Automatic Registration Research Articles

Multi-sensor remote sensing image alignment based on fast algorithms

Abstract Remote sensing image technology to the ground has important guiding significance in disaster assessment and emergency rescue deployment. In order to realize the fast automatic registration of multi-sensor remote sensing images, the remote sensing image block registration idea is introduced, and the image reconstruction is processed by using the conjugate gradient descent (CGD) method. The scale-invariant feature transformation (SIFT) algorithm is improved and optimized by combining the function-fitting method. By this way, it can improve the registration accuracy and efficiency of multi-sensor remote sensing images. The results show that the average peak signal-to-noise ratio of the image processed by the CGD method is 25.428. The average root mean square value is 17.442. The average image processing time is 6.093 s. These indicators are better than the passive filter algorithm and the gradient descent method. The average accuracy of image registration of the improved SIFT registration method is 96.37%, and the average image registration time is 2.14 s. These indicators are significantly better than the traditional SIFT algorithm and speeded-up robust features algorithm. It is proved that the improved SIFT registration method can effectively improve the accuracy and operation efficiency of multi-sensor remote sensing image registration methods. The improved SIFT registration method effectively solves the problems of low accuracy and long time consumption of traditional multi-sensor remote sensing image fast registration methods. While maintaining high registration accuracy, it improves the image registration speed and provides technical support for a rapid disaster assessment after major disasters such as earthquakes and floods. And it has an important value for the development of the efficient post-disaster rescue deployment.

Grid graph-based large-scale point clouds registration

ABSTRACT Automatic registration of unordered point clouds is the prerequisite for urban reconstruction. However, most of the existing technologies still suffer from some limitations. On one hand, most of them are sensitive to noise and repetitive structures, which makes them infeasible for the registration of large-scale point clouds. On the other hand, most of them dealing with point clouds with limited overlaps and unpredictable location. All these problems make it difficult for registration technology to obtain qualified results in outdoor point cloud. To overcome these limitations, this paper presents a grid graph-based point cloud registration (GGR) algorithm to align pairwise scans. First, point cloud is divided into a set of 3D grids. We propose a voting strategy to measure the similarity between two grids based on feature descriptor, transforming the superficial correspondence into 3D grid expression. Next, a graph matching is proposed to capture the spatial consistency from putative correspondences, and graph matching hierarchically refines the corresponding grids until obtaining point-to-point correspondences. Comprehensive experiments demonstrated that the proposed algorithm obtains good performance in terms of successful registration rate, rotation error, translation error, and outperformed the state-of-the-art approaches.

Automatic multi-view registration of point clouds via a high-quality descriptor and a novel 3D transformation estimation technique

Automatic multi-view registration of point clouds via a high-quality descriptor and a novel 3D transformation estimation technique

Automatic landmark detection and registration of brain cortical surfaces via quasi-conformal geometry and convolutional neural networks

In medical imaging, surface registration is extensively used for performing systematic comparisons between anatomical structures, with a prime example being the highly convoluted brain cortical surfaces. To obtain a meaningful registration, a common approach is to identify prominent features on the surfaces and establish a low-distortion mapping between them with the feature correspondence encoded as landmark constraints. Prior registration works have primarily focused on using manually labeled landmarks and solving highly nonlinear optimization problems, which are time-consuming and hence hinder practical applications. In this work, we propose a novel framework for the automatic landmark detection and registration of brain cortical surfaces using quasi-conformal geometry and convolutional neural networks. We first develop a landmark detection network (LD-Net) that allows for the automatic extraction of landmark curves given two prescribed starting and ending points based on the surface geometry. We then utilize the detected landmarks and quasi-conformal theory for achieving the surface registration. Specifically, we develop a coefficient prediction network (CP-Net) for predicting the Beltrami coefficients associated with the desired landmark-based registration and a mapping network called the disk Beltrami solver network (DBS-Net) for generating quasi-conformal mappings from the predicted Beltrami coefficients, with the bijectivity guaranteed by quasi-conformal theory. Experimental results are presented to demonstrate the effectiveness of our proposed framework. Altogether, our work paves a new way for surface-based morphometry and medical shape analysis.

Feature-Based Electromagnetic Tracking Registration Using Bioelectric Sensing

Catheter tracking is essential during minimally invasive endovascular procedures, and Electromagnetic (EM) tracking is a widely used technology for this purpose. When preoperative patient images are available, they can be used to guide EM-tracked interventions. However, a registration step between preoperative images and intraoperative EM tracking space is usually required. Most existing solutions for this registration process require manual interactions, which can add additional steps to the workflow. In this paper, a novel automatic feature-based registration method is proposed, based on electric sensing of vascular geometry by the catheter, also known as Bioelectric sensing. The technique employs the Bioelectric sensing capabilities of the catheter to identify vascular features, such as bifurcations, aneurysms, or stenosis. The known EM position of these features is then utilized to register the EM tracking space and the preoperative images. The registration is refined using iterative closest point (ICP) registration algorithms. Unlike existing solutions, the proposed method does not require external markers, interventional imaging, or additional surgeon actions, and hence does not impact the interventional workflow.

Machine learning for automated and real-time two-dimensional to three-dimensional registration of the spine using a single radiograph.

The goal of this work was to methodically evaluate, optimize, and validate a self-supervised machine learning algorithm capable of real-time automatic registration and fluoroscopic localization of the spine using a single radiograph or fluoroscopic frame. The authors propose a two-dimensional to three-dimensional (2D-3D) registration algorithm that maximizes an image similarity metric between radiographic images to identify the position of a C-arm relative to a 3D volume. This work utilizes digitally reconstructed radiographs (DRRs), which are synthetic radiographic images generated by simulating the x-ray projections as they would pass through a CT volume. To evaluate the algorithm, the authors used cone-beam CT data for 127 patients obtained from an open-source de-identified registry of cervical, thoracic, and lumbar scans. They systematically evaluated and tuned the algorithm, then quantified the convergence rate of the model by simulating C-arm registrations with 80 randomly simulated DRRs for each CT volume. The endpoints of this study were time to convergence, accuracy of convergence for each of the C-arm's degrees of freedom, and overall registration accuracy based on a voxel-by-voxel measurement. A total of 10,160 unique radiographic images were simulated from 127 CT scans. The algorithm successfully converged to the correct solution 82% of the time with an average of 1.96 seconds of computation. The radiographic images for which the algorithm converged to the solution demonstrated 99.9% registration accuracy despite utilizing only single-precision computation for speed. The algorithm was found to be optimized for convergence when the search space was limited to a ± 45° offset in the right anterior oblique/left anterior oblique, cranial/caudal, and receiver rotation angles with the radiographic isocenter contained within 8000 cm3 of the volumetric center of the CT volume. The investigated machine learning algorithm has the potential to aid surgeons in level localization, surgical planning, and intraoperative navigation through a completely automated 2D-3D registration process. Future work will focus on algorithmic optimizations to improve the convergence rate and speed profile.

AUTOMATIC REGISTRATION OF DECLARATIONS FOR GOODS:
 TYPICAL ERRORS

The article deals with topical issues related to the automatic registration of declarations for goods, errors that arise in practice at the stage of filing and registration of declarations for goods, as well as measures taken by the customs authorities in order to eliminate them.

Robust H-K Curvature Map Matching for Patient-to-CT Registration in Neurosurgical Navigation Systems

Image-to-patient registration is a coordinate system matching process between real patients and medical images to actively utilize medical images such as computed tomography (CT) during surgery. This paper mainly deals with a markerless method utilizing scan data of patients and 3D data from CT images. The 3D surface data of the patient are registered to CT data using computer-based optimization methods such as iterative closest point (ICP) algorithms. However, if a proper initial location is not set up, the conventional ICP algorithm has the disadvantages that it takes a long converging time and also suffers from the local minimum problem during the process. We propose an automatic and robust 3D data registration method that can accurately find a proper initial location for the ICP algorithm using curvature matching. The proposed method finds and extracts the matching area for 3D registration by converting 3D CT data and 3D scan data to 2D curvature images and by performing curvature matching between them. Curvature features have characteristics that are robust to translation, rotation, and even some deformation. The proposed image-to-patient registration is implemented with the precise 3D registration of the extracted partial 3D CT data and the patient’s scan data using the ICP algorithm.

The Determinants of Electoral Registration Quality: A Cross-National Analysis

ABSTRACT Electoral registers provide the definitive record of who can participate in an election, but there is often thought to be considerable variations in their quality cross-nationally. This leads to concerns about eligible voters being de facto disenfranchised on election day; but also ineligible voters or fictitious names appearing on the roll which can enable electoral fraud. In either case, the legitimacy of the election can be questioned. The electoral register is also used for other purposes such as drawing electoral boundaries. This article introduces some common international terminology for electoral register quality and a conceptualisation of the different ways in which an electoral register can be compiled. It then introduces a new global dataset on registration procedures (n = 159). The article hypotheses that automatic voter registration, as well as organisational and structural factors, strongly affects accuracy and completeness. The results show that automatic voter registration increases the completeness of the electoral register and also has a positive impact on accuracy. The organisational performance of the electoral management body was also shown to have positive effects on completeness and accuracy, suggesting an additional means of improving electoral registers beyond the registration model, which also rest in the hands of policy makers.

MO-0302 Automatic applicator-based image registration workflow for image-guided adaptive brachytherapy

MO-0302 Automatic applicator-based image registration workflow for image-guided adaptive brachytherapy

Improving the registration stability of cone-beam computed tomography with the Sphere-Mask Optical Positioning System: a feasibility study.

Cone-beam computed tomography (CBCT) is an important tool for patient positioning in radiotherapy due to its outstanding advantages. However, the CBCT registration shows errors due to the limitations of the automatic registration algorithm and the nonuniqueness of manual verification results. The purpose of this study was to verify the feasibility of using the Sphere-Mask Optical Positioning System (S-M_OPS) to improve the registration stability of CBCT through clinical trials. From November 2021 to February 2022, 28 patients who received intensity-modulated radiotherapy and site verification with CBCT were included in this study. S-M_OPS was used as an independent third-party system to supervise the CBCT registration result in real time. The supervision error was calculated based on the CBCT registration result and using the S-M_OPS registration result as the standard. For the head and neck, patients with a supervision error ≥3 or ≤-3 mm in 1 direction were selected. For the thorax, abdomen, pelvis, or other body parts, patients with a supervision error ≥5 or ≤-5 mm in 1 direction were selected. Then, re-registration was performed for all patients (selected and unselected). The registration errors of CBCT and S-M_OPS were calculated based on the re-registration results as the standard. For selected patients with large supervision errors, CBCT registration errors (mean ± standard deviation) in the latitudinal (LAT; left/right), vertical (VRT; superior/inferior), and longitudinal (LNG; anterior/posterior) directions were 0.90±3.20, -1.70±0.98, and 7.30±2.14 mm, respectively. The S-M_OPS registration errors were 0.40±0.14, 0.32±0.66, and 0.24±1.12 mm in the LAT, VRT, and LNG directions, respectively. For all patients, CBCT registration errors in the LAT, VRT, and LNG directions were 0.39±2.69, -0.82±1.47, and 2.39±2.93 mm, respectively. The S-M_OPS registration errors were -0.25±1.33, 0.55±1.27, and 0.36±1.34 mm for all patients in the LAT, VRT, and LNG directions, respectively. This study shows that S-M_OPS registration offers comparable accuracy to CBCT for daily registration. S-M_OPS, as an independent third-party tool, can prevent large errors in CBCT registration, thereby improving the accuracy and stability of CBCT registration.

In situ guidance for MRI interventions using projected feedback.

To develop and evaluate an augmented reality instrument guidance system for MRI-guided needle placement procedures such as musculoskeletal biopsy and arthrography. Our system guides the physician to insert a needle toward a target while looking at the insertion site without requiring special headgear. The system is comprised of a pair of stereo cameras, a projector, and a computational unit with a touch screen. All components are designed to be used within the MRI suite (Zone 4). Multi-modality fiducial markers called VisiMARKERs, detectable in both MRI and camera images, facilitate automatic registration after the initial scan. The navigation feedback is projected directly onto the intervention site allowing the interventionalist to keep their focus on the insertion site instead of a secondary monitor which is often not in front of them. We evaluated the feasibility and accuracy of this system on custom-built shoulder phantoms. Two radiologists used the system to select targets and entry points on initial MRIs of these phantoms over three sessions. They performed 80 needle insertions following the projected guidance. The system targeting error was 1.09mm, and the overall error was 2.29mm. We demonstrated both feasibility and accuracy of this MRI navigation system. The system operated without any problems inside the MRI suite close to the MRI bore. The two radiologists were able to easily follow the guidance and place the needle close to the target without any intermediate imaging.

Validation of a surgical navigation system for hypertensive intracerebral hemorrhage based on mixed reality using an automatic registration method

Validation of a surgical navigation system for hypertensive intracerebral hemorrhage based on mixed reality using an automatic registration method

A robust and automatic CT-3D ultrasound registration method based on segmentation, context, and edge hybrid metric.

The fusion of computed tomography (CT) and ultrasound (US) image can enhance lesion detection ability and improve the success rate of liver interventional radiology. The image-based fusion methods encounter the challenge of registration initialization due to the random scanning pose and limited field of view of US. Existing automatic methods those used vessel geometric information and intensity-based metric are sensitive to parameters and have low success rate. The learning-based methods require a large number of registered datasets for training. The aim of this study is to provide a fully automatic and robust US-3D CT registration method without registered training data and user-specified parameters assisted by the revolutionary deep learning-based segmentation, which can further be used for preparing training samples for the study of learning-basedmethods. We propose a fully automatic CT-3D US registration method by two improved registration metrics. We propose to use 3D U-Net-based multi-organ segmentation of US and CT to assist the conventional registration. The rigid transform is searched in the space of any paired vessel bifurcation planes where the best transform is decided by a segmentation overlap metric, which is more related to the segmentation precision than Dice coefficient. In nonrigid registration phase, we propose a hybrid context and edge based image similarity metric with a simple mask that can remove most noisy US voxels to guide the B-spline transform registration. We evaluate our method on 42 paired CT-3D US datasets scanned with two different US devices from two hospitals. We compared our methods with other exsiting methods with both quantitative measures of target registration error (TRE) and the Jacobian determinent with paired t-test and qualitative registration imagingresults. The results show that our method achieves fully automatic rigid registration TRE of 4.895 mm, deformable registration TRE of 2.995 mm in average, which outperforms state-of-the-art automatic linear methods and nonlinear registration metrics with paired t-test's p value less than 0.05. The proposed overlap metric achieves better results than self similarity description (SSD), edge matching (EM), and block matching (BM) with p values of 1.624E-10, 4.235E-9, and 0.002, respectively. The proposed hybrid edge and context-based metric outperforms context-only, edge-only, and intensity statistics-only-based metrics with p values of 0.023, 3.81E-5, and 1.38E-15, respectively. The 3D US segmentation has achieved mean Dice similarity coefficient (DSC) of 0.799, 0.724, 0.788, and precision of 0.871, 0.769, 0.862 for gallbladder, vessel, and branch vessel,respectively. The deep learning-based US segmentation can achieve satisfied result to assist robust conventional rigid registration. The Dice similarity coefficient-based metrics, hybrid context, and edge image similarity metric contribute to robust and accurateregistration.

360 Assessment of Pedicle Screw Placement Accuracy Utilizing a Novel Augmented Reality and Artificial Intelligence Surgical Guidance System

INTRODUCTION: The integration of artificial intelligence (AI) and augmented reality (AR) technologies in spine navigation has the potential to minimize pedicle screw misplacement, decrease radiation exposure, and improve procedural efficiency, addressing some of the challenges associated with spinal instrumentation. METHODS: Sixty lumbosacral pedicle screws were placed by 4 Orthopedics surgeons in 6 cadavers using the AR/AI surgical navigation system. Two CT intraoperative scans (before and after screw placement) of the cadaveric specimens were obtained for the study to measure the 3D positional and angular discrepancies between virtual and real screws. The validation procedure consisted of 3 steps: (1) automatic registration and 3D coordinate transformation, where the preop CT scan was aligned with the postop scan to determine the virtual screw 3D positions and orientations in postop scan; (2) analysis of the postop scan to determine the real screw 3D positions and orientations as they appeared in the scan volume; (3) automatic comparison between the 3D positions and orientations of the virtual and real screws. RESULTS: A one-sample t-test statistical analysis yielded a mean positional error of 2.16 mm ± 1.00 mm (99% CI: 1.81-2.49 mm), and a mean angular error of 1.49± 0.73° (99% CI: 1.25-1.74°). These results were independent of anatomical level, side, and surgeon. CONCLUSIONS: This study indicates that this AR/AI surgical guidance system provides mean positional and angular errors lower than 3.0 mm and 3.0° respectively, thus complying with FDA-required regulatory acceptance criteria. The system was FDA-cleared as a US 510k Orthopedic Stereotaxic Instrument.

P‐2.21: Research on multi‐lidar detection technology for autonomous driving

Nowadays autonomous driving has become a hot topic, many people pay attention to the field. Target detection through sensors is the basis of autonomous driving and plays an important role in high-level autonomous driving. The effective environmental information obtained by using a single sensor is less, and the visual sensor is easily affected by the weather and the algorithm is complex, so there are problems in real-time detection and accuracy. Multi-lidar detection is also a popular research in this field and has important research value and application prospect.This paper mainly describes the key steps of multi-lidar detection and proposes a fully automatic point cloud registration scheme. The fully automatic calibration method does not require manual initialization and does not rely on additional sensors at all, which can play a good role in multi-lidar detection. After that, the fused point cloud data is filtered to remove interference points and reduce the number of point clouds. In view of the under-segmentation of ground point cloud, the point cloud is mapped to the raster graph, and the ground point and non-ground point are separated by Random sample consensus algorithm and least square method. Then, distance-based clustering is used for obstacle detection on non-ground points.

Political Communication: Uncovering Knowledge, Attitudes and Young Voters’ Readiness for PRU-15 in Batang Padang District, Perak

Malaysia made history when the Dewan Rakyat approved an amendment to the Federal Constitution that lowered the voting age from 21 to 18 years old. The country’s democratic system was colored by the automatic registration of those who turned 18 as voters for the 15th Malaysian General Election (GE-15). This amendment aims to democratize the political process in this nation and promote mature, fair, and equitable elections. This ongoing study aims to discover young voters' knowledge, attitudes, and readiness toward the 15th general election (PRU-15) in Batang Padang, Perak district. Methodologically, a quantitative approach was used where 462 youth participants completed questionnaires. The findings discovered that although attitudes and readiness among young voters in the research population are at a moderate level, they have a high level of knowledge. The result also uncovered five key factors that influence young voters’ readiness towards making their decisions for PRU-15 namely voting is my responsibility, the good reputation of elected representatives, peer influence, the implementation of the Undi-18 policy by the government and family support. The study thus contributed to a new landscape of political communication through young voters’ contribution not only to casting the ballot during the election but also to participating in the country’s leadership as members of parliament. Keywords: Political communication, Undi-18, PRU-15, knowledge, young voters.

IDCF: information distribution composite feature for multi-modal image registration

ABSTRACT Automatic registration of multi-modal remote sensing data is a challenging task. Due to the significant nonlinear radiation difference and noise between multi-mode images, the register points extracted by traditional gradient algorithms (such as SIFT) have low repetition rate and poor feature similarity, which makes it difficult to register multi-mode images. To solve these problems, this paper proposes a method which is called Multi-modal Image Matching Based on Information Distribution Composite Feature (IDCF). Firstly, an adaptive information entropy map (AIEM) is proposed. AIEM not only describes the information distribution characteristics of the image, but also the distribution of the contour features of the image. Compared with traditional contour feature extraction operators, AIEM extraction results are clearer, more comprehensive and more detailed. In addition, AIEM is more robust to NRD compared to gradient, which is greatly affected by NRD. Then, IDCF extracts corner points on AIEM as feature points because corner points have better repeatability. After that, a composite feature description model based on maximum information index map (MIIM) and information trend graph (ITM) is defined. MIIM describes the main change direction of image information and ITM describes the overall change trend of image information. Finally, the similarity criterion SAD is used to match the feature points. The proposed IDCF aims to capture the structural similarity between images and has been tested with a variety of optical, Lidar, SAR, and map data. The results show that IDCF is robust against complex NRD outperforms the advanced algorithms (i.e. RIFT, PSO-SIFT and OS-SIFT) in matching performance.

Automatic registration of dental CT and 3D scanned model using deep split jaw and surface curvature

Background and objectives: In the medical field, various image registration applications have been studied. In dentistry, the registration of computed tomography (CT) volume data and 3D optically scanned models is essential for various clinical applications, including orthognathic surgery, implant surgical planning, and augmented reality. Our purpose was to present a fully automatic registration method of dental CT data and 3D scanned models.Methods: We use a 2D convolutional neural network to regress a curve splitting the maxilla (i.e., upper jaw) and mandible (i.e., lower jaw) and the points specifying the front and back ends of the crown from the CT data. Using this regressed information, we extract the point cloud and vertices corresponding to the tooth crown from the CT and scanned data, respectively. We introduce a novel metric, called curvature variance of neighbor (CVN), to discriminate between highly fluctuating and smoothly varying regions of the tooth crown. The registration based on CVN enables more accurate fine registration while reducing the effects of metal artifacts. Moreover, the proposed method does not require any preprocessing such as extracting the iso-surface for the tooth crown from the CT data, thereby significantly reducing the computation time.Results: We evaluated the proposed method with the comparison to several promising registration techniques. Our experimental results using three datasets demonstrated that the proposed method exhibited higher registration accuracy (i.e., 2.85, 1.92, and 7.73 times smaller distance errors for individual datasets) and smaller computation time (i.e., 4.12 times faster registration) than one of the state-of-the-art methods. Moreover, the proposed method worked considerably well for partially scanned data, whereas other methods suffered from the unbalancing of information between the CT and scanned data.Conclusions: The proposed method was able to perform fully automatic and highly accurate registration of dental CT data and 3D scanned models, even with severe metal artifacts. In addition, it could achieve fast registration because it did not require any preprocessing for iso-surface reconstruction from the CT data.

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver.

Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians' workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.