangulation network.Finally,the apple phenotypic parameters were calculated by means of segmentation,surface complement and integral of the fruit model.Total of 200 apples were used as samples in the experiment.By this method,the phenotypic parameters of the apples were estimated based on their 3D reconstruction model,and the linear regression analysis was carried out between the estimated values and the real values.The results showed that R2 of the linear regression fitting of each parameter was higher than 0.90.Among them,the fitting of volume was the best with R2 of 0.97.In addition,the average errors of apple volume,height,fruit shape index,maximum diameter D and minimum diameter d were 8.73 cm3,1.43 mm,1.28%,0.90 mm,and 1.23 mm,respectively.According to the Chinese national standard of“fresh Apple”,the average error of the estimated result is within the range of allowable error.It indicates that the method of apple phenotypic parameter estimation based on 3D reconstruction has a high accuracy and practicability,and it can be used as the support for fruit sorting.

Neuropsychiatric symptoms associated with the COVID-19 and its potential nervous system infection mechanism:the role of imaging in the study

The epidemic of coronavirus disease 2019(COVID-19)has broken the normal spread mode of respiratory viruses,namely,mainly spread in winter,resulting in over 230 million confirmed cases of COVID-19.Many studies have shown that severe acute respiratory syndrome coronavirus-2(SARS-CoV-2)can affect the nervous system by varying degrees.In this review,we look at the acute neuropsychiatric impacts of COVID-19 patients,including acute ischemic stroke,encephalitis,acute necrotizing encephalopathy,dysosmia,and epilepsy,as well as the long-term neuropsychiatric sequelae of COVID-19 survivors:mental disorder and neurodegenerative diseases.In particular,this review discusses long-term changes in brain structure and function associated with COVID-19 infection.We believe that the traditional imaging sequences are important in the acute phase,while the nontraditional imaging sequences are more meaningful for the detection of long-term neuropsychiatric sequelae.These long-term follow-up changes in structure and function may also help us understand the causes of neuropsychiatric symptoms in COVID-19 survivors.Finally,we review previous studies and discuss some potential mechanisms of SARS-CoV-2 infection in the nervous system.Continuous focus on neuropsychiatric sequelae and a comprehensive understanding of the long-term impacts of the virus to the nervous system is significant for formulating effective sequelae prevention andmanagement strategies,andmay provide important clues for nervous system damage in future public health crises.