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Sample records for medical image registration

  1. Deformable Medical Image Registration: A Survey

    PubMed Central

    Sotiras, Aristeidis; Davatzikos, Christos; Paragios, Nikos

    2013-01-01

    Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner. PMID:23739795

  2. Medical image registration using fuzzy theory.

    PubMed

    Pan, Meisen; Tang, Jingtian; Xiong, Qi

    2012-01-01

    Mutual information (MI)-based registration, which uses MI as the similarity measure, is a representative method in medical image registration. It has an excellent robustness and accuracy, but with the disadvantages of a large amount of calculation and a long processing time. In this paper, by computing the medical image moments, the centroid is acquired. By applying fuzzy c-means clustering, the coordinates of the medical image are divided into two clusters to fit a straight line, and the rotation angles of the reference and floating images are computed, respectively. Thereby, the initial values for registering the images are determined. When searching the optimal geometric transformation parameters, we put forward the two new concepts of fuzzy distance and fuzzy signal-to-noise ratio (FSNR), and we select FSNR as the similarity measure between the reference and floating images. In the experiments, the Simplex method is chosen as multi-parameter optimisation. The experimental results show that this proposed method has a simple implementation, a low computational cost, a fast registration and good registration accuracy. Moreover, it can effectively avoid trapping into the local optima. It is adapted to both mono-modality and multi-modality image registrations.

  3. Image registration method for medical image sequences

    DOEpatents

    Gee, Timothy F.; Goddard, James S.

    2013-03-26

    Image registration of low contrast image sequences is provided. In one aspect, a desired region of an image is automatically segmented and only the desired region is registered. Active contours and adaptive thresholding of intensity or edge information may be used to segment the desired regions. A transform function is defined to register the segmented region, and sub-pixel information may be determined using one or more interpolation methods.

  4. A survey of medical image registration - under review.

    PubMed

    Viergever, Max A; Maintz, J B Antoine; Klein, Stefan; Murphy, Keelin; Staring, Marius; Pluim, Josien P W

    2016-10-01

    A retrospective view on the past two decades of the field of medical image registration is presented, guided by the article "A survey of medical image registration" (Maintz and Viergever, 1998). It shows that the classification of the field introduced in that article is still usable, although some modifications to do justice to advances in the field would be due. The main changes over the last twenty years are the shift from extrinsic to intrinsic registration, the primacy of intensity-based registration, the breakthrough of nonlinear registration, the progress of inter-subject registration, and the availability of generic image registration software packages. Two problems that were called urgent already 20 years ago, are even more urgent nowadays: Validation of registration methods, and translation of results of image registration research to clinical practice. It may be concluded that the field of medical image registration has evolved, but still is in need of further development in various aspects.

  5. A Review on Medical Image Registration as an Optimization Problem.

    PubMed

    Song, Guoli; Han, Jianda; Zhao, Yiwen; Wang, Zheng; Du, Huibin

    2017-08-01

    In the course of clinical treatment, several medical media are required by a phy-sician in order to provide accurate and complete information about a patient. Medical image registra-tion techniques can provide a richer diagnosis and treatment information to doctors and to provide a comprehensive reference source for the researchers involved in image registration as an optimization problem. The essence of image registration is associating two or more different images spatial asso-ciation, and getting the translation of their spatial relationship. For medical image registration, its pro-cess is not absolute. Its core purpose is finding the conversion relationship between different images. The major step of image registration includes the change of geometrical dimensions, and change of the image of the combination, image similarity measure, iterative optimization and interpo-lation process. The contribution of this review is sort of related image registration research methods, can provide a brief reference for researchers about image registration.

  6. Active edge maps for medical image registration

    NASA Astrophysics Data System (ADS)

    Kerwin, William; Yuan, Chun

    2001-07-01

    Applying edge detection prior to performing image registration yields several advantages over raw intensity- based registration. Advantages include the ability to register multicontrast or multimodality images, immunity to intensity variations, and the potential for computationally efficient algorithms. In this work, a common framework for edge-based image registration is formulated as an adaptation of snakes used in boundary detection. Called active edge maps, the new formulation finds a one-to-one transformation T(x) that maps points in a source image to corresponding locations in a target image using an energy minimization approach. The energy consists of an image component that is small when edge features are well matched in the two images, and an internal term that restricts T(x) to allowable configurations. The active edge map formulation is illustrated here with a specific example developed for affine registration of carotid artery magnetic resonance images. In this example, edges are identified using a magnitude of gradient operator, image energy is determined using a Gaussian weighted distance function, and the internal energy includes separate, adjustable components that control volume preservation and rigidity.

  7. Medical image registration using sparse coding of image patches.

    PubMed

    Afzali, Maryam; Ghaffari, Aboozar; Fatemizadeh, Emad; Soltanian-Zadeh, Hamid

    2016-06-01

    Image registration is a basic task in medical image processing applications like group analysis and atlas construction. Similarity measure is a critical ingredient of image registration. Intensity distortion of medical images is not considered in most previous similarity measures. Therefore, in the presence of bias field distortions, they do not generate an acceptable registration. In this paper, we propose a sparse based similarity measure for mono-modal images that considers non-stationary intensity and spatially-varying distortions. The main idea behind this measure is that the aligned image is constructed by an analysis dictionary trained using the image patches. For this purpose, we use "Analysis K-SVD" to train the dictionary and find the sparse coefficients. We utilize image patches to construct the analysis dictionary and then we employ the proposed sparse similarity measure to find a non-rigid transformation using free form deformation (FFD). Experimental results show that the proposed approach is able to robustly register 2D and 3D images in both simulated and real cases. The proposed method outperforms other state-of-the-art similarity measures and decreases the transformation error compared to the previous methods. Even in the presence of bias field distortion, the proposed method aligns images without any preprocessing.

  8. A Review on Medical Image Registration as an Optimization Problem

    PubMed Central

    Song, Guoli; Han, Jianda; Zhao, Yiwen; Wang, Zheng; Du, Huibin

    2017-01-01

    Objective: In the course of clinical treatment, several medical media are required by a phy-sician in order to provide accurate and complete information about a patient. Medical image registra-tion techniques can provide a richer diagnosis and treatment information to doctors and to provide a comprehensive reference source for the researchers involved in image registration as an optimization problem. Methods: The essence of image registration is associating two or more different images spatial asso-ciation, and getting the translation of their spatial relationship. For medical image registration, its pro-cess is not absolute. Its core purpose is finding the conversion relationship between different images. Result: The major step of image registration includes the change of geometrical dimensions, and change of the image of the combination, image similarity measure, iterative optimization and interpo-lation process. Conclusion: The contribution of this review is sort of related image registration research methods, can provide a brief reference for researchers about image registration. PMID:28845149

  9. elastix: a toolbox for intensity-based medical image registration.

    PubMed

    Klein, Stefan; Staring, Marius; Murphy, Keelin; Viergever, Max A; Pluim, Josien P W

    2010-01-01

    Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), different time points (e.g., follow-up scans), and/or different subjects (in case of population studies). A large number of methods for image registration are described in the literature. Unfortunately, there is not one method that works for all applications. We have therefore developed elastix, a publicly available computer program for intensity-based medical image registration. The software consists of a collection of algorithms that are commonly used to solve medical image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. The command-line interface enables automated processing of large numbers of data sets, by means of scripting. The usage of elastix for comparing different registration methods is illustrated with three example experiments, in which individual components of the registration method are varied.

  10. Slice-to-volume medical image registration: A survey.

    PubMed

    Ferrante, Enzo; Paragios, Nikos

    2017-07-01

    During the last decades, the research community of medical imaging has witnessed continuous advances in image registration methods, which pushed the limits of the state-of-the-art and enabled the development of novel medical procedures. A particular type of image registration problem, known as slice-to-volume registration, played a fundamental role in areas like image guided surgeries and volumetric image reconstruction. However, to date, and despite the extensive literature available on this topic, no survey has been written to discuss this challenging problem. This paper introduces the first comprehensive survey of the literature about slice-to-volume registration, presenting a categorical study of the algorithms according to an ad-hoc taxonomy and analyzing advantages and disadvantages of every category. We draw some general conclusions from this analysis and present our perspectives on the future of the field. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Restricted surface matching: a new registration method for medical images

    NASA Astrophysics Data System (ADS)

    Gong, JianXing; Zamorano, Lucia J.; Jiang, Zhaowei; Nolte, Lutz P.; Diaz, Fernando

    1998-06-01

    Since its introduction to neurological surgery in the early 1980's, computer assisted surgery (CAS) with and without robotics navigation has been applied to several medical fields. The common issue all CAS systems is registration between two pre-operative 3D image modalities (for example, CT/MRI/PET et al) and the 3D image references of the patient in the operative room. In Wayne State University, a new way is introduced for medical image registration, which is different from traditional fiducial point registration and surface registration. We call it restricted surface matching (RSM). The method fast, convenient, accurate and robust. It combines the advantages from two registration methods mentioned before. Because of a penalty function introduced in its cost function, it is called `RSM'. The surface of a 3D image modality is pre-operatively extracted using segmentation techniques, and a distance map is created from such surface. The surface of another 3D reference is presented by a cloud of 3D points. At least three rough landmarks are used to restrict a registration not far away from global minimum. The local minimum issue is solved by use of a restriction for in the cost function and larger number of random starting points. The accuracy of matching is achieved by gradually releasing the restriction and limiting the influence of outliers. It only needs about half a minute to find the global minimum (for 256 X 256 X 56 images) in a SunSparc 10 station.

  12. Improved elastic medical image registration using mutual information

    NASA Astrophysics Data System (ADS)

    Ens, Konstantin; Schumacher, Hanno; Franz, Astrid; Fischer, Bernd

    2007-03-01

    One of the future-oriented areas of medical image processing is to develop fast and exact algorithms for image registration. By joining multi-modal images we are able to compensate the disadvantages of one imaging modality with the advantages of another modality. For instance, a Computed Tomography (CT) image containing the anatomy can be combined with metabolic information of a Positron Emission Tomography (PET) image. It is quite conceivable that a patient will not have the same position in both imaging systems. Furthermore some regions for instance in the abdomen can vary in shape and position due to different filling of the rectum. So a multi-modal image registration is needed to calculate a deformation field for one image in order to maximize the similarity between the two images, described by a so-called distance measure. In this work, we present a method to adapt a multi-modal distance measure, here mutual information (MI), with weighting masks. These masks are used to enhance relevant image structures and suppress image regions which otherwise would disturb the registration process. The performance of our method is tested on phantom data and real medical images.

  13. Collocation for Diffeomorphic Deformations in Medical Image Registration.

    PubMed

    Darkner, Sune; Pai, Akshay; G Liptrot, Matthew; Sporring, Jon

    2017-07-21

    Diffeomorphic deformation is a popular choice in medical image registration. A fundamental property of diffeomorphisms is in vertibility, implying that once the relation between two points A to B is found, then the relation B to A is given per definition. Consistency is a measure of a numerical algorithm's ability to mimic this invertibility, and achieving consistency has proven to be a challenge for many state-of-the-art algorithms.

  14. [Multimodal medical image registration using cubic spline interpolation method].

    PubMed

    He, Yuanlie; Tian, Lianfang; Chen, Ping; Wang, Lifei; Ye, Guangchun; Mao, Zongyuan

    2007-12-01

    Based on the characteristic of the PET-CT multimodal image series, a novel image registration and fusion method is proposed, in which the cubic spline interpolation method is applied to realize the interpolation of PET-CT image series, then registration is carried out by using mutual information algorithm and finally the improved principal component analysis method is used for the fusion of PET-CT multimodal images to enhance the visual effect of PET image, thus satisfied registration and fusion results are obtained. The cubic spline interpolation method is used for reconstruction to restore the missed information between image slices, which can compensate for the shortage of previous registration methods, improve the accuracy of the registration, and make the fused multimodal images more similar to the real image. Finally, the cubic spline interpolation method has been successfully applied in developing 3D-CRT (3D Conformal Radiation Therapy) system.

  15. Physical Constraint Finite Element Model for Medical Image Registration

    PubMed Central

    Zhang, Jingya; Wang, Jiajun; Wang, Xiuying; Gao, Xin; Feng, Dagan

    2015-01-01

    Due to being derived from linear assumption, most elastic body based non-rigid image registration algorithms are facing challenges for soft tissues with complex nonlinear behavior and with large deformations. To take into account the geometric nonlinearity of soft tissues, we propose a registration algorithm on the basis of Newtonian differential equation. The material behavior of soft tissues is modeled as St. Venant-Kirchhoff elasticity, and the nonlinearity of the continuum represents the quadratic term of the deformation gradient under the Green- St.Venant strain. In our algorithm, the elastic force is formulated as the derivative of the deformation energy with respect to the nodal displacement vectors of the finite element; the external force is determined by the registration similarity gradient flow which drives the floating image deforming to the equilibrium condition. We compared our approach to three other models: 1) the conventional linear elastic finite element model (FEM); 2) the dynamic elastic FEM; 3) the robust block matching (RBM) method. The registration accuracy was measured using three similarities: MSD (Mean Square Difference), NC (Normalized Correlation) and NMI (Normalized Mutual Information), and was also measured using the mean and max distance between the ground seeds and corresponding ones after registration. We validated our method on 60 image pairs including 30 medical image pairs with artificial deformation and 30 clinical image pairs for both the chest chemotherapy treatment in different periods and brain MRI normalization. Our method achieved a distance error of 0.320±0.138 mm in x direction and 0.326±0.111 mm in y direction, MSD of 41.96±13.74, NC of 0.9958±0.0019, NMI of 1.2962±0.0114 for images with large artificial deformations; and average NC of 0.9622±0.008 and NMI of 1.2764±0.0089 for the real clinical cases. Student’s t-test demonstrated that our model statistically outperformed the other methods in comparison (p

  16. Physical Constraint Finite Element Model for Medical Image Registration.

    PubMed

    Zhang, Jingya; Wang, Jiajun; Wang, Xiuying; Gao, Xin; Feng, Dagan

    2015-01-01

    Due to being derived from linear assumption, most elastic body based non-rigid image registration algorithms are facing challenges for soft tissues with complex nonlinear behavior and with large deformations. To take into account the geometric nonlinearity of soft tissues, we propose a registration algorithm on the basis of Newtonian differential equation. The material behavior of soft tissues is modeled as St. Venant-Kirchhoff elasticity, and the nonlinearity of the continuum represents the quadratic term of the deformation gradient under the Green- St.Venant strain. In our algorithm, the elastic force is formulated as the derivative of the deformation energy with respect to the nodal displacement vectors of the finite element; the external force is determined by the registration similarity gradient flow which drives the floating image deforming to the equilibrium condition. We compared our approach to three other models: 1) the conventional linear elastic finite element model (FEM); 2) the dynamic elastic FEM; 3) the robust block matching (RBM) method. The registration accuracy was measured using three similarities: MSD (Mean Square Difference), NC (Normalized Correlation) and NMI (Normalized Mutual Information), and was also measured using the mean and max distance between the ground seeds and corresponding ones after registration. We validated our method on 60 image pairs including 30 medical image pairs with artificial deformation and 30 clinical image pairs for both the chest chemotherapy treatment in different periods and brain MRI normalization. Our method achieved a distance error of 0.320±0.138 mm in x direction and 0.326±0.111 mm in y direction, MSD of 41.96±13.74, NC of 0.9958±0.0019, NMI of 1.2962±0.0114 for images with large artificial deformations; and average NC of 0.9622±0.008 and NMI of 1.2764±0.0089 for the real clinical cases. Student's t-test demonstrated that our model statistically outperformed the other methods in comparison (p

  17. Combining global and local parallel optimization for medical image registration

    NASA Astrophysics Data System (ADS)

    Wachowiak, Mark P.; Peters, Terry M.

    2005-04-01

    Optimization is an important component in linear and nonlinear medical image registration. While common non-derivative approaches such as Powell's method are accurate and efficient, they cannot easily be adapted for parallel hardware. In this paper, new optimization strategies are proposed for parallel, shared-memory (SM) architectures. The Dividing Rectangles (DIRECT) global method is combined with the local Generalized Pattern Search (GPS) and Multidirectional Search (MDS) and to improve efficiency on multiprocessor systems. These methods require no derivatives, and can be used with all similarity metrics. In a multiresolution framework, DIRECT is performed with relaxed convergence criteria, followed by local refinement with MDS or GPS. In 3D-3D MRI rigid registration of simulated MS lesion volumes to normal brains with varying noise levels, DIRECT/MDS had the highest success rate, followed by DIRECT/GPS. DIRECT/GPS was the most efficient (5--10 seconds with 8 CPUs, and 10--20 seconds with 4 CPUs). DIRECT followed by MDS or GPS greatly increased efficiency while maintaining accuracy. Powell's method generally required more than 30 seconds (1 CPU) with a low success rate (0.3 or lower). This work indicates that parallel optimization on shared memory systems can markedly improve registration speed and accuracy, particularly for large initial misorientations.

  18. Non-rigid registration of medical images based on ordinal feature and manifold learning

    NASA Astrophysics Data System (ADS)

    Li, Qi; Liu, Jin; Zang, Bo

    2015-12-01

    With the rapid development of medical imaging technology, medical image research and application has become a research hotspot. This paper offers a solution to non-rigid registration of medical images based on ordinal feature (OF) and manifold learning. The structural features of medical images are extracted by combining ordinal features with local linear embedding (LLE) to improve the precision and speed of the registration algorithm. A physical model based on manifold learning and optimization search is constructed according to the complicated characteristics of non-rigid registration. The experimental results demonstrate the robustness and applicability of the proposed registration scheme.

  19. A survey of medical image registration on graphics hardware.

    PubMed

    Fluck, O; Vetter, C; Wein, W; Kamen, A; Preim, B; Westermann, R

    2011-12-01

    The rapidly increasing performance of graphics processors, improving programming support and excellent performance-price ratio make graphics processing units (GPUs) a good option for a variety of computationally intensive tasks. Within this survey, we give an overview of GPU accelerated image registration. We address both, GPU experienced readers with an interest in accelerated image registration, as well as registration experts who are interested in using GPUs. We survey programming models and interfaces and analyze different approaches to programming on the GPU. We furthermore discuss the inherent advantages and challenges of current hardware architectures, which leads to a description of the details of the important building blocks for successful implementations. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  20. [A method for the medical image registration based on the statistics samples averaging distribution theory].

    PubMed

    Xu, Peng; Yao, Dezhong; Luo, Fen

    2005-08-01

    The registration method based on mutual information is currently a popular technique for the medical image registration, but the computation for the mutual information is complex and the registration speed is slow. In engineering process, a subsampling technique is taken to accelerate the registration speed at the cost of registration accuracy. In this paper a new method based on statistics sample theory is developed, which has both a higher speed and a higher accuracy as compared with the normal subsampling method, and the simulation results confirm the validity of the new method.

  1. A Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images.

    PubMed

    Du, Xiaogang; Dang, Jianwu; Wang, Yangping; Wang, Song; Lei, Tao

    2016-01-01

    The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD) plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD) is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs) to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG) optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU).

  2. A Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images

    PubMed Central

    Wang, Yangping; Wang, Song

    2016-01-01

    The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD) plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD) is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs) to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG) optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU). PMID:28053653

  3. Non-rigid registration of medical images based on estimation of deformation states

    NASA Astrophysics Data System (ADS)

    Marami, Bahram; Sirouspour, Shahin; Capson, David W.

    2014-11-01

    A unified framework for automatic non-rigid 3D-3D and 3D-2D registration of medical images with static and dynamic deformations is proposed in this paper. The problem of non-rigid image registration is approached as a classical state estimation problem using a generic deformation model for the soft tissue. The registration technique employs a dynamic linear elastic continuum mechanics model of the tissue deformation, which is discretized using the finite element method. In the proposed method, the registration is achieved through a Kalman-like filtering process, which incorporates information from the deformation model and a vector of observation prediction errors computed from an intensity-based similarity/distance metric between images. With this formulation, single and multiple-modality, 3D-3D and 3D-2D image registration problems can all be treated within the same framework. The performance of the proposed registration technique was evaluated in a number of different registration scenarios. First, 3D magnetic resonance (MR) images of uncompressed and compressed breast tissue were co-registered. 3D MR images of the uncompressed breast tissue were also registered to a sequence of simulated 2D interventional MR images of the compressed breast. Finally, the registration algorithm was employed to dynamically track a target sub-volume inside the breast tissue during the process of the biopsy needle insertion based on registering pre-insertion 3D MR images to a sequence of real-time simulated 2D interventional MR images. Registration results indicate that the proposed method can be effectively employed for the registration of medical images in image-guided procedures, such as breast biopsy in which the tissue undergoes static and dynamic deformations.

  4. Non-rigid registration of medical images based on estimation of deformation states.

    PubMed

    Marami, Bahram; Sirouspour, Shahin; Capson, David W

    2014-11-21

    A unified framework for automatic non-rigid 3D-3D and 3D-2D registration of medical images with static and dynamic deformations is proposed in this paper. The problem of non-rigid image registration is approached as a classical state estimation problem using a generic deformation model for the soft tissue. The registration technique employs a dynamic linear elastic continuum mechanics model of the tissue deformation, which is discretized using the finite element method. In the proposed method, the registration is achieved through a Kalman-like filtering process, which incorporates information from the deformation model and a vector of observation prediction errors computed from an intensity-based similarity/distance metric between images. With this formulation, single and multiple-modality, 3D-3D and 3D-2D image registration problems can all be treated within the same framework. The performance of the proposed registration technique was evaluated in a number of different registration scenarios. First, 3D magnetic resonance (MR) images of uncompressed and compressed breast tissue were co-registered. 3D MR images of the uncompressed breast tissue were also registered to a sequence of simulated 2D interventional MR images of the compressed breast. Finally, the registration algorithm was employed to dynamically track a target sub-volume inside the breast tissue during the process of the biopsy needle insertion based on registering pre-insertion 3D MR images to a sequence of real-time simulated 2D interventional MR images. Registration results indicate that the proposed method can be effectively employed for the registration of medical images in image-guided procedures, such as breast biopsy in which the tissue undergoes static and dynamic deformations.

  5. Image Registration Workshop Proceedings

    NASA Technical Reports Server (NTRS)

    LeMoigne, Jacqueline (Editor)

    1997-01-01

    Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research.

  6. [Research on non-rigid registration of multi-modal medical image based on Demons algorithm].

    PubMed

    Hao, Peibo; Chen, Zhen; Jiang, Shaofeng; Wang, Yang

    2014-02-01

    Non-rigid medical image registration is a popular subject in the research areas of the medical image and has an important clinical value. In this paper we put forward an improved algorithm of Demons, together with the conservation of gray model and local structure tensor conservation model, to construct a new energy function processing multi-modal registration problem. We then applied the L-BFGS algorithm to optimize the energy function and solve complex three-dimensional data optimization problem. And finally we used the multi-scale hierarchical refinement ideas to solve large deformation registration. The experimental results showed that the proposed algorithm for large de formation and multi-modal three-dimensional medical image registration had good effects.

  7. Convex hull matching and hierarchical decomposition for multimodality medical image registration.

    PubMed

    Yang, Jian; Fan, Jingfan; Fu, Tianyu; Ai, Danni; Zhu, Jianjun; Li, Qin; Wang, Yongtian

    2015-01-01

    This study proposes a novel hierarchical pyramid strategy for 3D registration of multimodality medical images. The surfaces of the source and target volume data are first extracted, and the surface point clouds are then aligned roughly using convex hull matching. The convex hull matching registration procedure could align images with large-scale transformations. The original images are divided into blocks and the corresponding blocks in the two images are registered by affine and non-rigid registration procedures. The sub-blocks are iteratively smoothed by the Gaussian kernel with different sizes during the registration procedure. The registration result of the large kernel is taken as the input of the small kernel registration. The fine registration of the two volume data sets is achieved by iteratively increasing the number of blocks, in which increase in similarity measure is taken as a criterion for acceptation of each iteration level. Results demonstrate the effectiveness and robustness of the proposed method in registering the multiple modalities of medical images.

  8. An ITK framework for deterministic global optimization for medical image registration

    NASA Astrophysics Data System (ADS)

    Dru, Florence; Wachowiak, Mark P.; Peters, Terry M.

    2006-03-01

    Similarity metric optimization is an essential step in intensity-based rigid and nonrigid medical image registration. For clinical applications, such as image guidance of minimally invasive procedures, registration accuracy and efficiency are prime considerations. In addition, clinical utility is enhanced when registration is integrated into image analysis and visualization frameworks, such as the popular Insight Toolkit (ITK). ITK is an open source software environment increasingly used to aid the development, testing, and integration of new imaging algorithms. In this paper, we present a new ITK-based implementation of the DIRECT (Dividing Rectangles) deterministic global optimization algorithm for medical image registration. Previously, it has been shown that DIRECT improves the capture range and accuracy for rigid registration. Our ITK class also contains enhancements over the original DIRECT algorithm by improving stopping criteria, adaptively adjusting a locality parameter, and by incorporating Powell's method for local refinement. 3D-3D registration experiments with ground-truth brain volumes and clinical cardiac volumes show that combining DIRECT with Powell's method improves registration accuracy over Powell's method used alone, is less sensitive to initial misorientation errors, and, with the new stopping criteria, facilitates adequate exploration of the search space without expending expensive iterations on non-improving function evaluations. Finally, in this framework, a new parallel implementation for computing mutual information is presented, resulting in near-linear speedup with two processors.

  9. Medical image registration by combining global and local information: a chain-type diffeomorphic demons algorithm.

    PubMed

    Liu, Xiaozheng; Yuan, Zhenming; Zhu, Junming; Xu, Dongrong

    2013-12-07

    The demons algorithm is a popular algorithm for non-rigid image registration because of its computational efficiency and simple implementation. The deformation forces of the classic demons algorithm were derived from image gradients by considering the deformation to decrease the intensity dissimilarity between images. However, the methods using the difference of image intensity for medical image registration are easily affected by image artifacts, such as image noise, non-uniform imaging and partial volume effects. The gradient magnitude image is constructed from the local information of an image, so the difference in a gradient magnitude image can be regarded as more reliable and robust for these artifacts. Then, registering medical images by considering the differences in both image intensity and gradient magnitude is a straightforward selection. In this paper, based on a diffeomorphic demons algorithm, we propose a chain-type diffeomorphic demons algorithm by combining the differences in both image intensity and gradient magnitude for medical image registration. Previous work had shown that the classic demons algorithm can be considered as an approximation of a second order gradient descent on the sum of the squared intensity differences. By optimizing the new dissimilarity criteria, we also present a set of new demons forces which were derived from the gradients of the image and gradient magnitude image. We show that, in controlled experiments, this advantage is confirmed, and yields a fast convergence.

  10. Medical image registration algorithms assesment: Bronze Standard application enactment on grids using the MOTEUR workflow engine.

    PubMed

    Glatard, Tristan; Montagnat, Johan; Pennec, Xavier

    2006-01-01

    Medical image registration is pre-processing needed for many medical image analysis procedures. A very large number of registration algorithms are available today, but their performance is often not known and very difficult to assess due to the lack of gold standard. The Bronze Standard algorithm is a very data and compute intensive statistical approach for quantifying registration algorithms accuracy. In this paper, we describe the Bronze Standard application and we discuss the need for grids to tackle such computations on medical image databases. We demonstrate MOTEUR, a service-based workflow engine optimized for dealing with data intensive applications. MOTEUR eases the enactment of the Bronze Standard and similar applications on the EGEE production grid infrastructure. It is a generic workflow engine, based on current standards and freely available, that can be used to instrument legacy application code at low cost.

  11. A new region descriptor for multi-modal medical image registration and region detection.

    PubMed

    Xiaonan Wan; Dongdong Yu; Feng Yang; Caiyun Yang; Chengcai Leng; Min Xu; Jie Tian

    2015-08-01

    Establishing accurate anatomical correspondences plays a critical role in multi-modal medical image registration and region detection. Although many features based registration methods have been proposed to detect these correspondences, they are mostly based on the point descriptor which leads to high memory cost and could not represent local region information. In this paper, we propose a new region descriptor which depicts the features in each region, instead of in each point, as a vector. First, feature attributes of each point are extracted by a Gabor filter bank combined with a gradient filter. Then, the region descriptor is defined as the covariance of feature attributes of each point inside the region, based on which a cost function is constructed for multi-modal image registration. Finally, our proposed region descriptor is applied to both multi-modal region detection and similarity metric measurement in multi-modal image registration. Experiments demonstrate the feasibility and effectiveness of our proposed region descriptor.

  12. Challenges in accurate registration of 3D medical imaging and histopathology in primary prostate cancer

    PubMed Central

    Meyer, Charles; Ma, Bing; Kunju, Lakshmi P; Davenport, Matthew; Piert, Morand

    2013-01-01

    Due to poor correlation of slice thickness and orientation, verification of medical imaging results with histology is difficult. Often validation of imaging findings of lesions suspicious for prostate cancer is driven by a subjective, visual approach to correlate in vivo images with histopathology. This manuscript describes fallacious assumptions for correlation of imaging findings with pathology and identifies the lack of accurate registration as a major obstacle in the validation of PET and PET/CT imaging in primary prostate cancer. Specific registration techniques that facilitate the most difficult part of the registration process—the mapping of pathology onto high-resolution imaging, preferably aided by the ex vivo prostate specimen—are discussed. PMID:23503575

  13. A new fast accurate nonlinear medical image registration program including surface preserving regularization.

    PubMed

    Gruslys, Audrunas; Acosta-Cabronero, Julio; Nestor, Peter J; Williams, Guy B; Ansorge, Richard E

    2014-11-01

    Recently inexpensive graphical processing units (GPUs) have become established as a viable alternative to traditional CPUs for many medical image processing applications. GPUs offer the potential of very significant improvements in performance at low cost and with low power consumption. One way in which GPU programs differ from traditional CPU programs is that increasingly elaborate calculations per voxel may not impact of the overall processing time because memory accesses can dominate execution time. This paper presents a new GPU based elastic image registration program named Ezys. The Ezys image registration algorithm belongs to the wide class of diffeomorphic demons but uses surface preserving image smoothing and regularization filters designed for a GPU that would be computationally expensive on a CPU. We describe the methods used in Ezys and present results from two important neuroscience applications. Firstly inter-subject registration for transfer of anatomical labels and secondly longitudinal intra-subject registration to quantify atrophy in individual subjects. Both experiments showed that Ezys registration compares favorably with other popular elastic image registration programs. We believe Ezys is a useful tool for neuroscience and other applications, and also demonstrates the value of developing of novel image processing filters specifically designed for GPUs.

  14. Hierarchical and successive approximate registration of the non-rigid medical image based on thin-plate splines

    NASA Astrophysics Data System (ADS)

    Hu, Jinyan; Li, Li; Yang, Yunfeng

    2017-06-01

    The hierarchical and successive approximate registration method of non-rigid medical image based on the thin-plate splines is proposed in the paper. There are two major novelties in the proposed method. First, the hierarchical registration based on Wavelet transform is used. The approximate image of Wavelet transform is selected as the registered object. Second, the successive approximation registration method is used to accomplish the non-rigid medical images registration, i.e. the local regions of the couple images are registered roughly based on the thin-plate splines, then, the current rough registration result is selected as the object to be registered in the following registration procedure. Experiments show that the proposed method is effective in the registration process of the non-rigid medical images.

  15. 3D nonrigid medical image registration using a new information theoretic measure

    NASA Astrophysics Data System (ADS)

    Li, Bicao; Yang, Guanyu; Coatrieux, Jean Louis; Li, Baosheng; Shu, Huazhong

    2015-11-01

    This work presents a novel method for the nonrigid registration of medical images based on the Arimoto entropy, a generalization of the Shannon entropy. The proposed method employed the Jensen-Arimoto divergence measure as a similarity metric to measure the statistical dependence between medical images. Free-form deformations were adopted as the transformation model and the Parzen window estimation was applied to compute the probability distributions. A penalty term is incorporated into the objective function to smooth the nonrigid transformation. The goal of registration is to optimize an objective function consisting of a dissimilarity term and a penalty term, which would be minimal when two deformed images are perfectly aligned using the limited memory BFGS optimization method, and thus to get the optimal geometric transformation. To validate the performance of the proposed method, experiments on both simulated 3D brain MR images and real 3D thoracic CT data sets were designed and performed on the open source elastix package. For the simulated experiments, the registration errors of 3D brain MR images with various magnitudes of known deformations and different levels of noise were measured. For the real data tests, four data sets of 4D thoracic CT from four patients were selected to assess the registration performance of the method, including ten 3D CT images for each 4D CT data covering an entire respiration cycle. These results were compared with the normalized cross correlation and the mutual information methods and show a slight but true improvement in registration accuracy.

  16. 3D nonrigid medical image registration using a new information theoretic measure.

    PubMed

    Li, Bicao; Yang, Guanyu; Coatrieux, Jean Louis; Li, Baosheng; Shu, Huazhong

    2015-11-21

    This work presents a novel method for the nonrigid registration of medical images based on the Arimoto entropy, a generalization of the Shannon entropy. The proposed method employed the Jensen-Arimoto divergence measure as a similarity metric to measure the statistical dependence between medical images. Free-form deformations were adopted as the transformation model and the Parzen window estimation was applied to compute the probability distributions. A penalty term is incorporated into the objective function to smooth the nonrigid transformation. The goal of registration is to optimize an objective function consisting of a dissimilarity term and a penalty term, which would be minimal when two deformed images are perfectly aligned using the limited memory BFGS optimization method, and thus to get the optimal geometric transformation. To validate the performance of the proposed method, experiments on both simulated 3D brain MR images and real 3D thoracic CT data sets were designed and performed on the open source elastix package. For the simulated experiments, the registration errors of 3D brain MR images with various magnitudes of known deformations and different levels of noise were measured. For the real data tests, four data sets of 4D thoracic CT from four patients were selected to assess the registration performance of the method, including ten 3D CT images for each 4D CT data covering an entire respiration cycle. These results were compared with the normalized cross correlation and the mutual information methods and show a slight but true improvement in registration accuracy.

  17. Efficient 3-D medical image registration using a distributed blackboard architecture.

    PubMed

    Tait, Roger J; Schaefer, Gerald; Hopgood, Adrian A; Zhu, Shao Ying

    2006-01-01

    A major drawback of 3-D medical image registration techniques is the performance bottleneck associated with re-sampling and similarity computation. Such bottlenecks limit registration applications in clinical situations where fast execution times are required and become particularly apparent in the case of registering 3-D data sets. In this paper a novel framework for high performance intensity-based volume registration is presented. Geometric alignment of both reference and sensed volume sets is achieved through a combination of scaling, translation, and rotation. Crucially, resampling and similarity computation is performed intelligently by a set of knowledge sources. The knowledge sources work in parallel and communicate with each other by means of a distributed blackboard architecture. Partitioning of the blackboard is used to balance communication and processing workloads. Large-scale registrations with substantial speedups, when compared with a conventional implementation, have been demonstrated.

  18. Automatic segmentation of medical images using image registration: diagnostic and simulation applications.

    PubMed

    Barber, D C; Hose, D R

    2005-01-01

    Automatic identification of the boundaries of significant structure (segmentation) within a medical image is an are of ongoing research. Various approaches have been proposed but only two methods have achieved widespread use: manual delineation of boundaries and segmentation using intensity values. In this paper we describe an approach based on image registration. A reference image is prepared and segmented, by hand or otherwise. A patient image is registered to the reference image and the mapping then applied to ther reference segmentation to map it back to the patient image. In general a high-resolution nonlinear mapping is required to achieve accurate segmentation. This paper describes an algorithm that can efficiently generate such mappings, and outlines the uses of this tool in two relevant applications. An important feature of the approach described in this paper is that the algorithm is independent of the segmentation problem being addresses. All knowledge about the problem at hand is contained in files of reference data. A secondary benefit is that the continuous three-dimensional mapping generated is well suited to the generation of patient-specific numerical models (e.g. finite element meshes) from the library models. Smoothness constraints in the morphing algorithm tend to maintain the geometric quality of the reference mesh.

  19. FZUImageReg: A toolbox for medical image registration and dose fusion in cervical cancer radiotherapy

    PubMed Central

    Bai, Penggang; Du, Min; Ni, Xiaolei; Ke, Dongzhong; Tong, Tong

    2017-01-01

    The combination external-beam radiotherapy and high-dose-rate brachytherapy is a standard form of treatment for patients with locally advanced uterine cervical cancer. Personalized radiotherapy in cervical cancer requires efficient and accurate dose planning and assessment across these types of treatment. To achieve radiation dose assessment, accurate mapping of the dose distribution from HDR-BT onto EBRT is extremely important. However, few systems can achieve robust dose fusion and determine the accumulated dose distribution during the entire course of treatment. We have therefore developed a toolbox (FZUImageReg), which is a user-friendly dose fusion system based on hybrid image registration for radiation dose assessment in cervical cancer radiotherapy. The main part of the software consists of a collection of medical image registration algorithms and a modular design with a user-friendly interface, which allows users to quickly configure, test, monitor, and compare different registration methods for a specific application. Owing to the large deformation, the direct application of conventional state-of-the-art image registration methods is not sufficient for the accurate alignment of EBRT and HDR-BT images. To solve this problem, a multi-phase non-rigid registration method using local landmark-based free-form deformation is proposed for locally large deformation between EBRT and HDR-BT images, followed by intensity-based free-form deformation. With the transformation, the software also provides a dose mapping function according to the deformation field. The total dose distribution during the entire course of treatment can then be presented. Experimental results clearly show that the proposed system can achieve accurate registration between EBRT and HDR-BT images and provide radiation dose warping and fusion results for dose assessment in cervical cancer radiotherapy in terms of high accuracy and efficiency. PMID:28388623

  20. Multiscale registration of medical images based on edge preserving scale space with application in image-guided radiation therapy

    NASA Astrophysics Data System (ADS)

    Li, Dengwang; Li, Hongsheng; Wan, Honglin; Chen, Jinhu; Gong, Guanzhong; Wang, Hongjun; Wang, Liming; Yin, Yong

    2012-08-01

    Mutual information (MI) is a well-accepted similarity measure for image registration in medical systems. However, MI-based registration faces the challenges of high computational complexity and a high likelihood of being trapped into local optima due to an absence of spatial information. In order to solve these problems, multi-scale frameworks can be used to accelerate registration and improve robustness. Traditional Gaussian pyramid representation is one such technique but it suffers from contour diffusion at coarse levels which may lead to unsatisfactory registration results. In this work, a new multi-scale registration framework called edge preserving multiscale registration (EPMR) was proposed based upon an edge preserving total variation L1 norm (TV-L1) scale space representation. TV-L1 scale space is constructed by selecting edges and contours of images according to their size rather than the intensity values of the image features. This ensures more meaningful spatial information with an EPMR framework for MI-based registration. Furthermore, we design an optimal estimation of the TV-L1 parameter in the EPMR framework by training and minimizing the transformation offset between the registered pairs for automated registration in medical systems. We validated our EPMR method on both simulated mono- and multi-modal medical datasets with ground truth and clinical studies from a combined positron emission tomography/computed tomography (PET/CT) scanner. We compared our registration framework with other traditional registration approaches. Our experimental results demonstrated that our method outperformed other methods in terms of the accuracy and robustness for medical images. EPMR can always achieve a small offset value, which is closer to the ground truth both for mono-modality and multi-modality, and the speed can be increased 5-8% for mono-modality and 10-14% for multi-modality registration under the same condition. Furthermore, clinical application by adaptive

  1. [Rapid 2D-3D medical image registration based on CUDA].

    PubMed

    Li, Lingzhi; Zou, Beiji

    2014-08-01

    The medical image registration between preoperative three-dimensional (3D) scan data and intraoperative two-dimensional (2D) image is a key technology in the surgical navigation. Most previous methods need to generate 2D digitally reconstructed radiographs (DRR) images from the 3D scan volume data, then use conventional image similarity function for comparison. This procedure includes a large amount of calculation and is difficult to archive real-time processing. In this paper, with using geometric feature and image density mixed characteristics, we proposed a new similarity measure function for fast 2D-3D registration of preoperative CT and intraoperative X-ray images. This algorithm is easy to implement, and the calculation process is very short, while the resulting registration accuracy can meet the clinical use. In addition, the entire calculation process is very suitable for highly parallel numerical calculation by using the algorithm based on CUDA hardware acceleration to satisfy the requirement of real-time application in surgery.

  2. A multi-object statistical atlas adaptive for deformable registration errors in anomalous medical image segmentation

    NASA Astrophysics Data System (ADS)

    Botter Martins, Samuel; Vallin Spina, Thiago; Yasuda, Clarissa; Falcão, Alexandre X.

    2017-02-01

    Statistical Atlases have played an important role towards automated medical image segmentation. However, a challenge has been to make the atlas more adaptable to possible errors in deformable registration of anomalous images, given that the body structures of interest for segmentation might present significant differences in shape and texture. Recently, deformable registration errors have been accounted by a method that locally translates the statistical atlas over the test image, after registration, and evaluates candidate objects from a delineation algorithm in order to choose the best one as final segmentation. In this paper, we improve its delineation algorithm and extend the model to be a multi-object statistical atlas, built from control images and adaptable to anomalous images, by incorporating a texture classifier. In order to provide a first proof of concept, we instantiate the new method for segmenting, object-by-object and all objects simultaneously, the left and right brain hemispheres, and the cerebellum, without the brainstem, and evaluate it on MRT1-images of epilepsy patients before and after brain surgery, which removed portions of the temporal lobe. The results show efficiency gain with statistically significant higher accuracy, using the mean Average Symmetric Surface Distance, with respect to the original approach.

  3. Multi-Modality fiducial marker for validation of registration of medical images with histology

    NASA Astrophysics Data System (ADS)

    Shojaii, Rushin; Martel, Anne L.

    2010-03-01

    A multi-modality fiducial marker is presented in this work, which can be used for validating the correlation of histology images with medical images. This marker can also be used for landmark-based image registration. Seven different fiducial markers including a catheter, spaghetti, black spaghetti, cuttlefish ink, and liquid iron are implanted in a mouse specimen and then investigated based on visibility, localization, size, and stability. The black spaghetti and the mixture of cuttlefish ink and flour are shown to be the most suitable markers. Based on the size of the markers, black spaghetti is more suitable for big specimens and the mixture of the cuttlefish ink, flour, and water injected in a catheter is more suitable for small specimens such as mouse tumours. These markers are visible on medical images and also detectable on histology and optical images of the tissue blocks. The main component in these agents which enhances the contrast is iron.

  4. Multimodality medical image registration and fusion techniques using mutual information and genetic algorithm-based approaches.

    PubMed

    Bhattacharya, Mahua; Das, Arpita

    2011-01-01

    Medical image fusion has been used to derive the useful complimentary information from multimodal images. The prior step of fusion is registration or proper alignment of test images for accurate extraction of detail information. For this purpose, the images to be fused are geometrically aligned using mutual information (MI) as similarity measuring metric followed by genetic algorithm to maximize MI. The proposed fusion strategy incorporating multi-resolution approach extracts more fine details from the test images and improves the quality of composite fused image. The proposed fusion approach is independent of any manual marking or knowledge of fiducial points and starts the procedure automatically. The performance of proposed genetic-based fusion methodology is compared with fuzzy clustering algorithm-based fusion approach, and the experimental results show that genetic-based fusion technique improves the quality of the fused image significantly over the fuzzy approaches.

  5. Investigating the Use of Cloudbursts for High-Throughput Medical Image Registration

    PubMed Central

    Kim, Hyunjoo; Parashar, Manish; Foran, David J.; Yang, Lin

    2010-01-01

    This paper investigates the use of clouds and autonomic cloudbursting to support a medical image registration. The goal is to enable a virtual computational cloud that integrates local computational environments and public cloud services on-the-fly, and support image registration requests from different distributed researcher groups with varied computational requirements and QoS constraints. The virtual cloud essentially implements shared and coordinated task-spaces, which coordinates the scheduling of jobs submitted by a dynamic set of research groups to their local job queues. A policy-driven scheduling agent uses the QoS constraints along with performance history and the state of the resources to determine the appropriate size and mix of the public and private cloud resource that should be allocated to a specific request. The virtual computational cloud and the medical image registration service have been developed using the CometCloud engine and have been deployed on a combination of private clouds at Rutgers University and the Cancer Institute of New Jersey and Amazon EC2. An experimental evaluation is presented and demonstrates the effectiveness of autonomic cloudbursts and policy-based autonomic scheduling for this application. PMID:20640235

  6. A practical salient region feature based 3D multi-modality registration method for medical images

    NASA Astrophysics Data System (ADS)

    Hahn, Dieter A.; Wolz, Gabriele; Sun, Yiyong; Hornegger, Joachim; Sauer, Frank; Kuwert, Torsten; Xu, Chenyang

    2006-03-01

    We present a novel representation of 3D salient region features and its integration into a hybrid rigid-body registration framework. We adopt scale, translation and rotation invariance properties of those intrinsic 3D features to estimate a transform between underlying mono- or multi-modal 3D medical images. Our method combines advantageous aspects of both feature- and intensity-based approaches and consists of three steps: an automatic extraction of a set of 3D salient region features on each image, a robust estimation of correspondences and their sub-pixel accurate refinement with outliers elimination. We propose a region-growing based approach for the extraction of 3D salient region features, a solution to the problem of feature clustering and a reduction of the correspondence search space complexity. Results of the developed algorithm are presented for both mono- and multi-modal intra-patient 3D image pairs (CT, PET and SPECT) that have been acquired for change detection, tumor localization, and time based intra-person studies. The accuracy of the method is clinically evaluated by a medical expert with an approach that measures the distance between a set of selected corresponding points consisting of both anatomical and functional structures or lesion sites. This demonstrates the robustness of the proposed method to image overlap, missing information and artefacts. We conclude by discussing potential medical applications and possibilities for integration into a non-rigid registration framework.

  7. An innovative multimodal/multispectral image registration method for medical images based on the Expectation-Maximization algorithm.

    PubMed

    Arce-Santana, Edgar; Campos-Delgado, Daniel U; Mejia-Rodriguez, Aldo; Reducindo, Isnardo

    2015-01-01

    In this paper, we present a methodology for multimodal/ multispectral image registration of medical images. This approach is formulated by using the Expectation-Maximization (EM) methodology, such that we estimate the parameters of a geometric transformation that aligns multimodal/multispectral images. In this framework, the hidden random variables are associated to the intensity relations between the studied images, which allow to compare multispectral intensity values between images of different modalities. The methodology is basically composed by an iterative two-step procedure, where at each step, a new estimation of the joint conditional multispectral intensity distribution and the geometric transformation is computed. The proposed algorithm was tested with different kinds of medical images, and the obtained results show that the proposed methodology can be used to efficiently align multimodal/multispectral medical images.

  8. Validation of histology image registration

    NASA Astrophysics Data System (ADS)

    Shojaii, Rushin; Karavardanyan, Tigran; Yaffe, Martin; Martel, Anne L.

    2011-03-01

    The aim of this paper is to validate an image registration pipeline used for histology image alignment. In this work a set of histology images are registered to their correspondent optical blockface images to make a histology volume. Then multi-modality fiducial markers are used to validate the alignment of histology images. The fiducial markers are catheters perfused with a mixture of cuttlefish ink and flour. Based on our previous investigations this fiducial marker is visible in medical images, optical blockface images and it can also be localized in histology images. The properties of this fiducial marker make it suitable for validation of the registration techniques used for histology image alignment. This paper reports on the accuracy of a histology image registration approach by calculation of target registration error using these fiducial markers.

  9. Evaluation of optimization methods for nonrigid medical image registration using mutual information and B-splines.

    PubMed

    Klein, Stefan; Staring, Marius; Pluim, Josien P W

    2007-12-01

    A popular technique for nonrigid registration of medical images is based on the maximization of their mutual information, in combination with a deformation field parameterized by cubic B-splines. The coordinate mapping that relates the two images is found using an iterative optimization procedure. This work compares the performance of eight optimization methods: gradient descent (with two different step size selection algorithms), quasi-Newton, nonlinear conjugate gradient, Kiefer-Wolfowitz, simultaneous perturbation, Robbins-Monro, and evolution strategy. Special attention is paid to computation time reduction by using fewer voxels to calculate the cost function and its derivatives. The optimization methods are tested on manually deformed CT images of the heart, on follow-up CT chest scans, and on MR scans of the prostate acquired using a BFFE, T1, and T2 protocol. Registration accuracy is assessed by computing the overlap of segmented edges. Precision and convergence properties are studied by comparing deformation fields. The results show that the Robbins-Monro method is the best choice in most applications. With this approach, the computation time per iteration can be lowered approximately 500 times without affecting the rate of convergence by using a small subset of the image, randomly selected in every iteration, to compute the derivative of the mutual information. From the other methods the quasi-Newton and the nonlinear conjugate gradient method achieve a slightly higher precision, at the price of larger computation times.

  10. Value of a probabilistic atlas in medical image segmentation regarding non-rigid registration of abdominal CT scans

    NASA Astrophysics Data System (ADS)

    Park, Hyunjin; Meyer, Charles R.

    2012-10-01

    A probabilistic atlas provides important information to help segmentation and registration applications in medical image analysis. We construct a probabilistic atlas by picking a target geometry and mapping other training scans onto that target and then summing the results into one probabilistic atlas. By choosing an atlas space close to the desired target, we construct an atlas that represents the population well. Image registration used to map one image geometry onto another is a primary task in atlas building. One of the main parameters of registration is the choice of degrees of freedom (DOFs) of the geometric transform. Herein, we measure the effect of the registration's DOFs on the segmentation performance of the resulting probabilistic atlas. Twenty-three normal abdominal CT scans were used, and four organs (liver, spinal cord, left and right kidneys) were segmented for each scan. A well-known manifold learning method, ISOMAP, was used to find the best target space to build an atlas. In summary, segmentation performance was high for high DOF registrations regardless of the chosen target space, while segmentation performance was lowered for low DOF registrations if a target space was far from the best target space. At the 0.05 level of statistical significance, there were no significant differences at high DOF registrations while there were significant differences at low DOF registrations when choosing different targets.

  11. A Log-Euclidean polyaffine registration for articulated structures in medical images.

    PubMed

    Martín-Fernández, Miguel Angel; Martín-Fernández, Marcos; Alberola-López, Carlos

    2009-01-01

    In this paper we generalize the Log-Euclidean polyaffine registration framework of Arsigny et al. to deal with articulated structures. This framework has very useful properties as it guarantees the invertibility of smooth geometric transformations. In articulated registration a skeleton model is defined for rigid structures such as bones. The final transformation is affine for the bones and elastic for other tissues in the image. We extend the Arsigny el al.'s method to deal with locally-affine registration of pairs of wires. This enables the possibility of using this registration framework to deal with articulated structures. In this context, the design of the weighting functions, which merge the affine transformations defined for each pair of wires, has a great impact not only on the final result of the registration algorithm, but also on the invertibility of the global elastic transformation. Several experiments, using both synthetic images and hand radiographs, are also presented.

  12. PSO-based methods for medical image registration and change assessment of pigmented skin

    NASA Astrophysics Data System (ADS)

    Kacenjar, Steve; Zook, Matthew; Balint, Michael

    2011-03-01

    There are various scientific and technological areas in which it is imperative to rapidly detect and quantify changes in imagery over time. In fields such as earth remote sensing, aerospace systems, and medical imaging, searching for timedependent, regional changes across deformable topographies is complicated by varying camera acquisition geometries, lighting environments, background clutter conditions, and occlusion. Under these constantly-fluctuating conditions, the use of standard, rigid-body registration approaches often fail to provide sufficient fidelity to overlay image scenes together. This is problematic because incorrect assessments of the underlying changes of high-level topography can result in systematic errors in the quantification and classification of interested areas. For example, in the current naked-eye detection strategies of melanoma, a dermatologist often uses static morphological attributes to identify suspicious skin lesions for biopsy. This approach does not incorporate temporal changes which suggest malignant degeneration. By performing the co-registration of time-separated skin imagery, a dermatologist may more effectively detect and identify early morphological changes in pigmented lesions; enabling the physician to detect cancers at an earlier stage resulting in decreased morbidity and mortality. This paper describes an image processing system which will be used to detect changes in the characteristics of skin lesions over time. The proposed system consists of three main functional elements: 1.) coarse alignment of timesequenced imagery, 2.) refined alignment of local skin topographies, and 3.) assessment of local changes in lesion size. During the coarse alignment process, various approaches can be used to obtain a rough alignment, including: 1.) a manual landmark/intensity-based registration method1, and 2.) several flavors of autonomous optical matched filter methods2. These procedures result in the rough alignment of a patient

  13. Patient-specific model of brain deformation: application to medical image registration.

    PubMed

    Wittek, Adam; Miller, Karol; Kikinis, Ron; Warfield, Simon K

    2007-01-01

    This contribution presents finite element computation of the deformation field within the brain during craniotomy-induced brain shift. The results were used to illustrate the capabilities of non-linear (i.e. accounting for both geometric and material non-linearities) finite element analysis in non-rigid registration of pre- and intra-operative magnetic resonance images of the brain. We used patient-specific hexahedron-dominant finite element mesh, together with realistic material properties for the brain tissue and appropriate contact conditions at boundaries. The model was loaded by the enforced motion of nodes (i.e. through prescribed motion of a boundary) at the brain surface in the craniotomy area. We suggest using explicit time-integration scheme for discretised equations of motion, as the computational times are much shorter and accuracy, for practical purposes, the same as in the case of implicit integration schemes. Application of the computed deformation field to register (i.e. align) the pre-operative images with the intra-operative ones indicated that the model very accurately predicts the displacements of the tumour and the lateral ventricles even for limited information about the brain surface deformation. The prediction accuracy improves when information about deformation of not only exposed (during craniotomy) but also unexposed parts of the brain surface is used when prescribing loading. However, it appears that the accuracy achieved using information only about the deformation of the exposed surface, that can be determined without intra-operative imaging, is acceptable. The presented results show that non-linear biomechanical models can complement medical image processing techniques when conducting non-rigid registration. Important advantage of such models over the previously used linear ones is that they do not require unrealistic assumptions that brain deformations are infinitesimally small and brain stress-strain relationship is linear.

  14. Medical image registration using machine learning-based interest point detector

    NASA Astrophysics Data System (ADS)

    Sergeev, Sergey; Zhao, Yang; Linguraru, Marius George; Okada, Kazunori

    2012-02-01

    This paper presents a feature-based image registration framework which exploits a novel machine learning (ML)-based interest point detection (IPD) algorithm for feature selection and correspondence detection. We use a feed-forward neural network (NN) with back-propagation as our base ML detector. Literature on ML-based IPD is scarce and to our best knowledge no previous research has addressed feature selection strategy for IPD purpose with cross-validation (CV) detectability measure. Our target application is the registration of clinical abdominal CT scans with abnormal anatomies. We evaluated the correspondence detection performance of the proposed ML-based detector against two well-known IPD algorithms: SIFT and SURF. The proposed method is capable of performing affine rigid registrations of 2D and 3D CT images, demonstrating more than two times better accuracy in correspondence detection than SIFT and SURF. The registration accuracy has been validated manually using identified landmark points. Our experimental results shows an improvement in 3D image registration quality of 18.92% compared with affine transformation image registration method from standard ITK affine registration toolkit.

  15. An object-oriented framework for medical image registration, fusion, and visualization.

    PubMed

    Zhu, Yang-Ming; Cochoff, Steven M

    2006-06-01

    An object-oriented framework for image registration, fusion, and visualization was developed based on the classic model-view-controller paradigm. The framework employs many design patterns to facilitate legacy code reuse, manage software complexity, and enhance the maintainability and portability of the framework. Three sample applications built a-top of this framework are illustrated to show the effectiveness of this framework: the first one is for volume image grouping and re-sampling, the second one is for 2D registration and fusion, and the last one is for visualization of single images as well as registered volume images.

  16. High-speed registration of three- and four-dimensional medical images by using voxel similarity.

    PubMed

    Shekhar, Raj; Zagrodsky, Vladimir; Castro-Pareja, Carlos R; Walimbe, Vivek; Jagadeesh, Jogikal M

    2003-01-01

    A generalized, accurate, automatic, retrospective method of image registration for three-dimensional images has been developed. The method is based on mutual information, a specific measure of voxel similarity, and is applicable to a wide range of imaging modalities and organs, rigid or deformable. A drawback of mutual information-based image registration is long execution times. To overcome the speed problem, low-cost, customized hardware to accelerate this computationally intensive task was developed. Individual hardware accelerator units (each, in principle, 25-fold faster than a comparable software implementation) can be concatenated to perform image registration at any user-desired speed. A first-generation prototype board with two processing units provided a 12- to 16-fold increase in speed. Enhancements for increasing the speed further are being developed. These advances have enabled many nontraditional applications of image registration and have made the traditional applications more efficient. Clinical applications include fusion of computed tomographic (CT), magnetic resonance, and positron emission tomographic (PET) images of the brain; fusion of whole-body CT and PET images; fusion of four-dimensional spatiotemporal ultrasonographic (US) and single photon emission CT images of the heart; and correction of misalignment between pre- and poststress four-dimensional US images. Copyright RSNA, 2003

  17. SU-E-J-137: Image Registration Tool for Patient Setup in Korea Heavy Ion Medical Accelerator Center

    SciTech Connect

    Kim, M; Suh, T; Cho, W; Jung, W

    2015-06-15

    Purpose: A potential validation tool for compensating patient positioning error was developed using 2D/3D and 3D/3D image registration. Methods: For 2D/3D registration, digitally reconstructed radiography (DRR) and three-dimensional computed tomography (3D-CT) images were applied. The ray-casting algorithm is the most straightforward method for generating DRR. We adopted the traditional ray-casting method, which finds the intersections of a ray with all objects, voxels of the 3D-CT volume in the scene. The similarity between the extracted DRR and orthogonal image was measured by using a normalized mutual information method. Two orthogonal images were acquired from a Cyber-Knife system from the anterior-posterior (AP) and right lateral (RL) views. The 3D-CT and two orthogonal images of an anthropomorphic phantom and head and neck cancer patient were used in this study. For 3D/3D registration, planning CT and in-room CT image were applied. After registration, the translation and rotation factors were calculated to position a couch to be movable in six dimensions. Results: Registration accuracies and average errors of 2.12 mm ± 0.50 mm for transformations and 1.23° ± 0.40° for rotations were acquired by 2D/3D registration using an anthropomorphic Alderson-Rando phantom. In addition, registration accuracies and average errors of 0.90 mm ± 0.30 mm for transformations and 1.00° ± 0.2° for rotations were acquired using CT image sets. Conclusion: We demonstrated that this validation tool could compensate for patient positioning error. In addition, this research could be the fundamental step for compensating patient positioning error at the first Korea heavy-ion medical accelerator treatment center.

  18. A faster method for 3D/2D medical image registration--a simulation study.

    PubMed

    Birkfellner, Wolfgang; Wirth, Joachim; Burgstaller, Wolfgang; Baumann, Bernard; Staedele, Harald; Hammer, Beat; Gellrich, Niels Claudius; Jacob, Augustinus Ludwig; Regazzoni, Pietro; Messmer, Peter

    2003-08-21

    3D/2D patient-to-computed-tomography (CT) registration is a method to determine a transformation that maps two coordinate systems by comparing a projection image rendered from CT to a real projection image. Iterative variation of the CT's position between rendering steps finally leads to exact registration. Applications include exact patient positioning in radiation therapy, calibration of surgical robots, and pose estimation in computer-aided surgery. One of the problems associated with 3D/2D registration is the fact that finding a registration includes solving a minimization problem in six degrees of freedom (dof) in motion. This results in considerable time requirements since for each iteration step at least one volume rendering has to be computed. We show that by choosing an appropriate world coordinate system and by applying a 2D/2D registration method in each iteration step, the number of iterations can be grossly reduced from n6 to n5. Here, n is the number of discrete variations around a given coordinate. Depending on the configuration of the optimization algorithm, this reduces the total number of iterations necessary to at least 1/3 of it's original value. The method was implemented and extensively tested on simulated x-ray images of a tibia, a pelvis and a skull base. When using one projective image and a discrete full parameter space search for solving the optimization problem, average accuracy was found to be 1.0 +/- 0.6(degrees) and 4.1 +/- 1.9 (mm) for a registration in six parameters, and 1.0 +/- 0.7(degrees) and 4.2 +/- 1.6 (mm) when using the 5 + 1 dof method described in this paper. Time requirements were reduced by a factor 3.1. We conclude that this hardware-independent optimization of 3D/2D registration is a step towards increasing the acceptance of this promising method for a wide number of clinical applications.

  19. ACIR: automatic cochlea image registration

    NASA Astrophysics Data System (ADS)

    Al-Dhamari, Ibraheem; Bauer, Sabine; Paulus, Dietrich; Lissek, Friedrich; Jacob, Roland

    2017-02-01

    Efficient Cochlear Implant (CI) surgery requires prior knowledge of the cochlea's size and its characteristics. This information helps to select suitable implants for different patients. To get these measurements, a segmentation method of cochlea medical images is needed. An important pre-processing step for good cochlea segmentation involves efficient image registration. The cochlea's small size and complex structure, in addition to the different resolutions and head positions during imaging, reveals a big challenge for the automated registration of the different image modalities. In this paper, an Automatic Cochlea Image Registration (ACIR) method for multi- modal human cochlea images is proposed. This method is based on using small areas that have clear structures from both input images instead of registering the complete image. It uses the Adaptive Stochastic Gradient Descent Optimizer (ASGD) and Mattes's Mutual Information metric (MMI) to estimate 3D rigid transform parameters. The use of state of the art medical image registration optimizers published over the last two years are studied and compared quantitatively using the standard Dice Similarity Coefficient (DSC). ACIR requires only 4.86 seconds on average to align cochlea images automatically and to put all the modalities in the same spatial locations without human interference. The source code is based on the tool elastix and is provided for free as a 3D Slicer plugin. Another contribution of this work is a proposed public cochlea standard dataset which can be downloaded for free from a public XNAT server.

  20. Nonrigid registration of dynamic medical imaging data using nD + t B-splines and a groupwise optimization approach.

    PubMed

    Metz, C T; Klein, S; Schaap, M; van Walsum, T; Niessen, W J

    2011-04-01

    A registration method for motion estimation in dynamic medical imaging data is proposed. Registration is performed directly on the dynamic image, thus avoiding a bias towards a specifically chosen reference time point. Both spatial and temporal smoothness of the transformations are taken into account. Optionally, cyclic motion can be imposed, which can be useful for visualization (viewing the segmentation sequentially) or model building purposes. The method is based on a 3D (2D+time) or 4D (3D+time) free-form B-spline deformation model, a similarity metric that minimizes the intensity variances over time and constrained optimization using a stochastic gradient descent method with adaptive step size estimation. The method was quantitatively compared with existing registration techniques on synthetic data and 3D+t computed tomography data of the lungs. This showed subvoxel accuracy while delivering smooth transformations, and high consistency of the registration results. Furthermore, the accuracy of semi-automatic derivation of left ventricular volume curves from 3D+t computed tomography angiography data of the heart was evaluated. On average, the deviation from the curves derived from the manual annotations was approximately 3%. The potential of the method for other imaging modalities was shown on 2D+t ultrasound and 2D+t magnetic resonance images. The software is publicly available as an extension to the registration package elastix.

  1. Image registration by parts

    NASA Technical Reports Server (NTRS)

    Chalermwat, Prachya; El-Ghazawi, Tarek; LeMoigne, Jacqueline

    1997-01-01

    In spite of the large number of different image registration techniques, most of these techniques use the correlation operation to match spatial image characteristics. Correlation is known to be one of the most computationally intensive operations and its computational needs grow rapidly with the increase in the image sizes. In this article, we show that, in many cases, it might be sufficient to determine image transformations by considering only one or several parts of the image rather than the entire image, which could result in substantial computational savings. This paper introduces the concept of registration by parts and investigates its viability. It describes alternative techniques for such image registration by parts and presents early empirical results that address the underlying trade-offs.

  2. SU-E-J-91: FFT Based Medical Image Registration Using a Graphics Processing Unit (GPU).

    PubMed

    Luce, J; Hoggarth, M; Lin, J; Block, A; Roeske, J

    2012-06-01

    To evaluate the efficiency gains obtained from using a Graphics Processing Unit (GPU) to perform a Fourier Transform (FT) based image registration. Fourier-based image registration involves obtaining the FT of the component images, and analyzing them in Fourier space to determine the translations and rotations of one image set relative to another. An important property of FT registration is that by enlarging the images (adding additional pixels), one can obtain translations and rotations with sub-pixel resolution. The expense, however, is an increased computational time. GPUs may decrease the computational time associated with FT image registration by taking advantage of their parallel architecture to perform matrix computations much more efficiently than a Central Processor Unit (CPU). In order to evaluate the computational gains produced by a GPU, images with known translational shifts were utilized. A program was written in the Interactive Data Language (IDL; Exelis, Boulder, CO) to performCPU-based calculations. Subsequently, the program was modified using GPU bindings (Tech-X, Boulder, CO) to perform GPU-based computation on the same system. Multiple image sizes were used, ranging from 256×256 to 2304×2304. The time required to complete the full algorithm by the CPU and GPU were benchmarked and the speed increase was defined as the ratio of the CPU-to-GPU computational time. The ratio of the CPU-to- GPU time was greater than 1.0 for all images, which indicates the GPU is performing the algorithm faster than the CPU. The smallest improvement, a 1.21 ratio, was found with the smallest image size of 256×256, and the largest speedup, a 4.25 ratio, was observed with the largest image size of 2304×2304. GPU programming resulted in a significant decrease in computational time associated with a FT image registration algorithm. The inclusion of the GPU may provide near real-time, sub-pixel registration capability. © 2012 American Association of Physicists in

  3. Geometry-based vs. intensity-based medical image registration: A comparative study on 3D CT data.

    PubMed

    Savva, Antonis D; Economopoulos, Theodore L; Matsopoulos, George K

    2016-02-01

    Spatial alignment of Computed Tomography (CT) data sets is often required in numerous medical applications and it is usually achieved by applying conventional exhaustive registration techniques, which are mainly based on the intensity of the subject data sets. Those techniques consider the full range of data points composing the data, thus negatively affecting the required processing time. Alternatively, alignment can be performed using the correspondence of extracted data points from both sets. Moreover, various geometrical characteristics of those data points can be used, instead of their chromatic properties, for uniquely characterizing each point, by forming a specific geometrical descriptor. This paper presents a comparative study reviewing variations of geometry-based, descriptor-oriented registration techniques, as well as conventional, exhaustive, intensity-based methods for aligning three-dimensional (3D) CT data pairs. In this context, three general image registration frameworks were examined: a geometry-based methodology featuring three distinct geometrical descriptors, an intensity-based methodology using three different similarity metrics, as well as the commonly used Iterative Closest Point algorithm. All techniques were applied on a total of thirty 3D CT data pairs with both known and unknown initial spatial differences. After an extensive qualitative and quantitative assessment, it was concluded that the proposed geometry-based registration framework performed similarly to the examined exhaustive registration techniques. In addition, geometry-based methods dramatically improved processing time over conventional exhaustive registration. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Three-Dimensional Medical Image Registration Using a Patient Space Correlation Technique

    DTIC Science & Technology

    1991-12-01

    to which a tumor has invaded a particular portion of the brain , or when assessing stroke induced cerebral damage (6). A variation of this method...Pelizzari, C . A. et al. "Accurate Three-Dimensional Registration of CT, PET and/or MR Images of the Brain ," J. Computer Assisted Tomography, 13(1):20-26...Lothar R. et al. "Three Dimensional inage Correlation of CT, MR, and PET Studies in Radiotherapy Treatment Planning of Brain Tumors ," J. Computer

  5. Nonrigid Medical Image Registration by Finite-Element Deformable Sheet-Curve Models

    PubMed Central

    Wang, Yue; Freedman, Matthew T.; Adali, Tulay; Shields, Peter

    2006-01-01

    Image-based change quantitation has been recognized as a promising tool for accurate assessment of tumor's early response to chemoprevention in cancer research. For example, various changes on breast density and vascularity in glandular tissue are the indicators of early response to treatment. Accurate extraction of glandular tissue from pre- and postcontrast magnetic resonance (MR) images requires a nonrigid registration of sequential MR images embedded with local deformations. This paper reports a newly developed registration method that aligns MR breast images using finite-element deformable sheet-curve models. Specifically, deformable curves are constructed to match the boundaries dynamically, while a deformable sheet of thin-plate splines is designed to model complex local deformations. The experimental results on both digital phantoms and real MR breast images using the new method have been compared to point-based thin-plate-spline (TPS) approach, and have demonstrated a significant and robust improvement in both boundary alignment and local deformation recovery. PMID:23165046

  6. Analysis of the setup errors of medical image registration-based cone-beam CT for lung cancer.

    PubMed

    Li, Jun; Tang, Xiao-Bin; Zhang, Xi-Zhi; Zhang, Xian-Wen; Ge, Yun; Chen, Da; Chai, Lei

    2016-04-07

    This study aimed to investigate the feasibility of efficiently using a rigid image registration (RIR) algorithm or a deformable image registration (DIR) algorithm to match medical images and evaluate the impact of setup errors on intensity modulated radiation therapy of lung cancer patients. Ten lung cancer patients were chosen randomly each day and were subjected to image-guided radiotherapy. The clinical registration between cone-beam computed tomography (CBCT) images and treatment planning system CT images was performed by applying both RIR and DIR; the clinical registration was evaluated on the basis of the contour index, including dice similarity coefficient, sensitivity, and positive predictive value; the optimal scheme of image registration was selected to ensure that the actual irradiation isocenter was consistent with the treatment planning isocenter. In each patient, the translational errors in the right-left (x), superior-inferior (y), and anterior-posterior (z) directions and the rotational errors in the u, υ, and w directions formed by the x, y, and z directions were calculated and analyzed daily in the whole course of treatment; margins were calculated according to this equation: M = 2.5∑+ 0.7δ. The tumors and the surrounding soft tissues of the patients are shown more clearly in the CBCT images than in the CT images. DIR can be applied more efficiently than RIR to determine the morphological and positional changes in the organs shown in the images with the same or different modalities in the different period. The setup errors in translation in the x, y and z axes were 0.05±0.16, 0.09±0.32 and -0.02±0.13 cm, respectively; by contrast, the setup errors in rotation in u, υ and w directions were (0.41±0.64)°, (-0.08±0.57)° and (-0.03±0.62)°, respectively. The setup errors in the x, y and z axes of the patients indicated that the margins expansions were 0.82, 1.15 and 0.72 cm, respectively. CBCT with DIR can measure and correct the

  7. Spacecraft camera image registration

    NASA Technical Reports Server (NTRS)

    Kamel, Ahmed A. (Inventor); Graul, Donald W. (Inventor); Chan, Fred N. T. (Inventor); Gamble, Donald W. (Inventor)

    1987-01-01

    A system for achieving spacecraft camera (1, 2) image registration comprises a portion external to the spacecraft and an image motion compensation system (IMCS) portion onboard the spacecraft. Within the IMCS, a computer (38) calculates an image registration compensation signal (60) which is sent to the scan control loops (84, 88, 94, 98) of the onboard cameras (1, 2). At the location external to the spacecraft, the long-term orbital and attitude perturbations on the spacecraft are modeled. Coefficients (K, A) from this model are periodically sent to the onboard computer (38) by means of a command unit (39). The coefficients (K, A) take into account observations of stars and landmarks made by the spacecraft cameras (1, 2) themselves. The computer (38) takes as inputs the updated coefficients (K, A) plus synchronization information indicating the mirror position (AZ, EL) of each of the spacecraft cameras (1, 2), operating mode, and starting and stopping status of the scan lines generated by these cameras (1, 2), and generates in response thereto the image registration compensation signal (60). The sources of periodic thermal errors on the spacecraft are discussed. The system is checked by calculating measurement residuals, the difference between the landmark and star locations predicted at the external location and the landmark and star locations as measured by the spacecraft cameras (1, 2).

  8. Image Registration for Stability Testing of MEMS

    NASA Technical Reports Server (NTRS)

    Memarsadeghi, Nargess; LeMoigne, Jacqueline; Blake, Peter N.; Morey, Peter A.; Landsman, Wayne B.; Chambers, Victor J.; Moseley, Samuel H.

    2011-01-01

    Image registration, or alignment of two or more images covering the same scenes or objects, is of great interest in many disciplines such as remote sensing, medical imaging. astronomy, and computer vision. In this paper, we introduce a new application of image registration algorithms. We demonstrate how through a wavelet based image registration algorithm, engineers can evaluate stability of Micro-Electro-Mechanical Systems (MEMS). In particular, we applied image registration algorithms to assess alignment stability of the MicroShutters Subsystem (MSS) of the Near Infrared Spectrograph (NIRSpec) instrument of the James Webb Space Telescope (JWST). This work introduces a new methodology for evaluating stability of MEMS devices to engineers as well as a new application of image registration algorithms to computer scientists.

  9. Image Registration for Stability Testing of MEMS

    NASA Technical Reports Server (NTRS)

    Memarsadeghi, Nargess; LeMoigne, Jacqueline; Blake, Peter N.; Morey, Peter A.; Landsman, Wayne B.; Chambers, Victor J.; Moseley, Samuel H.

    2011-01-01

    Image registration, or alignment of two or more images covering the same scenes or objects, is of great interest in many disciplines such as remote sensing, medical imaging. astronomy, and computer vision. In this paper, we introduce a new application of image registration algorithms. We demonstrate how through a wavelet based image registration algorithm, engineers can evaluate stability of Micro-Electro-Mechanical Systems (MEMS). In particular, we applied image registration algorithms to assess alignment stability of the MicroShutters Subsystem (MSS) of the Near Infrared Spectrograph (NIRSpec) instrument of the James Webb Space Telescope (JWST). This work introduces a new methodology for evaluating stability of MEMS devices to engineers as well as a new application of image registration algorithms to computer scientists.

  10. Asymmetric Image-Template Registration

    PubMed Central

    Sabuncu, Mert R.; Yeo, B.T. Thomas; Van Leemput, Koen; Vercauteren, Tom; Golland, Polina

    2010-01-01

    A natural requirement in pairwise image registration is that the resulting deformation is independent of the order of the images. This constraint is typically achieved via a symmetric cost function and has been shown to reduce the effects of local optima. Consequently, symmetric registration has been successfully applied to pairwise image registration as well as the spatial alignment of individual images with a template. However, recent work has shown that the relationship between an image and a template is fundamentally asymmetric. In this paper, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. PMID:20426033

  11. Image registration with uncertainty analysis

    DOEpatents

    Simonson, Katherine M [Cedar Crest, NM

    2011-03-22

    In an image registration method, edges are detected in a first image and a second image. A percentage of edge pixels in a subset of the second image that are also edges in the first image shifted by a translation is calculated. A best registration point is calculated based on a maximum percentage of edges matched. In a predefined search region, all registration points other than the best registration point are identified that are not significantly worse than the best registration point according to a predetermined statistical criterion.

  12. Bayesian technique for image classifying registration.

    PubMed

    Hachama, Mohamed; Desolneux, Agnès; Richard, Frédéric J P

    2012-09-01

    In this paper, we address a complex image registration issue arising while the dependencies between intensities of images to be registered are not spatially homogeneous. Such a situation is frequently encountered in medical imaging when a pathology present in one of the images modifies locally intensity dependencies observed on normal tissues. Usual image registration models, which are based on a single global intensity similarity criterion, fail to register such images, as they are blind to local deviations of intensity dependencies. Such a limitation is also encountered in contrast-enhanced images where there exist multiple pixel classes having different properties of contrast agent absorption. In this paper, we propose a new model in which the similarity criterion is adapted locally to images by classification of image intensity dependencies. Defined in a Bayesian framework, the similarity criterion is a mixture of probability distributions describing dependencies on two classes. The model also includes a class map which locates pixels of the two classes and weighs the two mixture components. The registration problem is formulated both as an energy minimization problem and as a maximum a posteriori estimation problem. It is solved using a gradient descent algorithm. In the problem formulation and resolution, the image deformation and the class map are estimated simultaneously, leading to an original combination of registration and classification that we call image classifying registration. Whenever sufficient information about class location is available in applications, the registration can also be performed on its own by fixing a given class map. Finally, we illustrate the interest of our model on two real applications from medical imaging: template-based segmentation of contrast-enhanced images and lesion detection in mammograms. We also conduct an evaluation of our model on simulated medical data and show its ability to take into account spatial variations

  13. Image Registration: A Necessary Evil

    NASA Technical Reports Server (NTRS)

    Bell, James; McLachlan, Blair; Hermstad, Dexter; Trosin, Jeff; George, Michael W. (Technical Monitor)

    1995-01-01

    Registration of test and reference images is a key component of nearly all PSP data reduction techniques. This is done to ensure that a test image pixel viewing a particular point on the model is ratioed by the reference image pixel which views the same point. Typically registration is needed to account for model motion due to differing airloads when the wind-off and wind-on images are taken. Registration is also necessary when two cameras are used for simultaneous acquisition of data from a dual-frequency paint. This presentation will discuss the advantages and disadvantages of several different image registration techniques. In order to do so, it is necessary to propose both an accuracy requirement for image registration and a means for measuring the accuracy of a particular technique. High contrast regions in the unregistered images are most sensitive to registration errors, and it is proposed that these regions be used to establish the error limits for registration. Once this is done, the actual registration error can be determined by locating corresponding points on the test and reference images, and determining how well a particular registration technique matches them. An example of this procedure is shown for three transforms used to register images of a semispan model. Thirty control points were located on the model. A subset of the points were used to determine the coefficients of each registration transform, and the error with which each transform aligned the remaining points was determined. The results indicate the general superiority of a third-order polynomial over other candidate transforms, as well as showing how registration accuracy varies with number of control points. Finally, it is proposed that image registration may eventually be done away with completely. As more accurate image resection techniques and more detailed model surface grids become available, it will be possible to map raw image data onto the model surface accurately. Intensity

  14. SU-E-I-23: Design and Clinical Application of External Marking Body in Multi- Mode Medical Images Registration and Fusion

    SciTech Connect

    Chen, Z; Gong, G

    2014-06-01

    Purpose: To design an external marking body (EMB) that could be visible on computed tomography (CT), magnetic resonance (MR), positron emission tomography (PET) and single-photon emission computed tomography (SPECT) images and to investigate the use of the EMB for multiple medical images registration and fusion in the clinic. Methods: We generated a solution containing paramagnetic metal ions and iodide ions (CT'MR dual-visible solution) that could be viewed on CT and MR images and multi-mode image visible solution (MIVS) that could be obtained by mixing radioactive nuclear material. A globular plastic theca (diameter: 3–6 mm) that mothball the MIVS and the EMB was brought by filling MIVS. The EMBs were fixed on the patient surface and CT, MR, PET and SPECT scans were obtained. The feasibility of clinical application and the display and registration error of EMB among different image modalities were investigated. Results: The dual-visible solution was highly dense on CT images (HU>700). A high signal was also found in all MR scanning (T1, T2, STIR and FLAIR) images, and the signal was higher than subcutaneous fat. EMB with radioactive nuclear material caused a radionuclide concentration area on PET and SPECT images, and the signal of EMB was similar to or higher than tumor signals. The theca with MIVS was clearly visible on all the images without artifact, and the shape was round or oval with a sharp edge. The maximum diameter display error was 0.3 ± 0.2mm on CT and MRI images, and 1.0 ± 0.3mm on PET and SPECT images. In addition, the registration accuracy of the theca center among multi-mode images was less than 1mm. Conclusion: The application of EMB with MIVS improves the registration and fusion accuracy of multi-mode medical images. Furthermore, it has the potential to ameliorate disease diagnosis and treatment outcome.

  15. Multi-modal image registration using structural features.

    PubMed

    Kasiri, Keyvan; Clausi, David A; Fieguth, Paul

    2014-01-01

    Multi-modal image registration has been a challenging task in medical images because of the complex intensity relationship between images to be aligned. Registration methods often rely on the statistical intensity relationship between the images which suffers from problems such as statistical insufficiency. The proposed registration method works based on extracting structural features by utilizing the complex phase and gradient-based information. By employing structural relationships between different modalities instead of complex similarity measures, the multi-modal registration problem is converted into a mono-modal one. Therefore, conventional mono-modal similarity measures can be utilized to evaluate the registration results. This new registration paradigm has been tested on magnetic resonance (MR) brain images of different modes. The method has been evaluated based on target registration error (TRE) to determine alignment accuracy. Quantitative results demonstrate that the proposed method is capable of achieving comparable registration accuracy compared to the conventional mutual information.

  16. Reflectance and fluorescence hyperspectral elastic image registration

    NASA Astrophysics Data System (ADS)

    Lange, Holger; Baker, Ross; Hakansson, Johan; Gustafsson, Ulf P.

    2004-05-01

    Science and Technology International (STI) presents a novel multi-modal elastic image registration approach for a new hyperspectral medical imaging modality. STI's HyperSpectral Diagnostic Imaging (HSDI) cervical instrument is used for the early detection of uterine cervical cancer. A Computer-Aided-Diagnostic (CAD) system is being developed to aid the physician with the diagnosis of pre-cancerous and cancerous tissue regions. The CAD system uses the fusion of multiple data sources to optimize its performance. The key enabling technology for the data fusion is image registration. The difficulty lies in the image registration of fluorescence and reflectance hyperspectral data due to the occurrence of soft tissue movement and the limited resemblance of these types of imagery. The presented approach is based on embedding a reflectance image in the fluorescence hyperspectral imagery. Having a reflectance image in both data sets resolves the resemblance problem and thereby enables the use of elastic image registration algorithms required to compensate for soft tissue movements. Several methods of embedding the reflectance image in the fluorescence hyperspectral imagery are described. Initial experiments with human subject data are presented where a reflectance image is embedded in the fluorescence hyperspectral imagery.

  17. Registration of interferometric SAR images

    NASA Technical Reports Server (NTRS)

    Lin, Qian; Vesecky, John F.; Zebker, Howard A.

    1992-01-01

    Interferometric synthetic aperture radar (INSAR) is a new way of performing topography mapping. Among the factors critical to mapping accuracy is the registration of the complex SAR images from repeated orbits. A new algorithm for registering interferometric SAR images is presented. A new figure of merit, the average fluctuation function of the phase difference image, is proposed to evaluate the fringe pattern quality. The process of adjusting the registration parameters according to the fringe pattern quality is optimized through a downhill simplex minimization algorithm. The results of applying the proposed algorithm to register two pairs of Seasat SAR images with a short baseline (75 m) and a long baseline (500 m) are shown. It is found that the average fluctuation function is a very stable measure of fringe pattern quality allowing very accurate registration.

  18. Development of an Amendment to X3D to Create a Standard Specification of Medical Image Volume Rendering, Segmentation, and Registration

    DTIC Science & Technology

    2006-11-01

    medical imaging data. Extensible 3D (X3D) is an International Standards Organization (ISO) ratified, freely available standard that defines a runtime system and delivery mechanism for 3D graphics on the World Wide Web. The Web3D Consortium, which administers X3D, has developed a draft extension to X3D for a volume rendering, registration and segmentation component to define a file format...of 3D medical imaging data. A formal ISO working project has been initiated to begin the process of ISO ratification of this

  19. Multi-modality image registration using the decomposition model

    NASA Astrophysics Data System (ADS)

    Ibrahim, Mazlinda; Chen, Ke

    2017-04-01

    In medical image analysis, image registration is one of the crucial steps required to facilitate automatic segmentation, treatment planning and other application involving imaging machines. Image registration, also known as image matching, aims to align two or more images so that information obtained can be compared and combined. Different imaging modalities and their characteristics make the task more challenging. We propose a decomposition model combining parametric and non-parametric deformation for multi-modality image registration. Numerical results show that the normalised gradient field perform better than the mutual information with the decomposition model.

  20. Registration Of SAR Images With Multisensor Images

    NASA Technical Reports Server (NTRS)

    Evans, Diane L.; Burnette, Charles F.; Van Zyl, Jakob J.

    1993-01-01

    Semiautomated technique intended primarily to facilitate registration of polarimetric synthetic-aperture-radar (SAR) images with other images of same or partly overlapping terrain while preserving polarization information conveyed by SAR data. Technique generally applicable in sense one or both of images to be registered with each other generated by polarimetric or nonpolarimetric SAR, infrared radiometry, conventional photography, or any other applicable sensing method.

  1. Supporting registration decisions during 3D medical volume reconstructions

    NASA Astrophysics Data System (ADS)

    Bajcsy, Peter; Lee, Sang-Chul; Clutter, David

    2006-03-01

    We propose a methodology for making optimal registration decisions during 3D volume reconstruction in terms of (a) anticipated accuracy of aligned images, (b) uncertainty of obtained results during the registration process, (c) algorithmic repeatability of alignment procedure, and (d) computational requirements. We researched and developed a web-enabled, web services based, data-driven, registration decision support system. The registration decisions include (1) image spatial size (image sub-area or entire image), (2) transformation model (e.g., rigid, affine or elastic), (3) invariant registration feature (intensity, morphology or a sequential combination of the two), (4) automation level (manual, semi-automated, or fully-automated), (5) evaluations of registration results (multiple metrics and methods for establishing ground truth), and (6) assessment of resources (computational resources and human expertise, geographically local or distributed). Our goal is to provide mechanisms for evaluating the tradeoffs of each registration decision in terms of the aforementioned impacts. First, we present a medical registration methodology for making registration decisions that lead to registration results with well-understood accuracy, uncertainty, consistency and computational complexity characteristics. Second, we have built software tools that enable geographically distributed researchers to optimize their data-driven registration decisions by using web services and supercomputing resources. The support developed for registration decisions about 3D volume reconstruction is available to the general community with the access to the NCSA supercomputing resources. We illustrate performance by considering 3D volume reconstruction of blood vessels in histological sections of uveal melanoma from serial fluorescent labeled paraffin sections labeled with antibodies to CD34 and laminin. The specimens are studied by fluorescence confocal laser scanning microscopy (CLSM) images.

  2. Video Image Stabilization and Registration (VISAR) Software

    NASA Technical Reports Server (NTRS)

    1999-01-01

    Two scientists at NASA Marshall Space Flight Center, atmospheric scientist Paul Meyer (left) and solar physicist Dr. David Hathaway, have developed promising new software, called Video Image Stabilization and Registration (VISAR), that may help law enforcement agencies to catch criminals by improving the quality of video recorded at crime scenes, VISAR stabilizes camera motion in the horizontal and vertical as well as rotation and zoom effects; produces clearer images of moving objects; smoothes jagged edges; enhances still images; and reduces video noise of snow. VISAR could also have applications in medical and meteorological imaging. It could steady images of Ultrasounds which are infamous for their grainy, blurred quality. It would be especially useful for tornadoes, tracking whirling objects and helping to determine the tornado's wind speed. This image shows two scientists reviewing an enhanced video image of a license plate taken from a moving automobile.

  3. Video Image Stabilization and Registration (VISAR) Software

    NASA Technical Reports Server (NTRS)

    1999-01-01

    Two scientists at NASA Marshall Space Flight Center, atmospheric scientist Paul Meyer (left) and solar physicist Dr. David Hathaway, have developed promising new software, called Video Image Stabilization and Registration (VISAR), that may help law enforcement agencies to catch criminals by improving the quality of video recorded at crime scenes, VISAR stabilizes camera motion in the horizontal and vertical as well as rotation and zoom effects; produces clearer images of moving objects; smoothes jagged edges; enhances still images; and reduces video noise of snow. VISAR could also have applications in medical and meteorological imaging. It could steady images of Ultrasounds which are infamous for their grainy, blurred quality. It would be especially useful for tornadoes, tracking whirling objects and helping to determine the tornado's wind speed. This image shows two scientists reviewing an enhanced video image of a license plate taken from a moving automobile.

  4. Deformable medical image registration of pleural cavity for photodynamic therapy by using finite-element based method.

    PubMed

    Penjweini, Rozhin; Kim, Michele M; Dimofte, Andrea; Finlay, Jarod C; Zhu, Timothy C

    2016-03-07

    When the pleural cavity is opened during the surgery portion of pleural photodynamic therapy (PDT) of malignant mesothelioma, the pleural volume will deform. This impacts the delivered dose when using highly conformal treatment techniques. To track the anatomical changes and contour the lung and chest cavity, an infrared camera-based navigation system (NDI) is used during PDT. In the same patient, a series of computed tomography (CT) scans of the lungs are also acquired before the surgery. The reconstructed three-dimensional contours from both NDI and CTs are imported into COMSOL Multiphysics software, where a finite element-based (FEM) deformable image registration is obtained. The CT contour is registered to the corresponding NDI contour by overlapping the center of masses and aligning their orientations. The NDI contour is considered as the reference contour, and the CT contour is used as the target one, which will be deformed. Deformed Geometry model is applied in COMSOL to obtain a deformed target contour. The distortion of the volume at X, Y and Z is mapped to illustrate the transformation of the target contour. The initial assessment shows that FEM-based image deformable registration can fuse images acquired by different modalities. It provides insights into the deformation of anatomical structures along X, Y and Z-axes. The deformed contour has good matches to the reference contour after the dynamic matching process. The resulting three-dimensional deformation map can be used to obtain the locations of other critical anatomic structures, e.g., heart, during surgery.

  5. Deformable medical image registration of pleural cavity for photodynamic therapy by using finite-element based method

    PubMed Central

    Penjweini, Rozhin; Kim, Michele M.; Dimofte, Andrea; Finlay, Jarod C; Zhu, Timothy C.

    2016-01-01

    When the pleural cavity is opened during the surgery portion of pleural photodynamic therapy (PDT) of malignant mesothelioma, the pleural volume will deform. This impacts the delivered dose when using highly conformal treatment techniques. To track the anatomical changes and contour the lung and chest cavity, an infrared camera–based navigation system (NDI) is used during PDT. In the same patient, a series of computed tomography (CT) scans of the lungs are also acquired before the surgery. The reconstructed three-dimensional contours from both NDI and CTs are imported into COMSOL Multiphysics software, where a finite element-based (FEM) deformable image registration is obtained. The CT contour is registered to the corresponding NDI contour by overlapping the center of masses and aligning their orientations. The NDI contour is considered as the reference contour, and the CT contour is used as the target one, which will be deformed. Deformed Geometry model is applied in COMSOL to obtain a deformed target contour. The distortion of the volume at X, Y and Z is mapped to illustrate the transformation of the target contour. The initial assessment shows that FEM-based image deformable registration can fuse images acquired by different modalities. It provides insights into the deformation of anatomical structures along X, Y and Z-axes. The deformed contour has good matches to the reference contour after the dynamic matching process. The resulting three-dimensional deformation map can be used to obtain the locations of other critical anatomic structures, e.g., heart, during surgery. PMID:27053826

  6. Deformable medical image registration of pleural cavity for photodynamic therapy by using finite-element based method

    NASA Astrophysics Data System (ADS)

    Penjweini, Rozhin; Kim, Michele M.; Dimofte, Andrea; Finlay, Jarod C.; Zhu, Timothy C.

    2016-03-01

    When the pleural cavity is opened during the surgery portion of pleural photodynamic therapy (PDT) of malignant mesothelioma, the pleural volume will deform. This impacts the delivered dose when using highly conformal treatment techniques. To track the anatomical changes and contour the lung and chest cavity, an infrared camera-based navigation system (NDI) is used during PDT. In the same patient, a series of computed tomography (CT) scans of the lungs are also acquired before the surgery. The reconstructed three-dimensional contours from both NDI and CTs are imported into COMSOL Multiphysics software, where a finite element-based (FEM) deformable image registration is obtained. The CT contour is registered to the corresponding NDI contour by overlapping the center of masses and aligning their orientations. The NDI contour is considered as the reference contour, and the CT contour is used as the target one, which will be deformed. Deformed Geometry model is applied in COMSOL to obtain a deformed target contour. The distortion of the volume at X, Y and Z is mapped to illustrate the transformation of the target contour. The initial assessment shows that FEM-based image deformable registration can fuse images acquired by different modalities. It provides insights into the deformation of anatomical structures along X, Y and Z-axes. The deformed contour has good matches to the reference contour after the dynamic matching process. The resulting three-dimensional deformation map can be used to obtain the locations of other critical anatomic structures, e.g., heart, during surgery.

  7. Video Image Stabilization and Registration (VISAR) Software

    NASA Technical Reports Server (NTRS)

    1999-01-01

    Two scientists at NASA's Marshall Space Flight Center, atmospheric scientist Paul Meyer and solar physicist Dr. David Hathaway, developed promising new software, called Video Image Stabilization and Registration (VISAR), which is illustrated in this Quick Time movie. VISAR is a computer algorithm that stabilizes camera motion in the horizontal and vertical as well as rotation and zoom effects producing clearer images of moving objects, smoothes jagged edges, enhances still images, and reduces video noise or snow. It could steady images of ultrasounds, which are infamous for their grainy, blurred quality. VISAR could also have applications in law enforcement, medical, and meteorological imaging. The software can be used for defense application by improving reconnaissance video imagery made by military vehicles, aircraft, and ships traveling in harsh, rugged environments.

  8. Video Image Stabilization and Registration (VISAR) Software

    NASA Technical Reports Server (NTRS)

    1999-01-01

    Two scientists at NASA's Marshall Space Flight Center,atmospheric scientist Paul Meyer and solar physicist Dr. David Hathaway, developed promising new software, called Video Image stabilization and Registration (VISAR), which is illustrated in this Quick Time movie. VISAR is a computer algorithm that stabilizes camera motion in the horizontal and vertical as well as rotation and zoom effects producing clearer images of moving objects, smoothes jagged edges, enhances still images, and reduces video noise or snow. It could steady images of ultrasounds, which are infamous for their grainy, blurred quality. VISAR could also have applications in law enforcement, medical, and meteorological imaging. The software can be used for defense application by improving reconnaissance video imagery made by military vehicles, aircraft, and ships traveling in harsh, rugged environments.

  9. Video Image Stabilization and Registration (VISAR) Software

    NASA Technical Reports Server (NTRS)

    1999-01-01

    Two scientists at NASA's Marshall Space Flight Center, atmospheric scientist Paul Meyer and solar physicist Dr. David Hathaway, developed promising new software, called Video Image Stabilization and Registration (VISAR), which is illustrated in this Quick Time movie. VISAR is a computer algorithm that stabilizes camera motion in the horizontal and vertical as well as rotation and zoom effects producing clearer images of moving objects, smoothes jagged edges, enhances still images, and reduces video noise or snow. It could steady images of ultrasounds, which are infamous for their grainy, blurred quality. VISAR could also have applications in law enforcement, medical, and meteorological imaging. The software can be used for defense application by improving reconnaissance video imagery made by military vehicles, aircraft, and ships traveling in harsh, rugged environments.

  10. Video Image Stabilization and Registration (VISAR) Software

    NASA Technical Reports Server (NTRS)

    1999-01-01

    Two scientists at NASA's Marshall Space Flight Center,atmospheric scientist Paul Meyer and solar physicist Dr. David Hathaway, developed promising new software, called Video Image stabilization and Registration (VISAR), which is illustrated in this Quick Time movie. VISAR is a computer algorithm that stabilizes camera motion in the horizontal and vertical as well as rotation and zoom effects producing clearer images of moving objects, smoothes jagged edges, enhances still images, and reduces video noise or snow. It could steady images of ultrasounds, which are infamous for their grainy, blurred quality. VISAR could also have applications in law enforcement, medical, and meteorological imaging. The software can be used for defense application by improving reconnaissance video imagery made by military vehicles, aircraft, and ships traveling in harsh, rugged environments.

  11. "Nonparametric Local Smoothing" is not image registration.

    PubMed

    Rohlfing, Torsten; Avants, Brian

    2012-11-01

    Image registration is one of the most important and universally useful computational tasks in biomedical image analysis. A recent article by Xing & Qiu (IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10):2081-2092, 2011) is based on an inappropriately narrow conceptualization of the image registration problem as the task of making two images look alike, which disregards whether the established spatial correspondence is plausible. The authors propose a new algorithm, Nonparametric Local Smoothing (NLS) for image registration, but use image similarities alone as a measure of registration performance, although these measures do not relate reliably to the realism of the correspondence map. Using data obtained from its authors, we show experimentally that the method proposed by Xing & Qiu is not an effective registration algorithm. While it optimizes image similarity, it does not compute accurate, interpretable transformations. Even judged by image similarity alone, the proposed method is consistently outperformed by a simple pixel permutation algorithm, which is known by design not to compute valid registrations. This study has demonstrated that the NLS algorithm proposed recently for image registration, and published in one of the most respected journals in computer science, is not, in fact, an effective registration method at all. Our results also emphasize the general need to apply registration evaluation criteria that are sensitive to whether correspondences are accurate and mappings between images are physically interpretable. These goals cannot be achieved by simply reporting image similarities.

  12. Medical Imaging.

    ERIC Educational Resources Information Center

    Barker, M. C. J.

    1996-01-01

    Discusses four main types of medical imaging (x-ray, radionuclide, ultrasound, and magnetic resonance) and considers their relative merits. Describes important recent and possible future developments in image processing. (Author/MKR)

  13. High-accuracy registration of intraoperative CT imaging

    NASA Astrophysics Data System (ADS)

    Oentoro, A.; Ellis, R. E.

    2010-02-01

    Image-guided interventions using intraoperative 3D imaging can be less cumbersome than systems dependent on preoperative images, especially by needing neither potentially invasive image-to-patient registration nor a lengthy process of segmenting and generating a 3D surface model. In this study, a method for computer-assisted surgery using direct navigation on intraoperative imaging is presented. In this system the registration step of a navigated procedure was divided into two stages: preoperative calibration of images to a ceiling-mounted optical tracking system, and intraoperative tracking during acquisition of the 3D medical image volume. The preoperative stage used a custom-made multi-modal calibrator that could be optically tracked and also contained fiducial spheres for radiological detection; a robust registration algorithm was used to compensate for the very high false-detection rate that was due to the high physical density of the optical light-emitting diodes. Intraoperatively, a tracking device was attached to plastic bone models that were also instrumented with radio-opaque spheres; A calibrated pointer was used to contact the latter spheres as a validation of the registration. Experiments showed that the fiducial registration error of the preoperative calibration stage was approximately 0.1 mm. The target registration error in the validation stage was approximately 1.2 mm. This study suggests that direct registration, coupled with procedure-specific graphical rendering, is potentially a highly accurate means of performing image-guided interventions in a fast, simple manner.

  14. Heuristic approach to image registration

    NASA Astrophysics Data System (ADS)

    Gertner, Izidor; Maslov, Igor V.

    2000-08-01

    Image registration, i.e. correct mapping of images obtained from different sensor readings onto common reference frame, is a critical part of multi-sensor ATR/AOR systems based on readings from different types of sensors. In order to fuse two different sensor readings of the same object, the readings have to be put into a common coordinate system. This task can be formulated as optimization problem in a space of all possible affine transformations of an image. In this paper, a combination of heuristic methods is explored to register gray- scale images. The modification of Genetic Algorithm is used as the first step in global search for optimal transformation. It covers the entire search space with (randomly or heuristically) scattered probe points and helps significantly reduce the search space to a subspace of potentially most successful transformations. Due to its discrete character, however, Genetic Algorithm in general can not converge while coming close to the optimum. Its termination point can be specified either as some predefined number of generations or as achievement of a certain acceptable convergence level. To refine the search, potential optimal subspaces are searched using more delicate and efficient for local search Taboo and Simulated Annealing methods.

  15. Comparison and evaluation of retrospective intermodality image registration techniques

    NASA Astrophysics Data System (ADS)

    West, Jay B.; Fitzpatrick, J. Michael; Wang, Matthew Y.; Dawant, Benoit M.; Maurer, Calvin R., Jr.; Kessler, Robert M.; Maciunas, Robert J.; Barillot, Christian; Lemoine, Didier; Collignon, Andre M. F.; Maes, Frederik; Suetens, Paul; Vandermeulen, Dirk; van den Elsen, Petra A.; Hemler, Paul F.; Napel, Sandy; Sumanaweera, Thilaka S.; Harkness, Beth A.; Hill, Derek L.; Studholme, Colin; Malandain, Gregoire; Pennec, Xavier; Noz, Marilyn E.; Maguire, Gerald Q., Jr.; Pollack, Michael; Pelizzari, Charles A.; Robb, Richard A.; Hanson, Dennis P.; Woods, Roger P.

    1996-04-01

    All retrospective image registration methods have attached to them some intrinsic estimate of registration error. However, this estimate of accuracy may not always be a good indicator of the distance between actual and estimated positions of targets within the cranial cavity. This paper describes a project whose principal goal is to use a prospective method based on fiducial markers as a 'gold standard' to perform an objective, blinded evaluation of the accuracy of several retrospective image-to-image registration techniques. Image volumes of three modalities -- CT, MR, and PET -- were taken of patients undergoing neurosurgery at Vanderbilt University Medical Center. These volumes had all traces of the fiducial markers removed, and were provided to project collaborators outside Vanderbilt, who then performed retrospective registrations on the volumes, calculating transformations from CT to MR and/or from PET to MR, and communicated their transformations to Vanderbilt where the accuracy of each registration was evaluated. In this evaluation the accuracy is measured at multiple 'regions of interest,' i.e. areas in the brain which would commonly be areas of neurological interest. A region is defined in the MR image and its centroid C is determined. Then the prospective registration is used to obtain the corresponding point C' in CT or PET. To this point the retrospective registration is then applied, producing C' in MR. Statistics are gathered on the target registration error (TRE), which is the disparity between the original point C and its corresponding point C'. A second goal of the project is to evaluate the importance of correcting geometrical distortion in MR images, by comparing the retrospective TRE in the rectified images, i.e., those which have had the distortion correction applied, with that of the same images before rectification. This paper presents preliminary results of this study along with a brief description of each registration technique and an

  16. Digital image registration by correlation techniques.

    NASA Technical Reports Server (NTRS)

    Popp, D. J.; Mccormack, D. S.; Lee, G. M.

    1972-01-01

    This study considers the translation problem associated with digital image registration and develops a means for comparing commonly used correlation techniques. Using suitably defined constraints, an optimum and four suboptimum registration techniques are defined and evaluated. A computational comparison is made and Gaussian image statistics are used to compare the selected techniques in terms of radial position location error.

  17. Research relative to automated multisensor image registration

    NASA Technical Reports Server (NTRS)

    Kanal, L. N.

    1983-01-01

    The basic aproaches to image registration are surveyed. Three image models are presented as models of the subpixel problem. A variety of approaches to the analysis of subpixel analysis are presented using these models.

  18. The role of image registration in brain mapping

    PubMed Central

    Toga, A.W.; Thompson, P.M.

    2008-01-01

    Image registration is a key step in a great variety of biomedical imaging applications. It provides the ability to geometrically align one dataset with another, and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or across time. Registration algorithms also enable the pooling and comparison of experimental findings across laboratories, the construction of population-based brain atlases, and the creation of systems to detect group patterns in structural and functional imaging data. We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain. PMID:19890483

  19. The role of image registration in brain mapping.

    PubMed

    Toga, A W; Thompson, P M

    2001-01-01

    Image registration is a key step in a great variety of biomedical imaging applications. It provides the ability to geometrically align one dataset with another, and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or across time. Registration algorithms also enable the pooling and comparison of experimental findings across laboratories, the construction of population-based brain atlases, and the creation of systems to detect group patterns in structural and functional imaging data. We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain.

  20. Enhancing retinal images by nonlinear registration

    NASA Astrophysics Data System (ADS)

    Molodij, G.; Ribak, E. N.; Glanc, M.; Chenegros, G.

    2015-05-01

    Being able to image the human retina in high resolution opens a new era in many important fields, such as pharmacological research for retinal diseases, researches in human cognition, nervous system, metabolism and blood stream, to name a few. In this paper, we propose to share the knowledge acquired in the fields of optics and imaging in solar astrophysics in order to improve the retinal imaging in the perspective to perform a medical diagnosis. The main purpose would be to assist health care practitioners by enhancing the spatial resolution of the retinal images and increase the level of confidence of the abnormal feature detection. We apply a nonlinear registration method using local correlation tracking to increase the field of view and follow structure evolutions using correlation techniques borrowed from solar astronomy technique expertise. Another purpose is to define the tracer of movements after analyzing local correlations to follow the proper motions of an image from one moment to another, such as changes in optical flows that would be of high interest in a medical diagnosis.

  1. Automatic registration and mosaicking of conservation images

    NASA Astrophysics Data System (ADS)

    Conover, Damon M.; Delaney, John K.; Loew, Murray H.

    2013-05-01

    As high-resolution conservation images, acquired using various imaging modalities, become more widely available, it is increasingly important to achieve accurate registration between the images. Accurate registration allows information unavailable in any one image to be compiled from several images and then used to provide a better understanding of how a painting was constructed. We have developed an algorithm that solves several important conservation problems: 1) registration and mosaicking of multiple X-ray films, ultraviolet images, and infrared reflectograms to a color reference image at high spatial-resolution (200 to 500 dpi) of paintings (both panel and canvas) and of works on paper, 2) registration of the images within visible and infrared multispectral reflectance and luminescence image cubes, and 3) mosaicking of hyperspectral image cubes (400 to 2500 nm). The registration/mosaicking algorithm corrects for several kinds of distortion, small rotation and scale errors, and keystone effects between the images. Thus images acquired with different cameras, illumination, and geometries can be registered/mosaicked. This automatic algorithm for registering/mosaicking multimodal conservation images is expected to be a valuable tool for conservators attempting to answer questions regarding the creation and preservation history of paintings. For example, an analysis of the reflectance spectra obtained from the sub-pixel registered multispectral image cubes can be used to separate, map, and identify artist materials in situ. And, by comparing the corresponding images in the X-ray, visible, and infrared regions, conservators can obtain a deeper understanding of compositional changes.

  2. Edge-based correlation image registration for multispectral imaging

    DOEpatents

    Nandy, Prabal

    2009-11-17

    Registration information for images of a common target obtained from a plurality of different spectral bands can be obtained by combining edge detection and phase correlation. The images are edge-filtered, and pairs of the edge-filtered images are then phase correlated to produce phase correlation images. The registration information can be determined based on these phase correlation images.

  3. Video Image Stabilization and Registration (VISAR) Software

    NASA Technical Reports Server (NTRS)

    1999-01-01

    Two scientists at NASA's Marshall Space Flight Center,atmospheric scientist Paul Meyer and solar physicist Dr. David Hathaway, developed promising new software, called Video Image Stabilization and Registration (VISAR). VISAR may help law enforcement agencies catch criminals by improving the quality of video recorded at crime scenes. In this photograph, the single frame at left, taken at night, was brightened in order to enhance details and reduce noise or snow. To further overcome the video defects in one frame, Law enforcement officials can use VISAR software to add information from multiple frames to reveal a person. Images from less than a second of videotape were added together to create the clarified image at right. VISAR stabilizes camera motion in the horizontal and vertical as well as rotation and zoom effects producing clearer images of moving objects, smoothes jagged edges, enhances still images, and reduces video noise or snow. VISAR could also have applications in medical and meteorological imaging. It could steady images of ultrasounds, which are infamous for their grainy, blurred quality. The software can be used for defense application by improving recornaissance video imagery made by military vehicles, aircraft, and ships traveling in harsh, rugged environments.

  4. Video Image Stabilization and Registration (VISAR) Software

    NASA Technical Reports Server (NTRS)

    1999-01-01

    Two scientists at NASA's Marshall Space Flight Center,atmospheric scientist Paul Meyer and solar physicist Dr. David Hathaway, developed promising new software, called Video Image Stabilization and Registration (VISAR). VISAR may help law enforcement agencies catch criminals by improving the quality of video recorded at crime scenes. In this photograph, the single frame at left, taken at night, was brightened in order to enhance details and reduce noise or snow. To further overcome the video defects in one frame, Law enforcement officials can use VISAR software to add information from multiple frames to reveal a person. Images from less than a second of videotape were added together to create the clarified image at right. VISAR stabilizes camera motion in the horizontal and vertical as well as rotation and zoom effects producing clearer images of moving objects, smoothes jagged edges, enhances still images, and reduces video noise or snow. VISAR could also have applications in medical and meteorological imaging. It could steady images of ultrasounds, which are infamous for their grainy, blurred quality. The software can be used for defense application by improving recornaissance video imagery made by military vehicles, aircraft, and ships traveling in harsh, rugged environments.

  5. A Multistage Approach for Image Registration.

    PubMed

    Bowen, Francis; Hu, Jianghai; Du, Eliza Yingzi

    2016-09-01

    Successful image registration is an important step for object recognition, target detection, remote sensing, multimodal content fusion, scene blending, and disaster assessment and management. The geometric and photometric variations between images adversely affect the ability for an algorithm to estimate the transformation parameters that relate the two images. Local deformations, lighting conditions, object obstructions, and perspective differences all contribute to the challenges faced by traditional registration techniques. In this paper, a novel multistage registration approach is proposed that is resilient to view point differences, image content variations, and lighting conditions. Robust registration is realized through the utilization of a novel region descriptor which couples with the spatial and texture characteristics of invariant feature points. The proposed region descriptor is exploited in a multistage approach. A multistage process allows the utilization of the graph-based descriptor in many scenarios thus allowing the algorithm to be applied to a broader set of images. Each successive stage of the registration technique is evaluated through an effective similarity metric which determines subsequent action. The registration of aerial and street view images from pre- and post-disaster provide strong evidence that the proposed method estimates more accurate global transformation parameters than traditional feature-based methods. Experimental results show the robustness and accuracy of the proposed multistage image registration methodology.

  6. Landmark constrained genus-one surface Teichmüller map applied to surface registration in medical imaging.

    PubMed

    Lam, Ka Chun; Gu, Xianfeng; Lui, Lok Ming

    2015-10-01

    We address the registration problem of genus-one surfaces (such as vertebrae bones) with prescribed landmark constraints. The high-genus topology of the surfaces makes it challenging to obtain a unique and bijective surface mapping that matches landmarks consistently. This work proposes to tackle this registration problem using a special class of quasi-conformal maps called Teichmüller maps (T-Maps). A landmark constrained T-Map is the unique mapping between genus-1 surfaces that minimizes the maximal conformality distortion while matching the prescribed feature landmarks. Existence and uniqueness of the landmark constrained T-Map are theoretically guaranteed. This work presents an iterative algorithm to compute the T-Map. The main idea is to represent the set of diffeomorphism using the Beltrami coefficients (BC). The BC is iteratively adjusted to an optimal one, which corresponds to our desired T-Map that matches the prescribed landmarks and satisfies the periodic boundary condition on the universal covering space. Numerical experiments demonstrate the effectiveness of our proposed algorithm. The method has also been applied to register vertebrae bones with prescribed landmark points and curves, which gives accurate surface registrations.

  7. Introduction to Remote Sensing Image Registration

    NASA Technical Reports Server (NTRS)

    Le Moigne, Jacqueline

    2017-01-01

    For many applications, accurate and fast image registration of large amounts of multi-source data is the first necessary step before subsequent processing and integration. Image registration is defined by several steps and each step can be approached by various methods which all present diverse advantages and drawbacks depending on the type of data, the type of applications, the a prior information known about the data and the type of accuracy that is required. This paper will first present a general overview of remote sensing image registration and then will go over a few specific methods and their applications

  8. Nonrigid image registration with crystal dislocation energy.

    PubMed

    Luo, Yishan; Chung, Albert C S

    2013-01-01

    The goal of nonrigid image registration is to find a suitable transformation such that the transformed moving image becomes similar to the reference image. The image registration problem can also be treated as an optimization problem, which tries to minimize an objective energy function that measures the differences between two involved images. In this paper, we consider image matching as the process of aligning object boundaries in two different images. The registration energy function can be defined based on the total energy associated with the object boundaries. The optimal transformation is obtained by finding the equilibrium state when the total energy is minimized, which indicates the object boundaries find their correspondences and stop deforming. We make an analogy between the above processes with the dislocation system in physics. The object boundaries are viewed as dislocations (line defects) in crystal. Then the well-developed dislocation energy is used to derive the energy assigned to object boundaries in images. The newly derived registration energy function takes the global gradient information of the entire image into consideration, and produces an orientation-dependent and long-range interaction between two images to drive the registration process. This property of interaction endows the new registration framework with both fast convergence rate and high registration accuracy. Moreover, the new energy function can be adapted to realize symmetric diffeomorphic transformation so as to ensure one-to-one matching between subjects. In this paper, the superiority of the new method is theoretically proven, experimentally tested and compared with the state-of-the-art SyN method. Experimental results with 3-D magnetic resonance brain images demonstrate that the proposed method outperforms the compared methods in terms of both registration accuracy and computation time.

  9. Fast 3D fluid registration of brain magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Leporé, Natasha; Chou, Yi-Yu; Lopez, Oscar L.; Aizenstein, Howard J.; Becker, James T.; Toga, Arthur W.; Thompson, Paul M.

    2008-03-01

    Fluid registration is widely used in medical imaging to track anatomical changes, to correct image distortions, and to integrate multi-modality data. Fluid mappings guarantee that the template image deforms smoothly into the target, without tearing or folding, even when large deformations are required for accurate matching. Here we implemented an intensity-based fluid registration algorithm, accelerated by using a filter designed by Bro-Nielsen and Gramkow. We validated the algorithm on 2D and 3D geometric phantoms using the mean square difference between the final registered image and target as a measure of the accuracy of the registration. In tests on phantom images with different levels of overlap, varying amounts of Gaussian noise, and different intensity gradients, the fluid method outperformed a more commonly used elastic registration method, both in terms of accuracy and in avoiding topological errors during deformation. We also studied the effect of varying the viscosity coefficients in the viscous fluid equation, to optimize registration accuracy. Finally, we applied the fluid registration algorithm to a dataset of 2D binary corpus callosum images and 3D volumetric brain MRIs from 14 healthy individuals to assess its accuracy and robustness.

  10. Effects of Spatial Resolution on Image Registration

    PubMed Central

    Zhao, Can; Carass, Aaron; Jog, Amod; Prince, Jerry L.

    2016-01-01

    This paper presents a theoretical analysis of the effect of spatial resolution on image registration. Based on the assumption of additive Gaussian noise on the images, the mean and variance of the distribution of the sum of squared differences (SSD) were estimated. Using these estimates, we evaluate a distance between the SSD distributions of aligned images and non-aligned images. The experimental results show that by matching the resolutions of the moving and fixed images one can get a better image registration result. The results agree with our theoretical analysis of SSD, but also suggest that it may be valid for mutual information as well. PMID:27773960

  11. Automated Registration Of Images From Multiple Sensors

    NASA Technical Reports Server (NTRS)

    Rignot, Eric J. M.; Kwok, Ronald; Curlander, John C.; Pang, Shirley S. N.

    1994-01-01

    Images of terrain scanned in common by multiple Earth-orbiting remote sensors registered automatically with each other and, where possible, on geographic coordinate grid. Simulated image of terrain viewed by sensor computed from ancillary data, viewing geometry, and mathematical model of physics of imaging. In proposed registration algorithm, simulated and actual sensor images matched by area-correlation technique.

  12. Cellular recurrent deep network for image registration

    NASA Astrophysics Data System (ADS)

    Alam, M.; Vidyaratne, L.; Iftekharuddin, Khan M.

    2015-09-01

    Image registration using Artificial Neural Network (ANN) remains a challenging learning task. Registration can be posed as a two-step problem: parameter estimation and actual alignment/transformation using the estimated parameters. To date ANN based image registration techniques only perform the parameter estimation, while affine equations are used to perform the actual transformation. In this paper, we propose a novel deep ANN based image rigid registration that combines parameter estimation and transformation as a simultaneous learning task. Our previous work shows that a complex universal approximator known as Cellular Simultaneous Recurrent Network (CSRN) can successfully approximate affine transformations with known transformation parameters. This study introduces a deep ANN that combines a feed forward network with a CSRN to perform full rigid registration. Layer wise training is used to pre-train feed forward network for parameter estimation and followed by a CSRN for image transformation respectively. The deep network is then fine-tuned to perform the final registration task. Our result shows that the proposed deep ANN architecture achieves comparable registration accuracy to that of image affine transformation using CSRN with known parameters. We also demonstrate the efficacy of our novel deep architecture by a performance comparison with a deep clustered MLP.

  13. Parallel image registration method for snapshot Fourier transform imaging spectroscopy

    NASA Astrophysics Data System (ADS)

    Zhang, Yu; Zhu, Shuaishuai; Lin, Jie; Zhu, Feijia; Jin, Peng

    2017-08-01

    A fast and precise registration method for multi-image snapshot Fourier transform imaging spectroscopy is proposed. This method accomplishes registration of an image array using the positional relationship between homologous points in the subimages, which are obtained offline by preregistration. Through the preregistration process, the registration problem is converted to the problem of using a registration matrix to interpolate subimages. Therefore, the hardware interpolation of graphics processing unit (GPU) texture memory, which has speed advantages for its parallel computing, can be used to significantly enhance computational efficiency. Compared to a central processing unit, GPU performance showed ˜27 times acceleration in registration efficiency.

  14. Evaluation of various deformable image registration algorithms for thoracic images.

    PubMed

    Kadoya, Noriyuki; Fujita, Yukio; Katsuta, Yoshiyuki; Dobashi, Suguru; Takeda, Ken; Kishi, Kazuma; Kubozono, Masaki; Umezawa, Rei; Sugawara, Toshiyuki; Matsushita, Haruo; Jingu, Keiichi

    2014-01-01

    We evaluated the accuracy of one commercially available and three publicly available deformable image registration (DIR) algorithms for thoracic four-dimensional (4D) computed tomography (CT) images. Five patients with esophagus cancer were studied. Datasets of the five patients were provided by DIR-lab (dir-lab.com) and consisted of thoracic 4D CT images and a coordinate list of anatomical landmarks that had been manually identified. Expert landmark correspondence was used for evaluating DIR spatial accuracy. First, the manually measured displacement vector field (mDVF) was obtained from the coordinate list of anatomical landmarks. Then the automatically calculated displacement vector field (aDVF) was calculated by using the following four DIR algorithms: B-spine implemented in Velocity AI (Velocity Medical, Atlanta, GA, USA), free-form deformation (FFD), Horn-Schunk optical flow (OF) and Demons in DIRART of MATLAB software. Registration error is defined as the difference between mDVF and aDVF. The mean 3D registration errors were 2.7 ± 0.8 mm for B-spline, 3.6 ± 1.0 mm for FFD, 2.4 ± 0.9 mm for OF and 2.4 ± 1.2 mm for Demons. The results showed that reasonable accuracy was achieved in B-spline, OF and Demons, and that these algorithms have the potential to be used for 4D dose calculation, automatic image segmentation and 4D CT ventilation imaging in patients with thoracic cancer. However, for all algorithms, the accuracy might be improved by using the optimized parameter setting. Furthermore, for B-spline in Velocity AI, the 3D registration error was small with displacements of less than ∼10 mm, indicating that this software may be useful in this range of displacements.

  15. Image registration techniques for multimodal sensors

    NASA Astrophysics Data System (ADS)

    Altinalev, Tevfik; Cetin, Enis A.; Yardimci, Yasemin C.

    2002-08-01

    Image registration refers to the problem of spatially aligning two or more images. A challenging problem in this area is the registration of images obtained by different types of sensors. In general such images have different gray level characteristics and commonly used techniques such as those based on area correlations cannot be applied directly. On the other hand, contours representing the region boundaries are preserved in most cases. Therefore, contour based registration techniques are applicable to multimodal sensors. In this paper, various registration techniques based on subband decomposition and projection along x and y directions are introduced. The effect of binarization is investigated. Unknown translation and scaling parameters are computed using cross-correlation methods over the projections. Performance of the algorithms is compared.

  16. Cross contrast multi-channel image registration using image synthesis for MR brain images.

    PubMed

    Chen, Min; Carass, Aaron; Jog, Amod; Lee, Junghoon; Roy, Snehashis; Prince, Jerry L

    2017-02-01

    Multi-modal deformable registration is important for many medical image analysis tasks such as atlas alignment, image fusion, and distortion correction. Whereas a conventional method would register images with different modalities using modality independent features or information theoretic metrics such as mutual information, this paper presents a new framework that addresses the problem using a two-channel registration algorithm capable of using mono-modal similarity measures such as sum of squared differences or cross-correlation. To make it possible to use these same-modality measures, image synthesis is used to create proxy images for the opposite modality as well as intensity-normalized images from each of the two available images. The new deformable registration framework was evaluated by performing intra-subject deformation recovery, intra-subject boundary alignment, and inter-subject label transfer experiments using multi-contrast magnetic resonance brain imaging data. Three different multi-channel registration algorithms were evaluated, revealing that the framework is robust to the multi-channel deformable registration algorithm that is used. With a single exception, all results demonstrated improvements when compared against single channel registrations using the same algorithm with mutual information. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Global image registration using a symmetric block-matching approach.

    PubMed

    Modat, Marc; Cash, David M; Daga, Pankaj; Winston, Gavin P; Duncan, John S; Ourselin, Sébastien

    2014-07-01

    Most medical image registration algorithms suffer from a directionality bias that has been shown to largely impact subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of nonlinear registration, but little work has been done for global registration. We propose a symmetric approach based on a block-matching technique and least-trimmed square regression. The proposed method is suitable for multimodal registration and is robust to outliers in the input images. The symmetric framework is compared with the original asymmetric block-matching technique and is shown to outperform it in terms of accuracy and robustness. The methodology presented in this article has been made available to the community as part of the NiftyReg open-source package.

  18. Absolute image registration for geosynchronous satellites

    NASA Technical Reports Server (NTRS)

    Nankervis, R.; Koch, D.; Sielski, H.; Hall, D.

    1980-01-01

    A procedure for the absolute registration of earth images acquired by cameras on geosynchronous satellites is described. A conventional least squares process is used to estimate navigational parameters and camera pointing biases from observed minus computed landmark line and element numbers. These estimated parameters along with orbit and attitude dynamic models are used to register images, employing an automated grey-level correlation technique, inside the span represented by the landmark data. Experimental results obtained from processing the SMS-2 observation data base covering May 2, 1979 through May 20, 1979 show registration accuracies with a standard deviation of less than two pixels if the registration is within the landmark data span. It is also found that accurate registration can be expected for images obtained up to 48 hours outside of the landmark data span.

  19. Onboard Image Registration from Invariant Features

    NASA Technical Reports Server (NTRS)

    Wang, Yi; Ng, Justin; Garay, Michael J.; Burl, Michael C

    2008-01-01

    This paper describes a feature-based image registration technique that is potentially well-suited for onboard deployment. The overall goal is to provide a fast, robust method for dynamically combining observations from multiple platforms into sensors webs that respond quickly to short-lived events and provide rich observations of objects that evolve in space and time. The approach, which has enjoyed considerable success in mainstream computer vision applications, uses invariant SIFT descriptors extracted at image interest points together with the RANSAC algorithm to robustly estimate transformation parameters that relate one image to another. Experimental results for two satellite image registration tasks are presented: (1) automatic registration of images from the MODIS instrument on Terra to the MODIS instrument on Aqua and (2) automatic stabilization of a multi-day sequence of GOES-West images collected during the October 2007 Southern California wildfires.

  20. Robust linear registration of CT images using random regression forests

    NASA Astrophysics Data System (ADS)

    Konukoglu, Ender; Criminisi, Antonio; Pathak, Sayan; Robertson, Duncan; White, Steve; Haynor, David; Siddiqui, Khan

    2011-03-01

    Global linear registration is a necessary first step for many different tasks in medical image analysis. Comparing longitudinal studies1, cross-modality fusion2, and many other applications depend heavily on the success of the automatic registration. The robustness and efficiency of this step is crucial as it affects all subsequent operations. Most common techniques cast the linear registration problem as the minimization of a global energy function based on the image intensities. Although these algorithms have proved useful, their robustness in fully automated scenarios is still an open question. In fact, the optimization step often gets caught in local minima yielding unsatisfactory results. Recent algorithms constrain the space of registration parameters by exploiting implicit or explicit organ segmentations, thus increasing robustness4,5. In this work we propose a novel robust algorithm for automatic global linear image registration. Our method uses random regression forests to estimate posterior probability distributions for the locations of anatomical structures - represented as axis aligned bounding boxes6. These posterior distributions are later integrated in a global linear registration algorithm. The biggest advantage of our algorithm is that it does not require pre-defined segmentations or regions. Yet it yields robust registration results. We compare the robustness of our algorithm with that of the state of the art Elastix toolbox7. Validation is performed via 1464 pair-wise registrations in a database of very diverse 3D CT images. We show that our method decreases the "failure" rate of the global linear registration from 12.5% (Elastix) to only 1.9%.

  1. Non-rigid image registration with SalphaS filters.

    PubMed

    Liao, Shu; Chung, Albert C S

    2008-01-01

    In this paper, based on the SalphaS distributions, we design SalphaS filters and use the filters as a new feature extraction method for non-rigid medical image registration. In brain MR images, the energy distributions of different frequency bands often exhibit heavy-tailed behavior. Such non-Gaussian behavior is essential for non-rigid image registration but cannot be satisfactorily modeled by the conventional Gabor filters. This leads to unsatisfactory modeling of voxels located at the salient regions of the images. To this end, we propose the SalphaS filters for modeling the heavy-tailed behavior of the energy distributions of brain MR images, and show that the Gabor filter is a special case of the SalphaS filter. The maximum response orientation selection criterion is defined for each frequency band to achieve rotation invariance. In our framework, if the brain MR images are already segmented, each voxel can be automatically assigned a weighting factor based on the Fisher's separation criterion and it is shown that the registration performance can be further improved. The proposed method has been compared with the free-form-deformation based method, Demons algorithm and a method using Gabor features by conducting non-rigid image registration experiments. It is observed that the proposed method achieves the best registration accuracy among all the compared methods in both the simulated and real datasets obtained from the BrainWeb and IBSR respectively.

  2. Medical imaging

    NASA Astrophysics Data System (ADS)

    Elliott, Alex

    2005-07-01

    Diagnostic medical imaging is a fundamental part of the practice of modern medicine and is responsible for the expenditure of considerable amounts of capital and revenue monies in healthcare systems around the world. Much research and development work is carried out, both by commercial companies and the academic community. This paper reviews briefly each of the major diagnostic medical imaging techniques—X-ray (planar and CT), ultrasound, nuclear medicine (planar, SPECT and PET) and magnetic resonance. The technical challenges facing each are highlighted, with some of the most recent developments. In terms of the future, interventional/peri-operative imaging, the advancement of molecular medicine and gene therapy are identified as potential areas of expansion.

  3. TU-B-19A-01: Image Registration II: TG132-Quality Assurance for Image Registration

    SciTech Connect

    Brock, K; Mutic, S

    2014-06-15

    AAPM Task Group 132 was charged with a review of the current approaches and solutions for image registration in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes. As the results of image registration are always used as the input of another process for planning or delivery, it is important for the user to understand and document the uncertainty associate with the algorithm in general and the Result of a specific registration. The recommendations of this task group, which at the time of abstract submission are currently being reviewed by the AAPM, include the following components. The user should understand the basic image registration techniques and methods of visualizing image fusion. The disclosure of basic components of the image registration by commercial vendors is critical in this respect. The physicists should perform end-to-end tests of imaging, registration, and planning/treatment systems if image registration is performed on a stand-alone system. A comprehensive commissioning process should be performed and documented by the physicist prior to clinical use of the system. As documentation is important to the safe implementation of this process, a request and report system should be integrated into the clinical workflow. Finally, a patient specific QA practice should be established for efficient evaluation of image registration results. The implementation of these recommendations will be described and illustrated during this educational session. Learning Objectives: Highlight the importance of understanding the image registration techniques used in their clinic. Describe the end-to-end tests needed for stand-alone registration systems. Illustrate a comprehensive commissioning program using both phantom data and clinical images. Describe a request and report system to ensure communication and documentation. Demonstrate an clinically-efficient patient QA practice for efficient evaluation of image

  4. Intraoperative ultrasound to stereocamera registration using interventional photoacoustic imaging

    NASA Astrophysics Data System (ADS)

    Vyas, Saurabh; Su, Steven; Kim, Robert; Kuo, Nathanael; Taylor, Russell H.; Kang, Jin U.; Boctor, Emad M.

    2012-02-01

    There are approximately 6000 hospitals in the United States, of which approximately 5400 employ minimally invasive surgical robots for a variety of procedures. Furthermore, 95% of these robots require extensive registration before they can be fitted into the operating room. These "registrations" are performed by surgical navigation systems, which allow the surgical tools, the robot and the surgeon to be synchronized together-hence operating in concert. The most common surgical navigation modalities include: electromagnetic (EM) tracking and optical tracking. Currently, these navigation systems are large, intrusive, come with a steep learning curve, require sacrifices on the part of the attending medical staff, and are quite expensive (since they require several components). Recently, photoacoustic (PA) imaging has become a practical and promising new medical imaging technology. PA imaging only requires the minimal equipment standard with most modern ultrasound (US) imaging systems as well as a common laser source. In this paper, we demonstrate that given a PA imaging system, as well as a stereocamera (SC), the registration between the US image of a particular anatomy and the SC image of the same anatomy can be obtained with reliable accuracy. In our experiments, we collected data for N = 80 trials of sample 3D US and SC coordinates. We then computed the registration between the SC and the US coordinates. Upon validation, the mean error and standard deviation between the predicted sample coordinates and the corresponding ground truth coordinates were found to be 3.33 mm and 2.20 mm respectively.

  5. GPUs benchmarking in subpixel image registration algorithm

    NASA Astrophysics Data System (ADS)

    Sanz-Sabater, Martin; Picazo-Bueno, Jose Angel; Micó, Vicente; Ferrerira, Carlos; Granero, Luis; Garcia, Javier

    2015-05-01

    Image registration techniques are used among different scientific fields, like medical imaging or optical metrology. The straightest way to calculate shifting between two images is using the cross correlation, taking the highest value of this correlation image. Shifting resolution is given in whole pixels which cannot be enough for certain applications. Better results can be achieved interpolating both images, as much as the desired resolution we want to get, and applying the same technique described before, but the memory needed by the system is significantly higher. To avoid memory consuming we are implementing a subpixel shifting method based on FFT. With the original images, subpixel shifting can be achieved multiplying its discrete Fourier transform by a linear phase with different slopes. This method is high time consuming method because checking a concrete shifting means new calculations. The algorithm, highly parallelizable, is very suitable for high performance computing systems. GPU (Graphics Processing Unit) accelerated computing became very popular more than ten years ago because they have hundreds of computational cores in a reasonable cheap card. In our case, we are going to register the shifting between two images, doing the first approach by FFT based correlation, and later doing the subpixel approach using the technique described before. We consider it as `brute force' method. So we will present a benchmark of the algorithm consisting on a first approach (pixel resolution) and then do subpixel resolution approaching, decreasing the shifting step in every loop achieving a high resolution in few steps. This program will be executed in three different computers. At the end, we will present the results of the computation, with different kind of CPUs and GPUs, checking the accuracy of the method, and the time consumed in each computer, discussing the advantages, disadvantages of the use of GPUs.

  6. Supervised local error estimation for nonlinear image registration using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Eppenhof, Koen A. J.; Pluim, Josien P. W.

    2017-02-01

    Error estimation in medical image registration is valuable when validating, comparing, or combining registration methods. To validate a nonlinear image registration method, ideally the registration error should be known for the entire image domain. We propose a supervised method for the estimation of a registration error map for nonlinear image registration. The method is based on a convolutional neural network that estimates the norm of the residual deformation from patches around each pixel in two registered images. This norm is interpreted as the registration error, and is defined for every pixel in the image domain. The network is trained using a set of artificially deformed images. Each training example is a pair of images: the original image, and a random deformation of that image. No manually labeled ground truth error is required. At test time, only the two registered images are required as input. We train and validate the network on registrations in a set of 2D digital subtraction angiography sequences, such that errors up to eight pixels can be estimated. We show that for this range of errors the convolutional network is able to learn the registration error in pairs of 2D registered images at subpixel precision. Finally, we present a proof of principle for the extension to 3D registration problems in chest CTs, showing that the method has the potential to estimate errors in 3D registration problems.

  7. Mid-space-independent deformable image registration.

    PubMed

    Aganj, Iman; Iglesias, Juan Eugenio; Reuter, Martin; Sabuncu, Mert Rory; Fischl, Bruce

    2017-05-15

    Aligning images in a mid-space is a common approach to ensuring that deformable image registration is symmetric - that it does not depend on the arbitrary ordering of the input images. The results are, however, generally dependent on the mathematical definition of the mid-space. In particular, the set of possible solutions is typically restricted by the constraints that are enforced on the transformations to prevent the mid-space from drifting too far from the native image spaces. The use of an implicit atlas has been proposed as an approach to mid-space image registration. In this work, we show that when the atlas is aligned to each image in the native image space, the data term of implicit-atlas-based deformable registration is inherently independent of the mid-space. In addition, we show that the regularization term can be reformulated independently of the mid-space as well. We derive a new symmetric cost function that only depends on the transformation morphing the images to each other, rather than to the atlas. This eliminates the need for anti-drift constraints, thereby expanding the space of allowable deformations. We provide an implementation scheme for the proposed framework, and validate it through diffeomorphic registration experiments on brain magnetic resonance images. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Image registration for DSA quality enhancement.

    PubMed

    Buzug, T M; Weese, J

    1998-01-01

    A generalized framework for histogram-based similarity measures is presented and applied to the image-enhancement task in digital subtraction angiography (DSA). The class of differentiable, strictly convex weighting functions is identified as suitable weightings of histograms for measuring the degree of clustering that goes along with registration. With respect to computation time, the energy similarity measure is the function of choice for the registration of mask and contrast image prior to subtraction. The robustness of the energy measure is studied for geometrical image distortions like rotation and scaling. Additionally, it is investigated how the histogram binning and inhomogeneous motion inside the templates influence the quality of the similarity measure. Finally, the registration success for the automated procedure is compared with the manually shift-corrected image pair of the head.

  9. A MULTICORE BASED PARALLEL IMAGE REGISTRATION METHOD

    PubMed Central

    Yang, Lin; Gong, Leiguang; Zhang, Hong; Nosher, John L.; Foran, David J.

    2012-01-01

    Image registration is a crucial step for many image-assisted clinical applications such as surgery planning and treatment evaluation. In this paper we proposed a landmark based nonlinear image registration algorithm for matching 2D image pairs. The algorithm was shown to be effective and robust under conditions of large deformations. In landmark based registration, the most important step is establishing the correspondence among the selected landmark points. This usually requires an extensive search which is often computationally expensive. We introduced a nonregular data partition algorithm using the K-means clustering algorithm to group the landmarks based on the number of available processing cores. The step optimizes the memory usage and data transfer. We have tested our method using IBM Cell Broadband Engine (Cell/B.E.) platform. PMID:19964921

  10. SAR image registration based on Susan algorithm

    NASA Astrophysics Data System (ADS)

    Wang, Chun-bo; Fu, Shao-hua; Wei, Zhong-yi

    2011-10-01

    Synthetic Aperture Radar (SAR) is an active remote sensing system which can be installed on aircraft, satellite and other carriers with the advantages of all day and night and all-weather ability. It is the important problem that how to deal with SAR and extract information reasonably and efficiently. Particularly SAR image geometric correction is the bottleneck to impede the application of SAR. In this paper we introduces image registration and the Susan algorithm knowledge firstly, then introduces the process of SAR image registration based on Susan algorithm and finally presents experimental results of SAR image registration. The Experiment shows that this method is effective and applicable, no matter from calculating the time or from the calculation accuracy.

  11. Laplacian eigenmaps for multimodal groupwise image registration

    NASA Astrophysics Data System (ADS)

    Polfliet, Mathias; Klein, Stefan; Niessen, Wiro J.; Vandemeulebroucke, Jef

    2017-02-01

    Multimodal groupwise registration has been of growing interest to the image processing community due to developments in scanner technologies (e.g. multiparametric MRI, DCE-CT or PET-MR) that increased both the number of modalities and number of images under consideration. In this work a novel methodology is presented for multimodal groupwise registration that is based on Laplacian eigenmaps, a nonlinear dimensionality reduction technique. Compared to recently proposed dissimilarity metrics based on principal component analysis, the proposed metric should enable a better capture of the intensity relationships between different images in the group. The metric is constructed to be the second smallest eigenvalue from the eigenvector problem defined in Laplacian eigenmaps. The method was validated in three distinct experiments: a non-linear synthetic registration experiment, the registration of quantitative MRI data of the carotid artery, and the registration of multimodal data of the brain (RIRE). The results show increased accuracy and robustness compared to other state-of-the-art groupwise registration methodologies.

  12. Regional manifold learning for deformable registration of brain MR images.

    PubMed

    Ye, Dong Hye; Hamm, Jihun; Kwon, Dongjin; Davatzikos, Christos; Pohl, Kilian M

    2012-01-01

    We propose a method for deformable registration based on learning the manifolds of individual brain regions. Recent publications on registration of medical images advocate the use of manifold learning in order to confine the search space to anatomically plausible deformations. Existing methods construct manifolds based on a single metric over the entire image domain thus frequently miss regional brain variations. We address this issue by first learning manifolds for specific regions and then computing region-specific deformations from these manifolds. We then determine deformations for the entire image domain by learning the global manifold in such a way that it preserves the region-specific deformations. We evaluate the accuracy of our method by applying it to the LPBA40 dataset and measuring the overlap of the deformed segmentations. The result shows significant improvement in registration accuracy on cortex regions compared to other state of the art methods.

  13. An accurate registration technique for distorted images

    NASA Technical Reports Server (NTRS)

    Delapena, Michele; Shaw, Richard A.; Linde, Peter; Dravins, Dainis

    1990-01-01

    Accurate registration of International Ultraviolet Explorer (IUE) images is crucial because the variability of the geometrical distortions that are introduced by the SEC-Vidicon cameras ensures that raw science images are never perfectly aligned with the Intensity Transfer Functions (ITFs) (i.e., graded floodlamp exposures that are used to linearize and normalize the camera response). A technique for precisely registering IUE images which uses a cross correlation of the fixed pattern that exists in all raw IUE images is described.

  14. Automated Image Registration (AIR) of MTI Imagery

    SciTech Connect

    Pope, P. A.; Theiler, J. P.

    2003-01-01

    This paper describes an algorithm for the registration of imagery collected by the Multispectral Thermal Imager (MTI). The Automated Image Registration (AIR) algorithm is entirely image-based and is implemented in an automated fashion, which avoids any requirement for human interaction. The AIR method differs from the 'direct georeferencing' method used to create our standard coregistered product since explicit information about the satellite's trajectory and the sensor geometry are not required. The AIR method makes use of a maximum cross-correlation (MCC) algorithm, which is applied locally about numerous points within any two images being compared. The MCC method is used to determine the row and column translations required to register the bands of imagery collected by a single SCA (band-to-band registration), and the raw and column translations required to regisler the imagery collected by the three SCAs for any individual band (SCA-to-SCA registration). Of particular note is the use of reciprocity and a weighted least squares approach to obtaining the band-to-band registration shifts. Reciprocity is enforced by using the MCC method to determine the row and column translations between all pair-wise combinations of bands. This information is then used in a weighted least squares approach to determine the optimum shift values between an arbitrarily selected reference band and the other 15 bands. The individual steps of the AIR methodology, and the results of registering MTI imagery through use of this algorithm, are described.

  15. Segmentation by surface-to-image registration

    NASA Astrophysics Data System (ADS)

    Xie, Zhiyong; Tamez-Pena, Jose; Gieseg, Michael; Liachenko, Serguei; Dhamija, Shantanu; Chiao, Ping

    2006-03-01

    This paper presents a new image segmentation algorithm using surface-to-image registration. The algorithm employs multi-level transformations and multi-resolution image representations to progressively register atlas surfaces (modeling anatomical structures) to subject images based on weighted external forces in which weights and forces are determined by gradients and local intensity profiles obtained from images. The algorithm is designed to prevent atlas surfaces converging to unintended strong edges or leaking out of structures of interest through weak edges where the image contrast is low. Segmentation of bone structures on MR images of rat knees analyzed in this manner performs comparably to technical experts using a semi-automatic tool.

  16. Non-rigid registration of tomographic images with Fourier transforms

    NASA Astrophysics Data System (ADS)

    Osorio, Ar; Isoardi, Ra; Mato, G.

    2007-11-01

    Spatial image registration of deformable body parts such as thorax and abdomen has important medical applications, but at the same time, it represents an important computational challenge. In this work we propose an automatic algorithm to perform non-rigid registration of tomographic images using a non-rigid model based on Fourier transforms. As a measure of similarity, we use the correlation coefficient, finding that the optimal order of the transformation is n = 3 (36 parameters). We apply this method to a digital phantom and to 7 pairs of patient images corresponding to clinical CT scans. The preliminary results indicate a fairly good agreement according to medical experts, with an average registration error of 2 mm for the case of clinical images. For 2D images (dimensions 512×512), the average running time for the algorithm is 15 seconds using a standard personal computer. Summarizing, we find that intra-modality registration of the abdomen can be achieved with acceptable accuracy for slight deformations and can be extended to 3D with a reasonable execution time.

  17. Software for Automated Image-to-Image Co-registration

    NASA Technical Reports Server (NTRS)

    Benkelman, Cody A.; Hughes, Heidi

    2007-01-01

    The project objectives are: a) Develop software to fine-tune image-to-image co-registration, presuming images are orthorectified prior to input; b) Create a reusable software development kit (SDK) to enable incorporation of these tools into other software; d) provide automated testing for quantitative analysis; and e) Develop software that applies multiple techniques to achieve subpixel precision in the co-registration of image pairs.

  18. Groupwise Image Registration Guided by a Dynamic Digraph of Images.

    PubMed

    Tang, Zhenyu; Fan, Yong

    2016-04-01

    For groupwise image registration, graph theoretic methods have been adopted for discovering the manifold of images to be registered so that accurate registration of images to a group center image can be achieved by aligning similar images that are linked by the shortest graph paths. However, the image similarity measures adopted to build a graph of images in the extant methods are essentially pairwise measures, not effective for capturing the groupwise similarity among multiple images. To overcome this problem, we present a groupwise image similarity measure that is built on sparse coding for characterizing image similarity among all input images and build a directed graph (digraph) of images so that similar images are connected by the shortest paths of the digraph. Following the shortest paths determined according to the digraph, images are registered to a group center image in an iterative manner by decomposing a large anatomical deformation field required to register an image to the group center image into a series of small ones between similar images. During the iterative image registration, the digraph of images evolves dynamically at each iteration step to pursue an accurate estimation of the image manifold. Moreover, an adaptive dictionary strategy is adopted in the groupwise image similarity measure to ensure fast convergence of the iterative registration procedure. The proposed method has been validated based on both simulated and real brain images, and experiment results have demonstrated that our method was more effective for learning the manifold of input images and achieved higher registration accuracy than state-of-the-art groupwise image registration methods.

  19. Automated image registration for FDOPA PET studies

    NASA Astrophysics Data System (ADS)

    Lin, Kang-Ping; Huang, Sung-Cheng; Yu, Dan-Chu; Melega, William; Barrio, Jorge R.; Phelps, Michael E.

    1996-12-01

    In this study, various image registration methods are investigated for their suitability for registration of L-6-[18F]-fluoro-DOPA (FDOPA) PET images. Five different optimization criteria including sum of absolute difference (SAD), mean square difference (MSD), cross-correlation coefficient (CC), standard deviation of pixel ratio (SDPR), and stochastic sign change (SSC) were implemented and Powell's algorithm was used to optimize the criteria. The optimization criteria were calculated either unidirectionally (i.e. only evaluating the criteria for comparing the resliced image 1 with the original image 2) or bidirectionally (i.e. averaging the criteria for comparing the resliced image 1 with the original image 2 and those for the sliced image 2 with the original image 1). Monkey FDOPA images taken at various known orientations were used to evaluate the accuracy of different methods. A set of human FDOPA dynamic images was used to investigate the ability of the methods for correcting subject movement. It was found that a large improvement in performance resulted when bidirectional rather than unidirectional criteria were used. Overall, the SAD, MSD and SDPR methods were found to be comparable in performance and were suitable for registering FDOPA images. The MSD method gave more adequate results for frame-to-frame image registration for correcting subject movement during a dynamic FDOPA study. The utility of the registration method is further demonstrated by registering FDOPA images in monkeys before and after amphetamine injection to reveal more clearly the changes in spatial distribution of FDOPA due to the drug intervention.

  20. Sorted self-similarity for multi-modal image registration.

    PubMed

    Kasiri, Keyvan; Fieguth, Paul; Clausi, David A

    2016-08-01

    In medical image analysis, registration of multimodal images has been challenging due to the complex intensity relationship between images. Classical multi-modal registration approaches evaluate the degree of the alignment by measuring the statistical dependency of the intensity values between images to be aligned. Employing statistical similarity measures, such as mutual information, is not promising in those cases with complex and spatially dependent intensity relations. A new similarity measure is proposed based on the assessing the similarity of pixels within an image, based on the idea that similar structures in an image are more probable to undergo similar intensity transformations. The most significant pixel similarity values are considered to transmit the most significant self-similarity information. The proposed method is employed in a framework to register different modalities of real brain scans and the performance of the method is compared to the conventional multi-modal registration approach. Quantitative evaluation of the method demonstrates the better registration accuracy in both rigid and non-rigid deformations.

  1. Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning.

    PubMed

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C; Shen, Dinggang

    2016-07-01

    Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked autoencoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework, image registration experiments were conducted on 7.0-T brain MR images. In all experiments, the results showed that the new image registration framework consistently demonstrated more accurate registration results when compared to state of the art.

  2. Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

    PubMed Central

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C.

    2015-01-01

    Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data,, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked auto-encoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework image registration experiments were conducted on 7.0-tesla brain MR images. In all experiments, the results showed the new image registration framework consistently demonstrated more accurate registration results when compared to state-of-the-art. PMID:26552069

  3. Image registration for luminescent paint applications

    NASA Technical Reports Server (NTRS)

    Bell, James H.; Mclachlan, Blair G.

    1993-01-01

    The use of pressure sensitive luminescent paints is a viable technique for the measurement of surface pressure on wind tunnel models. This technique requires data reduction of images obtained under known as well as test conditions and spatial transformation of the images. A general transform which registers images to subpixel accuracy is presented and the general characteristics of transforms for image registration and their derivation are discussed. Image resection and its applications are described. The mapping of pressure data to the three dimensional model surface for small wind tunnel models to a spatial accuracy of 0.5 percent of the model length is demonstrated.

  4. Image registration using binary boundary maps

    NASA Technical Reports Server (NTRS)

    Andrus, J. F.; Campbell, C. W.; Jayroe, R. R.

    1978-01-01

    Registration technique that matches binary boundary maps extracted from raw data, rather than matching actual data, is considerably faster than other techniques. Boundary maps, which are digital representations of regions where image amplitudes change significantly, typically represent data compression of 60 to 70 percent. Maps allow average products to be computed with addition rather than multiplication, further reducing computation time.

  5. Advances and challenges in deformable image registration: From image fusion to complex motion modelling.

    PubMed

    Schnabel, Julia A; Heinrich, Mattias P; Papież, Bartłomiej W; Brady, Sir J Michael

    2016-10-01

    Over the past 20 years, the field of medical image registration has significantly advanced from multi-modal image fusion to highly non-linear, deformable image registration for a wide range of medical applications and imaging modalities, involving the compensation and analysis of physiological organ motion or of tissue changes due to growth or disease patterns. While the original focus of image registration has predominantly been on correcting for rigid-body motion of brain image volumes acquired at different scanning sessions, often with different modalities, the advent of dedicated longitudinal and cross-sectional brain studies soon necessitated the development of more sophisticated methods that are able to detect and measure local structural or functional changes, or group differences. Moving outside of the brain, cine imaging and dynamic imaging required the development of deformable image registration to directly measure or compensate for local tissue motion. Since then, deformable image registration has become a general enabling technology. In this work we will present our own contributions to the state-of-the-art in deformable multi-modal fusion and complex motion modelling, and then discuss remaining challenges and provide future perspectives to the field.

  6. Video Image Stabilization and Registration

    NASA Technical Reports Server (NTRS)

    Hathaway, David H. (Inventor); Meyer, Paul J. (Inventor)

    2002-01-01

    A method of stabilizing and registering a video image in multiple video fields of a video sequence provides accurate determination of the image change in magnification, rotation and translation between video fields, so that the video fields may be accurately corrected for these changes in the image in the video sequence. In a described embodiment, a key area of a key video field is selected which contains an image which it is desired to stabilize in a video sequence. The key area is subdivided into nested pixel blocks and the translation of each of the pixel blocks from the key video field to a new video field is determined as a precursor to determining change in magnification, rotation and translation of the image from the key video field to the new video field.

  7. Video Image Stabilization and Registration

    NASA Technical Reports Server (NTRS)

    Hathaway, David H. (Inventor); Meyer, Paul J. (Inventor)

    2003-01-01

    A method of stabilizing and registering a video image in multiple video fields of a video sequence provides accurate determination of the image change in magnification, rotation and translation between video fields, so that the video fields may be accurately corrected for these changes in the image in the video sequence. In a described embodiment, a key area of a key video field is selected which contains an image which it is desired to stabilize in a video sequence. The key area is subdivided into nested pixel blocks and the translation of each of the pixel blocks from the key video field to a new video field is determined as a precursor to determining change in magnification, rotation and translation of the image from the key video field to the new video field.

  8. Video Image Stabilization and Registration

    NASA Technical Reports Server (NTRS)

    Hathaway, David H. (Inventor); Meyer, Paul J. (Inventor)

    2002-01-01

    A method of stabilizing and registering a video image in multiple video fields of a video sequence provides accurate determination of the image change in magnification, rotation and translation between video fields, so that the video fields may be accurately corrected for these changes in the image in the video sequence. In a described embodiment, a key area of a key video field is selected which contains an image which it is desired to stabilize in a video sequence. The key area is subdivided into nested pixel blocks and the translation of each of the pixel blocks from the key video field to a new video field is determined as a precursor to determining change in magnification, rotation and translation of the image from the key video field to the new video field.

  9. Image Segmentation, Registration, Compression, and Matching

    NASA Technical Reports Server (NTRS)

    Yadegar, Jacob; Wei, Hai; Yadegar, Joseph; Ray, Nilanjan; Zabuawala, Sakina

    2011-01-01

    A novel computational framework was developed of a 2D affine invariant matching exploiting a parameter space. Named as affine invariant parameter space (AIPS), the technique can be applied to many image-processing and computer-vision problems, including image registration, template matching, and object tracking from image sequence. The AIPS is formed by the parameters in an affine combination of a set of feature points in the image plane. In cases where the entire image can be assumed to have undergone a single affine transformation, the new AIPS match metric and matching framework becomes very effective (compared with the state-of-the-art methods at the time of this reporting). No knowledge about scaling or any other transformation parameters need to be known a priori to apply the AIPS framework. An automated suite of software tools has been created to provide accurate image segmentation (for data cleaning) and high-quality 2D image and 3D surface registration (for fusing multi-resolution terrain, image, and map data). These tools are capable of supporting existing GIS toolkits already in the marketplace, and will also be usable in a stand-alone fashion. The toolkit applies novel algorithmic approaches for image segmentation, feature extraction, and registration of 2D imagery and 3D surface data, which supports first-pass, batched, fully automatic feature extraction (for segmentation), and registration. A hierarchical and adaptive approach is taken for achieving automatic feature extraction, segmentation, and registration. Surface registration is the process of aligning two (or more) data sets to a common coordinate system, during which the transformation between their different coordinate systems is determined. Also developed here are a novel, volumetric surface modeling and compression technique that provide both quality-guaranteed mesh surface approximations and compaction of the model sizes by efficiently coding the geometry and connectivity

  10. Landsat image registration for agricultural applications

    NASA Technical Reports Server (NTRS)

    Wolfe, R. H., Jr.; Juday, R. D.; Wacker, A. G.; Kaneko, T.

    1982-01-01

    An image registration system has been developed at the NASA Johnson Space Center (JSC) to spatially align multi-temporal Landsat acquisitions for use in agriculture and forestry research. Working in conjunction with the Master Data Processor (MDP) at the Goddard Space Flight Center, it functionally replaces the long-standing LACIE Registration Processor as JSC's data supplier. The system represents an expansion of the techniques developed for the MDP and LACIE Registration Processor, and it utilizes the experience gained in an IBM/JSC effort evaluating the performance of the latter. These techniques are discussed in detail. Several tests were developed to evaluate the registration performance of the system. The results indicate that 1/15-pixel accuracy (about 4m for Landsat MSS) is achievable in ideal circumstances, sub-pixel accuracy (often to 0.2 pixel or better) was attained on a representative set of U.S. acquisitions, and a success rate commensurate with the LACIE Registration Processor was realized. The system has been employed in a production mode on U.S. and foreign data, and a performance similar to the earlier tests has been noted.

  11. TIMER: Tensor Image Morphing for Elastic Registration

    PubMed Central

    Yap, Pew-Thian; Wu, Guorong; Zhu, Hongtu; Lin, Weili; Shen, Dinggang

    2009-01-01

    We propose a novel diffusion tensor imaging (DTI) registration algorithm, called Tensor Image Morphing for Elastic Registration (TIMER), which leverages the hierarchical guidance of regional distributions and local boundaries, both extracted directly from the tensors. Currently available DTI registration methods generally extract tensor scalar features from each tensor to construct scalar maps. Subsequently, regional integration and other operations such as edge detection are performed to extract more features to guide the registration. However, there are two major limitations with these approaches. First, the computed regional features might not reflect the actual regional tensor distributions. Second, by the same token, gradient maps calculated from the tensor-derived scalar feature maps might not represent the actual tissue tensor boundaries. To overcome these limitations, we propose a new approach which extracts regional and edge information directly from a tensor neighborhood. Regional tensor distribution information, such as mean and variance, is computed in a multiscale fashion directly from the tensors by taking into account the voxel neighborhood of different sizes, and hence capturing tensor information at different scales, which in turn can be employed to hierarchically guide the registration. Such multiscale scheme can help alleviate the problem of local minimum and is also more robust to noise since one can better determine the statistical properties of each voxel by taking into account the properties of its surrounding. Also incorporated in our method is edge information extracted directly from the tensors, which is crucial to facilitate registration of tissue boundaries. Experiments involving real subjects, simulated subjects, fiber tracking, and atrophy detection indicate that TIMER performs better than the other methods in comparison (Yang et al., 2008a; Zhang et al., 2006). PMID:19398022

  12. TIMER: tensor image morphing for elastic registration.

    PubMed

    Yap, Pew-Thian; Wu, Guorong; Zhu, Hongtu; Lin, Weili; Shen, Dinggang

    2009-08-15

    We propose a novel diffusion tensor imaging (DTI) registration algorithm, called Tensor Image Morphing for Elastic Registration (TIMER), which leverages the hierarchical guidance of regional distributions and local boundaries, both extracted directly from the tensors. Currently available DTI registration methods generally extract tensor scalar features from each tensor to construct scalar maps. Subsequently, regional integration and other operations such as edge detection are performed to extract more features to guide the registration. However, there are two major limitations with these approaches. First, the computed regional features might not reflect the actual regional tensor distributions. Second, by the same token, gradient maps calculated from the tensor-derived scalar feature maps might not represent the actual tissue tensor boundaries. To overcome these limitations, we propose a new approach which extracts regional and edge information directly from a tensor neighborhood. Regional tensor distribution information, such as mean and variance, is computed in a multiscale fashion directly from the tensors by taking into account the voxel neighborhood of different sizes, and hence capturing tensor information at different scales, which in turn can be employed to hierarchically guide the registration. Such multiscale scheme can help alleviate the problem of local minimum and is also more robust to noise since one can better determine the statistical properties of each voxel by taking into account the properties of its surrounding. Also incorporated in our method is edge information extracted directly from the tensors, which is crucial to facilitate registration of tissue boundaries. Experiments involving real subjects, simulated subjects, fiber tracking, and atrophy detection indicate that TIMER performs better than the other methods (Yang et al., 2008; Zhang et al., 2006).

  13. Optimal atlas construction through hierarchical image registration

    NASA Astrophysics Data System (ADS)

    Grevera, George J.; Udupa, Jayaram K.; Odhner, Dewey; Torigian, Drew A.

    2016-03-01

    Atlases (digital or otherwise) are common in medicine. However, there is no standard framework for creating them from medical images. One traditional approach is to pick a representative subject and then proceed to label structures/regions of interest in this image. Another is to create a "mean" or average subject. Atlases may also contain more than a single representative (e.g., the Visible Human contains both a male and a female data set). Other criteria besides gender may be used as well, and the atlas may contain many examples for a given criterion. In this work, we propose that atlases be created in an optimal manner using a well-established graph theoretic approach using a min spanning tree (or more generally, a collection of them). The resulting atlases may contain many examples for a given criterion. In fact, our framework allows for the addition of new subjects to the atlas to allow it to evolve over time. Furthermore, one can apply segmentation methods to the graph (e.g., graph-cut, fuzzy connectedness, or cluster analysis) which allow it to be separated into "sub-atlases" as it evolves. We demonstrate our method by applying it to 50 3D CT data sets of the chest region, and by comparing it to a number of traditional methods using measures such as Mean Squared Difference, Mattes Mutual Information, and Correlation, and for rigid registration. Our results demonstrate that optimal atlases can be constructed in this manner and outperform other methods of construction using freely available software.

  14. Registration of plantar pressure images.

    PubMed

    Oliveira, Francisco P M; Tavares, João Manuel R S

    2012-01-01

    In this work, five computational methodologies to register plantar pressure images are compared: (1) the first methodology is based on matching the external contours of the feet; (2) the second uses the phase correlation technique; (3) the third addresses the direct maximization of cross-correlation using the Fourier transform; (4) the fourth minimizes the sum of squared differences using the Fourier transform; and (5) the fifth methodology iteratively optimizes an intensity (dis)similarity measure based on Powell's method. The accuracy and robustness of the five methodologies were assessed by using images from three common plantar pressure acquisition devices: a Footscan system, an EMED system, and a light reflection system. Using the residual error as a measure of accuracy, all methodologies revealed to be very accurate even in the presence of noise. The most accurate was the methodology based on the iterative optimization, when the mean squared error was minimized. It achieved a residual error inferior to 0.01 mm and 0.6 mm for non-noisy and noisy images, respectively. On the other hand, the methodology based on image contour matching was the fastest, but its accuracy was the lowest.

  15. Geometric assessment of image quality using digital image registration techniques

    NASA Technical Reports Server (NTRS)

    Tisdale, G. E.

    1976-01-01

    Image registration techniques were developed to perform a geometric quality assessment of multispectral and multitemporal image pairs. Based upon LANDSAT tapes, accuracies to a small fraction of a pixel were demonstrated. Because it is insensitive to the choice of registration areas, the technique is well suited to performance in an automatic system. It may be implemented at megapixel-per-second rates using a commercial minicomputer in combination with a special purpose digital preprocessor.

  16. Voxel similarity measures for automated image registration

    NASA Astrophysics Data System (ADS)

    Hill, Derek L.; Studholme, Colin; Hawkes, David J.

    1994-09-01

    We present the concept of the feature space sequence: 2D distributions of voxel features of two images generated at registration and a sequence of misregistrations. We provide an explanation of the structure seen in these images. Feature space sequences have been generated for a pair of MR image volumes identical apart from the addition of Gaussian noise to one, MR image volumes with and without Gadolinium enhancement, MR and PET-FDG image volumes and MR and CT image volumes, all of the head. The structure seen in the feature space sequences was used to devise two new measures of similarity which in turn were used to produce plots of cost versus misregistration for the 6 degrees of freedom of rigid body motion. One of these, the third order moment of the feature space histogram, was used to register the MR image volumes with and without Gadolinium enhancement. These techniques have the potential for registration accuracy to within a small fraction of a voxel or resolution element and therefore interpolation errors in image transformation can be the dominant source of error in subtracted images. We present a method for removing these errors using sinc interpolation and show how interpolation errors can be reduced by over two orders of magnitude.

  17. Validation of elastic registration algorithms based on adaptive irregular grids for medical applications

    NASA Astrophysics Data System (ADS)

    Franz, Astrid; Carlsen, Ingwer C.; Renisch, Steffen; Wischmann, Hans-Aloys

    2006-03-01

    Elastic registration of medical images is an active field of current research. Registration algorithms have to be validated in order to show that they fulfill the requirements of a particular clinical application. Furthermore, validation strategies compare the performance of different registration algorithms and can hence judge which algorithm is best suited for a target application. In the literature, validation strategies for rigid registration algorithms have been analyzed. For a known ground truth they assess the displacement error at a few landmarks, which is not sufficient for elastic transformations described by a huge number of parameters. Hence we consider the displacement error averaged over all pixels in the whole image or in a region-of-interest of clinical relevance. Using artificially, but realistically deformed images of the application domain, we use this quality measure to analyze an elastic registration based on transformations defined on adaptive irregular grids for the following clinical applications: Magnetic Resonance (MR) images of freely moving joints for orthopedic investigations, thoracic Computed Tomography (CT) images for the detection of pulmonary embolisms, and transmission images as used for the attenuation correction and registration of independently acquired Positron Emission Tomography (PET) and CT images. The definition of a region-of-interest allows to restrict the analysis of the registration accuracy to clinically relevant image areas. The behaviour of the displacement error as a function of the number of transformation control points and their placement can be used for identifying the best strategy for the initial placement of the control points.

  18. Canny edge-based deformable image registration

    NASA Astrophysics Data System (ADS)

    Kearney, Vasant; Huang, Yihui; Mao, Weihua; Yuan, Baohong; Tang, Liping

    2017-02-01

    This work focuses on developing a 2D Canny edge-based deformable image registration (Canny DIR) algorithm to register in vivo white light images taken at various time points. This method uses a sparse interpolation deformation algorithm to sparsely register regions of the image with strong edge information. A stability criterion is enforced which removes regions of edges that do not deform in a smooth uniform manner. Using a synthetic mouse surface ground truth model, the accuracy of the Canny DIR algorithm was evaluated under axial rotation in the presence of deformation. The accuracy was also tested using fluorescent dye injections, which were then used for gamma analysis to establish a second ground truth. The results indicate that the Canny DIR algorithm performs better than rigid registration, intensity corrected Demons, and distinctive features for all evaluation matrices and ground truth scenarios. In conclusion Canny DIR performs well in the presence of the unique lighting and shading variations associated with white-light-based image registration.

  19. The image registration of multi-band images by geometrical optics

    NASA Astrophysics Data System (ADS)

    Yan, Yung-Jhe; Chiang, Hou-Chi; Tsai, Yu-Hsiang; Huang, Ting-Wei; Mang, Ou-Yang

    2015-09-01

    The image fusion is combination of two or more images into one image. The fusion of multi-band spectral images has been in many applications, such as thermal system, remote sensing, medical treatment, etc. Images are taken with the different imaging sensors. If the sensors take images through the different optical paths in the same time, it will be in the different positions. The task of the image registration will be more difficult. Because the images are in the different field of views (F.O.V.), the different resolutions and the different view angles. It is important to build the relationship of the viewpoints in one image to the other image. In this paper, we focus on the problem of image registration for two non-pinhole sensors. The affine transformation between the 2-D image and the 3-D real world can be derived from the geometrical optics of the sensors. In the other word, the geometrical affine transformation function of two images are derived from the intrinsic and extrinsic parameters of two sensors. According to the affine transformation function, the overlap of the F.O.V. in two images can be calculated and resample two images in the same resolution. Finally, we construct the image registration model by the mapping function. It merges images for different imaging sensors. And, imaging sensors absorb different wavebands of electromagnetic spectrum at the different position in the same time.

  20. Medical imaging.

    PubMed Central

    Kreel, L.

    1991-01-01

    There is now a wide choice of medical imaging to show both focal and diffuse pathologies in various organs. Conventional radiology with plain films, fluoroscopy and contrast medium have many advantages, being readily available with low-cost apparatus and a familiarity that almost leads to contempt. The use of plain films in chest disease and in trauma does not need emphasizing, yet there are still too many occasions when the answer obtainable from a plain radiograph has not been available. The film may have been mislaid, or the examination was not requested, or the radiograph had been misinterpreted. The converse is also quite common. Examinations are performed that add nothing to patient management, such as skull films when CT will in any case be requested or views of the internal auditory meatus and heal pad thickness in acromegaly, to quote some examples. Other issues are more complicated. Should the patient who clinically has gall-bladder disease have more than a plain film that shows gall-stones? If the answer is yes, then why request a plain film if sonography will in any case be required to 'exclude' other pathologies especially of the liver or pancreas? But then should cholecystography, CT or scintigraphy be added for confirmation? Quite clearly there will be individual circumstances to indicate further imaging after sonography but in the vast majority of patients little or no extra information will be added. Statistics on accuracy and specificity will, in the case of gall-bladder pathology, vary widely if adenomyomatosis is considered by some to be a cause of symptoms or if sonographic examinations 'after fatty meals' are performed. The arguments for or against routine contrast urography rather than sonography are similar but the possibility of contrast reactions and the need to limit ionizing radiation must be borne in mind. These diagnostic strategies are also being influenced by their cost and availability; purely pragmatic considerations are not

  1. Surface driven biomechanical breast image registration

    NASA Astrophysics Data System (ADS)

    Eiben, Björn; Vavourakis, Vasileios; Hipwell, John H.; Kabus, Sven; Lorenz, Cristian; Buelow, Thomas; Williams, Norman R.; Keshtgar, M.; Hawkes, David J.

    2016-03-01

    Biomechanical modelling enables large deformation simulations of breast tissues under different loading conditions to be performed. Such simulations can be utilised to transform prone Magnetic Resonance (MR) images into a different patient position, such as upright or supine. We present a novel integration of biomechanical modelling with a surface registration algorithm which optimises the unknown material parameters of a biomechanical model and performs a subsequent regularised surface alignment. This allows deformations induced by effects other than gravity, such as those due to contact of the breast and MR coil, to be reversed. Correction displacements are applied to the biomechanical model enabling transformation of the original pre-surgical images to the corresponding target position. The algorithm is evaluated for the prone-to-supine case using prone MR images and the skin outline of supine Computed Tomography (CT) scans for three patients. A mean target registration error (TRE) of 10:9 mm for internal structures is achieved. For the prone-to-upright scenario, an optical 3D surface scan of one patient is used as a registration target and the nipple distances after alignment between the transformed MRI and the surface are 10:1 mm and 6:3 mm respectively.

  2. Automated landmark-guided deformable image registration.

    PubMed

    Kearney, Vasant; Chen, Susie; Gu, Xuejun; Chiu, Tsuicheng; Liu, Honghuan; Jiang, Lan; Wang, Jing; Yordy, John; Nedzi, Lucien; Mao, Weihua

    2015-01-07

    The purpose of this work is to develop an automated landmark-guided deformable image registration (LDIR) algorithm between the planning CT and daily cone-beam CT (CBCT) with low image quality. This method uses an automated landmark generation algorithm in conjunction with a local small volume gradient matching search engine to map corresponding landmarks between the CBCT and the planning CT. The landmarks act as stabilizing control points in the following Demons deformable image registration. LDIR is implemented on graphics processing units (GPUs) for parallel computation to achieve ultra fast calculation. The accuracy of the LDIR algorithm has been evaluated on a synthetic case in the presence of different noise levels and data of six head and neck cancer patients. The results indicate that LDIR performed better than rigid registration, Demons, and intensity corrected Demons for all similarity metrics used. In conclusion, LDIR achieves high accuracy in the presence of multimodality intensity mismatch and CBCT noise contamination, while simultaneously preserving high computational efficiency.

  3. Automated landmark-guided deformable image registration

    NASA Astrophysics Data System (ADS)

    Kearney, Vasant; Chen, Susie; Gu, Xuejun; Chiu, Tsuicheng; Liu, Honghuan; Jiang, Lan; Wang, Jing; Yordy, John; Nedzi, Lucien; Mao, Weihua

    2015-01-01

    The purpose of this work is to develop an automated landmark-guided deformable image registration (LDIR) algorithm between the planning CT and daily cone-beam CT (CBCT) with low image quality. This method uses an automated landmark generation algorithm in conjunction with a local small volume gradient matching search engine to map corresponding landmarks between the CBCT and the planning CT. The landmarks act as stabilizing control points in the following Demons deformable image registration. LDIR is implemented on graphics processing units (GPUs) for parallel computation to achieve ultra fast calculation. The accuracy of the LDIR algorithm has been evaluated on a synthetic case in the presence of different noise levels and data of six head and neck cancer patients. The results indicate that LDIR performed better than rigid registration, Demons, and intensity corrected Demons for all similarity metrics used. In conclusion, LDIR achieves high accuracy in the presence of multimodality intensity mismatch and CBCT noise contamination, while simultaneously preserving high computational efficiency.

  4. Medical imaging

    SciTech Connect

    Schneider, R.H.; Dwyer, S.J.

    1987-01-01

    This book contains papers from 26 sessions. Some of the session titles are: Tomographic Reconstruction, Radiography, Fluoro/Angio, Imaging Performance Measures, Perception, Image Processing, 3-D Display, and Printers, Displays, and Digitizers.

  5. [Registration and monitoring of radiation exposure from radiological imaging].

    PubMed

    Jungmann, F; Pinto dos Santos, D; Hempel, J; Düber, C; Mildenberger, P

    2013-06-01

    Strategies for reducing radiation exposure are an important part of optimizing medical imaging and therefore a relevant quality factor in radiology. Regarding the medical radiation exposure, computed tomography has a special relevance. The use of the integrating the healthcare enterprise (IHE) radiation exposure monitoring (REM) profile is the upcoming standard for organizing and collecting exposure data in radiology. Currently most installed base devices do not support this profile generating the required digital imaging and communication in medicine (DICOM) dose structured reporting (SR). For this reason different solutions had been developed to register dose exposure measurements without having the dose SR object.Registration and analysis of dose-related parameters is required for constantly optimizing examination protocols, especially computed tomography (CT) examinations based on the latest research results in order to minimize the individual radiation dose exposure from medical imaging according to the principle as low as reasonably achievable (ALARA).

  6. Spatially weighted mutual information image registration for image guided radiation therapy

    SciTech Connect

    Park, Samuel B.; Rhee, Frank C.; Monroe, James I.; Sohn, Jason W.

    2010-09-15

    Purpose: To develop a new metric for image registration that incorporates the (sub)pixelwise differential importance along spatial location and to demonstrate its application for image guided radiation therapy (IGRT). Methods: It is well known that rigid-body image registration with mutual information is dependent on the size and location of the image subset on which the alignment analysis is based [the designated region of interest (ROI)]. Therefore, careful review and manual adjustments of the resulting registration are frequently necessary. Although there were some investigations of weighted mutual information (WMI), these efforts could not apply the differential importance to a particular spatial location since WMI only applies the weight to the joint histogram space. The authors developed the spatially weighted mutual information (SWMI) metric by incorporating an adaptable weight function with spatial localization into mutual information. SWMI enables the user to apply the selected transform to medically ''important'' areas such as tumors and critical structures, so SWMI is neither dominated by, nor neglects the neighboring structures. Since SWMI can be utilized with any weight function form, the authors presented two examples of weight functions for IGRT application: A Gaussian-shaped weight function (GW) applied to a user-defined location and a structures-of-interest (SOI) based weight function. An image registration example using a synthesized 2D image is presented to illustrate the efficacy of SWMI. The convergence and feasibility of the registration method as applied to clinical imaging is illustrated by fusing a prostate treatment planning CT with a clinical cone beam CT (CBCT) image set acquired for patient alignment. Forty-one trials are run to test the speed of convergence. The authors also applied SWMI registration using two types of weight functions to two head and neck cases and a prostate case with clinically acquired CBCT/MVCT image sets. The

  7. Manifold-based feature point matching for multi-modal image registration.

    PubMed

    Hu, Liang; Wang, Manning; Song, Zhijian

    2013-03-01

    Images captured using different modalities usually have significant variations in their intensities, which makes it difficult to reveal their internal structural similarities and achieve accurate registration. Most conventional feature-based image registration techniques are fast and efficient, but they cannot be used directly for the registration of multi-modal images because of these intensity variations. This paper introduces the theory of manifold learning to transform the original images into mono-modal modalities, which is a feature-based method that is applicable to multi-modal image registration. Subsequently, scale-invariant feature transform is used to detect highly distinctive local descriptors and matches between corresponding images, and a point-based registration is executed. The algorithm was tested with T1- and T2-weighted magnetic resonance (MR) images obtained from BrainWeb. Both qualitative and quantitative evaluations of the method were performed and the results compared with those produced previously. The experiments showed that feature point matching after manifold learning achieved more accurate results than did the similarity measure for multi-modal image registration. This study provides a new manifold-based feature point matching method for multi-modal medical image registration, especially for MR images. The proposed method performs better than do conventional intensity-based techniques in terms of its registration accuracy and is suitable for clinical procedures. Copyright © 2012 John Wiley & Sons, Ltd.

  8. Adaptive registration of diffusion tensor images on lie groups

    NASA Astrophysics Data System (ADS)

    Liu, Wei; Chen, LeiTing; Cai, HongBin; Qiu, Hang; Fei, Nanxi

    2016-08-01

    With diffusion tensor imaging (DTI), more exquisite information on tissue microstructure is provided for medical image processing. In this paper, we present a locally adaptive topology preserving method for DTI registration on Lie groups. The method aims to obtain more plausible diffeomorphisms for spatial transformations via accurate approximation for the local tangent space on the Lie group manifold. In order to capture an exact geometric structure of the Lie group, the local linear approximation is efficiently optimized by using the adaptive selection of the local neighborhood sizes on the given set of data points. Furthermore, numerical comparative experiments are conducted on both synthetic data and real DTI data to demonstrate that the proposed method yields a higher degree of topology preservation on a dense deformation tensor field while improving the registration accuracy.

  9. Groupwise Registration Based on Hierarchical Image Clustering and Atlas Synthesis

    PubMed Central

    Wang, Qian; Chen, Liya; Yap, Pew-Thian; Wu, Guorong; Shen, Dinggang

    2010-01-01

    Groupwise registration has recently been proposed for simultaneous and consistent registration of all images in a group. Since many deformation parameters need to be optimized for each image under registration, the number of images that can be effectively handled by conventional groupwise registration methods is limited. Moreover, the robustness of registration is at stake due to significant intersubject variability. To overcome these problems, we present a groupwise registration framework, which is based on a hierarchical image clustering and atlas synthesis strategy. The basic idea is to decompose a large-scale groupwise registration problem into a series of small-scale problems, each of which is relatively easy to solve using a general computer. In particular, we employ a method called affinity propagation, which is designed for fast and robust clustering, to hierarchically cluster images into a pyramid of classes. Intraclass registration is then performed to register all images within individual classes, resulting in a representative center image for each class. These center images of different classes are further registered, from the bottom to the top in the pyramid. Once the registration reaches the summit of the pyramid, a single center image, or an atlas, is synthesized. Utilizing this strategy, we can efficiently and effectively register a large image group, construct their atlas, and, at the same time, establish shape correspondences between each image and the atlas. We have evaluated our framework using real and simulated data, and the results indicate that our framework achieves better robustness and registration accuracy compared to conventional methods. PMID:20063349

  10. A hybrid multimodal non-rigid registration of MR images based on diffeomorphic demons.

    PubMed

    Lu, Huanxiang; Cattin, Philippe C; Reyes, Mauricio

    2010-01-01

    In this paper we present a novel hybrid approach for multimodal medical image registration based on diffeomorphic demons. Diffeomorphic demons have proven to be a robust and efficient way for intensity-based image registration. A very recent extension even allows to use mutual information (MI) as a similarity measure to registration multimodal images. However, due to the intensity correspondence uncertainty existing in some anatomical parts, it is difficult for a purely intensity-based algorithm to solve the registration problem. Therefore, we propose to combine the resulting transformations from both intensity-based and landmark-based methods for multimodal non-rigid registration based on diffeomorphic demons. Several experiments on different types of MR images were conducted, for which we show that a better anatomical correspondence between the images can be obtained using the hybrid approach than using either intensity information or landmarks alone.

  11. Nonlinear image registration with bidirectional metric and reciprocal regularization

    PubMed Central

    Ying, Shihui; Li, Dan; Xiao, Bin; Peng, Yaxin; Du, Shaoyi; Xu, Meifeng

    2017-01-01

    Nonlinear registration is an important technique to align two different images and widely applied in medical image analysis. In this paper, we develop a novel nonlinear registration framework based on the diffeomorphic demons, where a reciprocal regularizer is introduced to assume that the deformation between two images is an exact diffeomorphism. In detail, first, we adopt a bidirectional metric to improve the symmetry of the energy functional, whose variables are two reciprocal deformations. Secondly, we slack these two deformations into two independent variables and introduce a reciprocal regularizer to assure the deformations being the exact diffeomorphism. Then, we utilize an alternating iterative strategy to decouple the model into two minimizing subproblems, where a new closed form for the approximate velocity of deformation is calculated. Finally, we compare our proposed algorithm on two data sets of real brain MR images with two relative and conventional methods. The results validate that our proposed method improves accuracy and robustness of registration, as well as the gained bidirectional deformations are actually reciprocal. PMID:28231342

  12. Nonlinear image registration with bidirectional metric and reciprocal regularization.

    PubMed

    Ying, Shihui; Li, Dan; Xiao, Bin; Peng, Yaxin; Du, Shaoyi; Xu, Meifeng

    2017-01-01

    Nonlinear registration is an important technique to align two different images and widely applied in medical image analysis. In this paper, we develop a novel nonlinear registration framework based on the diffeomorphic demons, where a reciprocal regularizer is introduced to assume that the deformation between two images is an exact diffeomorphism. In detail, first, we adopt a bidirectional metric to improve the symmetry of the energy functional, whose variables are two reciprocal deformations. Secondly, we slack these two deformations into two independent variables and introduce a reciprocal regularizer to assure the deformations being the exact diffeomorphism. Then, we utilize an alternating iterative strategy to decouple the model into two minimizing subproblems, where a new closed form for the approximate velocity of deformation is calculated. Finally, we compare our proposed algorithm on two data sets of real brain MR images with two relative and conventional methods. The results validate that our proposed method improves accuracy and robustness of registration, as well as the gained bidirectional deformations are actually reciprocal.

  13. Mono- and multimodal registration of optical breast images

    NASA Astrophysics Data System (ADS)

    Pearlman, Paul C.; Adams, Arthur; Elias, Sjoerd G.; Mali, Willem P. Th. M.; Viergever, Max A.; Pluim, Josien P. W.

    2012-08-01

    Optical breast imaging offers the possibility of noninvasive, low cost, and high sensitivity imaging of breast cancers. Poor spatial resolution and a lack of anatomical landmarks in optical images of the breast make interpretation difficult and motivate registration and fusion of these data with subsequent optical images and other breast imaging modalities. Methods used for registration and fusion of optical breast images are reviewed. Imaging concerns relevant to the registration problem are first highlighted, followed by a focus on both monomodal and multimodal registration of optical breast imaging. Where relevant, methods pertaining to other imaging modalities or imaged anatomies are presented. The multimodal registration discussion concerns digital x-ray mammography, ultrasound, magnetic resonance imaging, and positron emission tomography.

  14. Imaging medical imaging

    NASA Astrophysics Data System (ADS)

    Journeau, P.

    2015-03-01

    This paper presents progress on imaging the research field of Imaging Informatics, mapped as the clustering of its communities together with their main results by applying a process to produce a dynamical image of the interactions between their results and their common object(s) of research. The basic side draws from a fundamental research on the concept of dimensions and projective space spanning several streams of research about three-dimensional perceptivity and re-cognition and on their relation and reduction to spatial dimensionality. The application results in an N-dimensional mapping in Bio-Medical Imaging, with dimensions such as inflammatory activity, MRI acquisition sequencing, spatial resolution (voxel size), spatiotemporal dimension inferred, toxicity, depth penetration, sensitivity, temporal resolution, wave length, imaging duration, etc. Each field is represented through the projection of papers' and projects' `discriminating' quantitative results onto the specific N-dimensional hypercube of relevant measurement axes, such as listed above and before reduction. Past published differentiating results are represented as red stars, achieved unpublished results as purple spots and projects at diverse progress advancement levels as blue pie slices. The goal of the mapping is to show the dynamics of the trajectories of the field in its own experimental frame and their direction, speed and other characteristics. We conclude with an invitation to participate and show a sample mapping of the dynamics of the community and a tentative predictive model from community contribution.

  15. Medical Imaging.

    ERIC Educational Resources Information Center

    Jaffe, C. Carl

    1982-01-01

    Describes principle imaging techniques, their applications, and their limitations in terms of diagnostic capability and possible adverse biological effects. Techniques include film radiography, computed tomography, nuclear medicine, positron emission tomography (PET), ultrasonography, nuclear magnetic resonance, and digital radiography. PET has…

  16. Medical Imaging.

    ERIC Educational Resources Information Center

    Jaffe, C. Carl

    1982-01-01

    Describes principle imaging techniques, their applications, and their limitations in terms of diagnostic capability and possible adverse biological effects. Techniques include film radiography, computed tomography, nuclear medicine, positron emission tomography (PET), ultrasonography, nuclear magnetic resonance, and digital radiography. PET has…

  17. TU-A-19A-01: Image Registration I: Deformable Image Registration, Contour Propagation and Dose Mapping: 101 and 201

    SciTech Connect

    Kessler, M

    2014-06-15

    Deformable image registration, contour propagation and dose mapping have become common, possibly essential tools for modern image-guided radiation therapy. Historically, these tools have been largely developed at academic medical centers and used in a rather limited and well controlled fashion. Today these tools are now available to the radiotherapy community at large, both as stand-alone applications and as integrated components of both treatment planning and treatment delivery systems. Unfortunately, the details of how these tools work and their limitations are not generally documented or described by the vendors that provide them. Although “it looks right”, determining that unphysical deformations may have occurred is crucial. Because of this, understanding how and when to use, and not use these tools to support everyday clinical decisions is far from straight forward. The goal of this session will be to present both the theory (basic and advanced) and practical clinical use of deformable image registration, contour propagation and dose mapping. To the extent possible, the “secret sauce” that different vendor use to produce reasonable/acceptable results will be described. A detailed explanation of the possible sources of errors and actual examples of these will be presented. Knowing the underlying principles of the process and understanding the confounding factors will help the practicing medical physicist be better able to make decisions (about making decisions) using these tools available. Learning Objectives: Understand the basic (101) and advanced (201) principles of deformable image registration, contour propagation and dose mapping data mapping. Understand the sources and impact of errors in registration and data mapping and the methods for evaluating the performance of these tools. Understand the clinical use and value of these tools, especially when used as a “black box”.

  18. Registration of multi-view apical 3D echocardiography images

    NASA Astrophysics Data System (ADS)

    Mulder, H. W.; van Stralen, M.; van der Zwaan, H. B.; Leung, K. Y. E.; Bosch, J. G.; Pluim, J. P. W.

    2011-03-01

    Real-time three-dimensional echocardiography (RT3DE) is a non-invasive method to visualize the heart. Disadvantageously, it suffers from non-uniform image quality and a limited field of view. Image quality can be improved by fusion of multiple echocardiography images. Successful registration of the images is essential for prosperous fusion. Therefore, this study examines the performance of different methods for intrasubject registration of multi-view apical RT3DE images. A total of 14 data sets was annotated by two observers who indicated the position of the apex and four points on the mitral valve ring. These annotations were used to evaluate registration. Multi-view end-diastolic (ED) as well as end-systolic (ES) images were rigidly registered in a multi-resolution strategy. The performance of single-frame and multi-frame registration was examined. Multi-frame registration optimizes the metric for several time frames simultaneously. Furthermore, the suitability of mutual information (MI) as similarity measure was compared to normalized cross-correlation (NCC). For initialization of the registration, a transformation that describes the probe movement was obtained by manually registering five representative data sets. It was found that multi-frame registration can improve registration results with respect to single-frame registration. Additionally, NCC outperformed MI as similarity measure. If NCC was optimized in a multi-frame registration strategy including ED and ES time frames, the performance of the automatic method was comparable to that of manual registration. In conclusion, automatic registration of RT3DE images performs as good as manual registration. As registration precedes image fusion, this method can contribute to improved quality of echocardiography images.

  19. “Nonparametric Local Smoothing” is not image registration

    PubMed Central

    2012-01-01

    Background Image registration is one of the most important and universally useful computational tasks in biomedical image analysis. A recent article by Xing & Qiu (IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10):2081–2092, 2011) is based on an inappropriately narrow conceptualization of the image registration problem as the task of making two images look alike, which disregards whether the established spatial correspondence is plausible. The authors propose a new algorithm, Nonparametric Local Smoothing (NLS) for image registration, but use image similarities alone as a measure of registration performance, although these measures do not relate reliably to the realism of the correspondence map. Results Using data obtained from its authors, we show experimentally that the method proposed by Xing & Qiu is not an effective registration algorithm. While it optimizes image similarity, it does not compute accurate, interpretable transformations. Even judged by image similarity alone, the proposed method is consistently outperformed by a simple pixel permutation algorithm, which is known by design not to compute valid registrations. Conclusions This study has demonstrated that the NLS algorithm proposed recently for image registration, and published in one of the most respected journals in computer science, is not, in fact, an effective registration method at all. Our results also emphasize the general need to apply registration evaluation criteria that are sensitive to whether correspondences are accurate and mappings between images are physically interpretable. These goals cannot be achieved by simply reporting image similarities. PMID:23116330

  20. An effective non-rigid registration approach for ultrasound image based on "demons" algorithm.

    PubMed

    Liu, Yan; Cheng, H D; Huang, Jianhua; Zhang, Yingtao; Tang, Xianglong; Tian, Jiawei

    2013-06-01

    Medical image registration is an important component of computer-aided diagnosis system in diagnostics, therapy planning, and guidance of surgery. Because of its low signal/noise ratio (SNR), ultrasound (US) image registration is a difficult task. In this paper, a fully automatic non-rigid image registration algorithm based on demons algorithm is proposed for registration of ultrasound images. In the proposed method, an "inertia force" derived from the local motion trend of pixels in a Moore neighborhood system is produced and integrated into optical flow equation to estimate the demons force, which is helpful to handle the speckle noise and preserve the geometric continuity of US images. In the experiment, a series of US images and several similarity measure metrics are utilized for evaluating the performance. The experimental results demonstrate that the proposed method can register ultrasound images efficiently, robust to noise, quickly and automatically.

  1. Ray-tracing based registration for HRCT images of the lungs.

    PubMed

    Busayara, Sata; Zrimec, Tatjana

    2006-01-01

    Image registration is a fundamental problem in medical imaging. It is especially challenging in lung images compared, for example, with the brain. The challenges include large anatomical variations of human lung and a lack of fixed landmarks inside the lung. This paper presents a new method for lung HRCT image registration. It employs a landmark-based global transformation and a novel ray-tracing-based lung surface registration. The proposed surface registration method has two desirable properties: 1) it is fully reversible, and 2) it ensures that the registered lung will be inside the target lung. We evaluated the registration performance by applying it to lung regions mapping. Tested on 46 scans, the registered regions were 89% accurate compared with the ground-truth.

  2. Research on image registration based on D-Nets

    NASA Astrophysics Data System (ADS)

    Wu, Cengceng; Liu, Zhaoguang; Cheng, Hongtan

    2017-06-01

    Image registration is the key technology of digital imaging applications, it is used widely. We researched the image registration techniques in this paper. Based on the basis of D-Nets image registration algorithms, we propose a new innovation. We turn first to process image, so we can get synthetic images of original images and enhanced images. Then we extract SIFT feature in the original image. Next, in order to reduce noise of the image, we use the Gauss filter to process the synthesized image. Then we do experiments with synthetic images in the process of image registration. In this process, we use the D-Nets algorithm to achieve. Compared to the existing method, it can greatly improve the accuracy and recall.

  3. Image Registration for Targeted MRI-guided Transperineal Prostate Biopsy

    PubMed Central

    Fedorov, Andriy; Tuncali, Kemal; Fennessy, Fiona M.; Tokuda, Junichi; Hata, Nobuhiko; Wells, William M.; Kikinis, Ron; Tempany, Clare M.

    2012-01-01

    Purpose To develop and evaluate image registration methodology for automated re-identification of tumor-suspicious foci from pre-procedural MR exams during MR-guided transperineal prostate core biopsy. Materials and Methods A hierarchical approach for automated registration between planning and intra-procedural T2-weighted prostate MRI was developed and evaluated on the images acquired during 10 consecutive MR-guided biopsies. Registration accuracy was quantified at image-based landmarks and by evaluating spatial overlap for the manually segmented prostate and sub-structures. Registration reliability was evaluated by simulating initial mis-registration and analyzing the convergence behavior. Registration precision was characterized at the planned biopsy targets. Results The total computation time was compatible with a clinical setting, being at most 2 minutes. Deformable registration led to a significant improvement in spatial overlap of the prostate and peripheral zone contours compared to both rigid and affine registration. Average in-slice landmark registration error was 1.3±0.5 mm. Experiments simulating initial mis-registration resulted in an estimated average capture range of 6 mm and an average in-slice registration precision of ±0.3 mm. Conclusion Our registration approach requires minimum user interaction and is compatible with the time constraints of our interventional clinical workflow. The initial evaluation shows acceptable accuracy, reliability and consistency of the method. PMID:22645031

  4. INVITED REVIEW--IMAGE REGISTRATION IN VETERINARY RADIATION ONCOLOGY: INDICATIONS, IMPLICATIONS, AND FUTURE ADVANCES.

    PubMed

    Feng, Yang; Lawrence, Jessica; Cheng, Kun; Montgomery, Dean; Forrest, Lisa; Mclaren, Duncan B; McLaughlin, Stephen; Argyle, David J; Nailon, William H

    2016-01-01

    The field of veterinary radiation therapy (RT) has gained substantial momentum in recent decades with significant advances in conformal treatment planning, image-guided radiation therapy (IGRT), and intensity-modulated (IMRT) techniques. At the root of these advancements lie improvements in tumor imaging, image alignment (registration), target volume delineation, and identification of critical structures. Image registration has been widely used to combine information from multimodality images such as computerized tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) to improve the accuracy of radiation delivery and reliably identify tumor-bearing areas. Many different techniques have been applied in image registration. This review provides an overview of medical image registration in RT and its applications in veterinary oncology. A summary of the most commonly used approaches in human and veterinary medicine is presented along with their current use in IGRT and adaptive radiation therapy (ART). It is important to realize that registration does not guarantee that target volumes, such as the gross tumor volume (GTV), are correctly identified on the image being registered, as limitations unique to registration algorithms exist. Research involving novel registration frameworks for automatic segmentation of tumor volumes is ongoing and comparative oncology programs offer a unique opportunity to test the efficacy of proposed algorithms.

  5. Groupwise registration of MR brain images with tumors

    NASA Astrophysics Data System (ADS)

    Tang, Zhenyu; Wu, Yihong; Fan, Yong

    2017-09-01

    A novel groupwise image registration framework is developed for registering MR brain images with tumors. Our method iteratively estimates a normal-appearance counterpart for each tumor image to be registered and constructs a directed graph (digraph) of normal-appearance images to guide the groupwise image registration. Particularly, our method maps each tumor image to its normal appearance counterpart by identifying and inpainting brain tumor regions with intensity information estimated using a low-rank plus sparse matrix decomposition based image representation technique. The estimated normal-appearance images are groupwisely registered to a group center image guided by a digraph of images so that the total length of ‘image registration paths’ to be the minimum, and then the original tumor images are warped to the group center image using the resulting deformation fields. We have evaluated our method based on both simulated and real MR brain tumor images. The registration results were evaluated with overlap measures of corresponding brain regions and average entropy of image intensity information, and Wilcoxon signed rank tests were adopted to compare different methods with respect to their regional overlap measures. Compared with a groupwise image registration method that is applied to normal-appearance images estimated using the traditional low-rank plus sparse matrix decomposition based image inpainting, our method achieved higher image registration accuracy with statistical significance (p  =  7.02  ×  10-9).

  6. Groupwise registration of MR brain images with tumors.

    PubMed

    Tang, Zhenyu; Wu, Yihong; Fan, Yong

    2017-08-04

    A novel groupwise image registration framework is developed for registering MR brain images with tumors. Our method iteratively estimates a normal-appearance counterpart for each tumor image to be registered and constructs a directed graph (digraph) of normal-appearance images to guide the groupwise image registration. Particularly, our method maps each tumor image to its normal appearance counterpart by identifying and inpainting brain tumor regions with intensity information estimated using a low-rank plus sparse matrix decomposition based image representation technique. The estimated normal-appearance images are groupwisely registered to a group center image guided by a digraph of images so that the total length of 'image registration paths' to be the minimum, and then the original tumor images are warped to the group center image using the resulting deformation fields. We have evaluated our method based on both simulated and real MR brain tumor images. The registration results were evaluated with overlap measures of corresponding brain regions and average entropy of image intensity information, and Wilcoxon signed rank tests were adopted to compare different methods with respect to their regional overlap measures. Compared with a groupwise image registration method that is applied to normal-appearance images estimated using the traditional low-rank plus sparse matrix decomposition based image inpainting, our method achieved higher image registration accuracy with statistical significance (p  =  7.02  ×  10(-9)).

  7. Effects of Image Contrast on Functional MRI Image Registration

    PubMed Central

    Gonzalez-Castillo, Javier; Duthie, Kristen N.; Saad, Ziad S.; Chu, Carlton; Bandettini, Peter A.; Luh, Wen-Ming

    2012-01-01

    Lack of tissue contrast and existing inhomogeneous bias fields from multi-channel coils have the potential to degrade the output of registration algorithms; and consequently degrade group analysis and any attempt to accurately localize brain function. Non-invasive ways to improve tissue contrast in fMRI images include the use of low flip angles (FAs) well below the Ernst angle and longer repetition times (TR). Techniques to correct intensity inhomogeneity are also available in most mainstream fMRI data analysis packages; but are not used as part of the pre-processing pipeline in many studies. In this work, we use a combination of real data and simulations to show that simple-to-implement acquisition/pre-processing techniques can significantly improve the outcome of both functional-to-functional and anatomical-to-functional image registrations. We also emphasize the need of tissue contrast on EPI images to be able to appropriately evaluate the quality of the alignment. In particular, we show that the use of low FAs (e.g., θ≤40°), when physiological noise considerations permit such an approach, significantly improves accuracy, consistency and stability of registration for data acquired at relatively short TRs (TR≤2s). Moreover, we also show that the application of bias correction techniques significantly improves alignment both for array-coil data (known to contain high intensity inhomogeneity) as well as birdcage-coil data. Finally, improvements in alignment derived from the use of the first infinite-TR volumes (ITVs) as targets for registration are also demonstrated. For the purpose of quantitatively evaluating the different scenarios, two novel metrics were developed: Mean Voxel Distance (MVD) to evaluate registration consistency, and Deviation of Mean Voxel Distance (dMVD) to evaluate registration stability across successive alignment attempts. PMID:23128074

  8. Registration of Optical Data with High-Resolution SAR Data: a New Image Registration Solution

    NASA Astrophysics Data System (ADS)

    Bahr, T.; Jin, X.

    2013-04-01

    Accurate image-to-image registration is critical for many image processing workflows, including georeferencing, change detection, data fusion, image mosaicking, DEM extraction and 3D modeling. Users need a solution to generate tie points accurately and geometrically align the images automatically. To solve these requirements we developed the Hybrid Powered Auto-Registration Engine (HyPARE). HyPARE combines all available spatial reference information with a number of image registration approaches to improve the accuracy, performance, and automation of tie point generation and image registration. We demonstrate this approach by the registration of a Pléiades-1a image with a TerraSAR-X SpotLight image of Hannover, Germany. Registering images with different modalities is a known challenging problem; e.g. manual tie point collection is prone to error. The registration engine allows to generate tie points automatically, using an optimized mutual information-based matching method. It produces more accurate results than traditional correlation-based measures. In this example the resulting tie points are well distributed across the overlapping areas, even as the images have significant local feature differences.

  9. Unsupervised Deep Feature Learning for Deformable Registration of MR Brain Images

    PubMed Central

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Gao, Yaozong; Liao, Shu; Shen, Dinggang

    2014-01-01

    Establishing accurate anatomical correspondences is critical for medical image registration. Although many hand-engineered features have been proposed for correspondence detection in various registration applications, no features are general enough to work well for all image data. Although many learning-based methods have been developed to help selection of best features for guiding correspondence detection across subjects with large anatomical variations, they are often limited by requiring the known correspondences (often presumably estimated by certain registration methods) as the ground truth for training. To address this limitation, we propose using an unsupervised deep learning approach to directly learn the basis filters that can effectively represent all observed image patches. Then, the coefficients by these learnt basis filters in representing the particular image patch can be regarded as the morphological signature for correspondence detection during image registration. Specifically, a stacked two-layer convolutional network is constructed to seek for the hierarchical representations for each image patch, where the high-level features are inferred from the responses of the low-level network. By replacing the hand-engineered features with our learnt data-adaptive features for image registration, we achieve promising registration results, which demonstrates that a general approach can be built to improve image registration by using data-adaptive features through unsupervised deep learning. PMID:24579196

  10. Visualization of Deformable Image Registration Quality using Local Image Dissimilarity.

    PubMed

    Schlachter, Matthias; Fechter, Tobias; Jurisic, Miro; Schimek-Jasch, Tanja; Oehlke, Oliver; Adebahr, Sonja; Birkfellner, Wolfgang; Nestle, Ursula; Buhler, Katja

    2016-04-29

    Deformable image registration (DIR) has the potential to improve modern radiotherapy in many aspects, including volume definition, treatment planning and image-guided adaptive radiotherapy. Studies have shown its possible clinical benefits. However, measuring DIR accuracy is difficult without known ground truth, but necessary before integration in the radiotherapy workflow. Visual assessment is an important step towards clinical acceptance. We propose a visualization framework which supports the exploration and the assessment of DIR accuracy. It offers different interaction and visualization features for exploration of candidate regions to simplify the process of visual assessment. The visualization is based on voxel-wise comparison of local image patches for which dissimilarity measures are computed and visualized to indicate locally the registration results. We performed an evaluation with three radiation oncologists to demonstrate the viability of our approach. In the evaluation, lung regions were rated by the participants with regards to their visual accuracy and compared to the registration error measured with expert defined landmarks. Regions rated as "accepted" had an average registration error of 1.8 mm, with the highest single landmark error being 3.3 mm. Additionally, survey results show that the proposed visualizations support a fast and intuitive investigation of DIR accuracy, and are suitable for finding even small errors.

  11. Visualization of Deformable Image Registration Quality Using Local Image Dissimilarity.

    PubMed

    Schlachter, Matthias; Fechter, Tobias; Jurisic, Miro; Schimek-Jasch, Tanja; Oehlke, Oliver; Adebahr, Sonja; Birkfellner, Wolfgang; Nestle, Ursula; Bu Hler, Katja

    2016-10-01

    Deformable image registration (DIR) has the potential to improve modern radiotherapy in many aspects, including volume definition, treatment planning and image-guided adaptive radiotherapy. Studies have shown its possible clinical benefits. However, measuring DIR accuracy is difficult without known ground truth, but necessary before integration in the radiotherapy workflow. Visual assessment is an important step towards clinical acceptance. We propose a visualization framework which supports the exploration and the assessment of DIR accuracy. It offers different interaction and visualization features for exploration of candidate regions to simplify the process of visual assessment. The visualization is based on voxel-wise comparison of local image patches for which dissimilarity measures are computed and visualized to indicate locally the registration results. We performed an evaluation with three radiation oncologists to demonstrate the viability of our approach. In the evaluation, lung regions were rated by the participants with regards to their visual accuracy and compared to the registration error measured with expert defined landmarks. Regions rated as "accepted" had an average registration error of 1.8 mm, with the highest single landmark error being 3.3 mm. Additionally, survey results show that the proposed visualizations support a fast and intuitive investigation of DIR accuracy, and are suitable for finding even small errors.

  12. Registration of Visible and Infrared Images Based on Gradient Information

    NASA Astrophysics Data System (ADS)

    Geng, Yingnan; Wang, Yanan

    2017-06-01

    Multi-modality image registration is very challenging due to disparate imaging theory of sensors. Extraction of similar features and evaluation of algorithms have been two key difficulties. In this paper, gradient of RGB vector space, as a similar image feature, is shown the viability of using for the registration of infrared and visible still stereo pairs. Based on adaptive support-window algorithm, a novel method, which formulates the registration problem as correspondences between gradients of RGB vector space and obtains the best matching by minimizing gradient difference in sliding correspondence support-windows, is proposed. Evaluation experiments demonstrate high rates of successful registration by yielding qualitative and quantitative results.

  13. Biomechanical model as a registration tool for image-guided neurosurgery: evaluation against BSpline registration

    PubMed Central

    Mostayed, Ahmed; Garlapati, Revanth Reddy; Joldes, Grand Roman; Wittek, Adam; Roy, Aditi; Kikinis, Ron; Warfield, Simon K.; Miller, Karol

    2013-01-01

    In this paper we evaluate the accuracy of warping of neuro-images using brain deformation predicted by means of a patient-specific biomechanical model against registration using a BSpline-based free form deformation algorithm. Unlike the Bspline algorithm, biomechanics-based registration does not require an intra-operative MR image which is very expensive and cumbersome to acquire. Only sparse intra-operative data on the brain surface is sufficient to compute deformation for the whole brain. In this contribution the deformation fields obtained from both methods are qualitatively compared and overlaps of Canny edges extracted from the images are examined. We define an edge based Hausdorff distance metric to quantitatively evaluate the accuracy of registration for these two algorithms. The qualitative and quantitative evaluations indicate that our biomechanics-based registration algorithm, despite using much less input data, has at least as high registration accuracy as that of the BSpline algorithm. PMID:23771299

  14. Progressive piecewise registration of orthophotos and airborne scanner images

    NASA Astrophysics Data System (ADS)

    Chen, Lin-Chi; Yang, T. T.

    1994-08-01

    From the image-to-image registration point of view, we propose a scheme to iteratively register airborne multi-spectral imagery onto its counterpart, i.e., orthographic photography. The required registration control point pairs are automatically augmented first. Then a local registration procedure is applied according to the generated registration control point pairs. The coordinate transformation uses thin plate spline function. Through a consistency check, if the disparities between the reference image and the transformed airborne multi-spectral image is too large to accept, next iteration is performed. During the second iteration, some best matched feature points used in the consistency check of the first iteration append to the existing registration control points. This iteration procedure continues until the disparities are small enough. Experimental results indicate that the output image attain an excellent geometrical similarity with respect to the reference image. The rms of the disparities is less than 0.5 pixels.

  15. Constrained non-rigid registration for whole body image registration: method and validation

    NASA Astrophysics Data System (ADS)

    Li, Xia; Yankeelov, Thomas E.; Peterson, Todd E.; Gore, John C.; Dawant, Benoit M.

    2007-03-01

    3D intra- and inter-subject registration of image volumes is important for tasks that include measurements and quantification of temporal/longitudinal changes, atlas-based segmentation, deriving population averages, or voxel and tensor-based morphometry. A number of methods have been proposed to tackle this problem but few of them have focused on the problem of registering whole body image volumes acquired either from humans or small animals. These image volumes typically contain a large number of articulated structures, which makes registration more difficult than the registration of head images, to which the vast majority of registration algorithms have been applied. To solve this problem, we have previously proposed an approach, which initializes an intensity-based non-rigid registration algorithm with a point based registration technique [1, 2]. In this paper, we introduce new constraints into our non-rigid registration algorithm to prevent the bones from being deformed inaccurately. Results we have obtained show that the new constrained algorithm leads to better registration results than the previous one.

  16. SAR/LANDSAT image registration study

    NASA Technical Reports Server (NTRS)

    Murphrey, S. W. (Principal Investigator)

    1978-01-01

    The author has identified the following significant results. Temporal registration of synthetic aperture radar data with LANDSAT-MSS data is both feasible (from a technical standpoint) and useful (from an information-content viewpoint). The greatest difficulty in registering aircraft SAR data to corrected LANDSAT-MSS data is control-point location. The differences in SAR and MSS data impact the selection of features that will serve as a good control points. The SAR and MSS data are unsuitable for automatic computer correlation of digital control-point data. The gray-level data can not be compared by the computer because of the different response characteristics of the MSS and SAR images.

  17. Diffeomorphic image registration of diffusion MRI using spherical harmonics.

    PubMed

    Geng, Xiujuan; Ross, Thomas J; Gu, Hong; Shin, Wanyong; Zhan, Wang; Chao, Yi-Ping; Lin, Ching-Po; Schuff, Norbert; Yang, Yihong

    2011-03-01

    Nonrigid registration of diffusion magnetic resonance imaging (MRI) is crucial for group analyses and building white matter and fiber tract atlases. Most current diffusion MRI registration techniques are limited to the alignment of diffusion tensor imaging (DTI) data. We propose a novel diffeomorphic registration method for high angular resolution diffusion images by mapping their orientation distribution functions (ODFs). ODFs can be reconstructed using q-ball imaging (QBI) techniques and represented by spherical harmonics (SHs) to resolve intra-voxel fiber crossings. The registration is based on optimizing a diffeomorphic demons cost function. Unlike scalar images, deforming ODF maps requires ODF reorientation to maintain its consistency with the local fiber orientations. Our method simultaneously reorients the ODFs by computing a Wigner rotation matrix at each voxel, and applies it to the SH coefficients during registration. Rotation of the coefficients avoids the estimation of principal directions, which has no analytical solution and is time consuming. The proposed method was validated on both simulated and real data sets with various metrics, which include the distance between the estimated and simulated transformation fields, the standard deviation of the general fractional anisotropy and the directional consistency of the deformed and reference images. The registration performance using SHs with different maximum orders were compared using these metrics. Results show that the diffeomorphic registration improved the affine alignment, and registration using SHs with higher order SHs further improved the registration accuracy by reducing the shape difference and improving the directional consistency of the registered and reference ODF maps.

  18. Research of Registration Approaches of Thermal Infrared Images and Intensity Images of Point Cloud

    NASA Astrophysics Data System (ADS)

    Liu, L.; Wei, Z.; Liu, X.; Yang, Z.

    2017-09-01

    In order to realize the analysis of thermal energy of the objects in 3D vision, the registration approach of thermal infrared images and TLS (Terrestrial Laser Scanner) point cloud was studied. The original data was pre-processed. For the sake of making the scale and brightness contrast of the two kinds of data meet the needs of basic matching, the intensity image of point cloud was produced and projected to spherical coordinate system, histogram equalization processing was done for thermal infrared image.This paper focused on the research of registration approaches of thermal infrared images and intensity images of point cloud based on SIFT EOH-SIFT and PIIFD operators. The latter of which is usually used for medical image matching with different spectral character. The comparison results of the experiments showed that PIIFD operator got much more accurate feature point correspondences compared to SIFT and EOH-SIFT operators. The thermal infrared image and intensity image also have ideal overlap results by quadratic polynomial transformation. Therefore, PIIFD can be used as the basic operator for the registration of thermal infrared images and intensity images, and the operator can also be further improved by incorporating the iteration method.

  19. a New Approach for Optical and SAR Satellite Image Registration

    NASA Astrophysics Data System (ADS)

    Merkle, N.; Müller, R.; Schwind, P.; Palubinskas, G.; Reinartz, P.

    2015-03-01

    Over the last years several research studies have shown the high geometric accuracy of high resolution radar satellites like TerraSARX. Due to this fact, the impact of high resolution SAR images for image registration has increased. An aim of high accuracy image registration is the improvement of the absolute geometric accuracy of optical images by using SAR images as references. High accuracy image registration is required for different remote sensing applications and is an on-going research topic. The registration of images acquired by different sensor types, like optical and SAR images, is a challenging task. In our work, a novel approach is proposed, which is a combination of the classical feature-based and intensity-based registration approaches. In the first step of the method, spatial features, here roundabouts, are detected in the optical image. In the second step, the detected features are used to generate SAR like roundabout templates. In the third step, the templates are matched with the corresponding parts of the SAR image by using an intensitybased matching process. The proposed method is tested for a pair of TerraSAR-X and QuickBird images and a pair of TerraSAR-X and WorldView-2 images of a suburban area. The results show that the proposed method offers an alternative approach compared to the common optical and SAR images registration methods and it can be used for the geometric accuracy improvement of optical images.

  20. Registration of structurally dissimilar images in MRI-based brachytherapy

    NASA Astrophysics Data System (ADS)

    Berendsen, F. F.; Kotte, A. N. T. J.; de Leeuw, A. A. C.; Jürgenliemk-Schulz, I. M.; Viergever, M. A.; Pluim, J. P. W.

    2014-08-01

    A serious challenge in image registration is the accurate alignment of two images in which a certain structure is present in only one of the two. Such topological changes are problematic for conventional non-rigid registration algorithms. We propose to incorporate in a conventional free-form registration framework a geometrical penalty term that minimizes the volume of the missing structure in one image. We demonstrate our method on cervical MR images for brachytherapy. The intrapatient registration problem involves one image in which a therapy applicator is present and one in which it is not. By including the penalty term, a substantial improvement in the surface distance to the gold standard anatomical position and the residual volume of the applicator void are obtained. Registration of neighboring structures, i.e. the rectum and the bladder is generally improved as well, albeit to a lesser degree.

  1. Diffeomorphic Image Registration of Diffusion MRI Using Spherical Harmonics

    PubMed Central

    Geng, Xiujuan; Ross, Thomas J.; Gu, Hong; Shin, Wanyong; Zhan, Wang; Chao, Yi-Ping; Lin, Ching-Po; Schuff, Norbert; Yang, Yihong

    2013-01-01

    Non-rigid registration of diffusion MRI is crucial for group analyses and building white matter and fiber tract atlases. Most current diffusion MRI registration techniques are limited to the alignment of diffusion tensor imaging (DTI) data. We propose a novel diffeomorphic registration method for high angular resolution diffusion images by mapping their orientation distribution functions (ODFs). ODFs can be reconstructed using q-ball imaging (QBI) techniques and represented by spherical harmonics (SHs) to resolve intra-voxel fiber crossings. The registration is based on optimizing a diffeomorphic demons cost function. Unlike scalar images, deforming ODF maps requires ODF reorientation to maintain its consistency with the local fiber orientations. Our method simultaneously reorients the ODFs by computing a Wigner rotation matrix at each voxel, and applies it to the SH coefficients during registration. Rotation of the coefficients avoids the estimation of principal directions, which has no analytical solution and is time consuming. The proposed method was validated on both simulated and real data sets with various metrics, which include the distance between the estimated and simulated transformation fields, the standard deviation of the general fractional anisotropy and the directional consistency of the deformed and reference images. The registration performance using SHs with different maximum orders were compared using these metrics. Results show that the diffeomorphic registration improved the affine alignment, and registration using SHs with higher order SHs further improved the registration accuracy by reducing the shape difference and improving the directional consistency of the registered and reference ODF maps. PMID:21134814

  2. Binary image registration using cellular simultaneous recurrent networks

    NASA Astrophysics Data System (ADS)

    Anderson, Keith; Iftekharuddin, Khan; Kim, Paul

    2009-08-01

    Cellular simultaneous recurrent networks (CSRN)s have been traditionally exploited to solve digital control and conventional maze traversing problems. In a previous work [1], we investigate the use of CSRNs for image registration under affine transformations for binary images. In Ref. [1], we attempt to register simulated binary images with in-plane rotations between +/-20° using two different CSRN implementations such as with 1) a general multi-layered perceptron (GMLP) architecture; and (2) a modified MLP architecture with multi-layered feedback. Our results in Ref. 1 show that both architectures achieve moderate local registration, with our modified MLP architecture producing a best result of around 64% for cost function accuracy and 98% for image registration accuracy. In this current work, for the first time in literature, we investigate gray scale image registration using CSRNs. We exploit both types of CSRNs for registration of realistic images and perform complete evaluation of both binary and gray-scale image registration. Simulation results with both CSRN architectures show an average cost function accuracy of 40.5% and an average image accuracy of 33.2%, with a best result of 46.2% and 40.3%, respectively. Image results clearly show that the CSRN shows promise for use in registration of gray-scale images.

  3. Shearlet Features for Registration of Remotely Sensed Multitemporal Images

    NASA Technical Reports Server (NTRS)

    Murphy, James M.; Le Moigne, Jacqueline

    2015-01-01

    We investigate the role of anisotropic feature extraction methods for automatic image registration of remotely sensed multitemporal images. Building on the classical use of wavelets in image registration, we develop an algorithm based on shearlets, a mathematical generalization of wavelets that offers increased directional sensitivity. Initial experimental results on LANDSAT images are presented, which indicate superior performance of the shearlet algorithm when compared to classical wavelet algorithms.

  4. Dynamic algorithm selection for multi-sensor image registration

    NASA Astrophysics Data System (ADS)

    DelMarco, Stephen; Tom, Victor; Webb, Helen; Lefebvre, David

    2009-05-01

    Modern sensors have a range of modalities including SAR, EO, and IR. Registration of multimodal imagery from such sensors is becoming an increasingly common pre-processing step for various image exploitation activities such as image fusion for ATR. Over the past decades, several approaches to multisensor image registration have been developed. However, performance of these image registration algorithms is highly dependent on scene content and sensor operating conditions, with no single algorithm working well across the entire operating conditions space. To address this problem, in this paper we present an approach for dynamic selection of an appropriate registration algorithm, tuned to the scene content and feature manifestation of the imagery under consideration. We consider feature-based registration using Harris corners, Canny edge detection, and CFAR features, as well as pixel-based registration using cross-correlation and mutual information. We develop an approach for selecting the optimal combination of algorithms to use in the dynamic selection algorithm. We define a performance measure which balances contributions from convergence redundancy and convergence coverage components calculated over sample imagery, and optimize the measure to define an optimal algorithm set. We present numerical results demonstrating the improvement in registration performance through use of the dynamic algorithm selection approach over results generated through use of a fixed registration algorithm approach. The results provide registration convergence probabilities for geo-registering test SAR imagery against associated EO reference imagery. We present convergence results for various match score normalizations used in the dynamic selection algorithm.

  5. Microscopic neural image registration based on the structure of mitochondria

    NASA Astrophysics Data System (ADS)

    Cao, Huiwen; Han, Hua; Rao, Qiang; Xiao, Chi; Chen, Xi

    2017-02-01

    Microscopic image registration is a key component of the neural structure reconstruction with serial sections of neural tissue. The goal of microscopic neural image registration is to recover the 3D continuity and geometrical properties of specimen. During image registration, various distortions need to be corrected, including image rotation, translation, tissue deformation et.al, which come from the procedure of sample cutting, staining and imaging. Furthermore, there is only certain similarity between adjacent sections, and the degree of similarity depends on local structure of the tissue and the thickness of the sections. These factors make the microscopic neural image registration a challenging problem. To tackle the difficulty of corresponding landmarks extraction, we introduce a novel image registration method for Scanning Electron Microscopy (SEM) images of serial neural tissue sections based on the structure of mitochondria. The ellipsoidal shape of mitochondria ensures that the same mitochondria has similar shape between adjacent sections, and its characteristic of broad distribution in the neural tissue guarantees that landmarks based on the mitochondria distributed widely in the image. The proposed image registration method contains three parts: landmarks extraction between adjacent sections, corresponding landmarks matching and image deformation based on the correspondences. We demonstrate the performance of our method with SEM images of drosophila brain.

  6. Insight into efficient image registration techniques and the demons algorithm.

    PubMed

    Vercauteren, Tom; Pennec, Xavier; Malis, Ezio; Perchant, Aymeric; Ayache, Nicholas

    2007-01-01

    As image registration becomes more and more central to many biomedical imaging applications, the efficiency of the algorithms becomes a key issue. Image registration is classically performed by optimizing a similarity criterion over a given spatial transformation space. Even if this problem is considered as almost solved for linear registration, we show in this paper that some tools that have recently been developed in the field of vision-based robot control can outperform classical solutions. The adequacy of these tools for linear image registration leads us to revisit non-linear registration and allows us to provide interesting theoretical roots to the different variants of Thirion's demons algorithm. This analysis predicts a theoretical advantage to the symmetric forces variant of the demons algorithm. We show that, on controlled experiments, this advantage is confirmed, and yields a faster convergence.

  7. Research Issues in Image Registration for Remote Sensing

    NASA Technical Reports Server (NTRS)

    Eastman, Roger D.; LeMoigne, Jacqueline; Netanyahu, Nathan S.

    2007-01-01

    Image registration is an important element in data processing for remote sensing with many applications and a wide range of solutions. Despite considerable investigation the field has not settled on a definitive solution for most applications and a number of questions remain open. This article looks at selected research issues by surveying the experience of operational satellite teams, application-specific requirements for Earth science, and our experiments in the evaluation of image registration algorithms with emphasis on the comparison of algorithms for subpixel accuracy. We conclude that remote sensing applications put particular demands on image registration algorithms to take into account domain-specific knowledge of geometric transformations and image content.

  8. Integrating segmentation information for improved MRF-based elastic image registration.

    PubMed

    Mahapatra, Dwarikanath; Sun, Ying

    2012-01-01

    In this paper, we propose a method to exploit segmentation information for elastic image registration using a Markov-random-field (MRF)-based objective function. MRFs are suitable for discrete labeling problems, and the labels are defined as the joint occurrence of displacement fields (for registration) and segmentation class probability. The data penalty is a combination of the image intensity (or gradient information) and the mutual dependence of registration and segmentation information. The smoothness is a function of the interaction between the defined labels. Since both terms are a function of registration and segmentation labels, the overall objective function captures their mutual dependence. A multiscale graph-cut approach is used to achieve subpixel registration and reduce the computation time. The user defines the object to be registered in the floating image, which is rigidly registered before applying our method. We test our method on synthetic image data sets with known levels of added noise and simulated deformations, and also on natural and medical images. Compared with other registration methods not using segmentation information, our proposed method exhibits greater robustness to noise and improved registration accuracy.

  9. EVolution: an edge-based variational method for non-rigid multi-modal image registration

    NASA Astrophysics Data System (ADS)

    de Senneville, B. Denis; Zachiu, C.; Ries, M.; Moonen, C.

    2016-10-01

    Image registration is part of a large variety of medical applications including diagnosis, monitoring disease progression and/or treatment effectiveness and, more recently, therapy guidance. Such applications usually involve several imaging modalities such as ultrasound, computed tomography, positron emission tomography, x-ray or magnetic resonance imaging, either separately or combined. In the current work, we propose a non-rigid multi-modal registration method (namely EVolution: an edge-based variational method for non-rigid multi-modal image registration) that aims at maximizing edge alignment between the images being registered. The proposed algorithm requires only contrasts between physiological tissues, preferably present in both image modalities, and assumes deformable/elastic tissues. Given both is shown to be well suitable for non-rigid co-registration across different image types/contrasts (T1/T2) as well as different modalities (CT/MRI). This is achieved using a variational scheme that provides a fast algorithm with a low number of control parameters. Results obtained on an annotated CT data set were comparable to the ones provided by state-of-the-art multi-modal image registration algorithms, for all tested experimental conditions (image pre-filtering, image intensity variation, noise perturbation). Moreover, we demonstrate that, compared to existing approaches, our method possesses increased robustness to transient structures (i.e. that are only present in some of the images).

  10. EVolution: an edge-based variational method for non-rigid multi-modal image registration.

    PubMed

    Denis de Senneville, B; Zachiu, C; Ries, M; Moonen, C

    2016-10-21

    Image registration is part of a large variety of medical applications including diagnosis, monitoring disease progression and/or treatment effectiveness and, more recently, therapy guidance. Such applications usually involve several imaging modalities such as ultrasound, computed tomography, positron emission tomography, x-ray or magnetic resonance imaging, either separately or combined. In the current work, we propose a non-rigid multi-modal registration method (namely EVolution: an edge-based variational method for non-rigid multi-modal image registration) that aims at maximizing edge alignment between the images being registered. The proposed algorithm requires only contrasts between physiological tissues, preferably present in both image modalities, and assumes deformable/elastic tissues. Given both is shown to be well suitable for non-rigid co-registration across different image types/contrasts (T1/T2) as well as different modalities (CT/MRI). This is achieved using a variational scheme that provides a fast algorithm with a low number of control parameters. Results obtained on an annotated CT data set were comparable to the ones provided by state-of-the-art multi-modal image registration algorithms, for all tested experimental conditions (image pre-filtering, image intensity variation, noise perturbation). Moreover, we demonstrate that, compared to existing approaches, our method possesses increased robustness to transient structures (i.e. that are only present in some of the images).

  11. Registration of renal SPECT and 2.5D US images.

    PubMed

    Galdames, Francisco J; Perez, Claudio A; Estévez, Pablo A; Held, Claudio M; Jaillet, Fabrice; Lobo, Gabriel; Donoso, Gilda; Coll, Claudia

    2011-06-01

    Image registration is the process of transforming different image data sets of an object into the same coordinate system. This is a relevant task in the field of medical imaging; one of its objectives is to combine information from different imaging modalities. The main goal of this study is the registration of renal SPECT (Single Photon Emission Computerized Tomography) images and a sparse set of ultrasound slices (2.5D US), combining functional and anatomical information. Registration is performed after kidney segmentation in both image types. The SPECT segmentation is achieved using a deformable model based on a simplex mesh. The 2.5D US image segmentation is carried out in each of the 2D slices employing a deformable contour and Gabor filters to capture multi-scale image features. Moreover, a renal medulla detection method was developed to improve the US segmentation. A nonlinear optimization algorithm is used for the registration. In this process, movements caused by patient breathing during US image acquisition are also corrected. Only a few reports describe registration between SPECT images and a sparse set of US slices of the kidney, and they usually employ an optical localizer, unlike our method, that performs movement correction using information only from the SPECT and US images. Moreover, it does not require simultaneous acquisition of both image types. The registration method and both segmentations were evaluated separately. The SPECT segmentation was evaluated qualitatively by medical experts, obtaining a score of 5 over a scale from 1 to 5, where 5 represents a perfect segmentation. The 2.5D US segmentation was evaluated quantitatively, by comparing our method with an expert manual segmentation, and obtaining an average error of 3.3mm. The registration was evaluated quantitatively and qualitatively. Quantitatively the distance between the manual segmentation of the US images and the model extracted from the SPECT image was measured, obtaining an

  12. Early medical registration in Australia. Part 2.

    PubMed

    Dammery, D

    2001-11-01

    The lure of land and gold, the hope for better health and an overcrowded profession in Britain led to an increasing number of doctors migrating to Australia. This migration was even more marked after the goldrush, especially in Victoria. The fifth article in this series looks at the way in which medical practice was controlled by excluding many of those doctors who were from non-British origins.

  13. Large deformation diffeomorphic registration of diffusion-weighted imaging data.

    PubMed

    Zhang, Pei; Niethammer, Marc; Shen, Dinggang; Yap, Pew-Thian

    2014-12-01

    Registration plays an important role in group analysis of diffusion-weighted imaging (DWI) data. It can be used to build a reference anatomy for investigating structural variation or tracking changes in white matter. Unlike traditional scalar image registration where spatial alignment is the only focus, registration of DWI data requires both spatial alignment of structures and reorientation of local signal profiles. As such, DWI registration is much more complex and challenging than scalar image registration. Although a variety of algorithms has been proposed to tackle the problem, most of them are restricted by the diffusion model used for registration, making it difficult to fit to the registered data a different model. In this paper we describe a method that allows any diffusion model to be fitted after registration for subsequent multifaceted analysis. This is achieved by directly aligning DWI data using a large deformation diffeomorphic registration framework. Our algorithm seeks the optimal coordinate mapping by simultaneously considering structural alignment, local signal profile reorientation, and deformation regularization. Our algorithm also incorporates a multi-kernel strategy to concurrently register anatomical structures at different scales. We demonstrate the efficacy of our approach using in vivo data and report detailed qualitative and quantitative results in comparison with several different registration strategies. Copyright © 2014 Elsevier B.V. All rights reserved.

  14. Large deformation diffeomorphic registration of diffusion-weighted imaging data

    PubMed Central

    Zhang, Pei; Niethammer, Marc; Shen, Dinggang; Yap, Pew-Thian

    2014-01-01

    Registration plays an important role in group analysis of diffusion-weighted imaging (DWI) data. It can be used to build a reference anatomy for investigating structural variation or tracking changes in white matter. Unlike traditional scalar image registration where spatial alignment is the only focus, registration of DWI data requires both spatial alignment of structures and reorientation of local signal profiles. As such, DWI registration is much more complex and challenging than scalar image registration. Although a variety of algorithms has been proposed to tackle the problem, most of them are restricted by the zdiffusion model used for registration, making it difficult to fit to the registered data a different model. In this paper we describe a method that allows any diffusion model to be fitted after registration for subsequent multifaceted analysis. This is achieved by directly aligning DWI data using a large deformation diffeomorphic registration framework. Our algorithm seeks the optimal coordinate mapping by simultaneously considering structural alignment, local signal profile reorientation, and deformation regularization. Our algorithm also incorporates a multi-kernel strategy to concurrently register anatomical structures at different scales. We demonstrate the efficacy of our approach using in vivo data and report detailed qualitative and quantitative results in comparison with several different registration strategies. PMID:25106710

  15. Automatic nonrigid registration of whole body CT mice images.

    PubMed

    Li, Xia; Yankeelov, Thomas E; Peterson, Todd E; Gore, John C; Dawant, Benoit M

    2008-04-01

    Three-dimensional intra- and intersubject registration of image volumes is important for tasks that include quantification of temporal/longitudinal changes, atlas-based segmentation, computing population averages, or voxel and tensor-based morphometry. While a number of methods have been proposed to address this problem, few have focused on the problem of registering whole body image volumes acquired either from humans or small animals. These image volumes typically contain a large number of articulated structures, which makes registration more difficult than the registration of head images, to which the majority of registration algorithms have been applied. This article presents a new method for the automatic registration of whole body computed tomography (CT) volumes, which consists of two main steps. Skeletons are first brought into approximate correspondence with a robust point-based method. Transformations so obtained are refined with an intensity-based nonrigid registration algorithm that includes spatial adaptation of the transformation's stiffness. The approach has been applied to whole body CT images of mice, to CT images of the human upper torso, and to human head and neck CT images. To validate the authors method on soft tissue structures, which are difficult to see in CT images, the authors use coregistered magnetic resonance images. They demonstrate that the approach they propose can successfully register image volumes even when these volumes are very different in size and shape or if they have been acquired with the subjects in different positions.

  16. Group-wise diffeomorphic diffusion tensor image registration.

    PubMed

    Geng, Xiujuan; Gu, Hong; Shin, Wanyong; Ross, Thomas J; Yang, Yihong

    2010-01-01

    We propose an unbiased group-wise diffeomorphic registration technique to normalize a group of diffusion tensor (DT) images. Our method uses an implicit reference group-wise registration framework to avoid bias caused by reference selection. Log-Euclidean metrics on diffusion tensors are used for the tensor interpolation and computation of the similarity cost functions. The overall energy function is constructed by a diffeomorphic demons approach. The tensor reorientation is performed and implicitly optimized during the registration procedure. The performance of the proposed method is compared with reference-based diffusion tensor imaging (DTI) registration methods. The registered DTI images have smaller shape differences in terms of reduced variance of the fractional anisotropy maps and more consistent tensor orientations. We demonstrate that fiber tract atlas construction can benefit from the group-wise registration by producing fiber bundles with higher overlaps.

  17. Medical Imaging System

    NASA Technical Reports Server (NTRS)

    1991-01-01

    The MD Image System, a true-color image processing system that serves as a diagnostic aid and tool for storage and distribution of images, was developed by Medical Image Management Systems, Huntsville, AL, as a "spinoff from a spinoff." The original spinoff, Geostar 8800, developed by Crystal Image Technologies, Huntsville, incorporates advanced UNIX versions of ELAS (developed by NASA's Earth Resources Laboratory for analysis of Landsat images) for general purpose image processing. The MD Image System is an application of this technology to a medical system that aids in the diagnosis of cancer, and can accept, store and analyze images from other sources such as Magnetic Resonance Imaging.

  18. Landmark optimization using local curvature for point-based nonlinear rodent brain image registration.

    PubMed

    Liu, Yutong; Sajja, Balasrinivasa R; Uberti, Mariano G; Gendelman, Howard E; Kielian, Tammy; Boska, Michael D

    2012-01-01

    Purpose. To develop a technique to automate landmark selection for point-based interpolating transformations for nonlinear medical image registration. Materials and Methods. Interpolating transformations were calculated from homologous point landmarks on the source (image to be transformed) and target (reference image). Point landmarks are placed at regular intervals on contours of anatomical features, and their positions are optimized along the contour surface by a function composed of curvature similarity and displacements of the homologous landmarks. The method was evaluated in two cases (n = 5 each). In one, MRI was registered to histological sections; in the second, geometric distortions in EPI MRI were corrected. Normalized mutual information and target registration error were calculated to compare the registration accuracy of the automatically and manually generated landmarks. Results. Statistical analyses demonstrated significant improvement (P < 0.05) in registration accuracy by landmark optimization in most data sets and trends towards improvement (P < 0.1) in others as compared to manual landmark selection.

  19. A first step toward uncovering the truth about weight tuning in deformable image registration

    NASA Astrophysics Data System (ADS)

    Pirpinia, Kleopatra; Bosman, Peter A. N.; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja

    2016-03-01

    Deformable image registration is currently predominantly solved by optimizing a weighted linear combination of objectives. Successfully tuning the weights associated with these objectives is not trivial, leading to trial-and-error approaches. Such an approach assumes an intuitive interplay between weights, optimization objectives, and target registration errors. However, it is not known whether this always holds for existing registration methods. To investigate the interplay between weights, optimization objectives, and registration errors, we employ multi-objective optimization. Here, objectives of interest are optimized simultaneously, causing a set of multiple optimal solutions to exist, called the optimal Pareto front. Our medical application is in breast cancer and includes the challenging prone-supine registration problem. In total, we studied the interplay in three different ways. First, we ran many random linear combinations of objectives using the well-known registration software elastix. Second, since the optimization algorithms used in registration are typically of a local-search nature, final solutions may not always form a Pareto front. We therefore employed a multi-objective evolutionary algorithm that finds weights that correspond to registration outcomes that do form a Pareto front. Third, we examined how the interplay differs if a true multi-objective (i.e., weight-free) image registration method is used. Results indicate that a trial-and-error weight-adaptation approach can be successful for the easy prone to prone breast image registration case, due to the absence of many local optima. With increasing problem difficulty the use of more advanced approaches can be of value in finding and selecting the optimal registration outcomes.

  20. Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease.

    PubMed

    Shamonin, Denis P; Bron, Esther E; Lelieveldt, Boudewijn P F; Smits, Marion; Klein, Stefan; Staring, Marius

    2013-01-01

    Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4-5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15-60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license.

  1. Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease

    PubMed Central

    Shamonin, Denis P.; Bron, Esther E.; Lelieveldt, Boudewijn P. F.; Smits, Marion; Klein, Stefan; Staring, Marius

    2013-01-01

    Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4–5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15–60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license

  2. Nonrigid registration of multiple sclerosis brain images using lesion inpainting for morphometry or lesion mapping.

    PubMed

    Sdika, Michaël; Pelletier, Daniel

    2009-04-01

    Morphometric studies of medical images often include a nonrigid registration step from a subject to a common reference. The presence of white matter multiple sclerosis lesions will distort and bias the output of the registration. In this article, we present a method to remove this bias by filling such lesions to make the brain look like a healthy brain before the registration. We finally propose a dedicated method to fill the lesions and present numerical results showing that our method outperforms current state of the art method. 2008 Wiley-Liss, Inc.

  3. Visible and infrared image registration based on visual salient features

    NASA Astrophysics Data System (ADS)

    Wu, Feihong; Wang, Bingjian; Yi, Xiang; Li, Min; Hao, Jingya; Qin, Hanlin; Zhou, Huixin

    2015-09-01

    In order to improve the precision of visible and infrared (VIS/IR) image registration, an image registration method based on visual salient (VS) features is presented. First, a VS feature detector based on the modified visual attention model is presented to extract VS points. Because the iterative, within-feature competition method used in visual attention models is time consuming, an alternative fast visual salient (FVS) feature detector is proposed to make VS features more efficient. Then, a descriptor-rearranging (DR) strategy is adopted to describe feature points. This strategy combines information of both IR image and its negative image to overcome the contrast reverse problem between VIS and IR images, making it easier to find the corresponding points on VIS/IR images. Experiments show that both VS and FVS detectors have higher repeatability scores than scale invariant feature transform in the cases of blurring, brightness change, JPEG compression, noise, and viewpoint, except big scale change. The combination of VS detector and DR registration strategy can achieve precise image registration, but it is time-consuming. The combination of FVS detector and DR registration strategy can also reach a good registration of VIS/IR images but in a shorter time.

  4. INTER-GROUP IMAGE REGISTRATION BY HIERARCHICAL GRAPH SHRINKAGE.

    PubMed

    Ying, Shihui; Wu, Guorong; Liao, Shu; Shen, Dinggang

    2013-12-31

    In this paper, we propose a novel inter-group image registration method to register different groups of images (e.g., young and elderly brains) simultaneously. Specifically, we use a hierarchical two-level graph to model the distribution of entire images on the manifold, with intra-graph representing the image distribution in each group and the inter-graph describing the relationship between two groups. Then the procedure of inter-group registration is formulated as a dynamic evolution of graph shrinkage. The advantage of our method is that the topology of entire image distribution is explored to guide the image registration. In this way, each image coordinates with its neighboring images on the manifold to deform towards the population center, by following the deformation pathway simultaneously optimized within the graph. Our proposed method has been also compared with other state-of-the-art inter-group registration methods, where our method achieves better registration results in terms of registration accuracy and robustness.

  5. Reconstruction-based 3D/2D image registration.

    PubMed

    Tomazevic, Dejan; Likar, Bostjan; Pernus, Franjo

    2005-01-01

    In this paper we present a novel 3D/2D registration method, where first, a 3D image is reconstructed from a few 2D X-ray images and next, the preoperative 3D image is brought into the best possible spatial correspondence with the reconstructed image by optimizing a similarity measure. Because the quality of the reconstructed image is generally low, we introduce a novel asymmetric mutual information similarity measure, which is able to cope with low image quality as well as with different imaging modalities. The novel 3D/2D registration method has been evaluated using standardized evaluation methodology and publicly available 3D CT, 3DRX, and MR and 2D X-ray images of two spine phantoms, for which gold standard registrations were known. In terms of robustness, reliability and capture range the proposed method outperformed the gradient-based method and the method based on digitally reconstructed radiographs (DRRs).

  6. Nonrigid brain MR image registration using uniform spherical region descriptor.

    PubMed

    Liao, Shu; Chung, Albert C S

    2012-01-01

    There are two main issues that make nonrigid image registration a challenging task. First, voxel intensity similarity may not be necessarily equivalent to anatomical similarity in the image correspondence searching process. Second, during the imaging process, some interferences such as unexpected rotations of input volumes and monotonic gray-level bias fields can adversely affect the registration quality. In this paper, a new feature-based nonrigid image registration method is proposed. The proposed method is based on a new type of image feature, namely, uniform spherical region descriptor (USRD), as signatures for each voxel. The USRD is rotation and monotonic gray-level transformation invariant and can be efficiently calculated. The registration process is therefore formulated as a feature matching problem. The USRD feature is integrated with the Markov random field labeling framework in which energy function is defined for registration. The energy function is then optimized by the α-expansion algorithm. The proposed method has been compared with five state-of-the-art registration approaches on both the simulated and real 3-D databases obtained from the BrainWeb and Internet Brain Segmentation Repository, respectively. Experimental results demonstrate that the proposed method can achieve high registration accuracy and reliable robustness behavior.

  7. Multi-modal registration for correlative microscopy using image analogies.

    PubMed

    Cao, Tian; Zach, Christopher; Modla, Shannon; Powell, Debbie; Czymmek, Kirk; Niethammer, Marc

    2014-08-01

    Correlative microscopy is a methodology combining the functionality of light microscopy with the high resolution of electron microscopy and other microscopy technologies for the same biological specimen. In this paper, we propose an image registration method for correlative microscopy, which is challenging due to the distinct appearance of biological structures when imaged with different modalities. Our method is based on image analogies and allows to transform images of a given modality into the appearance-space of another modality. Hence, the registration between two different types of microscopy images can be transformed to a mono-modality image registration. We use a sparse representation model to obtain image analogies. The method makes use of corresponding image training patches of two different imaging modalities to learn a dictionary capturing appearance relations. We test our approach on backscattered electron (BSE) scanning electron microscopy (SEM)/confocal and transmission electron microscopy (TEM)/confocal images. We perform rigid, affine, and deformable registration via B-splines and show improvements over direct registration using both mutual information and sum of squared differences similarity measures to account for differences in image appearance. Copyright © 2013 Elsevier B.V. All rights reserved.

  8. Multi-modal Registration for Correlative Microscopy using Image Analogies

    PubMed Central

    Cao, Tian; Zach, Christopher; Modla, Shannon; Powell, Debbie; Czymmek, Kirk; Niethammer, Marc

    2014-01-01

    Correlative microscopy is a methodology combining the functionality of light microscopy with the high resolution of electron microscopy and other microscopy technologies for the same biological specimen. In this paper, we propose an image registration method for correlative microscopy, which is challenging due to the distinct appearance of biological structures when imaged with different modalities. Our method is based on image analogies and allows to transform images of a given modality into the appearance-space of another modality. Hence, the registration between two different types of microscopy images can be transformed to a mono-modality image registration. We use a sparse representation model to obtain image analogies. The method makes use of corresponding image training patches of two different imaging modalities to learn a dictionary capturing appearance relations. We test our approach on backscattered electron (BSE) scanning electron microscopy (SEM)/confocal and transmission electron microscopy (TEM)/confocal images. We perform rigid, affine, and deformable registration via B-splines and show improvements over direct registration using both mutual information and sum of squared differences similarity measures to account for differences in image appearance. PMID:24387943

  9. Parallel image registration with a thin client interface

    NASA Astrophysics Data System (ADS)

    Saiprasad, Ganesh; Lo, Yi-Jung; Plishker, William; Lei, Peng; Ahmad, Tabassum; Shekhar, Raj

    2010-03-01

    Despite its high significance, the clinical utilization of image registration remains limited because of its lengthy execution time and a lack of easy access. The focus of this work was twofold. First, we accelerated our course-to-fine, volume subdivision-based image registration algorithm by a novel parallel implementation that maintains the accuracy of our uniprocessor implementation. Second, we developed a thin-client computing model with a user-friendly interface to perform rigid and nonrigid image registration. Our novel parallel computing model uses the message passing interface model on a 32-core cluster. The results show that, compared with the uniprocessor implementation, the parallel implementation of our image registration algorithm is approximately 5 times faster for rigid image registration and approximately 9 times faster for nonrigid registration for the images used. To test the viability of such systems for clinical use, we developed a thin client in the form of a plug-in in OsiriX, a well-known open source PACS workstation and DICOM viewer, and used it for two applications. The first application registered the baseline and follow-up MR brain images, whose subtraction was used to track progression of multiple sclerosis. The second application registered pretreatment PET and intratreatment CT of radiofrequency ablation patients to demonstrate a new capability of multimodality imaging guidance. The registration acceleration coupled with the remote implementation using a thin client should ultimately increase accuracy, speed, and access of image registration-based interpretations in a number of diagnostic and interventional applications.

  10. Image registration under affine transformation using cellular simultaneous recurrent networks

    NASA Astrophysics Data System (ADS)

    Iftekharuddin, Khan M.; Anderson, Keith

    2010-08-01

    Cellular simultaneous recurrent networks (CSRN)s have been traditionally exploited to solve the digital control and conventional maze traversing problems. In previous works, we investigated the use of CSRNs to register simulated binary images with in-plane rotations between +/-20° using two different CSRN architectures such as one with a general multi-layered perceptron (GMLP) architecture; and another with modified MLP architecture with multilayered feedback. We further exploit the CSRN for registration of realistic binary and gray scale images under rotation. In this current work we report results of applying CSRNs to perform image registration under affine transformations such as rotation and translation. We further provide extensive analyses of CSRN affine registration results for appropriate cost function formulation. Our CSRN results analyses show that formulation of locally varying cost function is desirable for robust image registration under affine transformation.

  11. Bivariate gamma distributions for image registration and change detection.

    PubMed

    Chatelain, Florent; Tourneret, Jean-Yves; Inglada, Jordi; Ferrari, André

    2007-07-01

    This paper evaluates the potential interest of using bivariate gamma distributions for image registration and change detection. The first part of this paper studies estimators for the parameters of bivariate gamma distributions based on the maximum likelihood principle and the method of moments. The performance of both methods are compared in terms of estimated mean square errors and theoretical asymptotic variances. The mutual information is a classical similarity measure which can be used for image registration or change detection. The second part of the paper studies some properties of the mutual information for bivariate Gamma distributions. Image registration and change detection techniques based on bivariate gamma distributions are finally investigated. Simulation results conducted on synthetic and real data are very encouraging. Bivariate gamma distributions are good candidates allowing us to develop new image registration algorithms and new change detectors.

  12. Multimodal image registration based on binary gradient angle descriptor.

    PubMed

    Jiang, Dongsheng; Shi, Yonghong; Yao, Demin; Fan, Yifeng; Wang, Manning; Song, Zhijian

    2017-08-31

    Multimodal image registration plays an important role in image-guided interventions/therapy and atlas building, and it is still a challenging task due to the complex intensity variations in different modalities. The paper addresses the problem and proposes a simple, compact, fast and generally applicable modality-independent binary gradient angle descriptor (BGA) based on the rationale of gradient orientation alignment. The BGA can be easily calculated at each voxel by coding the quadrant in which a local gradient vector falls, and it has an extremely low computational complexity, requiring only three convolutions, two multiplication operations and two comparison operations. Meanwhile, the binarized encoding of the gradient orientation makes the BGA more resistant to image degradations compared with conventional gradient orientation methods. The BGA can extract similar feature descriptors for different modalities and enable the use of simple similarity measures, which makes it applicable within a wide range of optimization frameworks. The results for pairwise multimodal and monomodal registrations between various images (T1, T2, PD, T1c, Flair) consistently show that the BGA significantly outperforms localized mutual information. The experimental results also confirm that the BGA can be a reliable alternative to the sum of absolute difference in monomodal image registration. The BGA can also achieve an accuracy of [Formula: see text], similar to that of the SSC, for the deformable registration of inhale and exhale CT scans. Specifically, for the highly challenging deformable registration of preoperative MRI and 3D intraoperative ultrasound images, the BGA achieves a similar registration accuracy of [Formula: see text] compared with state-of-the-art approaches, with a computation time of 18.3 s per case. The BGA improves the registration performance in terms of both accuracy and time efficiency. With further acceleration, the framework has the potential for

  13. Avoiding stair-step artifacts in image registration for GOES-R navigation and registration assessment

    NASA Astrophysics Data System (ADS)

    Grycewicz, Thomas J.; Tan, Bin; Isaacson, Peter J.; De Luccia, Frank J.; Dellomo, John

    2016-09-01

    In developing software for independent verification and validation (IV and V) of the Image Navigation and Registration (INR) capability for the Geostationary Operational Environmental Satellite - R Series (GOES-R) Advanced Baseline Imager (ABI), we have encountered an image registration artifact which limits the accuracy of image offset estimation at the subpixel scale using image correlation. Where the two images to be registered have the same pixel size, subpixel image registration preferentially selects registration values where the image pixel boundaries are close to lined up. Because of the shape of a curve plotting input displacement to estimated offset, we call this a stair-step artifact. When one image is at a higher resolution than the other, the stair-step artifact is minimized by correlating at the higher resolution. For validating ABI image navigation, GOES-R images are correlated with Landsat-based ground truth maps. To create the ground truth map, the Landsat image is first transformed to the perspective seen from the GOES-R satellite, and then is scaled to an appropriate pixel size. Minimizing processing time motivates choosing the map pixels to be the same size as the GOES-R pixels. At this pixel size image processing of the shift estimate is efficient, but the stair-step artifact is present. If the map pixel is very small, stair-step is not a problem, but image correlation is computation-intensive. This paper describes simulation-based selection of the scale for truth maps for registering GOES-R ABI images.

  14. Avoiding Stair-Step Artifacts in Image Registration for GOES-R Navigation and Registration Assessment

    NASA Technical Reports Server (NTRS)

    Grycewicz, Thomas J.; Tan, Bin; Isaacson, Peter J.; De Luccia, Frank J.; Dellomo, John

    2016-01-01

    In developing software for independent verification and validation (IVV) of the Image Navigation and Registration (INR) capability for the Geostationary Operational Environmental Satellite R Series (GOES-R) Advanced Baseline Imager (ABI), we have encountered an image registration artifact which limits the accuracy of image offset estimation at the subpixel scale using image correlation. Where the two images to be registered have the same pixel size, subpixel image registration preferentially selects registration values where the image pixel boundaries are close to lined up. Because of the shape of a curve plotting input displacement to estimated offset, we call this a stair-step artifact. When one image is at a higher resolution than the other, the stair-step artifact is minimized by correlating at the higher resolution. For validating ABI image navigation, GOES-R images are correlated with Landsat-based ground truth maps. To create the ground truth map, the Landsat image is first transformed to the perspective seen from the GOES-R satellite, and then is scaled to an appropriate pixel size. Minimizing processing time motivates choosing the map pixels to be the same size as the GOES-R pixels. At this pixel size image processing of the shift estimate is efficient, but the stair-step artifact is present. If the map pixel is very small, stair-step is not a problem, but image correlation is computation-intensive. This paper describes simulation-based selection of the scale for truth maps for registering GOES-R ABI images.

  15. Registration of Heat Capacity Mapping Mission day and night images

    NASA Technical Reports Server (NTRS)

    Watson, K.; Hummer-Miller, S.; Sawatzky, D. L. (Principal Investigator)

    1982-01-01

    Neither iterative registration, using drainage intersection maps for control, nor cross correlation techniques were satisfactory in registering day and night HCMM imagery. A procedure was developed which registers the image pairs by selecting control points and mapping the night thermal image to the daytime thermal and reflectance images using an affine transformation on a 1300 by 1100 pixel image. The resulting image registration is accurate to better than two pixels (RMS) and does not exhibit the significant misregistration that was noted in the temperature-difference and thermal-inertia products supplied by NASA. The affine transformation was determined using simple matrix arithmetic, a step that can be performed rapidly on a minicomputer.

  16. MR to CT Registration of Brains using Image Synthesis.

    PubMed

    Roy, Snehashis; Carass, Aaron; Jog, Amod; Prince, Jerry L; Lee, Junghoon

    2014-03-21

    Computed tomography (CT) is the standard imaging modality for patient dose calculation for radiation therapy. Magnetic resonance (MR) imaging (MRI) is used along with CT to identify brain structures due to its superior soft tissue contrast. Registration of MR and CT is necessary for accurate delineation of the tumor and other structures, and is critical in radiotherapy planning. Mutual information (MI) or its variants are typically used as a similarity metric to register MRI to CT. However, unlike CT, MRI intensity does not have an accepted calibrated intensity scale. Therefore, MI-based MR-CT registration may vary from scan to scan as MI depends on the joint histogram of the images. In this paper, we propose a fully automatic framework for MR-CT registration by synthesizing a synthetic CT image from MRI using a co-registered pair of MR and CT images as an atlas. Patches of the subject MRI are matched to the atlas and the synthetic CT patches are estimated in a probabilistic framework. The synthetic CT is registered to the original CT using a deformable registration and the computed deformation is applied to the MRI. In contrast to most existing methods, we do not need any manual intervention such as picking landmarks or regions of interests. The proposed method was validated on ten brain cancer patient cases, showing 25% improvement in MI and correlation between MR and CT images after registration compared to state-of-the-art registration methods.

  17. MR to CT registration of brains using image synthesis

    NASA Astrophysics Data System (ADS)

    Roy, Snehashis; Carass, Aaron; Jog, Amod; Prince, Jerry L.; Lee, Junghoon

    2014-03-01

    Computed tomography (CT) is the preferred imaging modality for patient dose calculation for radiation therapy. Magnetic resonance (MR) imaging (MRI) is used along with CT to identify brain structures due to its superior soft tissue contrast. Registration of MR and CT is necessary for accurate delineation of the tumor and other structures, and is critical in radiotherapy planning. Mutual information (MI) or its variants are typically used as a similarity metric to register MRI to CT. However, unlike CT, MRI intensity does not have an accepted calibrated intensity scale. Therefore, MI-based MR-CT registration may vary from scan to scan as MI depends on the joint histogram of the images. In this paper, we propose a fully automatic framework for MR-CT registration by synthesizing a synthetic CT image from MRI using a co-registered pair of MR and CT images as an atlas. Patches of the subject MRI are matched to the atlas and the synthetic CT patches are estimated in a probabilistic framework. The synthetic CT is registered to the original CT using a deformable registration and the computed deformation is applied to the MRI. In contrast to most existing methods, we do not need any manual intervention such as picking landmarks or regions of interests. The proposed method was validated on ten brain cancer patient cases, showing 25% improvement in MI and correlation between MR and CT images after registration compared to state-of-the-art registration methods.

  18. Accurate registration of temporal CT images for pulmonary nodules detection

    NASA Astrophysics Data System (ADS)

    Yan, Jichao; Jiang, Luan; Li, Qiang

    2017-02-01

    Interpretation of temporal CT images could help the radiologists to detect some subtle interval changes in the sequential examinations. The purpose of this study was to develop a fully automated scheme for accurate registration of temporal CT images for pulmonary nodule detection. Our method consisted of three major registration steps. Firstly, affine transformation was applied in the segmented lung region to obtain global coarse registration images. Secondly, B-splines based free-form deformation (FFD) was used to refine the coarse registration images. Thirdly, Demons algorithm was performed to align the feature points extracted from the registered images in the second step and the reference images. Our database consisted of 91 temporal CT cases obtained from Beijing 301 Hospital and Shanghai Changzheng Hospital. The preliminary results showed that approximately 96.7% cases could obtain accurate registration based on subjective observation. The subtraction images of the reference images and the rigid and non-rigid registered images could effectively remove the normal structures (i.e. blood vessels) and retain the abnormalities (i.e. pulmonary nodules). This would be useful for the screening of lung cancer in our future study.

  19. Infrared thermal facial image sequence registration analysis and verification

    NASA Astrophysics Data System (ADS)

    Chen, Chieh-Li; Jian, Bo-Lin

    2015-03-01

    To study the emotional responses of subjects to the International Affective Picture System (IAPS), infrared thermal facial image sequence is preprocessed for registration before further analysis such that the variance caused by minor and irregular subject movements is reduced. Without affecting the comfort level and inducing minimal harm, this study proposes an infrared thermal facial image sequence registration process that will reduce the deviations caused by the unconscious head shaking of the subjects. A fixed image for registration is produced through the localization of the centroid of the eye region as well as image translation and rotation processes. Thermal image sequencing will then be automatically registered using the two-stage genetic algorithm proposed. The deviation before and after image registration will be demonstrated by image quality indices. The results show that the infrared thermal image sequence registration process proposed in this study is effective in localizing facial images accurately, which will be beneficial to the correlation analysis of psychological information related to the facial area.

  20. 3D-3D registration of partial capitate bones using spin-images

    NASA Astrophysics Data System (ADS)

    Breighner, Ryan; Holmes, David R.; Leng, Shuai; An, Kai-Nan; McCollough, Cynthia; Zhao, Kristin

    2013-03-01

    It is often necessary to register partial objects in medical imaging. Due to limited field of view (FOV), the entirety of an object cannot always be imaged. This study presents a novel application of an existing registration algorithm to this problem. The spin-image algorithm [1] creates pose-invariant representations of global shape with respect to individual mesh vertices. These `spin-images,' are then compared for two different poses of the same object to establish correspondences and subsequently determine relative orientation of the poses. In this study, the spin-image algorithm is applied to 4DCT-derived capitate bone surfaces to assess the relative accuracy of registration with various amounts of geometry excluded. The limited longitudinal coverage under the 4DCT technique (38.4mm, [2]), results in partial views of the capitate when imaging wrist motions. This study assesses the ability of the spin-image algorithm to register partial bone surfaces by artificially restricting the capitate geometry available for registration. Under IRB approval, standard static CT and 4DCT scans were obtained on a patient. The capitate was segmented from the static CT and one phase of 4DCT in which the whole bone was available. Spin-image registration was performed between the static and 4DCT. Distal portions of the 4DCT capitate (10-70%) were then progressively removed and registration was repeated. Registration accuracy was evaluated by angular errors and the percentage of sub-resolution fitting. It was determined that 60% of the distal capitate could be omitted without appreciable effect on registration accuracy using the spin-image algorithm (angular error < 1.5 degree, sub-resolution fitting < 98.4%).

  1. Medical Image Databases

    PubMed Central

    Tagare, Hemant D.; Jaffe, C. Carl; Duncan, James

    1997-01-01

    Abstract Information contained in medical images differs considerably from that residing in alphanumeric format. The difference can be attributed to four characteristics: (1) the semantics of medical knowledge extractable from images is imprecise; (2) image information contains form and spatial data, which are not expressible in conventional language; (3) a large part of image information is geometric; (4) diagnostic inferences derived from images rest on an incomplete, continuously evolving model of normality. This paper explores the differentiating characteristics of text versus images and their impact on design of a medical image database intended to allow content-based indexing and retrieval. One strategy for implementing medical image databases is presented, which employs object-oriented iconic queries, semantics by association with prototypes, and a generic schema. PMID:9147338

  2. Inverse consistent non-rigid image registration based on robust point set matching

    PubMed Central

    2014-01-01

    Background Robust point matching (RPM) has been extensively used in non-rigid registration of images to robustly register two sets of image points. However, except for the location at control points, RPM cannot estimate the consistent correspondence between two images because RPM is a unidirectional image matching approach. Therefore, it is an important issue to make an improvement in image registration based on RPM. Methods In our work, a consistent image registration approach based on the point sets matching is proposed to incorporate the property of inverse consistency and improve registration accuracy. Instead of only estimating the forward transformation between the source point sets and the target point sets in state-of-the-art RPM algorithms, the forward and backward transformations between two point sets are estimated concurrently in our algorithm. The inverse consistency constraints are introduced to the cost function of RPM and the fuzzy correspondences between two point sets are estimated based on both the forward and backward transformations simultaneously. A modified consistent landmark thin-plate spline registration is discussed in detail to find the forward and backward transformations during the optimization of RPM. The similarity of image content is also incorporated into point matching in order to improve image matching. Results Synthetic data sets, medical images are employed to demonstrate and validate the performance of our approach. The inverse consistent errors of our algorithm are smaller than RPM. Especially, the topology of transformations is preserved well for our algorithm for the large deformation between point sets. Moreover, the distance errors of our algorithm are similar to that of RPM, and they maintain a downward trend as whole, which demonstrates the convergence of our algorithm. The registration errors for image registrations are evaluated also. Again, our algorithm achieves the lower registration errors in same iteration number

  3. Inverse consistent non-rigid image registration based on robust point set matching.

    PubMed

    Yang, Xuan; Pei, Jihong; Shi, Jingli

    2014-01-01

    Robust point matching (RPM) has been extensively used in non-rigid registration of images to robustly register two sets of image points. However, except for the location at control points, RPM cannot estimate the consistent correspondence between two images because RPM is a unidirectional image matching approach. Therefore, it is an important issue to make an improvement in image registration based on RPM. In our work, a consistent image registration approach based on the point sets matching is proposed to incorporate the property of inverse consistency and improve registration accuracy. Instead of only estimating the forward transformation between the source point sets and the target point sets in state-of-the-art RPM algorithms, the forward and backward transformations between two point sets are estimated concurrently in our algorithm. The inverse consistency constraints are introduced to the cost function of RPM and the fuzzy correspondences between two point sets are estimated based on both the forward and backward transformations simultaneously. A modified consistent landmark thin-plate spline registration is discussed in detail to find the forward and backward transformations during the optimization of RPM. The similarity of image content is also incorporated into point matching in order to improve image matching. Synthetic data sets, medical images are employed to demonstrate and validate the performance of our approach. The inverse consistent errors of our algorithm are smaller than RPM. Especially, the topology of transformations is preserved well for our algorithm for the large deformation between point sets. Moreover, the distance errors of our algorithm are similar to that of RPM, and they maintain a downward trend as whole, which demonstrates the convergence of our algorithm. The registration errors for image registrations are evaluated also. Again, our algorithm achieves the lower registration errors in same iteration number. The determinant of the

  4. A hyperspectral vessel image registration method for blood oxygenation mapping.

    PubMed

    Wang, Qian; Li, Qingli; Zhou, Mei; Sun, Zhen; Liu, Hongying; Wang, Yiting

    2017-01-01

    Blood oxygenation mapping by the means of optical oximetry is of significant importance in clinical trials. This paper uses hyperspectral imaging technology to obtain in vivo images for blood oxygenation detection. The experiment involves dorsal skin fold window chamber preparation which was built on adult (8-10 weeks of age) female BALB/c nu/nu mice and in vivo image acquisition which was performed by hyperspectral imaging system. To get the accurate spatial and spectral information of targets, an automatic registration scheme is proposed. An adaptive feature detection method which combines the local threshold method and the level-set filter is presented to extract target vessels. A reliable feature matching algorithm with the correlative information inherent in hyperspectral images is used to kick out the outliers. Then, the registration images are used for blood oxygenation mapping. Registration evaluation results show that most of the false matches are removed and the smooth and concentrated spectra are obtained. This intensity invariant feature detection with outliers-removing feature matching proves to be effective in hyperspectral vessel image registration. Therefore, in vivo hyperspectral imaging system by the assistance of the proposed registration scheme provides a technique for blood oxygenation research.

  5. Deformable image registration between pathological images and MR image via an optical macro image.

    PubMed

    Ohnishi, Takashi; Nakamura, Yuka; Tanaka, Toru; Tanaka, Takuya; Hashimoto, Noriaki; Haneishi, Hideaki; Batchelor, Tracy T; Gerstner, Elizabeth R; Taylor, Jennie W; Snuderl, Matija; Yagi, Yukako

    2016-10-01

    Computed tomography (CT) and magnetic resonance (MR) imaging have been widely used for visualizing the inside of the human body. However, in many cases, pathological diagnosis is conducted through a biopsy or resection of an organ to evaluate the condition of tissues as definitive diagnosis. To provide more advanced information onto CT or MR image, it is necessary to reveal the relationship between tissue information and image signals. We propose a registration scheme for a set of PT images of divided specimens and a 3D-MR image by reference to an optical macro image (OM image) captured by an optical camera. We conducted a fundamental study using a resected human brain after the death of a brain cancer patient. We constructed two kinds of registration processes using the OM image as the base for both registrations to make conversion parameters between the PT and MR images. The aligned PT images had shapes similar to the OM image. On the other hand, the extracted cross-sectional MR image was similar to the OM image. From these resultant conversion parameters, the corresponding region on the PT image could be searched and displayed when an arbitrary pixel on the MR image was selected. The relationship between the PT and MR images of the whole brain can be analyzed using the proposed method. We confirmed that same regions between the PT and MR images could be searched and displayed using resultant information obtained by the proposed method. In terms of the accuracy of proposed method, the TREs were 0.56±0.39mm and 0.87±0.42mm. We can analyze the relationship between tissue information and MR signals using the proposed method.

  6. Deformable image registration between pathological images and MR image via an optical macro image

    PubMed Central

    Ohnishi, Takashi; Nakamura, Yuka; Tanaka, Toru; Tanaka, Takuya; Hashimoto, Noriaki; Haneishi, Hideaki; Batchelor, Tracy T.; Gerstner, Elizabeth R.; Taylor, Jennie W.; Snuderl, Matija; Yagi, Yukako

    2016-01-01

    Computed tomography (CT) and magnetic resonance (MR) imaging have been widely used for visualizing the inside of the human body. However, in many cases, pathological diagnosis is conducted through a biopsy or resection of an organ to evaluate the condition of tissues as definitive diagnosis. To provide more advanced information onto CT or MR image, it is necessary to reveal the relationship between tissue information and image signals. We propose a registration scheme for a set of PT images of divided specimens and a 3D-MR image by reference to an optical macro image (OM image) captured by an optical camera. We conducted a fundamental study using a resected human brain after the death of a brain cancer patient. We constructed two kinds of registration processes using the OM image as the base for both registrations to make conversion parameters between the PT and MR images. The aligned PT images had shapes similar to the OM image. On the other hand, the extracted cross-sectional MR image was similar to the OM image. From these resultant conversion parameters, the corresponding region on the PT image could be searched and displayed when an arbitrary pixel on the MR image was selected. The relationship between the PT and MR images of the whole brain can be analyzed using the proposed method. We confirmed that same regions between the PT and MR images could be searched and displayed using resultant information obtained by the proposed method. In terms of the accuracy of proposed method, the TREs were 0.56 ± 0.39 mm and 0.87 ± 0.42 mm. We can analyze the relationship between tissue information and MR signals using the proposed method. PMID:27613662

  7. Symmetric Biomechanically Guided Prone-to-Supine Breast Image Registration.

    PubMed

    Eiben, Björn; Vavourakis, Vasileios; Hipwell, John H; Kabus, Sven; Buelow, Thomas; Lorenz, Cristian; Mertzanidou, Thomy; Reis, Sara; Williams, Norman R; Keshtgar, Mohammed; Hawkes, David J

    2016-01-01

    Prone-to-supine breast image registration has potential application in the fields of surgical and radiotherapy planning, image guided interventions, and multi-modal cancer diagnosis, staging, and therapy response prediction. However, breast image registration of three dimensional images acquired in different patient positions is a challenging problem, due to large deformations induced to the soft breast tissue caused by the change in gravity loading. We present a symmetric, biomechanical simulation based registration framework which aligns the images in a central, virtually unloaded configuration. The breast tissue is modelled as a neo-Hookean material and gravity is considered as the main source of deformation in the original images. In addition to gravity, our framework successively applies image derived forces directly into the unloading simulation in place of a subsequent image registration step. This results in a biomechanically constrained deformation. Using a finite difference scheme avoids an explicit meshing step and enables simulations to be performed directly in the image space. The explicit time integration scheme allows the motion at the interface between chest and breast to be constrained along the chest wall. The feasibility and accuracy of the approach presented here was assessed by measuring the target registration error (TRE) using a numerical phantom with known ground truth deformations, nine clinical prone MRI and supine CT image pairs, one clinical prone-supine CT image pair and four prone-supine MRI image pairs. The registration reduced the mean TRE for the numerical phantom experiment from initially 19.3 to 0.9 mm and the combined mean TRE for all fourteen clinical data sets from 69.7 to 5.6 mm.

  8. Multi-modality image registration for effective thermographic fever screening

    NASA Astrophysics Data System (ADS)

    Dwith, C. Y. N.; Ghassemi, Pejhman; Pfefer, Joshua; Casamento, Jon; Wang, Quanzeng

    2017-02-01

    Fever screening based on infrared thermographs (IRTs) is a viable mass screening approach during infectious disease pandemics, such as Ebola and Severe Acute Respiratory Syndrome (SARS), for temperature monitoring in public places like hospitals and airports. IRTs have been found to be powerful, quick and non-invasive methods for detecting elevated temperatures. Moreover, regions medially adjacent to the inner canthi (called the canthi regions in this paper) are preferred sites for fever screening. Accurate localization of the canthi regions can be achieved through multi-modality registration of infrared (IR) and white-light images. Here we propose a registration method through a coarse-fine registration strategy using different registration models based on landmarks and edge detection on eye contours. We have evaluated the registration accuracy to be within +/- 2.7 mm, which enables accurate localization of the canthi regions.

  9. Temporal mammogram image registration using optimized curvilinear coordinates.

    PubMed

    Abdel-Nasser, Mohamed; Moreno, Antonio; Puig, Domenec

    2016-04-01

    Registration of mammograms plays an important role in breast cancer computer-aided diagnosis systems. Radiologists usually compare mammogram images in order to detect abnormalities. The comparison of mammograms requires a registration between them. A temporal mammogram registration method is proposed in this paper. It is based on the curvilinear coordinates, which are utilized to cope both with global and local deformations in the breast area. Temporal mammogram pairs are used to validate the proposed method. After registration, the similarity between the mammograms is maximized, and the distance between manually defined landmarks is decreased. In addition, a thorough comparison with the state-of-the-art mammogram registration methods is performed to show its effectiveness. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  10. Landmark Constrained Non-parametric Image Registration with Isotropic Tolerances

    NASA Astrophysics Data System (ADS)

    Papenberg, Nils; Olesch, Janine; Lange, Thomas; Schlag, Peter M.; Fischer, Bernd

    The incorporation of additional user knowledge into a nonrigid registration process is a promising topic in modern registration schemes. The combination of intensity based registration and some interactively chosen landmark pairs is a major approach in this direction. There exist different possibilities to incorporate landmark pairs into a variational non-parametric registration framework. As the interactive localization of point landmarks is always prone to errors, a demand for precise landmark matching is bound to fail. Here, the treatment of the distances of corresponding landmarks as penalties within a constrained optimization problem offers the possibility to control the quality of the matching of each landmark pair individually. More precisely, we introduce inequality constraints, which allow for a sphere-like tolerance around each landmark. We illustrate the performance of this new approach for artificial 2D images as well as for the challenging registration of preoperative CT data to intra-operative 3D ultrasound data of the liver.

  11. Practical pseudo-3D registration for large tomographic images

    NASA Astrophysics Data System (ADS)

    Liu, Xuan; Laperre, Kjell; Sasov, Alexander

    2014-09-01

    Image registration is a powerful tool in various tomographic applications. Our main focus is on microCT applications in which samples/animals can be scanned multiple times under different conditions or at different time points. For this purpose, a registration tool capable of handling fairly large volumes has been developed, using a novel pseudo-3D method to achieve fast and interactive registration with simultaneous 3D visualization. To reduce computation complexity in 3D registration, we decompose it into several 2D registrations, which are applied to the orthogonal views (transaxial, sagittal and coronal) sequentially and iteratively. After registration in each view, the next view is retrieved with the new transformation matrix for registration. This reduces the computation complexity significantly. For rigid transform, we only need to search for 3 parameters (2 shifts, 1 rotation) in each of the 3 orthogonal views instead of 6 (3 shifts, 3 rotations) for full 3D volume. In addition, the amount of voxels involved is also significantly reduced. For the proposed pseudo-3D method, image-based registration is employed, with Sum of Square Difference (SSD) as the similarity measure. The searching engine is Powell's conjugate direction method. In this paper, only rigid transform is used. However, it can be extended to affine transform by adding scaling and possibly shearing to the transform model. We have noticed that more information can be used in the 2D registration if Maximum Intensity Projections (MIP) or Parallel Projections (PP) is used instead of the orthogonal views. Also, other similarity measures, such as covariance or mutual information, can be easily incorporated. The initial evaluation on microCT data shows very promising results. Two application examples are shown: dental samples before and after treatment and structural changes in materials before and after compression. Evaluation on registration accuracy between pseudo-3D method and true 3D method has

  12. [Medical image enhancement: Sharpening].

    PubMed

    Kats, L; Vered, M

    2015-04-01

    Most digital imaging systems provide opportunities for image enhancement operations. These are applied to improve the original image and to make the image more appealing visually. One possible means of enhancing digital radiographic image is sharpening. The purpose of sharpening filters is to improve image quality by removing noise or edge enhancement. Sharpening filters may make the radiographic images subjectively more appealing. But during this process, important radiographic features may disappear while artifacts that simulate pathological process might be generated. Therefore, it is of utmost importance for dentists to be familiar with and aware of the use of image enhancement operations, provided by medical digital imaging programs.

  13. An adaptive patient specific deformable registration for breast images of positron emission tomography and magnetic resonance imaging using finite element approach

    NASA Astrophysics Data System (ADS)

    Xue, Cheng; Tang, Fuk-Hay

    2014-03-01

    A patient specific registration model based on finite element method was investigated in this study. Image registration of Positron Emission Tomography (PET) and Magnetic Resonance imaging (MRI) has been studied a lot. Surface-based registration is extensively applied in medical imaging. We develop and evaluate a registration method combine surface-based registration with biomechanical modeling. .Four sample cases of patients with PET and MRI breast scans performed within 30 days were collected from hospital. K-means clustering algorithm was used to segment images into two parts, which is fat tissue and neoplasm [2]. Instead of placing extrinsic landmarks on patients' body which may be invasive, we proposed a new boundary condition to simulate breast deformation during two screening. Then a three dimensional model with meshes was built. Material properties were assigned to this model according to previous studies. The whole registration was based on a biomechanical finite element model, which could simulate deformation of breast under pressure.

  14. Quicksilver: Fast predictive image registration - A deep learning approach.

    PubMed

    Yang, Xiao; Kwitt, Roland; Styner, Martin; Niethammer, Marc

    2017-07-11

    This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. Semi-automatic elastic registration on thyroid gland ultrasonic image

    NASA Astrophysics Data System (ADS)

    Xu, Xia; Zhong, Yue; Luo, Yan; Li, Deyu; Lin, Jiangli; Wang, Tianfu

    2007-12-01

    Knowledge of in vivo thyroid volume has both diagnostic and therapeutic importance and could lead to a more precise quantification of absolute activity contained in the thyroid gland. However, the shape of thyroid gland is irregular and difficult to calculate. For precise estimation of thyroid volume by ultrasound imaging, this paper presents a novel semiautomatic minutiae matching method in thyroid gland ultrasonic image by means of thin-plate spline model. Registration consists of four basic steps: feature detection, feature matching, mapping function design, and image transformation and resampling. Due to the connectivity of thyroid gland boundary, we choose active contour model as feature detector, and radials from centric points for feature matching. The proposed approach has been used in thyroid gland ultrasound images registration. Registration results of 18 healthy adults' thyroid gland ultrasound images show this method consumes less time and energy with good objectivity than algorithms selecting landmarks manually.

  16. Mass preserving registration for heart MR images.

    PubMed

    Zhu, Lei; Haker, Steven; Tannenbaum, Allen

    2005-01-01

    This paper presents a new algorithm for non-rigid registration between two doubly-connected regions. Our algorithm is based on harmonic analysis and the theory of optimal mass transport. It assumes an underlining continuum model, in which the total amount of mass is exactly preserved during the transformation of tissues. We use a finite element approach to numerically implement the algorithm.

  17. Registration and 3D visualization of large microscopy images

    NASA Astrophysics Data System (ADS)

    Mosaliganti, Kishore; Pan, Tony; Sharp, Richard; Ridgway, Randall; Iyengar, Srivathsan; Gulacy, Alexandra; Wenzel, Pamela; de Bruin, Alain; Machiraju, Raghu; Huang, Kun; Leone, Gustavo; Saltz, Joel

    2006-03-01

    Inactivation of the retinoblastoma gene in mouse embryos causes tissue infiltrations into critical sections of the placenta, which has been shown to affect fetal survivability. Our collaborators in cancer genetics are extremely interested in examining the three dimensional nature of these infiltrations given a stack of two dimensional light microscopy images. Three sets of wildtype and mutant placentas was sectioned serially and digitized using a commercial light microscopy scanner. Each individual placenta dataset consisted of approximately 1000 images totaling 700 GB in size, which were registered into a volumetric dataset using National Library of Medicine's (NIH/NLM) Insight Segmentation and Registration Toolkit (ITK). This paper describes our method for image registration to aid in volume visualization of tissue level intermixing for both wildtype and Rb - specimens. The registration process faces many challenges arising from the large image sizes, damages during sectioning, staining gradients both within and across sections, and background noise. These issues limit the direct application of standard registration techniques due to frequent convergence to local solutions. In this work, we develop a mixture of automated and semi-automated enhancements with ground-truth validation for the mutual information-based registration algorithm. Our final volume renderings clearly show tissue intermixing differences between both wildtype and Rb - specimens which are not obvious prior to registration.

  18. Rigid and elastic registration for coronary artery IVUS images.

    PubMed

    Sun, Zheng; Bai, Hua; Liu, Bingru

    2016-04-29

    Intravascular ultrasound (IVUS) has been widely used in diagnosis and interventional treatment of cardiac vessel diseases. The coronary artery IVUS images are usually polluted by motion artifacts caused by cardiac motion, pulsatile blood and catheter twist during continuous pullback acquisition. Strategies for rigid and elastic registration of coronary artery IVUS studies are developed to suppress the longitudinal motion and misalignment between successive frames. Rigid registration is performed by searching for the optimal matching for each frame in other cycles based on the cyclic variation of gray-scale features. The image sequence is gated to properly identify the frames in each cardiac phase. Then, elastic registration between frames is achieved through an optimization algorithm based on thin plate spline (TPS) to correct the misalignment of successive slices. Experimental results with in vivo image data shows that the rigid registration performs better than the offline ECG gating. The elastic mapping relation between lumen contours in successive frames is smooth and continuous. The serrated vessel wall borders in longitudinal cuts are smoothed after rigid registration while image segmentation and feature extraction are required. The point-to-point correspondence between lumen contours detected from two matched frames is obtained with elastic registration.

  19. Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information.

    PubMed

    Maes, F; Vandermeulen, D; Suetens, P

    1999-12-01

    Maximization of mutual information of voxel intensities has been demonstrated to be a very powerful criterion for three-dimensional medical image registration, allowing robust and accurate fully automated affine registration of multimodal images in a variety of applications, without the need for segmentation or other preprocessing of the images. In this paper, we investigate the performance of various optimization methods and multiresolution strategies for maximization of mutual information, aiming at increasing registration speed when matching large high-resolution images. We show that mutual information is a continuous function of the affine registration parameters when appropriate interpolation is used and we derive analytic expressions of its derivatives that allow numerically exact evaluation of its gradient. Various multiresolution gradient- and non-gradient-based optimization strategies, such as Powell, simplex, steepest-descent, conjugate-gradient, quasi-Newton and Levenberg-Marquardt methods, are evaluated for registration of computed tomography (CT) and magnetic resonance images of the brain. Speed-ups of a factor of 3 on average compared to Powell's method at full resolution are achieved with similar precision and without a loss of robustness with the simplex, conjugate-gradient and Levenberg-Marquardt method using a two-level multiresolution scheme. Large data sets such as 256(2) x 128 MR and 512(2) x 48 CT images can be registered with subvoxel precision in <5 min CPU time on current workstations.

  20. Diffeomorphic Registration of Images with Variable Contrast Enhancement

    PubMed Central

    Janssens, Guillaume; Jacques, Laurent; Orban de Xivry, Jonathan; Geets, Xavier; Macq, Benoit

    2011-01-01

    Nonrigid image registration is widely used to estimate tissue deformations in highly deformable anatomies. Among the existing methods, nonparametric registration algorithms such as optical flow, or Demons, usually have the advantage of being fast and easy to use. Recently, a diffeomorphic version of the Demons algorithm was proposed. This provides the advantage of producing invertible displacement fields, which is a necessary condition for these to be physical. However, such methods are based on the matching of intensities and are not suitable for registering images with different contrast enhancement. In such cases, a registration method based on the local phase like the Morphons has to be used. In this paper, a diffeomorphic version of the Morphons registration method is proposed and compared to conventional Morphons, Demons, and diffeomorphic Demons. The method is validated in the context of radiotherapy for lung cancer patients on several 4D respiratory-correlated CT scans of the thorax with and without variable contrast enhancement. PMID:21197460

  1. Diffeomorphic registration of images with variable contrast enhancement.

    PubMed

    Janssens, Guillaume; Jacques, Laurent; Orban de Xivry, Jonathan; Geets, Xavier; Macq, Benoit

    2011-01-01

    Nonrigid image registration is widely used to estimate tissue deformations in highly deformable anatomies. Among the existing methods, nonparametric registration algorithms such as optical flow, or Demons, usually have the advantage of being fast and easy to use. Recently, a diffeomorphic version of the Demons algorithm was proposed. This provides the advantage of producing invertible displacement fields, which is a necessary condition for these to be physical. However, such methods are based on the matching of intensities and are not suitable for registering images with different contrast enhancement. In such cases, a registration method based on the local phase like the Morphons has to be used. In this paper, a diffeomorphic version of the Morphons registration method is proposed and compared to conventional Morphons, Demons, and diffeomorphic Demons. The method is validated in the context of radiotherapy for lung cancer patients on several 4D respiratory-correlated CT scans of the thorax with and without variable contrast enhancement.

  2. A Demons algorithm for image registration with locally adaptive regularization.

    PubMed

    Cahill, Nathan D; Noble, J Alison; Hawkes, David J

    2009-01-01

    Thirion's Demons is a popular algorithm for nonrigid image registration because of its linear computational complexity and ease of implementation. It approximately solves the diffusion registration problem by successively estimating force vectors that drive the deformation toward alignment and smoothing the force vectors by Gaussian convolution. In this article, we show how the Demons algorithm can be generalized to allow image-driven locally adaptive regularization in a manner that preserves both the linear complexity and ease of implementation of the original Demons algorithm. We show that the proposed algorithm exhibits lower target registration error and requires less computational effort than the original Demons algorithm on the registration of serial chest CT scans of patients with lung nodules.

  3. Image panoramic mosaicing with global and local registration

    NASA Astrophysics Data System (ADS)

    Li, Qi; Ji, Zhen; Zhang, Jihong

    2001-09-01

    This paper presents techniques for constructing full view panoramic mosaics from sequences of images. The goal of this work is to remove too many limitations for pure panning motion. The best reference block is critical for the block- matching method for improving the robustness and performance. It is automatically selected in the high- frequency image, which always contains the plenty visible features. In order to reduce accumulated registration errors, the global registration using the phase-correlation matching method with rotation adjustment is applied to the whole sequence of images, which results in an optimal image mosaic with resolving translational or rotational motion. The local registration using the Levenberg-Marquardt iterative non-linear minimization algorithm is applied to compensate for small amounts of motion parallax introduced by translations of the camera and other unmodeled distortions, when minimize the discrepancy after applying the global registration. The accumulated misregistration errors may cause a visible gap between the two images. A smoothing filter is introduced, derived from Marr's computer vision theory for removing the visible artifact. By combining both global and local registration, together with artifact smoothing, the quality of the image mosaics is significantly improved, thereby enabling the creation of full view panoramic mosaics with hand-held cameras.

  4. Geodesic active fields--a geometric framework for image registration.

    PubMed

    Zosso, Dominique; Bresson, Xavier; Thiran, Jean-Philippe

    2011-05-01

    In this paper we present a novel geometric framework called geodesic active fields for general image registration. In image registration, one looks for the underlying deformation field that best maps one image onto another. This is a classic ill-posed inverse problem, which is usually solved by adding a regularization term. Here, we propose a multiplicative coupling between the registration term and the regularization term, which turns out to be equivalent to embed the deformation field in a weighted minimal surface problem. Then, the deformation field is driven by a minimization flow toward a harmonic map corresponding to the solution of the registration problem. This proposed approach for registration shares close similarities with the well-known geodesic active contours model in image segmentation, where the segmentation term (the edge detector function) is coupled with the regularization term (the length functional) via multiplication as well. As a matter of fact, our proposed geometric model is actually the exact mathematical generalization to vector fields of the weighted length problem for curves and surfaces introduced by Caselles-Kimmel-Sapiro. The energy of the deformation field is measured with the Polyakov energy weighted by a suitable image distance, borrowed from standard registration models. We investigate three different weighting functions, the squared error and the approximated absolute error for monomodal images, and the local joint entropy for multimodal images. As compared to specialized state-of-the-art methods tailored for specific applications, our geometric framework involves important contributions. Firstly, our general formulation for registration works on any parametrizable, smooth and differentiable surface, including nonflat and multiscale images. In the latter case, multiscale images are registered at all scales simultaneously, and the relations between space and scale are intrinsically being accounted for. Second, this method is, to

  5. Image processing and registration in a point set representation

    NASA Astrophysics Data System (ADS)

    Gao, Yi; Tannenbaum, Allen

    2010-03-01

    An image, being a continuous function, is commonly discretely represented as a set of sample values, namely the intensities, associated with the spatial grids. After that, all types of the operations are then carried out there in. We denote such representation as the discrete function representation (DFR). In this paper we provide another discrete representation for images using the point sets, called the point set representation (PSR). Essentially, the image is normalized to have the unit integral and is treated as a probability density function of some random variable. Then the PSR is formed by drawing samples of such random variable. In contrast with the DFR, here the image is purely represented as points and no values are associated. Besides being an equivalent discrete representation for images, we show that certain image operations benefit from such representation in the numerical stability, performance, or both. Examples are given in the Perona-Malik type diffusion where in the PSR there is no such problem as the numerical instability. Furthermore, PSR naturally bridges the fields of image registration with the point set registration. This helps handle some otherwise difficult problems in image registration such as partial image registration, with much faster convergence speed.

  6. Fast Registration of Terrestrial LIDAR Point Cloud and Sequence Images

    NASA Astrophysics Data System (ADS)

    Shao, J.; Zhang, W.; Zhu, Y.; Shen, A.

    2017-09-01

    Image has rich color information, and it can help to promote recognition and classification of point cloud. The registration is an important step in the application of image and point cloud. In order to give the rich texture and color information for LiDAR point cloud, the paper researched a fast registration method of point cloud and sequence images based on the ground-based LiDAR system. First, calculating transformation matrix of one of sequence images based on 2D image and LiDAR point cloud; second, using the relationships of position and attitude information among multi-angle sequence images to calculate all transformation matrixes in the horizontal direction; last, completing the registration of point cloud and sequence images based on the collinear condition of image point, projective center and LiDAR point. The experimental results show that the method is simple and fast, and the stitching error between adjacent images is litter; meanwhile, the overall registration accuracy is high, and the method can be used in engineering application.

  7. Biomechanical based image registration for head and neck radiation treatment

    NASA Astrophysics Data System (ADS)

    Al-Mayah, Adil; Moseley, Joanne; Hunter, Shannon; Velec, Mike; Chau, Lily; Breen, Stephen; Brock, Kristy

    2010-02-01

    Deformable image registration of four head and neck cancer patients was conducted using biomechanical based model. Patient specific 3D finite element models have been developed using CT and cone beam CT image data of the planning and a radiation treatment session. The model consists of seven vertebrae (C1 to C7), mandible, larynx, left and right parotid glands, tumor and body. Different combinations of boundary conditions are applied in the model in order to find the configuration with a minimum registration error. Each vertebra in the planning session is individually aligned with its correspondence in the treatment session. Rigid alignment is used for each individual vertebra and to the mandible since deformation is not expected in the bones. In addition, the effect of morphological differences in external body between the two image sessions is investigated. The accuracy of the registration is evaluated using the tumor, and left and right parotid glands by comparing the calculated Dice similarity index of these structures following deformation in relation to their true surface defined in the image of the second session. The registration improves when the vertebrae and mandible are aligned in the two sessions with the highest Dice index of 0.86+/-0.08, 0.84+/-0.11, and 0.89+/-0.04 for the tumor, left and right parotid glands, respectively. The accuracy of the center of mass location of tumor and parotid glands is also improved by deformable image registration where the error in the tumor and parotid glands decreases from 4.0+/-1.1, 3.4+/-1.5, and 3.8+/-0.9 mm using rigid registration to 2.3+/-1.0, 2.5+/-0.8 and 2.0+/-0.9 mm in the deformable image registration when alignment of vertebrae and mandible is conducted in addition to the surface projection of the body.

  8. SAR image registration based on SIFT and MSA

    NASA Astrophysics Data System (ADS)

    Yi, Zhaoxiang; Zhang, Xiongmei; Mu, Xiaodong; Wang, Kui; Song, Jianshe

    2014-02-01

    Referring to the problem of SAR image registration, an image registration method based on Scale Invariant Feature Transform (SIFT) and Multi-Scale Autoconvolution (MSA) is proposed. Based on the extraction of SIFT descriptors and the MSA affine invariant moments of the region around the keypoints, the feature fusion method based on canonical correlation analysis (CCA) is employed to fuse them together to be a new descriptor. After the control points are rough matched, the distance and gray correlation around the rough matched points are combined to build the similarity matrix and the singular value decomposition (SVD) method is employed to realize precise image registration. Finally, the affine transformation parameters are obtained and the images are registered. Experimental results show that the proposed method outperforms the SIFT method and achieves high accuracy in sub-pixel level.

  9. Longitudinal image registration with non-uniform appearance change.

    PubMed

    Csapo, Istvan; Davis, Brad; Shi, Yundi; Sanchez, Mar; Styner, Martin; Niethammer, Marc

    2012-01-01

    Longitudinal imaging studies are frequently used to investigate temporal changes in brain morphology. Image intensity may also change over time, for example when studying brain maturation. However, such intensity changes are not accounted for in image similarity measures for standard image registration methods. Hence, (i) local similarity measures, (ii) methods estimating intensity transformations between images, and (iii) metamorphosis approaches have been developed to either achieve robustness with respect to intensity changes or to simultaneously capture spatial and intensity changes. For these methods, longitudinal intensity changes are not explicitly modeled and images are treated as independent static samples. Here, we propose a model-based image similarity measure for longitudinal image registration in the presence of spatially non-uniform intensity change.

  10. Registration of multitemporal aerial optical images using line features

    NASA Astrophysics Data System (ADS)

    Zhao, Chenyang; Goshtasby, A. Ardeshir

    2016-07-01

    Registration of multitemporal images is generally considered difficult because scene changes can occur between the times the images are obtained. Since the changes are mostly radiometric in nature, features are needed that are insensitive to radiometric differences between the images. Lines are geometric features that represent straight edges of rigid man-made structures. Because such structures rarely change over time, lines represent stable geometric features that can be used to register multitemporal remote sensing images. An algorithm to establish correspondence between lines in two images of a planar scene is introduced and formulas to relate the parameters of a homography transformation to the parameters of corresponding lines in images are derived. Results of the proposed image registration on various multitemporal images are presented and discussed.

  11. Discontinuity Preserving Image Registration through Motion Segmentation: A Primal-Dual Approach.

    PubMed

    Kiriyanthan, Silja; Fundana, Ketut; Majeed, Tahir; Cattin, Philippe C

    2016-01-01

    Image registration is a powerful tool in medical image analysis and facilitates the clinical routine in several aspects. There are many well established elastic registration methods, but none of them can so far preserve discontinuities in the displacement field. These discontinuities appear in particular at organ boundaries during the breathing induced organ motion. In this paper, we exploit the fact that motion segmentation could play a guiding role during discontinuity preserving registration. The motion segmentation is embedded in a continuous cut framework guaranteeing convexity for motion segmentation. Furthermore we show that a primal-dual method can be used to estimate a solution to this challenging variational problem. Experimental results are presented for MR images with apparent breathing induced sliding motion of the liver along the abdominal wall.

  12. Discontinuity Preserving Image Registration through Motion Segmentation: A Primal-Dual Approach

    PubMed Central

    Kiriyanthan, Silja; Majeed, Tahir

    2016-01-01

    Image registration is a powerful tool in medical image analysis and facilitates the clinical routine in several aspects. There are many well established elastic registration methods, but none of them can so far preserve discontinuities in the displacement field. These discontinuities appear in particular at organ boundaries during the breathing induced organ motion. In this paper, we exploit the fact that motion segmentation could play a guiding role during discontinuity preserving registration. The motion segmentation is embedded in a continuous cut framework guaranteeing convexity for motion segmentation. Furthermore we show that a primal-dual method can be used to estimate a solution to this challenging variational problem. Experimental results are presented for MR images with apparent breathing induced sliding motion of the liver along the abdominal wall. PMID:27721897

  13. Mass Preserving Registration for Heart MR Images

    PubMed Central

    Zhu, Lei; Haker, Steven; Tannenbaum, Allen

    2013-01-01

    This paper presents a new algorithm for non-rigid registration between two doubly-connected regions. Our algorithm is based on harmonic analysis and the theory of optimal mass transport. It assumes an underlining continuum model, in which the total amount of mass is exactly preserved during the transformation of tissues. We use a finite element approach to numerically implement the algorithm. PMID:16685954

  14. GPU-accelerated Block Matching Algorithm for Deformable Registration of Lung CT Images

    PubMed Central

    Li, Min; Xiang, Zhikang; Xiao, Liang; Castillo, Edward; Castillo, Richard; Guerrero, Thomas

    2016-01-01

    Deformable registration (DR) is a key technology in the medical field. However, many of the existing DR methods are time-consuming and the registration accuracy needs to be improved, which prevents their clinical applications. In this study, we propose a parallel block matching algorithm for lung CT image registration, in which the sum of squared difference metric is modified as the cost function and the moving least squares approach is used to generate the full displacement field. The algorithm is implemented on Graphic Processing Unit (GPU) with the Compute Unified Device Architecture (CUDA). Results show that the proposed parallel block matching method achieves a fast runtime while maintaining an average registration error (standard deviation) of 1.08 (0.69) mm. PMID:28042622

  15. GPU-accelerated Block Matching Algorithm for Deformable Registration of Lung CT Images.

    PubMed

    Li, Min; Xiang, Zhikang; Xiao, Liang; Castillo, Edward; Castillo, Richard; Guerrero, Thomas

    2015-12-01

    Deformable registration (DR) is a key technology in the medical field. However, many of the existing DR methods are time-consuming and the registration accuracy needs to be improved, which prevents their clinical applications. In this study, we propose a parallel block matching algorithm for lung CT image registration, in which the sum of squared difference metric is modified as the cost function and the moving least squares approach is used to generate the full displacement field. The algorithm is implemented on Graphic Processing Unit (GPU) with the Compute Unified Device Architecture (CUDA). Results show that the proposed parallel block matching method achieves a fast runtime while maintaining an average registration error (standard deviation) of 1.08 (0.69) mm.

  16. Medical imaging systems

    DOEpatents

    Frangioni, John V

    2013-06-25

    A medical imaging system provides simultaneous rendering of visible light and diagnostic or functional images. The system may be portable, and may include adapters for connecting various light sources and cameras in open surgical environments or laparascopic or endoscopic environments. A user interface provides control over the functionality of the integrated imaging system. In one embodiment, the system provides a tool for surgical pathology.

  17. Automated rejection of contaminated surface measurements for improved surface registration in image guided neurosurgery.

    PubMed

    Bucholz, R; Macneil, W; Fewings, P; Ravindra, A; McDurmont, L; Baumann, C

    2000-01-01

    Most image guided Neurosurgery employs adhesively mounted external fiducials for registration of medical images to the surgical workspace. Due to high logistical costs associated with these artificial landmarks, we strive to eliminate the need for these markers. At our institution, we developed a handheld laser stripe triangulation device to capture the surface contours of the patient's head while oriented for surgery. Anatomical surface registration algorithms rely on the assumption that the patient's anatomy bears the same geometry as the 3D model of the patient constructed from the imaging modality employed. During the time interval from which the patient is imaged and placed in the Mayfield head clamp in the operating room, the skin of the head bulges at the pinsite and the skull fixation equipment itself optically interferes with the image capture laser. We have developed software to reject points belonging to objects of known geometry while calculating the registration. During the course of development of the laser scanning unit, we have acquired surface contours of 13 patients and 2 cadavers. Initial analysis revealed that this automated rejection of points improved the registrations in all cases, but the accuracy of the fiducial method was not surpassed. Only points belonging to the offending instrument are removed. Skin bulges caused by the clamps and instruments remain in the data. We anticipate that careful removal of the points in these skin bulges will yield registrations that at least match the accuracy of the fiducial method.

  18. Multimodal registration of remotely sensed images based on Jeffrey's divergence

    NASA Astrophysics Data System (ADS)

    Xu, Xiaocong; Li, Xia; Liu, Xiaoping; Shen, Huanfeng; Shi, Qian

    2016-12-01

    Entropy-based measures (e.g., mutual information, also known as Kullback-Leiber divergence), which quantify the similarity between two signals, are widely used as similarity measures for image registration. Although they are proven superior to many classical statistical measures, entropy-based measures, such as mutual information, may fail to yield the optimum registration if the multimodal image pair has insufficient scene overlap region. To overcome this challenge, we proposed using the symmetric form of Kullback-Leiber divergence, namely Jeffrey's divergence, as the similarity measure in practical multimodal image registration tasks. Mathematical analysis was performed to investigate the causes accounting for the limitation of mutual information when dealing with insufficient scene overlap image pairs. Experimental registrations of SPOT image, Landsat TM image, ALOS PalSAR image, and DEM data were carried out to compare the performance of Jeffrey's divergence and mutual information. Results indicate that Jeffrey's divergence is capable of providing larger feasible search space, which is favorable for exploring optimum transformation parameters in a larger range. This superiority of Jeffrey's divergence was further confirmed by a series of paradigms. Thus, the proposed model is more applicable for registering image pairs that are greatly misaligned or have an insufficient scene overlap region.

  19. PCA-based groupwise image registration for quantitative MRI.

    PubMed

    Huizinga, W; Poot, D H J; Guyader, J-M; Klaassen, R; Coolen, B F; van Kranenburg, M; van Geuns, R J M; Uitterdijk, A; Polfliet, M; Vandemeulebroucke, J; Leemans, A; Niessen, W J; Klein, S

    2016-04-01

    Quantitative magnetic resonance imaging (qMRI) is a technique for estimating quantitative tissue properties, such as the T1 and T2 relaxation times, apparent diffusion coefficient (ADC), and various perfusion measures. This estimation is achieved by acquiring multiple images with different acquisition parameters (or at multiple time points after injection of a contrast agent) and by fitting a qMRI signal model to the image intensities. Image registration is often necessary to compensate for misalignments due to subject motion and/or geometric distortions caused by the acquisition. However, large differences in image appearance make accurate image registration challenging. In this work, we propose a groupwise image registration method for compensating misalignment in qMRI. The groupwise formulation of the method eliminates the requirement of choosing a reference image, thus avoiding a registration bias. The method minimizes a cost function that is based on principal component analysis (PCA), exploiting the fact that intensity changes in qMRI can be described by a low-dimensional signal model, but not requiring knowledge on the specific acquisition model. The method was evaluated on 4D CT data of the lungs, and both real and synthetic images of five different qMRI applications: T1 mapping in a porcine heart, combined T1 and T2 mapping in carotid arteries, ADC mapping in the abdomen, diffusion tensor mapping in the brain, and dynamic contrast-enhanced mapping in the abdomen. Each application is based on a different acquisition model. The method is compared to a mutual information-based pairwise registration method and four other state-of-the-art groupwise registration methods. Registration accuracy is evaluated in terms of the precision of the estimated qMRI parameters, overlap of segmented structures, distance between corresponding landmarks, and smoothness of the deformation. In all qMRI applications the proposed method performed better than or equally well as

  20. A New Technique For Elastic Registration Of Tomographic Images

    NASA Astrophysics Data System (ADS)

    Ratib, Osman; Bidaut, Luc; Schelbert, Heinrich R.; Phelps, Michael E.

    1988-06-01

    A simple algorithm for geometric registration of tomographic images was developed to be applied to images of the heart obtained by Positron Emission Tomography (PET). Commonly, image misregistrations are not only caused by simple positioning differences like translation and rotation, but also by deformations that cannot be expressed by simple geometric transformations. The algorithm that we developed is a combination of two methods: a "rigid" registration followed by an "elastic" deformation. A variable number of reference points is used to register the images. The first part of this method consists of calculating the best match between the reference points of the two images based on the evaluation of the center of gravity, the axes of inertia and the surface extension. A set of geometric rotation, translation and scaling operations are then applied to the image to match. In the second phase an elastic model is further applied to perfectly match the reference points. An algorithm based on a weighted average of the displacement of every reference point is used. This second phase produces local deformations of the image to match every reference point. A set of validation experiments was performed using digitized images of isolated slices of a dog's heart where several deformations were applied. Further experiments on PET images showed that this method provides adequate automatic 2D registration of the tomographic images. The same approach could potentially be used to match images from different imaging modalities like MRI or CT scans.

  1. Automatic image registration performance for two different CBCT systems; variation with imaging dose

    NASA Astrophysics Data System (ADS)

    Barber, J.; Sykes, J. R.; Holloway, L.; Thwaites, D. I.

    2014-03-01

    The performance of an automatic image registration algorithm was compared on image sets collected with two commercial CBCT systems, and the relationship with imaging dose was explored. CBCT images of a CIRS Virtually Human Male Pelvis phantom (VHMP) were collected on Varian TrueBeam/OBI and Elekta Synergy/XVI linear accelerators, across a range of mAs settings. Each CBCT image was registered 100 times, with random initial offsets introduced. Image registration was performed using the grey value correlation ratio algorithm in the Elekta XVI software, to a mask of the prostate volume with 5 mm expansion. Residual registration errors were calculated after correcting for the initial introduced phantom set-up error. Registration performance with the OBI images was similar to that of XVI. There was a clear dependence on imaging dose for the XVI images with residual errors increasing below 4mGy. It was not possible to acquire images with doses lower than ~5mGy with the OBI system and no evidence of reduced performance was observed at this dose. Registration failures (maximum target registration error > 3.6 mm on the surface of a 30mm sphere) occurred in 5% to 9% of registrations except for the lowest dose XVI scan (31%). The uncertainty in automatic image registration with both OBI and XVI images was found to be adequate for clinical use within a normal range of acquisition settings.

  2. Multi-institutional Validation Study of Commercially Available Deformable Image Registration Software for Thoracic Images.

    PubMed

    Kadoya, Noriyuki; Nakajima, Yujiro; Saito, Masahide; Miyabe, Yuki; Kurooka, Masahiko; Kito, Satoshi; Fujita, Yukio; Sasaki, Motoharu; Arai, Kazuhiro; Tani, Kensuke; Yagi, Masashi; Wakita, Akihisa; Tohyama, Naoki; Jingu, Keiichi

    2016-10-01

    To assess the accuracy of the commercially available deformable image registration (DIR) software for thoracic images at multiple institutions. Thoracic 4-dimensional (4D) CT images of 10 patients with esophageal or lung cancer were used. Datasets for these patients were provided by DIR-lab (dir-lab.com) and included a coordinate list of anatomic landmarks (300 bronchial bifurcations) that had been manually identified. Deformable image registration was performed between the peak-inhale and -exhale images. Deformable image registration error was determined by calculating the difference at each landmark point between the displacement calculated by DIR software and that calculated by the landmark. Eleven institutions participated in this study: 4 used RayStation (RaySearch Laboratories, Stockholm, Sweden), 5 used MIM Software (Cleveland, OH), and 3 used Velocity (Varian Medical Systems, Palo Alto, CA). The ranges of the average absolute registration errors over all cases were as follows: 0.48 to 1.51 mm (right-left), 0.53 to 2.86 mm (anterior-posterior), 0.85 to 4.46 mm (superior-inferior), and 1.26 to 6.20 mm (3-dimensional). For each DIR software package, the average 3-dimensional registration error (range) was as follows: RayStation, 3.28 mm (1.26-3.91 mm); MIM Software, 3.29 mm (2.17-3.61 mm); and Velocity, 5.01 mm (4.02-6.20 mm). These results demonstrate that there was moderate variation among institutions, although the DIR software was the same. We evaluated the commercially available DIR software using thoracic 4D-CT images from multiple centers. Our results demonstrated that DIR accuracy differed among institutions because it was dependent on both the DIR software and procedure. Our results could be helpful for establishing prospective clinical trials and for the widespread use of DIR software. In addition, for clinical care, we should try to find the optimal DIR procedure using thoracic 4D-CT data. Copyright © 2016 Elsevier Inc. All rights

  3. Manifold learning based registration algorithms applied to multimodal images.

    PubMed

    Azampour, Mohammad Farid; Ghaffari, Aboozar; Hamidinekoo, Azam; Fatemizadeh, Emad

    2014-01-01

    Manifold learning algorithms are proposed to be used in image processing based on their ability in preserving data structures while reducing the dimension and the exposure of data structure in lower dimension. Multi-modal images have the same structure and can be registered together as monomodal images if only structural information is shown. As a result, manifold learning is able to transform multi-modal images to mono-modal ones and subsequently do the registration using mono-modal methods. Based on this application, in this paper novel similarity measures are proposed for multi-modal images in which Laplacian eigenmaps are employed as manifold learning algorithm and are tested against rigid registration of PET/MR images. Results show the feasibility of using manifold learning as a way of calculating the similarity between multimodal images.

  4. 3D image registration using a fast noniterative algorithm.

    PubMed

    Zhilkin, P; Alexander, M E

    2000-11-01

    This note describes the implementation of a three-dimensional (3D) registration algorithm, generalizing a previous 2D version [Alexander, Int J Imaging Systems and Technology 1999;10:242-57]. The algorithm solves an integrated form of linearized image matching equation over a set of 3D rectangular sub-volumes ('patches') in the image domain. This integrated form avoids numerical instabilities due to differentiation of a noisy image over a lattice, and in addition renders the algorithm robustness to noise. Registration is implemented by first convolving the unregistered images with a set of computationally fast [O(N)] filters, providing four bandpass images for each input image, and integrating the image matching equation over the given patch. Each filter and each patch together provide an independent set of constraints on the displacement field derived by solving a set of linear regression equations. Furthermore, the filters are implemented at a variety of spatial scales, enabling registration parameters at one scale to be used as an input approximation for deriving refined values of those parameters at a finer scale of resolution. This hierarchical procedure is necessary to avoid false matches occurring. Both downsampled and oversampled (undecimating) filtering is implemented. Although the former is computationally fast, it lacks the translation invariance of the latter. Oversampling is required for accurate interpolation that is used in intermediate stages of the algorithm to reconstruct the partially registered from the unregistered image. However, downsampling is useful, and computationally efficient, for preliminary stages of registration when large mismatches are present. The 3D registration algorithm was implemented using a 12-parameter affine model for the displacement: u(x) = Ax + b. Linear interpolation was used throughout. Accuracy and timing results for registering various multislice images, obtained by scanning a melon and human volunteers in various

  5. Fast and robust multimodal image registration using a local derivative pattern.

    PubMed

    Jiang, Dongsheng; Shi, Yonghong; Chen, Xinrong; Wang, Manning; Song, Zhijian

    2017-02-01

    Deformable multimodal image registration, which can benefit radiotherapy and image guided surgery by providing complementary information, remains a challenging task in the medical image analysis field due to the difficulty of defining a proper similarity measure. This article presents a novel, robust and fast binary descriptor, the discriminative local derivative pattern (dLDP), which is able to encode images of different modalities into similar image representations. dLDP calculates a binary string for each voxel according to the pattern of intensity derivatives in its neighborhood. The descriptor similarity is evaluated using the Hamming distance, which can be efficiently computed, instead of conventional L1 or L2 norms. For the first time, we validated the effectiveness and feasibility of the local derivative pattern for multimodal deformable image registration with several multi-modal registration applications. dLDP was compared with three state-of-the-art methods in artificial image and clinical settings. In the experiments of deformable registration between different magnetic resonance imaging (MRI) modalities from BrainWeb, between computed tomography and MRI images from patient data, and between MRI and ultrasound images from BITE database, we show our method outperforms localized mutual information and entropy images in terms of both accuracy and time efficiency. We have further validated dLDP for the deformable registration of preoperative MRI and three-dimensional intraoperative ultrasound images. Our results indicate that dLDP reduces the average mean target registration error from 4.12 mm to 2.30 mm. This accuracy is statistically equivalent to the accuracy of the state-of-the-art methods in the study; however, in terms of computational complexity, our method significantly outperforms other methods and is even comparable to the sum of the absolute difference. The results reveal that dLDP can achieve superior performance regarding both accuracy and

  6. Simultaneous registration of multiple images: similarity metrics and efficient optimization.

    PubMed

    Wachinger, Christian; Navab, Nassir

    2013-05-01

    We address the alignment of a group of images with simultaneous registration. Therefore, we provide further insights into a recently introduced framework for multivariate similarity measures, referred to as accumulated pair-wise estimates (APE), and derive efficient optimization methods for it. More specifically, we show a strict mathematical deduction of APE from a maximum-likelihood framework and establish a connection to the congealing framework. This is only possible after an extension of the congealing framework with neighborhood information. Moreover, we address the increased computational complexity of simultaneous registration by deriving efficient gradient-based optimization strategies for APE: Gauss-Newton and the efficient second-order minimization (ESM). We present next to SSD the usage of intrinsically nonsquared similarity measures in this least squares optimization framework. The fundamental assumption of ESM, the approximation of the perfectly aligned moving image through the fixed image, limits its application to monomodal registration. We therefore incorporate recently proposed structural representations of images which allow us to perform multimodal registration with ESM. Finally, we evaluate the performance of the optimization strategies with respect to the similarity measures, leading to very good results for ESM. The extension to multimodal registration is in this context very interesting because it offers further possibilities for evaluations, due to publicly available datasets with ground-truth alignment.

  7. Large deformation diffeomorphic registration of diffusion-weighted images.

    PubMed

    Zhang, Pei; Niethammer, Marc; Shen, Dinggang; Yap, Pew-Thian

    2012-01-01

    Registration of Diffusion-weighted imaging (DWI) data emerges as an important topic in magnetic resonance (MR) image analysis. As existing methods are often designed for specific diffusion models, it is difficult to fit to the registered data different models other than the one used for registration. In this paper we describe a diffeomorphic registration algorithm for DWI data in a large deformation setting. Our method generates spatially normalized DWI data and it is thus possible to fit various diffusion models after registration for comparison purposes. Our algorithm includes (1) a reorientation component, where each diffusion profile (DWI signal as a function on a unit sphere) is decomposed, reoriented and recomposed to form the orientation-corrected DWI profile, and (2) a large deformation diffeomorphic registration component to ensure one-to-one mapping in a large-structural-variation scenario. In addition our algorithm uses a geodesic shooting mechanism to avoid the huge computational resources that are needed to register high-dimensional vector-valued data. We also incorporate into our algorithm a multi-kernel strategy where anatomical structures at different scales are considered simultaneously during registration. We demonstrate the efficacy of our method using in vivo data.

  8. Stochastic optimization with randomized smoothing for image registration.

    PubMed

    Sun, Wei; Poot, Dirk H J; Smal, Ihor; Yang, Xuan; Niessen, Wiro J; Klein, Stefan

    2017-01-01

    Image registration is typically formulated as an optimization process, which aims to find the optimal transformation parameters of a given transformation model by minimizing a cost function. Local minima may exist in the optimization landscape, which could hamper the optimization process. To eliminate local minima, smoothing the cost function would be desirable. In this paper, we investigate the use of a randomized smoothing (RS) technique for stochastic gradient descent (SGD) optimization, to effectively smooth the cost function. In this approach, Gaussian noise is added to the transformation parameters prior to computing the cost function gradient in each iteration of the SGD optimizer. The approach is suitable for both rigid and nonrigid registrations. Experiments on synthetic images, cell images, public CT lung data, and public MR brain data demonstrate the effectiveness of the novel RS technique in terms of registration accuracy and robustness.

  9. Registration of 2D to 3D joint images using phase-based mutual information

    NASA Astrophysics Data System (ADS)

    Dalvi, Rupin; Abugharbieh, Rafeef; Pickering, Mark; Scarvell, Jennie; Smith, Paul

    2007-03-01

    Registration of two dimensional to three dimensional orthopaedic medical image data has important applications particularly in the area of image guided surgery and sports medicine. Fluoroscopy to computer tomography (CT) registration is an important case, wherein digitally reconstructed radiographs derived from the CT data are registered to the fluoroscopy data. Traditional registration metrics such as intensity-based mutual information (MI) typically work well but often suffer from gross misregistration errors when the image to be registered contains a partial view of the anatomy visible in the target image. Phase-based MI provides a robust alternative similarity measure which, in addition to possessing the general robustness and noise immunity that MI provides, also employs local phase information in the registration process which makes it less susceptible to the aforementioned errors. In this paper, we propose using the complex wavelet transform for computing image phase information and incorporating that into a phase-based MI measure for image registration. Tests on a CT volume and 6 fluoroscopy images of the knee are presented. The femur and the tibia in the CT volume were individually registered to the fluoroscopy images using intensity-based MI, gradient-based MI and phase-based MI. Errors in the coordinates of fiducials present in the bone structures were used to assess the accuracy of the different registration schemes. Quantitative results demonstrate that the performance of intensity-based MI was the worst. Gradient-based MI performed slightly better, while phase-based MI results were the best consistently producing the lowest errors.

  10. Multi-Image Registration for an Enhanced Vision System

    NASA Technical Reports Server (NTRS)

    Hines, Glenn; Rahman, Zia-Ur; Jobson, Daniel; Woodell, Glenn

    2002-01-01

    An Enhanced Vision System (EVS) utilizing multi-sensor image fusion is currently under development at the NASA Langley Research Center. The EVS will provide enhanced images of the flight environment to assist pilots in poor visibility conditions. Multi-spectral images obtained from a short wave infrared (SWIR), a long wave infrared (LWIR), and a color visible band CCD camera, are enhanced and fused using the Retinex algorithm. The images from the different sensors do not have a uniform data structure: the three sensors not only operate at different wavelengths, but they also have different spatial resolutions, optical fields of view (FOV), and bore-sighting inaccuracies. Thus, in order to perform image fusion, the images must first be co-registered. Image registration is the task of aligning images taken at different times, from different sensors, or from different viewpoints, so that all corresponding points in the images match. In this paper, we present two methods for registering multiple multi-spectral images. The first method performs registration using sensor specifications to match the FOVs and resolutions directly through image resampling. In the second method, registration is obtained through geometric correction based on a spatial transformation defined by user selected control points and regression analysis.

  11. Deformable image registration for tissues with large displacements.

    PubMed

    Huang, Xishi; Ren, Jing; Abdalbari, Anwar; Green, Mark

    2017-01-01

    Image registration for internal organs and soft tissues is considered extremely challenging due to organ shifts and tissue deformation caused by patients' movements such as respiration and repositioning. In our previous work, we proposed a fast registration method for deformable tissues with small rotations. We extend our method to deformable registration of soft tissues with large displacements. We analyzed the deformation field of the liver by decomposing the deformation into shift, rotation, and pure deformation components and concluded that in many clinical cases, the liver deformation contains large rotations and small deformations. This analysis justified the use of linear elastic theory in our image registration method. We also proposed a region-based neuro-fuzzy transformation model to seamlessly stitch together local affine and local rigid models in different regions. We have performed the experiments on a liver MRI image set and showed the effectiveness of the proposed registration method. We have also compared the performance of the proposed method with the previous method on tissues with large rotations and showed that the proposed method outperformed the previous method when dealing with the combination of pure deformation and large rotations. Validation results show that we can achieve a target registration error of [Formula: see text] and an average centerline distance error of [Formula: see text]. The proposed technique has the potential to significantly improve registration capabilities and the quality of intraoperative image guidance. To the best of our knowledge, this is the first time that the complex displacement of the liver is explicitly separated into local pure deformation and rigid motion.

  12. Vascular image registration techniques: A living review.

    PubMed

    Matl, Stefan; Brosig, Richard; Baust, Maximilian; Navab, Nassir; Demirci, Stefanie

    2017-01-01

    Registration of vascular structures is crucial for preoperative planning, intraoperative navigation, and follow-up assessment. Typical applications include, but are not limited to, Trans-catheter Aortic Valve Implantation and monitoring of tumor vasculature or aneurysm growth. In order to achieve the aforementioned goals, a large number of various registration algorithms has been developed. With this review paper we provide a comprehensive overview over the plethora of existing techniques with a particular focus on the suitable classification criteria such as the involved modalities of the employed optimization methods. However, we wish to go beyond a static literature review which is naturally doomed to be outdated after a certain period of time due to the research progress. We augment this review paper with an extendable and interactive database in order to obtain a living review whose currency goes beyond the one of a printed paper. All papers in this database are labeled with one or multiple tags according to 13 carefully defined categories. The classification of all entries can then be visualized as one or multiple trees which are presented via a web-based interactive app (http://livingreview.in.tum.de) allowing the user to choose a unique perspective for literature review. In addition, the user can search the underlying database for specific tags or publications related to vessel registration. Many applications of this framework are conceivable, including the use for getting a general overview on the topic or the utilization by physicians for deciding about the best-suited algorithm for a specific application.

  13. Deformable image registration by combining uncertainty estimates from supervoxel belief propagation.

    PubMed

    Heinrich, Mattias P; Simpson, Ivor J A; Papież, BartŁomiej W; Brady, Sir Michael; Schnabel, Julia A

    2016-01-01

    Discrete optimisation strategies have a number of advantages over their continuous counterparts for deformable registration of medical images. For example: it is not necessary to compute derivatives of the similarity term; dense sampling of the search space reduces the risk of becoming trapped in local optima; and (in principle) an optimum can be found without resorting to iterative coarse-to-fine warping strategies. However, the large complexity of high-dimensional medical data renders a direct voxel-wise estimation of deformation vectors impractical. For this reason, previous work on medical image registration using graphical models has largely relied on using a parameterised deformation model and on the use of iterative coarse-to-fine optimisation schemes. In this paper, we propose an approach that enables accurate voxel-wise deformable registration of high-resolution 3D images without the need for intermediate image warping or a multi-resolution scheme. This is achieved by representing the image domain as multiple comprehensive supervoxel layers and making use of the full marginal distribution of all probable displacement vectors after inferring regularity of the deformations using belief propagation. The optimisation acts on the coarse scale representation of supervoxels, which provides sufficient spatial context and is robust to noise in low contrast areas. Minimum spanning trees, which connect neighbouring supervoxels, are employed to model pair-wise deformation dependencies. The optimal displacement for each voxel is calculated by considering the probabilities for all displacements over all overlapping supervoxel graphs and subsequently seeking the mode of this distribution. We demonstrate the applicability of this concept for two challenging applications: first, for intra-patient motion estimation in lung CT scans; and second, for atlas-based segmentation propagation of MRI brain scans. For lung registration, the voxel-wise mode of displacements is found

  14. Cross Correlation versus Normalized Mutual Information on Image Registration

    NASA Technical Reports Server (NTRS)

    Tan, Bin; Tilton, James C.; Lin, Guoqing

    2016-01-01

    This is the first study to quantitatively assess and compare cross correlation and normalized mutual information methods used to register images in subpixel scale. The study shows that the normalized mutual information method is less sensitive to unaligned edges due to the spectral response differences than is cross correlation. This characteristic makes the normalized image resolution a better candidate for band to band registration. Improved band-to-band registration in the data from satellite-borne instruments will result in improved retrievals of key science measurements such as cloud properties, vegetation, snow and fire.

  15. Scope and applications of translation invariant wavelets to image registration

    NASA Technical Reports Server (NTRS)

    Chettri, Samir; LeMoigne, Jacqueline; Campbell, William

    1997-01-01

    The first part of this article introduces the notion of translation invariance in wavelets and discusses several wavelets that have this property. The second part discusses the possible applications of such wavelets to image registration. In the case of registration of affinely transformed images, we would conclude that the notion of translation invariance is not really necessary. What is needed is affine invariance and one way to do this is via the method of moment invariants. Wavelets or, in general, pyramid processing can then be combined with the method of moment invariants to reduce the computational load.

  16. Towards local estimation of emphysema progression using image registration

    NASA Astrophysics Data System (ADS)

    Staring, M.; Bakker, M. E.; Shamonin, D. P.; Stolk, J.; Reiber, J. H. C.; Stoel, B. C.

    2009-02-01

    Progression measurement of emphysema is required to evaluate the health condition of a patient and the effect of drugs. To locally estimate progression we use image registration, which allows for volume correction using the determinant of the Jacobian of the transformation. We introduce an adaptation of the so-called sponge model that circumvents its constant-mass assumption. Preliminary results from CT scans of a lung phantom and from CT data sets of three patients suggest that image registration may be a suitable method to locally estimate emphysema progression.

  17. Landmark-based image registration using Gneiting's compactly supported functions

    NASA Astrophysics Data System (ADS)

    Cavoretto, Roberto; De Rossi, Alessandra

    2012-09-01

    In this paper we consider landmark-based image registration with compactly supported radial basis functions. Since using globally supported functions, as for example radial basis functions, a single landmark pair change may influence the whole registration result, in 2001 Fornefett et al. introduced the use of Wendland's compactly supported functions to define landmark-based image transformations [6]. Here we propose transformations defined by means of another family of compactly supported radial basis functions, known as Gneiting's functions [7, 5]. Firstly, we briefly review their definitions and properties. Secondly, numerical experiments are presented, where we compare Gneiting's functions with other interpolants.

  18. Analysis of deformable image registration accuracy using computational modeling

    SciTech Connect

    Zhong Hualiang; Kim, Jinkoo; Chetty, Indrin J.

    2010-03-15

    Computer aided modeling of anatomic deformation, allowing various techniques and protocols in radiation therapy to be systematically verified and studied, has become increasingly attractive. In this study the potential issues in deformable image registration (DIR) were analyzed based on two numerical phantoms: One, a synthesized, low intensity gradient prostate image, and the other a lung patient's CT image data set. Each phantom was modeled with region-specific material parameters with its deformation solved using a finite element method. The resultant displacements were used to construct a benchmark to quantify the displacement errors of the Demons and B-Spline-based registrations. The results show that the accuracy of these registration algorithms depends on the chosen parameters, the selection of which is closely associated with the intensity gradients of the underlying images. For the Demons algorithm, both single resolution (SR) and multiresolution (MR) registrations required approximately 300 iterations to reach an accuracy of 1.4 mm mean error in the lung patient's CT image (and 0.7 mm mean error averaged in the lung only). For the low gradient prostate phantom, these algorithms (both SR and MR) required at least 1600 iterations to reduce their mean errors to 2 mm. For the B-Spline algorithms, best performance (mean errors of 1.9 mm for SR and 1.6 mm for MR, respectively) on the low gradient prostate was achieved using five grid nodes in each direction. Adding more grid nodes resulted in larger errors. For the lung patient's CT data set, the B-Spline registrations required ten grid nodes in each direction for highest accuracy (1.4 mm for SR and 1.5 mm for MR). The numbers of iterations or grid nodes required for optimal registrations depended on the intensity gradients of the underlying images. In summary, the performance of the Demons and B-Spline registrations have been quantitatively evaluated using numerical phantoms. The results show that parameter

  19. Comparison of simultaneous and sequential two-view registration for 3D/2D registration of vascular images.

    PubMed

    Pathak, Chetna; Van Horn, Mark; Weeks, Susan; Bullitt, Elizabeth

    2005-01-01

    Accurate 3D/2D vessel registration is complicated by issues of image quality, occlusion, and other problems. This study performs a quantitative comparison of 3D/2D vessel registration in which vessels segmented from preoperative CT or MR are registered with biplane x-ray angiograms by either a) simultaneous two-view registration with advance calculation of the relative pose of the two views, or b) sequential registration with each view. We conclude on the basis of phantom studies that, even in the absence of image errors, simultaneous two-view registration is more accurate than sequential registration. In more complex settings, including clinical conditions, the relative accuracy of simultaneous two-view registration is even greater.

  20. Efficient multi-atlas registration using an intermediate template image

    NASA Astrophysics Data System (ADS)

    Dewey, Blake E.; Carass, Aaron; Blitz, Ari M.; Prince, Jerry L.

    2017-03-01

    Multi-atlas label fusion is an accurate but time-consuming method of labeling the human brain. Using an intermediate image as a registration target can allow researchers to reduce time constraints by storing the deformations required of the atlas images. In this paper, we investigate the effect of registration through an intermediate template image on multi-atlas label fusion and propose a novel registration technique to counteract the negative effects of through-template registration. We show that overall computation time can be decreased dramatically with minimal impact on final label accuracy and time can be exchanged for improved results in a predictable manner. We see almost complete recovery of Dice similarity over a simple through-template registration using the corrected method and still maintain a 3-4 times speed increase. Further, we evaluate the effectiveness of this method on brains of patients with normal-pressure hydrocephalus, where abnormal brain shape presents labeling difficulties, specifically the ventricular labels. Our correction method creates substantially better ventricular labeling than traditional methods and maintains the speed increase seen in healthy subjects.

  1. Efficient Multi-Atlas Registration using an Intermediate Template Image

    PubMed Central

    Dewey, Blake E.; Carass, Aaron; Blitz, Ari M.; Prince, Jerry L.

    2017-01-01

    Multi-atlas label fusion is an accurate but time-consuming method of labeling the human brain. Using an intermediate image as a registration target can allow researchers to reduce time constraints by storing the deformations required of the atlas images. In this paper, we investigate the effect of registration through an intermediate template image on multi-atlas label fusion and propose a novel registration technique to counteract the negative effects of through-template registration. We show that overall computation time can be decreased dramatically with minimal impact on final label accuracy and time can be exchanged for improved results in a predictable manner. We see almost complete recovery of Dice similarity over a simple through-template registration using the corrected method and still maintain a 3–4 times speed increase. Further, we evaluate the effectiveness of this method on brains of patients with normal-pressure hydrocephalus, where abnormal brain shape presents labeling difficulties, specifically the ventricular labels. Our correction method creates substantially better ventricular labeling than traditional methods and maintains the speed increase seen in healthy subjects. PMID:28943702

  2. Efficient Multi-Atlas Registration using an Intermediate Template Image.

    PubMed

    Dewey, Blake E; Carass, Aaron; Blitz, Ari M; Prince, Jerry L

    2017-02-01

    Multi-atlas label fusion is an accurate but time-consuming method of labeling the human brain. Using an intermediate image as a registration target can allow researchers to reduce time constraints by storing the deformations required of the atlas images. In this paper, we investigate the effect of registration through an intermediate template image on multi-atlas label fusion and propose a novel registration technique to counteract the negative effects of through-template registration. We show that overall computation time can be decreased dramatically with minimal impact on final label accuracy and time can be exchanged for improved results in a predictable manner. We see almost complete recovery of Dice similarity over a simple through-template registration using the corrected method and still maintain a 3-4 times speed increase. Further, we evaluate the effectiveness of this method on brains of patients with normal-pressure hydrocephalus, where abnormal brain shape presents labeling difficulties, specifically the ventricular labels. Our correction method creates substantially better ventricular labeling than traditional methods and maintains the speed increase seen in healthy subjects.

  3. Agile multi-scale decompositions for automatic image registration

    NASA Astrophysics Data System (ADS)

    Murphy, James M.; Leija, Omar Navarro; Le Moigne, Jacqueline

    2016-05-01

    In recent works, the first and third authors developed an automatic image registration algorithm based on a multiscale hybrid image decomposition with anisotropic shearlets and isotropic wavelets. This prototype showed strong performance, improving robustness over registration with wavelets alone. However, this method imposed a strict hierarchy on the order in which shearlet and wavelet features were used in the registration process, and also involved an unintegrated mixture of MATLAB and C code. In this paper, we introduce a more agile model for generating features, in which a flexible and user-guided mix of shearlet and wavelet features are computed. Compared to the previous prototype, this method introduces a flexibility to the order in which shearlet and wavelet features are used in the registration process. Moreover, the present algorithm is now fully coded in C, making it more efficient and portable than the mixed MATLAB and C prototype. We demonstrate the versatility and computational efficiency of this approach by performing registration experiments with the fully-integrated C algorithm. In particular, meaningful timing studies can now be performed, to give a concrete analysis of the computational costs of the flexible feature extraction. Examples of synthetically warped and real multi-modal images are analyzed.

  4. High Speed Method for in Situ Multispectral Image Registration

    SciTech Connect

    Perrine, Kenneth A.; Lamarche, Brian L.; Hopkins, Derek F.; Budge, Scott E.; Opresko, Lee; Wiley, H. S.; Sowa, Marianne B.

    2007-01-29

    Multispectral confocal spinning disk microscopy provides a high resolution method for real-time live cell imaging. However, optical distortions and the physical misalignments introduced by the use of multiple acquisition cameras can obscure spatial information contained in the captured images. In this manuscript, we describe a multispectral method for real-time image registration whereby the image from one camera is warped onto the image from a second camera via a polynomial correction. This method provides a real-time pixel-for-pixel match between images obtained over physically distinct optical paths. Using an in situ calibration method, the polynomial is characterized by a set of coefficients using a least squares solver. Error analysis demonstrates optimal performance results from the use of cubic polynomials. High-speed evaluation of the warp is then performed through forward differencing with fixed-point data types. Image reconstruction errors are reduced through bilinear interpolation. The registration techniques described here allow for successful registration of multispectral images in real-time (exceeding 15 frame/sec) and have a broad applicability to imaging methods requiring pixel matching over multiple data channels.

  5. Diffusion Tensor Image Registration Using Hybrid Connectivity and Tensor Features

    PubMed Central

    Wang, Qian; Yap, Pew-Thian; Wu, Guorong; Shen, Dinggang

    2014-01-01

    Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone. PMID:24293159

  6. Morphological Feature Extraction for Automatic Registration of Multispectral Images

    NASA Technical Reports Server (NTRS)

    Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

    2007-01-01

    The task of image registration can be divided into two major components, i.e., the extraction of control points or features from images, and the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual extraction of control features can be subjective and extremely time consuming, and often results in few usable points. On the other hand, automated feature extraction allows using invariant target features such as edges, corners, and line intersections as relevant landmarks for registration purposes. In this paper, we present an extension of a recently developed morphological approach for automatic extraction of landmark chips and corresponding windows in a fully unsupervised manner for the registration of multispectral images. Once a set of chip-window pairs is obtained, a (hierarchical) robust feature matching procedure, based on a multiresolution overcomplete wavelet decomposition scheme, is used for registration purposes. The proposed method is validated on a pair of remotely sensed scenes acquired by the Advanced Land Imager (ALI) multispectral instrument and the Hyperion hyperspectral instrument aboard NASA's Earth Observing-1 satellite.

  7. Reducing uncertainties in volumetric image based deformable organ registration.

    PubMed

    Liang, J; Yan, D

    2003-08-01

    Applying volumetric image feedback in radiotherapy requires image based deformable organ registration. The foundation of this registration is the ability of tracking subvolume displacement in organs of interest. Subvolume displacement can be calculated by applying biomechanics model and the finite element method to human organs manifested on the multiple volumetric images. The calculation accuracy, however, is highly dependent on the determination of the corresponding organ boundary points. Lacking sufficient information for such determination, uncertainties are inevitable-thus diminishing the registration accuracy. In this paper, a method of consuming energy minimization was developed to reduce these uncertainties. Starting from an initial selection of organ boundary point correspondence on volumetric image sets, the subvolume displacement and stress distribution of the whole organ are calculated and the consumed energy due to the subvolume displacements is computed accordingly. The corresponding positions of the initially selected boundary points are then iteratively optimized to minimize the consuming energy under geometry and stress constraints. In this study, a rectal wall delineated from patient CT image was artificially deformed using a computer simulation and utilized to test the optimization. Subvolume displacements calculated based on the optimized boundary point correspondence were compared to the true displacements, and the calculation accuracy was thereby evaluated. Results demonstrate that a significant improvement on the accuracy of the deformable organ registration can be achieved by applying the consuming energy minimization in the organ deformation calculation.

  8. Sequential Registration-Based Segmentation of the Prostate Gland in MR Image Volumes.

    PubMed

    Khalvati, Farzad; Salmanpour, Aryan; Rahnamayan, Shahryar; Haider, Masoom A; Tizhoosh, H R

    2016-04-01

    Accurate and fast segmentation and volume estimation of the prostate gland in magnetic resonance (MR) images are necessary steps in the diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semi-automated segmentation of individual slices in T2-weighted MR image sequences. The proposed sequential registration-based segmentation (SRS) algorithm, which was inspired by the clinical workflow during medical image contouring, relies on inter-slice image registration and user interaction/correction to segment the prostate gland without the use of an anatomical atlas. It automatically generates contours for each slice using a registration algorithm, provided that the user edits and approves the marking in some previous slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid). Five radiation oncologists participated in the study where they contoured the prostate MR (T2-weighted) images of 15 patients both manually and using the SRS algorithm. Compared to the manual segmentation, on average, the SRS algorithm reduced the contouring time by 62% (a speedup factor of 2.64×) while maintaining the segmentation accuracy at the same level as the intra-user agreement level (i.e., Dice similarity coefficient of 91 versus 90%). The proposed algorithm exploits the inter-slice similarity of volumetric MR image series to achieve highly accurate results while significantly reducing the contouring time.

  9. Landsat image registration - A study of system parameters

    NASA Technical Reports Server (NTRS)

    Wacker, A. G.; Juday, R. D.; Wolfe, R. H., Jr.

    1984-01-01

    Some applications of Landsat data, particularily agricultural and forestry applications, require the ability to geometrically superimpose or register data acquired at different times and possibly by different satellites. An experimental investigation relating to a registration processor used by the Johnson Space Center for this purpose is the subject of this paper. Correlation of small subareas of images is at the heart of this registration processor and the manner in which various system parameters affect the correlation process is the prime area of investigation. Parameters investigated include preprocessing methods, methods for detecting sucessful correlations, fitting a surface to the correlation patch, fraction of pixels designated as edge pixels in edge detection adn local versus global generation of edge images. A suboptimum search procedure is used to find a good parameter set for this registration processor.

  10. Towards real-time registration of 4D ultrasound images.

    PubMed

    Foroughi, Pezhman; Abolmaesumi, Purang; Hashtrudi-Zaad, Keyvan

    2006-01-01

    In this paper, we demonstrate a method for fast registration of sequences of 3D liver images, which could be used for the future real-time applications. In our method, every image is elastically registered to a so called fixed ultrasound image exploiting the information from previous registration. A few feature points are automatically selected, and tracked inside the images, while the deformation of other points are extrapolated with respect to the tracked points employing a fast free-form approach. The main intended application of the proposed method is real-time tracking of tumors for radiosurgery. The algorithm is evaluated on both naturally and artificially deformed images. Experimental results show that for around 85 percent accuracy, the process of tracking is completed very close to real time.

  11. Automated dental implantation using image-guided robotics: registration results.

    PubMed

    Sun, Xiaoyan; McKenzie, Frederic D; Bawab, Sebastian; Li, Jiang; Yoon, Yongki; Huang, Jen-K

    2011-09-01

    One of the most important factors affecting the outcome of dental implantation is the accurate insertion of the implant into the patient's jaw bone, which requires a high degree of anatomical accuracy. With the accuracy and stability of robots, image-guided robotics is expected to provide more reliable and successful outcomes for dental implantation. Here, we proposed the use of a robot for drilling the implant site in preparation for the insertion of the implant. An image-guided robotic system for automated dental implantation is described in this paper. Patient-specific 3D models are reconstructed from preoperative Cone-beam CT images, and implantation planning is performed with these virtual models. A two-step registration procedure is applied to transform the preoperative plan of the implant insertion into intra-operative operations of the robot with the help of a Coordinate Measurement Machine (CMM). Experiments are carried out with a phantom that is generated from the patient-specific 3D model. Fiducial Registration Error (FRE) and Target Registration Error (TRE) values are calculated to evaluate the accuracy of the registration procedure. FRE values are less than 0.30 mm. Final TRE values after the two-step registration are 1.42 ± 0.70 mm (N = 5). The registration results of an automated dental implantation system using image-guided robotics are reported in this paper. Phantom experiments show that the practice of robot in the dental implantation is feasible and the system accuracy is comparable to other similar systems for dental implantation.

  12. 2D/3D Image Registration using Regression Learning

    PubMed Central

    Chou, Chen-Rui; Frederick, Brandon; Mageras, Gig; Chang, Sha; Pizer, Stephen

    2013-01-01

    In computer vision and image analysis, image registration between 2D projections and a 3D image that achieves high accuracy and near real-time computation is challenging. In this paper, we propose a novel method that can rapidly detect an object’s 3D rigid motion or deformation from a 2D projection image or a small set thereof. The method is called CLARET (Correction via Limited-Angle Residues in External Beam Therapy) and consists of two stages: registration preceded by shape space and regression learning. In the registration stage, linear operators are used to iteratively estimate the motion/deformation parameters based on the current intensity residue between the target projec-tion(s) and the digitally reconstructed radiograph(s) (DRRs) of the estimated 3D image. The method determines the linear operators via a two-step learning process. First, it builds a low-order parametric model of the image region’s motion/deformation shape space from its prior 3D images. Second, using learning-time samples produced from the 3D images, it formulates the relationships between the model parameters and the co-varying 2D projection intensity residues by multi-scale linear regressions. The calculated multi-scale regression matrices yield the coarse-to-fine linear operators used in estimating the model parameters from the 2D projection intensity residues in the registration. The method’s application to Image-guided Radiation Therapy (IGRT) requires only a few seconds and yields good results in localizing a tumor under rigid motion in the head and neck and under respiratory deformation in the lung, using one treatment-time imaging 2D projection or a small set thereof. PMID:24058278

  13. 2D/3D Image Registration using Regression Learning.

    PubMed

    Chou, Chen-Rui; Frederick, Brandon; Mageras, Gig; Chang, Sha; Pizer, Stephen

    2013-09-01

    In computer vision and image analysis, image registration between 2D projections and a 3D image that achieves high accuracy and near real-time computation is challenging. In this paper, we propose a novel method that can rapidly detect an object's 3D rigid motion or deformation from a 2D projection image or a small set thereof. The method is called CLARET (Correction via Limited-Angle Residues in External Beam Therapy) and consists of two stages: registration preceded by shape space and regression learning. In the registration stage, linear operators are used to iteratively estimate the motion/deformation parameters based on the current intensity residue between the target projec-tion(s) and the digitally reconstructed radiograph(s) (DRRs) of the estimated 3D image. The method determines the linear operators via a two-step learning process. First, it builds a low-order parametric model of the image region's motion/deformation shape space from its prior 3D images. Second, using learning-time samples produced from the 3D images, it formulates the relationships between the model parameters and the co-varying 2D projection intensity residues by multi-scale linear regressions. The calculated multi-scale regression matrices yield the coarse-to-fine linear operators used in estimating the model parameters from the 2D projection intensity residues in the registration. The method's application to Image-guided Radiation Therapy (IGRT) requires only a few seconds and yields good results in localizing a tumor under rigid motion in the head and neck and under respiratory deformation in the lung, using one treatment-time imaging 2D projection or a small set thereof.

  14. Deep Learning in Medical Image Analysis

    PubMed Central

    Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

    2016-01-01

    The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements. PMID:28301734

  15. Automated Image Registration Using Morphological Region of Interest Feature Extraction

    NASA Technical Reports Server (NTRS)

    Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

    2005-01-01

    With the recent explosion in the amount of remotely sensed imagery and the corresponding interest in temporal change detection and modeling, image registration has become increasingly important as a necessary first step in the integration of multi-temporal and multi-sensor data for applications such as the analysis of seasonal and annual global climate changes, as well as land use/cover changes. The task of image registration can be divided into two major components: (1) the extraction of control points or features from images; and (2) the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual control feature extraction can be subjective and extremely time consuming, and often results in few usable points. Automated feature extraction is a solution to this problem, where desired target features are invariant, and represent evenly distributed landmarks such as edges, corners and line intersections. In this paper, we develop a novel automated registration approach based on the following steps. First, a mathematical morphology (MM)-based method is used to obtain a scale-orientation morphological profile at each image pixel. Next, a spectral dissimilarity metric such as the spectral information divergence is applied for automated extraction of landmark chips, followed by an initial approximate matching. This initial condition is then refined using a hierarchical robust feature matching (RFM) procedure. Experimental results reveal that the proposed registration technique offers a robust solution in the presence of seasonal changes and other interfering factors. Keywords-Automated image registration, multi-temporal imagery, mathematical morphology, robust feature matching.

  16. A translational registration system for LANDSAT image segments

    NASA Technical Reports Server (NTRS)

    Parada, N. D. J. (Principal Investigator); Erthal, G. J.; Velasco, F. R. D.; Mascarenhas, N. D. D.

    1983-01-01

    The use of satellite images obtained from various dates is essential for crop forecast systems. In order to make possible a multitemporal analysis, it is necessary that images belonging to each acquisition have pixel-wise correspondence. A system developed to obtain, register and record image segments from LANDSAT images in computer compatible tapes is described. The translational registration of the segments is performed by correlating image edges in different acquisitions. The system was constructed for the Burroughs B6800 computer in ALGOL language.

  17. The Insight ToolKit image registration framework

    PubMed Central

    Avants, Brian B.; Tustison, Nicholas J.; Stauffer, Michael; Song, Gang; Wu, Baohua; Gee, James C.

    2014-01-01

    Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit (ITK4) seeks to establish new standards in publicly available image registration methodology. ITK4 makes several advances in comparison to previous versions of ITK. ITK4 supports both multivariate images and objective functions; it also unifies high-dimensional (deformation field) and low-dimensional (affine) transformations with metrics that are reusable across transform types and with composite transforms that allow arbitrary series of geometric mappings to be chained together seamlessly. Metrics and optimizers take advantage of multi-core resources, when available. Furthermore, ITK4 reduces the parameter optimization burden via principled heuristics that automatically set scaling across disparate parameter types (rotations vs. translations). A related approach also constrains steps sizes for gradient-based optimizers. The result is that tuning for different metrics and/or image pairs is rarely necessary allowing the researcher to more easily focus on design/comparison of registration strategies. In total, the ITK4 contribution is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. Finally, we contextualize this work with a reference registration evaluation study with application to pediatric brain labeling.1 PMID:24817849

  18. Registration of multimodal brain images: some experimental results

    NASA Astrophysics Data System (ADS)

    Chen, Hua-mei; Varshney, Pramod K.

    2002-03-01

    Joint histogram of two images is required to uniquely determine the mutual information between the two images. It has been pointed out that, under certain conditions, existing joint histogram estimation algorithms like partial volume interpolation (PVI) and linear interpolation may result in different types of artifact patterns in the MI based registration function by introducing spurious maxima. As a result, the artifacts may hamper the global optimization process and limit registration accuracy. In this paper we present an extensive study of interpolation-induced artifacts using simulated brain images and show that similar artifact patterns also exist when other intensity interpolation algorithms like cubic convolution interpolation and cubic B-spline interpolation are used. A new joint histogram estimation scheme named generalized partial volume estimation (GPVE) is proposed to eliminate the artifacts. A kernel function is involved in the proposed scheme and when the 1st order B-spline is chosen as the kernel function, it is equivalent to the PVI. A clinical brain image database furnished by Vanderbilt University is used to compare the accuracy of our algorithm with that of PVI. Our experimental results show that the use of higher order kernels can effectively remove the artifacts and, in cases when MI based registration result suffers from the artifacts, registration accuracy can be improved significantly.

  19. Biomechanical-based image registration for head and neck radiation treatment

    NASA Astrophysics Data System (ADS)

    Al-Mayah, Adil; Moseley, Joanne; Hunter, Shannon; Velec, Mike; Chau, Lily; Breen, Stephen; Brock, Kristy

    2010-11-01

    Deformable image registration of four head and neck cancer patients has been conducted using a biomechanical-based model. Patient-specific 3D finite element models have been developed using CT and cone-beam CT image data of the planning and a radiation treatment session. The model consists of seven vertebrae (C1 to C7), mandible, larynx, left and right parotid glands, tumor and body. Different combinations of boundary conditions are applied in the model in order to find the configuration with a minimum registration error. Each vertebra in the planning session is individually aligned with its correspondence in the treatment session. Rigid alignment is used for each individual vertebra and the mandible since no deformation is expected in the bones. In addition, the effect of morphological differences in the external body between the two image sessions is investigated. The accuracy of the registration is evaluated using the tumor and both parotid glands by comparing the calculated Dice similarity index of these structures following deformation in relation to their true surface defined in the image of the second session. The registration is improved when the vertebrae and mandible are aligned in the two sessions with the highest average Dice index of 0.86 ± 0.08, 0.84 ± 0.11 and 0.89 ± 0.04 for the tumor, left and right parotid glands, respectively. The accuracy of the center of mass location of tumor and parotid glands is also improved by deformable image registration where the errors in the tumor and parotid glands decrease from 4.0 ± 1.1, 3.4 ± 1.5 and 3.8 ± 0.9 mm using rigid registration to 2.3 ± 1.0, 2.5 ± 0.8 and 2.0 ± 0.9 mm in the deformable image registration when alignment of vertebrae and mandible is conducted in addition to the surface projection of the body. This work was presented at the SPIE conference, California, 2010: Al-Mayah A, Moseley J, Chau L, Breen S, and Brock K 2010 Biomechanical based deformable image registration of head and neck

  20. Applying the algorithm "assessing quality using image registration circuits" (AQUIRC) to multi-atlas segmentation

    NASA Astrophysics Data System (ADS)

    Datteri, Ryan; Asman, Andrew J.; Landman, Bennett A.; Dawant, Benoit M.

    2014-03-01

    Multi-atlas registration-based segmentation is a popular technique in the medical imaging community, used to transform anatomical and functional information from a set of atlases onto a new patient that lacks this information. The accuracy of the projected information on the target image is dependent on the quality of the registrations between the atlas images and the target image. Recently, we have developed a technique called AQUIRC that aims at estimating the error of a non-rigid registration at the local level and was shown to correlate to error in a simulated case. Herein, we extend upon this work by applying AQUIRC to atlas selection at the local level across multiple structures in cases in which non-rigid registration is difficult. AQUIRC is applied to 6 structures, the brainstem, optic chiasm, left and right optic nerves, and the left and right eyes. We compare the results of AQUIRC to that of popular techniques, including Majority Vote, STAPLE, Non-Local STAPLE, and Locally-Weighted Vote. We show that AQUIRC can be used as a method to combine multiple segmentations and increase the accuracy of the projected information on a target image, and is comparable to cutting edge methods in the multi-atlas segmentation field.

  1. Warped document image correction method based on heterogeneous registration strategies

    NASA Astrophysics Data System (ADS)

    Tong, Lijing; Zhan, Guoliang; Peng, Quanyao; Li, Yang; Li, Yifan

    2013-03-01

    With the popularity of digital camera and the application requirement of digitalized document images, using digital cameras to digitalize document images has become an irresistible trend. However, the warping of the document surface impacts on the quality of the Optical Character Recognition (OCR) system seriously. To improve the warped document image's vision quality and the OCR rate, this paper proposed a warped document image correction method based on heterogeneous registration strategies. This method mosaics two warped images of the same document from different viewpoints. Firstly, two feature points are selected from one image. Then the two feature points are registered in the other image base on heterogeneous registration strategies. At last, image mosaics are done for the two images, and the best mosaiced image is selected by OCR recognition results. As a result, for the best mosaiced image, the distortions are mostly removed and the OCR results are improved markedly. Experimental results show that the proposed method can resolve the issue of warped document image correction more effectively.

  2. Improving supervised classification accuracy using non-rigid multimodal image registration: detecting prostate cancer

    NASA Astrophysics Data System (ADS)

    Chappelow, Jonathan; Viswanath, Satish; Monaco, James; Rosen, Mark; Tomaszewski, John; Feldman, Michael; Madabhushi, Anant

    2008-03-01

    Computer-aided diagnosis (CAD) systems for the detection of cancer in medical images require precise labeling of training data. For magnetic resonance (MR) imaging (MRI) of the prostate, training labels define the spatial extent of prostate cancer (CaP); the most common source for these labels is expert segmentations. When ancillary data such as whole mount histology (WMH) sections, which provide the gold standard for cancer ground truth, are available, the manual labeling of CaP can be improved by referencing WMH. However, manual segmentation is error prone, time consuming and not reproducible. Therefore, we present the use of multimodal image registration to automatically and accurately transcribe CaP from histology onto MRI following alignment of the two modalities, in order to improve the quality of training data and hence classifier performance. We quantitatively demonstrate the superiority of this registration-based methodology by comparing its results to the manual CaP annotation of expert radiologists. Five supervised CAD classifiers were trained using the labels for CaP extent on MRI obtained by the expert and 4 different registration techniques. Two of the registration methods were affi;ne schemes; one based on maximization of mutual information (MI) and the other method that we previously developed, Combined Feature Ensemble Mutual Information (COFEMI), which incorporates high-order statistical features for robust multimodal registration. Two non-rigid schemes were obtained by succeeding the two affine registration methods with an elastic deformation step using thin-plate splines (TPS). In the absence of definitive ground truth for CaP extent on MRI, classifier accuracy was evaluated against 7 ground truth surrogates obtained by different combinations of the expert and registration segmentations. For 26 multimodal MRI-WMH image pairs, all four registration methods produced a higher area under the receiver operating characteristic curve compared to that

  3. Multimodal image registration using floating regressors in the joint intensity scatter plot.

    PubMed

    Orchard, Jeff

    2008-08-01

    This paper presents a new approach for multimodal medical image registration and compares it to normalized mutual information (NMI) and the correlation ratio (CR). Like NMI and CR, the new method's measure of registration quality is based on the distribution of points in the joint intensity scatter plot (JISP); compact clusters indicate good registration. This method iteratively fits the JISP clusters with regressors (in the form of points and line segments), and uses those regressors to efficiently compute the next motion increment. The result is a striking, dynamic process in which the regressors float around the JISP, tracking groups of points as they contract into tight clusters. One of the method's strengths is that it is intuitive and customizable, offering a multitude of ways to incorporate prior knowledge to guide the registration process. Moreover, the method is adaptive, and can adjust itself to fit data that does not quite match the prior model. Finally, the method is efficiently expandable to higher-dimensional scatter plots, avoiding the "curse of dimensionality" inherent in histogram-based registration methods such as MI and NMI. In two sets of experiments, a simple implementation of the new registration framework is shown to be comparable to (if not superior to) state-of-the-art implementations of NMI and CR in both accuracy and convergence robustness.

  4. An accelerated image matching technique for UAV orthoimage registration

    NASA Astrophysics Data System (ADS)

    Tsai, Chung-Hsien; Lin, Yu-Ching

    2017-06-01

    Using an Unmanned Aerial Vehicle (UAV) drone with an attached non-metric camera has become a popular low-cost approach for collecting geospatial data. A well-georeferenced orthoimage is a fundamental product for geomatics professionals. To achieve high positioning accuracy of orthoimages, precise sensor position and orientation data, or a number of ground control points (GCPs), are often required. Alternatively, image registration is a solution for improving the accuracy of a UAV orthoimage, as long as a historical reference image is available. This study proposes a registration scheme, including an Accelerated Binary Robust Invariant Scalable Keypoints (ABRISK) algorithm and spatial analysis of corresponding control points for image registration. To determine a match between two input images, feature descriptors from one image are compared with those from another image. A ;Sorting Ring; is used to filter out uncorrected feature pairs as early as possible in the stage of matching feature points, to speed up the matching process. The results demonstrate that the proposed ABRISK approach outperforms the vector-based Scale Invariant Feature Transform (SIFT) approach where radiometric variations exist. ABRISK is 19.2 times and 312 times faster than SIFT for image sizes of 1000 × 1000 pixels and 4000 × 4000 pixels, respectively. ABRISK is 4.7 times faster than Binary Robust Invariant Scalable Keypoints (BRISK). Furthermore, the positional accuracy of the UAV orthoimage after applying the proposed image registration scheme is improved by an average of root mean square error (RMSE) of 2.58 m for six test orthoimages whose spatial resolutions vary from 6.7 cm to 10.7 cm.

  5. Video image stabilization and registration--plus

    NASA Technical Reports Server (NTRS)

    Hathaway, David H. (Inventor)

    2009-01-01

    A method of stabilizing a video image displayed in multiple video fields of a video sequence includes the steps of: subdividing a selected area of a first video field into nested pixel blocks; determining horizontal and vertical translation of each of the pixel blocks in each of the pixel block subdivision levels from the first video field to a second video field; and determining translation of the image from the first video field to the second video field by determining a change in magnification of the image from the first video field to the second video field in each of horizontal and vertical directions, and determining shear of the image from the first video field to the second video field in each of the horizontal and vertical directions.

  6. Signal subspace registration of 3D images

    NASA Astrophysics Data System (ADS)

    Soumekh, Mehrdad

    1998-06-01

    This paper addresses the problem of fusing the information content of two uncalibrated sensors. This problem arises in registering images of a scene when it is viewed via two different sensory systems, or detecting change in a scene when it is viewed at two different time points by a sensory system (or via two different sensory systems or observation channels). We are concerned with sensory systems which have not only a relative shift, scaling and rotational calibration error, but also an unknown point spread function (that is time-varying for a single sensor, or different for two sensors). By modeling one image in terms of an unknown linear combination of the other image, its powers and their spatially-transformed (shift, rotation and scaling) versions, a signal subspace processing is developed for fusing uncalibrated sensors. Numerical results with realistic 3D magnetic resonance images of a patient with multiple sclerosis, which are acquired at two different time points, are provided.

  7. Dense image registration through MRFs and efficient linear programming.

    PubMed

    Glocker, Ben; Komodakis, Nikos; Tziritas, Georgios; Navab, Nassir; Paragios, Nikos

    2008-12-01

    In this paper, we introduce a novel and efficient approach to dense image registration, which does not require a derivative of the employed cost function. In such a context, the registration problem is formulated using a discrete Markov random field objective function. First, towards dimensionality reduction on the variables we assume that the dense deformation field can be expressed using a small number of control points (registration grid) and an interpolation strategy. Then, the registration cost is expressed using a discrete sum over image costs (using an arbitrary similarity measure) projected on the control points, and a smoothness term that penalizes local deviations on the deformation field according to a neighborhood system on the grid. Towards a discrete approach, the search space is quantized resulting in a fully discrete model. In order to account for large deformations and produce results on a high resolution level, a multi-scale incremental approach is considered where the optimal solution is iteratively updated. This is done through successive morphings of the source towards the target image. Efficient linear programming using the primal dual principles is considered to recover the lowest potential of the cost function. Very promising results using synthetic data with known deformations and real data demonstrate the potentials of our approach.

  8. Finite-Dimensional Lie Algebras for Fast Diffeomorphic Image Registration.

    PubMed

    Zhang, Miaomiao; Fletcher, P Thomas

    2015-01-01

    This paper presents a fast geodesic shooting algorithm for diffeomorphic image registration. We first introduce a novel finite-dimensional Lie algebra structure on the space of bandlimited velocity fields. We then show that this space can effectively represent initial velocities for diffeomorphic image registration at much lower dimensions than typically used, with little to no loss in registration accuracy. We then leverage the fact that the geodesic evolution equations, as well as the adjoint Jacobi field equations needed for gradient descent methods, can be computed entirely in this finite-dimensional Lie algebra. The result is a geodesic shooting method for large deformation metric mapping (LDDMM) that is dramatically faster and less memory intensive than state-of-the-art methods. We demonstrate the effectiveness of our model to register 3D brain images and compare its registration accuracy, run-time, and memory consumption with leading LDDMM methods. We also show how our algorithm breaks through the prohibitive time and memory requirements of diffeomorphic atlas building.

  9. F-TIMER: fast tensor image morphing for elastic registration.

    PubMed

    Yap, Pew-Thian; Wu, Guorong; Zhu, Hongtu; Lin, Weili; Shen, Dinggang

    2010-05-01

    We propose a novel diffusion tensor imaging (DTI) registration algorithm, called fast tensor image morphing for elastic registration (F-TIMER). F-TIMER leverages multiscale tensor regional distributions and local boundaries for hierarchically driving deformable matching of tensor image volumes. Registration is achieved by utilizing a set of automatically determined structural landmarks, via solving a soft correspondence problem. Based on the estimated correspondences, thin-plate splines are employed to generate a smooth, topology preserving, and dense transformation, and to avoid arbitrary mapping of nonlandmark voxels. To mitigate the problem of local minima, which is common in the estimation of high dimensional transformations, we employ a hierarchical strategy where a small subset of voxels with more distinctive attribute vectors are first deployed as landmarks to estimate a relatively robust low-degrees-of-freedom transformation. As the registration progresses, an increasing number of voxels are permitted to participate in refining the correspondence matching. A scheme as such allows less conservative progression of the correspondence matching towards the optimal solution, and hence results in a faster matching speed. Compared with its predecessor TIMER, which has been shown to outperform state-of-the-art algorithms, experimental results indicate that F-TIMER is capable of achieving comparable accuracy at only a fraction of the computation cost.

  10. Medical image analysis with artificial neural networks.

    PubMed

    Jiang, J; Trundle, P; Ren, J

    2010-12-01

    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

  11. Image registration via level-set motion: applications to atlas-based segmentation.

    PubMed

    Vemuri, B C; Ye, J; Chen, Y; Leonard, C M

    2003-03-01

    Image registration is an often encountered problem in various fields including medical imaging, computer vision and image processing. Numerous algorithms for registering image data have been reported in these areas. In this paper, we present a novel curve evolution approach expressed in a level-set framework to achieve image intensity morphing and a simple non-linear PDE for the corresponding coordinate registration. The key features of the intensity morphing model are that (a) it is very fast and (b) existence and uniqueness of the solution for the evolution model are established in a Sobolev space as opposed to using viscosity methods. The salient features of the coordinate registration model are its simplicity and computational efficiency. The intensity morph is easily achieved via evolving level-sets of one image into the level-sets of the other. To explicitly estimate the coordinate transformation between the images, we derive a non-linear PDE-based motion model which can be solved very efficiently. We demonstrate the performance of our algorithm on a variety of images including synthetic and real data. As an application of the PDE-based motion model, atlas based segmentation of hippocampal shape from several MR brain scans is depicted. In each of these experiments, automated hippocampal shape recovery results are validated via manual "expert" segmentations.

  12. Verification and Validation of a Fingerprint Image Registration Software

    NASA Astrophysics Data System (ADS)

    Desovski, Dejan; Gandikota, Vijai; Liu, Yan; Jiang, Yue; Cukic, Bojan

    2006-12-01

    The need for reliable identification and authentication is driving the increased use of biometric devices and systems. Verification and validation techniques applicable to these systems are rather immature and ad hoc, yet the consequences of the wide deployment of biometric systems could be significant. In this paper we discuss an approach towards validation and reliability estimation of a fingerprint registration software. Our validation approach includes the following three steps: (a) the validation of the source code with respect to the system requirements specification; (b) the validation of the optimization algorithm, which is in the core of the registration system; and (c) the automation of testing. Since the optimization algorithm is heuristic in nature, mathematical analysis and test results are used to estimate the reliability and perform failure analysis of the image registration module.

  13. Bidirectional Elastic Image Registration Using B-Spline Affine Transformation

    PubMed Central

    Gu, Suicheng; Meng, Xin; Sciurba, Frank C.; Wang, Chen; Kaminski, Naftali; Pu, Jiantao

    2014-01-01

    A registration scheme termed as B-spline affine transformation (BSAT) is presented in this study to elastically align two images. We define an affine transformation instead of the traditional translation at each control point. Mathematically, BSAT is a generalized form of the affine transformation and the traditional B-Spline transformation (BST). In order to improve the performance of the iterative closest point (ICP) method in registering two homologous shapes but with large deformation, a bi-directional instead of the traditional unidirectional objective / cost function is proposed. In implementation, the objective function is formulated as a sparse linear equation problem, and a sub-division strategy is used to achieve a reasonable efficiency in registration. The performance of the developed scheme was assessed using both two-dimensional (2D) synthesized dataset and three-dimensional (3D) volumetric computed tomography (CT) data. Our experiments showed that the proposed B-spline affine model could obtain reasonable registration accuracy. PMID:24530210

  14. Bidirectional elastic image registration using B-spline affine transformation.

    PubMed

    Gu, Suicheng; Meng, Xin; Sciurba, Frank C; Ma, Hongxia; Leader, Joseph; Kaminski, Naftali; Gur, David; Pu, Jiantao

    2014-06-01

    A registration scheme termed as B-spline affine transformation (BSAT) is presented in this study to elastically align two images. We define an affine transformation instead of the traditional translation at each control point. Mathematically, BSAT is a generalized form of the affine transformation and the traditional B-spline transformation (BST). In order to improve the performance of the iterative closest point (ICP) method in registering two homologous shapes but with large deformation, a bidirectional instead of the traditional unidirectional objective/cost function is proposed. In implementation, the objective function is formulated as a sparse linear equation problem, and a sub-division strategy is used to achieve a reasonable efficiency in registration. The performance of the developed scheme was assessed using both two-dimensional (2D) synthesized dataset and three-dimensional (3D) volumetric computed tomography (CT) data. Our experiments showed that the proposed B-spline affine model could obtain reasonable registration accuracy.

  15. Hierarchical model-based interferometric synthetic aperture radar image registration

    NASA Astrophysics Data System (ADS)

    Wang, Yang; Huang, Haifeng; Dong, Zhen; Wu, Manqing

    2014-01-01

    With the rapid development of spaceborne interferometric synthetic aperture radar technology, classical image registration methods are incompetent for high-efficiency and high-accuracy masses of real data processing. Based on this fact, we propose a new method. This method consists of two steps: coarse registration that is realized by cross-correlation algorithm and fine registration that is realized by hierarchical model-based algorithm. Hierarchical model-based algorithm is a high-efficiency optimization algorithm. The key features of this algorithm are a global model that constrains the overall structure of the motion estimated, a local model that is used in the estimation process, and a coarse-to-fine refinement strategy. Experimental results from different kinds of simulated and real data have confirmed that the proposed method is very fast and has high accuracy. Comparing with a conventional cross-correlation method, the proposed method provides markedly improved performance.

  16. Assessing the reliability of MRI-CBCT image registration to visualize temporomandibular joints

    PubMed Central

    Jaremko, J L; Alsufyani, N; Jibri, Z; Lai, H; Major, P W

    2015-01-01

    Objectives: To evaluate image quality of two methods of registering MRI and CBCT images of the temporomandibular joint (TMJ), particularly regarding TMJ articular disc–condyle relationship and osseous abnormality. Methods: MR and CBCT images for 10 patients (20 TMJs) were obtained and co-registered using two methods (non-guided and marker guided) using Mirada XD software (Mirada Medical Ltd, Oxford, UK). Three radiologists independently and blindly evaluated three types of images (MRI, CBCT and registered MRI-CBCT) at two times (T1 and T2) on two criteria: (1) quality of MRI-CBCT registrations (excellent, fair or poor) and (2) TMJ disc–condylar position and articular osseous abnormalities (osteophytes, erosions and subcortical cyst, surface flattening, sclerosis). Results: 75% of the non-guided registered images showed excellent quality, and 95% of the marker-guided registered images showed poor quality. Significant difference was found between the non-guided and marker-guided registration (χ2 = 108.5; p < 0.01). The interexaminer variability of the disc position in MRI [intraclass correlation coefficient (ICC) = 0.50 at T1, 0.56 at T2] was lower than that in MRI-CBCT registered images [ICC = 0.80 (0.52–0.92) at T1, 0.84 (0.62–0.93) at T2]. Erosions and subcortical cysts were noticed less frequently in the MRI-CBCT images than in CBCT images. Conclusions: Non-guided registration proved superior to marker-guided registration. Although MRI-CBCT fused images were slightly more limited than CBCT alone to detect osseous abnormalities, use of the fused images improved the consistency among examiners in detecting disc position in relation to the condyle. PMID:25734241

  17. The impact of registration accuracy on imaging validation study design: A novel statistical power calculation.

    PubMed

    Gibson, Eli; Fenster, Aaron; Ward, Aaron D

    2013-10-01

    Novel imaging modalities are pushing the boundaries of what is possible in medical imaging, but their signal properties are not always well understood. The evaluation of these novel imaging modalities is critical to achieving their research and clinical potential. Image registration of novel modalities to accepted reference standard modalities is an important part of characterizing the modalities and elucidating the effect of underlying focal disease on the imaging signal. The strengths of the conclusions drawn from these analyses are limited by statistical power. Based on the observation that in this context, statistical power depends in part on uncertainty arising from registration error, we derive a power calculation formula relating registration error, number of subjects, and the minimum detectable difference between normal and pathologic regions on imaging, for an imaging validation study design that accommodates signal correlations within image regions. Monte Carlo simulations were used to evaluate the derived models and test the strength of their assumptions, showing that the model yielded predictions of the power, the number of subjects, and the minimum detectable difference of simulated experiments accurate to within a maximum error of 1% when the assumptions of the derivation were met, and characterizing sensitivities of the model to violations of the assumptions. The use of these formulae is illustrated through a calculation of the number of subjects required for a case study, modeled closely after a prostate cancer imaging validation study currently taking place at our institution. The power calculation formulae address three central questions in the design of imaging validation studies: (1) What is the maximum acceptable registration error? (2) How many subjects are needed? (3) What is the minimum detectable difference between normal and pathologic image regions?

  18. Deep Learning in Medical Image Analysis.

    PubMed

    Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

    2017-03-09

    This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement. Expected final online publication date for the Annual Review of Biomedical Engineering Volume 19 is June 4, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

  19. Medical Imaging and Infertility.

    PubMed

    Peterson, Rebecca

    2016-11-01

    Infertility affects many couples, and medical imaging plays a vital role in its diagnosis and treatment. Radiologic technologists benefit from having a broad understanding of infertility risk factors and causes. This article describes the typical structure and function of the male and female reproductive systems, as well as congenital and acquired conditions that could lead to a couple's inability to conceive. Medical imaging procedures performed for infertility diagnosis are discussed, as well as common interventional options available to patients. © 2016 American Society of Radiologic Technologists.

  20. Slipping objects in image registration: improved motion field estimation with direction-dependent regularization.

    PubMed

    Schmidt-Richberg, Alexander; Ehrhardt, Jan; Werner, Rene; Handels, Heinz

    2009-01-01

    The computation of accurate motion fields is a crucial aspect in 4D medical imaging. It is usually done using a non-linear registration without further modeling of physiological motion properties. However, a globally homogeneous smoothing (regularization) of the motion field during the registration process can contradict the characteristics of motion dynamics. This is particularly the case when two organs slip along each other which leads to discontinuities in the motion field. In this paper, we present a diffusion-based model for incorporating physiological knowledge in image registration. By decoupling normal- and tangential-directed smoothing, we are able to estimate slipping motion at the organ borders while ensuring smooth motion fields in the inside and preventing gaps to arise in the field. We evaluate our model focusing on the estimation of respiratory lung motion. By accounting for the discontinuous motion of visceral and parietal pleurae, we are able to show a significant increase of registration accuracy with respect to the target registration error (TRE).

  1. Elastic image registration via rigid object motion induced deformation

    NASA Astrophysics Data System (ADS)

    Zheng, Xiaofen; Udupa, Jayaram K.; Hirsch, Bruce E.

    2011-03-01

    In this paper, we estimate the deformations induced on soft tissues by the rigid independent movements of hard objects and create an admixture of rigid and elastic adaptive image registration transformations. By automatically segmenting and independently estimating the movement of rigid objects in 3D images, we can maintain rigidity in bones and hard tissues while appropriately deforming soft tissues. We tested our algorithms on 20 pairs of 3D MRI datasets pertaining to a kinematic study of the flexibility of the ankle complex of normal feet as well as ankles affected by abnormalities in foot architecture and ligament injuries. The results show that elastic image registration via rigid object-induced deformation outperforms purely rigid and purely nonrigid approaches.

  2. Demons deformable registration for cone-beam CT guidance: registration of pre- and intra-operative images

    NASA Astrophysics Data System (ADS)

    Nithiananthan, S.; Brock, K. K.; Daly, M. J.; Chan, H.; Irish, J. C.; Siewerdsen, J. H.

    2010-02-01

    High-quality intraoperative 3D imaging systems such as cone-beam CT (CBCT) hold considerable promise for imageguided surgical procedures in the head and neck. With a large amount of preoperative imaging and planning information available in addition to the intraoperative images, it becomes desirable to be able to integrate all sources of imaging information within the same anatomical frame of reference using deformable image registration. Fast intensity-based algorithms are available which can perform deformable image registration within a period of time short enough for intraoperative use. However, CBCT images often contain voxel intensity inaccuracy which can hinder registration accuracy - for example, due to x-ray scatter, truncation, and/or erroneous scaling normalization within the 3D reconstruction algorithm. In this work, we present a method of integrating an iterative intensity matching step within the operation of a multi-scale Demons registration algorithm. Registration accuracy was evaluated in a cadaver model and showed that a conventional Demons implementation (with either no intensity match or a single histogram match) introduced anatomical distortion and degradation in target registration error (TRE). The iterative intensity matching procedure, on the other hand, provided robust registration across a broad range of intensity inaccuracies.

  3. Elastic registration of multiphase CT images of liver

    NASA Astrophysics Data System (ADS)

    Heldmann, Stefan; Zidowitz, Stephan

    2009-02-01

    In this work we present a novel approach for elastic image registration of multi-phase contrast enhanced CT images of liver. A problem in registration of multiphase CT is that the images contain similar but complementary structures. In our application each image shows a different part of the vessel system, e.g., portal/hepatic venous/arterial, or biliary vessels. Portal, arterial and biliary vessels run in parallel and abut on each other forming the so called portal triad, while hepatic veins run independent. Naive registration will tend to align complementary vessel. Our new approach is based on minimizing a cost function consisting of a distance measure and a regularizer. For the distance we use the recently proposed normalized gradient field measure that focuses on the alignment of edges. For the regularizer we use the linear elastic potential. The key feature of our approach is an additional penalty term using segmentations of the different vessel systems in the images to avoid overlaps of complementary structures. We successfully demonstrate our new method by real data examples.

  4. The ANACONDA algorithm for deformable image registration in radiotherapy

    SciTech Connect

    Weistrand, Ola; Svensson, Stina

    2015-01-15

    Purpose: The purpose of this work was to describe a versatile algorithm for deformable image registration with applications in radiotherapy and to validate it on thoracic 4DCT data as well as CT/cone beam CT (CBCT) data. Methods: ANAtomically CONstrained Deformation Algorithm (ANACONDA) combines image information (i.e., intensities) with anatomical information as provided by contoured image sets. The registration problem is formulated as a nonlinear optimization problem and solved with an in-house developed solver, tailored to this problem. The objective function, which is minimized during optimization, is a linear combination of four nonlinear terms: 1. image similarity term; 2. grid regularization term, which aims at keeping the deformed image grid smooth and invertible; 3. a shape based regularization term which works to keep the deformation anatomically reasonable when regions of interest are present in the reference image; and 4. a penalty term which is added to the optimization problem when controlling structures are used, aimed at deforming the selected structure in the reference image to the corresponding structure in the target image. Results: To validate ANACONDA, the authors have used 16 publically available thoracic 4DCT data sets for which target registration errors from several algorithms have been reported in the literature. On average for the 16 data sets, the target registration error is 1.17 ± 0.87 mm, Dice similarity coefficient is 0.98 for the two lungs, and image similarity, measured by the correlation coefficient, is 0.95. The authors have also validated ANACONDA using two pelvic cases and one head and neck case with planning CT and daily acquired CBCT. Each image has been contoured by a physician (radiation oncologist) or experienced radiation therapist. The results are an improvement with respect to rigid registration. However, for the head and neck case, the sample set is too small to show statistical significance. Conclusions: ANACONDA

  5. Translation and Rotation Invariant Multiscale Image Registration

    DTIC Science & Technology

    2002-03-01

    wavelet transform . We extend this work by creating a new multiscale transform to register two images with translation or rotation differences, independent...continuous wavelet transform to mimic the two-dimensional redundant discrete wavelet transform . This allows us to obtain multiple subbands at various scales...while maintaining the desirable properties of the redundant discrete wavelet transform . Whereas the discrete wavelet transform produces results only

  6. Accelerated Nonrigid Intensity-Based Image Registration Using Importance Sampling

    PubMed Central

    Bhagalia, Roshni; Fessler, Jeffrey A.; Kim, Boklye

    2015-01-01

    Nonrigid image registration methods using intensity-based similarity metrics are becoming increasingly common tools to estimate many types of deformations. Nonrigid warps can be very flexible with a large number of parameters and gradient optimization schemes are widely used to estimate them. However for large datasets, the computation of the gradient of the similarity metric with respect to these many parameters becomes very time consuming. Using a small random subset of image voxels to approximate the gradient can reduce computation time. This work focuses on the use of importance sampling to improve accuracy and reduce the variance of this gradient approximation. The proposed importance sampling framework is based on an edge-dependent adaptive sampling distribution designed for use with intensity-based registration algorithms. We compare the performance of registration based on stochastic approximations with and without importance sampling to that using deterministic gradient descent. Empirical results, on simulated MR brain data and real CT inhale-exhale lung data from 8 subjects, show that a combination of stochastic approximation methods and importance sampling improves the rate of convergence of the registration process while preserving accuracy. PMID:19211343

  7. Accelerated nonrigid intensity-based image registration using importance sampling.

    PubMed

    Bhagalia, Roshni; Fessler, Jeffrey A; Kim, Boklye

    2009-08-01

    Nonrigid image registration methods using intensity-based similarity metrics are becoming increasingly common tools to estimate many types of deformations. Nonrigid warps can be very flexible with a large number of parameters and gradient optimization schemes are widely used to estimate them. However, for large datasets, the computation of the gradient of the similarity metric with respect to these many parameters becomes very time consuming. Using a small random subset of image voxels to approximate the gradient can reduce computation time. This work focuses on the use of importance sampling to reduce the variance of this gradient approximation. The proposed importance sampling framework is based on an edge-dependent adaptive sampling distribution designed for use with intensity-based registration algorithms. We compare the performance of registration based on stochastic approximations with and without importance sampling to that using deterministic gradient descent. Empirical results, on simulated magnetic resonance brain data and real computed tomography inhale-exhale lung data from eight subjects, show that a combination of stochastic approximation methods and importance sampling accelerates the registration process while preserving accuracy.

  8. Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable

    PubMed Central

    Rohlfing, Torsten

    2011-01-01

    The accuracy of nonrigid image registrations is commonly approximated using surrogate measures such as tissue label overlap scores, image similarity, image difference, or transformation inverse consistency error. This paper provides experimental evidence that these measures, even when used in combination, cannot distinguish accurate from inaccurate registrations. To this end, we introduce a “registration” algorithm that generates highly inaccurate image transformations, yet performs extremely well in terms of the surrogate measures. Of the tested criteria, only overlap scores of localized anatomical regions reliably distinguish reasonable from inaccurate registrations, whereas image similarity and tissue overlap do not. We conclude that tissue overlap and image similarity, whether used alone or together, do not provide valid evidence for accurate registrations and should thus not be reported or accepted as such. PMID:21827972

  9. Statistical Methods for Image Registration and Denoising

    DTIC Science & Technology

    2008-06-19

    Perona and Malik [40] create an anisotropic diffusion algorithm that accounts for edges and preserves edges in the smoothed image by assuming them to be...where g(∇I) is proposed variously by Perona and Malik [40] as g(∇I) = exp ( −‖∇Î‖ σ2 ) , g(∇I) = 1 1 + ( ‖∇Î‖ σ )2 , and by Black et al. [6] as g...Magnetic Resonance in Medicine, 43 (5):705–715 (May 2000). 40. Perona P. and Malik J. “Scale-Space and Edge Detection Using Anisotropic Diffusion,” IEEE

  10. Estimation of lung lobar sliding using image registration

    NASA Astrophysics Data System (ADS)

    Amelon, Ryan; Cao, Kunlin; Reinhardt, Joseph M.; Christensen, Gary E.; Raghavan, Madhavan

    2012-03-01

    MOTIVATION: The lobes of the lungs slide relative to each other during breathing. Quantifying lobar sliding can aid in better understanding lung function, better modeling of lung dynamics, and a better understanding of the limits of image registration performance near fissures. We have developed a method to estimate lobar sliding in the lung from image registration of CT scans. METHODS: Six human lungs were analyzed using CT scans spanning functional residual capacity (FRC) to total lung capacity (TLC). The lung lobes were segmented and registered on a lobe-by-lobe basis. The displacement fields from the independent lobe registrations were then combined into a single image. This technique allows for displacement discontinuity at lobar boundaries. The displacement field was then analyzed as a continuum by forming finite elements from the voxel grid of the FRC image. Elements at a discontinuity will appear to have undergone significantly elevated 'shear stretch' compared to those within the parenchyma. Shear stretch is shown to be a good measure of sliding magnitude in this context. RESULTS: The sliding map clearly delineated the fissures of the lung. The fissure between the right upper and right lower lobes showed the greatest sliding in all subjects while the fissure between the right upper and right middle lobe showed the least sliding.

  11. Mobile medical image retrieval

    NASA Astrophysics Data System (ADS)

    Duc, Samuel; Depeursinge, Adrien; Eggel, Ivan; Müller, Henning

    2011-03-01

    Images are an integral part of medical practice for diagnosis, treatment planning and teaching. Image retrieval has gained in importance mainly as a research domain over the past 20 years. Both textual and visual retrieval of images are essential. In the process of mobile devices becoming reliable and having a functionality equaling that of formerly desktop clients, mobile computing has gained ground and many applications have been explored. This creates a new field of mobile information search & access and in this context images can play an important role as they often allow understanding complex scenarios much quicker and easier than free text. Mobile information retrieval in general has skyrocketed over the past year with many new applications and tools being developed and all sorts of interfaces being adapted to mobile clients. This article describes constraints of an information retrieval system including visual and textual information retrieval from the medical literature of BioMedCentral and of the RSNA journals Radiology and Radiographics. Solutions for mobile data access with an example on an iPhone in a web-based environment are presented as iPhones are frequently used and the operating system is bound to become the most frequent smartphone operating system in 2011. A web-based scenario was chosen to allow for a use by other smart phone platforms such as Android as well. Constraints of small screens and navigation with touch screens are taken into account in the development of the application. A hybrid choice had to be taken to allow for taking pictures with the cell phone camera and upload them for visual similarity search as most producers of smart phones block this functionality to web applications. Mobile information access and in particular access to images can be surprisingly efficient and effective on smaller screens. Images can be read on screen much faster and relevance of documents can be identified quickly through the use of images contained in

  12. a Novel Image Registration Algorithm for SAR and Optical Images Based on Virtual Points

    NASA Astrophysics Data System (ADS)

    Ai, C.; Feng, T.; Wang, J.; Zhang, S.

    2013-07-01

    Optical image is rich in spectral information, while SAR instrument can work in both day and night and obtain images through fog and clouds. Combination of these two types of complementary images shows the great advantages of better image interpretation. Image registration is an inevitable and critical problem for the applications of multi-source remote sensing images, such as image fusion, pattern recognition and change detection. However, the different characteristics between SAR and optical images, which are due to the difference in imaging mechanism and the speckle noises in SAR image, bring great challenges to the multi-source image registration. Therefore, a novel image registration algorithm based on the virtual points, derived from the corresponding region features, is proposed in this paper. Firstly, image classification methods are adopted to extract closed regions from SAR and optical images respectively. Secondly, corresponding region features are matched by constructing cost function with rotate invariant region descriptors such as area, perimeter, and the length of major and minor axes. Thirdly, virtual points derived from corresponding region features, such as the centroids, endpoints and cross points of major and minor axes, are used to calculate initial registration parameters. Finally, the parameters are corrected by an iterative calculation, which will be terminated when the overlap of corresponding region features reaches its maximum. In the experiment, WordView-2 and Radasat-2 with 0.5 m and 4.7 m spatial resolution respectively, obtained in August 2010 in Suzhou, are used to test the registration method. It is shown that the multi-source image registration algorithm presented above is effective, and the accuracy of registration is up to pixel level.

  13. Concepts for on-board satellite image registration, volume 1

    NASA Technical Reports Server (NTRS)

    Ruedger, W. H.; Daluge, D. R.; Aanstoos, J. V.

    1980-01-01

    The NASA-NEEDS program goals present a requirement for on-board signal processing to achieve user-compatible, information-adaptive data acquisition. One very specific area of interest is the preprocessing required to register imaging sensor data which have been distorted by anomalies in subsatellite-point position and/or attitude control. The concepts and considerations involved in using state-of-the-art positioning systems such as the Global Positioning System (GPS) in concert with state-of-the-art attitude stabilization and/or determination systems to provide the required registration accuracy are discussed with emphasis on assessing the accuracy to which a given image picture element can be located and identified, determining those algorithms required to augment the registration procedure and evaluating the technology impact on performing these procedures on-board the satellite.

  14. Batch settling curve registration via image data modeling.

    PubMed

    Derlon, Nicolas; Thürlimann, Christian; Dürrenmatt, David; Villez, Kris

    2017-05-01

    To this day, obtaining reliable characterization of sludge settling properties remains a challenging and time-consuming task. Without such assessments however, optimal design and operation of secondary settling tanks is challenging and conservative approaches will remain necessary. With this study, we show that automated sludge blanket height registration and zone settling velocity estimation is possible thanks to analysis of images taken during batch settling experiments. The experimental setup is particularly interesting for practical applications as it consists of off-the-shelf components only, no moving parts are required, and the software is released publicly. Furthermore, the proposed multivariate shape constrained spline model for image analysis appears to be a promising method for reliable sludge blanket height profile registration. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Error analysis of two methods for range-images registration

    NASA Astrophysics Data System (ADS)

    Liu, Xiaoli; Yin, Yongkai; Li, Ameng; He, Dong; Peng, Xiang

    2010-08-01

    With the improvements in range image registration techniques, this paper focuses on error analysis of two registration methods being generally applied in industry metrology including the algorithm comparison, matching error, computing complexity and different application areas. One method is iterative closest points, by which beautiful matching results with little error can be achieved. However some limitations influence its application in automatic and fast metrology. The other method is based on landmarks. We also present a algorithm for registering multiple range-images with non-coding landmarks, including the landmarks' auto-identification and sub-pixel location, 3D rigid motion, point pattern matching, global iterative optimization techniques et al. The registering results by the two methods are illustrated and a thorough error analysis is performed.

  16. Development of a piecewise linear omnidirectional 3D image registration method.

    PubMed

    Bae, Hyunsoo; Kang, Wonjin; Lee, SukGyu; Kim, Youngwoo

    2016-12-01

    This paper proposes a new piecewise linear omnidirectional image registration method. The proposed method segments an image captured by multiple cameras into 2D segments defined by feature points of the image and then stitches each segment geometrically by considering the inclination of the segment in the 3D space. Depending on the intended use of image registration, the proposed method can be used to improve image registration accuracy or reduce the computation time in image registration because the trade-off between the computation time and image registration accuracy can be controlled for. In general, nonlinear image registration methods have been used in 3D omnidirectional image registration processes to reduce image distortion by camera lenses. The proposed method depends on a linear transformation process for omnidirectional image registration, and therefore it can enhance the effectiveness of the geometry recognition process, increase image registration accuracy by increasing the number of cameras or feature points of each image, increase the image registration speed by reducing the number of cameras or feature points of each image, and provide simultaneous information on shapes and colors of captured objects.

  17. Development of a piecewise linear omnidirectional 3D image registration method

    NASA Astrophysics Data System (ADS)

    Bae, Hyunsoo; Kang, Wonjin; Lee, SukGyu; Kim, Youngwoo

    2016-12-01

    This paper proposes a new piecewise linear omnidirectional image registration method. The proposed method segments an image captured by multiple cameras into 2D segments defined by feature points of the image and then stitches each segment geometrically by considering the inclination of the segment in the 3D space. Depending on the intended use of image registration, the proposed method can be used to improve image registration accuracy or reduce the computation time in image registration because the trade-off between the computation time and image registration accuracy can be controlled for. In general, nonlinear image registration methods have been used in 3D omnidirectional image registration processes to reduce image distortion by camera lenses. The proposed method depends on a linear transformation process for omnidirectional image registration, and therefore it can enhance the effectiveness of the geometry recognition process, increase image registration accuracy by increasing the number of cameras or feature points of each image, increase the image registration speed by reducing the number of cameras or feature points of each image, and provide simultaneous information on shapes and colors of captured objects.

  18. Synthetic aperture radar/LANDSAT MSS image registration

    NASA Technical Reports Server (NTRS)

    Maurer, H. E. (Editor); Oberholtzer, J. D. (Editor); Anuta, P. E. (Editor)

    1979-01-01

    Algorithms and procedures necessary to merge aircraft synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) imagery were determined. The design of a SAR/LANDSAT data merging system was developed. Aircraft SAR images were registered to the corresponding LANDSAT MSS scenes and were the subject of experimental investigations. Results indicate that the registration of SAR imagery with LANDSAT MSS imagery is feasible from a technical viewpoint, and useful from an information-content viewpoint.

  19. Automatic deformable MR-ultrasound registration for image-guided neurosurgery.

    PubMed

    Rivaz, Hassan; Chen, Sean Jy-Shyang; Collins, D Louis

    2015-02-01

    In this work, we present a novel algorithm for registration of 3-D volumetric ultrasound (US) and MR using Robust PaTch-based cOrrelation Ratio (RaPTOR). RaPTOR computes local correlation ratio (CR) values on small patches and adds the CR values to form a global cost function. It is therefore invariant to large amounts of spatial intensity inhomogeneity. We also propose a novel outlier suppression technique based on the orientations of the RaPTOR gradients. Our deformation is modeled with free-form cubic B-splines. We analytically derive the derivatives of RaPTOR with respect to the transformation, i.e., the displacement of the B-spline nodes, and optimize RaPTOR using a stochastic gradient descent approach. RaPTOR is validated on MR and tracked US images of neurosurgery. Deformable registration of the US and MR images acquired, respectively, preoperation and postresection is of significant clinical significance, but challenging due to, among others, the large amount of missing correspondences between the two images. This work is also novel in that it performs automatic registration of this challenging dataset. To validate the results, we manually locate corresponding anatomical landmarks in the US and MR images of tumor resection in brain surgery. Compared to rigid registration based on the tracking system alone, RaPTOR reduces the mean initial mTRE over 13 patients from 5.9 to 2.9 mm, and the maximum initial TRE from 17.0 to 5.9 mm. Each volumetric registration using RaPTOR takes about 30 sec on a single CPU core. An important challenge in the field of medical image analysis is the shortage of publicly available dataset, which can both facilitate the advancement of new algorithms to clinical settings and provide a benchmark for comparison. To address this problem, we will make our manually located landmarks available online.

  20. A review of 3D/2D registration methods for image-guided interventions.

    PubMed

    Markelj, P; Tomaževič, D; Likar, B; Pernuš, F

    2012-04-01

    Registration of pre- and intra-interventional data is one of the key technologies for image-guided radiation therapy, radiosurgery, minimally invasive surgery, endoscopy, and interventional radiology. In this paper, we survey those 3D/2D data registration methods that utilize 3D computer tomography or magnetic resonance images as the pre-interventional data and 2D X-ray projection images as the intra-interventional data. The 3D/2D registration methods are reviewed with respect to image modality, image dimensionality, registration basis, geometric transformation, user interaction, optimization procedure, subject, and object of registration.

  1. Validation of 3D ultrasound: CT registration of prostate images

    NASA Astrophysics Data System (ADS)

    Firle, Evelyn A.; Wesarg, Stefan; Karangelis, Grigoris; Dold, Christian

    2003-05-01

    All over the world 20% of men are expected to develop prostate cancer sometime in his life. In addition to surgery - being the traditional treatment for cancer - the radiation treatment is getting more popular. The most interesting radiation treatment regarding prostate cancer is Brachytherapy radiation procedure. For the safe delivery of that therapy imaging is critically important. In several cases where a CT device is available a combination of the information provided by CT and 3D Ultrasound (U/S) images offers advantages in recognizing the borders of the lesion and delineating the region of treatment. For these applications the CT and U/S scans should be registered and fused in a multi-modal dataset. Purpose of the present development is a registration tool (registration, fusion and validation) for available CT volumes with 3D U/S images of the same anatomical region, i.e. the prostate. The combination of these two imaging modalities interlinks the advantages of the high-resolution CT imaging and low cost real-time U/S imaging and offers a multi-modality imaging environment for further target and anatomy delineation. This tool has been integrated into the visualization software "InViVo" which has been developed over several years in Fraunhofer IGD in Darmstadt.

  2. Medical imaging systems

    SciTech Connect

    Frangioni, John V

    2012-07-24

    A medical imaging system provides simultaneous rendering of visible light and fluorescent images. The system may employ dyes in a small-molecule form that remains in a subject's blood stream for several minutes, allowing real-time imaging of the subject's circulatory system superimposed upon a conventional, visible light image of the subject. The system may also employ dyes or other fluorescent substances associated with antibodies, antibody fragments, or ligands that accumulate within a region of diagnostic significance. In one embodiment, the system provides an excitation light source to excite the fluorescent substance and a visible light source for general illumination within the same optical guide that is used to capture images. In another embodiment, the system is configured for use in open surgical procedures by providing an operating area that is closed to ambient light. More broadly, the systems described herein may be used in imaging applications where a visible light image may be usefully supplemented by an image formed from fluorescent emissions from a fluorescent substance that marks areas of functional interest.

  3. Regional lung function and mechanics using image registration

    NASA Astrophysics Data System (ADS)

    Ding, Kai

    The main function of the respiratory system is gas exchange. Since many disease or injury conditions can cause biomechanical or material property changes that can alter lung function, there is a great interest in measuring regional lung function and mechanics. In this thesis, we present a technique that uses multiple respiratory-gated CT images of the lung acquired at different levels of inflation with both breath-hold static scans and retrospectively reconstructed 4D dynamic scans, along with non-rigid 3D image registration, to make local estimates of lung tissue function and mechanics. We validate our technique using anatomical landmarks and functional Xe-CT estimated specific ventilation. The major contributions of this thesis include: (1) developing the registration derived regional expansion estimation approach in breath-hold static scans and dynamic 4DCT scans, (2) developing a method to quantify lobar sliding from image registration derived displacement field, (3) developing a method for measurement of radiation-induced pulmonary function change following a course of radiation therapy, (4) developing and validating different ventilation measures in 4DCT. The ability of our technique to estimate regional lung mechanics and function as a surrogate of the Xe-CT ventilation imaging for the entire lung from quickly and easily obtained respiratory-gated images, is a significant contribution to functional lung imaging because of the potential increase in resolution, and large reductions in imaging time, radiation, and contrast agent exposure. Our technique may be useful to detect and follow the progression of lung disease such as COPD, may be useful as a planning tool during RT planning, may be useful for tracking the progression of toxicity to nearby normal tissue during RT, and can be used to evaluate the effectiveness of a treatment post-therapy.

  4. Explicit B-spline regularization in diffeomorphic image registration.

    PubMed

    Tustison, Nicholas J; Avants, Brian B

    2013-01-01

    Diffeomorphic mappings are central to image registration due largely to their topological properties and success in providing biologically plausible solutions to deformation and morphological estimation problems. Popular diffeomorphic image registration algorithms include those characterized by time-varying and constant velocity fields, and symmetrical considerations. Prior information in the form of regularization is used to enforce transform plausibility taking the form of physics-based constraints or through some approximation thereof, e.g., Gaussian smoothing of the vector fields [a la Thirion's Demons (Thirion, 1998)]. In the context of the original Demons' framework, the so-called directly manipulated free-form deformation (DMFFD) (Tustison et al., 2009) can be viewed as a smoothing alternative in which explicit regularization is achieved through fast B-spline approximation. This characterization can be used to provide B-spline "flavored" diffeomorphic image registration solutions with several advantages. Implementation is open source and available through the Insight Toolkit and our Advanced Normalization Tools (ANTs) repository. A thorough comparative evaluation with the well-known SyN algorithm (Avants et al., 2008), implemented within the same framework, and its B-spline analog is performed using open labeled brain data and open source evaluation tools.

  5. Explicit B-spline regularization in diffeomorphic image registration

    PubMed Central

    Tustison, Nicholas J.; Avants, Brian B.

    2013-01-01

    Diffeomorphic mappings are central to image registration due largely to their topological properties and success in providing biologically plausible solutions to deformation and morphological estimation problems. Popular diffeomorphic image registration algorithms include those characterized by time-varying and constant velocity fields, and symmetrical considerations. Prior information in the form of regularization is used to enforce transform plausibility taking the form of physics-based constraints or through some approximation thereof, e.g., Gaussian smoothing of the vector fields [a la Thirion's Demons (Thirion, 1998)]. In the context of the original Demons' framework, the so-called directly manipulated free-form deformation (DMFFD) (Tustison et al., 2009) can be viewed as a smoothing alternative in which explicit regularization is achieved through fast B-spline approximation. This characterization can be used to provide B-spline “flavored” diffeomorphic image registration solutions with several advantages. Implementation is open source and available through the Insight Toolkit and our Advanced Normalization Tools (ANTs) repository. A thorough comparative evaluation with the well-known SyN algorithm (Avants et al., 2008), implemented within the same framework, and its B-spline analog is performed using open labeled brain data and open source evaluation tools. PMID:24409140

  6. Hierarchical segmentation-assisted multimodal registration for MR brain images.

    PubMed

    Lu, Huanxiang; Beisteiner, Roland; Nolte, Lutz-Peter; Reyes, Mauricio

    2013-04-01

    Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.

  7. Refusal to grant provisional General Medical Council registration to U.K. medical graduates.

    PubMed

    David, Timothy J; Ellson, Sarah

    2015-09-01

    In the last five years, 2010-2014, there have been 17 instances when an application for provisional registration by a U.K. medical graduate was refused by the General Medical Council because the Registrar considered that the applicant's fitness to practise was impaired. While this number is small, the fact that this can happen is largely unappreciated by medical students and their teachers, the prevailing false assumption being that passing finals and graduation is the final hurdle before taking up a Foundation Programme post. It is a poorly recognised fact that just because a university fitness to practise committee has concluded that a student is fit to practise there is no guarantee that the General Medical Council will come to the same decision. This paper explains the reasons for these refusals and makes suggestions for students and medical schools.

  8. A reference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study archive.

    PubMed

    Castillo, Richard; Castillo, Edward; Fuentes, David; Ahmad, Moiz; Wood, Abbie M; Ludwig, Michelle S; Guerrero, Thomas

    2013-05-07

    Landmark point-pairs provide a strategy to assess deformable image registration (DIR) accuracy in terms of the spatial registration of the underlying anatomy depicted in medical images. In this study, we propose to augment a publicly available database (www.dir-lab.com) of medical images with large sets of manually identified anatomic feature pairs between breath-hold computed tomography (BH-CT) images for DIR spatial accuracy evaluation. Ten BH-CT image pairs were randomly selected from the COPDgene study cases. Each patient had received CT imaging of the entire thorax in the supine position at one-fourth dose normal expiration and maximum effort full dose inspiration. Using dedicated in-house software, an imaging expert manually identified large sets of anatomic feature pairs between images. Estimates of inter- and intra-observer spatial variation in feature localization were determined by repeat measurements of multiple observers over subsets of randomly selected features. 7298 anatomic landmark features were manually paired between the 10 sets of images. Quantity of feature pairs per case ranged from 447 to 1172. Average 3D Euclidean landmark displacements varied substantially among cases, ranging from 12.29 (SD: 6.39) to 30.90 (SD: 14.05) mm. Repeat registration of uniformly sampled subsets of 150 landmarks for each case yielded estimates of observer localization error, which ranged in average from 0.58 (SD: 0.87) to 1.06 (SD: 2.38) mm for each case. The additions to the online web database (www.dir-lab.com) described in this work will broaden the applicability of the reference data, providing a freely available common dataset for targeted critical evaluation of DIR spatial accuracy performance in multiple clinical settings. Estimates of observer variance in feature localization suggest consistent spatial accuracy for all observers across both four-dimensional CT and COPDgene patient cohorts.

  9. A Reference Dataset for Deformable Image Registration Spatial Accuracy Evaluation using the COPDgene Study Archive

    PubMed Central

    Castillo, Richard; Castillo, Edward; Fuentes, David; Ahmad, Moiz; Wood, Abbie M.; Ludwig, Michelle S.; Guerrero, Thomas

    2013-01-01

    Rationale and Objectives Landmark point-pairs provide a strategy to assess deformable image registration (DIR) accuracy in terms of the spatial registration of the underlying anatomy depicted in medical images. In this study, we propose to augment a publicly available database (www.dir-lab.com) of medical images with large sets of manually identified anatomic feature pairs between breath-hold computed tomography (BH-CT) images for DIR spatial accuracy evaluation. Materials and Methods 10 BH-CT image pairs were randomly selected from the COPDgene study cases. Each patient had received CT imaging of the entire thorax in the supine position at 1/4th dose normal expiration and maximum effort full dose inspiration. Using dedicated in-house software, an imaging expert manually identified large sets of anatomic feature pairs between images. Estimates of inter- and intra-observer spatial variation in feature localization were determined by repeat measurements of multiple observers over subsets of randomly selected features. Results 7298 anatomic landmark features were manually paired between the 10 sets of images. Quantity of feature pairs per case ranged from 447 to 1172. Average 3D Euclidean landmark displacements varied substantially among cases, ranging from 12.29 (SD: 6.39) to 30.90 (SD: 14.05) mm. Repeat registration of uniformly sampled subsets of 150 landmarks for each case yielded estimates of observer localization error, which ranged in average from 0.58 (SD: 0.87) to 1.06 (SD: 2.38) mm for each case. Conclusions The additions to the online web database (www.dir-lab.com) described in this work will broaden the applicability of the reference data, providing a freely available common dataset for targeted critical evaluation of DIR spatial accuracy performance in multiple clinical settings. Estimates of observer variance in feature localization suggest consistent spatial accuracy for all observers across both 4D CT and COPDgene patient cohorts. PMID:23571679

  10. A reference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study archive

    NASA Astrophysics Data System (ADS)

    Castillo, Richard; Castillo, Edward; Fuentes, David; Ahmad, Moiz; Wood, Abbie M.; Ludwig, Michelle S.; Guerrero, Thomas

    2013-05-01

    Landmark point-pairs provide a strategy to assess deformable image registration (DIR) accuracy in terms of the spatial registration of the underlying anatomy depicted in medical images. In this study, we propose to augment a publicly available database (www.dir-lab.com) of medical images with large sets of manually identified anatomic feature pairs between breath-hold computed tomography (BH-CT) images for DIR spatial accuracy evaluation. Ten BH-CT image pairs were randomly selected from the COPDgene study cases. Each patient had received CT imaging of the entire thorax in the supine position at one-fourth dose normal expiration and maximum effort full dose inspiration. Using dedicated in-house software, an imaging expert manually identified large sets of anatomic feature pairs between images. Estimates of inter- and intra-observer spatial variation in feature localization were determined by repeat measurements of multiple observers over subsets of randomly selected features. 7298 anatomic landmark features were manually paired between the 10 sets of images. Quantity of feature pairs per case ranged from 447 to 1172. Average 3D Euclidean landmark displacements varied substantially among cases, ranging from 12.29 (SD: 6.39) to 30.90 (SD: 14.05) mm. Repeat registration of uniformly sampled subsets of 150 landmarks for each case yielded estimates of observer localization error, which ranged in average from 0.58 (SD: 0.87) to 1.06 (SD: 2.38) mm for each case. The additions to the online web database (www.dir-lab.com) described in this work will broaden the applicability of the reference data, providing a freely available common dataset for targeted critical evaluation of DIR spatial accuracy performance in multiple clinical settings. Estimates of observer variance in feature localization suggest consistent spatial accuracy for all observers across both four-dimensional CT and COPDgene patient cohorts.

  11. Viewpoints on Medical Image Processing: From Science to Application

    PubMed Central

    Deserno (né Lehmann), Thomas M.; Handels, Heinz; Maier-Hein (né Fritzsche), Klaus H.; Mersmann, Sven; Palm, Christoph; Tolxdorff, Thomas; Wagenknecht, Gudrun; Wittenberg, Thomas

    2013-01-01

    Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment. PMID:24078804

  12. A fast image registration approach of neural activities in light-sheet fluorescence microscopy images

    NASA Astrophysics Data System (ADS)

    Meng, Hui; Hui, Hui; Hu, Chaoen; Yang, Xin; Tian, Jie

    2017-03-01

    The ability of fast and single-neuron resolution imaging of neural activities enables light-sheet fluorescence microscopy (LSFM) as a powerful imaging technique in functional neural connection applications. The state-of-art LSFM imaging system can record the neuronal activities of entire brain for small animal, such as zebrafish or C. elegans at single-neuron resolution. However, the stimulated and spontaneous movements in animal brain result in inconsistent neuron positions during recording process. It is time consuming to register the acquired large-scale images with conventional method. In this work, we address the problem of fast registration of neural positions in stacks of LSFM images. This is necessary to register brain structures and activities. To achieve fast registration of neural activities, we present a rigid registration architecture by implementation of Graphics Processing Unit (GPU). In this approach, the image stacks were preprocessed on GPU by mean stretching to reduce the computation effort. The present image was registered to the previous image stack that considered as reference. A fast Fourier transform (FFT) algorithm was used for calculating the shift of the image stack. The calculations for image registration were performed in different threads while the preparation functionality was refactored and called only once by the master thread. We implemented our registration algorithm on NVIDIA Quadro K4200 GPU under Compute Unified Device Architecture (CUDA) programming environment. The experimental results showed that the registration computation can speed-up to 550ms for a full high-resolution brain image. Our approach also has potential to be used for other dynamic image registrations in biomedical applications.

  13. Four dimensional deformable image registration using trajectory modeling

    PubMed Central

    Castillo, Edward; Castillo, Richard; Martinez, Josue; Shenoy, Maithili; Guerrero, Thomas

    2013-01-01

    A four-dimensional deformable image registration (4D DIR) algorithm, referred to as 4D local trajectory modeling (4DLTM), is presented and applied to thoracic 4D computed tomography (4DCT) image sets. The theoretical framework on which this algorithm is built exploits the incremental continuity present in 4DCT component images to calculate a dense set of parameterized voxel trajectories through space as functions of time. The spatial accuracy of the 4DLTM algorithm is compared with an alternative registration approach in which component phase to phase (CPP) DIR is utilized to determine the full displacement between maximum inhale and exhale images. A publically available DIR reference database (http://www.dir-lab.com) is utilized for the spatial accuracy assessment. The database consists of ten 4DCT image sets and corresponding manually identified landmark points between the maximum phases. A subset of points are propagated through the expiratory 4DCT component images. Cubic polynomials were found to provide sufficient flexibility and spatial accuracy for describing the point trajectories through the expiratory phases. The resulting average spatial error between the maximum phases was 1.25 mm for the 4DLTM and 1.44 mm for the CPP. The 4DLTM method captures the long-range motion between 4DCT extremes with high spatial accuracy. PMID:20009196

  14. Learning-based deformable image registration for infant MR images in the first year of life.

    PubMed

    Hu, Shunbo; Wei, Lifang; Gao, Yaozong; Guo, Yanrong; Wu, Guorong; Shen, Dinggang

    2017-01-01

    Many brain development studies have been devoted to investigate dynamic structural and functional changes in the first year of life. To quantitatively measure brain development in such a dynamic period, accurate image registration for different infant subjects with possible large age gap is of high demand. Although many state-of-the-art image registration methods have been proposed for young and elderly brain images, very few registration methods work for infant brain images acquired in the first year of life, because of (a) large anatomical changes due to fast brain development and (b) dynamic appearance changes due to white-matter myelination. To address these two difficulties, we propose a learning-based registration method to not only align the anatomical structures but also alleviate the appearance differences between two arbitrary infant MR images (with large age gap) by leveraging the regression forest to predict both the initial displacement vector and appearance changes. Specifically, in the training stage, two regression models are trained separately, with (a) one model learning the relationship between local image appearance (of one development phase) and its displacement toward the template (of another development phase) and (b) another model learning the local appearance changes between the two brain development phases. Then, in the testing stage, to register a new infant image to the template, we first predict both its voxel-wise displacement and appearance changes by the two learned regression models. Since such initializations can alleviate significant appearance and shape differences between new infant image and the template, it is easy to just use a conventional registration method to refine the remaining registration. We apply our proposed registration method to align 24 infant subjects at five different time points (i.e., 2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old), and achieve more accurate and robust registration

  15. Mitigation of spectral mis-registration effects in imaging spectrometers via cubic spline interpolation.

    PubMed

    Feng, Yutao; Xiang, Yang

    2008-09-29

    The effects of spectral curvature (smile) on the co-registration of measured radiance spectra were analyzed for a dispersive imaging spectrometer. The spectra measured by the imaging spectrometer with spectral curvature were re-sampled using cubic spline Interpolation to mitigate the mis-registration of spectral radiance measurement. After correction, the co-registration of measured radiance spectra is distinctly improved.

  16. Representing the dosimetric impact of deformable image registration errors

    NASA Astrophysics Data System (ADS)

    Vickress, Jason; Battista, Jerry; Barnett, Rob; Yartsev, Slav

    2017-09-01

    Deformable image registration (DIR) is emerging as a tool in radiation therapy for calculating the cumulative dose distribution across multiple fractions of treatment. Unfortunately, due to the variable nature of DIR algorithms and dependence of performance on image quality, registration errors can result in dose accumulation errors. In this study, landmarked images were used to characterize the DIR error throughout an image space and determine its impact on dosimetric analysis. Ten thoracic 4DCT images with 300 landmarks per image study matching the end-inspiration and end-expiration phases were obtained from ‘dir-labs’. DIR was performed using commercial software MIM Maestro. The range of dose uncertainty (RDU) was calculated at each landmark pair as the maximum and minimum of the doses within a sphere around the landmark in the end-expiration phase. The radius of the sphere was defined by a measure of DIR error which included either the actual DIR error, mean DIR error per study, constant errors of 2 or 5 mm, inverse consistency error, transitivity error or the distance discordance metric (DDM). The RDUs were evaluated using the magnitude of dose uncertainty (MDU) and inclusion rate (IR) of actual error lying within the predicted RDU. The RDU was calculated for 300 landmark pairs on each 4DCT study for all measures of DIR error. The most representative RDU was determined using the actual DIR error with a MDU of 2.5 Gy and IR of 97%. Across all other measures of DIR error, the DDM was most predictive with a MDU of 2.5 Gy and IR of 86%, closest to the actual DIR error. The proposed method represents the range of dosimetric uncertainty of DIR error using either landmarks at specific voxels or measures of registration accuracy throughout the volume.

  17. A one-bit approach for image registration

    NASA Astrophysics Data System (ADS)

    Nguyen, An Hung; Pickering, Mark; Lambert, Andrew

    2015-02-01

    Motion estimation or optic flow computation for automatic navigation and obstacle avoidance programs running on Unmanned Aerial Vehicles (UAVs) is a challenging task. These challenges come from the requirements of real-time processing speed and small light-weight image processing hardware with very limited resources (especially memory space) embedded on the UAVs. Solutions towards both simplifying computation and saving hardware resources have recently received much interest. This paper presents an approach for image registration using binary images which addresses these two requirements. This approach uses translational information between two corresponding patches of binary images to estimate global motion. These low bit-resolution images require a very small amount of memory space to store them and allow simple logic operations such as XOR and AND to be used instead of more complex computations such as subtractions and multiplications.

  18. An improved SIFT algorithm based on KFDA in image registration

    NASA Astrophysics Data System (ADS)

    Chen, Peng; Yang, Lijuan; Huo, Jinfeng

    2016-03-01

    As a kind of stable feature matching algorithm, SIFT has been widely used in many fields. In order to further improve the robustness of the SIFT algorithm, an improved SIFT algorithm with Kernel Discriminant Analysis (KFDA-SIFT) is presented for image registration. The algorithm uses KFDA to SIFT descriptors for feature extraction matrix, and uses the new descriptors to conduct the feature matching, finally chooses RANSAC to deal with the matches for further purification. The experiments show that the presented algorithm is robust to image changes in scale, illumination, perspective, expression and tiny pose with higher matching accuracy.

  19. Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms.

    PubMed

    Ger, Rachel B; Yang, Jinzhong; Ding, Yao; Jacobsen, Megan C; Fuller, Clifton D; Howell, Rebecca M; Li, Heng; Jason Stafford, R; Zhou, Shouhao; Court, Laurence E

    2017-06-16

    Accurate deformable image registration is necessary for longitudinal studies. The error associated with commercial systems has been evaluated using computed tomography (CT). Several in-house algorithms have been evaluated for use with magnetic resonance imaging (MRI), but there is still relatively little information about MRI deformable image registration. This work presents an evaluation of two deformable image registration systems, one commercial (Velocity) and one in-house (demons-based algorithm), with MRI using two different metrics to quantify the registration error. The registration error was analyzed with synthetic MR images. These images were generated from interpatient and intrapatient variation models trained on 28 patients. Four synthetic post-treatment images were generated for each of four synthetic pretreatment images, resulting in 16 image registrations for both the T1- and T2-weighted images. The synthetic post-treatment images were registered to their corresponding synthetic pretreatment image. The registration error was calculated between the known deformation vector field and the generated deformation vector field from the image registration system. The registration error was also analyzed using a porcine phantom with ten implanted 0.35-mm diameter gold markers. The markers were visible on CT but not MRI. CT, T1-weighted MR, and T2-weighted MR images were taken in four different positions. The markers were contoured on the CT images and rigidly registered to their corresponding MR images. The MR images were deformably registered and the distance between the projected marker location and true marker location was measured as the registration error. The synthetic images were evaluated only on Velocity. Root mean square errors (RMSEs) of 0.76 mm in the left-right (LR) direction, 0.76 mm in the anteroposterior (AP) direction, and 0.69 mm in the superior-inferior (SI) direction were observed for the T1-weighted MR images. RMSEs of 1.1 mm in the LR

  20. Image Quality Improvement in Adaptive Optics Scanning Laser Ophthalmoscopy Assisted Capillary Visualization Using B-spline-based Elastic Image Registration

    PubMed Central

    Uji, Akihito; Ooto, Sotaro; Hangai, Masanori; Arichika, Shigeta; Yoshimura, Nagahisa

    2013-01-01

    Purpose To investigate the effect of B-spline-based elastic image registration on adaptive optics scanning laser ophthalmoscopy (AO-SLO)-assisted capillary visualization. Methods AO-SLO videos were acquired from parafoveal areas in the eyes of healthy subjects and patients with various diseases. After nonlinear image registration, the image quality of capillary images constructed from AO-SLO videos using motion contrast enhancement was compared before and after B-spline-based elastic (nonlinear) image registration performed using ImageJ. For objective comparison of image quality, contrast-to-noise ratios (CNRS) for vessel images were calculated. For subjective comparison, experienced ophthalmologists ranked images on a 5-point scale. Results All AO-SLO videos were successfully stabilized by elastic image registration. CNR was significantly higher in capillary images stabilized by elastic image registration than in those stabilized without registration. The average ratio of CNR in images with elastic image registration to CNR in images without elastic image registration was 2.10 ± 1.73, with no significant difference in the ratio between patients and healthy subjects. Improvement of image quality was also supported by expert comparison. Conclusions Use of B-spline-based elastic image registration in AO-SLO-assisted capillary visualization was effective for enhancing image quality both objectively and subjectively. PMID:24265796

  1. A review of biomechanically informed breast image registration

    NASA Astrophysics Data System (ADS)

    Hipwell, John H.; Vavourakis, Vasileios; Han, Lianghao; Mertzanidou, Thomy; Eiben, Björn; Hawkes, David J.

    2016-01-01

    Breast radiology encompasses the full range of imaging modalities from routine imaging via x-ray mammography, magnetic resonance imaging and ultrasound (both two- and three-dimensional), to more recent technologies such as digital breast tomosynthesis, and dedicated breast imaging systems for positron emission mammography and ultrasound tomography. In addition new and experimental modalities, such as Photoacoustics, Near Infrared Spectroscopy and Electrical Impedance Tomography etc, are emerging. The breast is a highly deformable structure however, and this greatly complicates visual comparison of imaging modalities for the purposes of breast screening, cancer diagnosis (including image guided biopsy), tumour staging, treatment monitoring, surgical planning and simulation of the effects of surgery and wound healing etc. Due primarily to the challenges posed by these gross, non-rigid deformations, development of automated methods which enable registration, and hence fusion, of information within and across breast imaging modalities, and between the images and the physical space of the breast during interventions, remains an active research field which has yet to translate suitable methods into clinical practice. This review describes current research in the field of breast biomechanical modelling and identifies relevant publications where the resulting models have been incorporated into breast image registration and simulation algorithms. Despite these developments there remain a number of issues that limit clinical application of biomechanical modelling. These include the accuracy of constitutive modelling, implementation of representative boundary conditions, failure to meet clinically acceptable levels of computational cost, challenges associated with automating patient-specific model generation (i.e. robust image segmentation and mesh generation) and the complexity of applying biomechanical modelling methods in routine clinical practice.

  2. Multimodality imaging combination in small animal via point-based registration

    NASA Astrophysics Data System (ADS)

    Yang, C. C.; Wu, T. H.; Lin, M. H.; Huang, Y. H.; Guo, W. Y.; Chen, C. L.; Wang, T. C.; Yin, W. H.; Lee, J. S.

    2006-12-01

    We present a system of image co-registration in small animal study. Marker-based registration is chosen because of its considerable advantage that the fiducial feature is independent of imaging modality. We also experimented with different scanning protocols and different fiducial marker sizes to improve registration accuracy. Co-registration was conducted using rat phantom fixed by stereotactic frame. Overall, the co-registration accuracy was in sub-millimeter level and close to intrinsic system error. Therefore, we conclude that the system is an accurate co-registration method to be used in small animal studies.

  3. Wavelets in medical imaging

    SciTech Connect

    Zahra, Noor e; Sevindir, Huliya A.; Aslan, Zafar; Siddiqi, A. H.

    2012-07-17

    The aim of this study is to provide emerging applications of wavelet methods to medical signals and images, such as electrocardiogram, electroencephalogram, functional magnetic resonance imaging, computer tomography, X-ray and mammography. Interpretation of these signals and images are quite important. Nowadays wavelet methods have a significant impact on the science of medical imaging and the diagnosis of disease and screening protocols. Based on our initial investigations, future directions include neurosurgical planning and improved assessment of risk for individual patients, improved assessment and strategies for the treatment of chronic pain, improved seizure localization, and improved understanding of the physiology of neurological disorders. We look ahead to these and other emerging applications as the benefits of this technology become incorporated into current and future patient care. In this chapter by applying Fourier transform and wavelet transform, analysis and denoising of one of the important biomedical signals like EEG is carried out. The presence of rhythm, template matching, and correlation is discussed by various method. Energy of EEG signal is used to detect seizure in an epileptic patient. We have also performed denoising of EEG signals by SWT.

  4. Wavelets in medical imaging

    NASA Astrophysics Data System (ADS)

    Zahra, Noor e.; Sevindir, Hulya Kodal; Aslan, Zafer; Siddiqi, A. H.

    2012-07-01

    The aim of this study is to provide emerging applications of wavelet methods to medical signals and images, such as electrocardiogram, electroencephalogram, functional magnetic resonance imaging, computer tomography, X-ray and mammography. Interpretation of these signals and images are quite important. Nowadays wavelet methods have a significant impact on the science of medical imaging and the diagnosis of disease and screening protocols. Based on our initial investigations, future directions include neurosurgical planning and improved assessment of risk for individual patients, improved assessment and strategies for the treatment of chronic pain, improved seizure localization, and improved understanding of the physiology of neurological disorders. We look ahead to these and other emerging applications as the benefits of this technology become incorporated into current and future patient care. In this chapter by applying Fourier transform and wavelet transform, analysis and denoising of one of the important biomedical signals like EEG is carried out. The presence of rhythm, template matching, and correlation is discussed by various method. Energy of EEG signal is used to detect seizure in an epileptic patient. We have also performed denoising of EEG signals by SWT.

  5. Three-dimensional image registration as a tool for forensic odontology: a preliminary investigation.

    PubMed

    Abduo, Jaafar; Bennamoun, Mohammed

    2013-09-01

    Frequently, human dentition is utilized for victim identification. This report introduces a new human identification technique based on the principle of 3-dimensional (3D) image registration of the dentition. With the aid of a dry human skull, postmortem (PM) and antemortem (AM) scenarios were assumed. The skull in its initial state composed the PM scenario. Virtual 3D PM images were reconstructed from medical CT images. The AM scenario was achieved by reconstructing the missing hard and soft tissues of the skull by dental waxes. Virtual 3D AM images were obtained by laser surface scanning. The virtual PM and AM images were registered at 2 levels: arch level and tooth level. At arch level, the deviation between the 2 images was 0.147 mm for the maxilla and 0.166 mm for the mandible. At tooth level, the deviation average ranged from 0.077 to 0.237 mm. Qualitatively, even image fit was observed for the arches, intact teeth, and teeth with minimal deficiencies. As the tooth defect increased, the alignment discrepancy increased. It is concluded that 3D image registration ensured an accurate superimposition of the 3D images and can be used as a robust tool for forensic identification.

  6. Efficient 3D rigid-body registration of micro-MR and micro-CT trabecular bone images

    NASA Astrophysics Data System (ADS)

    Rajapakse, C. S.; Magland, J.; Wehrli, S. L.; Zhang, X. H.; Liu, X. S.; Guo, X. E.; Wehrli, F. W.

    2008-03-01

    Registration of 3D images acquired from different imaging modalities such as micro-magnetic resonance imaging (µMRI) and micro-computed tomography (µCT) are of interest in a number of medical imaging applications. Most general-purpose multimodality registration algorithms tend to be computationally intensive and do not take advantage of the shape of the imaging volume. Multimodality trabecular bone (TB) images of cylindrical cores, for example, tend to be misaligned along and around the axial direction more than that around other directions. Additionally, TB images acquired by µMRI can differ substantially from those acquired by µCT due to apparent trabecular thickening from magnetic susceptibility boundary effects and non-linear intensity correspondence. However, they share very similar contrast characteristics since the images essentially represent a binary tomographic system. The directional misalignment and the fundamental similarities of the two types of images can be exploited to achieve fast 3D registration. Here we present an intensity cross-correlation based 3D registration algorithm for registering 3D specimen images from cylindrical cores of cadaveric TB acquired by µMRI and µCT in the context of finite-element modeling to assess the bone's mechanical constants. The algorithm achieves the desired registration by first coarsely approximating the three translational and three rotational parameters required to align the µMR images to the µCT scan coordinate frame and fine-tuning the parameters in the neighborhood of the approximate solution. The algorithm described here is suitable for 3D rigid-body image registration applications where through-plane rotations are known to be relatively small. The accuracy of the technique is constrained by the image resolution and in-plane angular increments used.

  7. Robust image registration using adaptive coherent point drift method

    NASA Astrophysics Data System (ADS)

    Yang, Lijuan; Tian, Zheng; Zhao, Wei; Wen, Jinhuan; Yan, Weidong

    2016-04-01

    Coherent point drift (CPD) method is a powerful registration tool under the framework of the Gaussian mixture model (GMM). However, the global spatial structure of point sets is considered only without other forms of additional attribute information. The equivalent simplification of mixing parameters and the manual setting of the weight parameter in GMM make the CPD method less robust to outlier and have less flexibility. An adaptive CPD method is proposed to automatically determine the mixing parameters by embedding the local attribute information of features into the construction of GMM. In addition, the weight parameter is treated as an unknown parameter and automatically determined in the expectation-maximization algorithm. In image registration applications, the block-divided salient image disk extraction method is designed to detect sparse salient image features and local self-similarity is used as attribute information to describe the local neighborhood structure of each feature. The experimental results on optical images and remote sensing images show that the proposed method can significantly improve the matching performance.

  8. Fluid Registration of Diffusion Tensor Images Using Information Theory

    PubMed Central

    Chiang, Ming-Chang; Leow, Alex D.; Klunder, Andrea D.; Dutton, Rebecca A.; Barysheva, Marina; Rose, Stephen E.; McMahon, Katie L.; de Zubicaray, Greig I.; Toga, Arthur W.; Thompson, Paul M.

    2008-01-01

    We apply an information-theoretic cost metric, the symmetrized Kullback-Leibler (sKL) divergence, or J-divergence, to fluid registration of diffusion tensor images. The difference between diffusion tensors is quantified based on the sKL-divergence of their associated probability density functions (PDFs). Three-dimensional DTI data from 34 subjects were fluidly registered to an optimized target image. To allow large image deformations but preserve image topology, we regularized the flow with a large-deformation diffeomorphic mapping based on the kinematics of a Navier-Stokes fluid. A driving force was developed to minimize the J-divergence between the deforming source and target diffusion functions, while reorienting the flowing tensors to preserve fiber topography. In initial experiments, we showed that the sKL-divergence based on full diffusion PDFs is adaptable to higher-order diffusion models, such as high angular resolution diffusion imaging (HARDI). The sKL-divergence was sensitive to subtle differences between two diffusivity profiles, showing promise for nonlinear registration applications and multisubject statistical analysis of HARDI data. PMID:18390342

  9. Using image synthesis for multi-channel registration of different image modalities

    PubMed Central

    Chen, Min; Jog, Amod; Carass, Aaron; Prince, Jerry L.

    2015-01-01

    This paper presents a multi-channel approach for performing registration between magnetic resonance (MR) images with different modalities. In general, a multi-channel registration cannot be used when the moving and target images do not have analogous modalities. In this work, we address this limitation by using a random forest regression technique to synthesize the missing modalities from the available ones. This allows a single channel registration between two different modalities to be converted into a multi-channel registration with two mono-modal channels. To validate our approach, two openly available registration algorithms and five cost functions were used to compare the label transfer accuracy of the registration with (and without) our multi-channel synthesis approach. Our results show that the proposed method produced statistically significant improvements in registration accuracy (at an α level of 0.001) for both algorithms and all cost functions when compared to a standard multi-modal registration using the same algorithms with mutual information. PMID:26246653

  10. Using image synthesis for multi-channel registration of different image modalities.

    PubMed

    Chen, Min; Jog, Amod; Carass, Aaron; Prince, Jerry L

    2015-02-21

    This paper presents a multi-channel approach for performing registration between magnetic resonance (MR) images with different modalities. In general, a multi-channel registration cannot be used when the moving and target images do not have analogous modalities. In this work, we address this limitation by using a random forest regression technique to synthesize the missing modalities from the available ones. This allows a single channel registration between two different modalities to be converted into a multi-channel registration with two mono-modal channels. To validate our approach, two openly available registration algorithms and five cost functions were used to compare the label transfer accuracy of the registration with (and without) our multi-channel synthesis approach. Our results show that the proposed method produced statistically significant improvements in registration accuracy (at an α level of 0.001) for both algorithms and all cost functions when compared to a standard multi-modal registration using the same algorithms with mutual information.

  11. MIND Demons for MR-to-CT deformable image registration in image-guided spine surgery

    NASA Astrophysics Data System (ADS)

    Reaungamornrat, S.; De Silva, T.; Uneri, A.; Wolinsky, J.-P.; Khanna, A. J.; Kleinszig, G.; Vogt, S.; Prince, J. L.; Siewerdsen, J. H.

    2016-03-01

    Purpose: Localization of target anatomy and critical structures defined in preoperative MR images can be achieved by means of multi-modality deformable registration to intraoperative CT. We propose a symmetric diffeomorphic deformable registration algorithm incorporating a modality independent neighborhood descriptor (MIND) and a robust Huber metric for MR-to-CT registration. Method: The method, called MIND Demons, solves for the deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the velocity fields and the diffeomorphisms, a modality-insensitive similarity function suitable to multi-modality images, and constraints on geodesics in Lagrangian coordinates. Direct optimization (without relying on an exponential map of stationary velocity fields used in conventional diffeomorphic Demons) is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, in phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to conventional mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, and normalized MI (NMI) Demons. Result: The method yielded sub-voxel invertibility (0.006 mm) and nonsingular spatial Jacobians with capability to preserve local orientation and topology. It demonstrated improved registration accuracy in comparison to the reference methods, with mean target registration error (TRE) of 1.5 mm compared to 10.9, 2.3, and 4.6 mm for MI FFD, LMI FFD, and NMI Demons methods, respectively. Validation in clinical studies demonstrated realistic deformation with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine. Conclusions: A modality-independent deformable registration method has been developed to estimate a

  12. MIND Demons for MR-to-CT Deformable Image Registration In Image-Guided Spine Surgery

    PubMed Central

    Reaungamornrat, S.; De Silva, T.; Uneri, A.; Wolinsky, J.-P.; Khanna, A. J.; Kleinszig, G.; Vogt, S.; Prince, J. L.; Siewerdsen, J. H.

    2016-01-01

    Purpose Localization of target anatomy and critical structures defined in preoperative MR images can be achieved by means of multi-modality deformable registration to intraoperative CT. We propose a symmetric diffeomorphic deformable registration algorithm incorporating a modality independent neighborhood descriptor (MIND) and a robust Huber metric for MR-to-CT registration. Method The method, called MIND Demons, solves for the deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the velocity fields and the diffeomorphisms, a modality-insensitive similarity function suitable to multi-modality images, and constraints on geodesics in Lagrangian coordinates. Direct optimization (without relying on an exponential map of stationary velocity fields used in conventional diffeomorphic Demons) is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, in phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to conventional mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, and normalized MI (NMI) Demons. Result The method yielded sub-voxel invertibility (0.006 mm) and nonsingular spatial Jacobians with capability to preserve local orientation and topology. It demonstrated improved registration accuracy in comparison to the reference methods, with mean target registration error (TRE) of 1.5 mm compared to 10.9, 2.3, and 4.6 mm for MI FFD, LMI FFD, and NMI Demons methods, respectively. Validation in clinical studies demonstrated realistic deformation with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine. Conclusions A modality-independent deformable registration method has been developed to estimate a viscoelastic diffeomorphic map between preoperative MR and intraoperative CT. The

  13. MIND Demons for MR-to-CT Deformable Image Registration In Image-Guided Spine Surgery.

    PubMed

    Reaungamornrat, S; De Silva, T; Uneri, A; Wolinsky, J-P; Khanna, A J; Kleinszig, G; Vogt, S; Prince, J L; Siewerdsen, J H

    2016-02-27

    Localization of target anatomy and critical structures defined in preoperative MR images can be achieved by means of multi-modality deformable registration to intraoperative CT. We propose a symmetric diffeomorphic deformable registration algorithm incorporating a modality independent neighborhood descriptor (MIND) and a robust Huber metric for MR-to-CT registration. The method, called MIND Demons, solves for the deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the velocity fields and the diffeomorphisms, a modality-insensitive similarity function suitable to multi-modality images, and constraints on geodesics in Lagrangian coordinates. Direct optimization (without relying on an exponential map of stationary velocity fields used in conventional diffeomorphic Demons) is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, in phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to conventional mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, and normalized MI (NMI) Demons. The method yielded sub-voxel invertibility (0.006 mm) and nonsingular spatial Jacobians with capability to preserve local orientation and topology. It demonstrated improved registration accuracy in comparison to the reference methods, with mean target registration error (TRE) of 1.5 mm compared to 10.9, 2.3, and 4.6 mm for MI FFD, LMI FFD, and NMI Demons methods, respectively. Validation in clinical studies demonstrated realistic deformation with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine. A modality-independent deformable registration method has been developed to estimate a viscoelastic diffeomorphic map between preoperative MR and intraoperative CT. The method yields registration

  14. Iris-based cyclotorsional image alignment method for wavefront registration.

    PubMed

    Chernyak, Dimitri A

    2005-12-01

    In refractive surgery, especially wavefront-guided refractive surgery, correct registration of the treatment to the cornea is of paramount importance. The specificity of the custom ablation formula requires that the ablation be applied to the cornea only when it has been precisely aligned with the mapped area. If, however, the eye has rotated between measurement and ablation, and this cyclotorsion is not compensated for, the rotational misalignment could impair the effectiveness of the refractive surgery. To achieve precise registration, a noninvasive method for torsional rotational alignment of the captured wavefront image to the patient's eyes at surgery has been developed. This method applies a common coordinate system to the wavefront and the eye. Video cameras on the laser and wavefront devices precisely establish the spatial relationship between the optics of the eye and the natural features of the iris, enabling the surgeon to identify and compensate for cyclotorsional eye motion, whatever its cause.

  15. Non-parametric diffeomorphic image registration with the demons algorithm.

    PubMed

    Vercauteren, Tom; Pennec, Xavier; Perchant, Aymeric; Ayache, Nicholas

    2007-01-01

    We propose a non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm. The demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. The main idea of our algorithm is to adapt this procedure to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of free form deformations by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the true ones in terms of Jacobians.

  16. Direct Image-To Registration Using Mobile Sensor Data

    NASA Astrophysics Data System (ADS)

    Kehl, C.; Buckley, S. J.; Gawthorpe, R. L.; Viola, I.; Howell, J. A.

    2016-06-01

    Adding supplementary texture and 2D image-based annotations to 3D surface models is a useful next step for domain specialists to make use of photorealistic products of laser scanning and photogrammetry. This requires a registration between the new camera imagery and the model geometry to be solved, which can be a time-consuming task without appropriate automation. The increasing availability of photorealistic models, coupled with the proliferation of mobile devices, gives users the possibility to complement their models in real time. Modern mobile devices deliver digital photographs of increasing quality, as well as on-board sensor data, which can be used as input for practical and automatic camera registration procedures. Their familiar user interface also improves manual registration procedures. This paper introduces a fully automatic pose estimation method using the on-board sensor data for initial exterior orientation, and feature matching between an acquired photograph and a synthesised rendering of the orientated 3D scene as input for fine alignment. The paper also introduces a user-friendly manual camera registration- and pose estimation interface for mobile devices, based on existing surface geometry and numerical optimisation methods. The article further assesses the automatic algorithm's accuracy compared to traditional methods, and the impact of computational- and environmental parameters. Experiments using urban and geological case studies show a significant sensitivity of the automatic procedure to the quality of the initial mobile sensor values. Changing natural lighting conditions remain a challenge for automatic pose estimation techniques, although progress is presented here. Finally, the automatically-registered mobile images are used as the basis for adding user annotations to the input textured model.

  17. A cloud-based medical image repository

    NASA Astrophysics Data System (ADS)

    Maeder, Anthony J.; Planitz, Birgit M.; El Rifai, Diaa

    2012-02-01

    Many widely used digital medical image collections have been established but these are generally used as raw data sources without related image analysis toolsets. Providing associated functionality to allow specific types of operations to be performed on these images has proved beneficial in some cases (e.g. brain image registration and atlases). However, toolset development to provide generic image analysis functions on medical images has tended to be ad hoc, with Open Source options proliferating (e.g. ITK). Our Automated Medical Image Collection Annotation (AMICA) system is both an image repository, to which the research community can contribute image datasets, and a search/retrieval system that uses automated image annotation. AMICA was designed for the Windows Azure platform to leverage the flexibility and scalability of the cloud. It is intended that AMICA will expand beyond its initial pilot implementation (for brain CT, MR images) to accommodate a wide range of modalities and anatomical regions. This initiative aims to contribute to advances in clinical research by permitting a broader use and reuse of medical image data than is currently attainable. For example, cohort studies for cases with particular physiological or phenotypical profiles will be able to source and include enough cases to provide high statistical power, allowing more individualised risk factors to be assessed and thus allowing screening and staging processes to be optimised. Also, education, training and credentialing of clinicians in image interpretation, will be more effective because it will be possible to select instances of images with specific visual aspects, or correspond to types of cases where reading performance improvement is desirable.

  18. State estimation and absolute image registration for geosynchronous satellites

    NASA Technical Reports Server (NTRS)

    Nankervis, R.; Koch, D. W.; Sielski, H.

    1980-01-01

    Spacecraft state estimation and the absolute registration of Earth images acquired by cameras onboard geosynchronous satellites are described. The basic data type of the procedure consists of line and element numbers of image points called landmarks whose geodetic coordinates, relative to United States Geodetic Survey topographic maps, are known. A conventional least squares process is used to estimate navigational parameters and camera pointing biases from observed minus computed landmark line and element numbers. These estimated parameters along with orbit and attitude dynamic models are used to register images, using an automated grey level correlation technique, inside the span represented by the landmark data. In addition, the dynamic models can be employed to register images outside of the data span in a near real time mode. An important application of this mode is in support of meteorological studies where rapid data reduction is required for the rapid tracking and predicting of dynamic phenomena.

  19. 3D/2D image registration: the impact of X-ray views and their number.

    PubMed

    Tomazevic, Dejan; Likar, Bostjan; Pernus, Franjo

    2007-01-01

    An important part of image-guided radiation therapy or surgery is registration of a three-dimensional (3D) preoperative image to two-dimensional (2D) images of the patient. It is expected that the accuracy and robustness of a 3D/2D image registration method do not depend solely on the registration method itself but also on the number and projections (views) of intraoperative images. In this study, we systematically investigate these factors by using registered image data, comprising of CT and X-ray images of a cadaveric lumbar spine phantom and the recently proposed 3D/2D registration method. The results indicate that the proportion of successful registrations (robustness) significantly increases when more X-ray images are used for registration.

  20. Miniature stereoscopic video system provides real-time 3D registration and image fusion for minimally invasive surgery

    NASA Astrophysics Data System (ADS)

    Yaron, Avi; Bar-Zohar, Meir; Horesh, Nadav

    2007-02-01

    Sophisticated surgeries require the integration of several medical imaging modalities, like MRI and CT, which are three-dimensional. Many efforts are invested in providing the surgeon with this information in an intuitive & easy to use manner. A notable development, made by Visionsense, enables the surgeon to visualize the scene in 3D using a miniature stereoscopic camera. It also provides real-time 3D measurements that allow registration of navigation systems as well as 3D imaging modalities, overlaying these images on the stereoscopic video image in real-time. The real-time MIS 'see through tissue' fusion solutions enable the development of new MIS procedures in various surgical segments, such as spine, abdomen, cardio-thoracic and brain. This paper describes 3D surface reconstruction and registration methods using Visionsense camera, as a step toward fully automated multi-modality 3D registration.

  1. Image Registration Through The Exploitation Of Perspective Invariant Graphs

    NASA Astrophysics Data System (ADS)

    Gilmore, John F.

    1983-10-01

    This paper describes two new techniques of image registration as applied to scenes consisting of natural terrain. The first technique is a syntactic pattern recognition approach which combines the spatial relationships of a point pattern with point classifications to accurately perform image registration. In this approach, a preprocessor analyzes each image in order to identify points of interest and to classify these points based on statistical features. A classified graph possessing perspective invariant properties is created and is converted into a classification-based grammar string. A local match analysis is performed and the best global match is con-structed. A probability-of-match metric is computed in order to evaluate match confidence. The second technique described is an isomorphic graph matching approach called Mean Neighbors (MN). A MN graph is constructed from a given point pattern taking into account the elliptical projections of real world scenes onto a two dimensional surface. This approach exploits the spatial relationships of the given points of interest but neglects the point classifications used in syntactic processing. A projective, perspective invariant graph is constructed for both the reference and sensed images and a mapping of the coincidence edges occurs. A probability of match metric is used to evaluate the confidence of the best mapping.

  2. Multigrid optimal mass transport for image registration and morphing

    NASA Astrophysics Data System (ADS)

    Rehman, Tauseef ur; Tannenbaum, Allen

    2007-02-01

    In this paper we present a computationally efficient Optimal Mass Transport algorithm. This method is based on the Monge-Kantorovich theory and is used for computing elastic registration and warping maps in image registration and morphing applications. This is a parameter free method which utilizes all of the grayscale data in an image pair in a symmetric fashion. No landmarks need to be specified for correspondence. In our work, we demonstrate significant improvement in computation time when our algorithm is applied as compared to the originally proposed method by Haker et al [1]. The original algorithm was based on a gradient descent method for removing the curl from an initial mass preserving map regarded as 2D vector field. This involves inverting the Laplacian in each iteration which is now computed using full multigrid technique resulting in an improvement in computational time by a factor of two. Greater improvement is achieved by decimating the curl in a multi-resolutional framework. The algorithm was applied to 2D short axis cardiac MRI images and brain MRI images for testing and comparison.

  3. Medical Image Analysis Facility

    NASA Technical Reports Server (NTRS)

    1978-01-01

    To improve the quality of photos sent to Earth by unmanned spacecraft. NASA's Jet Propulsion Laboratory (JPL) developed a computerized image enhancement process that brings out detail not visible in the basic photo. JPL is now applying this technology to biomedical research in its Medical lrnage Analysis Facility, which employs computer enhancement techniques to analyze x-ray films of internal organs, such as the heart and lung. A major objective is study of the effects of I stress on persons with heart disease. In animal tests, computerized image processing is being used to study coronary artery lesions and the degree to which they reduce arterial blood flow when stress is applied. The photos illustrate the enhancement process. The upper picture is an x-ray photo in which the artery (dotted line) is barely discernible; in the post-enhancement photo at right, the whole artery and the lesions along its wall are clearly visible. The Medical lrnage Analysis Facility offers a faster means of studying the effects of complex coronary lesions in humans, and the research now being conducted on animals is expected to have important application to diagnosis and treatment of human coronary disease. Other uses of the facility's image processing capability include analysis of muscle biopsy and pap smear specimens, and study of the microscopic structure of fibroprotein in the human lung. Working with JPL on experiments are NASA's Ames Research Center, the University of Southern California School of Medicine, and Rancho Los Amigos Hospital, Downey, California.

  4. Panorama imaging for image-to-physical registration of narrow drill holes inside spongy bones

    NASA Astrophysics Data System (ADS)

    Bergmeier, Jan; Fast, Jacob Friedemann; Ortmaier, Tobias; Kahrs, Lüder Alexander

    2017-03-01

    Image-to-physical registration based on volumetric data like computed tomography on the one side and intraoperative endoscopic images on the other side is an important method for various surgical applications. In this contribution, we present methods to generate panoramic views from endoscopic recordings for image-to-physical registration of narrow drill holes inside spongy bone. One core application is the registration of drill poses inside the mastoid during minimally invasive cochlear implantations. Besides the development of image processing software for registration, investigations are performed on a miniaturized optical system, achieving 360° radial imaging with one shot by extending a conventional, small, rigid, rod lens endoscope. A reflective cone geometry is used to deflect radially incoming light rays into the endoscope optics. Therefore, a cone mirror is mounted in front of a conventional 0° endoscope. Furthermore, panoramic images of inner drill hole surfaces in artificial bone material are created. Prior to drilling, cone beam computed tomography data is acquired from this artificial bone and simulated endoscopic views are generated from this data. A qualitative and quantitative image comparison of resulting views in terms of image-to-image registration is performed. First results show that downsizing of panoramic optics to a diameter of 3mm is possible. Conventional rigid rod lens endoscopes can be extended to produce suitable panoramic one-shot image data. Using unrolling and stitching methods, images of the inner drill hole surface similar to computed tomography image data of the same surface were created. Registration is performed on ten perturbations of the search space and results in target registration errors of (0:487 +/- 0:438)mm at the entry point and (0:957 +/- 0:948)mm at the exit as well as an angular error of (1:763 +/- 1:536)°. The results show suitability of this image data for image-to-image registration. Analysis of the error

  5. Image navigation and registration for the geostationary lightning mapper (GLM)

    NASA Astrophysics Data System (ADS)

    van Bezooijen, Roel W. H.; Demroff, Howard; Burton, Gregory; Chu, Donald; Yang, Shu S.

    2016-10-01

    The Geostationary Lightning Mappers (GLM) for the Geostationary Operational Environmental Satellite (GOES) GOES-R series will, for the first time, provide hemispherical lightning information 24 hours a day from longitudes of 75 and 137 degrees west. The first GLM of a series of four is planned for launch in November, 2016. Observation of lightning patterns by GLM holds promise to improve tornado warning lead times to greater than 20 minutes while halving the present false alarm rates. In addition, GLM will improve airline traffic flow management, and provide climatology data allowing us to understand the Earth's evolving climate. The paper describes the method used for translating the pixel position of a lightning event to its corresponding geodetic longitude and latitude, using the J2000 attitude of the GLM mount frame reported by the spacecraft, the position of the spacecraft, and the alignment of the GLM coordinate frame relative to its mount frame. Because the latter alignment will experience seasonal variation, this alignment is determined daily using GLM background images collected over the previous 7 days. The process involves identification of coastlines in the background images and determination of the alignment change necessary to match the detected coastline with the coastline predicted using the GSHHS database. Registration is achieved using a variation of the Lucas-Kanade algorithm where we added a dither and average technique to improve performance significantly. An innovative water mask technique was conceived to enable self-contained detection of clear coastline sections usable for registration. Extensive simulations using accurate visible images from GOES13 and GOES15 have been used to demonstrate the performance of the coastline registration method, the results of which are presented in the paper.

  6. Registration and Fusion of Multiple Source Remotely Sensed Image Data

    NASA Technical Reports Server (NTRS)

    LeMoigne, Jacqueline

    2004-01-01

    Earth and Space Science often involve the comparison, fusion, and integration of multiple types of remotely sensed data at various temporal, radiometric, and spatial resolutions. Results of this integration may be utilized for global change analysis, global coverage of an area at multiple resolutions, map updating or validation of new instruments, as well as integration of data provided by multiple instruments carried on multiple platforms, e.g. in spacecraft constellations or fleets of planetary rovers. Our focus is on developing methods to perform fast, accurate and automatic image registration and fusion. General methods for automatic image registration are being reviewed and evaluated. Various choices for feature extraction, feature matching and similarity measurements are being compared, including wavelet-based algorithms, mutual information and statistically robust techniques. Our work also involves studies related to image fusion and investigates dimension reduction and co-kriging for application-dependent fusion. All methods are being tested using several multi-sensor datasets, acquired at EOS Core Sites, and including multiple sensors such as IKONOS, Landsat-7/ETM+, EO1/ALI and Hyperion, MODIS, and SeaWIFS instruments. Issues related to the coregistration of data from the same platform (i.e., AIRS and MODIS from Aqua) or from several platforms of the A-train (i.e., MLS, HIRDLS, OMI from Aura with AIRS and MODIS from Terra and Aqua) will also be considered.

  7. Registration and Fusion of Multiple Source Remotely Sensed Image Data

    NASA Technical Reports Server (NTRS)

    LeMoigne, Jacqueline

    2004-01-01

    Earth and Space Science often involve the comparison, fusion, and integration of multiple types of remotely sensed data at various temporal, radiometric, and spatial resolutions. Results of this integration may be utilized for global change analysis, global coverage of an area at multiple resolutions, map updating or validation of new instruments, as well as integration of data provided by multiple instruments carried on multiple platforms, e.g. in spacecraft constellations or fleets of planetary rovers. Our focus is on developing methods to perform fast, accurate and automatic image registration and fusion. General methods for automatic image registration are being reviewed and evaluated. Various choices for feature extraction, feature matching and similarity measurements are being compared, including wavelet-based algorithms, mutual information and statistically robust techniques. Our work also involves studies related to image fusion and investigates dimension reduction and co-kriging for application-dependent fusion. All methods are being tested using several multi-sensor datasets, acquired at EOS Core Sites, and including multiple sensors such as IKONOS, Landsat-7/ETM+, EO1/ALI and Hyperion, MODIS, and SeaWIFS instruments. Issues related to the coregistration of data from the same platform (i.e., AIRS and MODIS from Aqua) or from several platforms of the A-train (i.e., MLS, HIRDLS, OMI from Aura with AIRS and MODIS from Terra and Aqua) will also be considered.

  8. Dense deformation field estimation for brain intraoperative images registration

    NASA Astrophysics Data System (ADS)

    De Craene, Mathieu S.; du Bois d'Aische, Aloys; Talos, Ion-Florin; Ferrant, Matthieu; Black, Peter M.; Jolesz, Ferenc; Kikinis, Ron; Macq, Benoit; Warfield, Simon K.

    2004-05-01

    A new fast non rigid registration algorithm is presented. The algorithm estimates a dense deformation field by optimizing a criterion that measures image similarity by mutual information and regularizes with a linear elastic energy term. The optimal deformation field is found using a Simultaneous Perturbation Stochastic Approximation to the gradient. The implementation is parallelized for symmetric multi-processor architectures. This algorithm was applied to capture non-rigid brain deformations that occur during neurosurgery. Segmentation of the intra-operative data is not required but preoperative segmentation of the brain allows the algorithm to be robust to artifacts due to the craniotomy.

  9. Three modality image registration of brain SPECT/CT and MR images for quantitative analysis of dopamine transporter imaging

    NASA Astrophysics Data System (ADS)

    Yamaguchi, Yuzuho; Takeda, Yuta; Hara, Takeshi; Zhou, Xiangrong; Matsusako, Masaki; Tanaka, Yuki; Hosoya, Kazuhiko; Nihei, Tsutomu; Katafuchi, Tetsuro; Fujita, Hiroshi

    2016-03-01

    Important features in Parkinson's disease (PD) are degenerations and losses of dopamine neurons in corpus striatum. 123I-FP-CIT can visualize activities of the dopamine neurons. The activity radio of background to corpus striatum is used for diagnosis of PD and Dementia with Lewy Bodies (DLB). The specific activity can be observed in the corpus striatum on SPECT images, but the location and the shape of the corpus striatum on SPECT images only are often lost because of the low uptake. In contrast, MR images can visualize the locations of the corpus striatum. The purpose of this study was to realize a quantitative image analysis for the SPECT images by using image registration technique with brain MR images that can determine the region of corpus striatum. In this study, the image fusion technique was used to fuse SPECT and MR images by intervening CT image taken by SPECT/CT. The mutual information (MI) for image registration between CT and MR images was used for the registration. Six SPECT/CT and four MR scans of phantom materials are taken by changing the direction. As the results of the image registrations, 16 of 24 combinations were registered within 1.3mm. By applying the approach to 32 clinical SPECT/CT and MR cases, all of the cases were registered within 0.86mm. In conclusions, our registration method has a potential in superimposing MR images on SPECT images.

  10. Correction of geometric distortions in EP images using nonrigid registration to corresponding anatomic images

    NASA Astrophysics Data System (ADS)

    Skerl, Darko; Pan, Shiyan; Li, Rui; Fitzpatrick, J. Michael; Parks, Mitchell H.; Martin, Peter R.; Morgan, Victoria L.; Dawant, Benoit M.

    2001-07-01

    The spatial resolution of echo planar image (EPI) data acquired for functional MRI (fMRI) studies is low. To facilitate their interpretation, conventional T1-weighted anatomical images are often acquired prior to the acquisition of the EP images. T1-weighted and EP images are then registered and activation patterns computed from the EP images are superimposed on the anatomic images. Registration between the anatomic and the EP images is required to compensate for patient motion between the acquisitions and for geometric distortions affecting EP images. Recently, methods have been proposed to register EP and anatomic images using non-rigid registration techniques. In these approaches, the transformation is parameterized using splines. Here, we propose an alternative solution to this problem based on optical flow with non-stationary stiffness constraint. The approach we propose also includes several preprocessing steps such as automatic skull removal and intensity remapping. Results obtained with eight studies on normal volunteers are presented.

  11. Automatic parameter selection for feature-based multi-sensor image registration

    NASA Astrophysics Data System (ADS)

    DelMarco, Stephen; Tom, Victor; Webb, Helen; Chao, Alan

    2006-05-01

    Accurate image registration is critical for applications such as precision targeting, geo-location, change-detection, surveillance, and remote sensing. However, the increasing volume of image data is exceeding the current capacity of human analysts to perform manual registration. This image data glut necessitates the development of automated approaches to image registration, including algorithm parameter value selection. Proper parameter value selection is crucial to the success of registration techniques. The appropriate algorithm parameters can be highly scene and sensor dependent. Therefore, robust algorithm parameter value selection approaches are a critical component of an end-to-end image registration algorithm. In previous work, we developed a general framework for multisensor image registration which includes feature-based registration approaches. In this work we examine the problem of automated parameter selection. We apply the automated parameter selection approach of Yitzhaky and Peli to select parameters for feature-based registration of multisensor image data. The approach consists of generating multiple feature-detected images by sweeping over parameter combinations and using these images to generate estimated ground truth. The feature-detected images are compared to the estimated ground truth images to generate ROC points associated with each parameter combination. We develop a strategy for selecting the optimal parameter set by choosing the parameter combination corresponding to the optimal ROC point. We present numerical results showing the effectiveness of the approach using registration of collected SAR data to reference EO data.

  12. Estimation of the uncertainty of elastic image registration with the demons algorithm

    NASA Astrophysics Data System (ADS)

    Hub, M.; Karger, C. P.

    2013-05-01

    The accuracy of elastic image registration is limited. We propose an approach to detect voxels where registration based on the demons algorithm is likely to perform inaccurately, compared to other locations of the same image. The approach is based on the assumption that the local reproducibility of the registration can be regarded as a measure of uncertainty of the image registration. The reproducibility is determined as the standard deviation of the displacement vector components obtained from multiple registrations. These registrations differ in predefined initial deformations. The proposed approach was tested with artificially deformed lung images, where the ground truth on the deformation is known. In voxels where the result of the registration was less reproducible, the registration turned out to have larger average registration errors as compared to locations of the same image, where the registration was more reproducible. The proposed method can show a clinician in which area of the image the elastic registration with the demons algorithm cannot be expected to be accurate.

  13. Estimation of the uncertainty of elastic image registration with the demons algorithm.

    PubMed

    Hub, M; Karger, C P

    2013-05-07

    The accuracy of elastic image registration is limited. We propose an approach to detect voxels where registration based on the demons algorithm is likely to perform inaccurately, compared to other locations of the same image. The approach is based on the assumption that the local reproducibility of the registration can be regarded as a measure of uncertainty of the image registration. The reproducibility is determined as the standard deviation of the displacement vector components obtained from multiple registrations. These registrations differ in predefined initial deformations. The proposed approach was tested with artificially deformed lung images, where the ground truth on the deformation is known. In voxels where the result of the registration was less reproducible, the registration turned out to have larger average registration errors as compared to locations of the same image, where the registration was more reproducible. The proposed method can show a clinician in which area of the image the elastic registration with the demons algorithm cannot be expected to be accurate.

  14. MATHEMATICAL METHODS IN MEDICAL IMAGE PROCESSING

    PubMed Central

    ANGENENT, SIGURD; PICHON, ERIC; TANNENBAUM, ALLEN

    2013-01-01

    In this paper, we describe some central mathematical problems in medical imaging. The subject has been undergoing rapid changes driven by better hardware and software. Much of the software is based on novel methods utilizing geometric partial differential equations in conjunction with standard signal/image processing techniques as well as computer graphics facilitating man/machine interactions. As part of this enterprise, researchers have been trying to base biomedical engineering principles on rigorous mathematical foundations for the development of software methods to be integrated into complete therapy delivery systems. These systems support the more effective delivery of many image-guided procedures such as radiation therapy, biopsy, and minimally invasive surgery. We will show how mathematics may impact some of the main problems in this area, including image enhancement, registration, and segmentation. PMID:23645963

  15. Image registration and averaging of low laser power two-photon fluorescence images of mouse retina

    PubMed Central

    Alexander, Nathan S.; Palczewska, Grazyna; Stremplewski, Patrycjusz; Wojtkowski, Maciej; Kern, Timothy S.; Palczewski, Krzysztof

    2016-01-01

    Two-photon fluorescence microscopy (TPM) is now being used routinely to image live cells for extended periods deep within tissues, including the retina and other structures within the eye . However, very low laser power is a requirement to obtain TPM images of the retina safely. Unfortunately, a reduction in laser power also reduces the signal-to-noise ratio of collected images, making it difficult to visualize structural details. Here, image registration and averaging methods applied to TPM images of the eye in living animals (without the need for auxiliary hardware) demonstrate the structural information obtained with laser power down to 1 mW. Image registration provided between 1.4% and 13.0% improvement in image quality compared to averaging images without registrations when using a high-fluorescence template, and between 0.2% and 12.0% when employing the average of collected images as the template. Also, a diminishing return on image quality when more images were used to obtain the averaged image is shown. This work provides a foundation for obtaining informative TPM images with laser powers of 1 mW, compared to previous levels for imaging mice ranging between 6.3 mW [PalczewskaG., Nat Med. 20, 785 (2014)24952647 SharmaR., Biomed. Opt. Express 4, 1285 (2013)24009992]. PMID:27446697

  16. Image registration and averaging of low laser power two-photon fluorescence images of mouse retina.

    PubMed

    Alexander, Nathan S; Palczewska, Grazyna; Stremplewski, Patrycjusz; Wojtkowski, Maciej; Kern, Timothy S; Palczewski, Krzysztof

    2016-07-01

    Two-photon fluorescence microscopy (TPM) is now being used routinely to image live cells for extended periods deep within tissues, including the retina and other structures within the eye . However, very low laser power is a requirement to obtain TPM images of the retina safely. Unfortunately, a reduction in laser power also reduces the signal-to-noise ratio of collected images, making it difficult to visualize structural details. Here, image registration and averaging methods applied to TPM images of the eye in living animals (without the need for auxiliary hardware) demonstrate the structural information obtained with laser power down to 1 mW. Image registration provided between 1.4% and 13.0% improvement in image quality compared to averaging images without registrations when using a high-fluorescence template, and between 0.2% and 12.0% when employing the average of collected images as the template. Also, a diminishing return on image quality when more images were used to obtain the averaged image is shown. This work provides a foundation for obtaining informative TPM images with laser powers of 1 mW, compared to previous levels for imaging mice ranging between 6.3 mW [Palczewska G., Nat Med.20, 785 (2014) Sharma R., Biomed. Opt. Express4, 1285 (2013)].

  17. SU-E-J-264: Comparison of Two Commercially Available Software Platforms for Deformable Image Registration

    SciTech Connect

    Tuohy, R; Stathakis, S; Mavroidis, P; Bosse, C; Papanikolaou, N

    2014-06-01

    Purpose: To evaluate and compare the deformable image registration algorithms available in the Velocity (Velocity Medical Solutions, Atlanta, GA) and RayStation (RaySearch Americas, Inc., Garden city NY). Methods: Ten consecutive patient cone beam CTs (CBCT) for each fraction were collected. The CBCTs along with the simulation CT were exported to the Velocity and the RayStation software. Each CBCT was registered using deformable image registration to the simulation CT and the resulting deformable vector matrix was generated. Each registration was visually inspected by a physicist and the prescribing physician. The volumes of the critical organs were calculated for each deformable CT and used for comparison. Results: The resulting deformable registrations revealed differences between the two algorithms. These differences were realized when the organs at risk were contoured on each deformed CBCT. Differences in the order of 10% ±30% in volume were observed for bladder, 17 ±21% for rectum and 16±10% for sigmoid. The prostate and PTV volume differences were in the order of 3±5%. The volumetric differences observed had a respective impact on the DVHs of all organs at risk. Differences of 8–10% in the mean dose were observed for all organs above. Conclusion: Deformable registration is a powerful tool that aids in the definition of critical structures and is often used for the evaluation of daily dose delivered to the patient. It should be noted that extended QA should be performed before clinical implementation of the software and the users should be aware of advantages and limitations of the methods.

  18. Registration of whole immunohistochemical slide images: an efficient way to characterize biomarker colocalization.

    PubMed

    Moles Lopez, Xavier; Barbot, Paul; Van Eycke, Yves-Rémi; Verset, Laurine; Trépant, Anne-Laure; Larbanoix, Lionel; Salmon, Isabelle; Decaestecker, Christine

    2015-01-01

    Extracting accurate information from complex biological processes involved in diseases, such as cancers, requires the simultaneous targeting of multiple proteins and locating their respective expression in tissue samples. This information can be collected by imaging and registering adjacent sections from the same tissue sample and stained by immunohistochemistry (IHC). Registration accuracy should be on the scale of a few cells to enable protein colocalization to be assessed. We propose a simple and efficient method based on the open-source elastix framework to register virtual slides of adjacent sections from the same tissue sample. We characterize registration accuracies for different types of tissue and IHC staining. Our results indicate that this technique is suitable for the evaluation of the colocalization of biomarkers on the scale of a few cells. We also show that using this technique in conjunction with a sequential IHC labeling and erasing technique offers improved registration accuracies. Brightfield IHC enables to address the problem of large series of tissue samples, which are usually required in clinical research. However, this approach, which is simple at the tissue processing level, requires challenging image analysis processes, such as accurate registration, to view and extract the protein colocalization information. The method proposed in this work enables accurate registration (on the scale of a few cells) of virtual slides of adjacent tissue sections on which the expression of different proteins is evidenced by standard IHC. Furthermore, combining our method with a sequential labeling and erasing technique enables cell-scale colocalization. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.comFor numbered affiliations see end of article.

  19. BioImage Suite: An integrated medical image analysis suite: An update.

    PubMed

    Papademetris, Xenophon; Jackowski, Marcel P; Rajeevan, Nallakkandi; DiStasio, Marcello; Okuda, Hirohito; Constable, R Todd; Staib, Lawrence H

    2006-01-01

    BioImage Suite is an NIH-supported medical image analysis software suite developed at Yale. It leverages both the Visualization Toolkit (VTK) and the Insight Toolkit (ITK) and it includes many additional algorithms for image analysis especially in the areas of segmentation, registration, diffusion weighted image processing and fMRI analysis. BioImage Suite has a user-friendly user interface developed in the Tcl scripting language. A final beta version is freely available for download.

  20. Medical alert bracelet (image)

    MedlinePlus

    People with diabetes should always wear a medical alert bracelet or necklace that emergency medical workers will ... People with diabetes should always wear a medical alert bracelet or necklace that emergency medical workers will ...

  1. Algorithm for image registration and clutter and jitter noise reduction

    SciTech Connect

    Brower, K.L.

    1997-02-01

    This paper presents an analytical, computational method whereby two-dimensional images of an optical source represented in terms of a set of detector array signals can be registered with respect to a reference set of detector array signals. The detector image is recovered from the detector array signals and represented over a local region by a fourth order, two-dimensional taylor series. This local detector image can then be registered by a general linear transformation with respect to a reference detector image. The detector signal in the reference frame is reconstructed by integrating this detector image over the respective reference pixel. For cases in which the general linear transformation is uncertain by up to plus-or-minus two pixels, the general linear transformation can be determined by least squares fitting the detector image to the reference detector image. This registration process reduces clutter and jitter noise to a level comparable to the electronic noise level of the detector system. Test results with and without electronic noise using an analytical test function are presented.

  2. An extension of digital volume correlation for multimodality image registration

    NASA Astrophysics Data System (ADS)

    Tudisco, E.; Jailin, C.; Mendoza, A.; Tengattini, A.; Andò, E.; Hall, Stephen A.; Viggiani, Gioacchino; Hild, F.; Roux, S.

    2017-09-01

    The question of registering two images (or image volumes) acquired with different modalities, and thus exhibiting different contrast, at different positions is addressed based on an extension of global digital image (or volume) correlation. A specific comparison metric is introduced allowing the signature of the different phases to be related. A first solution consists of a Gaussian mixture to describe the joint distribution of gray levels, which not only provides a matching of both images, but also offers a natural segmentation indicator. A second ‘self-adapting’ solution does not include any postulated a priori model for the joint histogram and leads to a registration of the images based on their initial histograms. The algorithm is implemented with a pyramidal multiscale framework for the sake of robustness. The proposed multiscale technique is tested on two 3D images obtained from x-ray and neutron tomography respectively. The proposed approach brings the two images to coincidence with a sub-pixel accuracy and allows for a ‘natural’ segmentation of the different phases.

  3. Post-operative assessment in Deep Brain Stimulation based on multimodal images: registration workflow and validation

    NASA Astrophysics Data System (ADS)

    Lalys, Florent; Haegelen, Claire; Abadie, Alexandre; Jannin, Pierre

    2009-02-01

    Object Movement disorders in Parkinson disease patients may require functional surgery, when medical therapy isn't effective. In Deep Brain Stimulation (DBS) electrodes are implanted within the brain to stimulate deep structures such as SubThalamic Nucleus (STN). This paper describes successive steps for constructing a digital Atlas gathering patient's location of electrodes and contacts for post operative assessment. Materials and Method 12 patients who had undergone bilateral STN DBS have participated to the study. Contacts on post-operative CT scans were automatically localized, based on black artefacts. For each patient, post operative CT images were rigidly registered to pre operative MR images. Then, pre operative MR images were registered to a MR template (super-resolution Collin27 average MRI template). This last registration was the combination of global affine, local affine and local non linear registrations, respectively. Four different studies were performed in order to validate the MR patient to template registration process, based on anatomical landmarks and clinical scores (i.e., Unified Parkinson's disease rating Scale). Visualisation software was developed for displaying into the template images the stimulated contacts represented as cylinders with a colour code related to the improvement of the UPDRS. Results The automatic contact localization algorithm was successful for all the patients. Validation studies for the registration process gave a placement error of 1.4 +/- 0.2 mm and coherence with UPDRS scores. Conclusion The developed visualization tool allows post-operative assessment for previous interventions. Correlation with additional clinical scores will certainly permit to learn more about DBS and to better understand clinical side-effects.

  4. Multi-atlas segmentation with particle-based group-wise image registration

    NASA Astrophysics Data System (ADS)

    Lee, Joohwi; Lyu, Ilwoo; Styner, Martin

    2014-03-01

    We propose a novel multi-atlas segmentation method that employs a group-wise image registration method for the brain segmentation on rodent magnetic resonance (MR) images. The core element of the proposed segmentation is the use of a particle-guided image registration method that extends the concept of particle correspondence into the volumetric image domain. The registration method performs a group-wise image registration that simultaneously registers a set of images toward the space defined by the average of particles. The particle-guided image registration method is robust with low signal-to-noise ratio images as well as differing sizes and shapes observed in the developing rodent brain. Also, the use of an implicit common reference frame can prevent potential bias induced by the use of a single template in the segmentation process. We show that the use of a particle guided-image registration method can be naturally extended to a novel multi-atlas segmentation method and improves the registration method to explicitly use the provided template labels as an additional constraint. In the experiment, we show that our segmentation algorithm provides more accuracy with multi-atlas label fusion and stability against pair-wise image registration. The comparison with previous group-wise registration method is provided as well.

  5. Multi-atlas segmentation with particle-based group-wise image registration

    PubMed Central

    Lee, Joohwi; Lyu, Ilwoo; Styner, Martin

    2014-01-01

    We propose a novel multi-atlas segmentation method that employs a group-wise image registration method for the brain segmentation on rodent magnetic resonance (MR) images. The core element of the proposed segmentation is the use of a particle-guided image registration method that extends the concept of particle correspondence into the volumetric image domain. The registration method performs a group-wise image registration that simultaneously registers a set of images toward the space defined by the average of particles. The particle-guided image registration method is robust with low signal-to-noise ratio images as well as differing sizes and shapes observed in the developing rodent brain. Also, the use of an implicit common reference frame can prevent potential bias induced by the use of a single template in the segmentation process. We show that the use of a particle guided-image registration method can be naturally extended to a novel multi-atlas segmentation method and improves the registration method to explicitly use the provided template labels as an additional constraint. In the experiment, we show that our segmentation algorithm provides more accuracy with multi-atlas label fusion and stability against pair-wise image registration. The comparison with previous group-wise registration method is provided as well. PMID:25075158

  6. Hybrid feature-based diffeomorphic registration for tumor tracking in 2-D liver ultrasound images.

    PubMed

    Cifor, Amalia; Risser, Laurent; Chung, Daniel; Anderson, Ewan M; Schnabel, Julia A

    2013-09-01

    Real-time ultrasound image acquisition is a pivotal resource in the medical community, in spite of its limited image quality. This poses challenges to image registration methods, particularly to those driven by intensity values. We address these difficulties in a novel diffeomorphic registration technique for tumor tracking in series of 2-D liver ultrasound. Our method has two main characteristics: 1) each voxel is described by three image features: intensity, local phase, and phase congruency; 2) we compute a set of forces from either local information (Demons-type of forces), or spatial correspondences supplied by a block-matching scheme, from each image feature. A family of update deformation fields which are defined by these forces, and inform upon the local or regional contribution of each image feature are then composed to form the final transformation. The method is diffeomorphic, which ensures the invertibility of deformations. The qualitative and quantitative results yielded by both synthetic and real clinical data show the suitability of our method for the application at hand.

  7. Automated registration of intraoperative CT image data for navigated skull base surgery.

    PubMed

    Eggers, G; Kress, B; Mühling, J

    2008-02-01

    With a new intraoperative computed tomography (CT) imaging system, patient-to-image registration without any invasive registration markers is possible. Furthermore, registration can be performed fully automatically. The accuracy of this method for skull base surgery was investigated in this study. We employed a phantom study design. A phantom skull was equipped with 33 target markers in the regions of the anterior and lateral skull base. CT image data were acquired with an intraoperative CT suite. Image data were transferred as DICOM data to the navigation system, and registration was performed automatically. For registration, the position of the patient and the position of the CT gantry were monitored in the imaging process, using the infrared camera of a navigation system. Using the pointing device of the navigation system, the target markers were identified. The accuracy was measured as the spatial difference of the target markers in image space and on the phantom. Accuracy was always sufficient for image-guided surgery of any region of the skull base, with an average target registration error of below 1.2 mm. In contrast to traditional non-invasive registration methods, there was no difference in registration accuracy between the anterior skull base and the lateral skull base. Fully automated registration based on a tracked CT gantry is a robust and accurate registration method for skull base surgery.

  8. Conoscopic holography for image registration: a feasibility study

    NASA Astrophysics Data System (ADS)

    Lathrop, Ray A.; Cheng, Tiffany T.; Webster, Robert J., III

    2009-02-01

    Preoperative image data can facilitate intrasurgical guidance by revealing interior features of opaque tissues, provided image data can be accurately registered to the physical patient. Registration is challenging in organs that are deformable and lack features suitable for use as alignment fiducials (e.g. liver, kidneys, etc.). However, provided intraoperative sensing of surface contours can be accomplished, a variety of rigid and deformable 3D surface registration techniques become applicable. In this paper, we evaluate the feasibility of conoscopic holography as a new method to sense organ surface shape. We also describe potential advantages of conoscopic holography, including the promise of replacing open surgery with a laparoscopic approach. Our feasibility study investigated use of a tracked off-the-shelf conoscopic holography unit to perform a surface scans on several types of biological and synthetic phantom tissues. After first exploring baseline accuracy and repeatability of distance measurements, we performed a number of surface scan experiments on the phantom and ex vivo tissues with a variety of surface properties and shapes. These indicate that conoscopic holography is capable of generating surface point clouds of at least comparable (and perhaps eventually improved) accuracy in comparison to published experimental laser triangulation-based surface scanning results.

  9. A novel point cloud registration using 2D image features

    NASA Astrophysics Data System (ADS)

    Lin, Chien-Chou; Tai, Yen-Chou; Lee, Jhong-Jin; Chen, Yong-Sheng

    2017-01-01

    Since a 3D scanner only captures a scene of a 3D object at a time, a 3D registration for multi-scene is the key issue of 3D modeling. This paper presents a novel and an efficient 3D registration method based on 2D local feature matching. The proposed method transforms the point clouds into 2D bearing angle images and then uses the 2D feature based matching method, SURF, to find matching pixel pairs between two images. The corresponding points of 3D point clouds can be obtained by those pixel pairs. Since the corresponding pairs are sorted by their distance between matching features, only the top half of the corresponding pairs are used to find the optimal rotation matrix by the least squares approximation. In this paper, the optimal rotation matrix is derived by orthogonal Procrustes method (SVD-based approach). Therefore, the 3D model of an object can be reconstructed by aligning those point clouds with the optimal transformation matrix. Experimental results show that the accuracy of the proposed method is close to the ICP, but the computation cost is reduced significantly. The performance is six times faster than the generalized-ICP algorithm. Furthermore, while the ICP requires high alignment similarity of two scenes, the proposed method is robust to a larger difference of viewing angle.

  10. Army Medical Imaging System - ARMIS

    DTIC Science & Technology

    1992-08-08

    The Army Medical Imaging System (ARMIS) would use optical data cards, discs and small computers to perform the required functions of image...a filmless medical imaging system based on stimulable x-ray phosphors and optical data cards. Advantages of the system would be elimination of film

  11. Active registration models

    NASA Astrophysics Data System (ADS)

    Marstal, Kasper; Klein, Stefan

    2017-02-01

    We present the Active Registration Model (ARM) that couples medical image registration with regularization using a statistical model of intensity. Inspired by Active Appearance Models (AAMs), the statistical model is embedded in the registration procedure as a regularization term that penalize differences between a target image and a synthesized model reconstruction of that image. We demonstrate that the method generalizes AAMs to 3D images, many different transformation models, and many different gradient descent optimization methods. The method is validated on magnetic resonance images of human brains.

  12. An Automatic Registration-Fusion Scheme Based on Similarity Measures: An Application to Dental Imaging

    DTIC Science & Technology

    2007-11-02

    calculation of similarity measures between two dental radiographic images to be registered. Moreover, a fusion process has been developed to combine...information from registered dental images. Result on clinical data reveals the advantageous performance of the proposed automatic registration method...registration approach outperforms despite the fuzzy dental boundaries and the lack of characteristic edges of the radiographic images. These preliminary

  13. Digital image registration method based upon binary boundary maps

    NASA Technical Reports Server (NTRS)

    Jayroe, R. R., Jr.; Andrus, J. F.; Campbell, C. W.

    1974-01-01

    A relatively fast method is presented for matching or registering the digital data of imagery from the same ground scene acquired at different times, or from different multispectral images, sensors, or both. It is assumed that the digital images can be registed by using translations and rotations only, that the images are of the same scale, and that little or no distortion exists between images. It is further assumed that by working with several local areas of the image, the rotational effects in the local areas can be neglected. Thus, by treating the misalignments of local areas as translations, it is possible to determine rotational and translational misalignments for a larger portion of the image containing the local areas. This procedure of determining the misalignment and then registering the data according to the misalignment can be repeated until the desired degree of registration is achieved. The method to be presented is based upon the use of binary boundary maps produced from the raw digital imagery rather than the raw digital data.

  14. Registration of 4D time-series of cardiac images with multichannel Diffeomorphic Demons.

    PubMed

    Peyrat, Jean-Marc; Delingette, Hervé; Sermesant, Maxime; Pennec, Xavier; Xu, Chenyang; Ayache, Nicholas

    2008-01-01

    In this paper, we propose a generic framework for intersubject non-linear registration of 4D time-series images. In this framework, spatio-temporal registration is defined by mapping trajectories of physical points as opposed to spatial registration that solely aims at mapping homologous points. First, we determine the trajectories we want to register in each sequence using a motion tracking algorithm based on the Diffeomorphic Demons algorithm. Then, we perform simultaneously pairwise registrations of corresponding time-points with the constraint to map the same physical points over time. We show this trajectory registration can be formulated as a multichannel registration of 3D images. We solve it using the Diffeomorphic Demons algorithm extended to vector-valued 3D images. This framework is applied to the inter-subject non-linear registration of 4D cardiac CT sequences.

  15. SU-E-J-29: Automatic Image Registration Performance of Three IGRT Systems for Prostate Radiotherapy

    SciTech Connect

    Barber, J; Sykes, J; Holloway, L; Thwaites, D

    2015-06-15

    Purpose: To compare the performance of an automatic image registration algorithm on image sets collected on three commercial image guidance systems, and explore its relationship with imaging parameters such as dose and sharpness. Methods: Images of a CIRS Virtually Human Male Pelvis phantom (VHMP) were collected on the CBCT systems of Varian TrueBeam/OBI and Elekta Synergy/XVI linear accelerators, across a range of mAs settings; and MVCT on a Tomotherapy Hi-ART accelerator with a range of pitch. Using the 6D correlation ratio algorithm of XVI, each image was registered to a mask of the prostate volume with a 5 mm expansion. Registrations were repeated 100 times, with random initial offsets introduced to simulate daily matching. Residual registration errors were calculated by correcting for the initial phantom set-up error. Automatic registration was also repeated after reconstructing images with different sharpness filters. Results: All three systems showed good registration performance, with residual translations <0.5mm (1σ) for typical clinical dose and reconstruction settings. Residual rotational error had larger range, with 0.8°, 1.2° and 1.9° for 1σ in XVI, OBI and Tomotherapy respectively. The registration accuracy of XVI images showed a strong dependence on imaging dose, particularly below 4mGy. No evidence of reduced performance was observed at the lowest dose settings for OBI and Tomotherapy, but these were above 4mGy. Registration failures (maximum target registration error > 3.6 mm on the surface of a 30mm sphere) occurred in 5% to 10% of registrations. Changing the sharpness of image reconstruction had no significant effect on registration performance. Conclusions: Using the present automatic image registration algorithm, all IGRT systems tested provided satisfactory registrations for clinical use, within a normal range of acquisition settings.

  16. Image Registration of High-Resolution Uav Data: the New Hypare Algorithm

    NASA Astrophysics Data System (ADS)

    Bahr, T.; Jin, X.; Lasica, R.; Giessel, D.

    2013-08-01

    Unmanned aerial vehicles play an important role in the present-day civilian and military intelligence. Equipped with a variety of sensors, such as SAR imaging modes, E/O- and IR sensor technology, they are due to their agility suitable for many applications. Hence, the necessity arises to use fusion technologies and to develop them continuously. Here an exact image-to-image registration is essential. It serves as the basis for important image processing operations such as georeferencing, change detection, and data fusion. Therefore we developed the Hybrid Powered Auto-Registration Engine (HyPARE). HyPARE combines all available spatial reference information with a number of image registration approaches to improve the accuracy, performance, and automation of tie point generation and image registration. We demonstrate this approach by the registration of 39 still images from a high-resolution image stream, acquired with a Aeryon Photo3S™ camera on an Aeryon Scout micro-UAV™.

  17. Rolled fingerprint construction using MRF-based nonrigid image registration.

    PubMed

    Kwon, Dongjin; Yun, Il Dong; Lee, Sang Uk

    2010-12-01

    This paper proposes a new rolled fingerprint construction approach incorporating a state-of-the-art nonrigid image registration method based upon a Markov random field (MRF) energy model. The proposed method finds dense correspondences between images from a rolled fingerprint sequence and warps the entire fingerprint area to synthesize a rolled fingerprint. This method can generate conceptually more accurate rolled fingerprints by preserving the geometric properties of the finger surface as opposed to ink-based rolled impressions and other existing rolled fingerprint construction methods. To verify the accuracy of the proposed method, various comparative experiments were designed to reveal differences among the rolled construction methods. The results show that the proposed method is significantly superior in various aspects compared to previous approaches.

  18. Deformable image registration for multimodal lung-cancer staging

    NASA Astrophysics Data System (ADS)

    Cheirsilp, Ronnarit; Zang, Xiaonan; Bascom, Rebecca; Allen, Thomas W.; Mahraj, Rickhesvar P. M.; Higgins, William E.

    2016-03-01

    Positron emission tomography (PET) and X-ray computed tomography (CT) serve as major diagnostic imaging modalities in the lung-cancer staging process. Modern scanners provide co-registered whole-body PET/CT studies, collected while the patient breathes freely, and high-resolution chest CT scans, collected under a brief patient breath hold. Unfortunately, no method exists for registering a PET/CT study into the space of a high-resolution chest CT scan. If this could be done, vital diagnostic information offered by the PET/CT study could be brought seamlessly into the procedure plan used during live cancer-staging bronchoscopy. We propose a method for the deformable registration of whole-body PET/CT data into the space of a high-resolution chest CT study. We then demonstrate its potential for procedure planning and subsequent use in multimodal image-guided bronchoscopy.

  19. Automatic Image Registration Using Free and Open Source Software

    NASA Astrophysics Data System (ADS)

    Giri Babu, D.; Raja Shekhar, S. S.; Chandrasekar, K.; Sesha Sai, M. V. R.; Diwakar, P. G.; Dadhwal, V. K.

    2014-11-01

    Image registration is the most critical operation in remote sensing applications to enable location based referencing and analysis of earth features. This is the first step for any process involving identification, time series analysis or change detection using a large set of imagery over a region. Most of the reliable procedures involve time consuming and laborious manual methods of finding the corresponding matching features of the input image with respect to reference. Also the process, as it involves human interaction, does not converge with multiple operations at different times. Automated procedures rely on accurately determining the matching locations or points from both the images under comparison and the procedures are robust and consistent over time. Different algorithms are available to achieve this, based on pattern recognition, feature based detection, similarity techniques etc. In the present study and implementation, Correlation based methods have been used with a improvement over newly developed technique of identifying and pruning the false points of match. Free and Open Source Software (FOSS) have been used to develop the methodology to reach a wider audience, without any dependency on COTS (Commercially off the shelf) software. Standard deviation from foci of the ellipse of correlated points, is a statistical means of ensuring the best match of the points of interest based on both intensity values and location correspondence. The methodology is developed and standardised by enhancements to meet the registration requirements of remote sensing imagery. Results have shown a performance improvement, nearly matching the visual techniques and have been implemented in remote sensing operational projects. The main advantage of the proposed methodology is its viability in production mode environment. This paper also shows that the visualization capabilities of MapWinGIS, GDAL's image handling abilities and OSSIM's correlation facility can be efficiently

  20. A novel image registration method for InISAR imaging system

    NASA Astrophysics Data System (ADS)

    Tian, Biao; Li, Na; Liu, Yang; Tang, Da; Xu, Shiyou; Chen, Zengping

    2014-10-01

    This paper proposes a new image registration method based on grade-by-grade matching in interferometric inverse synthetic aperture radar (InISAR) imaging system using two antennas. The causation and quantitative analysis of the offset between two ISAR images for different antennas along each baseline is analyzed. Strong scatterer centers (SSCs) are extracted from the ISAR images of each antenna by OTSU method firstly. A standard matching is calculated by the image centroid. Then a mapping of region of interest (ROI) and correlation is carried out to get the precise registration. Simulation results demonstrate that the offset between two ISAR images can be compensated effectively when the proposed method is used, achieving a high quality 3D InISAR image consequently.

  1. Validating Dose Uncertainty Estimates Produced by AUTODIRECT: An Automated Program to Evaluate Deformable Image Registration Accuracy.

    PubMed

    Kim, Hojin; Chen, Josephine; Phillips, Justin; Pukala, Jason; Yom, Sue S; Kirby, Neil

    2017-01-01

    Deformable image registration is a powerful tool for mapping information, such as radiation therapy dose calculations, from one computed tomography image to another. However, deformable image registration is susceptible to mapping errors. Recently, an automated deformable image registration evaluation of confidence tool was proposed to predict voxel-specific deformable image registration dose mapping errors on a patient-by-patient basis. The purpose of this work is to conduct an extensive analysis of automated deformable image registration evaluation of confidence tool to show its effectiveness in estimating dose mapping errors. The proposed format of automated deformable image registration evaluation of confidence tool utilizes 4 simulated patient deformations (3 B-spline-based deformations and 1 rigid transformation) to predict the uncertainty in a deformable image registration algorithm's performance. This workflow is validated for 2 DIR algorithms (B-spline multipass from Velocity and Plastimatch) with 1 physical and 11 virtual phantoms, which have known ground-truth deformations, and with 3 pairs of real patient lung images, which have several hundred identified landmarks. The true dose mapping error distributions closely followed the Student t distributions predicted by automated deformable image registration evaluation of confidence tool for the validation tests: on average, the automated deformable image registration evaluation of confidence tool-produced confidence levels of 50%, 68%, and 95% contained 48.8%, 66.3%, and 93.8% and 50.1%, 67.6%, and 93.8% of the actual errors from Velocity and Plastimatch, respectively. Despite the sparsity of landmark points, the observed error distribution from the 3 lung patient data sets also followed the expected error distribution. The dose error distributions from automated deformable image registration evaluation of confidence tool also demonstrate good resemblance to the true dose error distributions. Automated

  2. A Parallel Point Matching Algorithm for Landmark Based Image Registration Using Multicore Platform

    PubMed Central

    Yang, Lin; Gong, Leiguang; Zhang, Hong; Nosher, John L.; Foran, David J.

    2013-01-01

    Point matching is crucial for many computer vision applications. Establishing the correspondence between a large number of data points is a computationally intensive process. Some point matching related applications, such as medical image registration, require real time or near real time performance if applied to critical clinical applications like image assisted surgery. In this paper, we report a new multicore platform based parallel algorithm for fast point matching in the context of landmark based medical image registration. We introduced a non-regular data partition algorithm which utilizes the K-means clustering algorithm to group the landmarks based on the number of available processing cores, which optimize the memory usage and data transfer. We have tested our method using the IBM Cell Broadband Engine (Cell/B.E.) platform. The results demonstrated a significant speed up over its sequential implementation. The proposed data partition and parallelization algorithm, though tested only on one multicore platform, is generic by its design. Therefore the parallel algorithm can be extended to other computing platforms, as well as other point matching related applications. PMID:24308014

  3. A Parallel Point Matching Algorithm for Landmark Based Image Registration Using Multicore Platform

    NASA Astrophysics Data System (ADS)

    Yang, Lin; Gong, Leiguang; Zhang, Hong; Nosher, John L.; Foran, David J.

    Point matching is crucial for many computer vision applications. Establishing the correspondence between a large number of data points is a computationally intensive process. Some point matching related applications, such as medical image registration, require real time or near real time performance if applied to critical clinical applications like image assisted surgery. In this paper, we report a new multicore platform based parallel algorithm for fast point matching in the context of landmark based medical image registration. We introduced a non-regular data partition algorithm which utilizes the K-means clustering algorithm to group the landmarks based on the number of available processing cores, which optimize the memory usage and data transfer. We have tested our method using the IBM Cell Broadband Engine (Cell/B.E.) platform. The results demonstrated a significant speed up over its sequential implementation. The proposed data partition and parallelization algorithm, though tested only on one multicore platform, is generic by its design. Therefore the parallel algorithm can be extended to other computing platforms, as well as other point matching related applications.

  4. A vascular image registration method based on network structure and circuit simulation.

    PubMed

    Chen, Li; Lian, Yuxi; Guo, Yi; Wang, Yuanyuan; Hatsukami, Thomas S; Pimentel, Kristi; Balu, Niranjan; Yuan, Chun

    2017-05-02

    Image registration is an important research topic in the field of image processing. Applying image registration to vascular image allows multiple images to be strengthened and fused, which has practical value in disease detection, clinical assisted therapy, etc. However, it is hard to register vascular structures with high noise and large difference in an efficient and effective method. Different from common image registration methods based on area or features, which were sensitive to distortion and uncertainty in vascular structure, we proposed a novel registration method based on network structure and circuit simulation. Vessel images were transformed to graph networks and segmented to branches to reduce the calculation complexity. Weighted graph networks were then converted to circuits, in which node voltages of the circuit reflecting the vessel structures were used for node registration. The experiments in the two-dimensional and three-dimensional simulation and clinical image sets showed the success of our proposed method in registration. The proposed vascular image registration method based on network structure and circuit simulation is stable, fault tolerant and efficient, which is a useful complement to the current mainstream image registration methods.

  5. An automated deformable image registration evaluation of confidence tool

    NASA Astrophysics Data System (ADS)

    Kirby, Neil; Chen, Josephine; Kim, Hojin; Morin, Olivier; Nie, Ke; Pouliot, Jean

    2016-04-01

    Deformable image registration (DIR) is a powerful tool for radiation oncology, but it can produce errors. Beyond this, DIR accuracy is not a fixed quantity and varies on a case-by-case basis. The purpose of this study is to explore the possibility of an automated program to create a patient- and voxel-specific evaluation of DIR accuracy. AUTODIRECT is a software tool that was developed to perform this evaluation for the application of a clinical DIR algorithm to a set of patient images. In brief, AUTODIRECT uses algorithms to generate deformations and applies them to these images (along with processing) to generate sets of test images, with known deformations that are similar to the actual ones and with realistic noise properties. The clinical DIR algorithm is applied to these test image sets (currently 4). From these tests, AUTODIRECT generates spatial and dose uncertainty estimates for each image voxel based on a Student’s t distribution. In this study, four commercially available DIR algorithms were used to deform a dose distribution associated with a virtual pelvic phantom image set, and AUTODIRECT was used to generate dose uncertainty estimates for each deformation. The virtual phantom image set has a known ground-truth deformation, so the true dose-warping errors of the DIR algorithms were also known. AUTODIRECT predicted error patterns that closely matched the actual error spatial distribution. On average AUTODIRECT overestimated the magnitude of the dose errors, but tuning the AUTODIRECT algorithms should improve agreement. This proof-of-principle test demonstrates the potential for the AUTODIRECT algorithm as an empirical method to predict DIR errors.

  6. Diffeomorphic demons: efficient non-parametric image registration.

    PubMed

    Vercauteren, Tom; Pennec, Xavier; Perchant, Aymeric; Ayache, Nicholas

    2009-03-01

    We propose an efficient non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm. In the first part of this paper, we show that Thirion's demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. We provide strong theoretical roots to the different variants of Thirion's demons algorithm. This analysis predicts a theoretical advantage for the symmetric forces variant of the demons algorithm. We show on controlled experiments that this advantage is confirmed in practice and yields a faster convergence. In the second part of this paper, we adapt the optimization procedure underlying the demons algorithm to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of displacement fields by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the gold standard, available in controlled experiments, in terms of Jacobians.

  7. Hierarchical attribute-guided symmetric diffeomorphic registration for MR brain images.

    PubMed

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Shen, Dinggang

    2012-01-01

    Deformable registration has been widely used in neuroscience studies for spatial normalization of brain images onto a standard space. Due to high anatomical variances across individual brains, registration performance could be limited when trying to estimate entire deformation pathway either from templ