3D Shape Reconstruction of Lumbar Vertebra From Two X-ray Images and a CT Model

Structure reconstruction of 3 D anatomy from biplanar X-ray images is a challenging topic. Traditionally, the elastic-model-based method was used to reconstruct 3 D shapes by deforming the control points on the elastic mesh. However, the reconstructed shape is not smooth because the limited control points are only distributed on the edge of the elastic mesh.Alternatively, statistical-model-based methods, which include shape-model-based and intensity-model-based methods, are introduced due to their smooth reconstruction. However, both suffer from limitations. With the shape-model-based method, only the boundary profile is considered, leading to the loss of valid intensity information. For the intensity-based-method, the computation speed is slow because it needs to calculate the intensity distribution in each iteration. To address these issues, we propose a new reconstruction method using X-ray images and a specimen’s CT data. Specifically, the CT data provides both the shape mesh and the intensity model of the vertebra. Intensity model is used to generate the deformation field from X-ray images, while the shape model is used to generate the patient specific model by applying the calculated deformation field.Experiments on the public synthetic dataset and clinical dataset show that the average reconstruction errors are 1.1 mm and1.2 mm, separately. The average reconstruction time is 3 minutes.

The diagnostic value of 3D spiral CT imaging of cholangiopancreatic ducts on obstructive jaundice

Objective: Computerized tomography (CT) plays an important role in the diagnosis of diseases of biliary tract. Recently, three dimensions (3D) spiral CT imaging has been used in surgical diseases gradually. This study was designed to evaluate the diagnostic value of 3D spiral CT imaging of cholangiopancreatic ducts on obstructive jaundice. Methods: Thirty patients with obstructive jaundice had received B-mode ultrasonography, CT, percutaneous transhepatic cholangiography (PTC) or endoscopic retrograde cholangiopancreatography (ERCP), and 3D spiral CT imaging of cholangiopancreatic ducts preoperatively. Then the diagnose accordance rate of these examinational methods were compared after operations. Results: The diagnose accordance rate of 3D spiral CT imaging of cholangiopancreatic ducts was higher than those of B-mode ultrasonography, CT, or single PTC or ERCP, which showed clear images of bile duct tree and pathological changes. As to malignant obstructive jaundice, this examinational technique could clearly display the adjacent relationship between tumor and liver tissue, biliary ducts, blood vessels, and intrahepatic metastases. Conclusion: 3D spiral CT imaging of cholangiopancreatic ducts has significant value for obstructive diseases of biliary ducts, which provides effective evidence for the feasibility of tumor-resection and surgical options.

Study on threshold segmentation of multi-resolution 3D human brain CT image

In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images,a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper.In this method,first,original 3D human brain image information is collected,and CT image filtering is performed to the collected information through the gradient value decomposition method,and edge contour features of the 3D human brain CT image are extracted.Then,the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points,and the 3D human brain CT image is reconstructed with the salient feature point as center.Simulation results show that the method proposed in this paper can provide accuracy up to 100%when the signal-to-noise ratio is 0,and with the increase of signal-to-noise ratio,the accuracy provided by this method is stable at 100%.Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is signicantly better than traditional methods in pathological feature estimation accuracy,and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.

Computer‑aided CT image processing and modeling method for tibia microstructure

We present a method for computed tomography(CT)image processing and modeling for tibia microstructure,achieved by using computer graphics and fractal theory.Given the large-scale image data of tibia species with DICOM standard for clinical applications,we take advantage of algorithms such as image binarization,hot pixel removing and close operation to obtain visually clear image for tibia microstructure.All of these images are based on 20 CT scanning images with 30μm slice thickness and 30μm interval and continuous changes in pores.For each pore,we determine its profile by using an improved algorithm for edge detection.Then,to calculate its three-dimensional fractal dimension,we measure the circumference perimeter and area of the pores of bone microstructure using a line fitting method based on the least squares.Subsequently,we put forward an algorithm for the pore profiles through ellipse fitting.The results show that the pores have significant fractal characteristics because of the good linear correlation between the perimeter and the area parameters in log–log scale coordinates system,and the ratio of the elliptical short axis to the long axis through ellipse fitting tends to 0.6501.Based on support vector machine and structural risk minimization principle,we put forward a mapping database theory of structure parameters among the pores of CT images and fractal dimension,Poisson’s ratios,porosity and equivalent aperture.On this basis,we put forward a new concept for 3D modeling called precision-measuring digital expressing to reconstruct tibia microstructure for human hard tissue.

PC-BASED SURFACE RECONSTRUCTION OF MEDICAL CT IMAGES

It is an active research area to reconstruct 3-D object and display its visible surfacesfrom cross-sectional images. In this paper, the methods of reconstructing 3-D object from medicalCT images and displaying the visible surfaces are discussed. A polygon approximation methodthat forms polygon with the same number of segment points and a fast interpolation method forcross-sectional contours are presented at first. Then the voxel set of a human liver is reconstructed.And then the liver voxel set is displayed using depth and gradient shading methods. The softwareis written in C programming language at a microcomputer image processing system with a PC/ATcomputer as the host and a PC-VISION board as the image processing unit. The result of theprocessing is satisfying.

Research on Automatic Elimination of Laptop Computer in Security CT Images Based on Projection Algorithm and YOLOv7-Seg

In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to individually present their laptops for inspection. The paper introduced a method for laptop removal. By combining projection algorithms with the YOLOv7-Seg model, a laptop’s three views were generated through projection, and instance segmentation of these views was achieved using YOLOv7-Seg. The resulting 2D masks from instance segmentation at different angles were employed to reconstruct a 3D mask through angle restoration. Ultimately, the intersection of this 3D mask with the original 3D data enabled the successful extraction of the laptop’s 3D information. Experimental results demonstrated that the fusion of projection and instance segmentation facilitated the automatic removal of laptops from CT data. Moreover, higher instance segmentation model accuracy leads to more precise removal outcomes. By implementing the laptop removal functionality, the civil aviation security screening process becomes more efficient and convenient. Passengers will no longer be required to individually handle their laptops, effectively enhancing the efficiency and accuracy of security screening.

Visualization of Three-dimensional Human Data Based on CT Image

Three-dimensional medical image visualization becomes an essential part for medical field, including computer aided diagnosis, surgery planning and simulation, artificial limb surgery, radiotherapy planning, and teaching etc. In this paper, marching cubes algorithm is adopted to reconstruct the 3-D images for the CT image sequence in DICOM format under theVC＋＋6.0 and the visual package VTK platform. The relatively simple interactive operations such as rotation and transfer can be realized on the platform. Moreover, the normal vector and interior point are calculated to form the virtual clipping plane, which is then used to incise the 3-D object. Information of the virtual slice can be obtained, in the mean while the virtual slice images are displayed on the screen. The technique can realize the real time interaction extraction of virtual slice on 3-D CT image. The cuboids structured can be zoomed, moved and eircumrotated by operating mouse to incise the 3-D reconstruction object. Real time interaction can be realized by clipping the reconstruction object. The coordinates can be acquired by the mouse clicking in the 3D space, to realize the point mouse pick-up as well angle and distance interactive measurement. We can get quantitative information about 3-D images through measurement.

3D characterization and analysis of pore structure of packed ore particle beds based on computed tomography images

Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional images of specimens with single particle size of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10 ram. Based on the in-house developed 3D image analysis programs using Matlab, the volume porosity, pore size distribution and degree of connectivity were calculated and analyzed in detail. The results indicate that the volume porosity, the mean diameter of pores and the effective pore size （d50） increase with the increasing of particle size. Lognormal distribution or Gauss distribution is mostly suitable to model the pore size distribution. The degree of connectivity investigated on the basis of cluster-labeling algorithm also increases with increasing the particle size approximately.

DIRECT VOXEL-PROJECTION FOR VOLUMETRIC DATA RENDERING IN MEDICAL IMAGERY

The volumetric rendering of 3 D medical image data is very effective method for communication about radiological studies to clinicians. Algorithms that produce images with artifacts and inaccuracies are not clinically useful. This paper proposed a direct voxel projection algorithm to implement volumetric data rendering. Using this algorithm, arbitrary volume rotation, transparent and cutaway views are generated satisfactorily. Compared with the existing raytracing methods, it improves the projection image quality greatly. Some experimental results about real medical CT image data demonstrate the advantages and fidelity of the proposed algorithm.

Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians.In our study,we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography(CT)images of cancer patients.Medical image data has more complex characteristics than normal image data,and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem.We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation.In strategies,we trained our custom convolutional neural network model which has a structure inspired by the U-net,and residual blocks were also applied to the architecture.The output of the network is added with a rectified linear unit(Re-Lu)function for each pixel to ensure there are no negative values,which are absurd with radiation doses.Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treatedwith radiation therapy.The results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score(DVH-score).In the best training cases,the Dose-score is 3.08 and the DVH-score is 1.78.In addition,we also conducted a comparison with the results of another study in the same context of using the loss function.

Use of high-resolution X-ray computed tomography and 3D image analysis to quantify mineral dissemination and pore space in oxide copper ore particles

Mineral dissemination and pore space distribution in ore particles are important features that influence heap leaching performance. To quantify the mineral dissemination and pore space distribution of an ore particle, a cylindrical copper oxide ore sample (I center dot 4.6 mm x 5.6 mm) was scanned using high-resolution X-ray computed tomography (HRXCT), a nondestructive imaging technology, at a spatial resolution of 4.85 mu m. Combined with three-dimensional (3D) image analysis techniques, the main mineral phases and pore space were segmented and the volume fraction of each phase was calculated. In addition, the mass fraction of each mineral phase was estimated and the result was validated with that obtained using traditional techniques. Furthermore, the pore phase features, including the pore size distribution, pore surface area, pore fractal dimension, pore centerline, and the pore connectivity, were investigated quantitatively. The pore space analysis results indicate that the pore size distribution closely fits a log-normal distribution and that the pore space morphology is complicated, with a large surface area and low connectivity. This study demonstrates that the combination of HRXCT and 3D image analysis is an effective tool for acquiring 3D mineralogical and pore structural data.

Virtual Anatomy: The Dissecting Theatre of the Future—Implementation of Cinematic Rendering in a Large 8 K High-Resolution Projection Environment

Modern computer techniques have been in use for several years to generate three-dimensional visualizations of human anatomy. Very good 3-D computer models of the human body are now available and used routinely in anatomy instruction. These techniques are subsumed under the heading “virtual anatomy” to distinguish them from the conventional study of anatomy entailing cadavers and anatomy textbooks. Moreover, other imaging procedures (X-ray, angiography, CT and MR) are also used in virtual anatomy instruction. A recently introduced three-dimensional post-processing technique named Cinematic Rendering now makes it possible to use the output of routine CT and MR examinations as the basis for highly photo-realistic 3-D depictions of human anatomy. We have installed Cinematic Rendering (enabled for stereoscopy) in a high-definition 8K 3-D projection space that accommodates an audience of 150. The space’s projection surface measures 16 × 9 meters;images can be projected on both the front wall and the floor. A game controller can be used to operate Cinematic Rendering software so that it can generate interactive real-time depictions of human anatomy on the basis of CT and MR data sets. This prototype installation was implemented without technical problems;in day-to-day, real-world use over a period of 22 months, there were no impairments of service due to software crashes or other technical problems. We are already employing this installation routinely for educational offerings open to the public, courses for students in the health professions, and (continuing) professional education units for medical interns, residents and specialists—in, so to speak, the dissecting theater of the future.

3-D Visualization of Medical Images with Arbitrary Sections

In this paper, with the general retrospect to the research on surface reconstruction and the marching cubes algorithm, we gave detailed description of an algorithm on the construction of object surfaces. The possible ambiguity problem in the original marching cubes algorithm was eliminated by its index mechanism. Some results on the MRI images were presented. Based on extracting and clipping contours from a set of medial slice images and setting the patch vertices values according to the gray images, this algorithm may be applied to form the arbitrary section images with three dimensional effects. It can also enhance the visual effect and interpretation of medical data.

Detection of the Single Image from DIBR Based on 3D Warping Trace and Edge Matching

Recently, the popularity of 3D content is on the rise because of its immersive experience to view- ers. While demands for 3D contents and 3D technologies increase, only a few copyright protection methods for 3D contents have been proposed. The simplest infringement is the illegal distribution of the single 2D image from 3D content. The leaked image is still valuable as 2D content and the leakage can be occurred in DIBR system. To detect the leaked image, we focus on the hole-filled region which is caused by the hole-filling procedure mandatory in DIBR system. To estimate the hole-filled regions, two different procedures are conducted to extract edges and to estimate 3D warping traces, respectively. After that, the hole-filled regions are estimated and the left-right-eye image discrimination (LR discrimination) is also conducted. Experimental results demonstrate the effectiveness of the proposed method using quantitative measures.

Application of X-ray Computed Tomography in Characterization Microstructure Changes of Cement Pastes in Carbonation Process

The microstructure characteristics and meso-defect volume changes of hardened cement paste before and after carbonation were investigated by three-dimensional （3D） X-ray computed tomograpby （XCT）, where three types water-to-cement ratio of 0.53, 0.35 and 0.23 were considered. The high-resolution 3D images of microstructure and filtered defects were reconstructed by an XCT VG Studio MAX 2.0 software, The meso- defect volume fractions and size distribution were analyzed based on 3D images through add-on modules of 3D defect analysis. The 3D meso-defects volume fractions before carbonation were 0.79%, 0.38% and 0.05% corresponding to w/c ratio=0.53, 0.35 and 0.23, respectively. The 3D meso-defects volume fractions after carbonation were 2.44%, 0.91% and 0.14% corresponding to w/c ratio=0.53, 0.35 and 0.23, respectively. The experimental results suggest that 3D meso-defects volume fractions after carbonation for above three w/c ratio increased significantly. At the same time, meso-cracks distribution of the carbonation shrinkage and gray values changes of the different w/c ratio and carbonation reactions were also investigated.

Fast and Low Cost X-Ray Stereoradiography Displayed on a 3D Monitor

Conventional x-ray stereoradiography based on film radiography is not practical due to its inconvenient and time-consuming procedures. In this research, an image viewing system consisted of a 30 cm × 30 cm gadolinium oxy-sulfide (GOS) fluorescent screen and a Cannon 500D digital camera were designed and constructed for real-time and near real-time x-ray imaging. The camera was connected to a laptop computer via USB port to allow remote camera setting and control as well as view image on the computer. The system was tested with x-rays generated from a Rigaku x-ray tube for its response at various camera settings and exposure times. The image brightness increased with increasing of the camera ISO setting and with the exposure time as expected. To test the system performance, two test specimens were radiographed including a video camera and a floppy disk drive as well as two simulated specimens. Each of the test specimens was also radiographed at two positions by moving the specimens approximately 6 cm from the first position. The two radiographs of each specimen were then combined to make an anaglyph image that could be viewed in 3D on a normal LCD or LED monitor by using appropriate color glasses. When the two radiographs were combined to make MPO (multiple object) file format, it could be viewed in 3D on a 3D monitor with or without 3D glasses depending on type of the monitor. The developed system could be conveniently employed for routine inspection of a specimen both in 2D and 3D within a minute.

Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.

纹理映射体绘制的Java 3D实现

目的设计实现一个虚拟心脏系统原型,重点讨论其中的基于纹理映射的体绘制方法,给出用Java 3D软件方法实现纹理映射体绘制。方法获取CT心脏断层数据,对其进行预处理,在此基础上进行图像的滤波、分割、配准及裁剪,最后实现基于纹理映射体绘制的三维重构。结果用Java+Java 3D实现了虚拟心脏系统原型中基于纹理映射的体绘制。结论采用软件方法(Java3D)实现了纹理映射硬件的功能,由于不需要三维纹理映射硬件的支持,降低了硬件成本。

A Systematic Literature Review of Deep Learning Algorithms for Segmentation of the COVID-19 Infection

Coronavirus has infected more than 753 million people,ranging in severity from one person to another,where more than six million infected people died worldwide.Computer-aided diagnostic(CAD)with artificial intelligence(AI)showed outstanding performance in effectively diagnosing this virus in real-time.Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients.This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs.We used the methodology of systematic reviews and meta-analyses(PRISMA)flow method.This research aims to systematically analyze the supervised deep learning methods,open resource datasets,data augmentation methods,and loss functions used for various segment shapes of COVID-19 infection from computerized tomography(CT)chest images.We have selected 56 primary studies relevant to the topic of the paper.We have compared different aspects of the algorithms used to segment infected areas in the CT images.Limitations to deep learning in the segmentation of infected areas still need to be developed to predict smaller regions of infection at the beginning of their appearance.

2D-3D registration for 3D analysis of lower limb alignment in a weight-bearing condition

X-ray imaging is the conventional method for diagnosing the orthopedic condition of a patient. Computerized Tomography(CT) scanning is another diagnostic method that provides patient’s 3D anatomical information. However, both methods have limitations when diagnosing the whole leg; X-ray imaging does not provide 3D information, and normal CT scanning cannot be performed with a standing posture. Obtaining 3D data regarding the whole leg in a standing posture is clinically important because it enables 3D analysis in the weight bearing condition.Based on these clinical needs, a hardware-based bi-plane X-ray imaging system has been developed; it uses two orthogonal X-ray images. However, such methods have not been made available in general clinics because of the hight cost. Therefore, we proposed a widely adaptive method for 2 D X-ray image and 3D CT scan data. By this method, it is possible to threedimensionally analyze the whole leg in standing posture. The optimal position that generates the most similar image is the captured X-ray image. The algorithm verifies the similarity using the performance of the proposed method by simulation-based experiments. Then, we analyzed the internal-external rotation angle of the femur using real patient data. Approximately 10.55 degrees of internal rotations were found relative to the defined anterior-posterior direction. In this paper, we present a useful registration method using the conventional X-ray image and 3D CT scan data to analyze the whole leg in the weight-bearing condition.