Image Registration Technology Research Articles

Feasibility and safety study of advanced prostate biopsy robot system based on MR-TRUS Image flexible fusion technology in animal experiments

The advanced prostate biopsy robot system has broad application prospects in clinical practice, but due to the deformation and distortion between MR-TRUS (magnetic resonance transrectal ultrasound) images, it poses challenges in biopsy accuracy and safety. The study utilized an advanced prostate biopsy robot system based on MR-TRUS image flexible registration technology and conducted experiments on animal models. Retrospective analysis of the puncture accuracy of 12 animal experiments undergoing prostate puncture using MR-TRUS flexible registration technology from May 2022 to October 2023, and observation of intraoperative and 7-day postoperative complications. The study obtained MR-TRUS images and utilized image processing algorithms for registration to reduce image deformation and distortion. Then, precise positioning and operation are carried out through the robot system to execute the prostate biopsy program. The experimental results indicate that the advanced prostate biopsy robot system based on MR-TRUS image flexible registration technology has demonstrated good feasibility and safety in animal experiments. Image registration technology has successfully reduced image distortion and deformation, improving biopsy accuracy. The precise positioning and operation of robot systems play a crucial role in the biopsy process, reducing the occurrence of complications.

Research on vehicle A-pillar visualization based on A-pillar detection and decision tree model

Abstract When turning or passing through intersections, the vehicle’s A-pillar blind spot may obscure road conditions. Using cameras to capture and display external images on the A-pillar screen can eliminate the A-pillar blind spot. However, this solution still faces a mismatch between the displayed screen image and the surrounding environment image. To obtain more accurate blind spot images, accurate blind spot cropping areas are calculated through vehicle A-pillar detection and image registration technology. By introducing eye position and posture as features and considering the corresponding blind spot cropping areas as target variables, a decision tree model is trained to predict the blind spot cropping areas. This model can rapidly and accurately predict the blind spot areas for display on the A-pillar screen, with an average pixel error of 12.7, a coefficient of determination of 0.85, and an image processing speed within one millisecond. Experiments show that the A-pillar blind spot visualization system based on A-pillar detection and decision tree model prediction effectively eliminates the A-pillar blind spot, enhancing driving safety.

High-precision point cloud registration method based on volume image correlation

With the rapid development of binocular reconstruction, fringe projection profilometry, and time of flight, 3D imaging technology has been widely applied in the field of 3D measurement. However, due to limited measurement range and self-occlusion, point cloud registration methods are often used to obtain larger or more complete 3D contours. Although many scholars have proposed various point cloud registration methods, the accuracy and efficiency of point cloud registration still need to be further improved, especially for point clouds with different density or non-rigid transformation. Image registration technology based on image correlation has been developed for many years and has achieved great success in fields such as computer vision, photomechanics, and photogrammetry. Therefore, a simple and direct idea in this paper is to transform the point cloud registration problem into volume image correlation problem. By this, an efficient image registration method based on fast Fourier transform and an inverse compositional Gaussian Newton optimization method that only needs to calculate the Hessian matrix once can be introduced into the point cloud registration field, which can greatly improve the speed and accuracy of point cloud registration. Comparative experiments have shown that our method has doubled the accuracy and efficiency compared to the iterative closest point (ICP) method, and its practicality has also been verified in impeller reconstruction experiments.

Spaceborne SAR Time-Series Images Change Detection Based on SAR-SIFT-Logarithm Background Subtraction

Synthetic Aperture Radar (SAR) image change detection aims to detect changes with images of the same area acquired at different times. It has wide applications in environmental monitoring, urban planning and resource management. Traditional change detection methods for spaceborne SAR time-series images typically adopt a pairwise comparison strategy to obtain multi-temporal change information. However, this kind of method has the problem of losing the overall change information, which is time consuming. To address this problem, this paper proposes a new change detection algorithm for spaceborne SAR time-series data based on SAR-SIFT-Logarithm Background Subtraction. This algorithm combines the SAR-SIFT image registration technology with Logarithm Background Subtraction. The method first preprocesses the input time-series data with steps like noise reducing and radiometric calibration. Then, the images will be coregistered by the SAR-SIFT step to avoid mismatches-induced detection performance degradation. Next, the parts that remained unchanged throughout the time period are modeled with a median filter to obtain the static background. The change information is then obtained via the subtraction of background and CFAR detection and clustering. The proposed algorithm is validated using the Sentinel-1 GRD and PAZ-1 time-series dataset. Experimental results demonstrate that the proposed method effectively detects the overall change information and reduces processing time compared to traditional pairwise comparison methods.

Research on multi-source remote sensing image registration technology based on Baker mapping

ABSTRACT To address the issues of inaccurate estimation of registration parameters and high mismatch rate in feature based remote sensing image registration, a registration method based on global feature triangle similarity is proposed. This method utilizes the similarity principle of feature triangles to evaluate the global geometric similarity of matching feature points to eliminate mismatched points. In addition, due to the sensitivity of phase information in the frequency domain to spatial transformations and structural differences, as well as its robustness to lighting and noise, a phase structure consistency measurement method is proposed for developing feature point position adjustment strategies. The results indicate that the registration method proposed by the research institute achieved the lowest RMSE with a size of 1.51. In terms of IRMSE indicators, compared to the RANSAC measurement model, the PH SSIM measurement model has a mean decrease of 0.253. This indicates that the improved registration model proposed in the study has advantages in improving registration accuracy. The innovation of this study lies in constructing a matching feature point evaluation model to eliminate mismatched points, and proposing a remote sensing image registration method based on mismatch point removal and feature point position adjustment.

EDIRNet: an unsupervised deformable registration model for X-ray and neutron images.

For high-precision industrial non-destructive testing, multimodal image registration technology can be employed to register X-ray and neutron images. X-ray and neutron image registration algorithms usually use conventional methods through iterative optimization. These methods will increase the cost of registration time and require more initialization parameters. The imaging results of internal sample structures can suffer from edge blurring due to the influence of a neutron beam collimator aperture, X-ray focal point, and imaging angles. We present an unsupervised learning model, EDIRNet, based on deep learning for deformable registration of X-ray and neutron images. We define the registration process as a function capable of estimating the flow field from input images. By leveraging deep learning techniques, we effectively parameterize this function. Consequently, given a registration image, our optimized network parameters enable rapid and direct estimation of the flow field between the images. We design an attention-based edge enhancement module to enhance the edge features of the image. For evaluating our presented network model, we utilize a dataset including 552 pairs of X-ray and neutron images. The experimental results show that the registration accuracy of EDIRNet reaches 93.09%. Compared with traditional algorithms, the accuracy of EDIRNet is improved by 3.17%, and the registration time is reduced by 28.75s.

Multiscale feature fusion network for 3D head MRI image registration.

Image registration technology has become an important medical image preprocessing step with the wide application of computer-aided diagnosis technology in various medical image analysis tasks. We propose a multiscale feature fusion registration based on deep learning to achieve the accurate registration and fusion of head magnetic resonance imaging (MRI) and solve the problem that general registration methods cannot handle the complex spatial information and position information of head MRI. Our proposed multiscale feature fusion registration network consists of three sequentially trained modules. The first is an affine registration module that implements affine transformation; the second is to realize non-rigid transformation, a deformable registration module composed of top-down and bottom-up feature fusion subnetworks in parallel; and the third is a deformable registration module that also realizes non-rigid transformation and is composed of two feature fusion subnetworks in series. The network decomposes the deformation field of large displacement into multiple deformation fields of small displacement by multiscale registration and registration, which reduces the difficulty of registration. Moreover, multiscale information in head MRI is learned in a targeted manner, which improves the registration accuracy, by connecting the two feature fusion subnetworks. We used 29 3D head MRIs for training and seven volumes for testing and calculated the values of the registration evaluation metrics for the new algorithm to register anterior and posterior lateral pterygoid muscles. The Dice similarity coefficient was 0.745±0.021, the Hausdorff distance was 3.441±0.935mm, the Average surface distance was 0.738±0.098mm, and the Standard deviation of the Jacobian matrix was 0.425±0.043. Our new algorithm achieved a higher registration accuracy compared with state-of-the-art registration methods. Our proposed multiscale feature fusion registration network can realize end-to-end deformable registration of 3D head MRI, which can effectively cope with the characteristics of large deformation displacement and the rich details of head images and provide reliable technical support for the diagnosis and analysis of head diseases.

Threshold method to improve the speed of “Registration-Frame accumulation” and its application in breast multispectral transmission images

Breast multispectral transmission imaging provides a new research direction for realizing low-cost and effective early breast cancer screening. However, during the acquisition of multispectral images, due to human body jitter and the strong absorption of light by breast tissue, the images of transmission image sequence are blurred and slightly moved. In order to solve this problem, this paper proposes to combine the image registration technology with the frame accumulation technology, and use the registration method based on Normalized Mutual Information (NMI) to correction the image sequence before the frame accumulation processing. At the same time, a threshold method is proposed to improve the operation speed of “Registration-Frame accumulation”. In this paper, four wavelengths of breast transmission images are processed to verify the proposed method. The results show that the combination of registration and frame accumulation can effectively improve the quality of frame accumulation image, and it shows a good effect on the images of four wavelengths. At the same time, the threshold method can reduce the operation time effectively by slightly losing the image quality within the acceptable range, which provides a new idea to improve the speed of transmission image processing. The method in this paper not only solves the problem of image jitter and improves image quality and operation speed, but also lays a foundation for the application of multispectral transmission imaging in early screening of breast cancer.

RI-MFM: A Novel Infrared and Visible Image Registration with Rotation Invariance and Multilevel Feature Matching

In the past ten years, multimodal image registration technology has been continuously developed, and a large number of researchers have paid attention to the problem of infrared and visible image registration. Due to the differences in grayscale distribution, resolution and viewpoint between two images, most of the existing infrared and visible image registration methods are still insufficient in accuracy. To solve such problems, we propose a new robust and accurate infrared and visible image registration method. For the purpose of generating more robust feature descriptors, we propose to generate feature descriptors using a concentric-circle-based feature-description algorithm. The method enhances the description of the main direction of feature points by introducing centroids, and, at the same time, uses concentric circles to ensure the rotation invariance of feature descriptors. To match feature points quickly and accurately, we propose a multi-level feature-matching algorithm using improved offset consistency for matching feature points. We redesigned the matching algorithm based on the offset consistency principle. The comparison experiments with several other state-of-the-art registration methods in CVC and homemade datasets show that our proposed method has significant advantages in both feature-point localization accuracy and correct matching rate.

A New Approach to Automatically Calibrate and Detect Building Cracks

Timely crack detection plays an important role in building damage assessment. In this study, an automatic crack detection method based on image registration and pixel-level segmentation (improved DeepLab_v3+) is proposed. Firstly, the moving images are calibrated by image registration, and the similarity method is adopted to evaluate the calibrated results. Secondly, the DeepLab_v3+ is improved and used to segment the fixed images and the calibrated images. Finally, the difference of crack pixels between the fixed and calibrated images is estimated, and the key parameter is investigated to find the optimal optimizer and learning rate. The results illustrate that: (1) the image registration technology shows excellent calibration achievement and the average error is only 4%; (2) with the resnet50 being selected as the backbone network of improved Deeplab_v3+, the automatic detection method proposed in this study is more efficient in comparison with other common pixel-level segmentation algorithms; (3) the best network optimizer of improved Deeplab_v3+ and learning rate of crack segmentation task are sgdm and 0.001, respectively. The crack detection method proposed in this study can significantly improves the technical level of crack detection in practical projects.

Digital Analysis of Smart Registration Methods for Magnetic Resonance Images in Public Healthcare.

Brain development and atrophy accompany people's life. Brain development diseases, such as autism and Alzheimer's disease, affect a large part of the population. Analyzing brain development is very important in public healthcare, and image registration is essential in medical brain image analysis. Many previous studies investigate registration accuracy by the “ground truth” dataset, marker-based similarity calculation, and expert check to find the best registration algorithms. But the evaluation of image registration technology only at the accuracy level is not comprehensive. Here, we compare the performance of three publicly available registration techniques in brain magnetic resonance imaging (MRI) analysis based on some key features widely used in previous MRI studies for classification and detection tasks. According to the analysis results, SPM12 has a stable speed and success rate, and it always works as a guiding tool for newcomers to medical image analysis. It can preserve maximum contrast information, which will facilitate studies such as tumor diagnosis. FSL is a mature and widely applicable toolkit for users, with a relatively stable success rate and good performance. It has complete functions and its function-based integrated toolbox can meet the requirements of different researchers. AFNI is a flexible and complex tool that is more suitable for professional researchers. It retains most details in medical image analysis, which makes it useful in fine-grained analysis such as volume estimation. Our study provides a new idea for comparing registration tools, where tool selection strategy mainly depends on the research task in which the selected tool can leverage its unique advantages.

A 3D Image Registration Method for Laparoscopic Liver Surgery Navigation

At present, laparoscopic augmented reality (AR) navigation has been applied to minimally invasive abdominal surgery, which can help doctors to see the location of blood vessels and tumors in organs, so as to perform precise surgery operations. Image registration is the process of optimally mapping one or more images to the target image, and it is also the core of laparoscopic AR navigation. The key is how to shorten the registration time and optimize the registration accuracy. We have studied the three-dimensional (3D) image registration technology in laparoscopic liver surgery navigation and proposed a new registration method combining rough registration and fine registration. First, the adaptive fireworks algorithm (AFWA) is applied to rough registration, and then the optimized iterative closest point (ICP) algorithm is applied to fine registration. We proposed a method that is validated by the computed tomography (CT) dataset 3D-IRCADb-01. Experimental results show that our method is superior to other registration methods based on stochastic optimization algorithms in terms of registration time and accuracy.

Performance Evaluation of Feature Matching Techniques for Detecting Reinforced Soil Retaining Wall Displacement

Image registration technology is widely applied in various matching methods. In this study, we aim to evaluate the feature matching performance and to find an optimal technique for detecting three types of behaviors—facing displacement, settlement, and combined displacement—in reinforced soil retaining walls (RSWs). For a single block with an artificial target and a multiblock structure with artificial and natural targets, five popular detectors and descriptors—KAZE, SURF, MinEigen, ORB, and BRISK—were used to evaluate the resolution performance. For comparison, the repeatability, matching score, and inlier matching features were analyzed based on the number of extracted and matched features. The axial registration error (ARE) was used to verify the accuracy of the methods by comparing the position between the estimated and real features. The results showed that the KAZE method was the best detector and descriptor for RSWs (block shape target), with the highest probability of successfully matching features. In the multiblock experiment, the block used as a natural target showed similar matching performance to that of the block with an artificial target attached. Therefore, the behaviors of RSW blocks can be analyzed using the KAZE method without installing an artificial target.

An Abdominal Registration Technology for Integration of Nanomaterial Imaging-Aided Diagnosis and Treatment.

Image registration technology is a key technology used in the process of nanomaterial imaging-aided diagnosis and targeted therapy effect monitoring for abdominal diseases. Recently, the deep-learning based methods have been increasingly used for large-scale medical image registration, because their iteration is much less than those of traditional ones. In this paper, a coarse-to-fine unsupervised learning-based three-dimensional (3D) abdominal CT image registration method is presented. Firstly, an affine transformation was used as an initial step to deal with large deformation between two images. Secondly, an unsupervised total loss function containing similarity, smoothness, and topology preservation measures was proposed to achieve better registration performances during convolutional neural network (CNN) training and testing. The experimental results demonstrated that the proposed method severally obtains the average MSE, PSNR, and SSIM values of 0.0055, 22.7950, and 0.8241, which outperformed some existing traditional and unsupervised learning-based methods. Moreover, our method can register 3D abdominal CT images with shortest time and is expected to become a real-time method for clinical application.

Non-rigid medical image registration using multi-scale residual deep fully convolutional networks

Medical image registration technology is important in medical image processing, which can establish correspondence between complex medical images and ensure the comparability of image data. Since the deep convolutional neural network model is prone to degradation and the convolution kernel has limited detection range during training, a Multi-scale residual full convolutional network (MS-ResFCN) model is proposed for unsupervised non-rigid medical image registration tasks. The model introduces a residual structure in the Fully Convolutional Network (FCN) model to ensure effective and stable training. At the same time, a hierarchical multi-scale convolution kernel is constructed within a single convolutional layer of the residual structure, which enhances the nonlinear mapping ability of the network. The local texture information and the contextual information of the image are synchronously extracted and fused to enrich the diversity of features. The experiment results on LPBA40 data sets show that the MS-ResFCN model can effectively eliminate the degradation phenomenon of the deep network during the training process and extract multi-scale features, which achieves better feature representation ability and registration accuracy.

A Fast Subpixel Registration Algorithm Based on Single-Step DFT Combined with Phase Correlation Constraint in Multimodality Brain Image.

Multimodality brain image registration technology is the key technology to determine the accuracy and speed of brain diagnosis and treatment. In order to achieve high-precision image registration, a fast subpixel registration algorithm based on single-step DFT combined with phase correlation constraint in multimodality brain image was proposed in this paper. Firstly, the coarse positioning at the pixel level was achieved by using the downsampling cross-correlation model, which reduced the Fourier transform dimension of the cross-correlation matrix and the multiplication of the discrete Fourier transform matrix, so as to speed up the coarse registration process. Then, the improved DFT multiplier of the matrix multiplication was used in the neighborhood of the coarse point, and the subpixel fast location was achieved by the bidirectional search strategy. Qualitative and quantitative simulation experiment results show that, compared with comparison registration algorithms, our proposed algorithm could greatly reduce space and time complexity without losing accuracy.

Study of novel deformable image registration in myocardial perfusion single-photon emission computed tomography.

In the present study, deformable image registration (DIR) technology was applied to gated myocardial perfusion single-photon emission computed tomography (G-MPS) reconstructed images in distorting all image phases. We aimed to define a new method of end-diastole compatible image registration and verify the clinical usability for any cardiac volume. Projection images were created using the Monte Carlo simulation. All image phases were shifted to fit the end-diastole phase by applying DIR to images that were reconstructed from projection images. Defect ratios were subsequently evaluated using the simulated images of the anterior wall simulated ischemia. Furthermore, receiver operating characteristic (ROC) analysis was performed for the clinical evaluation of DIR and nongated images. To this end, normal volume and small hearts of 33 patients without coronary artery disease and 55 with single vessel disease (coronary stenosis > 70%) were evaluated. Defect ratio analysis for voxel values of 25-100 were 75.7-21.3 for nongated and 74.7-15.6 for DIR images. For normal cardiac volume, the area under the ROC curve was 0.901 ± 0.088 for nongated and 0.925 ± 0.073 for DIR images (P = 0.078). Finally, for small cardiac volume, the area under the ROC curve was 0.651 ± 0.124 for nongated and 0.815 ± 0.119 for DIR (P < 0.01). In the present study, we developed a new registration technique by applying DIR to G-MPS images. When optimal DIR conditions were applied, the resolution of G-MPS images was improved. Furthermore, the diagnostic ability was improved in cases of small cardiac volume.

Research on texture image inpainting of jacquard fabric based on non-single vision

For the texture image inpainting of jacquard fabric with a large damaged region but complete data information around it, traditional exemplar-based image inpainting algorithms are not only have rapid falling traditional confidence value, but also the matching precision is greatly limited due to the lack of robustness, which results in a wrong guided direction and an unsatisfactory inpainting effect. To solve the above-mentioned problems, a novel digital image acquisition system for damaged fabric was designed and one set of texture image inpainting algorithms of jacquard fabric based on non-single vision was developed. In this image inpainting method, the images to be inpainted and target images were obtained through the non-single-vision imaging method. By using the image registration and Poisson image blending technology, the damaged region was filled by block matching first and then patches from the target image to the data missing region were copied. The experimental results clearly showed that the proposed method achieves better results in visual appearance and inpainting quality, compared with traditional approaches for inpainting. In addition, this method shows broad potential application prospects in the virtual restoration of ancient fabrics using image inpainting technology.

Research on image matching algorithm based on improved SIFT UAV

Aiming at the low accuracy of UAV image registration algorithm, and the complex calculation of SIFT algorithm feature descriptor and the low time efficiency, an image matching algorithm based on improved SIFT is proposed. Firstly, the image of UAV is preprocessed, and the feature point operator is detected by Shi-Tomasi corner, then is extracted by SIFT feature vector, and the dimension of feature vector is reduced by PCA. Then the nearest neighbor method is used for feature matching, and the BBF algorithm is used to search the feature values to improve the matching speed. Finally, the RANSAC algorithm is used to purify the feature points for accurate matching. Experiments show that compared with the traditional image matching method, the proposed algorithm improves the efficiency and accuracy of the UAV image registration technology.

Investigation of image registration method for the multi-directional image fusion of woven fabrics

Traditional single-side scanning or single-vision image acquisition methods have the limitation of incomplete information caused by the existence of blind spots. To collect the complete texture information of fabric images, a new multi-vision image acquisition and the related fusion method is developed to solve this problem. However, linear addition of image sequences acquired from multiple directions cannot achieve a good result of image fusion, it is necessary to conduct the image fusion based on the image registration between images at pixel level. Therefore, a new multi-directional digital image acquisition system for woven fabrics is established in this article; one set of image fusion algorithm based on image registration is proposed for the image enhancement of fabric. Fabric texture images are digitized by means of multi-directional vision imaging instead of unidirectional imaging, the structural information of fabric texture could be enhanced using image registration and fusion technology and the indexing and localization of texture corresponding points could be controlled using matching points or control points. Our experimental results show that the proposed method could be used to merge the effective information from the multi-directional vision images completely, it has the potential application for the rendering of woven fabrics using image driven virtual reality enhancement.