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.

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.

Magnetic resonance imaging with three-dimensional fast imaging employing steady-state acquisition with phase-cycled and short T1 inversion recovery pulse sequence for evaluating brachial plexus injury

There is a large amount of fat in the postganglionic segment of the brachial plexus nerve.The use of short T1 inversion recovery pulse sequence may improve signal strength of the brachial plexus postganglionic segment.The present study revealed that the combination of three-dimensional fast imaging employing steady-state acquisition with phase-cycled and short T1 inversion recovery pulse sequence clearly displayed the anatomical morphology and structure of the brachial plexus nerve,together with maximum intensity projection,volume rendering and other three-dimensional reconstruction techniques.Our results suggested that this method is also suitable for providing accurate assessment and diagnosis of the site,severity and scope of brachial plexus injury.

Experimental Study of Assessment on Ventricular Activation Origin and Contraction Sequence by Doppler Tissue Imaging

To evaluate the possibility and accuracy of Doppler tissue image (DTI) on assessment of normal and abnormal ventricular activation and contraction sequence, 9 open chest canine hearts were analyzed by acceleration mode, M mode, and spectrum mode DTI. Our results showed that: (1) Acceleration mode DTI could show the origin of activation and conduction sequence on line; (2) M mode DTI revealed that the activation in mid interventricular septum was earlier than that in mid left ventricular posterior wall at sinus activation; (3) Spectrum DTI showed the ventricular endocardium was activated earlier than the ventricular epicardium in all segments at sinus rhythm. The earliest site of activation of the normal ventricular wall was at middle interventricular septum; the latest site was at basal posterior wall; the contraction sequence was different at the different walls; (4) During abnormal ventricular activation, mid left ventricular posterior wall was activated earliest in accordance with the pacing sites. Abnormal ventricular activation was slower than sinus activation, and the contraction sequence varied at different sites of ventricular wall. It is concluded that DTI can be used to localize the origin of normal or abnormal myocardial activation and to assess the contraction sequence conveniently, accurately and non invasively.

Hepatic MR imaging using IDEAL-IQ sequence:Will Gd-EOB-DTPA interfere with reproductivity of fat fraction quantification?

BACKGROUND Iterative decomposition of water and fat with echo asymmetry and least squares estimation quantification sequence(IDEAL-IQ)is based on chemical shift-based water and fat separation technique to get proton density fat fraction.Multiple studies have shown that using IDEAL-IQ to test the stability and repeatability of liver fat is acceptable and has high accuracy.AIM To explore whether Gadoxetate Disodium(Gd-EOB-DTPA)interferes with the measurement of the hepatic fat content quantified with the IDEAL-IQ and to evaluate the robustness of this technique.METHODS IDEAL-IQ was used to quantify the liver fat content at 3.0T in 65 patients injected with Gd-EOB-DTPA contrast.After injection,IDEAL-IQ was estimated four times,and the fat fraction(FF)and R2* were measured at the following time points:Precontrast,between the portal phase(70 s)and the late phase(180 s),the delayed phase(5 min)and the hepatobiliary phase(20 min).One-way repeated-measures analysis was conducted to evaluate the difference in the FFs between the four time points.Bland-Altman plots were adopted to assess the FF changes before and after injection of the contrast agent.P<0.05 was considered statistically significant.RESULTS The assessment of the FF at the four time points in the liver,spleen and spine showed no significant differences,and the measurements of hepatic FF yielded good consistency between T1 and T2[95%confidence interval:-0.6768%,0.6658%],T1 and T3(-0.3900%,0.3178%),and T1 and T4(-0.3750%,0.2825%).R2* of the liver,spleen and spine increased significantly after injection(P<0.0001).CONCLUSION Using the IDEAL-IQ sequence to measure the FF,we can obtain results that will not be affected by Gd-EOB-DTPA.The high reproducibility of the IDEAL-IQ sequence makes it available in the scanning interval to save time during multiphase examinations.

Adaptive Virtual Source Imaging Using the Sequence Intensity Factor:Simulation and Experimental Study

Virtual source(VS)imaging has been proposed to improve image resolution in medical ultrasound imaging.However,VS obtains a limited contrast due to the non-adaptive delay-and-sum(DAS)beamforming.To improve the image contrast and provide an enhanced resolution,adaptive weighting algorithms were applied in VS imaging.In this paper,we proposed an adjustable generalized coherence factor(aGCF)for the synthetic aperture sequential beamforming(SASB)ofVS imaging to improve image quality.The value of aGCF is adjusted by a sequence intensity factor(SIF)that is defined as the ratio between the effective low resolution scan lines(LRLs)intensity and total LRLs strength.The aGCF-weighted VS(aGCF-VS)images were compared with standard VS images and GCF-weighted VS(GCF-VS)images.Simulation and experimental results demonstrated that the contrast ratio(CR)and contrastto-noise ratio(CNR)of aGCF-VS are greatly improved,compared with standard VS imaging.And in comparison with GCF-VS,aGCF-VS can obtain better CNR and speckle signal-to-noise ratio(sSNR)whilemaintaining similar CR.Therefore,aGCF is suitable for VS imaging to improve contrast and preserve speckle pattern.

Dedicated Magnetic Resonance Imaging Sequences:Contribution in the Diagnosis of Focal Epilepsy in the Lebanese Population

There have been many advances in the diagnosis and management of focal epilepsies particularly with neuroimaging techniques and magnetic resonance imaging(MRI).Special MRI sequences can be employed to localize and resect the epileptogenic lesions responsible for focal epilepsy.This study aims to show the benefit of dedicated epilepsy MRI sequences in the diagnosis of focal epilepsies.A general electric 1.5 Tesla MRI machine was used with standard and special sequences.Also,a Nihon Kohden electroencephalography(EEG)machine was used.This is a prospective observational study that included 51 patients with focal epilepsies who had an initial negative brain imaging.They underwent epilepsy MRI sequences along with a prolonged video EEG monitoring to localize the lesion,and then results were analyzed statistically using SPSS 22 program.The majority of patients were males(62.75%)with a mean age of 30 years.The grand majority of patients(74.5%,p value of 0.001)had their lesion localized by the epilepsy MRI.The most commonly found pathology was mesial temporal sclerosis.A significant number of patients(23.5%)were sent for an epilepsy surgery(p value 0.002).This study shows the significant impact of dedicated epilepsy MRI sequences on the diagnosis and management of focal epilepsy in the Lebanese population.

MOVING OBJECT TRACKING IN DYNAMIC IMAGE SEQUENCE BASED ON ESTIMATION OF MOTION VECTORS OF FEATURE POINTS

An improved estimation of motion vectors of feature points is proposed for tracking moving objects of dynamic image sequence. Feature points are firstly extracted by the improved minimum intensity change （MIC） algorithm. The matching points of these feature points are then determined by adaptive rood pattern searching. Based on the random sample consensus （RANSAC） method, the background motion is finally compensated by the parameters of an affine transform of the background motion. With reasonable morphological filtering, the moving objects are completely extracted from the background, and then tracked accurately. Experimental results show that the improved method is successful on the motion background compensation and offers great promise in tracking moving objects of the dynamic image sequence.

A novel color image encryption scheme using fractional-order hyperchaotic system and DNA sequence operations

In this paper, Adomian decomposition method （ADM） with high accuracy and fast convergence is introduced to solve the fractional-order piecewise-linear （PWL） hyperchaotic system. Based on the obtained hyperchaotic sequences, a novel color image encryption algorithm is proposed by employing a hybrid model of bidirectional circular permutation and DNA masking. In this scheme, the pixel positions of image are scrambled by circular permutation, and the pixel values are substituted by DNA sequence operations. In the DNA sequence operations, addition and substraction operations are performed according to traditional addition and subtraction in the binary, and two rounds of addition rules are used to encrypt the pixel values. The simulation results and security analysis show that the hyperchaotic map is suitable for image encryption, and the proposed encryption algorithm has good encryption effect and strong key sensitivity. It can resist brute-force attack, statistical attack, differential attack, known-plaintext, and chosen-plaintext attacks.

A Sequence Image Matching Method Based on Improved High-Dimensional Combined Features

Image matching technology is theoretically significant and practically promising in the field of autonomous navigation.Addressing shortcomings of existing image matching navigation technologies,the concept of high-dimensional combined feature is presented based on sequence image matching navigation.To balance between the distribution of high-dimensional combined features and the shortcomings of the only use of geometric relations,we propose a method based on Delaunay triangulation to improve the feature,and add the regional characteristics of the features together with their geometric characteristics.Finally,k-nearest neighbor(KNN)algorithm is adopted to optimize searching process.Simulation results show that the matching can be realized at the rotation angle of-8°to 8°and the scale factor of 0.9 to 1.1,and when the image size is 160 pixel×160 pixel,the matching time is less than 0.5 s.Therefore,the proposed algorithm can substantially reduce computational complexity,improve the matching speed,and exhibit robustness to the rotation and scale changes.

A New Images Hiding Scheme Based on Chaotic Sequences

We propose a data hidding technique in a still image. This technique is based on chaotic sequence in the transform domain of cover image. We use different chaotic random sequences multiplied by multiple sensitive images, respectively, to spread the spectrum of sensitive images. Multiple sensitive images are hidden in a covert image as a form of noise. The results of theoretical analysis and computer simulation show the new hiding technique have better properties with high security, imperceptibility and capacity for hidden information in comparison with the conventional scheme such as LSB (Least Significance Bit).

A Prediction Model for Detecting Dysthyroid Optic Neuropathy Based on Clinical Factors and Imaging Markers of the Optic Nerve and Cerebrospinal Fluid in the Optic Nerve Sheath

Objective This study aimed to develop and test a model for predicting dysthyroid optic neuropathy(DON)based on clinical factors and imaging markers of the optic nerve and cerebrospinal fluid(CSF)in the optic nerve sheath.Methods This retrospective study included patients with thyroid-associated ophthalmopathy(TAO)without DON and patients with TAO accompanied by DON at our hospital.The imaging markers of the optic nerve and CSF in the optic nerve sheath were measured on the water-fat images of each patient and,together with clinical factors,were screened by Least absolute shrinkage and selection operator.Subsequently,we constructed a prediction model using multivariate logistic regression.The accuracy of the model was verified using receiver operating characteristic curve analysis.Results In total,80 orbits from 44 DON patients and 90 orbits from 45 TAO patients were included in our study.Two variables(optic nerve subarachnoid space and the volume of the CSF in the optic nerve sheath)were found to be independent predictive factors and were included in the prediction model.In the development cohort,the mean area under the curve(AUC)was 0.994,with a sensitivity of 0.944,specificity of 0.967,and accuracy of 0.901.Moreover,in the validation cohort,the AUC was 0.960,the sensitivity was 0.889,the specificity was 0.893,and the accuracy was 0.890.Conclusions A combined model was developed using imaging data of the optic nerve and CSF in the optic nerve sheath,serving as a noninvasive potential tool to predict DON.

Apert syndrome diagnosed by prenatal ultrasound combined with magnetic resonance imaging and whole exome sequencing:A case report

BACKGROUND Most cases of Apert syndrome(AS)are found after birth.Cases of AS diagnosed by ultrasound combined with magnetic resonance imaging(MRI)and whole exome sequencing(WES)during pregnancy are rare.CASE SUMMARY We present the case of a 34-year old female patient(gravida 2,para 1)whose fetus was diagnosed with AS during pregnancy.Fetal ultrasound performed at 30,2/7 wk of pregnancy showed abnormalities.MRI and three-dimensional ultrasound performed at 31,1/7 wk of pregnancy showed the possibility of AS.Chromosome examination and core family WES were conducted at 31,5/7 wk of pregnancy.The results showed that FGFR2 in the fetus had a c.755C>G missense mutation in its nucleotide,and AS was confirmed.CONCLUSION This case highlights the importance of imaging examinations.Prenatal ultrasound combined with MRI can identify fetal morphological abnormalities accurately,which can be confirmed by WES.

Stability of Efficient Deterministic Compressed Sensing for Images with Chirps and Reed-Muller Sequences

We explore the stability of image reconstruction algorithms under deterministic compressed sensing. Recently, we have proposed [1-3] deterministic compressed sensing algorithms for 2D images. These algorithms are suitable when Daubechies wavelets are used as the sparsifying basis. In the initial work, we have shown that the algorithms perform well for images with sparse wavelets coefficients. In this work, we address the question of robustness and stability of the algorithms, specifically, if the image is not sparse and/or if noise is present. We show that our algorithms perform very well in the presence of a certain degree of noise. This is especially important for MRI and other real world applications where some level of noise is always present.

3D Motion Parameters Determination Based on Binocular Sequence Images

Exactly capturing three dimensional (3D) motion i nf ormation of an object is an essential and important task in computer vision, and is also one of the most difficult problems. In this paper, a binocular vision s ystem and a method for determining 3D motion parameters of an object from binocu lar sequence images are introduced. The main steps include camera calibration, t he matching of motion and stereo images, 3D feature point correspondences and re solving the motion parameters. Finally, the experimental results of acquiring th e motion parameters of the objects with uniform velocity and acceleration in the straight line based on the real binocular sequence images by the mentioned meth od are presented.

Measurement of 3-D motion parameters of jacket launch based on stereo image sequences

To make sure that the process of jacket launch occurs in a seml-controlled manner, this paper deals with measurement of kinematic parameters of jacket launch using stereo vision and motion analysis. The system captured stereo image sequences by two separate CCD cameras, and then rebuilt 3D coordinates of the feature points to analyze the jacket launch motion. The possibility of combining stereo vision and motion analysis for measurement was examined. Resuhs by experiments using scale model of jacket confirm the theoretical data.

Rapid Texture Mapping from Image Sequences for Building Geometry Model

An effective approach, mapping the texture for building model based on the digital photogrammetric theory, is proposed. The easily-acquired image sequences from digital video camera on helicopter are used as texture resource, and the correspondence between the space edge in building geometry model and its line feature in image sequences is determined semi-automatically. The experimental results in production of three-dimensional data for car navigation show us an attractive future both in efficiency and effect.

BIT-CORRELATION MATCHING FUNCTION FOR IMAGE SEQUENCE CODING

This paper proposes a new block matching criterion called the bit-correlation matching function for image sequence coding. When using the identical fast searching algorithm, the bit-correlation matching function not only results in nearly the same accuracy in displacement estimation as the mean square error function, but also makes the algorithm low in computation complexity and easy to parallel implementation, thus reducing the coding time of image sequence efficiently.

Fast interactive segmentation algorithm of image sequences based on relative fuzzy connectedness

A fast interactive segmentation algorithm of image-sequences based on relative fuzzy connectedness is presented. In comparison with the original algorithm, the proposed one, with the same accuracy, accelerates the segmentation speed by three times for single image. Meanwhile, this fast segmentation algorithm is extended from single object to multiple objects and from single-image to image-sequences. Thus the segmentation of multiple objects from complex hackground and batch segmentation of image-sequences can be achieved. In addition, a post-processing scheme is incorporated in this algorithm, which extracts smooth edge with one-pixel-width for each segmented object. The experimental results illustrate that the proposed algorithm can obtain the object regions of interest from medical image or image-sequences as well as man-made images quickly and reliably with only a little interaction.

Relative Navigation of Long-Range Non-Cooperative Targets Based on Monocular Sequence Images

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.