Multi-sensor image registration has been widely used in remote sensing and medical image field, but registration performance is degenerated when heterogeneous images are involved. An image registration method based on...Multi-sensor image registration has been widely used in remote sensing and medical image field, but registration performance is degenerated when heterogeneous images are involved. An image registration method based on multi-resolution shape analysis is proposed in this paper, to deal with the problem that the shape of similar objects is always invariant. The contours of shapes are first detected as visual features using an extended contour search algorithm in order to reduce effects of noise, and the multi-resolution shape descriptor is constructed through Fourier curvature representation of the contour’s chain code. Then a minimum distance function is used to judge the similarity between two contours. To avoid the effect of different resolution and intensity distribution, suitable resolution of each image is selected by maximizing the consistency of its pyramid shapes. Finally, the transformation parameters are estimated based on the matched control-point pairs which are the centers of gravity of the closed contours. Multi-sensor Landsat TM imagery and infrared imagery have been used as experimental data for comparison with the classical contour-based registration. Our results have been shown to be superior to the classical ones.展开更多
The geolocation of ground targets by airborne image sensors is an important task for unmanned aerial vehicles or surveillance aircraft.This paper proposes an Iterative Geolocation based on Cross-view Image Registratio...The geolocation of ground targets by airborne image sensors is an important task for unmanned aerial vehicles or surveillance aircraft.This paper proposes an Iterative Geolocation based on Cross-view Image Registration(IGCIR)that can provide real-time target location results with high precision.The proposed method has two key features.First,a cross-view image registration process is introduced,including a projective transformation and a two-stage multi-sensor registration.This process utilizes both gradient information and phase information of cross-view images.This allows the registration process to reach a good balance between matching precision and computational efficiency.By matching the airborne camera view to the preloaded digital map,the geolocation accuracy can reach the accuracy level of the digital map for any ground target appearing in the airborne camera view.Second,the proposed method uses the registration results to perform an iteration process,which compensates for the bias of the strap-down initial navigation module online.Although it is challenging to provide cross-view registration results with high frequency,such an iteration process allows the method to generate real-time,highly accurate location results.The effectiveness of the proposed IGCIR method is verified by a series of flying-test experiments.The results show that the location accuracy of the method can reach 4.18 m(at 10 km standoff distance).展开更多
This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualiz...This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization enhancement.However,fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is challenging.To deal with this problem,we propose a 3D multi-scale attention VoxelMorph(MAVoxelMorph)registration network.To alleviate the large deformation problem,a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction,and position-aware axial attention for long-distance dependencies between pixels capture.To further improve the large deformation and fine structure registration results,a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding layers.Our method was evaluated on four public lung datasets(DIR-Lab dataset,Creatis dataset,Learn2Reg dataset,OASIS dataset)and a local dataset.Results proved that the proposed method achieved better registration performance than current state-of-the-art methods,especially in handling the registration of large deformations and fine structures.It also proved to be fast in 3D image registration,using about 1.5 s,and faster than most methods.Qualitative and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.展开更多
Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue.Convolutional neural network(CNN)and generative adversarial ne...Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue.Convolutional neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image registration.However,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s disease.We argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator inGAN.In this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image registration.To begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific modalities.Additionally,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise results.Lastly,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s disease.Experimental results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.展开更多
Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes.However,these methods often lack constraint information and overlook se...Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes.However,these methods often lack constraint information and overlook semantic consistency,limiting their performance.To address these issues,we present a novel approach for medical image registration called theDual-VoxelMorph,featuring a dual-channel cross-constraint network.This innovative network utilizes both intensity and segmentation images,which share identical semantic information and feature representations.Two encoder-decoder structures calculate deformation fields for intensity and segmentation images,as generated by the dual-channel cross-constraint network.This design facilitates bidirectional communication between grayscale and segmentation information,enabling the model to better learn the corresponding grayscale and segmentation details of the same anatomical structures.To ensure semantic and directional consistency,we introduce constraints and apply the cosine similarity function to enhance semantic consistency.Evaluation on four public datasets demonstrates superior performance compared to the baselinemethod,achieving Dice scores of 79.9%,64.5%,69.9%,and 63.5%for OASIS-1,OASIS-3,LPBA40,and ADNI,respectively.展开更多
Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or modalities.Although several survey...Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or modalities.Although several surveys have reviewed the development of medical image registration,they have not systematically summarized the existing med-ical image registration methods.To this end,a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives,aiming to help audiences quickly understand the development of medical image registration.In particular,we review recent advances in retinal image registration,which has not attracted much attention.In addition,current challenges in retinal image registration are discussed and insights and prospects for future research provided.展开更多
To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation f...To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation for input image with angle difference between them. A hi erarchical feature matching algorithm was adopted to get the final transform parameters between the two images. The simulation results for two infrared images show that the method can effectively, quickly and accurately register images and be antinoise to some extent.展开更多
A methodology for alignment of an X-ray image and a CT image, based on the Chamfer 3-4 distance transform and simulated annealing optimization algorithm is presented. Firstly, an initial transformation matrix is const...A methodology for alignment of an X-ray image and a CT image, based on the Chamfer 3-4 distance transform and simulated annealing optimization algorithm is presented. Firstly, an initial transformation matrix is constructed. For the convenience of computing, geometric models of the X-ray device to reconstruct the calibration matrix are used. Then, by defining the distance between the 3-D protective and the 2-D object image, we optimize this distance matching problem, using the simulated annealing algorithm. This method is also integrated into medical intra-operation, dealing with the data set acquired from 3-D image workstation and active navigation.展开更多
Technique s for constructing full view panoramic mosaics from sequences of images are pres ented. The goal of this work is to remove too many limitations for pure panning motion. The best reference block is important...Technique s for constructing full view panoramic mosaics from sequences of images are pres ented. The goal of this work is to remove too many limitations for pure panning motion. The best reference block is important for the block-matching method for improving the robustness and performance. It is automatically selected in the h igh-frequency image, which always contains the plenty visible features. In orde r to reduce accumulated registration errors, the global registration using the p hase-correlation matching method with rotation adjustment is applied to the who le sequence of images, which results in an optimal image mosaic with resolving t ranslational or rotational motion. The local registration using the Levenberg-M arquardt iterative non-linear minimization algorithm is applied to compensating for small amounts of motion parallax introduced by translations of the camera a nd other unmodeled distortions, then minimizing the discrepancy after applying t he global registration. The accumulated misregistration errors may cause a visib le gap between the two images. A smoothing filter is introduced for removing the visible artifact.展开更多
A novel algorithm of 3-D surface image registration is proposed. It makes use of the array information of 3-D points and takes vector/vertex-like features as the basis of the matching. That array information of 3-D po...A novel algorithm of 3-D surface image registration is proposed. It makes use of the array information of 3-D points and takes vector/vertex-like features as the basis of the matching. That array information of 3-D points can be easily obtained when capturing original 3-D images. The iterative least-mean-squared （LMS） algorithm is applied to optimizing adaptively the transformation matrix parameters. These can effectively improve the registration performance and hurry up the matching process. Experimental results show that it can reach a good subjective impression on aligned 3-D images. Although the algorithm focuses primarily on the human head model, it can also be used for other objects with small modifications.展开更多
Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual informa...Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual information and gradient information to solve this problem and apply it to the non-rigid deformation image registration. To improve the accuracy, we provide some implemental issues, for example, the Powell searching algorithm, gray interpolation and consideration of outlier points. The experimental results show the accuracy of the method and the feasibility in non-rigid medical image registration.展开更多
Point features, as the basis of lines, surfaces, and bodies, are commonly used in medical image registration. To obtain an elegant spatial transformation of extracted feature points, many point set matching algorithms...Point features, as the basis of lines, surfaces, and bodies, are commonly used in medical image registration. To obtain an elegant spatial transformation of extracted feature points, many point set matching algorithms(PMs) have been developed to match two point sets by optimizing multifarious distance functions. There are ample reviews related to medical image registration and PMs which summarize their basic principles and main algorithms separately. However,to data, detailed summary of PMs used in medical image registration in different clinical environments has not been published. In this paper, we provide a comprehensive review of the existing key techniques of the PMs applied to medical image registration according to the basic principles and clinical applications. As the core technique of the PMs, geometric transformation models are elaborated in this paper, demonstrating the mechanism of point set registration. We also focus on the clinical applications of the PMs and propose a practical classification method according to their applications in different clinical surgeries. The aim of this paper is to provide a summary of pointfeaturebased methods used in medical image registration and to guide doctors or researchers interested in this field to choose appropriate techniques in their research.展开更多
Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to ...Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition,the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision tasks.This study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named Super Fusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion,and semantic segmentation tasks demonstrate the superiority of our Super Fusion compared to the state-of-the-art alternatives.The source code and pre-trained model are publicly available at https://github.com/Linfeng-Tang/Super Fusion.展开更多
Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi...Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.展开更多
AIM: To achieve symmetric boundaries for left and right breasts boundaries in thermal images by registration. METHODS: The proposed method for registration consists of two steps. In the first step, shape context, an a...AIM: To achieve symmetric boundaries for left and right breasts boundaries in thermal images by registration. METHODS: The proposed method for registration consists of two steps. In the first step, shape context, an approach as presented by Belongie and Malik was applied for registration of two breast boundaries. The shape context is an approach to measure shape similarity. Two sets of finite sample points from shape contours of two breasts are then presented. Consequently, the correspondences between the two shapes are found. By finding correspondences, the sample point which has the most similar shape context is obtained. RESULTS: In this study, a line up transformation which maps one shape onto the other has been estimated in order to complete shape. The used of a thin plate spline permitted good estimation of a plane transformation which has capability to map unselective points from one shape onto the other. The obtained aligningtransformation of boundaries points has been applied successfully to map the two breasts interior points. Some of advantages for using shape context method in this work are as follows:(1) no special land marks or key points are needed;(2) it is tolerant to all common shape deformation; and(3) although it is uncomplicated and straightforward to use, it gives remarkably powerful descriptor for point sets significantly upgrading point set registration. Results are very promising. The proposed algorithm was implemented for 32 cases. Boundary registration is done perfectly for 28 cases.CONCLUSION: We used shape contexts method that is simple and easy to implement to achieve symmetric boundaries for left and right breasts boundaries in thermal images.展开更多
Remote sensing image registration is still a challenging task owing to the significant influence of nonlinear differences between remote sensing images.To solve this problem,this paper proposes a novel approach with r...Remote sensing image registration is still a challenging task owing to the significant influence of nonlinear differences between remote sensing images.To solve this problem,this paper proposes a novel approach with regard to feature-based remote sensing image registration.There are two key contributions:1)we bring forward an improved strategy of composite nonlinear diffusion filtering according to the scale factors in multi-scale space and 2)we design a gradually decreasing resolution of multi-scale pyramid space.And a binary code string is served as feature descriptors to improve matching efficiency.Extensive experiments of different categories of remote image datasets on feature extraction and feature registration are performed.The experimental results demonstrate the superiority of our proposed scheme compared with other classical algorithms in terms of correct matching ratio,accuracy and computation efficiency.展开更多
Fresnel incoherent correlation holography(FINCH) is a unique three-dimensional(3D) imaging technique which has the advantages of scanning-free,high resolution,and easy matching with existing mature optical systems.In ...Fresnel incoherent correlation holography(FINCH) is a unique three-dimensional(3D) imaging technique which has the advantages of scanning-free,high resolution,and easy matching with existing mature optical systems.In this article,an incoherent digital holographic spectral imaging method with high accuracy of spectral reconstruction based on liquid crystal tunable filter(LCTF) and FINCH is proposed.Using the programmable characteristics of spatial light modulator(SLM),a series of phase masks,none of whose focal lengths changes with wavelength,is designed and made.For each wavelength of LCTF output,SLM calls three phase masks with different phase constants at the corresponding wavelength,and CCD records three holograms.The spectral images obtained by this method have a constant magnification,which can achieve pixel-level image registration,restrain image registration errors,and improve spectral reconstruction accuracy.The results show that this method can not only obtain the 3D spatial information and spectral information of the object simultaneously,but also have high accuracy of spectral reconstruction and excellent color reproducibility.展开更多
The proposed algorithm relies on a group of new formulas for calculating tangent slope so as to address angle feature of edge curves of image. It can utilize tangent angle features to estimate automatically and fully ...The proposed algorithm relies on a group of new formulas for calculating tangent slope so as to address angle feature of edge curves of image. It can utilize tangent angle features to estimate automatically and fully the rotation parameters of geometric transform and enable rough matching of images with huge rotation difference. After angle compensation, it can search for matching point sets by correlation criterion, then calculate parameters of affine transform, enable higher-precision emendation of rotation and transferring. Finally, it fulfills precise matching for images with relax-tense iteration method. Compared with the registration approach based on wavelet direction-angle features, the matching algorithm with tangent feature of image edge is more robust and realizes precise registration of various images. Furthermore, it is also helpful in graphics matching.展开更多
Interferometric Synthetic Aperture Radar (InSAR) allows production of high resolution DEM and detection of small earth motions using multiple pass SAR data sets obtained by remote sensing satellite. But the whole proc...Interferometric Synthetic Aperture Radar (InSAR) allows production of high resolution DEM and detection of small earth motions using multiple pass SAR data sets obtained by remote sensing satellite. But the whole process has not yet reached sufficient robustness to warrant automated DEM production as commonly produced by stereovision with optical images. The automatic algorithm for precision registration is one of the bottlenecks in InSAR data processing. In this paper, an automatic approach with multi-step image matching algorithm is presented. All procedures are automatically implemented. The experiment is carried out successfully with SIR-C/L-band InSAR data. The triangular piecewise rectification is also advanced in reducing local distortion between the images and processing the large scene image. The primary result has prospect in the precision registration for the repeat-track InSAR data and reveals the potential of the presented automatic strategy.展开更多
This paper presents a novel medical image registration algorithm named total variation constrained graphregularization for non-negative matrix factorization(TV-GNMF).The method utilizes non-negative matrix factorizati...This paper presents a novel medical image registration algorithm named total variation constrained graphregularization for non-negative matrix factorization(TV-GNMF).The method utilizes non-negative matrix factorization by total variation constraint and graph regularization.The main contributions of our work are the following.First,total variation is incorporated into NMF to control the diffusion speed.The purpose is to denoise in smooth regions and preserve features or details of the data in edge regions by using a diffusion coefficient based on gradient information.Second,we add graph regularization into NMF to reveal intrinsic geometry and structure information of features to enhance the discrimination power.Third,the multiplicative update rules and proof of convergence of the TV-GNMF algorithm are given.Experiments conducted on datasets show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms.展开更多
基金Project supported by the National Natural Science Foundation of China (No. 60272031), the Hi-Tech Research and Development Program (863) of China (No. 2003AA131032-2), and the Natural Science Foundation of Zhejiang Province (No. M603202), China
文摘Multi-sensor image registration has been widely used in remote sensing and medical image field, but registration performance is degenerated when heterogeneous images are involved. An image registration method based on multi-resolution shape analysis is proposed in this paper, to deal with the problem that the shape of similar objects is always invariant. The contours of shapes are first detected as visual features using an extended contour search algorithm in order to reduce effects of noise, and the multi-resolution shape descriptor is constructed through Fourier curvature representation of the contour’s chain code. Then a minimum distance function is used to judge the similarity between two contours. To avoid the effect of different resolution and intensity distribution, suitable resolution of each image is selected by maximizing the consistency of its pyramid shapes. Finally, the transformation parameters are estimated based on the matched control-point pairs which are the centers of gravity of the closed contours. Multi-sensor Landsat TM imagery and infrared imagery have been used as experimental data for comparison with the classical contour-based registration. Our results have been shown to be superior to the classical ones.
基金supported by the National Level Project of China(No.52-L0D01-0613-20/22)。
文摘The geolocation of ground targets by airborne image sensors is an important task for unmanned aerial vehicles or surveillance aircraft.This paper proposes an Iterative Geolocation based on Cross-view Image Registration(IGCIR)that can provide real-time target location results with high precision.The proposed method has two key features.First,a cross-view image registration process is introduced,including a projective transformation and a two-stage multi-sensor registration.This process utilizes both gradient information and phase information of cross-view images.This allows the registration process to reach a good balance between matching precision and computational efficiency.By matching the airborne camera view to the preloaded digital map,the geolocation accuracy can reach the accuracy level of the digital map for any ground target appearing in the airborne camera view.Second,the proposed method uses the registration results to perform an iteration process,which compensates for the bias of the strap-down initial navigation module online.Although it is challenging to provide cross-view registration results with high frequency,such an iteration process allows the method to generate real-time,highly accurate location results.The effectiveness of the proposed IGCIR method is verified by a series of flying-test experiments.The results show that the location accuracy of the method can reach 4.18 m(at 10 km standoff distance).
基金supported in part by the National Natural Science Foundation of China[62301374]Hubei Provincial Natural Science Foundation of China[2022CFB804]+2 种基金Hubei Provincial Education Research Project[B2022057]the Youths Science Foundation of Wuhan Institute of Technology[K202240]the 15th Graduate Education Innovation Fund of Wuhan Institute of Technology[CX2023295].
文摘This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization enhancement.However,fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is challenging.To deal with this problem,we propose a 3D multi-scale attention VoxelMorph(MAVoxelMorph)registration network.To alleviate the large deformation problem,a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction,and position-aware axial attention for long-distance dependencies between pixels capture.To further improve the large deformation and fine structure registration results,a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding layers.Our method was evaluated on four public lung datasets(DIR-Lab dataset,Creatis dataset,Learn2Reg dataset,OASIS dataset)and a local dataset.Results proved that the proposed method achieved better registration performance than current state-of-the-art methods,especially in handling the registration of large deformations and fine structures.It also proved to be fast in 3D image registration,using about 1.5 s,and faster than most methods.Qualitative and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.
文摘Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue.Convolutional neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image registration.However,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s disease.We argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator inGAN.In this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image registration.To begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific modalities.Additionally,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise results.Lastly,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s disease.Experimental results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.
基金National Natural Science Foundation of China(Grant Nos.62171130,62172197,61972093)the Natural Science Foundation of Fujian Province(Grant Nos.2020J01573,2022J01131257,2022J01607)+3 种基金Fujian University Industry University Research Joint Innovation Project(No.2022H6006)in part by the Fund of Cloud Computing and BigData for SmartAgriculture(GrantNo.117-612014063)NationalNatural Science Foundation of China(Grant No.62301160)Nature Science Foundation of Fujian Province(Grant No.2022J01607).
文摘Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes.However,these methods often lack constraint information and overlook semantic consistency,limiting their performance.To address these issues,we present a novel approach for medical image registration called theDual-VoxelMorph,featuring a dual-channel cross-constraint network.This innovative network utilizes both intensity and segmentation images,which share identical semantic information and feature representations.Two encoder-decoder structures calculate deformation fields for intensity and segmentation images,as generated by the dual-channel cross-constraint network.This design facilitates bidirectional communication between grayscale and segmentation information,enabling the model to better learn the corresponding grayscale and segmentation details of the same anatomical structures.To ensure semantic and directional consistency,we introduce constraints and apply the cosine similarity function to enhance semantic consistency.Evaluation on four public datasets demonstrates superior performance compared to the baselinemethod,achieving Dice scores of 79.9%,64.5%,69.9%,and 63.5%for OASIS-1,OASIS-3,LPBA40,and ADNI,respectively.
基金supported in part by General Program of National Natural Science Foundation of China,Nos.82102189 and 82272086Guangdong Provincial Department of Education,No.SJZLGC202202.
文摘Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or modalities.Although several surveys have reviewed the development of medical image registration,they have not systematically summarized the existing med-ical image registration methods.To this end,a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives,aiming to help audiences quickly understand the development of medical image registration.In particular,we review recent advances in retinal image registration,which has not attracted much attention.In addition,current challenges in retinal image registration are discussed and insights and prospects for future research provided.
文摘To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation for input image with angle difference between them. A hi erarchical feature matching algorithm was adopted to get the final transform parameters between the two images. The simulation results for two infrared images show that the method can effectively, quickly and accurately register images and be antinoise to some extent.
基金The National Natural Science Foundation of China (60272045) the Key Project of Ministry of Education of China.
文摘A methodology for alignment of an X-ray image and a CT image, based on the Chamfer 3-4 distance transform and simulated annealing optimization algorithm is presented. Firstly, an initial transformation matrix is constructed. For the convenience of computing, geometric models of the X-ray device to reconstruct the calibration matrix are used. Then, by defining the distance between the 3-D protective and the 2-D object image, we optimize this distance matching problem, using the simulated annealing algorithm. This method is also integrated into medical intra-operation, dealing with the data set acquired from 3-D image workstation and active navigation.
文摘Technique s for constructing full view panoramic mosaics from sequences of images are pres ented. The goal of this work is to remove too many limitations for pure panning motion. The best reference block is important for the block-matching method for improving the robustness and performance. It is automatically selected in the h igh-frequency image, which always contains the plenty visible features. In orde r to reduce accumulated registration errors, the global registration using the p hase-correlation matching method with rotation adjustment is applied to the who le sequence of images, which results in an optimal image mosaic with resolving t ranslational or rotational motion. The local registration using the Levenberg-M arquardt iterative non-linear minimization algorithm is applied to compensating for small amounts of motion parallax introduced by translations of the camera a nd other unmodeled distortions, then minimizing the discrepancy after applying t he global registration. The accumulated misregistration errors may cause a visib le gap between the two images. A smoothing filter is introduced for removing the visible artifact.
文摘A novel algorithm of 3-D surface image registration is proposed. It makes use of the array information of 3-D points and takes vector/vertex-like features as the basis of the matching. That array information of 3-D points can be easily obtained when capturing original 3-D images. The iterative least-mean-squared （LMS） algorithm is applied to optimizing adaptively the transformation matrix parameters. These can effectively improve the registration performance and hurry up the matching process. Experimental results show that it can reach a good subjective impression on aligned 3-D images. Although the algorithm focuses primarily on the human head model, it can also be used for other objects with small modifications.
文摘Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual information and gradient information to solve this problem and apply it to the non-rigid deformation image registration. To improve the accuracy, we provide some implemental issues, for example, the Powell searching algorithm, gray interpolation and consideration of outlier points. The experimental results show the accuracy of the method and the feasibility in non-rigid medical image registration.
基金Supported by the National Natural Science Foundation of China(Grant No.61533016)
文摘Point features, as the basis of lines, surfaces, and bodies, are commonly used in medical image registration. To obtain an elegant spatial transformation of extracted feature points, many point set matching algorithms(PMs) have been developed to match two point sets by optimizing multifarious distance functions. There are ample reviews related to medical image registration and PMs which summarize their basic principles and main algorithms separately. However,to data, detailed summary of PMs used in medical image registration in different clinical environments has not been published. In this paper, we provide a comprehensive review of the existing key techniques of the PMs applied to medical image registration according to the basic principles and clinical applications. As the core technique of the PMs, geometric transformation models are elaborated in this paper, demonstrating the mechanism of point set registration. We also focus on the clinical applications of the PMs and propose a practical classification method according to their applications in different clinical surgeries. The aim of this paper is to provide a summary of pointfeaturebased methods used in medical image registration and to guide doctors or researchers interested in this field to choose appropriate techniques in their research.
基金supported by the National Natural Science Foundation of China(62276192,62075169,62061160370)the Key Research and Development Program of Hubei Province(2020BAB113)。
文摘Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition,the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision tasks.This study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named Super Fusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion,and semantic segmentation tasks demonstrate the superiority of our Super Fusion compared to the state-of-the-art alternatives.The source code and pre-trained model are publicly available at https://github.com/Linfeng-Tang/Super Fusion.
基金supported by National Nature Science Foundation of China (Nos. 61462046 and 61762052)Natural Science Foundation of Jiangxi Province (Nos. 20161BAB202049 and 20161BAB204172)+2 种基金the Bidding Project of the Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG (Nos. WE2016003, WE2016013 and WE2016015)the Science and Technology Research Projects of Jiangxi Province Education Department (Nos. GJJ160741, GJJ170632 and GJJ170633)the Art Planning Project of Jiangxi Province (Nos. YG2016250 and YG2017381)
文摘Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.
文摘AIM: To achieve symmetric boundaries for left and right breasts boundaries in thermal images by registration. METHODS: The proposed method for registration consists of two steps. In the first step, shape context, an approach as presented by Belongie and Malik was applied for registration of two breast boundaries. The shape context is an approach to measure shape similarity. Two sets of finite sample points from shape contours of two breasts are then presented. Consequently, the correspondences between the two shapes are found. By finding correspondences, the sample point which has the most similar shape context is obtained. RESULTS: In this study, a line up transformation which maps one shape onto the other has been estimated in order to complete shape. The used of a thin plate spline permitted good estimation of a plane transformation which has capability to map unselective points from one shape onto the other. The obtained aligningtransformation of boundaries points has been applied successfully to map the two breasts interior points. Some of advantages for using shape context method in this work are as follows:(1) no special land marks or key points are needed;(2) it is tolerant to all common shape deformation; and(3) although it is uncomplicated and straightforward to use, it gives remarkably powerful descriptor for point sets significantly upgrading point set registration. Results are very promising. The proposed algorithm was implemented for 32 cases. Boundary registration is done perfectly for 28 cases.CONCLUSION: We used shape contexts method that is simple and easy to implement to achieve symmetric boundaries for left and right breasts boundaries in thermal images.
基金supported by National Nature Science Foundation of China(Nos.61640412 and 61762052)the Natural Science Foundation of Jiangxi Province(No.20192BAB207021)the Science and Technology Research Projects of Jiangxi Province Education Department(Nos.GJJ170633 and GJJ170632).
文摘Remote sensing image registration is still a challenging task owing to the significant influence of nonlinear differences between remote sensing images.To solve this problem,this paper proposes a novel approach with regard to feature-based remote sensing image registration.There are two key contributions:1)we bring forward an improved strategy of composite nonlinear diffusion filtering according to the scale factors in multi-scale space and 2)we design a gradually decreasing resolution of multi-scale pyramid space.And a binary code string is served as feature descriptors to improve matching efficiency.Extensive experiments of different categories of remote image datasets on feature extraction and feature registration are performed.The experimental results demonstrate the superiority of our proposed scheme compared with other classical algorithms in terms of correct matching ratio,accuracy and computation efficiency.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61505178,61307019,and 11504333)the Natural Science Foundation of Henan Province,China(Grant Nos.18A140032,15A140038,and 16A140035)。
文摘Fresnel incoherent correlation holography(FINCH) is a unique three-dimensional(3D) imaging technique which has the advantages of scanning-free,high resolution,and easy matching with existing mature optical systems.In this article,an incoherent digital holographic spectral imaging method with high accuracy of spectral reconstruction based on liquid crystal tunable filter(LCTF) and FINCH is proposed.Using the programmable characteristics of spatial light modulator(SLM),a series of phase masks,none of whose focal lengths changes with wavelength,is designed and made.For each wavelength of LCTF output,SLM calls three phase masks with different phase constants at the corresponding wavelength,and CCD records three holograms.The spectral images obtained by this method have a constant magnification,which can achieve pixel-level image registration,restrain image registration errors,and improve spectral reconstruction accuracy.The results show that this method can not only obtain the 3D spatial information and spectral information of the object simultaneously,but also have high accuracy of spectral reconstruction and excellent color reproducibility.
基金Supported by the National Natural Science Foundation of China (No.60141002) the Aviation Basic Science Foundation (02I53073)
文摘The proposed algorithm relies on a group of new formulas for calculating tangent slope so as to address angle feature of edge curves of image. It can utilize tangent angle features to estimate automatically and fully the rotation parameters of geometric transform and enable rough matching of images with huge rotation difference. After angle compensation, it can search for matching point sets by correlation criterion, then calculate parameters of affine transform, enable higher-precision emendation of rotation and transferring. Finally, it fulfills precise matching for images with relax-tense iteration method. Compared with the registration approach based on wavelet direction-angle features, the matching algorithm with tangent feature of image edge is more robust and realizes precise registration of various images. Furthermore, it is also helpful in graphics matching.
基金Project supported by the National Natural Science Foundation of China(No.69782001)
文摘Interferometric Synthetic Aperture Radar (InSAR) allows production of high resolution DEM and detection of small earth motions using multiple pass SAR data sets obtained by remote sensing satellite. But the whole process has not yet reached sufficient robustness to warrant automated DEM production as commonly produced by stereovision with optical images. The automatic algorithm for precision registration is one of the bottlenecks in InSAR data processing. In this paper, an automatic approach with multi-step image matching algorithm is presented. All procedures are automatically implemented. The experiment is carried out successfully with SIR-C/L-band InSAR data. The triangular piecewise rectification is also advanced in reducing local distortion between the images and processing the large scene image. The primary result has prospect in the precision registration for the repeat-track InSAR data and reveals the potential of the presented automatic strategy.
基金supported by the National Natural Science Foundation of China(61702251,41971424,61701191,U1605254)the Natural Science Basic Research Plan in Shaanxi Province of China(2018JM6030)+4 种基金the Key Technical Project of Fujian Province(2017H6015)the Science and Technology Project of Xiamen(3502Z20183032)the Doctor Scientific Research Starting Foundation of Northwest University(338050050)Youth Academic Talent Support Program of Northwest University(360051900151)the Natural Sciences and Engineering Research Council of Canada,Canada。
文摘This paper presents a novel medical image registration algorithm named total variation constrained graphregularization for non-negative matrix factorization(TV-GNMF).The method utilizes non-negative matrix factorization by total variation constraint and graph regularization.The main contributions of our work are the following.First,total variation is incorporated into NMF to control the diffusion speed.The purpose is to denoise in smooth regions and preserve features or details of the data in edge regions by using a diffusion coefficient based on gradient information.Second,we add graph regularization into NMF to reveal intrinsic geometry and structure information of features to enhance the discrimination power.Third,the multiplicative update rules and proof of convergence of the TV-GNMF algorithm are given.Experiments conducted on datasets show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms.
