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.

The Application Value of Ultrasound Imaging in the Differential Diagnosis of Benign and Malignant Breast Nodules of BI-RADS 3 and Above

Objective:To explore the diagnostic value of ultrasound imaging for breast nodules of breast imaging-reporting and data system(BI-RADS)category 3 and above.Methods:From June 2021 to July 2022,163 patients with breast nodules of BI-RADS 3 or above were selected as the research subjects.After pathological diagnosis,24 cases were malignant breast nodules of BI-RADS 3 or above,while 139 cases were benign breast nodules of BI-RADS 3 or above.The diagnosis rate of malignant and benign breast nodules of BI-RADS 3 or above,including 95%CI,was observed and analyzed.Results:The malignant and benign detection rates of conventional ultrasound were 88.63%and 75.00%,respectively,and the malignant and benign detection rates of ultrasound imaging were 93.18%and 87.50%,respectively,with 95%CIs greater than 0.7.Conclusion:Ultrasound imaging can help improve the diagnostic accuracy of benign and malignant breast nodules of BI-RADS 3 and above and reduce the misdiagnosis rate.

Vault predicting after implantable collamer lens implantation using random forest network based on different features in ultrasound biomicroscopy images

AIM:To analyze ultrasound biomicroscopy(UBM)images using random forest network to find new features to make predictions about vault after implantable collamer lens(ICL)implantation.METHODS:A total of 450 UBM images were collected from the Lixiang Eye Hospital to provide the patient’s preoperative parameters as well as the vault of the ICL after implantation.The vault was set as the prediction target,and the input elements were mainly ciliary sulcus shape parameters,which included 6 angular parameters,2 area parameters,and 2 parameters,distance between ciliary sulci,and anterior chamber height.A random forest regression model was applied to predict the vault,with the number of base estimators(n_estimators)of 2000,the maximum tree depth(max_depth)of 17,the number of tree features(max_features)of Auto,and the random state(random_state)of 40.0.RESULTS:Among the parameters selected in this study,the distance between ciliary sulci had a greater importance proportion,reaching 52%before parameter optimization is performed,and other features had less influence,with an importance proportion of about 5%.The importance of the distance between the ciliary sulci increased to 53% after parameter optimization,and the importance of angle 3 and area 1 increased to 5% and 8%respectively,while the importance of the other parameters remained unchanged,and the distance between the ciliary sulci was considered the most important feature.Other features,although they accounted for a relatively small proportion,also had an impact on the vault prediction.After parameter optimization,the best prediction results were obtained,with a predicted mean value of 763.688μm and an actual mean value of 776.9304μm.The R²was 0.4456 and the root mean square error was 201.5166.CONCLUSION:A study based on UBM images using random forest network can be performed for prediction of the vault after ICL implantation and can provide some reference for ICL size selection.

A Transfer Learning Approach Based on Ultrasound Images for Liver Cancer Detection

The convolutional neural network(CNN)is one of the main algorithms that is applied to deep transfer learning for classifying two essential types of liver lesions;Hemangioma and hepatocellular carcinoma(HCC).Ultrasound images,which are commonly available and have low cost and low risk compared to computerized tomography(CT)scan images,will be used as input for the model.A total of 350 ultrasound images belonging to 59 patients are used.The number of images with HCC is 202 and 148,respectively.These images were collected from ultrasound cases.info(28 Hemangiomas patients and 11 HCC patients),the department of radiology,the University of Washington(7 HCC patients),the Atlas of ultrasound Germany(3 HCC patients),and Radiopedia and others(10 HCC patients).The ultrasound images are divided into 225,52,and 73 for training,validation,and testing.A data augmentation technique is used to enhance the validation performance.We proposed an approach based on ensembles of the best-selected deep transfer models from the on-the-shelf models:VGG16,VGG19,DenseNet,Inception,InceptionResNet,ResNet,and EfficientNet.After tuning both the feature extraction and the classification layers,the best models are selected.Validation accuracy is used for model tuning and selection.The accuracy,sensitivity,specificity and AUROC are used to evaluate the performance.The experiments are concluded in five stages.The first stage aims to evaluate the base model performance by training the on-the-shelf models.The best accu-racy obtained in the first stage is 83.5%.In the second stage,we augmented the data and retrained the on-the-shelf models with the augmented data.The best accuracy we obtained in the second stage was 86.3%.In the third stage,we tuned the feature extraction layers of the on-the-shelf models.The best accuracy obtained in the third stage is 89%.In the fourth stage,we fine-tuned the classification layer and obtained an accuracy of 93%as the best accuracy.In the fifth stage,we applied the ensemble approach using the best three-performing models and obtained an accuracy,specificity,sensitivity,and AUROC of 94%,93.7%,95.1%,and 0.944,respectively.

Image-Based Ultrasound Speed Estimation: Phantom and Human Liver Studies

Purpose: A novel image-based method for speed of sound (SoS) estimation is proposed and experimentally validated on a tissue-mimicking ultrasound phantom and normal human liver in vivo using linear and curved array transducers. Methods: When the beamforming SoS settings are adjusted to match the real tissue’s SoS, the ultrasound image at regions of interest will be in focus and the image quality will be optimal. Based on this principle, both a tissue-mimicking ultrasound phantom and normal human liver in vivo were used in this study. Ultrasound image was acquired using different SoS settings in beamforming channels ranging from 1420 m/sec to 1600 m/sec. Two regions of interest (ROIs) were selected. One was in a fully developed speckle region, while the other contained specular reflectors. We evaluated the image quality of these two ROIs in images acquired at different SoS settings in beamforming channels by using the normalized autocorrelation function (ACF) of the image data. The values of the normalized ACF at a specific lag as a function of the SoS setting were computed. Subsequently, the soft tissue’s SoS was determined from the SoS setting at the minimum value of the normalized ACF. Results: The value of the ACF as a function of the SoS setting can be computed for phantom and human liver images. SoS in soft tissue can be determined from the SoS setting at the minimum value of the normalized ACF. The estimation results show that the SoS of the tissue-mimicking phantom is 1460 m/sec, which is consistent with the phantom manufacturer’s specification, and the SoS of the normal human liver is 1540 m/sec, which is within the range of the SoS in a healthy human liver in vivo. Conclusion: Soft tissue’s SoS can be determined by analyzing the normalized ACF of ultrasound images. The method is based on searching for a minimum of the normalized ACF of ultrasound image data with a specific lag among different SoS settings in beamforming channels.

Using shapes correlation for active contour segmentation of uterine fibroid ultrasound images in computer-aided therapy

Segmenting the lesion regions from the ultrasound （US） images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound （HIFU）, as a popular computer-aided therapy, has been widely used in the treatment of uterine fibroids. However, such segmentation in HIFU remains challenge for two reasons： （1） the blurry or missing boundaries of lesion regions in the HIFU images and （2） the deformation of uterine fibroids caused by the patient＇s breathing or an external force during the US imaging process, which can lead to complex shapes of lesion regions. These factors have prevented classical active contour-based segmentation methods from yielding desired results for uterine fibroids in US images. In this paper, a novel active contour-based segmentation method is proposed, which utilizes the correlation information of target shapes among a sequence of images as prior knowledge to aid the existing active contour method. This prior knowledge can be interpreted as a unsupervised clustering of shapes prior modeling. Meanwhile, it is also proved that the shapes correlation has the low-rank property in a linear space, and the theory of matrix recovery is used as an effective tool to impose the proposed prior on an existing active contour model. Finally, an accurate method is developed to solve the proposed model by using the Augmented Lagrange Multiplier （ALM）. Experimental results from both synthetic and clinical uterine fibroids US image sequences demonstrate that the proposed method can consistently improve the performance of active contour models and increase the robustness against missing or misleading boundaries, and can greatly improve the efficiency of HIFU therapy.

Quantitative ultrasound brain imaging with multiscale deconvolutional waveform inversion

High-resolution images of human brain are critical for monitoring the neurological conditions in a portable and safe manner.Sound speed mapping of brain tissues provides unique information for such a purpose.In addition,it is particularly important for building digital human acoustic models,which form a reference for future ultrasound research.Conventional ultrasound modalities can hardly image the human brain at high spatial resolution inside the skull due to the strong impedance contrast between hard tissue and soft tissue.We carry out numerical experiments to demonstrate that the time-domain waveform inversion technique,originating from the geophysics community,is promising to deliver quantitative images of human brains within the skull at a sub-millimeter level by using ultra-sound signals.The successful implementation of such an approach to brain imaging requires the following items:signals of sub-megahertz frequencies transmitting across the inside of skull,an accurate numerical wave equation solver simulating the wave propagation,and well-designed inversion schemes to reconstruct the physical parameters of targeted model based on the optimization theory.Here we propose an innovative modality of multiscale deconvolutional waveform inversion that improves ultrasound imaging resolution,by evaluating the similarity between synthetic data and observed data through using limited length Wiener filter.We implement the proposed approach to iteratively update the parametric models of the human brain.The quantitative imaging method paves the way for building the accurate acoustic brain model to diagnose associated diseases,in a potentially more portable,more dynamic and safer way than magnetic resonance imaging and x-ray computed tomography.

Value of ultrasound and magnetic resonance imaging combined with tumor markers in the diagnosis of ovarian tumors

BACKGROUND Compare the diagnostic performance of ultrasound(US),magnetic resonance imaging(MRI),and serum tumor markers alone or in combination for detecting ovarian tumors.AIM To investigate the diagnostic value of US,MRI combined with tumor markers in ovarian tumors.METHODS The data of 110 patients with ovarian tumors,confirmed by surgery and pathology,were collected in our hospital from February 2018 to May 2023.The dataset included 60 cases of benign tumors and 50 cases of malignant tumors.Prior to surgery,all patients underwent preoperative US and MRI examinations,as well as serum tumor marker tests[carbohydrate antigen 125(CA125),human epididymis protein 4(HE4)].The aim of the study was to compare the diagnostic performance of these three methods individually and in combination for ovarian tumors.RESULTS This study found statistically significant differences in the ultrasonic imaging characteristics between benign and malignant tumors.These differences include echo characteristics,presence or absence of a capsule,blood flow resistance index,clear tumor shape,and blood flow signal display rate(P<0.05).The apparent diffusion coefficient values of the solid and cystic parts in benign tumors were found to be higher compared to malignant tumors(P<0.05).Additionally,the time-intensity curve image features of benign and malignant tumors showed significant statistical differences(P<0.05).The levels of serum CA125 and HE4 in benign tumors were lower than those in malignant tumors(P<0.05).The combined use of US,MRI,and tumor markers in the diagnosis of ovarian tumors demonstrates higher accuracy,sensitivity,and specificity compared to using each method individually(P<0.05).CONCLUSION US,MRI,and tumor markers each have their own advantages and disadvantages when it comes to diagnosing ovarian tumors.However,by combining these three methods,we can significantly enhance the accuracy of ovarian tumor diagnosis,enabling early detection and identification of the tumor’s nature,and providing valuable guidance for clinical treatment.

Magnetic resonance image-guided versus ultrasound-guided high-intensity focused ultrasound in the treatment of breast cancer

Image-guided high-intensity focused ultrasound (HIFU) has been used for more than ten years, primarily in the treatment of liver and prostate cancers. HIFU has the advantages of precise cancer ablation and excellent protection of healthy tissue. Breast cancer is a common cancer in women. HIFU therapy, in combination with other therapies, has the potential to improve both oncologic and cosmetic outcomes for breast cancer patients by providing a curative therapy that conserves mammary shape. Currently, HIFU therapy is not commonly used in breast cancer treatment, and efforts to promote the application of HIFU is expected. In this article, we compare different image-guided models for HIFU and reviewed the status, drawbacks, and potential of HIFU therapy for breast cancer.

Design and Experiments of Ultrasound Image-Guided Multi-DOF Robot System for Brachytherapy

To implant radioactive seeds through a needle precisely and safely, a novel multi-DOF surgical robotic system is presented in this paper for percutaneous prostate intervention through the patient's perineum under real-time ultrasound image guidance. The proposed robot, which is designed with 9-DOF, consists of a 3-DOF automatic location platform for position adjustment, 2-DOF for automatic ultrasonic probe adjustment mounted with electromagnetic trackers, and 4-DOF for manually adjusting the guided template. Meanwhile, a new registration method based on the quaternion algorithm and least square method is developed, and the needle insertion is performed under the realtime guidance of a navigation system. Furthermore, the robot system has undergone some preliminary experiments with a laser tracker to evaluate the repeatability and accuracy of the robot system. The location error of the puncture needle tip can be controlled under 0.7 mm in air for the whole robotic system. The acquired results endorse the precision of the robot system for prostate seed implantation brachytherapy.

Diagnostic Accuracy and Complication Rates of Fusion Images Created Using Real-Time Ultrasound with CT for Identification of Peripheral Lung Lesions in Patients Undergoing Biopsy

Background: Fusion image improves lesion detectability and can be an effective tool for percutaneous ultrasound (US)-guide procedure. We describe the clinical benefit of US-guided lung biopsy using fusion image. Purpose: To retrospectively compare the diagnostic accuracy and complication rates of US-guided lung biopsy with B-mode alone and those of a fusion image created using real-time US and computed tomography (CT). Materials and Methods: Between September, 2013 and September, 2016, 50 peripheral lung lesions in 50 patients (40 males, 10 females;median, 74 years old) were performed by US-guided percutaneous cutting needle biopsy using the B-mode alone or fusion image. Final diagnoses were based on surgical outcomes or clinical follow-up results for at least 12 months after biopsy. To assess prebiopsy characteristics, all lesions were divided into two groups: group 1 (identification on B-mode) and group 2 (identification on fusion image). Results: Of 50 peripheral lesions, 40 lesions (80%) were detected by means of B-mode alone (group 1), and 10 lesions (20%) were identified by fusion image (group 2). The diagnostic accuracy of group 1 was 90% (36/40 lesions), and the diagnostic accuracy of group 2 was 100% (10/10 lesions). Nodule type and the size of the lesions showed significant group wise differences (p Conclusion: Fusion images created using real-time US and CT may be useful for identification of the minimal size of potential target lung lesions and may be more suitable for improved yields with US-guided lung biopsy.

Ultrasound diagnosis of congenital Morgagni hernias: Ten years of experience at two Chinese centers

BACKGROUND Morgagni hernias are rare anomalies that are easily misdiagnosed or missed.AIM To summarize the ultrasound(US)imaging characteristics of Morgagni hernias through a comparison of imaging and surgical results.METHODS The records of children with Morgagni hernias who were hospitalized at two hospitals between January 2013 and November 2023 were retrospectively re-viewed in terms of clinical findings,US features,and operative details.RESULTS Between 2013 and 2023,we observed nine(five male and four female)children with Morgagni hernias.Upper abdominal scanning revealed a widening of the prehepatic space,with an abnormal channel extending from the xiphoid process to the right or left side of the thoracic cavity.The channel had intestinal duct and intestinal gas echoes.Hernia contents were found in the transverse colon(n=6),the colon and small intestine(n=2),and the colon and stomach(n=1).Among the patients,seven had a right-sided lesion,two had a left-sided lesion,and all of them had hernial sacs.CONCLUSION US imaging can accurately determine the location,extent,and content of Morgagni hernias.For suspected Mor-gagni hernias,we recommend performing sonographic screening first.

Estimation of muscle pennation angle in ultrasound images using the beamlet transform

Ultrasound imaging has been widely used to investigate the architecture properties of skeletal muscle, including the measurement of the pennation angle. In this study, we propose a beamlet-based algorithm to detect the straight line- shaped patterns of aponeurosis, fascicle or bone, and then to quantify the pennation angle ix1 ultrasound images. The results demonstrate that the proposed algorithm can well detect the pennatoin angles in thirty ultrasound images with the correlation coefficient of 0.945, the standard root mean square error of 0.682°, and the relative root mean square error of 3.498%. The results suggest that this beamlet-based algorithm provides an alternative approach for the orientation estimation in muscu- loskeletal ultrasound images.

Despeckling of Ultrasound Images Using Modified Local Statistics Mean Variance Filter

This article presents an improved method of despeckling the ultrasound medical images.In this paper a modified local statistics mean variance filter method has been proposed.In the proposed method,more consideration is given to local statistics since local statistical features are more important rather than global features.Various parameters like mean square error,peak signal to noise ratio,quality index,and structural similarity index measure are calculated to analyze the quality of the despeckled image.

Medical ultrasound image segmentation by modified local histogram range image method

Fast and satisfied medical ultrasound segmentation is known to be difficult due to speckle noises and other artificial effects. Since speckle noise is formed from random signals which are emitted by an ultrasound system, we can’t encounter the same way as other image noises. Lack of information in ultrasound images is another problem. Thus, segmentation results may not be accurate enough by means of customary image segmentation methods. Those methods that can specify undesirable effects and segment them by eliminating artificial effects, should be chosen. It seems to be a complicated work with high computational load. The current study presents a different approach to ultrasound image segmentation that relies mainly on local evaluation, named as local histogram range image method which is modified by means of discrete wavelet transform. Thus, a significant decrease in computational load is then achieved. The results show that it is possible for tissues to be segmented correctly.

Imaging and endoscopic tools in pediatric inflammatory bowel disease: What’s new?

Pediatric inflammatory bowel disease(IBD)is a chronic inflammatory disorder,with increasing incidence and prevalence worldwide.There have been recent advances in imaging and endoscopic technology for disease diagnosis,treatment,and monitoring.Intestinal ultrasound,including transabdominal,transperineal,and endoscopic,has been emerging for the assessment of transmural bowel inflammation and disease complications(e.g.,fistula,abscess).Aside from surgery,IBD-related intestinal strictures now have endoscopic treatment options including through-the-scope balloon dilatation,injection,and needle knife stricturotomy and new evaluation tools such as endoscopic functional lumen imaging probe.Unsedated transnasal endoscopy may have a role in patients with upper gastrointestinal Crohn’s disease or those with IBD with new upper gastrointestinal symptoms.Improvements to dysplasia screening in pediatric patients with longstanding colonic disease or primary sclerosing cholangitis hold promise with the addition of virtual chromoendoscopy and ongoing research in the field of artificial intelligence-assisted endoscopic detection.Artificial intelligence and machine learning is a rapidly evolving field,with goals of further personalizing IBD diagnosis and treatment selection as well as prognostication.This review summarized these advancements,focusing on pediatric patients with IBD.

Medical ultrasound image segmentation using genetic active contour

Image segmentation is one of the earliest and most important stages of image processing and plays an important role in both qualitative and quantitative analysis of medical ultrasound images but ultrasound images have low level of contrast and are corrupted with strong speckle noise. Due to these effects, segmentation of ultrasound images is very challenging and traditional image segmentation methods may not be leads to satisfactory results. The active contour method has been one of the widely used techniques for image segmentation;however, due to low quality of ultrasound images, it has encountered difficulties. In this paper, we presented a segmental method combined genetic algorithm and active contour with an energy minimization procedure based on genetic algorithms. This method have been proposed to overcome some limits of classical active contours, as con-tour initialization and local minima (speckle noise), and have been successfully applied on medical ultrasound images. Experimental result on medical ultrasound image show that our presented method only can correctly segment the circular tissue’s on ultra-sound images.

Recovery of photoacoustic images based on accurate ultrasound positioning

Photoacoustic microscopy is an in vivo imaging technology based on the photoacoustic effect.It is widely used in various biomedical studies because it can provide high-resolution images while being label-free,safe,and harmless to biological tissue.Polygon-scanning is an effective scanning method in photoacoustic microscopy that can realize fast imaging of biological tissue with a large field of view.However,in polygon-scanning,fluctuations of the rotating motor speed and the geometric error of the rotating mirror cause image distortions,which seriously affect the photoacoustic-microscopy imaging quality.To improve the image quality of photoacoustic microscopy using polygon-scanning,an image correction method is proposed based on accurate ultrasound positioning.In this method,the photoacoustic and ultrasound imaging data of the sample are simultaneously obtained,and the angle information of each mirror used in the polygon-scanning is extracted from the ultrasonic data to correct the photoacoustic images.Experimental results show that the proposed method can significantly reduce image distortions in photoacoustic microscopy,with the image dislocation offset decreasing from 24.774 to 10.365μm.

A REVIEW ON ARTERY WALL SEGMENTATION TECHNIQUES AND INTIMA-MEDIA THICKNESS MEASUREMENT FOR CAROTID ULTRASOUND IMAGES

Stroke and heart attack,which could be led by a kind of cerebrovascular and cardiovascular disease named as atherosclerosis,would seriously cause human morbidity and mortality.It is important for the early stage diagnosis and monitoring medical intervention of the atherosclerosis.Carotid stenosis is a classical atherosclerotic lesion with vessel wall narrowing down and accumulating plaques burden.The carotid artery of intima-media thickness(IMT)is a key indicator to the disease.With the development of computer assisted diagnosis technology,the imaging techniques,segmentation algorithms,measurement methods,and evaluation tools have made considerable progress.Ultrasound imaging,being real-time,economic,reliable,and safe,now seems to become a standard in vascular assessment methodology especially for the measurement of IMT.This review firstly attempts to discuss the clinical relevance of measurements in clinical practice at first,and then followed by the challenges that one has to face when approaching the segmentation of ultrasound images.Secondly,the commonly used methods for the IMT segmentation and measurement are presented.Thirdly,discussion and evaluation of different segmentation techniques are performed.An overview of summary and future perspectives is given finally.

The Use of Ultrasound Images in Manual Therapy and Additionally in Assessment of Shoulder Impingement Syndrome

The use of the ultrasound imaging (USI) in physiotherapy is becoming increasingly common but is highly operator dependent and there are safe and professional issues regarding its practical use. Currently there are no specific training guidelines relating to physiotherapists using USI. The use of ultrasound technology for medical applications began in the 1950s and has proven to be an effective, safe, non-invasive, and relatively inexpensive tool for assessing morphologic characteristics and structural integrity of visceral organs and soft tissues. The use of ultrasound to assess muscle morphology and guide rehabilitation decision-making in physical therapy practice can be traced back to the late 1960s and has been found to be reliable and valid for specific muscles during particular movements. Over the last decade there has been rapid development of this technique with increased use both by clinicians and researchers. This method is defined in literature with the denomination of Rehabilitative Ultrasound Imaging (RUSI). In this work we will see how RUSI could be of help in the evaluation of shoulder impingement syndrome (SIS).