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Sample records for accurate image registration

  1. 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.

  2. Improving JWST Coronagraphic Performance with Accurate Image Registration

    NASA Astrophysics Data System (ADS)

    Van Gorkom, Kyle; Pueyo, Laurent; Lajoie, Charles-Philippe; JWST Coronagraphs Working Group

    2016-06-01

    The coronagraphs on the James Webb Space Telescope (JWST) will enable high-contrast observations of faint objects at small separations from bright hosts, such as circumstellar disks, exoplanets, and quasar disks. Despite attenuation by the coronagraphic mask, bright speckles in the host’s point spread function (PSF) remain, effectively washing out the signal from the faint companion. Suppression of these bright speckles is typically accomplished by repeating the observation with a star that lacks a faint companion, creating a reference PSF that can be subtracted from the science image to reveal any faint objects. Before this reference PSF can be subtracted, however, the science and reference images must be aligned precisely, typically to 1/20 of a pixel. Here, we present several such algorithms for performing image registration on JWST coronagraphic images. Using both simulated and pre-flight test data (taken in cryovacuum), we assess (1) the accuracy of each algorithm at recovering misaligned scenes and (2) the impact of image registration on achievable contrast. Proper image registration, combined with post-processing techniques such as KLIP or LOCI, will greatly improve the performance of the JWST coronagraphs.

  3. Accurate band-to-band registration of AOTF imaging spectrometer using motion detection technology

    NASA Astrophysics Data System (ADS)

    Zhou, Pengwei; Zhao, Huijie; Jin, Shangzhong; Li, Ningchuan

    2016-05-01

    This paper concerns the problem of platform vibration induced band-to-band misregistration with acousto-optic imaging spectrometer in spaceborne application. Registrating images of different bands formed at different time or different position is difficult, especially for hyperspectral images form acousto-optic tunable filter (AOTF) imaging spectrometer. In this study, a motion detection method is presented using the polychromatic undiffracted beam of AOTF. The factors affecting motion detect accuracy are analyzed theoretically, and calculations show that optical distortion is an easily overlooked factor to achieve accurate band-to-band registration. Hence, a reflective dual-path optical system has been proposed for the first time, with reduction of distortion and chromatic aberration, indicating the potential of higher registration accuracy. Consequently, a spectra restoration experiment using additional motion detect channel is presented for the first time, which shows the accurate spectral image registration capability of this technique.

  4. Curvelet-based sampling for accurate and efficient multimodal image registration

    NASA Astrophysics Data System (ADS)

    Safran, M. N.; Freiman, M.; Werman, M.; Joskowicz, L.

    2009-02-01

    We present a new non-uniform adaptive sampling method for the estimation of mutual information in multi-modal image registration. The method uses the Fast Discrete Curvelet Transform to identify regions along anatomical curves on which the mutual information is computed. Its main advantages of over other non-uniform sampling schemes are that it captures the most informative regions, that it is invariant to feature shapes, orientations, and sizes, that it is efficient, and that it yields accurate results. Extensive evaluation on 20 validated clinical brain CT images to Proton Density (PD) and T1 and T2-weighted MRI images from the public RIRE database show the effectiveness of our method. Rigid registration accuracy measured at 10 clinical targets and compared to ground truth measurements yield a mean target registration error of 0.68mm(std=0.4mm) for CT-PD and 0.82mm(std=0.43mm) for CT-T2. This is 0.3mm (1mm) more accurate in the average (worst) case than five existing sampling methods. Our method has the lowest registration errors recorded to date for the registration of CT-PD and CT-T2 images in the RIRE website when compared to methods that were tested on at least three patient datasets.

  5. Accurate CT-MR image registration for deep brain stimulation: a multi-observer evaluation study

    NASA Astrophysics Data System (ADS)

    Rühaak, Jan; Derksen, Alexander; Heldmann, Stefan; Hallmann, Marc; Meine, Hans

    2015-03-01

    Since the first clinical interventions in the late 1980s, Deep Brain Stimulation (DBS) of the subthalamic nucleus has evolved into a very effective treatment option for patients with severe Parkinson's disease. DBS entails the implantation of an electrode that performs high frequency stimulations to a target area deep inside the brain. A very accurate placement of the electrode is a prerequisite for positive therapy outcome. The assessment of the intervention result is of central importance in DBS treatment and involves the registration of pre- and postinterventional scans. In this paper, we present an image processing pipeline for highly accurate registration of postoperative CT to preoperative MR. Our method consists of two steps: a fully automatic pre-alignment using a detection of the skull tip in the CT based on fuzzy connectedness, and an intensity-based rigid registration. The registration uses the Normalized Gradient Fields distance measure in a multilevel Gauss-Newton optimization framework and focuses on a region around the subthalamic nucleus in the MR. The accuracy of our method was extensively evaluated on 20 DBS datasets from clinical routine and compared with manual expert registrations. For each dataset, three independent registrations were available, thus allowing to relate algorithmic with expert performance. Our method achieved an average registration error of 0.95mm in the target region around the subthalamic nucleus as compared to an inter-observer variability of 1.12 mm. Together with the short registration time of about five seconds on average, our method forms a very attractive package that can be considered ready for clinical use.

  6. Feasibility study for image guided kidney surgery: assessment of required intraoperative surface for accurate image to physical space registrations

    NASA Astrophysics Data System (ADS)

    Benincasa, Anne B.; Clements, Logan W.; Herrell, S. Duke; Chang, Sam S.; Cookson, Michael S.; Galloway, Robert L.

    2006-03-01

    Currently, the removal of kidney tumor masses uses only direct or laparoscopic visualizations, resulting in prolonged procedure and recovery times and reduced clear margin. Applying current image guided surgery (IGS) techniques, as those used in liver cases, to kidney resections (nephrectomies) presents a number of complications. Most notably is the limited field of view of the intraoperative kidney surface, which constrains the ability to obtain a surface delineation that is geometrically descriptive enough to drive a surface-based registration. Two different phantom orientations were used to model the laparoscopic and traditional partial nephrectomy views. For the laparoscopic view, fiducial point sets were compiled from a CT image volume using anatomical features such as the renal artery and vein. For the traditional view, markers attached to the phantom set-up were used for fiducials and targets. The fiducial points were used to perform a point-based registration, which then served as a guide for the surface-based registration. Laser range scanner (LRS) obtained surfaces were registered to each phantom surface using a rigid iterative closest point algorithm. Subsets of each phantom's LRS surface were used in a robustness test to determine the predictability of their registrations to transform the entire surface. Results from both orientations suggest that about half of the kidney's surface needs to be obtained intraoperatively for accurate registrations between the image surface and the LRS surface, suggesting the obtained kidney surfaces were geometrically descriptive enough to perform accurate registrations. This preliminary work paves the way for further development of kidney IGS systems.

  7. 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. PMID:24968094

  8. Accurate three-dimensional registration of magnetic resonance images for detecting local changes in cartilage thickness

    NASA Astrophysics Data System (ADS)

    Cheng, Yuanzhi; Jin, Quan; Zhao, Jie; Guo, Changyong; Bai, Jing

    2011-04-01

    The purpose of this study is to develop a three-dimensional registration method for monitoring knee joint disease from magnetic resonance (MR) image data sets. A global optimization technique was used for identifying anatomically corresponding points of knee femur surfaces (bone cartilage interfaces). In a first pre-registration step, we used the principal axes transformation to correct for different knee joint positions and orientations in the MR scanner. In a second step, we presented a global search algorithm based on Lipschitz optimization theory. This technique can simultaneously determine the translation and rotation parameters through searching a six-dimensional space of Euclidean motion metrics (translation and rotation) after calculating the point correspondences. The point correspondences were calculated by using the Hungarian algorithm. The accuracy of registration was evaluated using 20 porcine knees. There were 300 corresponding landmark points over the 20 pig knees. We evaluated the registration accuracy by measuring the root-mean-square distance (RMSD) error of corresponding landmark points between two femur surfaces (two time-points). The results show that the average RMSD was 1.22 +/- 0.10 mm (SD) by the iterative closest point (ICP) method, 1.17 +/- 0.10 mm the by expectation-maximization-ICP method, 1.02 +/- 0.06 mm by the genetic method, and 0.93 +/- 0.04 mm by the proposed method. Compared with the other three registration approaches, the proposed method achieved the highest registration accuracy.

  9. The level of detail required in a deformable phantom to accurately perform quality assurance of deformable image registration

    NASA Astrophysics Data System (ADS)

    Saenz, Daniel L.; Kim, Hojin; Chen, Josephine; Stathakis, Sotirios; Kirby, Neil

    2016-09-01

    The primary purpose of the study was to determine how detailed deformable image registration (DIR) phantoms need to adequately simulate human anatomy and accurately assess the quality of DIR algorithms. In particular, how many distinct tissues are required in a phantom to simulate complex human anatomy? Pelvis and head-and-neck patient CT images were used for this study as virtual phantoms. Two data sets from each site were analyzed. The virtual phantoms were warped to create two pairs consisting of undeformed and deformed images. Otsu’s method was employed to create additional segmented image pairs of n distinct soft tissue CT number ranges (fat, muscle, etc). A realistic noise image was added to each image. Deformations were applied in MIM Software (MIM) and Velocity deformable multi-pass (DMP) and compared with the known warping. Images with more simulated tissue levels exhibit more contrast, enabling more accurate results. Deformation error (magnitude of the vector difference between known and predicted deformation) was used as a metric to evaluate how many CT number gray levels are needed for a phantom to serve as a realistic patient proxy. Stabilization of the mean deformation error was reached by three soft tissue levels for Velocity DMP and MIM, though MIM exhibited a persisting difference in accuracy between the discrete images and the unprocessed image pair. A minimum detail of three levels allows a realistic patient proxy for use with Velocity and MIM deformation algorithms.

  10. The level of detail required in a deformable phantom to accurately perform quality assurance of deformable image registration.

    PubMed

    Saenz, Daniel L; Kim, Hojin; Chen, Josephine; Stathakis, Sotirios; Kirby, Neil

    2016-09-01

    The primary purpose of the study was to determine how detailed deformable image registration (DIR) phantoms need to adequately simulate human anatomy and accurately assess the quality of DIR algorithms. In particular, how many distinct tissues are required in a phantom to simulate complex human anatomy? Pelvis and head-and-neck patient CT images were used for this study as virtual phantoms. Two data sets from each site were analyzed. The virtual phantoms were warped to create two pairs consisting of undeformed and deformed images. Otsu's method was employed to create additional segmented image pairs of n distinct soft tissue CT number ranges (fat, muscle, etc). A realistic noise image was added to each image. Deformations were applied in MIM Software (MIM) and Velocity deformable multi-pass (DMP) and compared with the known warping. Images with more simulated tissue levels exhibit more contrast, enabling more accurate results. Deformation error (magnitude of the vector difference between known and predicted deformation) was used as a metric to evaluate how many CT number gray levels are needed for a phantom to serve as a realistic patient proxy. Stabilization of the mean deformation error was reached by three soft tissue levels for Velocity DMP and MIM, though MIM exhibited a persisting difference in accuracy between the discrete images and the unprocessed image pair. A minimum detail of three levels allows a realistic patient proxy for use with Velocity and MIM deformation algorithms. PMID:27494827

  11. Accurate quantification of local changes for carotid arteries in 3D ultrasound images using convex optimization-based deformable registration

    NASA Astrophysics Data System (ADS)

    Cheng, Jieyu; Qiu, Wu; Yuan, Jing; Fenster, Aaron; Chiu, Bernard

    2016-03-01

    Registration of longitudinally acquired 3D ultrasound (US) images plays an important role in monitoring and quantifying progression/regression of carotid atherosclerosis. We introduce an image-based non-rigid registration algorithm to align the baseline 3D carotid US with longitudinal images acquired over several follow-up time points. This algorithm minimizes the sum of absolute intensity differences (SAD) under a variational optical-flow perspective within a multi-scale optimization framework to capture local and global deformations. Outer wall and lumen were segmented manually on each image, and the performance of the registration algorithm was quantified by Dice similarity coefficient (DSC) and mean absolute distance (MAD) of the outer wall and lumen surfaces after registration. In this study, images for 5 subjects were registered initially by rigid registration, followed by the proposed algorithm. Mean DSC generated by the proposed algorithm was 79:3+/-3:8% for lumen and 85:9+/-4:0% for outer wall, compared to 73:9+/-3:4% and 84:7+/-3:2% generated by rigid registration. Mean MAD of 0:46+/-0:08mm and 0:52+/-0:13mm were generated for lumen and outer wall respectively by the proposed algorithm, compared to 0:55+/-0:08mm and 0:54+/-0:11mm generated by rigid registration. The mean registration time of our method per image pair was 143+/-23s.

  12. SU-E-J-08: A Hybrid Three Dimensional Registration Framework for Image-Guided Accurate Radiotherapy System ARTS-IGRT

    SciTech Connect

    Wu, Q; Pei, X; Cao, R; Hu, L; Wu, Y

    2014-06-01

    Purpose: The purpose of this work was to develop a registration framework and method based on the software platform of ARTS-IGRT and implement in C++ based on ITK libraries to register CT images and CBCT images. ARTS-IGRT was a part of our self-developed accurate radiation planning system ARTS. Methods: Mutual information (MI) registration treated each voxel equally. Actually, different voxels even having same intensity should be treated differently in the registration procedure. According to their importance values calculated from self-information, a similarity measure was proposed which combined the spatial importance of a voxel with MI (S-MI). For lung registration, Firstly, a global alignment method was adopted to minimize the margin error and achieve the alignment of these two images on the whole. The result obtained at the low resolution level was then interpolated to become the initial conditions for the higher resolution computation. Secondly, a new similarity measurement S-MI was established to quantify how close the two input image volumes were to each other. Finally, Demons model was applied to compute the deformable map. Results: Registration tools were tested for head-neck and lung images and the average region was 128*128*49. The rigid registration took approximately 2 min and converged 10% faster than traditional MI algorithm, the accuracy reached 1mm for head-neck images. For lung images, the improved symmetric Demons registration process was completed in an average of 5 min using a 2.4GHz dual core CPU. Conclusion: A registration framework was developed to correct patient's setup according to register the planning CT volume data and the daily reconstructed 3D CBCT data. The experiments showed that the spatial MI algorithm can be adopted for head-neck images. The improved Demons deformable registration was more suitable to lung images, and rigid alignment should be applied before deformable registration to get more accurate result. Supported by

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. Highly accurate fast lung CT registration

    NASA Astrophysics Data System (ADS)

    Rühaak, Jan; Heldmann, Stefan; Kipshagen, Till; Fischer, Bernd

    2013-03-01

    Lung registration in thoracic CT scans has received much attention in the medical imaging community. Possible applications range from follow-up analysis, motion correction for radiation therapy, monitoring of air flow and pulmonary function to lung elasticity analysis. In a clinical environment, runtime is always a critical issue, ruling out quite a few excellent registration approaches. In this paper, a highly efficient variational lung registration method based on minimizing the normalized gradient fields distance measure with curvature regularization is presented. The method ensures diffeomorphic deformations by an additional volume regularization. Supplemental user knowledge, like a segmentation of the lungs, may be incorporated as well. The accuracy of our method was evaluated on 40 test cases from clinical routine. In the EMPIRE10 lung registration challenge, our scheme ranks third, with respect to various validation criteria, out of 28 algorithms with an average landmark distance of 0.72 mm. The average runtime is about 1:50 min on a standard PC, making it by far the fastest approach of the top-ranking algorithms. Additionally, the ten publicly available DIR-Lab inhale-exhale scan pairs were registered to subvoxel accuracy at computation times of only 20 seconds. Our method thus combines very attractive runtimes with state-of-the-art accuracy in a unique way.

  18. Highly Accurate Inverse Consistent Registration: A Robust Approach

    PubMed Central

    Reuter, Martin; Rosas, H. Diana; Fischl, Bruce

    2010-01-01

    The registration of images is a task that is at the core of many applications in computer vision. In computational neuroimaging where the automated segmentation of brain structures is frequently used to quantify change, a highly accurate registration is necessary for motion correction of images taken in the same session, or across time in longitudinal studies where changes in the images can be expected. This paper, inspired by Nestares and Heeger (2000), presents a method based on robust statistics to register images in the presence of differences, such as jaw movement, differential MR distortions and true anatomical change. The approach we present guarantees inverse consistency (symmetry), can deal with different intensity scales and automatically estimates a sensitivity parameter to detect outlier regions in the images. The resulting registrations are highly accurate due to their ability to ignore outlier regions and show superior robustness with respect to noise, to intensity scaling and outliers when compared to state-of-the-art registration tools such as FLIRT (in FSL) or the coregistration tool in SPM. PMID:20637289

  19. Automatic digital image registration

    NASA Technical Reports Server (NTRS)

    Goshtasby, A.; Jain, A. K.; Enslin, W. R.

    1982-01-01

    This paper introduces a general procedure for automatic registration of two images which may have translational, rotational, and scaling differences. This procedure involves (1) segmentation of the images, (2) isolation of dominant objects from the images, (3) determination of corresponding objects in the two images, and (4) estimation of transformation parameters using the center of gravities of objects as control points. An example is given which uses this technique to register two images which have translational, rotational, and scaling differences.

  20. 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).

  1. Image registration with uncertainty analysis

    DOEpatents

    Simonson, Katherine M.

    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.

  2. How accurate are Scottish cancer registration data?

    PubMed Central

    Brewster, D.; Crichton, J.; Muir, C.

    1994-01-01

    In order to assess the accuracy of Scottish cancer registration data, a random sample of 2,200 registrations, attributed to the year 1990, was generated. Relevant medical records were available for review in 2,021 (92%) cases. Registration details were reabstracted from available records and compared with data in the registry. Discrepancies in identifying items of data (surname, forename, sex and date of birth) were found in 3.5% of cases. Most were trivial and would not disturb record linkage. Discrepancy rates of 7.1% in post code of residence at the time of diagnosis (excluding differences arising through boundary changes), 11.0% in anniversary date (excluding differences of 6 weeks or less), 7.7% in histological verification status, 5.4% in ICD-9 site codes (the first three digits) and 14.5% in ICD-O morphology codes (excluding 'inferred' morphology codes) were recorded. Overall, serious discrepancies were judged to have occurred in 2.8% of cases. In many respects, therefore, Scottish cancer registration data show a high level of accuracy that compares favourably to the reported accuracy of the few other cancer registries undertaking such analyses. PMID:7947104

  3. 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. PMID:26292357

  4. Image registration using redundant wavelet transforms

    NASA Astrophysics Data System (ADS)

    Brown, Richard K.; Claypoole, Roger L., Jr.

    2001-12-01

    Imagery is collected much faster and in significantly greater quantities today compared to a few years ago. Accurate registration of this imagery is vital for comparing the similarities and differences between multiple images. Image registration is a significant component in computer vision and other pattern recognition problems, medical applications such as Medical Resonance Images (MRI) and Positron Emission Tomography (PET), remotely sensed data for target location and identification, and super-resolution algorithms. Since human analysis is tedious and error prone for large data sets, we require an automatic, efficient, robust, and accurate method to register images. Wavelet transforms have proven useful for a variety of signal and image processing tasks. In our research, we present a fundamentally new wavelet-based registration algorithm utilizing redundant transforms and a masking process to suppress the adverse effects of noise and improve processing efficiency. The shift-invariant wavelet transform is applied in translation estimation and a new rotation-invariant polar wavelet transform is effectively utilized in rotation estimation. We demonstrate the robustness of these redundant wavelet transforms for the registration of two images (i.e., translating or rotating an input image to a reference image), but extensions to larger data sets are feasible. We compare the registration accuracy of our redundant wavelet transforms to the critically sampled discrete wavelet transform using the Daubechies wavelet to illustrate the power of our algorithm in the presence of significant additive white Gaussian noise and strongly translated or rotated images.

  5. Local image registration a comparison for bilateral registration mammography

    NASA Astrophysics Data System (ADS)

    Celaya-Padilaa, José M.; Rodriguez-Rojas, Juan; Trevino, Victor; Tamez-Pena, José G.

    2013-11-01

    Early tumor detection is key in reducing the number of breast cancer death and screening mammography is one of the most widely available and reliable method for early detection. However, it is difficult for the radiologist to process with the same attention each case, due the large amount of images to be read. Computer aided detection (CADe) systems improve tumor detection rate; but the current efficiency of these systems is not yet adequate and the correct interpretation of CADe outputs requires expert human intervention. Computer aided diagnosis systems (CADx) are being designed to improve cancer diagnosis accuracy, but they have not been efficiently applied in breast cancer. CADx efficiency can be enhanced by considering the natural mirror symmetry between the right and left breast. The objective of this work is to evaluate co-registration algorithms for the accurate alignment of the left to right breast for CADx enhancement. A set of mammograms were artificially altered to create a ground truth set to evaluate the registration efficiency of DEMONs , and SPLINE deformable registration algorithms. The registration accuracy was evaluated using mean square errors, mutual information and correlation. The results on the 132 images proved that the SPLINE deformable registration over-perform the DEMONS on mammography images.

  6. A Robust and Accurate Two-Step Auto-Labeling Conditional Iterative Closest Points (TACICP) Algorithm for Three-Dimensional Multi-Modal Carotid Image Registration

    PubMed Central

    Guo, Hengkai; Wang, Guijin; Huang, Lingyun; Hu, Yuxin; Yuan, Chun; Li, Rui; Zhao, Xihai

    2016-01-01

    Atherosclerosis is among the leading causes of death and disability. Combining information from multi-modal vascular images is an effective and efficient way to diagnose and monitor atherosclerosis, in which image registration is a key technique. In this paper a feature-based registration algorithm, Two-step Auto-labeling Conditional Iterative Closed Points (TACICP) algorithm, is proposed to align three-dimensional carotid image datasets from ultrasound (US) and magnetic resonance (MR). Based on 2D segmented contours, a coarse-to-fine strategy is employed with two steps: rigid initialization step and non-rigid refinement step. Conditional Iterative Closest Points (CICP) algorithm is given in rigid initialization step to obtain the robust rigid transformation and label configurations. Then the labels and CICP algorithm with non-rigid thin-plate-spline (TPS) transformation model is introduced to solve non-rigid carotid deformation between different body positions. The results demonstrate that proposed TACICP algorithm has achieved an average registration error of less than 0.2mm with no failure case, which is superior to the state-of-the-art feature-based methods. PMID:26881433

  7. 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. PMID:26585712

  8. Advances in image registration and fusion

    NASA Astrophysics Data System (ADS)

    Steer, Christopher; Rogers, Jeremy; Smith, Moira; Heather, Jamie; Bernhardt, Mark; Hickman, Duncan

    2008-03-01

    Many image fusion systems involving passive sensors require the accurate registration of the sensor data prior to performing fusion. Since depth information is not readily available in such systems, all registration algorithms are intrinsically approximations based upon various assumption about the depth field. Although often overlooked, many registration algorithms can break down in certain situations and this may adversely affect the image fusion performance. In this paper, we discuss a framework for quantifying the accuracy and robustness of image registration algorithms which allows a more precise understanding of their shortcomings. In addition, some novel algorithms have been investigated that overcome some of these limitations. A second aspect of this work has considered the treatment of images from multiple sensors whose angular and spatial separation is large and where conventional registration algorithms break down (typically greater than a few degrees of separation). A range of novel approaches is reported which exploit the use of parallax to estimate depth information and reconstruct a geometrical model of the scene. The imagery can then be combined with this geometrical model to render a variety of useful representations of the data. These techniques (which we term Volume Registration) show great promise as a means of gathering and presenting 3D and 4D scene information for both military and civilian applications.

  9. Adaptive deformable image registration of inhomogeneous tissues

    NASA Astrophysics Data System (ADS)

    Ren, Jing

    2015-03-01

    Physics based deformable registration can provide physically consistent image match of deformable soft tissues. In order to help radiologist/surgeons to determine the status of malicious tumors, we often need to accurately align the regions with embedded tumors. This is a very challenging task since the tumor and the surrounding tissues have very different tissue properties such as stiffness and elasticity. In order to address this problem, based on minimum strain energy principle in elasticity theory, we propose to partition the whole region of interest into smaller sub-regions and dynamically adjust weights of vessel segments and bifurcation points in each sub-region in the registration objective function. Our previously proposed fast vessel registration is used as a component in the inner loop. We have validated the proposed method using liver MR images from human subjects. The results show that our method can detect the large registration errors and improve the registration accuracy in the neighborhood of the tumors and guarantee the registration errors to be within acceptable accuracy. The proposed technique has the potential to significantly improve the registration capability and the quality of clinical diagnosis and treatment planning.

  10. The Role of Registration in Accurate Surgical Guidance

    PubMed Central

    Fitzpatrick, J. Michael

    2016-01-01

    Registration is presented as the central issue of surgical guidance. The focus is on the accuracy of approaches employed today, all of which use pre-operative images to guide surgery on rigid anatomy. The three most well established approaches to guidance—the stereotactic frame, point fiducials, and surface matching—are examined in detail, along with two new approaches based on microstereotactic frames. It is shown that each method relies on the registration of points in the image to corresponding points in the operating room, and therefore that the error patterns associated with point registration are similar for all of them. Three types of registration error—fiducial localization error (FLE), fiducial registration error (FRE), and target registration error (TRE) are highlighted, as well as two additional guidance errors—target localization error, and total targeting error, the latter of which is the overall error of the guidance system. Statistical relationships between TRE and FLE, between FRE and FLE, and among TRE, TLE and TTE are given. Finally some myths concerning fiducial registration are highlighted. PMID:20718266

  11. The role of registration in accurate surgical guidance.

    PubMed

    Fitzpatrick, J M

    2010-01-01

    Registration is presented as the central issue of surgical guidance. The focus is on the accuracy of approaches employed today, all of which use pre-operative images to guide surgery on rigid anatomy. The three most well-established approaches to guidance, namely the stereotactic frame, point fiducials, and surface matching, are examined in detail, together with two new approaches based on microstereotactic frames. It is shown that each method relies on the registration of points in the image to corresponding points in the operating room, and therefore that the error patterns associated with point registration are similar for all of them. Three types of registration error, namely fiducial localization error (FLE), fiducial registration error (FRE), and target registration error (TRE), are highlighted, as well as two additional guidance errors, namely target localization error and total targeting error, the latter of which is the overall error of the guidance system. Statistical relationships between TRE and FLE, between FRE and FLE, and between TRE, TLE, and TTE are given. Finally some myths concerning fiducial registration are highlighted. PMID:20718266

  12. 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.

  13. 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.

  14. Robust image registration of biological microscopic images.

    PubMed

    Wang, Ching-Wei; Ka, Shuk-Man; Chen, Ann

    2014-01-01

    Image registration of biological data is challenging as complex deformation problems are common. Possible deformation effects can be caused in individual data preparation processes, involving morphological deformations, stain variations, stain artifacts, rotation, translation, and missing tissues. The combining deformation effects tend to make existing automatic registration methods perform poor. In our experiments on serial histopathological images, the six state of the art image registration techniques, including TrakEM2, SURF + affine transformation, UnwarpJ, bUnwarpJ, CLAHE + bUnwarpJ and BrainAligner, achieve no greater than 70% averaged accuracies, while the proposed method achieves 91.49% averaged accuracy. The proposed method has also been demonstrated to be significantly better in alignment of laser scanning microscope brain images and serial ssTEM images than the benchmark automatic approaches (p < 0.001). The contribution of this study is to introduce a fully automatic, robust and fast image registration method for 2D image registration. PMID:25116443

  15. 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

  16. 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.

  17. SU-E-J-100: The Combination of Deformable Image Registration and Regions-Of-Interest Mapping Technique to Accomplish Accurate Dose Calculation On Cone Beam Computed Tomography for Esophageal Cancer

    SciTech Connect

    Huang, B-T; Lu, J-Y

    2015-06-15

    Purpose: We introduce a new method combined with the deformable image registration (DIR) and regions-of-interest mapping (ROIM) technique to accurately calculate dose on daily CBCT for esophageal cancer. Methods: Patients suffered from esophageal cancer were enrolled in the study. Prescription was set to 66 Gy/30 F and 54 Gy/30 F to the primary tumor (PTV66) and subclinical disease (PTV54) . Planning CT (pCT) were segmented into 8 substructures in terms of their differences in physical density, such as gross target volume (GTV), venae cava superior (SVC), aorta, heart, spinal cord, lung, muscle and bones. The pCT and its substructures were transferred to the MIM software to readout their mean HU values. Afterwards, a deformable planning CT to daily KV-CBCT image registration method was then utilized to acquire a new structure set on CBCT. The newly generated structures on CBCT were then transferred back to the treatment planning system (TPS) and its HU information were overridden manually with mean HU values obtained from pCT. Finally, the treatment plan was projected onto the CBCT images with the same beam arrangements and monitor units (MUs) to accomplish dose calculation. Planning target volume (PTV) and organs at risk (OARs) from both of the pCT and CBCT were compared to evaluate the dose calculation accuracy. Results: It was found that the dose distribution in the CBCT showed little differences compared to the pCT, regardless of whether PTV or OARs were concerned. Specifically, dose variation in GTV, PTV54, PTV66, SVC, lung and heart were within 0.1%. The maximum dose variation was presented in the spinal cord, which was up to 2.7% dose difference. Conclusion: The proposed method combined with DIR and ROIM technique to accurately calculate dose distribution on CBCT for esophageal cancer is feasible.

  18. 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.

  19. Evaluating Similarity Measures for Brain Image Registration

    PubMed Central

    Razlighi, Q. R.; Kehtarnavaz, N.; Yousefi, S.

    2013-01-01

    Evaluation of similarity measures for image registration is a challenging problem due to its complex interaction with the underlying optimization, regularization, image type and modality. We propose a single performance metric, named robustness, as part of a new evaluation method which quantifies the effectiveness of similarity measures for brain image registration while eliminating the effects of the other parts of the registration process. We show empirically that similarity measures with higher robustness are more effective in registering degraded images and are also more successful in performing intermodal image registration. Further, we introduce a new similarity measure, called normalized spatial mutual information, for 3D brain image registration whose robustness is shown to be much higher than the existing ones. Consequently, it tolerates greater image degradation and provides more consistent outcomes for intermodal brain image registration. PMID:24039378

  20. Interactive multigrid refinement for deformable image registration.

    PubMed

    Zhou, Wu; Xie, Yaoqin

    2013-01-01

    Deformable image registration is the spatial mapping of corresponding locations between images and can be used for important applications in radiotherapy. Although numerous methods have attempted to register deformable medical images automatically, such as salient-feature-based registration (SFBR), free-form deformation (FFD), and demons, no automatic method for registration is perfect, and no generic automatic algorithm has shown to work properly for clinical applications due to the fact that the deformation field is often complex and cannot be estimated well by current automatic deformable registration methods. This paper focuses on how to revise registration results interactively for deformable image registration. We can manually revise the transformed image locally in a hierarchical multigrid manner to make the transformed image register well with the reference image. The proposed method is based on multilevel B-spline to interactively revise the deformable transformation in the overlapping region between the reference image and the transformed image. The resulting deformation controls the shape of the transformed image and produces a nice registration or improves the registration results of other registration methods. Experimental results in clinical medical images for adaptive radiotherapy demonstrated the effectiveness of the proposed method. PMID:24232828

  1. 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.

  2. Semiautomated Multimodal Breast Image Registration

    PubMed Central

    Curtis, Charlotte; Frayne, Richard; Fear, Elise

    2012-01-01

    Consideration of information from multiple modalities has been shown to have increased diagnostic power in breast imaging. As a result, new techniques such as microwave imaging continue to be developed. Interpreting these novel image modalities is a challenge, requiring comparison to established techniques such as the gold standard X-ray mammography. However, due to the highly deformable nature of breast tissues, comparison of 3D and 2D modalities is a challenge. To enable this comparison, a registration technique was developed to map features from 2D mammograms to locations in the 3D image space. This technique was developed and tested using magnetic resonance (MR) images as a reference 3D modality, as MR breast imaging is an established technique in clinical practice. The algorithm was validated using a numerical phantom then successfully tested on twenty-four image pairs. Dice's coefficient was used to measure the external goodness of fit, resulting in an excellent overall average of 0.94. Internal agreement was evaluated by examining internal features in consultation with a radiologist, and subjective assessment concludes that reasonable alignment was achieved. PMID:22481910

  3. 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.

  4. 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.

  5. Video Image Stabilization and Registration

    NASA Astrophysics Data System (ADS)

    Hathaway, David H.; Meyer, Paul J.

    2002-10-01

    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.

  6. 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.

  7. 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.

  8. Piecewise nonlinear image registration using DCT basis functions

    NASA Astrophysics Data System (ADS)

    Gan, Lin; Agam, Gady

    2015-03-01

    The deformation field in nonlinear image registration is usually modeled by a global model. Such models are often faced with the problem that a locally complex deformation cannot be accurately modeled by simply increasing degrees of freedom (DOF). In addition, highly complex models require additional regularization which is usually ineffective when applied globally. Registering locally corresponding regions addresses this problem in a divide and conquer strategy. In this paper we propose a piecewise image registration approach using Discrete Cosine Transform (DCT) basis functions for a nonlinear model. The contributions of this paper are three-folds. First, we develop a multi-level piecewise registration framework that extends the concept of piecewise linear registration and works with any nonlinear deformation model. This framework is then applied to nonlinear DCT registration. Second, we show how adaptive model complexity and regularization could be applied for local piece registration, thus accounting for higher variability. Third, we show how the proposed piecewise DCT can overcome the fundamental problem of a large curvature matrix inversion in global DCT when using high degrees of freedoms. The proposed approach can be viewed as an extension of global DCT registration where the overall model complexity is increased while achieving effective local regularization. Experimental evaluation results provide comparison of the proposed approach to piecewise linear registration using an affine transformation model and a global nonlinear registration using DCT model. Preliminary results show that the proposed approach achieves improved performance.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Accurate tracking of tumor volume change during radiotherapy by CT-CBCT registration with intensity correction

    NASA Astrophysics Data System (ADS)

    Park, Seyoun; Robinson, Adam; Quon, Harry; Kiess, Ana P.; Shen, Colette; Wong, John; Plishker, William; Shekhar, Raj; Lee, Junghoon

    2016-03-01

    In this paper, we propose a CT-CBCT registration method to accurately predict the tumor volume change based on daily cone-beam CTs (CBCTs) during radiotherapy. CBCT is commonly used to reduce patient setup error during radiotherapy, but its poor image quality impedes accurate monitoring of anatomical changes. Although physician's contours drawn on the planning CT can be automatically propagated to daily CBCTs by deformable image registration (DIR), artifacts in CBCT often cause undesirable errors. To improve the accuracy of the registration-based segmentation, we developed a DIR method that iteratively corrects CBCT intensities by local histogram matching. Three popular DIR algorithms (B-spline, demons, and optical flow) with the intensity correction were implemented on a graphics processing unit for efficient computation. We evaluated their performances on six head and neck (HN) cancer cases. For each case, four trained scientists manually contoured the nodal gross tumor volume (GTV) on the planning CT and every other fraction CBCTs to which the propagated GTV contours by DIR were compared. The performance was also compared with commercial image registration software based on conventional mutual information (MI), VelocityAI (Varian Medical Systems Inc.). The volume differences (mean±std in cc) between the average of the manual segmentations and automatic segmentations are 3.70+/-2.30 (B-spline), 1.25+/-1.78 (demons), 0.93+/-1.14 (optical flow), and 4.39+/-3.86 (VelocityAI). The proposed method significantly reduced the estimation error by 9% (B-spline), 38% (demons), and 51% (optical flow) over the results using VelocityAI. Although demonstrated only on HN nodal GTVs, the results imply that the proposed method can produce improved segmentation of other critical structures over conventional methods.

  15. 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.

  16. 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

  17. A contour-based approach to multisensor image registration.

    PubMed

    Li, H; Manjunath, B S; Mitra, S K

    1995-01-01

    Image registration is concerned with the establishment of correspondence between images of the same scene. One challenging problem in this area is the registration of multispectral/multisensor images. In general, such images have different gray level characteristics, and simple techniques such as those based on area correlations cannot be applied directly. On the other hand, contours representing region boundaries are preserved in most cases. The authors present two contour-based methods which use region boundaries and other strong edges as matching primitives. The first contour matching algorithm is based on the chain-code correlation and other shape similarity criteria such as invariant moments. Closed contours and the salient segments along the open contours are matched separately. This method works well for image pairs in which the contour information is well preserved, such as the optical images from Landsat and Spot satellites. For the registration of the optical images with synthetic aperture radar (SAR) images, the authors propose an elastic contour matching scheme based on the active contour model. Using the contours from the optical image as the initial condition, accurate contour locations in the SAR image are obtained by applying the active contour model. Both contour matching methods are automatic and computationally quite efficient. Experimental results with various kinds of image data have verified the robustness of the algorithms, which have outperformed manual registration in terms of root mean square error at the control points. PMID:18289982

  18. 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

  19. 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.

  20. Automated Image Registration Using Geometrically Invariant Parameter Space Clustering (GIPSC)

    SciTech Connect

    Seedahmed, Gamal H.; Martucci, Louis M.

    2002-09-01

    Accurate, robust, and automatic image registration is a critical task in many typical applications, which employ multi-sensor and/or multi-date imagery information. In this paper we present a new approach to automatic image registration, which obviates the need for feature matching and solves for the registration parameters in a Hough-like approach. The basic idea underpinning, GIPSC methodology is to pair each data element belonging to two overlapping images, with all other data in each image, through a mathematical transformation. The results of pairing are encoded and exploited in histogram-like arrays as clusters of votes. Geometrically invariant features are adopted in this approach to reduce the computational complexity generated by the high dimensionality of the mathematical transformation. In this way, the problem of image registration is characterized, not by spatial or radiometric properties, but by the mathematical transformation that describes the geometrical relationship between the two images or more. While this approach does not require feature matching, it does permit recovery of matched features (e.g., points) as a useful by-product. The developed methodology incorporates uncertainty modeling using a least squares solution. Successful and promising experimental results of multi-date automatic image registration are reported in this paper.

  1. DR-TAMAS: Diffeomorphic Registration for Tensor Accurate Alignment of Anatomical Structures.

    PubMed

    Irfanoglu, M Okan; Nayak, Amritha; Jenkins, Jeffrey; Hutchinson, Elizabeth B; Sadeghi, Neda; Thomas, Cibu P; Pierpaoli, Carlo

    2016-05-15

    In this work, we propose DR-TAMAS (Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures), a novel framework for intersubject registration of Diffusion Tensor Imaging (DTI) data sets. This framework is optimized for brain data and its main goal is to achieve an accurate alignment of all brain structures, including white matter (WM), gray matter (GM), and spaces containing cerebrospinal fluid (CSF). Currently most DTI-based spatial normalization algorithms emphasize alignment of anisotropic structures. While some diffusion-derived metrics, such as diffusion anisotropy and tensor eigenvector orientation, are highly informative for proper alignment of WM, other tensor metrics such as the trace or mean diffusivity (MD) are fundamental for a proper alignment of GM and CSF boundaries. Moreover, it is desirable to include information from structural MRI data, e.g., T1-weighted or T2-weighted images, which are usually available together with the diffusion data. The fundamental property of DR-TAMAS is to achieve global anatomical accuracy by incorporating in its cost function the most informative metrics locally. Another important feature of DR-TAMAS is a symmetric time-varying velocity-based transformation model, which enables it to account for potentially large anatomical variability in healthy subjects and patients. The performance of DR-TAMAS is evaluated with several data sets and compared with other widely-used diffeomorphic image registration techniques employing both full tensor information and/or DTI-derived scalar maps. Our results show that the proposed method has excellent overall performance in the entire brain, while being equivalent to the best existing methods in WM. PMID:26931817

  2. 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.

  3. Image registration of MR and CT images using a frameless fiducial marker system.

    PubMed

    Kremser, C; Plangger, C; Bösecke, R; Pallua, A; Aichner, F; Felber, S R

    1997-01-01

    A new system of fiducial stereotactic markers that can easily be adapted to various imaging modalities without losing image registration was developed and tested. Utilizing MR and CT imaging the accuracy of the new system was evaluated with phantom studies and preliminary patient studies. The markers are clearly visible without artifacts on both imaging modalities. The clear delineation of the marker dots on the images enables an accurate automated marker detection. Using the marker system, image registration was found to yield an accuracy of up to 1 mm, depending on the imaging modality and the employed marker arrangement. The presented marker system shall improve patient comfort in comparison to conventional fixed stereotactic frames if repeated, highly accurate registrations are necessary over longer periods. PMID:9254002

  4. 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

  5. Multimodal image fusion with SIMS: Preprocessing with image registration.

    PubMed

    Tarolli, Jay Gage; Bloom, Anna; Winograd, Nicholas

    2016-06-01

    In order to utilize complementary imaging techniques to supply higher resolution data for fusion with secondary ion mass spectrometry (SIMS) chemical images, there are a number of aspects that, if not given proper consideration, could produce results which are easy to misinterpret. One of the most critical aspects is that the two input images must be of the same exact analysis area. With the desire to explore new higher resolution data sources that exists outside of the mass spectrometer, this requirement becomes even more important. To ensure that two input images are of the same region, an implementation of the insight segmentation and registration toolkit (ITK) was developed to act as a preprocessing step before performing image fusion. This implementation of ITK allows for several degrees of movement between two input images to be accounted for, including translation, rotation, and scale transforms. First, the implementation was confirmed to accurately register two multimodal images by supplying a known transform. Once validated, two model systems, a copper mesh grid and a group of RAW 264.7 cells, were used to demonstrate the use of the ITK implementation to register a SIMS image with a microscopy image for the purpose of performing image fusion. PMID:26772745

  6. 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. PMID:21442490

  7. Multimodality medical image fusion: probabilistic quantification, segmentation, and registration

    NASA Astrophysics Data System (ADS)

    Wang, Yue J.; Freedman, Matthew T.; Xuan, Jian Hua; Zheng, Qinfen; Mun, Seong K.

    1998-06-01

    Multimodality medical image fusion is becoming increasingly important in clinical applications, which involves information processing, registration and visualization of interventional and/or diagnostic images obtained from different modalities. This work is to develop a multimodality medical image fusion technique through probabilistic quantification, segmentation, and registration, based on statistical data mapping, multiple feature correlation, and probabilistic mean ergodic theorems. The goal of image fusion is to geometrically align two or more image areas/volumes so that pixels/voxels representing the same underlying anatomical structure can be superimposed meaningfully. Three steps are involved. To accurately extract the regions of interest, we developed the model supported Bayesian relaxation labeling, and edge detection and region growing integrated algorithms to segment the images into objects. After identifying the shift-invariant features (i.e., edge and region information), we provided an accurate and robust registration technique which is based on matching multiple binary feature images through a site model based image re-projection. The image was initially segmented into specified number of regions. A rough contour can be obtained by delineating and merging some of the segmented regions. We applied region growing and morphological filtering to extract the contour and get rid of some disconnected residual pixels after segmentation. The matching algorithm is implemented as follows: (1) the centroids of PET/CT and MR images are computed and then translated to the center of both images. (2) preliminary registration is performed first to determine an initial range of scaling factors and rotations, and the MR image is then resampled according to the specified parameters. (3) the total binary difference of the corresponding binary maps in both images is calculated for the selected registration parameters, and the final registration is achieved when the

  8. 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.

  9. 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. PMID:9719851

  10. A multicore based parallel image registration method.

    PubMed

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

    2009-01-01

    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

  11. Nonrigid image registration using an entropic similarity.

    PubMed

    Khader, Mohammed; Ben Hamza, A

    2011-09-01

    In this paper, we propose a nonrigid image registration technique by optimizing a generalized information-theoretic similarity measure using the quasi-Newton method as an optimization scheme and cubic B-splines for modeling the nonrigid deformation field between the fixed and moving 3-D image pairs. To achieve a compromise between the nonrigid registration accuracy and the associated computational cost, we implement a three-level hierarchical multiresolution approach such that the image resolution is increased in a coarse to fine fashion. Experimental results are provided to demonstrate the registration accuracy of our approach. The feasibility of the proposed method is demonstrated on a 3-D magnetic resonance data volume and also on clinically acquired 4-D CT image datasets. PMID:21690017

  12. Proximity corrected accurate in-die registration metrology

    NASA Astrophysics Data System (ADS)

    Daneshpanah, M.; Laske, F.; Wagner, M.; Roeth, K.-D.; Czerkas, S.; Yamaguchi, H.; Fujii, N.; Yoshikawa, S.; Kanno, K.; Takamizawa, H.

    2014-07-01

    193nm immersion lithography is the mainstream production technology for the 20nm and 14nm logic nodes. Multi-patterning of an increasing number of critical layers puts extreme pressure on wafer intra-field overlay, to which mask registration error is a major contributor [1]. The International Technology Roadmap for Semiconductors (ITRS [2]) requests a registration error below 4 nm for each mask of a multi-patterning set forming one layer on the wafer. For mask metrology at the 20nm and 14nm logic nodes, maintaining a precision-to-tolerance (P/T) ratio below 0.25 will be very challenging. Full characterization of mask registration errors in the active area of the die will become mandatory. It is well-known that differences in pattern density and asymmetries in the immediate neighborhood of a feature give rise to apparent shifts in position when measured by optical metrology systems, so-called optical proximity effects. These effects can easily be similar in magnitude to real mask placement errors, and uncorrected can result in mis-qualification of the mask. Metrology results from KLA-Tencor's next generation mask metrology system are reported, applying a model-based algorithm [3] which includes corrections for proximity errors. The proximity corrected, model-based measurements are compared to standard measurements and a methodology presented that verifies the correction performance of the new algorithm.

  13. 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

  14. 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. PMID:26552069

  15. An accurate 3D shape context based non-rigid registration method for mouse whole-body skeleton registration

    NASA Astrophysics Data System (ADS)

    Xiao, Di; Zahra, David; Bourgeat, Pierrick; Berghofer, Paula; Acosta Tamayo, Oscar; Wimberley, Catriona; Gregoire, Marie C.; Salvado, Olivier

    2011-03-01

    Small animal image registration is challenging because of its joint structure, and posture and position difference in each acquisition without a standard scan protocol. In this paper, we face the issue of mouse whole-body skeleton registration from CT images. A novel method is developed for analyzing mouse hind-limb and fore-limb postures based on geodesic path descriptor and then registering the major skeletons and fore limb skeletons initially by thin-plate spline (TPS) transform based on the obtained geodesic paths and their enhanced correspondence fields. A target landmark correction method is proposed for improving the registration accuracy of the improved 3D shape context non-rigid registration method we previously proposed. A novel non-rigid registration framework, combining the skeleton posture analysis, geodesic path based initial alignment and 3D shape context model, is proposed for mouse whole-body skeleton registration. The performance of the proposed methods and framework was tested on 12 pairs of mouse whole-body skeletons. The experimental results demonstrated the flexibility, stability and accuracy of the proposed framework for automatic mouse whole body skeleton registration.

  16. Image registration under symmetric conditions: novel approach

    NASA Astrophysics Data System (ADS)

    Duraisamy, Prakash; Yousef, Amr; Buckles, Bill; Jackson, Steve

    2015-03-01

    Registering the 2D images is one of the important pre-processing steps in many computer vision applications like 3D reconstruction, building panoramic images. Contemporary registration algorithm like SIFT (Scale Invariant Feature transform) was not quite success in registering the images under symmetric conditions and under poor illuminations using DoF (Difference of Gaussian) features. In this paper, we introduced a novel approach for registering the images under symmetric conditions.

  17. Voxel-based 2-D/3-D registration of fluoroscopy images and CT scans for image-guided surgery.

    PubMed

    Weese, J; Penney, G P; Desmedt, P; Buzug, T M; Hill, D L; Hawkes, D J

    1997-12-01

    Registration of intraoperative fluoroscopy images with preoperative three-dimensional (3-D) CT images can be used for several purposes in image-guided surgery. On the one hand, it can be used to display the position of surgical instruments, which are being tracked by a localizer, in the preoperative CT scan. On the other hand, the registration result can be used to project preoperative planning information or important anatomical structures visible in the CT image onto the fluoroscopy image. For this registration task, a novel voxel-based method in combination with a new similarity measure (pattern intensity) has been developed. The basic concept of the method is explained at the example of two-dimensional (2-D)/3-D registration of a vertebra in an X-ray fluoroscopy image with a 3-D CT image. The registration method is described, and the results for a spine phantom are presented and discussed. Registration has been carried out repeatedly with different starting estimates to study the capture range. Information about registration accuracy has been obtained by comparing the registration results with a highly accurate "ground-truth" registration, which has been derived from fiducial markers attached to the phantom prior to imaging. In addition, registration results for different vertebrae have been compared. The results show that the rotation parameters and the shifts parallel to the projection plane can accurately be determined from a single projection. Because of the projection geometry, the accuracy of the height above the projection plane is significantly lower. PMID:11020832

  18. 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

  19. Can dental registrants use the Index of Orthodontic Treatment Need accurately? Part 1: Knowledge of IOTN among dental registrants.

    PubMed

    Jawad, Z; Bates, C; Hodge, T

    2016-05-27

    Aim To determine whether dental registrants can use the dental health component (DHC) and aesthetic component (AC) of the Index of Orthodontic Treatment Need (IOTN) 'accurately' to an acceptable level of agreement and diagnostic validity.Method Participants from six different registrant groups were asked to score the IOTN for 14 cases based on study models and photographs as well as completing a short questionnaire. Participants in the study were all recruited at study days and annual conferences. The main outcome measures include the different registrant groups IOTN scores compared to expert panel scores using kappa statistics. To assess for diagnostic validity, individual participants sensitivity and specificity scores were calculated.Result Overall, 229 registrants took part in the study. For the DHC the specialist orthodontist (SO), postgraduate orthodontic student (PGOS) and the qualified orthodontic therapist (QOT) groups achieved a mean kappa ≥0.60 indicating 'acceptable' agreement with the expert panel scores. The dental foundation trainee (DFT) and general dental practitioner (GDP) group achieved a mean kappa of 0.20 and 0.22 respectively indicating poor and fair agreement. The student orthodontic therapist (SOT) group achieved a mean kappa of 0.55 indicating moderate agreement. For the AC none of the registrant groups achieved an acceptable level of agreement with the mean kappa scores for the different groups ranging from kappa 0.13-0.21, indicating poor to fair agreement.Conclusion Overall agreement for the DHC was varied for the different registrant groups ranging from fair to substantial agreement. Registrants were better at applying the DHC compared to the AC with agreement ranging from poor to fair. More needs to done to help registrants use the IOTN more 'accurately'. PMID:27228933

  20. CT image registration in sinogram space

    SciTech Connect

    Mao Weihua; Li Tianfang; Wink, Nicole; Xing Lei

    2007-09-15

    Object displacement in a CT scan is generally reflected in CT projection data or sinogram. In this work, the direct relationship between object motion and the change of CT projection data (sinogram) is investigated and this knowledge is applied to create a novel algorithm for sinogram registration. Calculated and experimental results demonstrate that the registration technique works well for registering rigid 2D or 3D motion in parallel and fan beam samplings. Problem and solution for 3D sinogram-based registration of metallic fiducials are also addressed. Since the motion is registered before image reconstruction, the presented algorithm is particularly useful when registering images with metal or truncation artifacts. In addition, this algorithm is valuable for dealing with situations where only limited projection data are available, making it appealing for various applications in image guided radiation therapy.

  1. 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.

  2. An image registration based ultrasound probe calibration

    NASA Astrophysics Data System (ADS)

    Li, Xin; Kumar, Dinesh; Sarkar, Saradwata; Narayanan, Ram

    2012-02-01

    Reconstructed 3D ultrasound of prostate gland finds application in several medical areas such as image guided biopsy, therapy planning and dose delivery. In our application, we use an end-fire probe rotated about its axis to acquire a sequence of rotational slices to reconstruct 3D TRUS (Transrectal Ultrasound) image. The image acquisition system consists of an ultrasound transducer situated on a cradle directly attached to a rotational sensor. However, due to system tolerances, axis of probe does not align exactly with the designed axis of rotation resulting in artifacts in the 3D reconstructed ultrasound volume. We present a rigid registration based automatic probe calibration approach. The method uses a sequence of phantom images, each pair acquired at angular separation of 180 degrees and registers corresponding image pairs to compute the deviation from designed axis. A modified shadow removal algorithm is applied for preprocessing. An attribute vector is constructed from image intensity and a speckle-insensitive information-theoretic feature. We compare registration between the presented method and expert-corrected images in 16 prostate phantom scans. Images were acquired at multiple resolutions, and different misalignment settings from two ultrasound machines. Screenshots from 3D reconstruction are shown before and after misalignment correction. Registration parameters from automatic and manual correction were found to be in good agreement. Average absolute differences of translation and rotation between automatic and manual methods were 0.27 mm and 0.65 degree, respectively. The registration parameters also showed lower variability for automatic registration (pooled standard deviation σtranslation = 0.50 mm, σrotation = 0.52 degree) compared to the manual approach (pooled standard deviation σtranslation = 0.62 mm, σrotation = 0.78 degree).

  3. 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. PMID:27427472

  4. Registration of In Vivo Prostate Magnetic Resonance Images to Digital Histopathology Images

    NASA Astrophysics Data System (ADS)

    Ward, A. D.; Crukley, C.; McKenzie, C.; Montreuil, J.; Gibson, E.; Gomez, J. A.; Moussa, M.; Bauman, G.; Fenster, A.

    Early and accurate diagnosis of prostate cancer enables minimally invasive therapies to cure the cancer with less morbidity. The purpose of this work is to non-rigidly register in vivo pre-prostatectomy prostate medical images to regionally-graded histopathology images from post-prostatectomy specimens, seeking a relationship between the multi parametric imaging and cancer distribution and aggressiveness. Our approach uses image-based registration in combination with a magnetically tracked probe to orient the physical slicing of the specimen to be parallel to the in vivo imaging planes, yielding a tractable 2D registration problem. We measured a target registration error of 0.85 mm, a mean slicing plane marking error of 0.7 mm, and a mean slicing error of 0.6 mm; these results compare favourably with our 2.2 mm diagnostic MR image thickness. Qualitative evaluation of in vivo imaging-histopathology fusion reveals excellent anatomic concordance between MR and digital histopathology.

  5. Automatic pose initialization for accurate 2D/3D registration applied to abdominal aortic aneurysm endovascular repair

    NASA Astrophysics Data System (ADS)

    Miao, Shun; Lucas, Joseph; Liao, Rui

    2012-02-01

    Minimally invasive abdominal aortic aneurysm (AAA) stenting can be greatly facilitated by overlaying the preoperative 3-D model of the abdominal aorta onto the intra-operative 2-D X-ray images. Accurate 2-D/3-D registration in 3-D space makes the 2-D/3-D overlay robust to the change of C-Arm angulations. By far, the 2-D/3-D registration methods based on simulated X-ray projection images using multiple image planes have been shown to be able to provide satisfactory 3-D registration accuracy. However, one drawback of the intensity-based 2-D/3-D registration methods is that the similarity measure is usually highly non-convex and hence the optimizer can easily be trapped into local minima. User interaction therefore is often needed in the initialization of the position of the 3-D model in order to get a successful 2-D/3-D registration. In this paper, a novel 3-D pose initialization technique is proposed, as an extension of our previously proposed bi-plane 2-D/3-D registration method for AAA intervention [4]. The proposed method detects vessel bifurcation points and spine centerline in both 2-D and 3-D images, and utilizes landmark information to bring the 3-D volume into a 15mm capture range. The proposed landmark detection method was validated on real dataset, and is shown to be able to provide a good initialization for 2-D/3-D registration in [4], thus making the workflow fully automatic.

  6. Image quality degradation and retrieval errors introduced by registration and interpolation of multispectral digital images

    SciTech Connect

    Henderson, B.G.; Borel, C.C.; Theiler, J.P.; Smith, B.W.

    1996-04-01

    Full utilization of multispectral data acquired by whiskbroom and pushbroom imagers requires that the individual channels be registered accurately. Poor registration introduces errors which can be significant, especially in high contrast areas such as boundaries between regions. We simulate the acquisition of multispectral imagery in order to estimate the errors that are introduced by co-registration of different channels and interpolation within the images. We compute the Modulation Transfer Function (MTF) and image quality degradation brought about by fractional pixel shifting and calculate errors in retrieved quantities (surface temperature and water vapor) that occur as a result of interpolation. We also present a method which might be used to estimate sensor platform motion for accurate registration of images acquired by a pushbroom scanner.

  7. Registration of 3-D images using weighted geometrical features

    SciTech Connect

    Maurer, C.R. Jr.; Aboutanos, G.B.; Dawant, B.M.; Maciunas, R.J.; Fitzpatrick, J.M.

    1996-12-01

    In this paper, the authors present a weighted geometrical features (WGF) registration algorithm. Its efficacy is demonstrated by combining points and a surface. The technique is an extension of Besl and McKay`s iterative closest point (ICP) algorithm. The authors use the WGF algorithm to register X-ray computed tomography (CT) and T2-weighted magnetic resonance (MR) volume head images acquired from eleven patients that underwent craniotomies in a neurosurgical clinical trial. Each patient had five external markers attached to transcutaneous posts screwed into the outer table of the skull. The authors define registration error as the distance between positions of corresponding markers that are not used for registration. The CT and MR images are registered using fiducial points (marker positions) only, a surface only, and various weighted combinations of points and a surface. The CT surface is derived from contours corresponding to the inner surface of the skull. The MR surface is derived from contours corresponding to the cerebrospinal fluid (CSF)-dura interface. Registration using points and a surface is found to be significantly more accurate than registration using only points or a surface.

  8. Automatic parameter selection for multimodal image registration.

    PubMed

    Hahn, Dieter A; Daum, Volker; Hornegger, Joachim

    2010-05-01

    Over the past ten years similarity measures based on intensity distributions have become state-of-the-art in automatic multimodal image registration. An implementation for clinical usage has to support a plurality of images. However, a generally applicable parameter configuration for the number and sizes of histogram bins, optimal Parzen-window kernel widths or background thresholds cannot be found. This explains why various research groups present partly contradictory empirical proposals for these parameters. This paper proposes a set of data-driven estimation schemes for a parameter-free implementation that eliminates major caveats of heuristic trial and error. We present the following novel approaches: a new coincidence weighting scheme to reduce the influence of background noise on the similarity measure in combination with Max-Lloyd requantization, and a tradeoff for the automatic estimation of the number of histogram bins. These methods have been integrated into a state-of-the-art rigid registration that is based on normalized mutual information and applied to CT-MR, PET-MR, and MR-MR image pairs of the RIRE 2.0 database. We compare combinations of the proposed techniques to a standard implementation using default parameters, which can be found in the literature, and to a manual registration by a medical expert. Additionally, we analyze the effects of various histogram sizes, sampling rates, and error thresholds for the number of histogram bins. The comparison of the parameter selection techniques yields 25 approaches in total, with 114 registrations each. The number of bins has no significant influence on the proposed implementation that performs better than both the manual and the standard method in terms of acceptance rates and target registration error (TRE). The overall mean TRE is 2.34 mm compared to 2.54 mm for the manual registration and 6.48 mm for a standard implementation. Our results show a significant TRE reduction for distortion

  9. Vectorial total variation-based regularization for variational image registration.

    PubMed

    Chumchob, Noppadol

    2013-11-01

    To use interdependence between the primary components of the deformation field for smooth and non-smooth registration problems, the channel-by-channel total variation- or standard vectorial total variation (SVTV)-based regularization has been extended to a more flexible and efficient technique, allowing high quality regularization procedures. Based on this method, this paper proposes a fast nonlinear multigrid (NMG) method for solving the underlying Euler-Lagrange system of two coupled second-order nonlinear partial differential equations. Numerical experiments using both synthetic and realistic images not only confirm that the recommended VTV-based regularization yields better registration qualities for a wide range of applications than those of the SVTV-based regularization, but also that the proposed NMG method is fast, accurate, and reliable in delivering visually-pleasing registration results. PMID:23893729

  10. A multi-scale registration of urban aerial image with airborne lidar data

    NASA Astrophysics Data System (ADS)

    Huang, Shuo; Chen, Siying; Zhang, Yinchao; Guo, Pan; Chen, He

    2015-11-01

    This paper presented a multi-scale progressive registration method of airborne LiDAR data with aerial image. The cores of the proposed method lie in the coarse registration with road networks and the fine registration method using regularized building corners. During the two-stage registration, the exterior orientation parameters (EOP) are continually refined. By validation of the actual flight data of Dunhuang, the experimental result shows that the proposed method can obtain accurate results with low-precision initial EOP, also improve the automatic degree of registration.

  11. 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.

  12. 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.

  13. A comparison of image registration techniques for the correlation of radiolabelled antibody distribution with tumour morphology.

    PubMed

    Flynn, A A; Green, A J; Boxer, G; Pedley, R B; Begent, R H

    1999-07-01

    Image registration is a powerful tool for correlating functional images with images of anatomical structure. This facilitates more accurate quantitation of regional radiopharmaceutical uptake. Similarly, registration of images of radiolabelled antibody distribution, in tissue sections, with the equivalent histological images allows the comparison and measurement of radiopharmaceutical distribution with morphological structure. The images used were obtained by storage phosphor plate technology, for the radiopharmaceutical distribution, and by digitization of the stained histological sections. Here we compare four fully automatic registration techniques and one manual technique in terms of their spatial accuracy. We have found that there was no difference in accuracy between cross-correlation, minimization of variance and mutual information. These techniques were more accurate than principal axes and the manual technique. However, minimization of variance and mutual information were more time-consuming than the other methods. Consequently, cross-correlation is the method of choice for automatic registration of large numbers of these image pairs. PMID:10442718

  14. Robust optical and SAR multi-sensor image registration

    NASA Astrophysics Data System (ADS)

    Wu, Yingdan; Ming, Yang

    2015-10-01

    This paper proposes a robust matching method for the multi-sensor imagery. Firstly, the SIFT feature matching and relaxation matching method are integrated in the highest pyramid to derive the approximate relationship between the reference and slave image. Then, the normalized Mutual Information and multi-grid multi-level RANSAC algorithm are adopted to find the correct conjugate points. Iteratively perform above steps until the original image level, the facet- based transformation model is used to carry out the image registration. Experiments have been made, and the results show that the method in this paper can deliver large number of evenly distributed conjugate points and realize the accurate registration of optical and SAR multi-sensor imagery.

  15. Stochastic inverse consistency in medical image registration.

    PubMed

    Yeung, Sai Kit; Shi, Pengcheng

    2005-01-01

    An essential goal in medical image registration is, the forward and reverse mapping matrices should be inverse to each other, i.e., inverse consistency. Conventional approaches enforce consistency in deterministic fashions, through incorporation of sub-objective cost function to impose source-destination symmetric property during the registration process. Assuming that the initial forward and reverse matching matrices have been computed and used as the inputs to our system, this paper presents a stochastic framework which yields perfect inverse consistency with the simultaneous considerations of the errors underneath the registration matrices and the imperfectness of the consistent constraint. An iterative generalized total least square (GTLS) strategy has been developed such that the inverse consistency is optimally imposed. PMID:16685959

  16. 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.

  17. 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.

  18. Geometric direct search algorithms for image registration.

    PubMed

    Lee, Seok; Choi, Minseok; Kim, Hyungmin; Park, Frank Chongwoo

    2007-09-01

    A widely used approach to image registration involves finding the general linear transformation that maximizes the mutual information between two images, with the transformation being rigid-body [i.e., belonging to SE(3)] or volume-preserving [i.e., belonging to SL(3)]. In this paper, we present coordinate-invariant, geometric versions of the Nelder-Mead optimization algorithm on the groups SL(3), SE(3), and their various subgroups, that are applicable to a wide class of image registration problems. Because the algorithms respect the geometric structure of the underlying groups, they are numerically more stable, and exhibit better convergence properties than existing local coordinate-based algorithms. Experimental results demonstrate the improved convergence properties of our geometric algorithms. PMID:17784595

  19. Fast 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 algorithm, called Fast Tensor Image Morphing for Elastic Registration or 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 aligning 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 non-landmark 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. Results indicate that better accuracy can be achieved by F-TIMER, compared with other deformable registration algorithms [1, 2], with significantly reduced computation time cost of 4–14 folds. PMID:20426052

  20. 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.

  1. Adaptive registration of diffusion tensor images on lie groups

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

    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.

  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. Digital image registration method using boundary maps

    NASA Technical Reports Server (NTRS)

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

    1975-01-01

    A new method of automatic image registration (matching) is presented. It requires that the original single or multichannel images first be converted to binary boundary maps having elements equal to zero or unity. The method corrects for both translational and rotational errors. One feature of the technique is the rapid calculation of a pseudo correlation matrix NCOR using only integer additions. It is argued that the use of boundary maps is advisable when the data from the two images are acquired under different conditions; i.e., weather conditions, lighting conditions, etc.

  4. Fundus image registration for vestibularis research

    NASA Astrophysics Data System (ADS)

    Ithapu, Vamsi K.; Fritsche, Armin; Oppelt, Ariane; Westhofen, Martin; Deserno, Thomas M.

    2010-03-01

    In research on vestibular nerve disorders, fundus images of both left and right eyes are acquired systematically to precisely assess the rotation of the eye ball that is induced by the rotation of entire head. The measurement is still carried out manually. Although various methods have been proposed for medical image registration, robust detection of rotation especially in images with varied quality in terms of illumination, aberrations, blur and noise still is challenging. This paper evaluates registration algorithms operating on different levels of semantics: (i) data-based using Fourier transform and log polar maps; (ii) point-based using scaled image feature transform (SIFT); (iii) edge-based using Canny edge maps; (iv) object-based using matched filters for vessel detection; (v) scene-based detecting papilla and macula automatically and (vi) manually by two independent medical experts. For evaluation, a database of 22 patients is used, where each of left and right eye images is captured in upright head position and in lateral tilt of +/-200. For 66 pairs of images (132 in total), the results are compared with ground truth, and the performance measures are tabulated. Best correctness of 89.3% were obtained using the pixel-based method and allowing 2.5° deviation from the manual measures. However, the evaluation shows that for applications in computer-aided diagnosis involving a large set of images with varied quality, like in vestibularis research, registration methods based on a single level of semantics are not sufficiently robust. A multi-level semantics approach will improve the results since failure occur on different images.

  5. A bronchoscopic navigation system using bronchoscope center calibration for accurate registration of electromagnetic tracker and CT volume without markers

    SciTech Connect

    Luo, Xiongbiao

    2014-06-15

    Purpose: Various bronchoscopic navigation systems are developed for diagnosis, staging, and treatment of lung and bronchus cancers. To construct electromagnetically navigated bronchoscopy systems, registration of preoperative images and an electromagnetic tracker must be performed. This paper proposes a new marker-free registration method, which uses the centerlines of the bronchial tree and the center of a bronchoscope tip where an electromagnetic sensor is attached, to align preoperative images and electromagnetic tracker systems. Methods: The chest computed tomography (CT) volume (preoperative images) was segmented to extract the bronchial centerlines. An electromagnetic sensor was fixed at the bronchoscope tip surface. A model was designed and printed using a 3D printer to calibrate the relationship between the fixed sensor and the bronchoscope tip center. For each sensor measurement that includes sensor position and orientation information, its corresponding bronchoscope tip center position was calculated. By minimizing the distance between each bronchoscope tip center position and the bronchial centerlines, the spatial alignment of the electromagnetic tracker system and the CT volume was determined. After obtaining the spatial alignment, an electromagnetic navigation bronchoscopy system was established to real-timely track or locate a bronchoscope inside the bronchial tree during bronchoscopic examinations. Results: The electromagnetic navigation bronchoscopy system was validated on a dynamic bronchial phantom that can simulate respiratory motion with a breath rate range of 0–10 min{sup −1}. The fiducial and target registration errors of this navigation system were evaluated. The average fiducial registration error was reduced from 8.7 to 6.6 mm. The average target registration error, which indicates all tracked or navigated bronchoscope position accuracy, was much reduced from 6.8 to 4.5 mm compared to previous registration methods. Conclusions: An

  6. 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

    2015-01-01

    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. PMID:25479095

  7. 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.

  8. Verifying radiotherapy treatment setup by interactive image registration.

    PubMed Central

    Boxwala, A. A.; Chaney, E. L.; Friedman, C. P.

    1996-01-01

    Digital image analysis techniques can be used to assist the physician in diagnostic or therapeutic decision making. In radiation oncology, portal image registration can improve the accuracy of detection of errors during radiation treatment. Following a discussion of the general paradigm of interactive image registration, we describe PortFolio, a workstation for portal image analysis. Images Figure 1 Figure 2 PMID:8947672

  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. Biomechanical modeling provides more accurate data for neuronavigation than rigid registration

    PubMed Central

    Garlapati, Revanth Reddy; Roy, Aditi; Joldes, Grand Roman; Wittek, Adam; Mostayed, Ahmed; Doyle, Barry; Warfield, Simon Keith; Kikinis, Ron; Knuckey, Neville; Bunt, Stuart; Miller, Karol

    2015-01-01

    It is possible to improve neuronavigation during image-guided surgery by warping the high-quality preoperative brain images so that they correspond with the current intraoperative configuration of the brain. In this work, the accuracy of registration results obtained using comprehensive biomechanical models is compared to the accuracy of rigid registration, the technology currently available to patients. This comparison allows us to investigate whether biomechanical modeling provides good quality image data for neuronavigation for a larger proportion of patients than rigid registration. Preoperative images for 33 cases of neurosurgery were warped onto their respective intraoperative configurations using both biomechanics-based method and rigid registration. We used a Hausdorff distance-based evaluation process that measures the difference between images to quantify the performance of both methods of registration. A statistical test for difference in proportions was conducted to evaluate the null hypothesis that the proportion of patients for whom improved neuronavigation can be achieved, is the same for rigid and biomechanics-based registration. The null hypothesis was confidently rejected (p-value<10−4). Even the modified hypothesis that less than 25% of patients would benefit from the use of biomechanics-based registration was rejected at a significance level of 5% (p-value = 0.02). The biomechanics-based method proved particularly effective for cases experiencing large craniotomy-induced brain deformations. The outcome of this analysis suggests that our nonlinear biomechanics-based methods are beneficial to a large proportion of patients and can be considered for use in the operating theatre as one possible method of improving neuronavigation and surgical outcomes. PMID:24460486

  12. Results of automatic image registration are dependent on initial manual registration.

    PubMed

    Johnson, Joshua E; Fischer, Kenneth J

    2015-01-01

    Measurement of static alignment of articulating joints is of clinical benefit and can be determined using image-based registration. We propose a method that could potentially improve the outcome of image-based registration by using initial manual registration. Magnetic resonance images of two wrist specimens were acquired in the relaxed position and during simulated grasp. Transformations were determined from voxel-based image registration between the two volumes. The volumes were manually aligned to match as closely as possible before auto-registration, from which standard transformations were obtained. Then, translation/rotation perturbations were applied to the manual registration to obtain altered initial positions, from which altered auto-registration transformations were obtained. Models of the radiolunate joint were also constructed from the images to simulate joint contact mechanics. We compared the sensitivity of transformations (translations and rotations) and contact mechanics to altering the initial registration condition from the defined standard. We observed that with increasing perturbation, transformation errors appeared to increase and values for contact force and contact area appeared to decrease. Based on these preliminary findings, it appears that the final registration outcome is sensitive to the initial registration. PMID:25408167

  13. Accurate mask registration on tilted lines for 6F2 DRAM manufacturing

    NASA Astrophysics Data System (ADS)

    Roeth, K. D.; Choi, W.; Lee, Y.; Kim, S.; Yim, D.; Laske, F.; Ferber, M.; Daneshpanah, M.; Kwon, E.

    2015-10-01

    193nm immersion lithography is the mainstream production technology for the 22nm half pitch (HP) DRAM manufacturing. Considering multi-patterning as the technology to solve the very low k1 situation in the resolution equation puts extreme pressure on the intra-field overlay, to which mask registration error may be a significant error contributor [3]. The International Technology Roadmap for Semiconductors (ITRS [1]) requests a registration error below 4 nm for each mask of a multi-patterning set forming one layer on the wafer. For mask metrology at the 22nm HP node, maintaining a precision-to-tolerance (P/T) ratio below 0.25 will be very challenging. Mask registration error impacts intra-field wafer overlay directly and has a major impact on wafer yield. DRAM makers moved several years ago to 6F2 (figure 1, [2]) cell design and thus printing tilted lines at 15 or 30 degree. Overlay of contact layer over buried line has to be well controlled. However, measuring mask registration performance accurately on tilted lines was a challenge. KLA Tencor applied the model-based algorithm to enable the accurate registration measurement of tilted lines on the Poly layer as well as the mask-to-mask overlay to the adjacent contact layers. The metrology solution is discussed and measurement results are provided.

  14. 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.

  15. Hierarchical Multi-modal Image Registration by Learning Common Feature Representations

    PubMed Central

    Ge, Hongkun; Wu, Guorong; Wang, Li; Gao, Yaozong

    2016-01-01

    Mutual information (MI) has been widely used for registering images with different modalities. Since most inter-modality registration methods simply estimate deformations in a local scale, but optimizing MI from the entire image, the estimated deformations for certain structures could be dominated by the surrounding unrelated structures. Also, since there often exist multiple structures in each image, the intensity correlation between two images could be complex and highly nonlinear, which makes global MI unable to precisely guide local image deformation. To solve these issues, we propose a hierarchical inter-modality registration method by robust feature matching. Specifically, we first select a small set of key points at salient image locations to drive the entire image registration. Since the original image features computed from different modalities are often difficult for direct comparison, we propose to learn their common feature representations by projecting them from their native feature spaces to a common space, where the correlations between corresponding features are maximized. Due to the large heterogeneity between two high-dimension feature distributions, we employ Kernel CCA (Canonical Correlation Analysis) to reveal such non-linear feature mappings. Then, our registration method can take advantage of the learned common features to reliably establish correspondences for key points from different modality images by robust feature matching. As more and more key points take part in the registration, our hierarchical feature-based image registration method can efficiently estimate the deformation pathway between two inter-modality images in a global to local manner. We have applied our proposed registration method to prostate CT and MR images, as well as the infant MR brain images in the first year of life. Experimental results show that our method can achieve more accurate registration results, compared to other state-of-the-art image registration

  16. Single- and multimodal subvoxel registration of dissimilar medical images using robust similarity measures

    NASA Astrophysics Data System (ADS)

    Nikou, Christophoros; Heitz, Fabrice; Armspach, Jean-Paul; Namer, Izzie-Jacques

    1998-06-01

    Although a large variety of image registration methods have been described in the literature, only a few approaches have attempted to address the rigid registration of medical images showing gross dissimilarities (due for instance to lesion evolution). In the present paper, we develop driven registration algorithms, relying on robust pixel similarity metrics, that enable an accurate (subvoxel) rigid registration of dissimilar single or multimodal 2D/3D images. In the proposed approach, gross dissimilarities are handled by considering similarity measures related to robust M-estimators. A `soft redescending' estimator (the Geman- McClure p-function) has been adopted to reject gross image dissimilarities during the registration. The registration parameters are estimated using a top down stochastic multigrid relaxation algorithm. Thanks to the stochastic multigrid strategy, the registration is not affected by local minima in the objective function and a manual initialization near the optimal solution is not necessary. The proposed robust similarity metrics compare favorably to the most popular standard similarity metrics, on patient image pairs showing gross dissimilarities. Two case studies are considered: the registration of MR/MR and MR/SPECT image volumes of patients suffering from multiple sclerosis and epilepsy.

  17. 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.

  18. Image registration of naval IR images

    NASA Astrophysics Data System (ADS)

    Rodland, Arne J.

    1996-06-01

    In a real world application an image from a stabilized sensor on a moving platform will not be 100 percent stabilized. There will always be a small unknown error in the stabilization due to factors such as dynamic deformations in the structure between sensor and reference Inertial Navigation Unit, servo inaccuracies, etc. For a high resolution imaging sensor this stabilization error causes the image to move several pixels in unknown direction between frames. TO be able to detect and track small moving objects from such a sensor, this unknown movement of the sensor image must be estimated. An algorithm that searches for land contours in the image has been evaluated. The algorithm searches for high contrast points distributed over the whole image. As long as moving objects in the scene only cover a small area of the scene, most of the points are located on solid ground. By matching the list of points from frame to frame, the movement of the image due to stabilization errors can be estimated and compensated. The point list is searched for points with diverging movement from the estimated stabilization error. These points are then assumed to be located on moving objects. Points assumed to be located on moving objects are gradually exchanged with new points located in the same area. Most of the processing is performed on the list of points and not on the complete image. The algorithm is therefore very fast and well suited for real time implementation. The algorithm has been tested on images from an experimental IR scanner. Stabilization errors were added artificially to the image such that the output from the algorithm could be compared with the artificially added stabilization errors.

  19. 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. PMID:27085311

  20. 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

  1. Unsupervised deep feature learning for deformable registration of MR brain images.

    PubMed

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

    2013-01-01

    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

  2. Registration of heat capacity mapping mission day and night images

    NASA Technical Reports Server (NTRS)

    Watson, K.; Hummer-Miller, S.; Sawatzky, D. L.

    1982-01-01

    Registration of thermal images is complicated by distinctive differences in the appearance of day and night features needed as control in the registration process. These changes are unlike those that occur between Landsat scenes and pose unique constraints. Experimentation with several potentially promising techniques has led to selection of a fairly simple scheme for registration of data from the experimental thermal satellite HCMM using an affine transformation. Two registration examples are provided.

  3. An Automatic Optical and SAR Image Registration Method Using Iterative Multi-Level and Refinement Model

    NASA Astrophysics Data System (ADS)

    Xu, C.; Sui, H. G.; Li, D. R.; Sun, K. M.; Liu, J. Y.

    2016-06-01

    Automatic image registration is a vital yet challenging task, particularly for multi-sensor remote sensing images. Given the diversity of the data, it is unlikely that a single registration algorithm or a single image feature will work satisfactorily for all applications. Focusing on this issue, the mainly contribution of this paper is to propose an automatic optical-to-SAR image registration method using -level and refinement model: Firstly, a multi-level strategy of coarse-to-fine registration is presented, the visual saliency features is used to acquire coarse registration, and then specific area and line features are used to refine the registration result, after that, sub-pixel matching is applied using KNN Graph. Secondly, an iterative strategy that involves adaptive parameter adjustment for re-extracting and re-matching features is presented. Considering the fact that almost all feature-based registration methods rely on feature extraction results, the iterative strategy improve the robustness of feature matching. And all parameters can be automatically and adaptively adjusted in the iterative procedure. Thirdly, a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features, and Voronoi diagram is introduced into Spectral Point Matching (VSPM) to further enhance the matching accuracy between two sets of matching points. Experimental results show that the proposed method can effectively and robustly generate sufficient, reliable point pairs and provide accurate registration.

  4. Registration of serial SPECT/CT images for three-dimensional dosimetry in radionuclide therapy.

    PubMed

    Sjögreen-Gleisner, K; Rueckert, D; Ljungberg, M

    2009-10-21

    For radionuclide therapy, individual patient pharmacokinetics can be measured in three dimensions by sequential SPECT imaging. Accurate registration of the time series of images is central for voxel-based calculations of the residence time and absorbed dose. In this work, rigid and non-rigid methods are evaluated for registration of 6-7 SPECT/CT images acquired over a week, in anatomical regions from the head-and-neck region down to the pelvis. A method for calculation of the absorbed dose, including a voxel mass determination from the CT images, is also described. Registration of the SPECT/CT images is based on a CT-derived spatial transformation. Evaluation is focused on the CT registration accuracy, and on its impact on values of residence time and absorbed dose. According to the CT evaluation, the non-rigid method produces a more accurate registration than the rigid one. For images of the residence time and absorbed dose, registration produces a sharpening of the images. For volumes-of-interest, the differences between rigid and non-rigid results are generally small. However, the non-rigid method is more consistent for regions where non-rigid patient movements are likely, such as in the head-neck-shoulder region. PMID:19794243

  5. 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.

  6. [Human cerebral image registration using generalized mutual information].

    PubMed

    Zhang, Jingzhou; Li, Ting; Zhang, Jia

    2008-12-01

    Medical image registration is a highlight of actual research on medical image processing. Based onsimilarity measure of Shannon entropy, a new generalized distance measurement based on Rényi entropy applied to image rigid registration is introduced and is called here generalized mutual information (GMI). It is used in three dimensional cerebral image registration experiments. The simulation results show that generalized distance measurement and Shannon entropy measurement apply to different areas; that the registration measure based o n generalized distance is a natural extension of mutual information of Shannon entropy. The results prove that generalized mutual information uses less time than simple mutual information does, and the new similarity measure manifests higher degree of consistency between the two cerebral registration images. Also, the registration results provide the clinical diagnoses with more important references. In conclusion, generalized mutual information has satisfied the demands of clinical application to a wide extent. PMID:19166197

  7. Accurate high-resolution measurements of 3-D tissue dynamics with registration-enhanced displacement encoded MRI.

    PubMed

    Gomez, Arnold D; Merchant, Samer S; Hsu, Edward W

    2014-06-01

    Displacement fields are important to analyze deformation, which is associated with functional and material tissue properties often used as indicators of health. Magnetic resonance imaging (MRI) techniques like DENSE and image registration methods like Hyperelastic Warping have been used to produce pixel-level deformation fields that are desirable in high-resolution analysis. However, DENSE can be complicated by challenges associated with image phase unwrapping, in particular offset determination. On the other hand, Hyperelastic Warping can be hampered by low local image contrast. The current work proposes a novel approach for measuring tissue displacement with both DENSE and Hyperelastic Warping, incorporating physically accurate displacements obtained by the latter to improve phase characterization in DENSE. The validity of the proposed technique is demonstrated using numerical and physical phantoms, and in vivo small animal cardiac MRI. PMID:24771572

  8. Accurate High-Resolution Measurements of 3-D Tissue Dynamics With Registration-Enhanced Displacement Encoded MRI

    PubMed Central

    Merchant, Samer S.; Hsu, Edward W.

    2014-01-01

    Displacement fields are important to analyze deformation, which is associated with functional and material tissue properties often used as indicators of health. Magnetic resonance imaging (MRI) techniques like DENSE and image registration methods like Hyperelastic Warping have been used to produce pixel-level deformation fields that are desirable in high-resolution analysis. However, DENSE can be complicated by challenges associated with image phase unwrapping, in particular offset determination. On the other hand, Hyperelastic Warping can be hampered by low local image contrast. The current work proposes a novel approach for measuring tissue displacement with both DENSE and Hyperelastic Warping, incorporating physically accurate displacements obtained by the latter to improve phase characterization in DENSE. The validity of the proposed technique is demonstrated using numerical and physical phantoms, and in vivo small animal cardiac MRI. PMID:24771572

  9. A stationary wavelet transform based approach to registration of planning CT and setup cone beam-CT images in radiotherapy.

    PubMed

    Deng, Jun-Min; Yue, Hai-Zhen; Zhuo, Zhi-Zheng; Yan, Hua-Gang; Liu, Di; Li, Hai-Yun

    2014-05-01

    Image registration between planning CT images and cone beam-CT (CBCT) images is one of the key technologies of image guided radiotherapy (IGRT). Current image registration methods fall roughly into two categories: geometric features-based and image grayscale-based. Mutual information (MI) based registration, which belongs to the latter category, has been widely applied to multi-modal and mono-modal image registration. However, the standard mutual information method only focuses on the image intensity information and overlooks spatial information, leading to the instability of intensity interpolation. Due to its use of positional information, wavelet transform has been applied to image registration recently. In this study, we proposed an approach to setup CT and cone beam-CT (CBCT) image registration in radiotherapy based on the combination of mutual information (MI) and stationary wavelet transform (SWT). Firstly, SWT was applied to generate gradient images and low frequency components produced in various levels of image decomposition were eliminated. Then inverse SWT was performed on the remaining frequency components. Lastly, the rigid registration of gradient images and original images was implemented using a weighting function with the normalized mutual information (NMI) being the similarity measure, which compensates for the lack of spatial information in mutual information based image registration. Our experiment results showed that the proposed method was highly accurate and robust, and indicated a significant clinical potential in improving the accuracy of target localization in image guided radiotherapy (IGRT). PMID:24729043

  10. Image registration with auto-mapped control volumes

    SciTech Connect

    Schreibmann, Eduard; Xing Lei

    2006-04-15

    Many image registration algorithms rely on the use of homologous control points on the two input image sets to be registered. In reality, the interactive identification of the control points on both images is tedious, difficult, and often a source of error. We propose a two-step algorithm to automatically identify homologous regions that are used as a priori information during the image registration procedure. First, a number of small control volumes having distinct anatomical features are identified on the model image in a somewhat arbitrary fashion. Instead of attempting to find their correspondences in the reference image through user interaction, in the proposed method, each of the control regions is mapped to the corresponding part of the reference image by using an automated image registration algorithm. A normalized cross-correlation (NCC) function or mutual information was used as the auto-mapping metric and a limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) was employed to optimize the function to find the optimal mapping. For rigid registration, the transformation parameters of the system are obtained by averaging that derived from the individual control volumes. In our deformable calculation, the mapped control volumes are treated as the nodes or control points with known positions on the two images. If the number of control volumes is not enough to cover the whole image to be registered, additional nodes are placed on the model image and then located on the reference image in a manner similar to the conventional BSpline deformable calculation. For deformable registration, the established correspondence by the auto-mapped control volumes provides valuable guidance for the registration calculation and greatly reduces the dimensionality of the problem. The performance of the two-step registrations was applied to three rigid registration cases (two PET-CT registrations and a brain MRI-CT registration) and one deformable registration of

  11. SAR image registration in absolute coordinates using GPS carrier phase position and velocity information

    SciTech Connect

    Burgett, S.; Meindl, M.

    1994-09-01

    It is useful in a variety of military and commercial application to accurately register the position of synthetic aperture radar (SAR) imagery in absolute coordinates. The two basic SAR measurements, range and doppler, can be used to solve for the position of the SAR image. Imprecise knowledge of the SAR collection platform`s position and velocity vectors introduce errors in the range and doppler measurements and can cause the apparent location of the SAR image on the ground to be in error by tens of meters. Recent advances in carrier phase GPS techniques can provide an accurate description of the collection vehicle`s trajectory during the image formation process. In this paper, highly accurate carrier phase GPS trajectory information is used in conjunction with SAR imagery to demonstrate a technique for accurate registration of SAR images in WGS-84 coordinates. Flight test data will be presented that demonstrates SAR image registration errors of less than 4 meters.

  12. Rethinking image registration on customizable hardware

    NASA Astrophysics Data System (ADS)

    Bowman, David; Tahtali, Murat; Lambert, Andrew

    2010-08-01

    Image registration is one of the most important tasks in image processing and is frequently one of the most computationally intensive. In cases where there is a high likelihood of finding the exact template in the search image, correlation-based methods predominate. Presumably this is because the computational complexity of a correlation operation can be reduced substantially by transforming the task into the frequency domain. Alternative methods such as minimum Sum of Squared Differences (minSSD) are not so tractable and are normally disfavored. This bias is justified when dealing with conventional computer processors since the operations must be conducted in an essentially sequential manner however we demonstrate it is normally unjustified when the processing is undertaken on customizable hardware such as FPGAs where tasks can be temporally and/or spatially parallelized. This is because the gate-based logic of an FPGA is better suited to the tasks of minSSD i.e. signed-addition hardware can be very cheaply implemented in FPGA fabric, and square operations are easily implemented via a look-up table. In contrast, correlationbased methods require extensive use of multiplier hardware which cannot be so cheaply implemented in the device. Even with modern DSP-oriented FPGAs which contain many "hard" multipliers we experience at least an order of magnitude increase in the number of minSSD hardware modules we can implement compared to cross-correlation modules. We demonstrate successful use and comparison of techniques within an FPGA for registration and correction of turbulence degraded images.

  13. High-performance computing in image registration

    NASA Astrophysics Data System (ADS)

    Zanin, Michele; Remondino, Fabio; Dalla Mura, Mauro

    2012-10-01

    Thanks to the recent technological advances, a large variety of image data is at our disposal with variable geometric, radiometric and temporal resolution. In many applications the processing of such images needs high performance computing techniques in order to deliver timely responses e.g. for rapid decisions or real-time actions. Thus, parallel or distributed computing methods, Digital Signal Processor (DSP) architectures, Graphical Processing Unit (GPU) programming and Field-Programmable Gate Array (FPGA) devices have become essential tools for the challenging issue of processing large amount of geo-data. The article focuses on the processing and registration of large datasets of terrestrial and aerial images for 3D reconstruction, diagnostic purposes and monitoring of the environment. For the image alignment procedure, sets of corresponding feature points need to be automatically extracted in order to successively compute the geometric transformation that aligns the data. The feature extraction and matching are ones of the most computationally demanding operations in the processing chain thus, a great degree of automation and speed is mandatory. The details of the implemented operations (named LARES) exploiting parallel architectures and GPU are thus presented. The innovative aspects of the implementation are (i) the effectiveness on a large variety of unorganized and complex datasets, (ii) capability to work with high-resolution images and (iii) the speed of the computations. Examples and comparisons with standard CPU processing are also reported and commented.

  14. 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.

  15. Color image registration based on quaternion Fourier transformation

    NASA Astrophysics Data System (ADS)

    Wang, Qiang; Wang, Zhengzhi

    2012-05-01

    The traditional Fourier Mellin transform is applied to quaternion algebra in order to investigate quaternion Fourier transformation properties useful for color image registration in frequency domain. Combining with the quaternion phase correlation, we propose a method for color image registration based on the quaternion Fourier transform. The registration method, which processes color image in a holistic manner, is convenient to realign color images differing in translation, rotation, and scaling. Experimental results on different types of color images indicate that the proposed method not only obtains high accuracy in similarity transform in the image plane but also is computationally efficient.

  16. Prospective image registration for automated scan prescription of follow-up knee images in quantitative studies.

    PubMed

    Goldenstein, Janet; Schooler, Joseph; Crane, Jason C; Ozhinsky, Eugene; Pialat, Jean-Baptiste; Carballido-Gamio, Julio; Majumdar, Sharmila

    2011-06-01

    Consistent scan prescription for MRI of the knee is very important for accurate comparison of images in a longitudinal study. However, consistent scan region selection is difficult due to the complexity of the knee joint. We propose a novel method for registering knee images using a mutual information registration algorithm to align images in a baseline and follow-up exam. The output of the registration algorithm, three translations and three Euler angles, is then used to redefine the region to be imaged and acquire an identical oblique imaging volume in the follow-up exam as in the baseline. This algorithm is robust to articulation of the knee and anatomical abnormalities due to disease (e.g., osteophytes). The registration method is performed only on the distal femur and is not affected by the proximal tibia or soft tissues. We have incorporated this approach in a clinical MR system and have demonstrated its utility in automatically obtaining consistent scan regions between baseline and follow-up examinations, thus improving the precision of quantitative evaluation of cartilage. Results show an improvement with prospective registration in the coefficient of variation for cartilage thickness, cartilage volume and T2 relaxation measurements. PMID:21546186

  17. 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

  18. Biomechanical deformable image registration of longitudinal lung CT images using vessel information.

    PubMed

    Cazoulat, Guillaume; Owen, Dawn; Matuszak, Martha M; Balter, James M; Brock, Kristy K

    2016-07-01

    Spatial correlation of lung tissue across longitudinal images, as the patient responds to treatment, is a critical step in adaptive radiotherapy. The goal of this work is to expand a biomechanical model-based deformable registration algorithm (Morfeus) to achieve accurate registration in the presence of significant anatomical changes. Six lung cancer patients previously treated with conventionally fractionated radiotherapy were retrospectively evaluated. Exhale CT scans were obtained at treatment planning and following three weeks of treatment. For each patient, the planning CT was registered to the follow-up CT using Morfeus, a biomechanical model-based deformable registration algorithm. To model the complex response of the lung, an extension to Morfeus has been developed: an initial deformation was estimated with Morfeus consisting of boundary conditions on the chest wall and incorporating a sliding interface with the lungs. It was hypothesized that the addition of boundary conditions based on vessel tree matching would provide a robust reduction of the residual registration error. To achieve this, the vessel trees were segmented on the two images by thresholding a vesselness image based on the Hessian matrix's eigenvalues. For each point on the reference vessel tree centerline, the displacement vector was estimated by applying a variant of the Demons registration algorithm between the planning CT and the deformed follow-up CT. An expert independently identified corresponding landmarks well distributed in the lung to compute target registration errors (TRE). The TRE was: [Formula: see text], [Formula: see text] and [Formula: see text] mm after rigid registration, Morfeus and Morfeus with boundary conditions on the vessel tree, respectively. In conclusion, the addition of boundary conditions on the vessels significantly improved the accuracy in modeling the response of the lung and tumor over the course of radiotherapy. Minimizing and modeling these geometrical

  19. Biomechanical deformable image registration of longitudinal lung CT images using vessel information

    NASA Astrophysics Data System (ADS)

    Cazoulat, Guillaume; Owen, Dawn; Matuszak, Martha M.; Balter, James M.; Brock, Kristy K.

    2016-07-01

    Spatial correlation of lung tissue across longitudinal images, as the patient responds to treatment, is a critical step in adaptive radiotherapy. The goal of this work is to expand a biomechanical model-based deformable registration algorithm (Morfeus) to achieve accurate registration in the presence of significant anatomical changes. Six lung cancer patients previously treated with conventionally fractionated radiotherapy were retrospectively evaluated. Exhale CT scans were obtained at treatment planning and following three weeks of treatment. For each patient, the planning CT was registered to the follow-up CT using Morfeus, a biomechanical model-based deformable registration algorithm. To model the complex response of the lung, an extension to Morfeus has been developed: an initial deformation was estimated with Morfeus consisting of boundary conditions on the chest wall and incorporating a sliding interface with the lungs. It was hypothesized that the addition of boundary conditions based on vessel tree matching would provide a robust reduction of the residual registration error. To achieve this, the vessel trees were segmented on the two images by thresholding a vesselness image based on the Hessian matrix’s eigenvalues. For each point on the reference vessel tree centerline, the displacement vector was estimated by applying a variant of the Demons registration algorithm between the planning CT and the deformed follow-up CT. An expert independently identified corresponding landmarks well distributed in the lung to compute target registration errors (TRE). The TRE was: 5.8+/- 2.9 , 3.4+/- 2.3 and 1.6+/- 1.3 mm after rigid registration, Morfeus and Morfeus with boundary conditions on the vessel tree, respectively. In conclusion, the addition of boundary conditions on the vessels significantly improved the accuracy in modeling the response of the lung and tumor over the course of radiotherapy. Minimizing and modeling these geometrical uncertainties will enable

  20. Exact surface registration of retinal surfaces from 3-D optical coherence tomography images.

    PubMed

    Lee, Sieun; Lebed, Evgeniy; Sarunic, Marinko V; Beg, Mirza Faisal

    2015-02-01

    Nonrigid registration of optical coherence tomography (OCT) images is an important problem in studying eye diseases, evaluating the effect of pharmaceuticals in treating vision loss, and performing group-wise cross-sectional analysis. High dimensional nonrigid registration algorithms required for cross-sectional and longitudinal analysis are still being developed for accurate registration of OCT image volumes, with the speckle noise in images presenting a challenge for registration. Development of algorithms for segmentation of OCT images to generate surface models of retinal layers has advanced considerably and several algorithms are now available that can segment retinal OCT images into constituent retinal surfaces. Important morphometric measurements can be extracted if accurate surface registration algorithm for registering retinal surfaces onto corresponding template surfaces were available. In this paper, we present a novel method to perform multiple and simultaneous retinal surface registration, targeted to registering surfaces extracted from ocular volumetric OCT images. This enables a point-to-point correspondence (homology) between template and subject surfaces, allowing for a direct, vertex-wise comparison of morphometric measurements across subject groups. We demonstrate that this approach can be used to localize and analyze regional changes in choroidal and nerve fiber layer thickness among healthy and glaucomatous subjects, allowing for cross-sectional population wise analysis. We also demonstrate the method's ability to track longitudinal changes in optic nerve head morphometry, allowing for within-individual tracking of morphometric changes. This method can also, in the future, be used as a precursor to 3-D OCT image registration to better initialize nonrigid image registration algorithms closer to the desired solution. PMID:25312906

  1. Deep Adaptive Log-Demons: Diffeomorphic Image Registration with Very Large Deformations

    PubMed Central

    Zhao, Liya; Jia, Kebin

    2015-01-01

    This paper proposes a new framework for capturing large and complex deformation in image registration. Traditionally, this challenging problem relies firstly on a preregistration, usually an affine matrix containing rotation, scale, and translation and afterwards on a nonrigid transformation. According to preregistration, the directly calculated affine matrix, which is obtained by limited pixel information, may misregistrate when large biases exist, thus misleading following registration subversively. To address this problem, for two-dimensional (2D) images, the two-layer deep adaptive registration framework proposed in this paper firstly accurately classifies the rotation parameter through multilayer convolutional neural networks (CNNs) and then identifies scale and translation parameters separately. For three-dimensional (3D) images, affine matrix is located through feature correspondences by a triplanar 2D CNNs. Then deformation removal is done iteratively through preregistration and demons registration. By comparison with the state-of-the-art registration framework, our method gains more accurate registration results on both synthetic and real datasets. Besides, principal component analysis (PCA) is combined with correlation like Pearson and Spearman to form new similarity standards in 2D and 3D registration. Experiment results also show faster convergence speed. PMID:26120356

  2. Deep Adaptive Log-Demons: Diffeomorphic Image Registration with Very Large Deformations.

    PubMed

    Zhao, Liya; Jia, Kebin

    2015-01-01

    This paper proposes a new framework for capturing large and complex deformation in image registration. Traditionally, this challenging problem relies firstly on a preregistration, usually an affine matrix containing rotation, scale, and translation and afterwards on a nonrigid transformation. According to preregistration, the directly calculated affine matrix, which is obtained by limited pixel information, may misregistrate when large biases exist, thus misleading following registration subversively. To address this problem, for two-dimensional (2D) images, the two-layer deep adaptive registration framework proposed in this paper firstly accurately classifies the rotation parameter through multilayer convolutional neural networks (CNNs) and then identifies scale and translation parameters separately. For three-dimensional (3D) images, affine matrix is located through feature correspondences by a triplanar 2D CNNs. Then deformation removal is done iteratively through preregistration and demons registration. By comparison with the state-of-the-art registration framework, our method gains more accurate registration results on both synthetic and real datasets. Besides, principal component analysis (PCA) is combined with correlation like Pearson and Spearman to form new similarity standards in 2D and 3D registration. Experiment results also show faster convergence speed. PMID:26120356

  3. Automatic 3D ultrasound calibration for image guided therapy using intramodality image registration

    NASA Astrophysics Data System (ADS)

    Schlosser, Jeffrey; Kirmizibayrak, Can; Shamdasani, Vijay; Metz, Steve; Hristov, Dimitre

    2013-11-01

    Many real time ultrasound (US) guided therapies can benefit from management of motion-induced anatomical changes with respect to a previously acquired computerized anatomy model. Spatial calibration is a prerequisite to transforming US image information to the reference frame of the anatomy model. We present a new method for calibrating 3D US volumes using intramodality image registration, derived from the ‘hand-eye’ calibration technique. The method is fully automated by implementing data rejection based on sensor displacements, automatic registration over overlapping image regions, and a self-consistency error metric evaluated continuously during calibration. We also present a novel method for validating US calibrations based on measurement of physical phantom displacements within US images. Both calibration and validation can be performed on arbitrary phantoms. Results indicate that normalized mutual information and localized cross correlation produce the most accurate 3D US registrations for calibration. Volumetric image alignment is more accurate and reproducible than point selection for validating the calibrations, yielding <1.5 mm root mean square error, a significant improvement relative to previously reported hand-eye US calibration results. Comparison of two different phantoms for calibration and for validation revealed significant differences for validation (p = 0.003) but not for calibration (p = 0.795).

  4. 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.

  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. 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.

  7. Simultaneous 3D–2D image registration and C-arm calibration: Application to endovascular image-guided interventions

    SciTech Connect

    Mitrović, Uroš; Pernuš, Franjo; Likar, Boštjan; Špiclin, Žiga

    2015-11-15

    Purpose: Three-dimensional to two-dimensional (3D–2D) image registration is a key to fusion and simultaneous visualization of valuable information contained in 3D pre-interventional and 2D intra-interventional images with the final goal of image guidance of a procedure. In this paper, the authors focus on 3D–2D image registration within the context of intracranial endovascular image-guided interventions (EIGIs), where the 3D and 2D images are generally acquired with the same C-arm system. The accuracy and robustness of any 3D–2D registration method, to be used in a clinical setting, is influenced by (1) the method itself, (2) uncertainty of initial pose of the 3D image from which registration starts, (3) uncertainty of C-arm’s geometry and pose, and (4) the number of 2D intra-interventional images used for registration, which is generally one and at most two. The study of these influences requires rigorous and objective validation of any 3D–2D registration method against a highly accurate reference or “gold standard” registration, performed on clinical image datasets acquired in the context of the intervention. Methods: The registration process is split into two sequential, i.e., initial and final, registration stages. The initial stage is either machine-based or template matching. The latter aims to reduce possibly large in-plane translation errors by matching a projection of the 3D vessel model and 2D image. In the final registration stage, four state-of-the-art intrinsic image-based 3D–2D registration methods, which involve simultaneous refinement of rigid-body and C-arm parameters, are evaluated. For objective validation, the authors acquired an image database of 15 patients undergoing cerebral EIGI, for which accurate gold standard registrations were established by fiducial marker coregistration. Results: Based on target registration error, the obtained success rates of 3D to a single 2D image registration after initial machine-based and

  8. 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. PMID:26736903

  9. 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.

  10. NOTE: A comparison of image registration techniques for the correlation of radiolabelled antibody distribution with tumour morphology

    NASA Astrophysics Data System (ADS)

    Flynn, A. A.; Green, A. J.; Boxer, G.; Pedley, R. B.; Begent, R. H. J.

    1999-07-01

    Image registration is a powerful tool for correlating functional images with images of anatomical structure. This facilitates more accurate quantitation of regional radiopharmaceutical uptake. Similarly, registration of images of radiolabelled antibody distribution, in tissue sections, with the equivalent histological images allows the comparison and measurement of radiopharmaceutical distribution with morphological structure. The images used were obtained by storage phosphor plate technology, for the radiopharmaceutical distribution, and by digitization of the stained histological sections. Here we compare four fully automatic registration techniques and one manual technique in terms of their spatial accuracy. We have found that there was no difference in accuracy between cross-correlation, minimization of variance and mutual information. These techniques were more accurate than principal axes and the manual technique. However, minimization of variance and mutual information were more time-consuming than the other methods. Consequently, cross-correlation is the method of choice for automatic registration of large numbers of these image pairs.

  11. Lucas-Kanade image registration using camera parameters

    NASA Astrophysics Data System (ADS)

    Cho, Sunghyun; Cho, Hojin; Tai, Yu-Wing; Moon, Young Su; Cho, Junguk; Lee, Shihwa; Lee, Seungyong

    2012-01-01

    The Lucas-Kanade algorithm and its variants have been successfully used for numerous works in computer vision, which include image registration as a component in the process. In this paper, we propose a Lucas-Kanade based image registration method using camera parameters. We decompose a homography into camera intrinsic and extrinsic parameters, and assume that the intrinsic parameters are given, e.g., from the EXIF information of a photograph. We then estimate only the extrinsic parameters for image registration, considering two types of camera motions, 3D rotations and full 3D motions with translations and rotations. As the known information about the camera is fully utilized, the proposed method can perform image registration more reliably. In addition, as the number of extrinsic parameters is smaller than the number of homography elements, our method runs faster than the Lucas-Kanade based registration method that estimates a homography itself.

  12. Analytic regularization for landmark-based image registration

    NASA Astrophysics Data System (ADS)

    Shusharina, Nadezhda; Sharp, Gregory

    2012-03-01

    Landmark-based registration using radial basis functions (RBF) is an efficient and mathematically transparent method for the registration of medical images. To ensure invertibility and diffeomorphism of the RBF-based vector field, various regularization schemes have been suggested. Here, we report a novel analytic method of RBF regularization and demonstrate its power for Gaussian RBF. Our analytic formula can be used to obtain a regularized vector field from the solution of a system of linear equations, exactly as in traditional RBF, and can be generalized to any RBF with infinite support. We statistically validate the method on global registration of synthetic and pulmonary images. Furthermore, we present several clinical examples of multistage intensity/landmark-based registrations, where regularized Gaussian RBF are successful in correcting locally misregistered areas resulting from automatic B-spline registration. The intended ultimate application of our method is rapid, interactive local correction of deformable registration with a small number of mouse clicks.

  13. 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.

  14. 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.

  15. Registration of Laser Scanning Point Clouds and Aerial Images Using either Artificial or Natural Tie Features

    NASA Astrophysics Data System (ADS)

    Rönnholm, P.; Haggrén, H.

    2012-07-01

    Integration of laser scanning data and photographs is an excellent combination regarding both redundancy and complementary. Applications of integration vary from sensor and data calibration to advanced classification and scene understanding. In this research, only airborne laser scanning and aerial images are considered. Currently, the initial registration is solved using direct orientation sensors GPS and inertial measurements. However, the accuracy is not usually sufficient for reliable integration of data sets, and thus the initial registration needs to be improved. A registration of data from different sources requires searching and measuring of accurate tie features. Usually, points, lines or planes are preferred as tie features. Therefore, the majority of resent methods rely highly on artificial objects, such as buildings, targets or road paintings. However, in many areas no such objects are available. For example in forestry areas, it would be advantageous to be able to improve registration between laser data and images without making additional ground measurements. Therefore, there is a need to solve registration using only natural features, such as vegetation and ground surfaces. Using vegetation as tie features is challenging, because the shape and even location of vegetation can change because of wind, for example. The aim of this article was to compare registration accuracies derived by using either artificial or natural tie features. The test area included urban objects as well as trees and other vegetation. In this area, two registrations were performed, firstly, using mainly built objects and, secondly, using only vegetation and ground surface. The registrations were solved applying the interactive orientation method. As a result, using artificial tie features leaded to a successful registration in all directions of the coordinate system axes. In the case of using natural tie features, however, the detection of correct heights was difficult causing

  16. High-accuracy 3D image-based registration of endoscopic video to C-arm cone-beam CT for image-guided skull base surgery

    NASA Astrophysics Data System (ADS)

    Mirota, Daniel J.; Uneri, Ali; Schafer, Sebastian; Nithiananthan, Sajendra; Reh, Douglas D.; Gallia, Gary L.; Taylor, Russell H.; Hager, Gregory D.; Siewerdsen, Jeffrey H.

    2011-03-01

    Registration of endoscopic video to preoperative CT facilitates high-precision surgery of the head, neck, and skull-base. Conventional video-CT registration is limited by the accuracy of the tracker and does not use the underlying video or CT image data. A new image-based video registration method has been developed to overcome the limitations of conventional tracker-based registration. This method adds to a navigation system based on intraoperative C-arm cone-beam CT (CBCT), in turn providing high-accuracy registration of video to the surgical scene. The resulting registration enables visualization of the CBCT and planning data within the endoscopic video. The system incorporates a mobile C-arm, integrated with an optical tracking system, video endoscopy, deformable registration of preoperative CT with intraoperative CBCT, and 3D visualization. Similarly to tracker-based approach, the image-based video-CBCT registration the endoscope is localized with optical tracking system followed by a direct 3D image-based registration of the video to the CBCT. In this way, the system achieves video-CBCT registration that is both fast and accurate. Application in skull-base surgery demonstrates overlay of critical structures (e.g., carotid arteries) and surgical targets with sub-mm accuracy. Phantom and cadaver experiments show consistent improvement of target registration error (TRE) in video overlay over conventional tracker-based registration-e.g., 0.92mm versus 1.82mm for image-based and tracker-based registration, respectively. The proposed method represents a two-fold advance-first, through registration of video to up-to-date intraoperative CBCT, and second, through direct 3D image-based video-CBCT registration, which together provide more confident visualization of target and normal tissues within up-to-date images.

  17. Effect of vertebral surface extraction on registration accuracy: a comparison of registration results for iso-intensity algorithms applied to computed tomography images

    NASA Astrophysics Data System (ADS)

    Herring, Jeannette L.; Maurer, Calvin R., Jr.; Muratore, Diane M.; Galloway, Robert L., Jr.; Dawant, Benoit M.

    1999-05-01

    This paper presents a comparison of iso-intensity-based surface extraction algorithms applied to computed tomography (CT) images of the spine. The extracted vertebral surfaces are used in surface-based registration of CT images to physical space, where our ultimate goal is the development of a technique that can be used for image-guided spinal surgery. The surface extraction process has a direct effect on image-guided surgery in two ways: the extracted surface must provide an accurate representation of the actual surface so that a good registration can be achieved, and the number of polygons in the mesh representation of the extracted surface must be small enough to allow the registration to be performed quickly. To examine the effect of the surface extraction process on registration error and run time, we have performed a large number of experiments on two plastic spine phantoms. Using a marker-based system to assess accuracy, we have found that submillimetric registration accuracy can be achieved using a point-to- surface registration algorithm with simplified and unsimplified members of the general class of iso-intensity- based surface extraction algorithms. This research has practical implications, since it shows that several versions of the widely available class of intensity-based surface extraction algorithms can be used to provide sufficient accuracy for vertebral registration. Since intensity-based algorithms are completely deterministic and fully automatic, this finding simplifies the pre-processing required for image-guided back surgery.

  18. A registration algorithm of improved correlation coefficient for image of rotation and scaling

    NASA Astrophysics Data System (ADS)

    Wei, Chun-tao; Hu, Tao; Yuan, Kai-min

    2015-12-01

    In stereo vision technology, image matching is one of the most important parts, and the coefficient of correlation matching is recognized to be more mature and stable matching algorithm. Correlation coefficient method has high sensitivity to image rotation, but do not have rotation invariance, and require a large computational complexity. Because of this it cannot be widely applied in the field of real-time image matching. This paper is aimed at this drawback to make its computational complexity greatly reduced, posses the scale and rotation invariance, so as to meet the requirements of real-time image matching system, this paper proposes a image registration algorithm of accurate registration combined with Fourier-Mellin transform and Radon transform of image. After the introduction of Fourier transform and correlation coefficient method to detect the correct rotation factor and scale factor, it is provided a reliable basis for correlation coefficient method of image registration to achieve both rotation and scaling invariance, image using this method is verified by the experiments on the feasibility of the registration, the registration accuracy is improved.

  19. Accurate and robust registration of high-speed railway viaduct point clouds using closing conditions and external geometric constraints

    NASA Astrophysics Data System (ADS)

    Ji, Zheng; Song, Mengxiao; Guan, Haiyan; Yu, Yongtao

    2015-08-01

    This paper proposes an automatic method for registering multiple laser scans without a control network. The proposed registration method first uses artificial targets to pair-wise register adjacent scans for initial transformation estimates; the proposed registration method then employs combined adjustments with closing conditions and external triangle constraints to globally register all scans along a long-range, high-speed railway corridor. The proposed registration method uses (1) closing conditions to eliminate registration errors that gradually accumulate as the length of a corridor (the number of scan stations) increases, and (2) external geometric constraints to ensure the shape correctness of an elongated high-speed railway. A 640-m high-speed railway viaduct with twenty-one piers is used to conduct experiments using our proposed registration method. A group of comparative experiments is undertaken to evaluate the robustness and efficiency of the proposed registration method to accurately register long-range corridors.

  20. Real-time SPECT and 2D ultrasound image registration.

    PubMed

    Bucki, Marek; Chassat, Fabrice; Galdames, Francisco; Asahi, Takeshi; Pizarro, Daniel; Lobo, Gabriel

    2007-01-01

    In this paper we present a technique for fully automatic, real-time 3D SPECT (Single Photon Emitting Computed Tomography) and 2D ultrasound image registration. We use this technique in the context of kidney lesion diagnosis. Our registration algorithm allows a physician to perform an ultrasound exam after a SPECT image has been acquired and see in real time the registration of both modalities. An automatic segmentation algorithm has been implemented in order to display in 3D the positions of the acquired US images with respect to the organs. PMID:18044572

  1. 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. PMID:19890483

  2. Accurate, fully-automated registration of coronary arteries for volumetric CT digital subtraction angiography

    NASA Astrophysics Data System (ADS)

    Razeto, Marco; Mohr, Brian; Arakita, Kazumasa; Schuijf, Joanne D.; Fuchs, Andreas; Kühl, J. Tobias; Chen, Marcus Y.; Kofoed, Klaus F.

    2014-03-01

    Diagnosis of coronary artery disease with Coronary Computed Tomography Angiography (CCTA) is complicated by the presence of signi cant calci cation or stents. Volumetric CT Digital Subtraction Angiography (CTDSA) has recently been shown to be e ective at overcoming these limitations. Precise registration of structures is essential as any misalignment can produce artifacts potentially inhibiting clinical interpretation of the data. The fully-automated registration method described in this paper addresses the problem by combining a dense deformation eld with rigid-body transformations where calci cations/stents are present. The method contains non-rigid and rigid components. Non-rigid registration recovers the majority of motion artifacts and produces a dense deformation eld valid over the entire scan domain. Discrete domains are identi ed in which rigid registrations very accurately align each calci cation/stent. These rigid-body transformations are combined within the immediate area of the deformation eld using a distance transform to minimize distortion of the surrounding tissue. A recent interim analysis of a clinical feasibility study evaluated reader con dence and diagnostic accuracy in conventional CCTA and CTDSA registered using this method. Conventional invasive coronary angiography was used as the reference. The study included 27 patients scanned with a second-generation 320-row CT detector in which 41 lesions were identi ed. Compared to conventional CCTA, CTDSA improved reader con dence in 13/36 (36%) of segments with severe calci cation and 3/5 (60%) of segments with coronary stents. Also, the false positive rate of CTDSA was reduced compared to conventional CCTA from 18% (24/130) to 14% (19/130).

  3. A Novel Technique for Prealignment in Multimodality Medical Image Registration

    PubMed Central

    Zhou, Wu; Zhang, Lijuan; Xie, Yaoqin; Liang, Changhong

    2014-01-01

    Image pair is often aligned initially based on a rigid or affine transformation before a deformable registration method is applied in medical image registration. Inappropriate initial registration may compromise the registration speed or impede the convergence of the optimization algorithm. In this work, a novel technique was proposed for prealignment in both monomodality and multimodality image registration based on statistical correlation of gradient information. A simple and robust algorithm was proposed to determine the rotational differences between two images based on orientation histogram matching accumulated from local orientation of each pixel without any feature extraction. Experimental results showed that it was effective to acquire the orientation angle between two unregistered images with advantages over the existed method based on edge-map in multimodalities. Applying the orientation detection into the registration of CT/MR, T1/T2 MRI, and monomadality images with respect to rigid and nonrigid deformation improved the chances of finding the global optimization of the registration and reduced the search space of optimization. PMID:25162024

  4. Comparing nonrigid registration techniques for motion corrected MR prostate diffusion imaging

    SciTech Connect

    Buerger, C. Sénégas, J.; Kabus, S.; Carolus, H.; Schulz, H.; Renisch, S.; Agarwal, H.; Turkbey, B.; Choyke, P. L.

    2015-01-15

    Purpose: T{sub 2}-weighted magnetic resonance imaging (MRI) is commonly used for anatomical visualization in the pelvis area, such as the prostate, with high soft-tissue contrast. MRI can also provide functional information such as diffusion-weighted imaging (DWI) which depicts the molecular diffusion processes in biological tissues. The combination of anatomical and functional imaging techniques is widely used in oncology, e.g., for prostate cancer diagnosis and staging. However, acquisition-specific distortions as well as physiological motion lead to misalignments between T{sub 2} and DWI and consequently to a reduced diagnostic value. Image registration algorithms are commonly employed to correct for such misalignment. Methods: The authors compare the performance of five state-of-the-art nonrigid image registration techniques for accurate image fusion of DWI with T{sub 2}. Results: Image data of 20 prostate patients with cancerous lesions or cysts were acquired. All registration algorithms were validated using intensity-based as well as landmark-based techniques. Conclusions: The authors’ results show that the “fast elastic image registration” provides most accurate results with a target registration error of 1.07 ± 0.41 mm at minimum execution times of 11 ± 1 s.

  5. 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

  6. INTER-GROUP IMAGE REGISTRATION BY HIERARCHICAL GRAPH SHRINKAGE

    PubMed Central

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

    2013-01-01

    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. PMID:24443692

  7. 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.

  8. Registration of multitemporal low-resolution synthetic aperture radar images based on a new similarity measure

    NASA Astrophysics Data System (ADS)

    Ren, Weilong; Song, Jianshe; Zhang, Xiongmei; Cai, Xingfu

    2016-01-01

    Image registration is concerned with the precise overlap of two images. One challenging problem in this area is the registration of low-resolution synthetic aperture radar (SAR) images. In general, extracting feature points from such images is difficult due to the coarse observation and the severe speckle. The use of area similarity for image registration is another important branch to solve the problem. A similarity measure based on a conditional density function (cdf) is proposed. The cdf is specially tailored for SAR images, where the speckle is generally assumed as multiplicative gamma noise with unit mean. Additionally, a two-step procedure is devised for the registration of intro-model SAR images to improve the computational efficiency. First, the two images are roughly aligned considering only the translational difference. Then small blocks from the two images are accurately aligned and the center point of each block is treated as a control point, which is finally used to obtain the precise affine transformation between the two images. Five SAR image datasets are tested in the experiment part, and the results demonstrate the efficiency and accuracy of the proposed method.

  9. Patient-specific biomechanical model as whole-body CT image registration tool.

    PubMed

    Li, Mao; Miller, Karol; Joldes, Grand Roman; Doyle, Barry; Garlapati, Revanth Reddy; Kikinis, Ron; Wittek, Adam

    2015-05-01

    Whole-body computed tomography (CT) image registration is important for cancer diagnosis, therapy planning and treatment. Such registration requires accounting for large differences between source and target images caused by deformations of soft organs/tissues and articulated motion of skeletal structures. The registration algorithms relying solely on image processing methods exhibit deficiencies in accounting for such deformations and motion. We propose to predict the deformations and movements of body organs/tissues and skeletal structures for whole-body CT image registration using patient-specific non-linear biomechanical modelling. Unlike the conventional biomechanical modelling, our approach for building the biomechanical models does not require time-consuming segmentation of CT scans to divide the whole body into non-overlapping constituents with different material properties. Instead, a Fuzzy C-Means (FCM) algorithm is used for tissue classification to assign the constitutive properties automatically at integration points of the computation grid. We use only very simple segmentation of the spine when determining vertebrae displacements to define loading for biomechanical models. We demonstrate the feasibility and accuracy of our approach on CT images of seven patients suffering from cancer and aortic disease. The results confirm that accurate whole-body CT image registration can be achieved using a patient-specific non-linear biomechanical model constructed without time-consuming segmentation of the whole-body images. PMID:25721296

  10. Patient-Specific Biomechanical Model as Whole-Body CT Image Registration Tool

    PubMed Central

    Li, Mao; Miller, Karol; Joldes, Grand Roman; Doyle, Barry; Garlapati, Revanth Reddy; Kikinis, Ron; Wittek, Adam

    2015-01-01

    Whole-body computed tomography (CT) image registration is important for cancer diagnosis, therapy planning and treatment. Such registration requires accounting for large differences between source and target images caused by deformations of soft organs/tissues and articulated motion of skeletal structures. The registration algorithms relying solely on image processing methods exhibit deficiencies in accounting for such deformations and motion. We propose to predict the deformations and movements of body organs/tissues and skeletal structures for whole-body CT image registration using patient-specific non-linear biomechanical modelling. Unlike the conventional biomechanical modelling, our approach for building the biomechanical models does not require time-consuming segmentation of CT scans to divide the whole body into non-overlapping constituents with different material properties. Instead, a Fuzzy C-Means (FCM) algorithm is used for tissue classification to assign the constitutive properties automatically at integration points of the computation grid. We use only very simple segmentation of the spine when determining vertebrae displacements to define loading for biomechanical models. We demonstrate the feasibility and accuracy of our approach on CT images of seven patients suffering from cancer and aortic disease. The results confirm that accurate whole-body CT image registration can be achieved using a patient-specific non-linear biomechanical model constructed without time-consuming segmentation of the whole-body images. PMID:25721296

  11. 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.

  12. Rigid 2D/3D registration of intraoperative digital x-ray images and preoperative CT and MR images

    NASA Astrophysics Data System (ADS)

    Tomazevic, Dejan; Likar, Bostjan; Pernus, Franjo

    2002-05-01

    This paper describes a novel approach to register 3D computed tomography (CT) or magnetic resonance (MR) images to a set of 2D X-ray images. Such a registration may be a valuable tool for intraoperative determination of the precise position and orientation of some anatomy of interest, defined in preoperative images. The registration is based solely on the information present in 2D and 3D images. It does not require fiducial markers, X-ray image segmentation, or construction of digitally reconstructed radiographs. The originality of the approach is in using normals to bone surfaces, preoperatively defined in 3D MR or CT data, and gradients of intraoperative X-ray images, which are back-projected towards the X-ray source. The registration is then concerned with finding that rigid transformation of a CT or MR volume, which provides the best match between surface normals and back projected gradients, considering their amplitudes and orientations. The method is tested on a lumbar spine phantom. Gold standard registration is obtained by fidicual markers attached to the phantom. Volumes of interest, containing single vertebrae, are registered to different pairs of X-ray images from different starting positions, chosen randomly and uniformly around the gold standard position. Target registration errors and rotation errors are in order of 0.3 mm and 0.35 degrees for the CT to X-ray registration and 1.3 mm and 1.5 degrees for MR to X-ray registration. The registration is shown to be fast and accurate.

  13. Co-registration and distortion correction of diffusion and anatomical images based on inverse contrast normalization.

    PubMed

    Bhushan, Chitresh; Haldar, Justin P; Choi, Soyoung; Joshi, Anand A; Shattuck, David W; Leahy, Richard M

    2015-07-15

    Diffusion MRI provides quantitative information about microstructural properties which can be useful in neuroimaging studies of the human brain. Echo planar imaging (EPI) sequences, which are frequently used for acquisition of diffusion images, are sensitive to inhomogeneities in the primary magnetic (B0) field that cause localized distortions in the reconstructed images. We describe and evaluate a new method for correction of susceptibility-induced distortion in diffusion images in the absence of an accurate B0 fieldmap. In our method, the distortion field is estimated using a constrained non-rigid registration between an undistorted T1-weighted anatomical image and one of the distorted EPI images from diffusion acquisition. Our registration framework is based on a new approach, INVERSION (Inverse contrast Normalization for VERy Simple registratION), which exploits the inverted contrast relationship between T1- and T2-weighted brain images to define a simple and robust similarity measure. We also describe how INVERSION can be used for rigid alignment of diffusion images and T1-weighted anatomical images. Our approach is evaluated with multiple in vivo datasets acquired with different acquisition parameters. Compared to other methods, INVERSION shows robust and consistent performance in rigid registration and shows improved alignment of diffusion and anatomical images relative to normalized mutual information for non-rigid distortion correction. PMID:25827811

  14. Nonrigid Medical Image Registration Based on Mesh Deformation Constraints

    PubMed Central

    Qiu, TianShuang; Guo, DongMei

    2013-01-01

    Regularizing the deformation field is an important aspect in nonrigid medical image registration. By covering the template image with a triangular mesh, this paper proposes a new regularization constraint in terms of connections between mesh vertices. The connection relationship is preserved by the spring analogy method. The method is evaluated by registering cerebral magnetic resonance imaging (MRI) image data obtained from different individuals. Experimental results show that the proposed method has good deformation ability and topology-preserving ability, providing a new way to the nonrigid medical image registration. PMID:23424604

  15. Rapid pedobarographic image registration based on contour curvature and optimization.

    PubMed

    Oliveira, Francisco P M; Tavares, João Manuel R S; Pataky, Todd C

    2009-11-13

    Image registration, the process of optimally aligning homologous structures in multiple images, has recently been demonstrated to support automated pixel-level analysis of pedobarographic images and, subsequently, to extract unique and biomechanically relevant information from plantar pressure data. Recent registration methods have focused on robustness, with slow but globally powerful algorithms. In this paper, we present an alternative registration approach that affords both speed and accuracy, with the goal of making pedobarographic image registration more practical for near-real-time laboratory and clinical applications. The current algorithm first extracts centroid-based curvature trajectories from pressure image contours, and then optimally matches these curvature profiles using optimization based on dynamic programming. Special cases of disconnected images (that occur in high-arched subjects, for example) are dealt with by introducing an artificial spatially linear bridge between adjacent image clusters. Two registration algorithms were developed: a 'geometric' algorithm, which exclusively matched geometry, and a 'hybrid' algorithm, which performed subsequent pseudo-optimization. After testing the two algorithms on 30 control image pairs considered in a previous study, we found that, when compared with previously published results, the hybrid algorithm improved overlap ratio (p=0.010), but both current algorithms had slightly higher mean-squared error, assumedly because they did not consider pixel intensity. Nonetheless, both algorithms greatly improved the computational efficiency (25+/-8 and 53+/-9 ms per image pair for geometric and hybrid registrations, respectively). These results imply that registration-based pixel-level pressure image analyses can, eventually, be implemented for practical clinical purposes. PMID:19647829

  16. 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.

  17. Infrared image non-rigid registration based on regional information entropy demons algorithm

    NASA Astrophysics Data System (ADS)

    Lu, Chaoliang; Ma, Lihua; Yu, Ming; Cui, Shumin; Wu, Qingrong

    2015-02-01

    Infrared imaging fault detection which is treated as an ideal, non-contact, non-destructive testing method is applied to the circuit board fault detection. Since Infrared images obtained by handheld infrared camera with wide-angle lens have both rigid and non-rigid deformations. To solve this problem, a new demons algorithm based on regional information entropy was proposed. The new method overcame the shortcomings of traditional demons algorithm that was sensitive to the intensity. First, the information entropy image was gotten by computing regional information entropy of the image. Then, the deformation between the two images was calculated that was the same as demons algorithm. Experimental results demonstrated that the proposed algorithm has better robustness in intensity inconsistent images registration compared with the traditional demons algorithm. Achieving accurate registration between intensity inconsistent infrared images provided strong support for the temperature contrast.

  18. A novel scheme for automatic nonrigid image registration using deformation invariant feature and geometric constraint

    NASA Astrophysics Data System (ADS)

    Deng, Zhipeng; Lei, Lin; Zhou, Shilin

    2015-10-01

    Automatic image registration is a vital yet challenging task, particularly for non-rigid deformation images which are more complicated and common in remote sensing images, such as distorted UAV (unmanned aerial vehicle) images or scanning imaging images caused by flutter. Traditional non-rigid image registration methods are based on the correctly matched corresponding landmarks, which usually needs artificial markers. It is a rather challenging task to locate the accurate position of the points and get accurate homonymy point sets. In this paper, we proposed an automatic non-rigid image registration algorithm which mainly consists of three steps: To begin with, we introduce an automatic feature point extraction method based on non-linear scale space and uniform distribution strategy to extract the points which are uniform distributed along the edge of the image. Next, we propose a hybrid point matching algorithm using DaLI (Deformation and Light Invariant) descriptor and local affine invariant geometric constraint based on triangulation which is constructed by K-nearest neighbor algorithm. Based on the accurate homonymy point sets, the two images are registrated by the model of TPS (Thin Plate Spline). Our method is demonstrated by three deliberately designed experiments. The first two experiments are designed to evaluate the distribution of point set and the correctly matching rate on synthetic data and real data respectively. The last experiment is designed on the non-rigid deformation remote sensing images and the three experimental results demonstrate the accuracy, robustness, and efficiency of the proposed algorithm compared with other traditional methods.

  19. Light Field Imaging Based Accurate Image Specular Highlight Removal

    PubMed Central

    Wang, Haoqian; Xu, Chenxue; Wang, Xingzheng; Zhang, Yongbing; Peng, Bo

    2016-01-01

    Specular reflection removal is indispensable to many computer vision tasks. However, most existing methods fail or degrade in complex real scenarios for their individual drawbacks. Benefiting from the light field imaging technology, this paper proposes a novel and accurate approach to remove specularity and improve image quality. We first capture images with specularity by the light field camera (Lytro ILLUM). After accurately estimating the image depth, a simple and concise threshold strategy is adopted to cluster the specular pixels into “unsaturated” and “saturated” category. Finally, a color variance analysis of multiple views and a local color refinement are individually conducted on the two categories to recover diffuse color information. Experimental evaluation by comparison with existed methods based on our light field dataset together with Stanford light field archive verifies the effectiveness of our proposed algorithm. PMID:27253083

  20. 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. PMID:27000285

  1. Tendon strain imaging using non-rigid image registration: a validation study

    NASA Astrophysics Data System (ADS)

    Almeida, Nuno M.; Slagmolen, Pieter; Barbosa, Daniel; Scheys, Lennart; Geukens, Leonie; Fukagawa, Shingo; Peers, Koen; Bellemans, Johan; Suetens, Paul; D'Hooge, Jan

    2012-03-01

    Ultrasound image has already been proved to be a useful tool for non-invasive strain quantifications in soft tissue. While clinical applications only include cardiac imaging, the development of techniques suitable for musculoskeletal system is an active area of research. On this study, a technique for speckle tracking on ultrasound images using non-rigid image registration is presented. This approach is based on a single 2D+t registration procedure, in which the temporal changes on the B-mode speckle patterns are locally assessed. This allows estimating strain from ultrasound image sequences of tissues under deformation while imposing temporal smoothness in the deformation field, originating smooth strain curves. METHODS: The tracking algorithm was systematically tested on synthetic images and gelatin phantoms, under sinusoidal deformations with amplitudes between 0.5% and 4.0%, at frequencies between 0.25Hz and 2.0Hz. Preliminary tests were also performed on Achilles tendons isolated from human cadavers. RESULTS: The strain was estimated with deviations of -0.011%+/-0.053% on the synthetic images and agreements of +/-0.28% on the phantoms. Some tests with real tendons show good tracking results. However, significant variability between the trials still exists. CONCLUSIONS: The proposed image registration methodology constitutes a robust tool for motion and deformation tracking in both simulated and real phantom data. Strain estimation in both cases reveals that the proposed method is accurate and provides good precision. Although the ex-vivo results are still preliminary, the potential of the proposed algorithm is promising. This suggests that further improvements, together with systematic testing, can lead to in-vivo and clinical applications.

  2. High-performance automatic image registration for remote sensing

    NASA Astrophysics Data System (ADS)

    Chalermwat, Prachya

    Image registration is one of the crucial steps in the analysis of remotely sensed data. A new acquired image must be transformed, using image registration techniques, to match the orientation and scale of previous related images. Image registration requires intensive computational effort not only because of its computational complexity, but also due to the continuous increase in image resolution and spectral bands. Thus, high-performance computing techniques for image registration are critically needed. Very few works have addressed image registration on contemporary high-performance computing systems. Furthermore, issues of load balancing, scalability, and formal analysis of algorithmic efficiency were seldom considered. This dissertation introduces high-performance automatic image registration (HAIR) algorithms. High performance is achieved by: (1) reduction in search data, (2) reduction in search space, and (3) parallel processing. Reduction in search data is achieved by performing registration using only subimages. A new metric called registrability is used to select those subimages such that accuracy is maintained. In addition, a histogram comparison is used to discard anomalous subimages, such as those with clouds. Further data reduction is obtained using an iterative refinement search (IRA), which exploits the wavelet multi-resolution representation. This technique starts searching images with lower resolution first, then refining the results using higher resolution images to use the least possible data points in the overall registration task. Reduction of search space is achieved through two methods. First, iterative refinement reduces dramatically the number of solutions examined. In addition, genetic algorithms were also used to further expedite the search. Parallel processing techniques have been utilized to provide coarse-grain load-balanced parallel algorithms based on iterative refinement as well as genetic algorithms. Two hybrid algorithms have been

  3. 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

  4. Registration of challenging pre-clinical brain images

    PubMed Central

    Crum, William R.; Modo, Michel; Vernon, Anthony C.; Barker, Gareth J.; Williams, Steven C.R.

    2013-01-01

    The size and complexity of brain imaging studies in pre-clinical populations are increasing, and automated image analysis pipelines are urgently required. Pre-clinical populations can be subjected to controlled interventions (e.g., targeted lesions), which significantly change the appearance of the brain obtained by imaging. Existing systems for registration (the systematic alignment of scans into a consistent anatomical coordinate system), which assume image similarity to a reference scan, may fail when applied to these images. However, affine registration is a particularly vital pre-processing step for subsequent image analysis which is assumed to be an effective procedure in recent literature describing sophisticated techniques such as manifold learning. Therefore, in this paper, we present an affine registration solution that uses a graphical model of a population to decompose difficult pairwise registrations into a composition of steps using other members of the population. We developed this methodology in the context of a pre-clinical model of stroke in which large, variable hyper-intense lesions significantly impact registration performance. We tested this technique systematically in a simulated human population of brain tumour images before applying it to pre-clinical models of Parkinson's disease and stroke. PMID:23558335

  5. 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.

  6. Target location for IR image based on IR/visual image registration

    NASA Astrophysics Data System (ADS)

    Liu, Zhao-ying; Zhou, Fu-gen; Bai, Xiang-zhi

    2009-07-01

    We propose an effective algorithm of IR target location based on image registration. This approach includes four steps--pre-processing, typical region and feature points extraction, point pattern matching, target location. Firstly, by analying the characters of the visual and IR images, a pre-processing procedure is introduced to improve the IR image quality and to make the gray distribution in IR and visual images more consistent. Secondly, mathematical morphology is used to extract typical regions around the target, and we mark the feature points based on the extracted typical regions. Thirdly, point pattern matching algorithm is applied to realize the preliminary registration of IR/visual images, triangle geometry similarity is utilized as the similarity measure to establish two points set correspondance. Finally, we take twostage location strategy to accurately locate the IR targets, least square method and mutual information theory are applied in the location strategy. Experiment results demonstrate a high rate (above 93%) of success for predicting target location, the results showed that this method can effectively meet the requirement of target detection in low resolution and low contrast IR images.

  7. 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.

  8. Registration of partially overlapping laser-radar range images

    NASA Astrophysics Data System (ADS)

    Lv, Dan; Sun, Jian-Feng; Li, Qi; Wang, Qi

    2015-10-01

    To register partially overlapping three-dimensional point sets from different viewpoints, it is necessary to remove spurious corresponding point pairs that are not located in overlapping regions. Most variants of the iterative closest point (ICP) algorithm require users to manually select the rejection parameters for discarding spurious point pairs between the registering views. This requirement often results in unreliable and inaccurate registration. To overcome this problem, we present an improved ICP algorithm that can automatically determine the rejection percentage to reliably and accurately align partially overlapping laser-radar (ladar) range images. The similarity of k neighboring features of each nonplanar point is employed to determine reasonable point pairs in nonplanar regions, and the distance measurement method is used to find reasonable point pairs in planar regions. The rejection percentage can be obtained from these two sets of reasonable pairs. The performance of our algorithm is compared with that of five other algorithms using various models with low and high curvatures. The experimental results show that our algorithm is more accurate and robust than the other algorithms.

  9. 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

  10. Automatic registration between reference and on-board digital tomosynthesis images for positioning verification.

    PubMed

    Ren, Lei; Godfrey, Devon J; Yan, Hui; Wu, Q Jackie; Yin, Fang-Fang

    2008-02-01

    The authors developed a hybrid multiresolution rigid-body registration technique to automatically register reference digital tomosynthesis (DTS) images with on-board DTS images to guide patient positioning in radiation therapy. This hybrid registration technique uses a faster but less accurate static method to achieve an initial registration, followed by a slower but more accurate adaptive method to fine tune the registration. A multiresolution scheme is employed in the registration to further improve the registration accuracy, robustness, and efficiency. Normalized mutual information is selected as the criterion for the similarity measure and the downhill simplex method is used as the search engine. This technique was tested using image data both from an anthropomorphic chest phantom and from eight head-and-neck cancer patients. The effects of the scan angle and the region-of-interest (ROI) size on the registration accuracy and robustness were investigated. The necessity of using the adaptive registration method in the hybrid technique was validated by comparing the results of the static method and the hybrid method. With a 44 degrees scan angle and a large ROI covering the entire DTS volume, the average of the registration capture ranges in single-axis simulations was between -31 and +34 deg for rotations and between -89 and +78 mm for translations in the phantom study, and between -38 and +38 deg for rotations and between -58 and +65 mm for translations in the patient study. Decreasing the DTS scan angle from 44 degrees to 22 degrees mainly degraded the registration accuracy and robustness for the out-of-plane rotations. Decreasing the ROI size from the entire DTS volume to the volume surrounding the spinal cord reduced the capture ranges to between -23 and +18 deg for rotations and between -33 and +43 mm for translations in the phantom study, and between -18 and +25 deg for rotations and between -35 and +39 mm for translations in the patient study. Results also

  11. Automatic registration between reference and on-board digital tomosynthesis images for positioning verification

    SciTech Connect

    Ren Lei; Godfrey, Devon J.; Yan, Hui; Wu, Q. Jackie; Yin, Fang-Fang

    2008-02-15

    The authors developed a hybrid multiresolution rigid-body registration technique to automatically register reference digital tomosynthesis (DTS) images with on-board DTS images to guide patient positioning in radiation therapy. This hybrid registration technique uses a faster but less accurate static method to achieve an initial registration, followed by a slower but more accurate adaptive method to fine tune the registration. A multiresolution scheme is employed in the registration to further improve the registration accuracy, robustness, and efficiency. Normalized mutual information is selected as the criterion for the similarity measure and the downhill simplex method is used as the search engine. This technique was tested using image data both from an anthropomorphic chest phantom and from eight head-and-neck cancer patients. The effects of the scan angle and the region-of-interest (ROI) size on the registration accuracy and robustness were investigated. The necessity of using the adaptive registration method in the hybrid technique was validated by comparing the results of the static method and the hybrid method. With a 44 deg. scan angle and a large ROI covering the entire DTS volume, the average of the registration capture ranges in single-axis simulations was between -31 and +34 deg. for rotations and between -89 and +78 mm for translations in the phantom study, and between -38 and +38 deg. for rotations and between -58 and +65 mm for translations in the patient study. Decreasing the DTS scan angle from 44 deg. to 22 deg. mainly degraded the registration accuracy and robustness for the out-of-plane rotations. Decreasing the ROI size from the entire DTS volume to the volume surrounding the spinal cord reduced the capture ranges to between -23 and +18 deg. for rotations and between -33 and +43 mm for translations in the phantom study, and between -18 and +25 deg. for rotations and between -35 and +39 mm for translations in the patient study. Results also

  12. A Rigid Image Registration Based on the Nonsubsampled Contourlet Transform and Genetic Algorithms

    PubMed Central

    Meskine, Fatiha; Chikr El Mezouar, Miloud; Taleb, Nasreddine

    2010-01-01

    Image registration is a fundamental task used in image processing to match two or more images taken at different times, from different sensors or from different viewpoints. The objective is to find in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. In this work, we present an efficient image registration algorithm that uses genetic algorithms within a multi-resolution framework based on the Non-Subsampled Contourlet Transform (NSCT). An adaptable genetic algorithm for registration is adopted in order to minimize the search space. This approach is used within a hybrid scheme applying the two techniques fitness sharing and elitism. Two NSCT based methods are proposed for registration. A comparative study is established between these methods and a wavelet based one. Because the NSCT is a shift-invariant multidirectional transform, the second method is adopted for its search speeding up property. Simulation results clearly show that both proposed techniques are really promising methods for image registration compared to the wavelet approach, while the second technique has led to the best performance results of all. Moreover, to demonstrate the effectiveness of these methods, these registration techniques have been successfully applied to register SPOT, IKONOS and Synthetic Aperture Radar (SAR) images. The algorithm has been shown to work perfectly well for multi-temporal satellite images as well, even in the presence of noise. PMID:22163672

  13. 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

  14. Narrow band deformable registration of prostate magnetic resonance imaging, magnetic resonance spectroscopic imaging, and computed tomography studies

    SciTech Connect

    Schreibmann, Eduard; Xing Lei . E-mail: lei@reyes.stanford.edu

    2005-06-01

    Purpose: Endorectal (ER) coil-based magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI) is often used to obtain anatomic and metabolic images of the prostate and to accurately identify and assess the intraprostatic lesions. Recent advancements in high-field (3 Tesla or above) MR techniques affords significantly enhanced signal-to-noise ratio and makes it possible to obtain high-quality MRI data. In reality, the use of rigid or inflatable endorectal probes deforms the shape of the prostate gland, and the images so obtained are not directly usable in radiation therapy planning. The purpose of this work is to apply a narrow band deformable registration model to faithfully map the acquired information from the ER-based MRI/MRSI onto treatment planning computed tomography (CT) images. Methods and Materials: A narrow band registration, which is a hybrid method combining the advantages of pixel-based and distance-based registration techniques, was used to directly register ER-based MRI/MRSI with CT. The normalized correlation between the two input images for registration was used as the metric, and the calculation was restricted to those points contained in the narrow bands around the user-delineated structures. The narrow band method is inherently efficient because of the use of a priori information of the meaningful contour data. The registration was performed in two steps. First, the two input images were grossly aligned using a rigid registration. The detailed mapping was then modeled by free form deformations based on B-spline. The limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS), which is known for its superior performance in dealing with high-dimensionality problems, was implemented to optimize the metric function. The convergence behavior of the algorithm was studied by self-registering an MR image with 100 randomly initiated relative positions. To evaluate the performance of the algorithm, an MR image was

  15. 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.

  16. Estimation of operative line of resection using preoperative image and nonrigid registration.

    PubMed

    Lee, Jong-Ha; Won, Chang-Hee; Kong, Seong-Gon

    2008-01-01

    Even though accurate diagnosis of organs is done using preoperative images such as CT or MRI, these information are not directly used in the operating room, because organs are nonrigid and their shapes change with time. In this paper, we propose to obtain an intraoperative image of an open organ and fuse the image with a preoperative image. The intraoperative image is obtained from a three-dimensional laser scanner. The registration of preoperative image to the intraoperative image can relate the information from the preoperative image to the open organ in the operating room. We do this by registering preoperative images to intraoperative images. An algorithm based on Robust Point Matching method is developed for this nonrigid image registration problem. We also propose a new metric called Non Overlapping Ratio to determine the registration error. The experiments demonstrate that the proposed method is capable of achieving region of interest estimation within 1.51 mm mean distance error and 0.66% Non Overlapping Ratio. PMID:19163585

  17. Improved Image Registration by Sparse Patch-Based Deformation Estimation

    PubMed Central

    Kim, Minjeong; Wu, Guorong; Wang, Qian; Shen, Dinggang

    2014-01-01

    Despite of intensive efforts for decades, deformable image registration is still a challenging problem due to the potential large anatomical differences across individual images, which limits the registration performance. Fortunately, this issue could be alleviated if a good initial deformation can be provided for the two images under registration, which are often termed as the moving subject and the fixed template, respectively. In this work, we present a novel patch-based initial deformation prediction framework for improving the performance of existing registration algorithms. Our main idea is to estimate the initial deformation between subject and template in a patch-wise fashion by using the sparse representation technique. We argue that two image patches should follow the same deformation towards the template image if their patch-wise appearance patterns are similar. To this end, our framework consists of two stages, i.e., the training stage and the application stage. In the training stage, we register all training images to the pre-selected template, such that the deformation of each training image with respect to the template is known. In the application stage, we apply the following four steps to efficiently calculate the initial deformation field for the new test subject: (1) We pick a small number of key points in the distinctive regions of the test subject; (2) For each key point, we extract a local patch and form a coupled appearance-deformation dictionary from training images where each dictionary atom consists of the image intensity patch as well as their respective local deformations; (3) A small set of training image patches in the coupled dictionary are selected to represent the image patch of each subject key point by sparse representation. Then, we can predict the initial deformation for each subject key point by propagating the pre-estimated deformations on the selected training patches with the same sparse representation coefficients. (4) We

  18. Multimodal registration of retinal images using self organizing maps.

    PubMed

    Matsopoulos, George K; Asvestas, Pantelis A; Mouravliansky, Nikolaos A; Delibasis, Konstantinos K

    2004-12-01

    In this paper, an automatic method for registering multimodal retinal images is presented. The method consists of three steps: the vessel centerline detection and extraction of bifurcation points only in the reference image, the automatic correspondence of bifurcation points in the two images using a novel implementation of the self organizing maps and the extraction of the parameters of the affine transform using the previously obtained correspondences. The proposed registration algorithm was tested on 24 multimodal retinal pairs and the obtained results show an advantageous performance in terms of accuracy with respect to the manual registration. PMID:15575412

  19. DIRBoost-an algorithm for boosting deformable image registration: application to lung CT intra-subject registration.

    PubMed

    Muenzing, Sascha E A; van Ginneken, Bram; Viergever, Max A; Pluim, Josien P W

    2014-04-01

    We introduce a boosting algorithm to improve on existing methods for deformable image registration (DIR). The proposed DIRBoost algorithm is inspired by the theory on hypothesis boosting, well known in the field of machine learning. DIRBoost utilizes a method for automatic registration error detection to obtain estimates of local registration quality. All areas detected as erroneously registered are subjected to boosting, i.e. undergo iterative registrations by employing boosting masks on both the fixed and moving image. We validated the DIRBoost algorithm on three different DIR methods (ANTS gSyn, NiftyReg, and DROP) on three independent reference datasets of pulmonary image scan pairs. DIRBoost reduced registration errors significantly and consistently on all reference datasets for each DIR algorithm, yielding an improvement of the registration accuracy by 5-34% depending on the dataset and the registration algorithm employed. PMID:24556079

  20. 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.

  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. Computed tomography lung iodine contrast mapping by image registration and subtraction

    NASA Astrophysics Data System (ADS)

    Goatman, Keith; Plakas, Costas; Schuijf, Joanne; Beveridge, Erin; Prokop, Mathias

    2014-03-01

    Pulmonary embolism (PE) is a relatively common and potentially life threatening disease, affecting around 600,000 people annually in the United States alone. Prompt treatment using anticoagulants is effective and saves lives, but unnecessary treatment risks life threatening haemorrhage. The specificity of any diagnostic test for PE is therefore as important as its sensitivity. Computed tomography (CT) angiography is routinely used to diagnose PE. However, there are concerns it may over-report the condition. Additional information about the severity of an occlusion can be obtained from an iodine contrast map that represents tissue perfusion. Such maps tend to be derived from dual-energy CT acquisitions. However, they may also be calculated by subtracting pre- and post-contrast CT scans. Indeed, there are technical advantages to such a subtraction approach, including better contrast-to-noise ratio for the same radiation dose, and bone suppression. However, subtraction relies on accurate image registration. This paper presents a framework for the automatic alignment of pre- and post-contrast lung volumes prior to subtraction. The registration accuracy is evaluated for seven subjects for whom pre- and post-contrast helical CT scans were acquired using a Toshiba Aquilion ONE scanner. One hundred corresponding points were annotated on the pre- and post-contrast scans, distributed throughout the lung volume. Surface-to-surface error distances were also calculated from lung segmentations. Prior to registration the mean Euclidean landmark alignment error was 2.57mm (range 1.43-4.34 mm), and following registration the mean error was 0.54mm (range 0.44-0.64 mm). The mean surface error distance was 1.89mm before registration and 0.47mm after registration. There was a commensurate reduction in visual artefacts following registration. In conclusion, a framework for pre- and post-contrast lung registration has been developed that is sufficiently accurate for lung subtraction

  3. Voxel-based registration of simulated and real patient CBCT data for accurate dental implant pose estimation

    NASA Astrophysics Data System (ADS)

    Moreira, António H. J.; Queirós, Sandro; Morais, Pedro; Rodrigues, Nuno F.; Correia, André Ricardo; Fernandes, Valter; Pinho, A. C. M.; Fonseca, Jaime C.; Vilaça, João. L.

    2015-03-01

    The success of dental implant-supported prosthesis is directly linked to the accuracy obtained during implant's pose estimation (position and orientation). Although traditional impression techniques and recent digital acquisition methods are acceptably accurate, a simultaneously fast, accurate and operator-independent methodology is still lacking. Hereto, an image-based framework is proposed to estimate the patient-specific implant's pose using cone-beam computed tomography (CBCT) and prior knowledge of implanted model. The pose estimation is accomplished in a threestep approach: (1) a region-of-interest is extracted from the CBCT data using 2 operator-defined points at the implant's main axis; (2) a simulated CBCT volume of the known implanted model is generated through Feldkamp-Davis-Kress reconstruction and coarsely aligned to the defined axis; and (3) a voxel-based rigid registration is performed to optimally align both patient and simulated CBCT data, extracting the implant's pose from the optimal transformation. Three experiments were performed to evaluate the framework: (1) an in silico study using 48 implants distributed through 12 tridimensional synthetic mandibular models; (2) an in vitro study using an artificial mandible with 2 dental implants acquired with an i-CAT system; and (3) two clinical case studies. The results shown positional errors of 67+/-34μm and 108μm, and angular misfits of 0.15+/-0.08° and 1.4°, for experiment 1 and 2, respectively. Moreover, in experiment 3, visual assessment of clinical data results shown a coherent alignment of the reference implant. Overall, a novel image-based framework for implants' pose estimation from CBCT data was proposed, showing accurate results in agreement with dental prosthesis modelling requirements.

  4. Co-registration of multispectral images for enhanced target recognition

    NASA Astrophysics Data System (ADS)

    Khaghani, Farbod; Nelson, Richard J.

    2007-04-01

    Unlike straightforward registration problems encountered in broadband imaging, spectral imaging in fielded instruments often suffers from a combination of imaging aberrations that make spatial co-registration of the images a challenging problem. Depending on the sensor architecture, typical problems to be mitigated include differing focus, magnification, and warping between the images in the various spectral bands due to optics differences; scene shift between spectral images due to parallax; and scene shift due to temporal misregistration between the spectral images. However, typical spectral images sometimes contain scene commonalities that can be exploited in traditional ways. As a first step toward automatic spatial co-registration for spectral images, we exploit manually-selected scene commonalities to produce transformation parameters in a four-channel spectral imager. The four bands consist of two mid-wave infrared channels and two short-wave infrared channels. Each of the four bands is blurred differently due to differing focal lengths of the imaging optics, magnified differently, warped differently, and translated differently. Centroid location techniques are used on the scene commonalities in order to generate sub-pixel values for the fiducial markers used in the transformation polygons, and conclusions are drawn about the effectiveness of such techniques in spectral imaging applications.

  5. Accurate calibration of a stereo-vision system in image-guided radiotherapy.

    PubMed

    Liu, Dezhi; Li, Shidong

    2006-11-01

    Image-guided radiotherapy using a three-dimensional (3D) camera as the on-board surface imaging system requires precise and accurate registration of the 3D surface images in the treatment machine coordinate system. Two simple calibration methods, an analytical solution as three-point matching and a least-squares estimation method as multipoint registration, were introduced to correlate the stereo-vision surface imaging frame with the machine coordinate system. Both types of calibrations utilized 3D surface images of a calibration template placed on the top of the treatment couch. Image transformational parameters were derived from corresponding 3D marked points on the surface images to their given coordinates in the treatment room coordinate system. Our experimental results demonstrated that both methods had provided the desired calibration accuracy of 0.5 mm. The multipoint registration method is more robust particularly for noisy 3D surface images. Both calibration methods have been used as our weekly QA tools for a 3D image-guided radiotherapy system. PMID:17153416

  6. Accurate calibration of a stereo-vision system in image-guided radiotherapy

    SciTech Connect

    Liu Dezhi; Li Shidong

    2006-11-15

    Image-guided radiotherapy using a three-dimensional (3D) camera as the on-board surface imaging system requires precise and accurate registration of the 3D surface images in the treatment machine coordinate system. Two simple calibration methods, an analytical solution as three-point matching and a least-squares estimation method as multipoint registration, were introduced to correlate the stereo-vision surface imaging frame with the machine coordinate system. Both types of calibrations utilized 3D surface images of a calibration template placed on the top of the treatment couch. Image transformational parameters were derived from corresponding 3D marked points on the surface images to their given coordinates in the treatment room coordinate system. Our experimental results demonstrated that both methods had provided the desired calibration accuracy of 0.5 mm. The multipoint registration method is more robust particularly for noisy 3D surface images. Both calibration methods have been used as our weekly QA tools for a 3D image-guided radiotherapy system.

  7. Nonlinear spatial warping for between-subjects pedobarographic image registration.

    PubMed

    Pataky, T C; Keijsers, N L W; Goulermas, J Y; Crompton, R H

    2009-04-01

    Foot size and shape vary between individuals and the foot adopts arbitrary stance phase postures, so traditional pedobarographic analyses regionalize foot pressure images to afford homologous data comparison. An alternative approach that does not require explicit anatomical labelling and that is used widely in other functional imaging domains is to register images such that homologous structures optimally overlap and then to compare images directly at the pixel level. Image registration represents the preprocessing cornerstone of such pixel-level techniques, so its performance warrants independent attention. The purpose of this study was to evaluate the performance of four between-subjects warping registration algorithms including: Principal Axes (PA), four-parameter Optimal Scaling (OS4), eight-parameter Optimal Projective (OP8), and locally affine Nonlinear (NL). Fifteen subjects performed 10 trials of self-paced walking, and their peak pressure images were registered within-subjects using an optimal rigid body transformation. The resulting mean images were then registered between-subjects using all four methods in all 210 (15x14) subject combinations. All registration methods improved alignment, and each method performed qualitatively well for certain image pairs. However, only the NL consistently performed satisfactorily because of disproportionate anatomical variation in toe lengths and rearfoot/forefoot width, for example. Using three independent image (dis)similarity metrics, MANOVA confirmed that the NL method yielded superior registration performance (p<0.001). These data demonstrate that nonlinear spatial warping is necessary for robust between-subject pedobarographic image registration and, by extension, robust homologous data comparison at the pixel level. PMID:19112023

  8. 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.

  9. 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.

  10. Deformable image registration with content mismatch: a demons variant to account for added material and surgical devices in the target image

    NASA Astrophysics Data System (ADS)

    Nithiananthan, S.; Uneri, A.; Schafer, S.; Mirota, D.; Otake, Y.; Stayman, J. W.; Zbijewski, W.; Khanna, A. J.; Reh, D. D.; Gallia, G. L.; Siewerdsen, J. H.

    2013-03-01

    Fast, accurate, deformable image registration is an important aspect of image-guided interventions. Among the factors that can confound registration is the presence of additional material in the intraoperative image - e.g., contrast bolus or a surgical implant - that was not present in the prior image. Existing deformable registration methods generally fail to account for tissue excised between image acquisitions and typically simply "move" voxels within the images with no ability to account for tissue that is removed or introduced between scans. We present a variant of the Demons algorithm to accommodate such content mismatch. The approach combines segmentation of mismatched content with deformable registration featuring an extra pseudo-spatial dimension representing a reservoir from which material can be drawn into the registered image. Previous work tested the registration method in the presence of tissue excision ("missing tissue"). The current paper tests the method in the presence of additional material in the target image and presents a general method by which either missing or additional material can be accommodated. The method was tested in phantom studies, simulations, and cadaver models in the context of intraoperative cone-beam CT with three examples of content mismatch: a variable-diameter bolus (contrast injection); surgical device (rod), and additional material (bone cement). Registration accuracy was assessed in terms of difference images and normalized cross correlation (NCC). We identify the difficulties that traditional registration algorithms encounter when faced with content mismatch and evaluate the ability of the proposed method to overcome these challenges.

  11. Sensitivity study of voxel-based PET image comparison to image registration algorithms

    SciTech Connect

    Yip, Stephen Chen, Aileen B.; Berbeco, Ross; Aerts, Hugo J. W. L.

    2014-11-01

    Purpose: Accurate deformable registration is essential for voxel-based comparison of sequential positron emission tomography (PET) images for proper adaptation of treatment plan and treatment response assessment. The comparison may be sensitive to the method of deformable registration as the optimal algorithm is unknown. This study investigated the impact of registration algorithm choice on therapy response evaluation. Methods: Sixteen patients with 20 lung tumors underwent a pre- and post-treatment computed tomography (CT) and 4D FDG-PET scans before and after chemoradiotherapy. All CT images were coregistered using a rigid and ten deformable registration algorithms. The resulting transformations were then applied to the respective PET images. Moreover, the tumor region defined by a physician on the registered PET images was classified into progressor, stable-disease, and responder subvolumes. Particularly, voxels with standardized uptake value (SUV) decreases >30% were classified as responder, while voxels with SUV increases >30% were progressor. All other voxels were considered stable-disease. The agreement of the subvolumes resulting from difference registration algorithms was assessed by Dice similarity index (DSI). Coefficient of variation (CV) was computed to assess variability of DSI between individual tumors. Root mean square difference (RMS{sub rigid}) of the rigidly registered CT images was used to measure the degree of tumor deformation. RMS{sub rigid} and DSI were correlated by Spearman correlation coefficient (R) to investigate the effect of tumor deformation on DSI. Results: Median DSI{sub rigid} was found to be 72%, 66%, and 80%, for progressor, stable-disease, and responder, respectively. Median DSI{sub deformable} was 63%–84%, 65%–81%, and 82%–89%. Variability of DSI was substantial and similar for both rigid and deformable algorithms with CV > 10% for all subvolumes. Tumor deformation had moderate to significant impact on DSI for progressor

  12. New AIRS: The medical imaging software for segmentation and registration of elastic organs in SPECT/CT

    NASA Astrophysics Data System (ADS)

    Widita, R.; Kurniadi, R.; Darma, Y.; Perkasa, Y. S.; Trianti, N.

    2012-06-01

    We have been successfully improved our software, Automated Image Registration and Segmentation (AIRS), to fuse the CT and SPECT images of elastic organs. Segmentation and registration of elastic organs presents many challenges. Many artifacts can arise in SPECT/CT scans. Also, different organs and tissues have very similar gray levels, which consign thresholding to limited utility. We have been developed a new software to solve different registration and segmentation problems that arises in tomographic data sets. It will be demonstrated that the information obtained by SPECT/CT is more accurate in evaluating patients/objects than that obtained from either SPECT or CT alone. We used multi-modality registration which is amenable for images produced by different modalities and having unclear boundaries between tissues. The segmentation components used in this software is region growing algorithms which have proven to be an effective approach for image segmentation. Our method is designed to perform with clinically acceptable speed, using accelerated techniques (multiresolution).

  13. Automatic arm removal in PET and CT images for deformable registration.

    PubMed

    Gong, Lixin; Pathak, Sayan; Alessio, Adam; Kinahan, Paul

    2006-12-01

    Positron emission tomography (PET) imaging is rapidly expanding its role in clinical practice for cancer management. The high sensitivity of PET for functional abnormalities associated with cancer can be confounded by the minimal anatomical information it provides for cancer localization. Computed tomography (CT) provides detailed anatomical information but is less sensitive to pathologies than PET. Thus, combining (i.e., registering) PET and CT images would enable both accurate and sensitive cancer localization with respect to detailed patient anatomy. An additional application area of registration is to align CT-CT scans from serial studies on a patient on a PET/CT scanner to facilitate accurate assessment of therapeutic response from the co-aligned PET images. To facilitate image fusion, we are developing a deformable registration software system using mutual information and a B-spline model of the deformation. When applying deformable registration to whole body images, one of the obstacles is that the arms are present in PET images but not in CT images or are in different positions in serial CT images. This feature mismatch requires a preprocessing step to remove the arms where present and thus adds a manual step in an otherwise automatic algorithm. In this paper, we present a simple yet effective method for automatic arm removal. We demonstrate the efficiency and robustness of this algorithm on both clinical PET and CT images. By streamlining the entire registration process, we expect that the fusion technology will soon find its way into clinics, greatly benefiting cancer diagnosis, staging, therapy planning and treatment monitoring. PMID:17084065

  14. Weighted medical image registration with automatic mask generation

    NASA Astrophysics Data System (ADS)

    Schumacher, Hanno; Franz, Astrid; Fischer, Bernd

    2006-03-01

    Registration of images is a crucial part of many medical imaging tasks. The problem is to find a transformation which aligns two given images. The resulting displacement fields may be for example described as a linear combination of pre-selected basis functions (parametric approach), or, as in our case, they may be computed as the solution of an associated partial differential equation (non-parametric approach). Here, the underlying functional consists of a smoothness term ensuring that the transformation is anatomically meaningful and a distance term describing the similarity between the two images. To be successful, the registration scheme has to be tuned for the problem under consideration. One way of incorporating user knowledge is the employment of weighting masks into the distance measure, and thereby enhancing or hiding dedicated image parts. In general, these masks are based on a given segmentation of both images. We present a method which generates a weighting mask for the second image, given the mask for the first image. The scheme is based on active contours and makes use of a gradient vector flow method. As an example application, we consider the registration of abdominal computer tomography (CT) images used for radiation therapy. The reference image is acquired well ahead of time and is used for setting up the radiation plan. The second image is taken just before the treatment and its processing is time-critical. We show that the proposed automatic mask generation scheme yields similar results as compared to the approach based on a pre-segmentation of both images. Hence for time-critical applications, as intra-surgery registration, we are able to significantly speed up the computation by avoiding a pre-segmentation of the second image.

  15. Robust methods for automatic image-to-world registration in cone-beam CT interventional guidance

    PubMed Central

    Dang, H.; Otake, Y.; Schafer, S.; Stayman, J. W.; Kleinszig, G.; Siewerdsen, J. H.

    2012-01-01

    Purpose: Real-time surgical navigation relies on accurate image-to-world registration to align the coordinate systems of the image and patient. Conventional manual registration can present a workflow bottleneck and is prone to manual error and intraoperator variability. This work reports alternative means of automatic image-to-world registration, each method involving an automatic registration marker (ARM) used in conjunction with C-arm cone-beam CT (CBCT). The first involves a Known-Model registration method in which the ARM is a predefined tool, and the second is a Free-Form method in which the ARM is freely configurable. Methods: Studies were performed using a prototype C-arm for CBCT and a surgical tracking system. A simple ARM was designed with markers comprising a tungsten sphere within infrared reflectors to permit detection of markers in both x-ray projections and by an infrared tracker. The Known-Model method exercised a predefined specification of the ARM in combination with 3D-2D registration to estimate the transformation that yields the optimal match between forward projection of the ARM and the measured projection images. The Free-Form method localizes markers individually in projection data by a robust Hough transform approach extended from previous work, backprojected to 3D image coordinates based on C-arm geometric calibration. Image-domain point sets were transformed to world coordinates by rigid-body point-based registration. The robustness and registration accuracy of each method was tested in comparison to manual registration across a range of body sites (head, thorax, and abdomen) of interest in CBCT-guided surgery, including cases with interventional tools in the radiographic scene. Results: The automatic methods exhibited similar target registration error (TRE) and were comparable or superior to manual registration for placement of the ARM within ∼200 mm of C-arm isocenter. Marker localization in projection data was robust across all

  16. Robust methods for automatic image-to-world registration in cone-beam CT interventional guidance

    SciTech Connect

    Dang, H.; Otake, Y.; Schafer, S.; Stayman, J. W.; Kleinszig, G.; Siewerdsen, J. H.

    2012-10-15

    Purpose: Real-time surgical navigation relies on accurate image-to-world registration to align the coordinate systems of the image and patient. Conventional manual registration can present a workflow bottleneck and is prone to manual error and intraoperator variability. This work reports alternative means of automatic image-to-world registration, each method involving an automatic registration marker (ARM) used in conjunction with C-arm cone-beam CT (CBCT). The first involves a Known-Model registration method in which the ARM is a predefined tool, and the second is a Free-Form method in which the ARM is freely configurable. Methods: Studies were performed using a prototype C-arm for CBCT and a surgical tracking system. A simple ARM was designed with markers comprising a tungsten sphere within infrared reflectors to permit detection of markers in both x-ray projections and by an infrared tracker. The Known-Model method exercised a predefined specification of the ARM in combination with 3D-2D registration to estimate the transformation that yields the optimal match between forward projection of the ARM and the measured projection images. The Free-Form method localizes markers individually in projection data by a robust Hough transform approach extended from previous work, backprojected to 3D image coordinates based on C-arm geometric calibration. Image-domain point sets were transformed to world coordinates by rigid-body point-based registration. The robustness and registration accuracy of each method was tested in comparison to manual registration across a range of body sites (head, thorax, and abdomen) of interest in CBCT-guided surgery, including cases with interventional tools in the radiographic scene. Results: The automatic methods exhibited similar target registration error (TRE) and were comparable or superior to manual registration for placement of the ARM within {approx}200 mm of C-arm isocenter. Marker localization in projection data was robust across all

  17. Non-Rigid Registration of Liver CT Images for CT-Guided Ablation of Liver Tumors.

    PubMed

    Luu, Ha Manh; Klink, Camiel; Niessen, Wiro; Moelker, Adriaan; Walsum, Theo van

    2016-01-01

    CT-guided percutaneous ablation for liver cancer treatment is a relevant technique for patients not eligible for surgery and with tumors that are inconspicuous on US imaging. The lack of real-time imaging and the use of a limited amount of CT contrast agent make targeting the tumor with the needle challenging. In this study, we evaluate a registration framework that allows the integration of diagnostic pre-operative contrast enhanced CT images and intra-operative non-contrast enhanced CT images to improve image guidance in the intervention. The liver and tumor are segmented in the pre-operative contrast enhanced CT images. Next, the contrast enhanced image is registered to the intra-operative CT images in a two-stage approach. First, the contrast-enhanced diagnostic image is non-rigidly registered to a non-contrast enhanced image that is conventionally acquired at the start of the intervention. In case the initial registration is not sufficiently accurate, a refinement step is applied using non-rigid registration method with a local rigidity term. In the second stage, the intra-operative CT-images that are used to check the needle position, which often consist of only a few slices, are registered rigidly to the intra-operative image that was acquired at the start of the intervention. Subsequently, the diagnostic image is registered to the current intra-operative image, using both transformations, this allows the visualization of the tumor region extracted from pre-operative data in the intra-operative CT images containing needle. The method is evaluated on imaging data of 19 patients at the Erasmus MC. Quantitative evaluation is performed using the Dice metric, mean surface distance of the liver border and corresponding landmarks in the diagnostic and the intra-operative images. The registration of the diagnostic CT image to the initial intra-operative CT image did not require a refinement step in 13 cases. For those cases, the resulting registration had a Dice

  18. 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.

  19. 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.

  20. Analysis of deformable image registration accuracy using computational modeling.

    PubMed

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

    2010-03-01

    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

  1. Elastic registration for auto-fluorescence image averaging.

    PubMed

    Kubecka, Libor; Jan, Jiri; Kolar, Radim; Jirik, Radovan

    2006-01-01

    The paper describes restitution of geometrical distortions and improvement of signal-to-noise ratio of auto-fluorescence retinal images, finally aimed at segmentation and area estimation of the lipofuscin spots as one of the features to be included in glaucoma diagnosis. The main problems - geometrical and illumination incompatibility of frames in the image sequence and a non-negligible "shear" distortion in the individual frames - have been solved by the presented registration procedure. The concept and some details of the MI-based regularized registration, together with evaluation of test results form the core of the contribution. PMID:17945684

  2. 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

  3. Registration of multimodal volume head images via attached markers

    NASA Astrophysics Data System (ADS)

    Mandava, Venkateswara R.; Fitzpatrick, J. Michael; Maurer, Calvin R., Jr.; Maciunas, Robert J.; Allen, George S.

    1992-06-01

    We investigate the accuracy of registering arbitrarily oriented, multimodal, volume images of the human head, both to other images and to physical space, by aligning a configuration of three or more fiducial points that are the centers of attached markers. To compute the centers we use an extension of an adaptive thresholding algorithm due to Kittler. Because the markers are indistinguishable it is necessary to establish their correspondence between images. We have evaluated geometric matching algorithms for this purpose. The inherent errors in fiducial localization arising with digital images limits the accuracy with which anatomical targets can be registered. To accommodate this error we apply a least-squares registration algorithm to the fiducials. To evaluate the resulting target registration accuracy we have conducted experiments on images of internally implanted markers in a cadaver and images of externally attached markers in volunteers. We have also produced computer simulations of volume images of a hemispherical model of the head, randomly picking corresponding fiducial points and targets in the images, introducing uniformly distributed error into the fiducial locations, registering the images, and measuring target registration accuracy at the 95% confidence level. Our results indicate that submillimetric accuracy is feasible for high resolution images with four markers.

  4. AIRS: The Medical Imaging Software for Segmentation and Registration in SPECT/CT

    NASA Astrophysics Data System (ADS)

    Widita, R.; Kurniadi, R.; Haryanto, F.; Darma, Y.; Perkasa, Y. S.; Zasneda, S. S.

    2010-06-01

    We have been successfully developed a new software, Automated Image Registration and Segmentation (AIRS), to fuse the CT and SPECT images. It is designed to solve different registration and segmentation problems that arises in tomographic data sets. AIRS is addressed to obtain anatomic information to be applied to NanoSpect system which is imaging for nano-tissues or small animals. It will be demonstrated that the information obtained by SPECT/CT is more accurate in evaluating patients/objects than that obtained from either SPECT or CT alone. The registration methods developed here are for both two-dimensional and three-dimensional registration. We used normalized mutual information (NMI) which is amenable for images produced by different modalities and having unclear boundaries between tissues. The segmentation components used in this software is region growing algorithms which have proven to be an effective approach for image segmentation. The implementations of region growing developed here are connected threshold and neighborhood connected. Our method is designed to perform with clinically acceptable speed, using accelerated techniques (multiresolution).

  5. A CNN based Hybrid approach towards automatic image registration

    NASA Astrophysics Data System (ADS)

    Arun, Pattathal V.; Katiyar, Sunil K.

    2013-06-01

    Image registration is a key component of various image processing operations which involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however inability to properly model object shape as well as contextual information had limited the attainable accuracy. In this paper, we propose a framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using corset optimization The salient features of this work are cellular neural network approach based SIFT feature point optimisation, adaptive resampling and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. System has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling. Rejestracja obrazu jest kluczowym składnikiem różnych operacji jego przetwarzania. W ostatnich latach do automatycznej rejestracji obrazu wykorzystuje się metody sztucznej inteligencji, których największą wadą, obniżającą dokładność uzyskanych wyników jest brak możliwości dobrego wymodelowania kształtu i informacji kontekstowych. W niniejszej pracy zaproponowano zasady dokładnego modelowania kształtu oraz adaptacyjnego resamplingu z wykorzystaniem zaawansowanych technik, takich jak Vector Machines (VM), komórkowa sieć neuronowa (CNN), przesiewanie (SIFT), Coreset i

  6. Barriers in Accurate and Complete Birth Registration in New York State.

    PubMed

    Melnik, Thomas A; Guldal, Cemile G; Schoen, Lawrence D; Alicandro, Jeanne; Henfield, Paul

    2015-09-01

    Birth records have important legal, administrative and public health uses. However, invalid and incomplete birth reporting is a significant problem in New York State (NYS) and nationwide. We aimed to identify current practices and potential barriers in data collection by birth registrars (BRs) in NYS facilities. Using a 28-question survey regarding birth data collection, we surveyed 127 BRs in August 2013. The response rate was 88.2% (n = 112), with 31.2% from New York City (NYC) and 68.8% from the Rest of State (ROS). NYC facilities were dedicating significantly fewer staff hours (0.98 h) per birth to electronic birth registration on average compared to facilities in the ROS (1.54 h/birth). ROS BRs reported significantly less support in continuing education/training for data quality, and supervisor or manager review, and significantly greater use of electronic reports to monitor data quality compared to NYC BRs. Fewer than half the BRs statewide reported being able to accurately report previous low-birthweight birth, previous preterm delivery, and date of last menses. In addition, NYC BRs reported being significantly less able to accurately report previous C-section, method of delivery, and birthweight compared to ROS BRs. Furthermore, NYC BRs had more problems with fetal presentation coding compared to ROS BRs. The implementation of good practices identified in this report and the elimination of barriers suggested by the results are being used to guide the development of statewide efforts to improve birth data accuracy and completeness. PMID:25652064

  7. Quantitative evaluation of image registration techniques in the case of retinal images

    NASA Astrophysics Data System (ADS)

    Gavet, Yann; Fernandes, Mathieu; Pinoli, Jean-Charles

    2012-04-01

    In human retina observation (with non mydriatic optical microscopes), an image registration process is often employed to enlarge the field of view. Analyzing all the images takes a lot of time. Numerous techniques have been proposed to perform the registration process. Its good evaluation is a difficult question that is then raising. This article presents the use of two quantitative criterions to evaluate and compare some classical feature-based image registration techniques. The images are first segmented and the resulting binary images are then registered. The good quality of the registration process is evaluated with a normalized criterion based on the ɛ dissimilarity criterion, and the figure of merit criterion (fom), for 25 pairs of images with a manual selection of control points. These criterions are normalized by the results of the affine method (considered as the most simple method). Then, for each pair, the influence of the number of points used to perform the registration is evaluated.

  8. Deformable Image Registration for Cone-Beam CT Guided Transoral Robotic Base of Tongue Surgery

    PubMed Central

    Reaungamornrat, S.; Liu, W. P.; Wang, A. S.; Otake, Y.; Nithiananthan, S.; Uneri, A.; Schafer, S.; Tryggestad, E.; Richmon, J.; Sorger, J. M.; Siewerdsen, J. H.; Taylor, R. H.

    2013-01-01

    Transoral robotic surgery (TORS) offers a minimally invasive approach to resection of base of tongue tumors. However, precise localization of the surgical target and adjacent critical structures can be challenged by the highly deformed intraoperative setup. We propose a deformable registration method using intraoperative cone-beam CT (CBCT) to accurately align preoperative CT or MR images with the intraoperative scene. The registration method combines a Gaussian mixture (GM) model followed by a variation of the Demons algorithm. First, following segmentation of the volume of interest (i.e., volume of the tongue extending to the hyoid), a GM model is applied to surface point clouds for rigid initialization (GM rigid) followed by nonrigid deformation (GM nonrigid). Second, the registration is refined using the Demons algorithm applied to distance map transforms of the (GM-registered) preoperative image and intraoperative CBCT. Performance was evaluated in repeat cadaver studies (25 image pairs) in terms of target registration error (TRE), entropy correlation coefficient (ECC), and normalized pointwise mutual information (NPMI). Retraction of the tongue in the TORS operative setup induced gross deformation >30 mm. The mean TRE following the GM rigid, GM nonrigid, and Demons steps was 4.6, 2.1, and 1.7 mm, respectively. The respective ECC was 0.57, 0.70, and 0.73 and NPMI was 0.46, 0.57, and 0.60. Registration accuracy was best across the superior aspect of the tongue and in proximity to the hyoid (by virtue of GM registration of surface points on these structures). The Demons step refined registration primarily in deeper portions of the tongue further from the surface and hyoid bone. Since the method does not use image intensities directly, it is suitable to multi-modality registration of preoperative CT or MR with intraoperative CBCT. Extending the 3D image registration to the fusion of image and planning data in stereo-endoscopic video is anticipated to support

  9. Deformable image registration for cone-beam CT guided transoral robotic base-of-tongue surgery

    NASA Astrophysics Data System (ADS)

    Reaungamornrat, S.; Liu, W. P.; Wang, A. S.; Otake, Y.; Nithiananthan, S.; Uneri, A.; Schafer, S.; Tryggestad, E.; Richmon, J.; Sorger, J. M.; Siewerdsen, J. H.; Taylor, R. H.

    2013-07-01

    Transoral robotic surgery (TORS) offers a minimally invasive approach to resection of base-of-tongue tumors. However, precise localization of the surgical target and adjacent critical structures can be challenged by the highly deformed intraoperative setup. We propose a deformable registration method using intraoperative cone-beam computed tomography (CBCT) to accurately align preoperative CT or MR images with the intraoperative scene. The registration method combines a Gaussian mixture (GM) model followed by a variation of the Demons algorithm. First, following segmentation of the volume of interest (i.e. volume of the tongue extending to the hyoid), a GM model is applied to surface point clouds for rigid initialization (GM rigid) followed by nonrigid deformation (GM nonrigid). Second, the registration is refined using the Demons algorithm applied to distance map transforms of the (GM-registered) preoperative image and intraoperative CBCT. Performance was evaluated in repeat cadaver studies (25 image pairs) in terms of target registration error (TRE), entropy correlation coefficient (ECC) and normalized pointwise mutual information (NPMI). Retraction of the tongue in the TORS operative setup induced gross deformation >30 mm. The mean TRE following the GM rigid, GM nonrigid and Demons steps was 4.6, 2.1 and 1.7 mm, respectively. The respective ECC was 0.57, 0.70 and 0.73, and NPMI was 0.46, 0.57 and 0.60. Registration accuracy was best across the superior aspect of the tongue and in proximity to the hyoid (by virtue of GM registration of surface points on these structures). The Demons step refined registration primarily in deeper portions of the tongue further from the surface and hyoid bone. Since the method does not use image intensities directly, it is suitable to multi-modality registration of preoperative CT or MR with intraoperative CBCT. Extending the 3D image registration to the fusion of image and planning data in stereo-endoscopic video is anticipated to

  10. 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.

  11. 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.

  12. A local fast marching-based diffusion tensor image registration algorithm by simultaneously considering spatial deformation and tensor orientation.

    PubMed

    Xue, Zhong; Li, Hai; Guo, Lei; Wong, Stephen T C

    2010-08-01

    It is a key step to spatially align diffusion tensor images (DTI) to quantitatively compare neural images obtained from different subjects or the same subject at different timepoints. Different from traditional scalar or multi-channel image registration methods, tensor orientation should be considered in DTI registration. Recently, several DTI registration methods have been proposed in the literature, but deformation fields are purely dependent on the tensor features not the whole tensor information. Other methods, such as the piece-wise affine transformation and the diffeomorphic non-linear registration algorithms, use analytical gradients of the registration objective functions by simultaneously considering the reorientation and deformation of tensors during the registration. However, only relatively local tensor information such as voxel-wise tensor-similarity is utilized. This paper proposes a new DTI image registration algorithm, called local fast marching (FM)-based simultaneous registration. The algorithm not only considers the orientation of tensors during registration but also utilizes the neighborhood tensor information of each voxel to drive the deformation, and such neighborhood tensor information is extracted from a local fast marching algorithm around the voxels of interest. These local fast marching-based tensor features efficiently reflect the diffusion patterns around each voxel within a spherical neighborhood and can capture relatively distinctive features of the anatomical structures. Using simulated and real DTI human brain data the experimental results show that the proposed algorithm is more accurate compared with the FA-based registration and is more efficient than its counterpart, the neighborhood tensor similarity-based registration. PMID:20382233

  13. Automatic registration and mosaicking of technical images of Old Master paintings

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

    The registration of technical art conservation images of Old Master paintings presents unique challenges. Specifically, X-radiographs and reflective infrared (1000-2500 nm) images reveal shifted, or new, compositional elements not visible on the surface of artworks. Here, we describe a new multimodal registration and mosaicking algorithm that is capable of providing accurate alignment of a variety of types of images, such as the registration of multispectral reflective infrared images, X-radiographs, hyperspectral image cubes, and X-ray fluorescence image cubes to reference color images taken at high spatial sampling (300-500 pixels per inch), even when content differences are present, and a validation study has been performed to quantify the algorithm's accuracy. Key to the algorithm's success is the use of subsets of wavelet images to select control points and a novel method for filtering candidate control-point pairs. The algorithm has been used to register more than 100 paintings at the National Gallery of Art, D.C. and The Art Institute of Chicago. Many of the resulting registered datasets have been published in online catalogues, providing scholars additional information to further their understanding of the paintings and the working methods of the artists who painted them.

  14. Change detection of lung cancer using image registration and thin-plate spline warping

    NASA Astrophysics Data System (ADS)

    Almasslawi, Dawood M. S.; Kabir, Ehsanollah

    2011-06-01

    Lung cancer has the lowest survival rate comparing to other types of cancer and determination of the patient's cancer stage is the most vital issue regarding the cancer treatment process. In most cases accurate estimation of the cancer stage is not easy to achieve. The changes in the size of the primary tumor can be detected using image registration techniques. The registration method proposed in this paper uses Normalized Mutual Information metric and Thin-Plate Spline transformation function for the accurate determination of the correspondence between series of the lung cancer Computed Tomography images. The Normalized Mutual Information is used as a metric for the rigid registration of the images to better estimate the global motion of the tissues and the Thin Plate Spline is used to deform the image in a locally supported manner. The Control Points needed for the transformation are extracted semiautomatically. This new approach in change detection of the lung cancer is implemented using the Insight Toolkit. The results from implementing this method on the CT images of 8 patients provided a satisfactory quality for change detection of the lung cancer.

  15. 3D registration through pseudo x-ray image generation.

    PubMed

    Viant, W J; Barnel, F

    2001-01-01

    Registration of a pre operative plan with the intra operative position of the patient is still a largely unsolved problem. Current techniques generally require fiducials, either artificial or anatomic, to achieve the registration solution. Invariably these fiducials require implantation and/or direct digitisation. The technique described in this paper requires no digitisation or implantation of fiducials, but instead relies on the shape and form of the anatomy through a fully automated image comparison process. A pseudo image, generated from a virtual image intensifier's view of a CT dataset, is intra operatively compared with a real x-ray image. The principle is to align the virtual with the real image intensifier. The technique is an extension to the work undertaken by Domergue [1] and based on original ideas by Weese [4]. PMID:11317805

  16. Improved registration of DCE-MR images of the liver using a prior segmentation of the region of interest

    NASA Astrophysics Data System (ADS)

    Zhang, Tian; Li, Zhang; Runge, Jurgen H.; Lavini, Cristina; Stoker, Jaap; van Gulik, Thomas; van Vliet, Lucas J.; Vos, Frans M.

    2016-03-01

    In Dynamic Contrast-Enhanced MRI (DCE-MRI) of the liver, a series of images is acquired over a period of 20 minutes. Due to the patient's breathing, the liver is subject to a substantial displacement between acquisitions. Furthermore, due to its location in the abdomen, the liver also undergoes marked deformation. The large deformations combined with variation in image contrast make accurate liver registration challenging. We present a registration framework that incorporates a liver segmentation to improve the registration accuracy. The segmented liver serves as region-of-interest to our in-house developed registration method called ALOST (autocorrelation of local image structure). ALOST is a continuous optimization method that uses local phase features to overcome space-variant intensity distortions. The proposed framework can confine the solution field to the liver and allow for ALOST to obtain a more accurate solution. For the segmentation part, we use a level-set method to delineate the liver in a so-called contrast enhancement map. This map is obtained by computing the difference between the last and registered first volume from the DCE series. Subsequently, we slightly dilate the segmentation, and apply it as the mask to the other DCE-MRI volumes during registration. It is shown that the registration result becomes more accurate compared with the original ALOST approach.

  17. Inter-subject MR-PET image registration and integration

    SciTech Connect

    Lin, K.P.; Chen, T.S.; Yao, W.F.

    1996-12-31

    A MR-PET inter-subject image integration technique is developed to provide more precise anatomical location based on a template MR image, and to examine the anatomical variation in sensory-motor stimulation or to obtain cross-subject signal averaging to enhance the delectability of focal brain activity detected by different subject PET images. In this study, a multimodality intrasubject image registration procedure is firstly applied to align MR and PET images of the same subject. The second procedure is to estimate an elastic image transformation that can nonlinearly deform each 3D brain MR image and map them to the template MR image. The estimation procedure of the elastic image transformation is based on a strategy that searches the best local image match to achieve an optimal global image match, iteratively. The final elastic image transformation estimated for each subject will then be used to deform the MR-PET registered PET image. After the nonlinear PET image deformation, MR-PET intersubject mapping, averaging, and fusing are simultaneously accomplished. The developed technique has been implemented to an UNIX based workstation with Motif window system. The software named Elastic-IRIS has few requirements of user interaction. The registered anatomical location of 10 different subjects has a standard deviation of {approximately}2mm. in the x, y, and z directions. The processing time for one MR-PET inter-subject registration ranged from 20 to 30 minutes on a SUN SPARC-20.

  18. Scale-invariant registration of monocular endoscopic images to CT-scans for sinus surgery.

    PubMed

    Burschka, Darius; Li, Ming; Ishii, Masaru; Taylor, Russell H; Hager, Gregory D

    2005-10-01

    In this paper, we present a novel method for intra-operative registration directly from monocular endoscopic images. This technique has the potential to provide a more accurate surface registration at the surgical site than existing methods. It can operate autonomously from as few as two images and can be particularly useful in revision cases where surgical landmarks may be absent. A by-product of video registration is an estimate of the local surface structure of the anatomy, thus providing the opportunity to dynamically update anatomical models as the surgery progresses. Our approach is based on a previously presented method [Burschka, D., Hager, G.D., 2004. V-GPS (SLAM):--Vision-based inertial system for mobile robots. In: Proceedings of ICRA, 409-415] for reconstruction of a scaled 3D model of the environment from unknown camera motion. We use this scaled reconstruction as input to a PCA-based algorithm that registers the reconstructed data to the CT data and recovers the scale and pose parameters of the camera in the coordinate frame of the CT scan. The result is used in an ICP registration step to refine the registration estimates. The details of our approach and the experimental results with a phantom of a human skull and a head of a pig cadaver are presented in this paper. PMID:16009593

  19. 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

  20. Multimodal image registration for preoperative planning and image-guided neurosurgical procedures.

    PubMed

    Risholm, Petter; Golby, Alexandra J; Wells, William

    2011-04-01

    Image registration is the process of transforming images acquired at different time points, or with different imaging modalities, into the same coordinate system. It is an essential part of any neurosurgical planning and navigation system because it facilitates combining images with important complementary, structural, and functional information to improve the information based on which a surgeon makes critical decisions. Brigham and Women's Hospital (BWH) has been one of the pioneers in developing intraoperative registration methods for aligning preoperative and intraoperative images of the brain. This article presents an overview of intraoperative registration and highlights some recent developments at BWH. PMID:21435571

  1. SU-E-J-91: Biomechanical Deformable Image Registration of Longitudinal Lung CT Images

    SciTech Connect

    Cazoulat, G; Owen, D; Matuszak, M; Balter, J; Brock, K

    2015-06-15

    Purpose: Spatial correlation of lung tissue across longitudinal images, as the patient responds to treatment, is a critical step in adaptive radiotherapy. The goal of this work is to expand a biomechanical model-based deformable registration algorithm (Morfeus) to achieve accurate registration in the presence of significant anatomical changes. Methods: Four lung cancer patients previously treated with conventionally fractionated radiotherapy that exhibited notable tumor shrinkage during treatment were retrospectively evaluated. Exhale breathhold CT scans were obtained at treatment planning (PCT) and following three weeks (W3CT) of treatment. For each patient, the PCT was registered to the W3CT using Morfeus, a biomechanical model-based deformable registration algorithm, consisting of boundary conditions on the lungs and incorporating a sliding interface between the lung and chest wall. To model the complex response of the lung, an extension to Morfeus has been developed: (i) The vessel tree was segmented by thresholding a vesselness image based on the Hessian matrix’s eigenvalues and the centerline was extracted; (ii) A 3D shape context method was used to find correspondences between the trees of the two images; (ii) Correspondences were used as additional boundary conditions (Morfeus+vBC). An expert independently identified corresponding landmarks well distributed in the lung to compute Target Registration Errors (TRE). Results: The TRE within 15mm of the tumor boundaries (on average 11 landmarks) is: 6.1±1.8, 4.6±1.1 and 3.8±2.3 mm after rigid registration, Morfeus and Morfeus+vBC, respectively. The TRE in the rest of the lung (on average 13 landmarks) is: 6.4±3.9, 4.7±2.2 and 3.6±1.9 mm, which is on the order of the 2mm isotropic dose grid vector (3.5mm). Conclusion: The addition of boundary conditions on the vessels improved the accuracy in modeling the response of the lung and tumor over the course of radiotherapy. Minimizing and modeling these

  2. 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.

  3. 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.

  4. 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

  5. A Local IDW Transformation Algorithm for Medical Image Registration

    NASA Astrophysics Data System (ADS)

    Cavoretto, Roberto; De Rossi, Alessandra

    2008-09-01

    In this paper we propose the use of a modified version of the Inverse Distance Weighted (IDW) method for landmark—based registration of medical images. More precisely, we consider radial basis functions (RBFs) as nodal functions in the modified IDW method, circumventing the drawback due to RBF global support.

  6. 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

  7. Temporal registration of multispectral digital satellite images using their edge images

    NASA Technical Reports Server (NTRS)

    Nack, M. L.

    1975-01-01

    An algorithm is described which will form an edge image by detecting the edges of features in a particular spectral band of a digital satellite image. It is capable also of forming composite multispectral edge images. In addition, an edge image correlation algorithm is presented which performs rapid automatic registration of the edge images and, consequently, the grey level images.

  8. 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.

  9. Automatic registration and segmentation algorithm for multiple electrophoresis images

    NASA Astrophysics Data System (ADS)

    Baker, Matthew S.; Busse, Harald; Vogt, Martin

    2000-06-01

    We present an algorithm for registering, segmenting and quantifying multiple scanned electrophoresis images. (2D gel) Electrophoresis is a technique for separating proteins or other macromolecules in organic material according to net charge and molecular mass and results in scanned grayscale images with dark spots against a light background marking the presence of such macromolecules. The algorithm begins by registering each of the images using a non-rigid registration algorithm. The registered images are then jointly segmented using a Markov random field approach to obtain a single segmentation. By using multiple images, the effect of noise is greatly reduced. We demonstrate the algorithm on several sets of real data.

  10. Evaluation and validation methods for intersubject nonrigid 3D image registration of the human brain

    NASA Astrophysics Data System (ADS)

    Guo, Ting; Starreveld, Yves P.; Peters, Terry M.

    2005-04-01

    This work presents methodologies for assessing the accuracy of non-rigid intersubject registration algorithms from both qualitative and quantitative perspectives. The first method was based on a set of 43 anatomical landmarks. MRI brain images of 12 subjects were non-rigidly registered to the standard MRI dataset. The "gold-standard" coordinates of the 43 landmarks in the target were estimated by averaging their coordinates after 6 tagging sessions. The Euclidean distance between each landmark of a subject after warping to the reference space and the homologous "gold-standard" landmark on the reference image was considered as the registration error. Another method based on visual inspection software displaying the spatial change of colour-coded spheres, before and after warping, was also developed to evaluate the performance of the non-rigid warping algorithms within the homogeneous regions in the deep-brain. Our methods were exemplified by assessing and comparing the accuracy of two intersubject non-rigid registration approaches, AtamaiWarp and ANIMAL algorithms. From the first method, the average registration error was 1.04mm +/- 0.65mm for AtamaiWarp, and 1.59mm +/- 1.47mm for ANIMAL. With maximum registration errors of 2.78mm and 3.90mm respectively, AtamaiWarp and ANIMAL located 58% and 35% landmarks respectively with registration errors less than 1mm. A paired t-test showed that the differences in registration error between AtamaiWarp and ANIMAL were significant (P < 0.002) demonstrating that AtamaiWarp, in addition to being over 60 times faster than ANIMAL, also provides more accurate results. From the second method, both algorithms treated the interior of homogeneous regions in an appropriate manner.

  11. Adaptive registration of magnetic resonance images based on a viscous fluid model.

    PubMed

    Chang, Herng-Hua; Tsai, Chih-Yuan

    2014-11-01

    This paper develops a new viscous fluid registration algorithm that makes use of a closed incompressible viscous fluid model associated with mutual information. In our approach, we treat the image pixels as the fluid elements of a viscous fluid governed by the nonlinear Navier-Stokes partial differential equation (PDE) that varies in both temporal and spatial domains. We replace the pressure term with an image-based body force to guide the transformation that is weighted by the mutual information between the template and reference images. A computationally efficient algorithm with staggered grids is introduced to obtain stable solutions of this modified PDE for transformation. The registration process of updating the body force, the velocity and deformation fields is repeated until the mutual information reaches a prescribed threshold. We have evaluated this new algorithm in a number of synthetic and medical images. As consistent with the theory of the viscous fluid model, we found that our method faithfully transformed the template images into the reference images based on the intensity flow. Experimental results indicated that the proposed scheme achieved stable registrations and accurate transformations, which is of potential in large-scale medical image deformation applications. PMID:25176596

  12. 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.

  13. An Iterative Image Registration Algorithm by Optimizing Similarity Measurement

    PubMed Central

    Chu, Wei; Ma, Li; Song, John; Vorburger, Theodore

    2010-01-01

    A new registration algorithm based on Newton-Raphson iteration is proposed to align images with rigid body transformation. A set of transformation parameters consisting of translation in x and y and rotation angle around z is calculated by optimizing a specified similarity metric using the Newton-Raphson method. This algorithm has been tested by registering and correlating pairs of topography measurements of nominally identical NIST Standard Reference Material (SRM 2461) standard cartridge cases, and very good registration accuracy has been obtained. PMID:27134776

  14. Registration of whole immunohistochemical slide images: an efficient way to characterize biomarker colocalization

    PubMed Central

    Moles Lopez, Xavier; Barbot, Paul; Van Eycke, Yves-Rémi; Verset, Laurine; Trépant, Anne-Laure; Larbanoix, Lionel; Salmon, Isabelle; Decaestecker, Christine

    2015-01-01

    Background and objective 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. Methods 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. Results 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. Discussion 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. Conclusions 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. PMID:25125687

  15. Automated registration of multispectral MR vessel wall images of the carotid artery

    SciTech Connect

    Klooster, R. van 't; Staring, M.; Reiber, J. H. C.; Lelieveldt, B. P. F.; Geest, R. J. van der; Klein, S.; Kwee, R. M.; Kooi, M. E.

    2013-12-15

    Purpose: Atherosclerosis is the primary cause of heart disease and stroke. The detailed assessment of atherosclerosis of the carotid artery requires high resolution imaging of the vessel wall using multiple MR sequences with different contrast weightings. These images allow manual or automated classification of plaque components inside the vessel wall. Automated classification requires all sequences to be in alignment, which is hampered by patient motion. In clinical practice, correction of this motion is performed manually. Previous studies applied automated image registration to correct for motion using only nondeformable transformation models and did not perform a detailed quantitative validation. The purpose of this study is to develop an automated accurate 3D registration method, and to extensively validate this method on a large set of patient data. In addition, the authors quantified patient motion during scanning to investigate the need for correction. Methods: MR imaging studies (1.5T, dedicated carotid surface coil, Philips) from 55 TIA/stroke patients with ipsilateral <70% carotid artery stenosis were randomly selected from a larger cohort. Five MR pulse sequences were acquired around the carotid bifurcation, each containing nine transverse slices: T1-weighted turbo field echo, time of flight, T2-weighted turbo spin-echo, and pre- and postcontrast T1-weighted turbo spin-echo images (T1W TSE). The images were manually segmented by delineating the lumen contour in each vessel wall sequence and were manually aligned by applying throughplane and inplane translations to the images. To find the optimal automatic image registration method, different masks, choice of the fixed image, different types of the mutual information image similarity metric, and transformation models including 3D deformable transformation models, were evaluated. Evaluation of the automatic registration results was performed by comparing the lumen segmentations of the fixed image and

  16. Retinal image registration via feature-guided Gaussian mixture model.

    PubMed

    Liu, Chengyin; Ma, Jiayi; Ma, Yong; Huang, Jun

    2016-07-01

    Registration of retinal images taken at different times, from different perspectives, or with different modalities is a critical prerequisite for the diagnoses and treatments of various eye diseases. This problem can be formulated as registration of two sets of sparse feature points extracted from the given images, and it is typically solved by first creating a set of putative correspondences and then removing the false matches as well as estimating the spatial transformation between the image pairs or solved by estimating the correspondence and transformation jointly involving an iteration process. However, the former strategy suffers from missing true correspondences, and the latter strategy does not make full use of local appearance information, which may be problematic for low-quality retinal images due to a lack of reliable features. In this paper, we propose a feature-guided Gaussian mixture model (GMM) to address these issues. We formulate point registration as the estimation of a feature-guided mixture of densities: A GMM is fitted to one point set, such that both the centers and local features of the Gaussian densities are constrained to coincide with the other point set. The problem is solved under a unified maximum-likelihood framework together with an iterative expectation-maximization algorithm initialized by the confident feature correspondences, where the image transformation is modeled by an affine function. Extensive experiments on various retinal images show the robustness of our approach, which consistently outperforms other state-of-the-art methods, especially when the data is badly degraded. PMID:27409682

  17. Co-Registration of Multitemporal Uav Image Datasets for Monitoring Applications: a New Approach

    NASA Astrophysics Data System (ADS)

    Aicardi, I.; Nyapwere, N.; Nex, F.; Gerke, M.; Lingua, A. M.; Koeva, M. N.

    2016-06-01

    In the last years we have witnessed a rapid development of UAVs (Unmanned Aerial Vehicles), especially for image collection. One of the advantages is the possibility to perform high resolution and repeated flights in a cheap way to detect changes over time. Thus, dynamic scenes can be monitored acquiring image blocks in different epochs in a flexible way. Anyway, most of UAVs are not able to provide accurate direct geo-referencing information, so image blocks from different epochs still need to be co-registered to efficiently detect changes. This task is mostly completed using GCPs (Ground Control Points), although this approach is time consuming as manual intervention is needed. This paper aims at investigating new techniques to automate the co-registration of image blocks without the use of GCPs, just relying on an image based co-registration (IBCR) approach. The image alignment is initially performed on a reference (anchor) epoch and the registration of the following (slave) epochs is performed including some (anchor) images from the reference epoch with fixed external orientation parameters. This allows constraining the Bundle Block Adjustment of the slave epoch to be consistent with the reference one. The study involved the use of 10 multi-temporal image block over a large building construction site, and spanning a time frame of 2 years. Different tests have been performed for the reference image choice with a manual approach and then evaluating the reached accuracy. The performed tests on the chosen test site have shown that the accuracy of the proposed methodology provides results comparable to the common GCPs registration approach.

  18. 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.

  19. 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

  20. 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. PMID:24530210

  1. A gradient feature weighted Minimax algorithm for registration of multiple portal images to 3DCT volumes in prostate radiotherapy

    SciTech Connect

    Chelikani, Sudhakar . E-mail: sudhakar.chelikani@yale.edu; Purushothaman, Kailasnath; Knisely, Jonathan; Chen, Zhe; Nath, Ravinder; Bansal, Ravi; Duncan, James

    2006-06-01

    Purpose: To develop an accurate, fast, and robust algorithm for registering portal and computed tomographic (CT) images for radiotherapy using a combination of sparse and dense field data that complement each other. Methods and Materials: Gradient Feature Weighted Minimax (GFW Minimax) method was developed to register multiple portal images to three-dimensional CT images. Its performance was compared with that of three others: Minimax, Mutual Information, and Gilhuijs' method. Phantom and prostate cancer patient images were used. Effects of registration errors on tumor control probability (TCP) and normal tissue complication probability (NTCP) were investigated as a relative measure. Results: Registration of four portals to CTs resulted in 30% lower error when compared with registration with two portals. Computation time increased by nearly 50%. GFW Minimax performed the best, followed by Gilhuijs' method, the Minimax method, and Mutual Information. Conclusions: Using four portals instead of two lowered the registration error. Reduced fields of view images with full feature sets gave similar results in shorter times as full fields of view images. In clinical situations where soft tissue targets are of importance, GFW Minimax algorithm was significantly more accurate and robust. With registration errors lower than 1 mm, margins may be scaled down to 4 mm without adversely affecting TCP and NTCP.

  2. 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.

  3. Can dental registrants use the Index of Orthodontic Treatment Need accurately? Part 2. Factors influencing knowledge of IOTN among dental registrants.

    PubMed

    Jawad, Z; Bates, C; Hodge, T

    2016-06-10

    tools to help dental registrants use the IOTN more accurately to an acceptable level. PMID:27283567

  4. 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. PMID:15804853

  5. 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.

  6. Automatic pre- to intra-operative CT registration for image-guided cochlear implant surgery

    PubMed Central

    Reda, Fitsum A.; Noble, Jack H.; Labadie, Robert F.; Dawant, Benoit M.

    2015-01-01

    Percutaneous cochlear implantation (PCI) is a minimally invasive image-guided cochlear implant approach, where access to the cochlea is achieved by drilling a linear channel from the skull surface to the cochlea. The PCI approach requires pre- and intra-operative planning. Computation of a safe linear drilling trajectory is performed in a pre-operative CT. This trajectory is mapped to intra-operative space using the transformation matrix that registers the pre- and intra-operative CTs. However, the difference in orientation between the pre- and intra-operative CTs is too extreme to be recovered by standard, gradient descent based registration methods. Thus far, the registration has been initialized manually by an expert. In this work we present a method that aligns the scans completely automatically. We compared the performance of the automatic approach to the registration approach when an expert does the manual initialization on 11 pairs of scans. There is a maximum difference of 0.18 mm between the entry and target points of the trajectory mapped with expert initialization and the automatic registration method. This suggests that the automatic registration method is accurate enough to be used in a PCI surgery. PMID:22922692

  7. 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

  8. A method of image registration for small animal, multi-modality imaging.

    PubMed

    Chow, Patrick L; Stout, David B; Komisopoulou, Evangelia; Chatziioannou, Arion F

    2006-01-21

    Many research institutions have a full suite of preclinical tomographic scanners to answer biomedical questions in vivo. Routine multi-modality imaging requires robust registration of images generated by various tomographs. We have implemented a hardware registration method for preclinical imaging that is similar to that used in the combined positron emission tomography (PET)/computed tomography (CT) scanners in the clinic. We designed an imaging chamber which can be rigidly and reproducibly mounted on separate microPET and microCT scanners. We have also designed a three-dimensional grid phantom with 1288 lines that is used to generate the spatial transformation matrix from software registration using a 15-parameter perspective model. The imaging chamber works in combination with the registration phantom synergistically to achieve the image registration goal. We verified that the average registration error between two imaging modalities is 0.335 mm using an in vivo mouse bone scan. This paper also estimates the impact of image misalignment on PET quantitation using attenuation corrections generated from misregistered images. Our technique is expected to produce PET quantitation errors of less than 5%. The methods presented are robust and appropriate for routine use in high throughput animal imaging facilities. PMID:16394345

  9. Simulation of electronic registration of multispectral remote sensing images to 0.1 pixel accuracy

    NASA Technical Reports Server (NTRS)

    Reitsema, H. J.; Mord, A. J.; Fraser, D.; Richard, H. L.; Speaker, E. E.

    1984-01-01

    Band-to-band coregistration of multispectral remote sensing images can be achieved by electronic signal processing techniques rather than by costly and difficult mechanical alignment. This paper describes the results of a study of the end-to-end performance of electronic registration. The software simulation includes steps which model the performance of the geometric calibration process, the instrument image quality, detector performance and the effects of achieving coregistration through image resampling. The image resampling step emulates the Pipelined Resampling Processor, a real-time image resampler. The study demonstrates that the electronic alignment technique produces multispectral images which are superior to those produced by an imager whose pixel geometry is accurate to 0.1 pixel rms. The implications of this approach for future earth observation programs are discussed.

  10. Biomechanical Role of Bone Anisotropy Estimated on Clinical CT Scans by Image Registration.

    PubMed

    Taghizadeh, Elham; Reyes, Mauricio; Zysset, Philippe; Latypova, Adeliya; Terrier, Alexandre; Büchler, Philippe

    2016-08-01

    Image-based modeling is a popular approach to perform patient-specific biomechanical simulations. Accurate modeling is critical for orthopedic application to evaluate implant design and surgical planning. It has been shown that bone strength can be estimated from the bone mineral density (BMD) and trabecular bone architecture. However, these findings cannot be directly and fully transferred to patient-specific modeling since only BMD can be derived from clinical CT. Therefore, the objective of this study was to propose a method to predict the trabecular bone structure using a µCT atlas and an image registration technique. The approach has been evaluated on femurs and patellae under physiological loading. The displacement and ultimate force for femurs loaded in stance position were predicted with an error of 2.5% and 3.7%, respectively, while predictions obtained with an isotropic material resulted in errors of 7.3% and 6.9%. Similar results were obtained for the patella, where the strain predicted using the registration approach resulted in an improved mean squared error compared to the isotropic model. We conclude that the registration of anisotropic information from of a single template bone enables more accurate patient-specific simulations from clinical image datasets than isotropic model. PMID:26790866

  11. Image registration using a weighted region adjacency graph

    NASA Astrophysics Data System (ADS)

    Al-Hasan, Muhannad; Fisher, Mark

    2005-04-01

    Image registration is an important problem for image processing and computer vision with many proposed applications in medical image analysis.1, 2 Image registration techniques attempt to map corresponding features between two images. The problem is particularly difficult as anatomy is subject to elastic deformations. This paper considers this problem in the context of graph matching. Firstly, weighted Region Adjacency Graphs (RAGs) are constructed from each image using an approach based on watershed saliency. 3 The vertices of the RAG represent salient regions in the image and the (weighted) edges represent the relationship (bonding) between each region. Correspondences between images are then determined using a weighted graph matching method. Graph matching is considered to be one of the most complex problems in computer vision, due to its combinatorial nature. Our approach uses a multi-spectral technique to graph matching first proposed by Umeyama4 to find an approximate solution to the weighted graph matching problem (WGMP) based on the singular value decomposition of the adjacency matrix. Results show the technique is successful in co-registering 2-D MRI images and the method could be useful in co-registering 3-D volumetric data (e.g. CT, MRI, SPECT, PET etc.).

  12. Robust bladder image registration by redefining data-term in total variational approach

    NASA Astrophysics Data System (ADS)

    Ali, Sharib; Daul, Christian; Galbrun, Ernest; Amouroux, Marine; Guillemin, François; Blondel, Walter

    2015-03-01

    Cystoscopy is the standard procedure for clinical diagnosis of bladder cancer diagnosis. Bladder carcinoma in situ are often multifocal and spread over large areas. In vivo, localization and follow-up of these tumors and their nearby sites is necessary. But, due to the small field of view (FOV) of the cystoscopic video images, urologists cannot easily interpret the scene. Bladder mosaicing using image registration facilitates this interpretation through the visualization of entire lesions with respect to anatomical landmarks. The reference white light (WL) modality is affected by a strong variability in terms of texture, illumination conditions and motion blur. Moreover, in the complementary fluorescence light (FL) modality, the texture is visually different from that of the WL. Existing algorithms were developed for a particular modality and scene conditions. This paper proposes a more general on fly image registration approach for dealing with these variability issues in cystoscopy. To do so, we present a novel, robust and accurate image registration scheme by redefining the data-term of the classical total variational (TV) approach. Quantitative results on realistic bladder phantom images are used for verifying accuracy and robustness of the proposed model. This method is also qualitatively assessed with patient data mosaicing for both WL and FL modalities.

  13. Cellular neural network-based hybrid approach toward automatic image registration

    NASA Astrophysics Data System (ADS)

    Arun, Pattathal VijayaKumar; Katiyar, Sunil Kumar

    2013-01-01

    Image registration is a key component of various image processing operations that involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however, inability to properly model object shape as well as contextual information has limited the attainable accuracy. A framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as vector machines, cellular neural network (CNN), scale invariant feature transform (SIFT), coreset, and cellular automata is proposed. CNN has been found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using coreset optimization. The salient features of this work are cellular neural network approach-based SIFT feature point optimization, adaptive resampling, and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. This system has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. This methodology is also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

  14. A new usage of ASIFT for the range image registration

    NASA Astrophysics Data System (ADS)

    Liu, Chun-Yang; Li, Dong; Tian, Jin-Dong

    2014-11-01

    This paper addresses the range image registration problem for views having overlapping area and which may include substantial noise. The current state of the art in range image registration is best represented by the well-known iterative closest point (ICP) algorithm and numerous variations on it. Although this method is effective in many domains, it nevertheless suffers from two key limitations: It requires prealignment of the range surfaces to a reasonable starting point and it is not robust to outliers arising either from noise or low surface overlap. This paper proposes a new approach that avoids these problems for precision range image registration, by using a new, robust method based on ASIFT followed by ICP. Up to now, this approach has been evaluated by experiment. We define the fitness function to calculate the time for the convergence stage of ICP, because the time required is very important. ASIFT are capable of image matching even when there is fully affine variant. The novel ICP search algorithm we present following ASIFT offers much faster convergence than prior ICP methods, and ensures more precise alignments, even in the presence of significant noise, than mean squared error or other well-known robust cost functions.

  15. 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.

  16. 3D registration through pseudo x-ray image generation.

    PubMed

    Domergue, G; Viant, W J

    2000-01-01

    One of the less effective processes within current Computer Assisted Surgery systems, utilizing pre-operative planning, is the registration of the plan with the intra-operative position of the patient. The technique described in this paper requires no digitisation of anatomical features or fiducial markers but instead relies on image matching between pseudo and real x-ray images generated by a virtual and a real image intensifier respectively. The technique is an extension to the work undertaken by Weese [1]. PMID:10977585

  17. 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. PMID:23877240

  18. A method for quantitative analysis of regional lung ventilation using deformable image registration of CT and hybrid hyperpolarized gas/1H MRI.

    PubMed

    Tahir, Bilal A; Swift, Andrew J; Marshall, Helen; Parra-Robles, Juan; Hatton, Matthew Q; Hartley, Ruth; Kay, Richard; Brightling, Christopher E; Vos, Wim; Wild, Jim M; Ireland, Rob H

    2014-12-01

    Hyperpolarized gas magnetic resonance imaging (MRI) generates highly detailed maps of lung ventilation and physiological function while CT provides corresponding anatomical and structural information. Fusion of such complementary images enables quantitative analysis of pulmonary structure-function. However, direct image registration of hyperpolarized gas MRI to CT is problematic, particularly in lungs whose boundaries are difficult to delineate due to ventilation heterogeneity. This study presents a novel indirect method of registering hyperpolarized gas MRI to CT utilizing (1)H-structural MR images that are acquired in the same breath-hold as the gas MRI. The feasibility of using this technique for regional quantification of ventilation of specific pulmonary structures is demonstrated for the lobes.The direct and indirect methods of hyperpolarized gas MRI to CT image registration were compared using lung images from 15 asthma patients. Both affine and diffeomorphic image transformations were implemented. Registration accuracy was evaluated using the target registration error (TRE) of anatomical landmarks identified on (1)H MRI and CT. The Wilcoxon signed-rank test was used to test statistical significance.For the affine transformation, the indirect method of image registration was significantly more accurate than the direct method (TRE = 14.7 ± 3.2 versus 19.6 ± 12.7 mm, p = 0.036). Using a deformable transformation, the indirect method was also more accurate than the direct method (TRE = 13.5 ± 3.3 versus 20.4 ± 12.8 mm, p = 0.006).Accurate image registration is critical for quantification of regional lung ventilation with hyperpolarized gas MRI within the anatomy delineated by CT. Automatic deformable image registration of hyperpolarized gas MRI to CT via same breath-hold (1)H MRI is more accurate than direct registration. Potential applications include improved multi-modality image fusion, functionally weighted radiotherapy planning, and quantification of

  19. 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.

  20. Improving multispectral satellite image compression using onboard subpixel registration

    NASA Astrophysics Data System (ADS)

    Albinet, Mathieu; Camarero, Roberto; Isnard, Maxime; Poulet, Christophe; Perret, Jokin

    2013-09-01

    Future CNES earth observation missions will have to deal with an ever increasing telemetry data rate due to improvements in resolution and addition of spectral bands. Current CNES image compressors implement a discrete wavelet transform (DWT) followed by a bit plane encoding (BPE) but only on a mono spectral basis and do not profit from the multispectral redundancy of the observed scenes. Recent CNES studies have proven a substantial gain on the achievable compression ratio, +20% to +40% on selected scenarios, by implementing a multispectral compression scheme based on a Karhunen Loeve transform (KLT) followed by the classical DWT+BPE. But such results can be achieved only on perfectly registered bands; a default of registration as low as 0.5 pixel ruins all the benefits of multispectral compression. In this work, we first study the possibility to implement a multi-bands subpixel onboard registration based on registration grids generated on-the-fly by the satellite attitude control system and simplified resampling and interpolation techniques. Indeed bands registration is usually performed on ground using sophisticated techniques too computationally intensive for onboard use. This fully quantized algorithm is tuned to meet acceptable registration performances within stringent image quality criteria, with the objective of onboard real-time processing. In a second part, we describe a FPGA implementation developed to evaluate the design complexity and, by extrapolation, the data rate achievable on a spacequalified ASIC. Finally, we present the impact of this approach on the processing chain not only onboard but also on ground and the impacts on the design of the instrument.

  1. Graphics processing unit-accelerated non-rigid registration of MR images to CT images during CT-guided percutaneous liver tumor ablations

    PubMed Central

    Tokuda, Junichi; Plishker, William; Torabi, Meysam; Olubiyi, Olutayo I; Zaki, George; Tatli, Servet; Silverman, Stuart G.; Shekhar, Raj; Hata, Nobuhiko

    2015-01-01

    Rationale and Objectives Accuracy and speed are essential for the intraprocedural nonrigid MR-to-CT image registration in the assessment of tumor margins during CT-guided liver tumor ablations. While both accuracy and speed can be improved by limiting the registration to a region of interest (ROI), manual contouring of the ROI prolongs the registration process substantially. To achieve accurate and fast registration without the use of an ROI, we combined a nonrigid registration technique based on volume subdivision with hardware acceleration using a graphical processing unit (GPU). We compared the registration accuracy and processing time of GPU-accelerated volume subdivision-based nonrigid registration technique to the conventional nonrigid B-spline registration technique. Materials and Methods Fourteen image data sets of preprocedural MR and intraprocedural CT images for percutaneous CT-guided liver tumor ablations were obtained. Each set of images was registered using the GPU-accelerated volume subdivision technique and the B-spline technique. Manual contouring of ROI was used only for the B-spline technique. Registration accuracies (Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HD)), and total processing time including contouring of ROIs and computation were compared using a paired Student’s t-test. Results Accuracy of the GPU-accelerated registrations and B-spline registrations, respectively were 88.3 ± 3.7% vs 89.3 ± 4.9% (p = 0.41) for DSC and 13.1 ± 5.2 mm vs 11.4 ± 6.3 mm (p = 0.15) for HD. Total processing time of the GPU-accelerated registration and B-spline registration techniques was 88 ± 14 s vs 557 ± 116 s (p < 0.000000002), respectively; there was no significant difference in computation time despite the difference in the complexity of the algorithms (p = 0.71). Conclusion The GPU-accelerated volume subdivision technique was as accurate as the B-spline technique and required significantly less processing time. The GPU

  2. 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.

  3. Robust Global Image Registration Based on a Hybrid Algorithm Combining Fourier- and Spatial-domain Techniques

    NASA Astrophysics Data System (ADS)

    Crabtree, P.; McNicholl, P.; Seanor, C.; Murray-Krezan, J.

    2012-09-01

    A variety of image registration techniques have been investigated for applications such as image analysis, fusion, compression, enhancement, and creating mosaics. In particular, robust registration is a key component for successful multi-frame processing aimed at super-resolution or high dynamic range imaging. Image registration techniques are broadly categorized as global (area) or feature-based, and can also be classified as being performed in either the Fourier- or spatial-domain. Spatial domain methods are typically used for applications requiring accurate estimation of sub-pixel motion, such as multi-frame super-resolution based on de-aliasing. However, these techniques often rely on the availability of a priori information (good initial guess), and are therefore limited in terms of the dynamic range of the global motion estimates. A Gaussian pyramid approach is one standard method for extending the region of convergence of spatial domain techniques. On the other hand, Fourier domain-based correlation techniques such as the log-polar FFT method provide fast and reasonably accurate estimates of global shifts, rotation, and uniform scale changes, and tend to perform well over a large range of frame-to-frame motion magnitudes. In this paper we explore several possible hybrid algorithms for robust global registration based on combining the log-polar FFT and spatial-domain techniques. This includes the straightforward use of the log-polar FFT algorithm to generate an initial guess for use by a spatial domain algorithm, as well as the intertwining of the two methods by applying both global correlation and spatial domain registration at each relevant step within the log-polar FFT algorithm. In addition, we explore the benefits of normalized gradient correlation in performing the coarse log-polar FFT registration. The use of normalized gradient correlation, as opposed to phase-only correlation, has recently been proposed for improving the log-polar FFT method in terms

  4. Registration scheme suitable to Mueller matrix imaging for biomedical applications

    NASA Astrophysics Data System (ADS)

    Guyot, Steve; Anastasiadou, Makrina; Deléchelle, Eric; de Martino, Antonello

    2007-06-01

    Most Mueller matrix imaging polarimeters implement sequential acquisition of at least 16 raw images of the same object with different incident and detected light polarizations. When this technique is implemented in vivo, the unavoidable motions of the subject may shift and distort the raw images to an extent such that the final Mueller images cannot be extracted. We describe a registration algorithm which solves this problem for the typical conditions of in vivo imaging, e.g. with spatially inhomogeneous medium to strong depolarization. The algorithm, based on the so called “optical flow,” is validated experimentally by comparing the Mueller images of a pig skin sample taken in static and in dynamic conditions.

  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. Diffusion tensor image registration using tensor geometry and orientation features.

    PubMed

    Yang, Jinzhong; Shen, Dinggang; Davatzikos, Christos; Verma, Ragini

    2008-01-01

    This paper presents a method for deformable registration of diffusion tensor (DT) images that integrates geometry and orientation features into a hierarchical matching framework. The geometric feature is derived from the structural geometry of diffusion and characterizes the shape of the tensor in terms of prolateness, oblateness, and sphericity of the tensor. Local spatial distributions of the prolate, oblate, and spherical geometry are used to create an attribute vector of geometric feature for matching. The orientation feature improves the matching of the WM fiber tracts by taking into account the statistical information of underlying fiber orientations. These features are incorporated into a hierarchical deformable registration framework to develop a diffusion tensor image registration algorithm. Extensive experiments on simulated and real brain DT data establish the superiority of this algorithm for deformable matching of diffusion tensors, thereby aiding in atlas creation. The robustness of the method makes it potentially useful for group-based analysis of DT images acquired in large studies to identify disease-induced and developmental changes. PMID:18982691

  7. 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.

  8. Deformable image registration of CT and truncated cone-beam CT for adaptive radiation therapy

    NASA Astrophysics Data System (ADS)

    Zhen, Xin; Yan, Hao; Zhou, Linghong; Jia, Xun; Jiang, Steve B.

    2013-11-01

    Truncation of a cone-beam computed tomography (CBCT) image, mainly caused by the limited field of view (FOV) of CBCT imaging, poses challenges to the problem of deformable image registration (DIR) between computed tomography (CT) and CBCT images in adaptive radiation therapy (ART). The missing information outside the CBCT FOV usually causes incorrect deformations when a conventional DIR algorithm is utilized, which may introduce significant errors in subsequent operations such as dose calculation. In this paper, based on the observation that the missing information in the CBCT image domain does exist in the projection image domain, we propose to solve this problem by developing a hybrid deformation/reconstruction algorithm. As opposed to deforming the CT image to match the truncated CBCT image, the CT image is deformed such that its projections match all the corresponding projection images for the CBCT image. An iterative forward-backward projection algorithm is developed. Six head-and-neck cancer patient cases are used to evaluate our algorithm, five with simulated truncation and one with real truncation. It is found that our method can accurately register the CT image to the truncated CBCT image and is robust against image truncation when the portion of the truncated image is less than 40% of the total image. Part of this work was presented at the 54th AAPM Annual Meeting (Charlotte, NC, USA, 29 July-2 August 2012).

  9. Evaluating the utility of 3D TRUS image information in guiding intra-procedure registration for motion compensation

    NASA Astrophysics Data System (ADS)

    De Silva, Tharindu; Cool, Derek W.; Romagnoli, Cesare; Fenster, Aaron; Ward, Aaron D.

    2014-03-01

    In targeted 3D transrectal ultrasound (TRUS)-guided biopsy, patient and prostate movement during the procedure can cause target misalignments that hinder accurate sampling of pre-planned suspicious tissue locations. Multiple solutions have been proposed for motion compensation via registration of intra-procedural TRUS images to a baseline 3D TRUS image acquired at the beginning of the biopsy procedure. While 2D TRUS images are widely used for intra-procedural guidance, some solutions utilize richer intra-procedural images such as bi- or multi-planar TRUS or 3D TRUS, acquired by specialized probes. In this work, we measured the impact of such richer intra-procedural imaging on motion compensation accuracy, to evaluate the tradeoff between cost and complexity of intra-procedural imaging versus improved motion compensation. We acquired baseline and intra-procedural 3D TRUS images from 29 patients at standard sextant-template biopsy locations. We used the planes extracted from the 3D intra-procedural scans to simulate 2D and 3D information available in different clinically relevant scenarios for registration. The registration accuracy was evaluated by calculating the target registration error (TRE) using manually identified homologous fiducial markers (micro-calcifications). Our results indicate that TRE improves gradually when the number of intra-procedural imaging planes used in registration is increased. Full 3D TRUS information helps the registration algorithm to robustly converge to more accurate solutions. These results can also inform the design of a fail-safe workflow during motion compensation in a system using a tracked 2D TRUS probe, by prescribing rotational acquisitions that can be performed quickly and easily by the physician immediately prior to needle targeting.

  10. 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

  11. 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

  12. 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. PMID:27364430

  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. 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.

  15. 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”.

  16. Satellite image registration based on the geometrical arrangement of objects

    NASA Astrophysics Data System (ADS)

    Bartl, Renate; Schneider, Werner

    1995-11-01

    The knowledge of the geometrical relationship between images is a prerequisite for registration. Assuming a conformal affine transformation, 4 transformation parameters have to be determined. This is done on the basis of the geometrical arrangement of characteristic objects extracted from images in a preprocessing step, for example a land use classification yielding forest, pond, or urban regions. The algorithm introduced establishes correspondence between (centers of gravity of) objects by building and matching so-called ANGLE CHAINS, a linear structure for representing a geometric (2D) arrangement. An example with satellite imagery illustrates the usefulness of the algorithm.

  17. 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.

  18. 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

  19. 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

  20. 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.

  1. An automated, fast and accurate registration method to link stranded seeds in permanent prostate implants.

    PubMed

    Westendorp, Hendrik; Nuver, Tonnis T; Moerland, Marinus A; Minken, André W

    2015-10-21

    The geometry of a permanent prostate implant varies over time. Seeds can migrate and edema of the prostate affects the position of seeds. Seed movements directly influence dosimetry which relates to treatment quality. We present a method that tracks all individual seeds over time allowing quantification of seed movements. This linking procedure was tested on transrectal ultrasound (TRUS) and cone-beam CT (CBCT) datasets of 699 patients. These datasets were acquired intraoperatively during a dynamic implantation procedure, that combines both imaging modalities. The procedure was subdivided in four automatic linking steps. (I) The Hungarian Algorithm was applied to initially link seeds in CBCT and the corresponding TRUS datasets. (II) Strands were identified and optimized based on curvature and linefits: non optimal links were removed. (III) The positions of unlinked seeds were reviewed and were linked to incomplete strands if within curvature- and distance-thresholds. (IV) Finally, seeds close to strands were linked, also if the curvature-threshold was violated. After linking the seeds an affine transformation was applied. The procedure was repeated until the results were stable or the 6th iteration ended. All results were visually reviewed for mismatches and uncertainties. Eleven implants showed a mismatch and in 12 cases an uncertainty was identified. On average the linking procedure took 42 ms per case. This accurate and fast method has the potential to be used for other time spans, like Day 30, and other imaging modalities. It can potentially be used during a dynamic implantation procedure to faster and better evaluate the quality of the permanent prostate implant. PMID:26439900

  2. An automated, fast and accurate registration method to link stranded seeds in permanent prostate implants

    NASA Astrophysics Data System (ADS)

    Westendorp, Hendrik; Nuver, Tonnis T.; Moerland, Marinus A.; Minken, André W.

    2015-10-01

    The geometry of a permanent prostate implant varies over time. Seeds can migrate and edema of the prostate affects the position of seeds. Seed movements directly influence dosimetry which relates to treatment quality. We present a method that tracks all individual seeds over time allowing quantification of seed movements. This linking procedure was tested on transrectal ultrasound (TRUS) and cone-beam CT (CBCT) datasets of 699 patients. These datasets were acquired intraoperatively during a dynamic implantation procedure, that combines both imaging modalities. The procedure was subdivided in four automatic linking steps. (I) The Hungarian Algorithm was applied to initially link seeds in CBCT and the corresponding TRUS datasets. (II) Strands were identified and optimized based on curvature and linefits: non optimal links were removed. (III) The positions of unlinked seeds were reviewed and were linked to incomplete strands if within curvature- and distance-thresholds. (IV) Finally, seeds close to strands were linked, also if the curvature-threshold was violated. After linking the seeds an affine transformation was applied. The procedure was repeated until the results were stable or the 6th iteration ended. All results were visually reviewed for mismatches and uncertainties. Eleven implants showed a mismatch and in 12 cases an uncertainty was identified. On average the linking procedure took 42 ms per case. This accurate and fast method has the potential to be used for other time spans, like Day 30, and other imaging modalities. It can potentially be used during a dynamic implantation procedure to faster and better evaluate the quality of the permanent prostate implant.

  3. A review of biomechanically informed breast image registration.

    PubMed

    Hipwell, John H; Vavourakis, Vasileios; Han, Lianghao; Mertzanidou, Thomy; Eiben, Björn; Hawkes, David J

    2016-01-21

    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. PMID:26733349

  4. 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.

  5. On-line range images registration with GPGPU

    NASA Astrophysics Data System (ADS)

    Będkowski, J.; Naruniec, J.

    2013-03-01

    This paper concerns implementation of algorithms in the two important aspects of modern 3D data processing: data registration and segmentation. Solution proposed for the first topic is based on the 3D space decomposition, while the latter on image processing and local neighbourhood search. Data processing is implemented by using NVIDIA compute unified device architecture (NIVIDIA CUDA) parallel computation. The result of the segmentation is a coloured map where different colours correspond to different objects, such as walls, floor and stairs. The research is related to the problem of collecting 3D data with a RGB-D camera mounted on a rotated head, to be used in mobile robot applications. Performance of the data registration algorithm is aimed for on-line processing. The iterative closest point (ICP) approach is chosen as a registration method. Computations are based on the parallel fast nearest neighbour search. This procedure decomposes 3D space into cubic buckets and, therefore, the time of the matching is deterministic. First technique of the data segmentation uses accele-rometers integrated with a RGB-D sensor to obtain rotation compensation and image processing method for defining pre-requisites of the known categories. The second technique uses the adapted nearest neighbour search procedure for obtaining normal vectors for each range point.

  6. 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.

  7. 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

  8. Fluorescence Lifetime Imaging and Intravascular Ultrasound: Co-Registration Study Using Ex Vivo Human Coronaries

    PubMed Central

    Gorpas, Dimitris; Fatakdawala, Hussain; Bec, Julien; Ma, Dinglong; Yankelevich, Diego R.; Qi, Jinyi

    2015-01-01

    Fluorescence lifetime imaging (FLIM) has demonstrated potential for robust assessment of atherosclerotic plaques biochemical composition and for complementing conventional intravascular ultrasound (IVUS), which provides information on plaque morphology. The success of such a bi-modal imaging modality depends on accurate segmentation of the IVUS images and proper angular registration between these two modalities. This paper reports a novel IVUS segmentation methodology addressing this issue. The image preprocessing consisted of denoising, using the Wiener filter, followed by image smoothing, implemented through the application of the alternating sequential filter on the edge separability metric images. Extraction of the lumen/intima and media/adventitia boundaries was achieved by tracing the gray-scale peaks over the A-lines of the IVUS preprocessed images. Cubic spline interpolation, in both cross-sectional and longitudinal directions, ensured boundary smoothness and continuity. The detection of the guide-wire artifact in both modalities is used for angular registration. Intraluminal studies were conducted in 13 ex vivo segments of human coronaries. The IVUS segmentation accuracy was assessed against independent manual tracings, providing 91.82% sensitivity and 97.55% specificity. The proposed methodology makes the bi-modal FLIM and IVUS approach feasible for comprehensive intravascular diagnosis by providing co-registered biochemical and morphological information of atherosclerotic plaques. PMID:25163056

  9. Fluorescence lifetime imaging and intravascular ultrasound: co-registration study using ex vivo human coronaries.

    PubMed

    Gorpas, Dimitris; Fatakdawala, Hussain; Bec, Julien; Ma, Dinglong; Yankelevich, Diego R; Qi, Jinyi; Marcu, Laura

    2015-01-01

    Fluorescence lifetime imaging (FLIM) has demonstrated potential for robust assessment of atherosclerotic plaques biochemical composition and for complementing conventional intravascular ultrasound (IVUS), which provides information on plaque morphology. The success of such a bi-modal imaging modality depends on accurate segmentation of the IVUS images and proper angular registration between these two modalities. This paper reports a novel IVUS segmentation methodology addressing this issue. The image preprocessing consisted of denoising, using the Wiener filter, followed by image smoothing, implemented through the application of the alternating sequential filter on the edge separability metric images. Extraction of the lumen/intima and media/adventitia boundaries was achieved by tracing the gray-scale peaks over the A-lines of the IVUS preprocessed images. Cubic spline interpolation, in both cross-sectional and longitudinal directions, ensured boundary smoothness and continuity. The detection of the guide-wire artifact in both modalities is used for angular registration. Intraluminal studies were conducted in 13 ex vivo segments of human coronaries. The IVUS segmentation accuracy was assessed against independent manual tracings, providing 91.82% sensitivity and 97.55% specificity. The proposed methodology makes the bi-modal FLIM and IVUS approach feasible for comprehensive intravascular diagnosis by providing co-registered biochemical and morphological information of atherosclerotic plaques. PMID:25163056

  10. Maximum-likelihood registration of range images with missing data.

    PubMed

    Sharp, Gregory C; Lee, Sang W; Wehe, David K

    2008-01-01

    Missing data are common in range images, due to geometric occlusions, limitations in the sensor field of view, poor reflectivity, depth discontinuities, and cast shadows. Using registration to align these data often fails, because points without valid correspondences can be incorrectly matched. This paper presents a maximum likelihood method for registration of scenes with unmatched or missing data. Using ray casting, correspondences are formed between valid and missing points in each view. These correspondences are used to classify points by their visibility properties, including occlusions, field of view, and shadow regions. The likelihood of each point match is then determined using statistical properties of the sensor, such as noise and outlier distributions. Experiments demonstrate a high rates of convergence on complex scenes with varying degrees of overlap. PMID:18000329

  11. 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.

  12. 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.

  13. Ridge-based retinal image registration algorithm involving OCT fundus images

    NASA Astrophysics Data System (ADS)

    Li, Ying; Gregori, Giovanni; Knighton, Robert W.; Lujan, Brandon J.; Rosenfeld, Philip J.; Lam, Byron L.

    2011-03-01

    This paper proposes an algorithm for retinal image registration involving OCT fundus images (OFIs). The first application of the algorithm is to register OFIs with color fundus photographs; such registration between multimodal retinal images can help correlate features across imaging modalities, which is important for both clinical and research purposes. The second application is to perform the montage of several OFIs, which allows us to construct 3D OCT images over a large field of view out of separate OCT datasets. We use blood vessel ridges as registration features. The brute force search and an Iterative Closest Point (ICP) algorithm are employed for image pair registration. Global alignment to minimize the distance between matching pixel pairs is used to obtain the montage of OFIs. Quality of OFIs is the big limitation factor of the registration algorithm. In the first experiment, the effect of manual OFI enhancement on registration was evaluated for the affine model on 11 image pairs from diseased eyes. The average root mean square error (RMSE) decreases from 58 μm to 40 μm. This indicates that the registration algorithm is robust to manual enhancement. In the second experiment for the montage of OFIs, the algorithm was tested on 6 sets from healthy eyes and 6 sets from diseased eyes, each set having 8 partially overlapping SD-OCT images. Visual evaluation showed that the montage performance was acceptable for normal cases, and not good for abnormal cases due to low visibility of blood vessels. The average RMSE for a typical montage case from a healthy eye is 2.3 pixels (69 μm).

  14. 3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning

    NASA Astrophysics Data System (ADS)

    Yang, Xiaofeng; Fei, Baowei

    2012-02-01

    We developed a three-dimensional (3D) segmentation method for transrectal ultrasound (TRUS) images, which is based on longitudinal image registration and machine learning. Using longitudinal images of each individual patient, we register previously acquired images to the new images of the same subject. Three orthogonal Gabor filter banks were used to extract texture features from each registered image. Patient-specific Gabor features from the registered images are used to train kernel support vector machines (KSVMs) and then to segment the newly acquired prostate image. The segmentation method was tested in TRUS data from five patients. The average surface distance between our and manual segmentation is 1.18 +/- 0.31 mm, indicating that our automatic segmentation method based on longitudinal image registration is feasible for segmenting the prostate in TRUS images.

  15. Avoiding symmetry-breaking spatial non-uniformity in deformable image registration via a quasi-volume-preserving constraint.

    PubMed

    Aganj, Iman; Reuter, Martin; Sabuncu, Mert R; Fischl, Bruce

    2015-02-01

    The choice of a reference image typically influences the results of deformable image registration, thereby making it asymmetric. This is a consequence of a spatially non-uniform weighting in the cost function integral that leads to general registration inaccuracy. The inhomogeneous integral measure--which is the local volume change in the transformation, thus varying through the course of the registration--causes image regions to contribute differently to the objective function. More importantly, the optimization algorithm is allowed to minimize the cost function by manipulating the volume change, instead of aligning the images. The approaches that restore symmetry to deformable registration successfully achieve inverse-consistency, but do not eliminate the regional bias that is the source of the error. In this work, we address the root of the problem: the non-uniformity of the cost function integral. We introduce a new quasi-volume-preserving constraint that allows for volume change only in areas with well-matching image intensities, and show that such a constraint puts a bound on the error arising from spatial non-uniformity. We demonstrate the advantages of adding the proposed constraint to standard (asymmetric and symmetrized) demons and diffeomorphic demons algorithms through experiments on synthetic images, and real X-ray and 2D/3D brain MRI data. Specifically, the results show that our approach leads to image alignment with more accurate matching of manually defined neuroanatomical structures, better tradeoff between image intensity matching and registration-induced distortion, improved native symmetry, and lower susceptibility to local optima. In summary, the inclusion of this space- and time-varying constraint leads to better image registration along every dimension that we have measured it. PMID:25449738

  16. 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.

  17. Registration of DRRs and portal images for verification of stereotactic body radiotherapy: a feasibility study in lung cancer treatment

    NASA Astrophysics Data System (ADS)

    Künzler, Thomas; Grezdo, Jozef; Bogner, Joachim; Birkfellner, Wolfgang; Georg, Dietmar

    2007-04-01

    at the periphery of the lung, close to backbone or diaphragm. Moreover, tumour movement during shallow breathing strongly influences image acquisition for patient positioning. Recapitulating, 2D/3D image registration for lung tumours is an attractive alternative compared to conventional CT verification of the tumour position. Nevertheless, size and location of the tumour are limiting parameters for an accurate registration process.

  18. Statistically deformable 2D/3D registration for accurate determination of post-operative cup orientation from single standard X-ray radiograph.

    PubMed

    Zheng, Guoyan

    2009-01-01

    The widely used procedure of evaluation of cup orientation following total hip arthroplasty using single standard anteroposterior (AP) radiograph is known inaccurate, largely due to the wide variability in individual pelvic orientation relative to X-ray plate. 2D/3D rigid image registration methods have been introduced for an accurate determination of the post-operative cup alignment with respect to an anatomical reference extracted from the CT data. Although encouraging results have been reported, their extensive usage in clinical routine is still limited. This may be explained by their requirement of a CAD model of the prosthesis, which is often difficult to be organized from the manufacturer due to the proprietary issue, and by their requirement of a pre-operative CT scan, which is not available for most retrospective studies. To address these issues, we developed and validated a statistically deformable 2D/3D registration approach for accurate determination of post-operative cup orientation. No CAD model and pre-operative CT data is required any more. Quantitative and qualitative results evaluated on cadaveric and clinical datasets are given, which indicate the validity of the approach. PMID:20426064

  19. Active illumination based 3D surface reconstruction and registration for image guided medialization laryngoplasty

    NASA Astrophysics Data System (ADS)

    Jin, Ge; Lee, Sang-Joon; Hahn, James K.; Bielamowicz, Steven; Mittal, Rajat; Walsh, Raymond

    2007-03-01

    The medialization laryngoplasty is a surgical procedure to improve the voice function of the patient with vocal fold paresis and paralysis. An image guided system for the medialization laryngoplasty will help the surgeons to accurately place the implant and thus reduce the failure rates of the surgery. One of the fundamental challenges in image guided system is to accurately register the preoperative radiological data to the intraoperative anatomical structure of the patient. In this paper, we present a combined surface and fiducial based registration method to register the preoperative 3D CT data to the intraoperative surface of larynx. To accurately model the exposed surface area, a structured light based stereo vision technique is used for the surface reconstruction. We combined the gray code pattern and multi-line shifting to generate the intraoperative surface of the larynx. To register the point clouds from the intraoperative stage to the preoperative 3D CT data, a shape priori based ICP method is proposed to quickly register the two surfaces. The proposed approach is capable of tracking the fiducial markers and reconstructing the surface of larynx with no damage to the anatomical structure. We used off-the-shelf digital cameras, LCD projector and rapid 3D prototyper to develop our experimental system. The final RMS error in the registration is less than 1mm.

  20. MR-CT registration using a Ni-Ti prostate stent in image-guided radiotherapy of prostate cancer

    SciTech Connect

    Korsager, Anne Sofie; Ostergaard, Lasse Riis; Carl, Jesper

    2013-06-15

    Purpose: In image-guided radiotherapy of prostate cancer defining the clinical target volume often relies on magnetic resonance (MR). The task of transferring the clinical target volume from MR to standard planning computed tomography (CT) is not trivial due to prostate mobility. In this paper, an automatic local registration approach is proposed based on a newly developed removable Ni-Ti prostate stent.Methods: The registration uses the voxel similarity measure mutual information in a two-step approach where the pelvic bones are used to establish an initial registration for the local registration.Results: In a phantom study, the accuracy was measured to 0.97 mm and visual inspection showed accurate registration of all 30 data sets. The consistency of the registration was examined where translation and rotation displacements yield a rotation error of 0.41 Degree-Sign {+-} 0.45 Degree-Sign and a translation error of 1.67 {+-} 2.24 mm.Conclusions: This study demonstrated the feasibility for an automatic local MR-CT registration using the prostate stent.

  1. 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.

  2. A novel parametric method for non-rigid image registration.

    PubMed

    Cuzol, Anne; Hellier, Pierre; Mémin, Etienne

    2005-01-01

    This paper presents a novel non-rigid registration method. The main contribution of the method is the modeling of the vorticity (respectively divergence) of the deformation field using vortex (respectively sink and source) particles. Two parameters are associated with a particle: the vorticity (or divergence) strength and the influence domain. This leads to a very compact representation of vorticity and divergence fields. In addition, the optimal position of these particles is determined using a mean shift process. 2D experiments of this method are presented and demonstrate its ability to recover evolving phenomena (MS lesions) so as to register images from 20 patients. PMID:17354717

  3. Distance-Dependent Multimodal Image Registration for Agriculture Tasks

    PubMed Central

    Berenstein, Ron; Hočevar, Marko; Godeša, Tone; Edan, Yael; Ben-Shahar, Ohad

    2015-01-01

    Image registration is the process of aligning two or more images of the same scene taken at different times; from different viewpoints; and/or by different sensors. This research focuses on developing a practical method for automatic image registration for agricultural systems that use multimodal sensory systems and operate in natural environments. While not limited to any particular modalities; here we focus on systems with visual and thermal sensory inputs. Our approach is based on pre-calibrating a distance-dependent transformation matrix (DDTM) between the sensors; and representing it in a compact way by regressing the distance-dependent coefficients as distance-dependent functions. The DDTM is measured by calculating a projective transformation matrix for varying distances between the sensors and possible targets. To do so we designed a unique experimental setup including unique Artificial Control Points (ACPs) and their detection algorithms for the two sensors. We demonstrate the utility of our approach using different experiments and evaluation criteria. PMID:26308000

  4. 2D-3D registration for prostate radiation therapy based on a statistical model of transmission images

    SciTech Connect

    Munbodh, Reshma; Tagare, Hemant D.; Chen Zhe; Jaffray, David A.; Moseley, Douglas J.; Knisely, Jonathan P. S.; Duncan, James S.

    2009-10-15

    Purpose: In external beam radiation therapy of pelvic sites, patient setup errors can be quantified by registering 2D projection radiographs acquired during treatment to a 3D planning computed tomograph (CT). We present a 2D-3D registration framework based on a statistical model of the intensity values in the two imaging modalities. Methods: The model assumes that intensity values in projection radiographs are independently but not identically distributed due to the nonstationary nature of photon counting noise. Two probability distributions are considered for the intensity values: Poisson and Gaussian. Using maximum likelihood estimation, two similarity measures, maximum likelihood with a Poisson (MLP) and maximum likelihood with Gaussian (MLG), distribution are derived. Further, we investigate the merit of the model-based registration approach for data obtained with current imaging equipment and doses by comparing the performance of the similarity measures derived to that of the Pearson correlation coefficient (ICC) on accurately collected data of an anthropomorphic phantom of the pelvis and on patient data. Results: Registration accuracy was similar for all three similarity measures and surpassed current clinical requirements of 3 mm for pelvic sites. For pose determination experiments with a kilovoltage (kV) cone-beam CT (CBCT) and kV projection radiographs of the phantom in the anterior-posterior (AP) view, registration accuracies were 0.42 mm (MLP), 0.29 mm (MLG), and 0.29 mm (ICC). For kV CBCT and megavoltage (MV) AP portal images of the same phantom, registration accuracies were 1.15 mm (MLP), 0.90 mm (MLG), and 0.69 mm (ICC). Registration of a kV CT and MV AP portal images of a patient was successful in all instances. Conclusions: The results indicate that high registration accuracy is achievable with multiple methods including methods that are based on a statistical model of a 3D CT and 2D projection images.

  5. Automated Registration of Images from Multiple Bands of Resourcesat-2 Liss-4 camera

    NASA Astrophysics Data System (ADS)

    Radhadevi, P. V.; Solanki, S. S.; Jyothi, M. V.; Varadan, G.

    2014-11-01

    Continuous and automated co-registration and geo-tagging of images from multiple bands of Liss-4 camera is one of the interesting challenges of Resourcesat-2 data processing. Three arrays of the Liss-4 camera are physically separated in the focal plane in alongtrack direction. Thus, same line on the ground will be imaged by extreme bands with a time interval of as much as 2.1 seconds. During this time, the satellite would have covered a distance of about 14 km on the ground and the earth would have rotated through an angle of 30". A yaw steering is done to compensate the earth rotation effects, thus ensuring a first level registration between the bands. But this will not do a perfect co-registration because of the attitude fluctuations, satellite movement, terrain topography, PSM steering and small variations in the angular placement of the CCD lines (from the pre-launch values) in the focal plane. This paper describes an algorithm based on the viewing geometry of the satellite to do an automatic band to band registration of Liss-4 MX image of Resourcesat-2 in Level 1A. The algorithm is using the principles of photogrammetric collinearity equations. The model employs an orbit trajectory and attitude fitting with polynomials. Then, a direct geo-referencing with a global DEM with which every pixel in the middle band is mapped to a particular position on the surface of the earth with the given attitude. Attitude is estimated by interpolating measurement data obtained from star sensors and gyros, which are sampled at low frequency. When the sampling rate of attitude information is low compared to the frequency of jitter or micro-vibration, images processed by geometric correction suffer from distortion. Therefore, a set of conjugate points are identified between the bands to perform a relative attitude error estimation and correction which will ensure the internal accuracy and co-registration of bands. Accurate calculation of the exterior orientation parameters with

  6. 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)]. PMID:27446697

  7. 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

  8. Validation of a deformable image registration technique for cone beam CT-based dose verification

    SciTech Connect

    Moteabbed, M. Sharp, G. C.; Wang, Y.; Trofimov, A.; Efstathiou, J. A.; Lu, H.-M.

    2015-01-15

    lengths decreased from 10.1 to 2.5 mm when CBCT was calibrated prior to registration. The results showed no dependence on the level of bladder filling. In comparison with the dose calculated on the primary deformed CT, differences in mean dose averaged over all organs were 0.2% and 3.9% for dose calculated on the secondary deformed CT with and without CBCT calibration, respectively, and 0.5% for dose calculated directly on the calibrated CBCT, for the full-bladder scenario. Gamma analysis for the distance to agreement of 2 mm and 2% of prescribed dose indicated a pass rate of 100% for both cases involving calibrated CBCT and on average 86% without CBCT calibration. Conclusions: Using deformable registration on the planning CT images to evaluate the IMRT dose based on daily CBCTs was found feasible. The proposed method will provide an accurate dose distribution using planning CT and pretreatment CBCT data, avoiding the additional uncertainties introduced by CBCT inhomogeneity and artifacts. This is a necessary initial step toward future image-guided adaptive radiotherapy of the prostate.

  9. Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration.

    PubMed

    Onofrey, John A; Staib, Lawrence H; Papademetris, Xenophon

    2015-01-01

    This paper presents a dictionary learning-based method to segment the brain surface in post-surgical CT images of epilepsy patients following surgical implantation of electrodes. Using the electrodes identified in the post-implantation CT, surgeons require accurate registration with pre-implantation functional and structural MR imaging to guide surgical resection of epileptic tissue. In this work, we use a surface-based registration method to align the MR and CT brain surfaces. The key challenge here is not the registration, but rather the extraction of the cortical surface from the CT image, which includes missing parts of the skull and artifacts introduced by the electrodes. To segment the brain from these images, we propose learning a model of appearance that captures both the normal tissue and the artifacts found along this brain surface boundary. Using clinical data, we demonstrate that our method both accurately extracts the brain surface and better localizes electrodes than intensity-based rigid and non-rigid registration methods. PMID:26221711

  10. Open-source image registration for MRI–TRUS fusion-guided prostate interventions

    PubMed Central

    Khallaghi, Siavash; Sánchez, C. Antonio; Lasso, Andras; Fels, Sidney; Tuncali, Kemal; Sugar, Emily Neubauer; Kapur, Tina; Zhang, Chenxi; Wells, William; Nguyen, Paul L.; Abolmaesumi, Purang; Tempany, Clare

    2015-01-01

    Purpose We propose two software tools for non-rigid registration of MRI and transrectal ultrasound (TRUS) images of the prostate. Our ultimate goal is to develop an open-source solution to support MRI–TRUS fusion image guidance of prostate interventions, such as targeted biopsy for prostate cancer detection and focal therapy. It is widely hypothesized that image registration is an essential component in such systems. Methods The two non-rigid registration methods are: (1) a deformable registration of the prostate segmentation distance maps with B-spline regularization and (2) a finite element-based deformable registration of the segmentation surfaces in the presence of partial data. We evaluate the methods retrospectively using clinical patient image data collected during standard clinical procedures. Computation time and Target Registration Error (TRE) calculated at the expert-identified anatomical landmarks were used as quantitative measures for the evaluation. Results The presented image registration tools were capable of completing deformable registration computation within 5 min. Average TRE was approximately 3 mm for both methods, which is comparable with the slice thickness in our MRI data. Both tools are available under nonrestrictive open-source license. Conclusions We release open-source tools that may be used for registration during MRI–TRUS-guided prostate interventions. Our tools implement novel registration approaches and produce acceptable registration results. We believe these tools will lower the barriers in development and deployment of interventional research solutions and facilitate comparison with similar tools. PMID:25847666

  11. Evaluation of five non-rigid image registration algorithms using the NIREP framework

    NASA Astrophysics Data System (ADS)

    Wei, Ying; Christensen, Gary E.; Song, Joo Hyun; Rudrauf, David; Bruss, Joel; Kuhl, Jon G.; Grabowski, Thomas J.

    2010-03-01

    Evaluating non-rigid image registration algorithm performance is a difficult problem since there is rarely a "gold standard" (i.e., known) correspondence between two images. This paper reports the analysis and comparison of five non-rigid image registration algorithms using the Non-Rigid Image Registration Evaluation Project (NIREP) (www.nirep.org) framework. The NIREP framework evaluates registration performance using centralized databases of well-characterized images and standard evaluation statistics (methods) which are implemented in a software package. The performance of five non-rigid registration algorithms (Affine, AIR, Demons, SLE and SICLE) was evaluated using 22 images from two NIREP neuroanatomical evaluation databases. Six evaluation statistics (relative overlap, intensity variance, normalized ROI overlap, alignment of calcarine sulci, inverse consistency error and transitivity error) were used to evaluate and compare image registration performance. The results indicate that the Demons registration algorithm produced the best registration results with respect to the relative overlap statistic but produced nearly the worst registration results with respect to the inverse consistency statistic. The fact that one registration algorithm produced the best result for one criterion and nearly the worst for another illustrates the need to use multiple evaluation statistics to fully assess performance.

  12. An image registration pipeline for analysis of transsynaptic tracing in mice

    NASA Astrophysics Data System (ADS)

    Kutten, Kwame S.; Eacker, Stephen M.; Dawson, Valina L.; Dawson, Ted M.; Ratnanather, Tilak; Miller, Michael I.

    2016-03-01

    Parkinson's Disease (PD) is a movement disorder characterized by the loss of dopamine neurons in the substantia nigra pars compacta (SNpc) and norepinephrine neurons in the locus coeruleus (LC). To further understand the pathophysiology of PD, the input neurons of the SNpc and LC will be transsynapticly traced in mice using a fluorescent recombinant rabies virus (RbV) and imaged using serial two-photon tomography (STP). A mapping between these images and a brain atlas must be found to accurately determine the locations of input neurons in the brain. Therefore a registration pipeline to align the Allen Reference Atlas (ARA) to these types of images was developed. In the preprocessing step, a brain mask was generated from the transsynaptic tracing images using simple morphological operators. The masks were then registered to the ARA using Large Deformation Diffeomorphic Metric Mapping (LDDMM), an algorithm specialized for calculating anatomically realistic transforms between images. The pipeline was then tested on an STP scan of a mouse brain labeled by an adeno-associated virus (AAV). Based on qualitative evaluation of the registration results, the pipeline was found to be sufficient for use with transsynaptic RbV tracing.

  13. A correlative imaging based methodology for accurate quantitative assessment of bone formation in additive manufactured implants.

    PubMed

    Geng, Hua; Todd, Naomi M; Devlin-Mullin, Aine; Poologasundarampillai, Gowsihan; Kim, Taek Bo; Madi, Kamel; Cartmell, Sarah; Mitchell, Christopher A; Jones, Julian R; Lee, Peter D

    2016-06-01

    A correlative imaging methodology was developed to accurately quantify bone formation in the complex lattice structure of additive manufactured implants. Micro computed tomography (μCT) and histomorphometry were combined, integrating the best features from both, while demonstrating the limitations of each imaging modality. This semi-automatic methodology registered each modality using a coarse graining technique to speed the registration of 2D histology sections to high resolution 3D μCT datasets. Once registered, histomorphometric qualitative and quantitative bone descriptors were directly correlated to 3D quantitative bone descriptors, such as bone ingrowth and bone contact. The correlative imaging allowed the significant volumetric shrinkage of histology sections to be quantified for the first time (~15 %). This technique demonstrated the importance of location of the histological section, demonstrating that up to a 30 % offset can be introduced. The results were used to quantitatively demonstrate the effectiveness of 3D printed titanium lattice implants. PMID:27153828

  14. Validation of Imaging With Pathology in Laryngeal Cancer: Accuracy of the Registration Methodology

    SciTech Connect

    Caldas-Magalhaes, Joana; Kasperts, Nicolien; Kooij, Nina; Berg, Cornelis A.T. van den; Terhaard, Chris H.J.; Raaijmakers, Cornelis P.J.; Philippens, Marielle E.P.

    2012-02-01

    Purpose: To investigate the feasibility and accuracy of an automated method to validate gross tumor volume (GTV) delineations with pathology in laryngeal and hypopharyngeal cancer. Methods and Materials: High-resolution computed tomography (CT{sub HR}), magnetic resonance imaging (MRI), and positron emission tomography (PET) scans were obtained from 10 patients before total laryngectomy. The GTV was delineated separately in each imaging modality. The laryngectomy specimen was sliced transversely in 3-mm-thick slices, and whole-mount hematoxylin-eosin stained (H and E) sections were obtained. A pathologist delineated tumor tissue in the H and E sections (GTV{sub PATH}). An automatic three-dimensional (3D) reconstruction of the specimen was performed, and the CT{sub HR}, MRI, and PET were semiautomatically and rigidly registered to the 3D specimen. The accuracy of the pathology-imaging registration and the specimen deformation and shrinkage were assessed. The tumor delineation inaccuracies were compared with the registration errors. Results: Good agreement was observed between anatomical landmarks in the 3D specimen and in the in vivo images. Limited deformations and shrinkage (3% {+-} 1%) were found inside the cartilage skeleton. The root mean squared error of the registration between the 3D specimen and the CT, MRI, and PET was on average 1.5, 3.0, and 3.3 mm, respectively, in the cartilage skeleton. The GTV{sub PATH} volume was 7.2 mL, on average. The GTVs based on CT, MRI, and PET generated a mean volume of 14.9, 18.3, and 9.8 mL and covered the GTV{sub PATH} by 85%, 88%, and 77%, respectively. The tumor delineation inaccuracies exceeded the registration error in all the imaging modalities. Conclusions: Validation of GTV delineations with pathology is feasible with an average overall accuracy below 3.5 mm inside the laryngeal skeleton. The tumor delineation inaccuracies were larger than the registration error. Therefore, an accurate histological validation of

  15. 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.

  16. Registration of 2D C-Arm and 3D CT Images for a C-Arm Image-Assisted Navigation System for Spinal Surgery.

    PubMed

    Chang, Chih-Ju; Lin, Geng-Li; Tse, Alex; Chu, Hong-Yu; Tseng, Ching-Shiow

    2015-01-01

    C-Arm image-assisted surgical navigation system has been broadly applied to spinal surgery. However, accurate path planning on the C-Arm AP-view image is difficult. This research studies 2D-3D image registration methods to obtain the optimum transformation matrix between C-Arm and CT image frames. Through the transformation matrix, the surgical path planned on preoperative CT images can be transformed and displayed on the C-Arm images for surgical guidance. The positions of surgical instruments will also be displayed on both CT and C-Arm in the real time. Five similarity measure methods of 2D-3D image registration including Normalized Cross-Correlation, Gradient Correlation, Pattern Intensity, Gradient Difference Correlation, and Mutual Information combined with three optimization methods including Powell's method, Downhill simplex algorithm, and genetic algorithm are applied to evaluate their performance in converge range, efficiency, and accuracy. Experimental results show that the combination of Normalized Cross-Correlation measure method with Downhill simplex algorithm obtains maximum correlation and similarity in C-Arm and Digital Reconstructed Radiograph (DRR) images. Spine saw bones are used in the experiment to evaluate 2D-3D image registration accuracy. The average error in displacement is 0.22 mm. The success rate is approximately 90% and average registration time takes 16 seconds. PMID:27018859

  17. 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.

  18. 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.

  19. 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™.

  20. Accelerating image registration of MRI by GPU-based parallel computation.

    PubMed

    Huang, Teng-Yi; Tang, Yu-Wei; Ju, Shiun-Ying

    2011-06-01

    Automatic image registration for MRI applications generally requires many iteration loops and is, therefore, a time-consuming task. This drawback prolongs data analysis and delays the workflow of clinical routines. Recent advances in the massively parallel computation of graphic processing units (GPUs) may be a solution to this problem. This study proposes a method to accelerate registration calculations, especially for the popular statistical parametric mapping (SPM) system. This study reimplemented the image registration of SPM system to achieve an approximately 14-fold increase in speed in registering single-modality intrasubject data sets. The proposed program is fully compatible with SPM, allowing the user to simply replace the original image registration library of SPM to gain the benefit of the computation power provided by commodity graphic processors. In conclusion, the GPU computation method is a practical way to accelerate automatic image registration. This technology promises a broader scope of application in the field of image registration. PMID:21531103

  1. Pulmonary CT image registration and warping for tracking tissue deformation during the respiratory cycle through 3D consistent image registration

    PubMed Central

    Li, Baojun; Christensen, Gary E.; Hoffman, Eric A.; McLennan, Geoffrey; Reinhardt, Joseph M.

    2008-01-01

    Tracking lung tissues during the respiratory cycle has been a challenging task for diagnostic CT and CT-guided radiotherapy. We propose an intensity- and landmark-based image registration algorithm to perform image registration and warping of 3D pulmonary CT image data sets, based on consistency constraints and matching corresponding airway branchpoints. In this paper, we demonstrate the effectivenss and accuracy of this algorithm in tracking lung tissues by both animal and human data sets. In the animal study, the result showed a tracking accuracy of 1.9 mm between 50% functional residual capacity (FRC) and 85% total lung capacity (TLC) for 12 metal seeds implanted in the lungs of a breathing sheep under precise volume control using a pulmonary ventilator. Visual inspection of the human subject results revealed the algorithm’s potential not only in matching the global shapes, but also in registering the internal structures (e.g., oblique lobe fissures, pulmonary artery branches, etc.). These results suggest that our algorithm has significant potential for warping and tracking lung tissue deformation with applications in diagnostic CT, CT-guided radiotherapy treatment planning, and therapeutic effect evaluation. PMID:19175115

  2. Ant colony optimization image registration algorithm based on wavelet transform and mutual information

    NASA Astrophysics Data System (ADS)

    Zhang, Hong; Sun, Yanfeng; Zhai, Bing; Wang, Yiding

    2013-07-01

    This paper studies on the image registration of the medical images. Wavelet transform is adopted to decompose the medical images because the resolution of the medical image is high and the computational amount of the registration is large. Firstly, the low frequency sub-images are matched. Then source images are matched. The image registration was fulfilled by the ant colony optimization algorithm to search the extremum of the mutual information. The experiment result demonstrates the proposed approach can not only reduce calculation amount, but also skip from the local extremum during optimization process, and search the optimization value.

  3. A novel approach for establishing benchmark CBCT/CT deformable image registrations in prostate cancer radiotherapy

    NASA Astrophysics Data System (ADS)

    Kim, Jinkoo; Kumar, Sanath; Liu, Chang; Zhong, Hualiang; Pradhan, Deepak; Shah, Mira; Cattaneo, Richard; Yechieli, Raphael; Robbins, Jared R.; Elshaikh, Mohamed A.; Chetty, Indrin J.

    2013-11-01

    Deformable image registration (DIR) is an integral component for adaptive radiation therapy. However, accurate registration between daily cone-beam computed tomography (CBCT) and treatment planning CT is challenging, due to significant daily variations in rectal and bladder fillings as well as the increased noise levels in CBCT images. Another significant challenge is the lack of ‘ground-truth’ registrations in the clinical setting, which is necessary for quantitative evaluation of various registration algorithms. The aim of this study is to establish benchmark registrations of clinical patient data. Three pairs of CT/CBCT datasets were chosen for this institutional review board approved retrospective study. On each image, in order to reduce the contouring uncertainty, ten independent sets of organs were manually delineated by five physicians. The mean contour set for each image was derived from the ten contours. A set of distinctive points (round natural calcifications and three implanted prostate fiducial markers) were also manually identified. The mean contours and point features were then incorporated as constraints into a B-spline based DIR algorithm. Further, a rigidity penalty was imposed on the femurs and pelvic bones to preserve their rigidity. A piecewise-rigid registration approach was adapted to account for the differences in femur pose and the sliding motion between bones. For each registration, the magnitude of the spatial Jacobian (|JAC|) was calculated to quantify the tissue compression and expansion. Deformation grids and finite-element-model-based unbalanced energy maps were also reviewed visually to evaluate the physical soundness of the resultant deformations. Organ DICE indices (indicating the degree of overlap between registered organs) and residual misalignments of the fiducial landmarks were quantified. Manual organ delineation on CBCT images varied significantly among physicians with overall mean DICE index of only 0.7 among redundant

  4. A novel approach for establishing benchmark CBCT/CT deformable image registrations in prostate cancer radiotherapy.

    PubMed

    Kim, Jinkoo; Kumar, Sanath; Liu, Chang; Zhong, Hualiang; Pradhan, Deepak; Shah, Mira; Cattaneo, Richard; Yechieli, Raphael; Robbins, Jared R; Elshaikh, Mohamed A; Chetty, Indrin J

    2013-11-21

    Deformable image registration (DIR) is an integral component for adaptive radiation therapy. However, accurate registration between daily cone-beam computed tomography (CBCT) and treatment planning CT is challenging, due to significant daily variations in rectal and bladder fillings as well as the increased noise levels in CBCT images. Another significant challenge is the lack of 'ground-truth' registrations in the clinical setting, which is necessary for quantitative evaluation of various registration algorithms. The aim of this study is to establish benchmark registrations of clinical patient data. Three pairs of CT/CBCT datasets were chosen for this institutional review board approved retrospective study. On each image, in order to reduce the contouring uncertainty, ten independent sets of organs were manually delineated by five physicians. The mean contour set for each image was derived from the ten contours. A set of distinctive points (round natural calcifications and three implanted prostate fiducial markers) were also manually identified. The mean contours and point features were then incorporated as constraints into a B-spline based DIR algorithm. Further, a rigidity penalty was imposed on the femurs and pelvic bones to preserve their rigidity. A piecewise-rigid registration approach was adapted to account for the differences in femur pose and the sliding motion between bones. For each registration, the magnitude of the spatial Jacobian (|JAC|) was calculated to quantify the tissue compression and expansion. Deformation grids and finite-element-model-based unbalanced energy maps were also reviewed visually to evaluate the physical soundness of the resultant deformations. Organ DICE indices (indicating the degree of overlap between registered organs) and residual misalignments of the fiducial landmarks were quantified. Manual organ delineation on CBCT images varied significantly among physicians with overall mean DICE index of only 0.7 among redundant

  5. Automatic morphing using image registration: Application to continuous tracking of radar reflectivity and rain fields

    NASA Astrophysics Data System (ADS)

    Vongsaard, Jearanai

    Rainfall is one of the most important natural phenomenon that influences human life. Accurate rainfall estimation and prediction are crucial for flood forecasting, flood control, climate diagnostics, and water resource management. Rain data may be collected from numerous sources. Conventional rain gauge networks or meteorological radars provide continuous coverage in time. Satellite observations provide snap-shots of precipitation fields at poor temporal resolution. While a number of spaceborne platforms have been deployed for rain observation, the development of continuous space/time rainfall remains a major challenge. This dissertation seeks alternative techniques to automatically generate continuous data streams of rainfall data from sparse or intermittent observations. In order to avoid human intervention in the process, an automatic procedure is needed for real-time operations. For this purpose, Automatic Morphing Using Image Registration (AMIR) model is developed by integrating automatic image registration and image morphing algorithm. The new AMIR technique uses automatic image registration as the basis for finding control points for the morphing process. In the study of data assimilation for weather forecasting, there is a need to generate continuous streams of rainfall data to alleviate the so-called "spin up" problem, or the inability to provide short-term forecasts [Road90]. The proposed algorithm has been tested using remote sensing images from Next Generation Weather Radars (NEXRAD) and Tropical Rainfall Measuring Mission (TRMM). Three cases of rainfall data have been used. These include the passage of a storm in Florida, hurricane Floyd, and scattered rain in the southwestern of the United States for the same period using NEXRAD radar data as surrogate for spaceborne observations. These cases have drastically different spatial and temporal characteristics and hence provide tests on the applicability of the AMIR method. Comparative experimental results

  6. 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.

  7. 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.

  8. A hybrid biomechanical intensity based deformable image registration of lung 4DCT.

    PubMed

    Samavati, Navid; Velec, Michael; Brock, Kristy

    2015-04-21

    Deformable image registration (DIR) has been extensively studied over the past two decades due to its essential role in many image-guided interventions (IGI). IGI demands a highly accurate registration that maintains its accuracy across the entire region of interest. This work evaluates the improvement in accuracy and consistency by refining the results of Morfeus, a biomechanical model-based DIR algorithm. A hybrid DIR algorithm is proposed based on, a biomechanical model-based DIR algorithm and a refinement step based on a B-spline intensity-based algorithm. Inhale and exhale reconstructions of four-dimensional computed tomography (4DCT) lung images from 31 patients were initially registered using the biomechanical DIR by modeling contact surface between the lungs and the chest cavity. The resulting deformations were then refined using the intensity-based algorithm to reduce any residual uncertainties. Important parameters in the intensity-based algorithm, including grid spacing, number of pyramids, and regularization coefficient, were optimized on 10 randomly-chosen patients (out of 31). Target registration error (TRE) was calculated by measuring the Euclidean distance of common anatomical points on both images after registration. For each patient a minimum of 30 points/lung were used. Grid spacing of 8 mm, 5 levels of grid pyramids, and regularization coefficient of 3.0 were found to provide optimal results on 10 randomly chosen patients. Overall the entire patient population (n = 31), the hybrid method resulted in mean ± SD (90th%) TRE of 1.5 ± 1.4 (2.9) mm compared to 3.1 ± 1.9 (5.6) using biomechanical DIR and 2.6 ± 2.5 (6.1) using intensity-based DIR alone. The proposed hybrid biomechanical modeling intensity based algorithm is a promising DIR technique which could be used in various IGI procedures. The current investigation shows the efficacy of this approach for the registration of 4DCT images of the lungs with average accuracy of 1.5 mm. PMID

  9. A hybrid biomechanical intensity based deformable image registration of lung 4DCT

    NASA Astrophysics Data System (ADS)

    Samavati, Navid; Velec, Michael; Brock, Kristy

    2015-04-01

    Deformable image registration (DIR) has been extensively studied over the past two decades due to its essential role in many image-guided interventions (IGI). IGI demands a highly accurate registration that maintains its accuracy across the entire region of interest. This work evaluates the improvement in accuracy and consistency by refining the results of Morfeus, a biomechanical model-based DIR algorithm. A hybrid DIR algorithm is proposed based on, a biomechanical model-based DIR algorithm and a refinement step based on a B-spline intensity-based algorithm. Inhale and exhale reconstructions of four-dimensional computed tomography (4DCT) lung images from 31 patients were initially registered using the biomechanical DIR by modeling contact surface between the lungs and the chest cavity. The resulting deformations were then refined using the intensity-based algorithm to reduce any residual uncertainties. Important parameters in the intensity-based algorithm, including grid spacing, number of pyramids, and regularization coefficient, were optimized on 10 randomly-chosen patients (out of 31). Target registration error (TRE) was calculated by measuring the Euclidean distance of common anatomical points on both images after registration. For each patient a minimum of 30 points/lung were used. Grid spacing of 8 mm, 5 levels of grid pyramids, and regularization coefficient of 3.0 were found to provide optimal results on 10 randomly chosen patients. Overall the entire patient population (n = 31), the hybrid method resulted in mean ± SD (90th%) TRE of 1.5 ± 1.4 (2.9) mm compared to 3.1 ± 1.9 (5.6) using biomechanical DIR and 2.6 ± 2.5 (6.1) using intensity-based DIR alone. The proposed hybrid biomechanical modeling intensity based algorithm is a promising DIR technique which could be used in various IGI procedures. The current investigation shows the efficacy of this approach for the registration of 4DCT images of the lungs with average accuracy of 1.5 mm.

  10. A hybrid biomechanical intensity based deformable image registration of lung 4DCT

    PubMed Central

    Samavati, Navid; Velec, Michael; Brock, Kristy

    2015-01-01

    Purpose Deformable Image Registration (DIR) has been extensively studied over the past two decades due to its essential role in many image-guided interventions (IGI). IGI demands a highly accurate registration that maintains its accuracy across the entire region of interest. This work evaluates the improvement in accuracy and consistency by refining the results of Morfeus, a biomechanical model-based DIR algorithm. Methods and Materials A Hybrid DIR algorithm is proposed based on, a biomechanical model–based DIR algorithm and a refinement step based on a B-spline intensity-based algorithm. Inhale and exhale reconstructions of 4DCT lung images from 31 patients were initially registered using the biomechanical DIR by modeling contact surface between the lungs and the chest cavity. The resulting deformations were then refined using the intensity-based algorithm to reduce any residual uncertainties. Important parameters in the intensity-based algorithm, including grid spacing, number of pyramids, and regularization coefficient, were optimized on 10 randomly-chosen patients (out of 31). Target Registration Error (TRE) was calculated by measuring the Euclidean distance of common anatomical points on both images after registration. For each patient a minimum of 30 points/lung were used. Results Grid spacing of 8 mm, 5 levels of grid pyramids, and regularization coefficient of 3.0 were found to provide optimal results on 10 randomly chosen patients. Overall the entire patient population (n = 31), the Hybrid method resulted in mean±SD (90th%) TRE of 1.5±1.4 (2.9) mm compared to 3.1±1.9 (5.6) using biomechanical DIR and 2.6±2.5 (6.1) using intensity-based DIR alone. Conclusions The proposed hybrid biomechanical modeling intensity based algorithm is a promising DIR technique which could be used in various IGI procedures. The current investigation shows the efficacy of this approach for the registration of 4DCT images of the lungs with average accuracy of 1.5 mm. PMID

  11. 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.

  12. Closed-loop optical stabilization and digital image registration in adaptive optics scanning light ophthalmoscopy

    PubMed Central

    Yang, Qiang; Zhang, Jie; Nozato, Koji; Saito, Kenichi; Williams, David R.; Roorda, Austin; Rossi, Ethan A.

    2014-01-01

    Eye motion is a major impediment to the efficient acquisition of high resolution retinal images with the adaptive optics (AO) scanning light ophthalmoscope (AOSLO). Here we demonstrate a solution to this problem by implementing both optical stabilization and digital image registration in an AOSLO. We replaced the slow scanning mirror with a two-axis tip/tilt mirror for the dual functions of slow scanning and optical stabilization. Closed-loop optical stabilization reduced the amplitude of eye-movement related-image motion by a factor of 10–15. The residual RMS error after optical stabilization alone was on the order of the size of foveal cones: ~1.66–2.56 μm or ~0.34–0.53 arcmin with typical fixational eye motion for normal observers. The full implementation, with real-time digital image registration, corrected the residual eye motion after optical stabilization with an accuracy of ~0.20–0.25 μm or ~0.04–0.05 arcmin RMS, which to our knowledge is more accurate than any method previously reported. PMID:25401030

  13. Target error for image-to-physical space registration: preliminary clinical results using laser range scanning

    NASA Astrophysics Data System (ADS)

    Cao, Aize; Miga, Michael I.; Dumpuri, P.; Ding, S.; Dawant, B. M.; Thompson, R. C.

    2007-03-01

    In this paper, preliminary results from an image-to-physical space registration platform are presented. The current platform employs traditional and novel methods of registration which use a variety of data sources to include: traditional synthetic skin-fiducial point-based registration, surface registration based on facial contours, brain feature point-based registration, brain vessel-to-vessel registration, and a more comprehensive cortical surface registration method that utilizes both geometric and intensity information from both the image volume and physical patient. The intraoperative face and cortical surfaces were digitized using a laser range scanner (LRS) capable of producing highly resolved textured point clouds. In two in vivo cases, a series of registrations were performed using these techniques and compared within the context of a true target error. One of the advantages of using a textured point cloud data stream is that true targets among the physical cortical surface and the preoperative image volume can be identified and used to assess image-to-physical registration methods. The results suggest that iterative closest point (ICP) method for intraoperative face surface registration is equivalent to point-based registration (PBR) method of skin fiducial markers. With regard to the initial image and physical space registration, for patient 1, mean target registration error (TRE) were 3.1+/-0.4 mm and 3.6 +/-0.9 mm for face ICP and skin fiducial PBR, respectively. For patient 2, the mean TRE were 5.7 +/-1.3 mm, and 6.6 +/-0.9 mm for face ICP and skin fiducial PBR, respectively. With regard to intraoperative cortical surface registration, SurfaceMI outperformed feature based PBR and vessel ICP with 1.7+/-1.8 mm for patient 1. For patient 2, the best result was achieved by using vessel ICP with 1.9+/-0.5 mm.

  14. Two Phase Non-Rigid Multi-Modal Image Registration Using Weber Local Descriptor-Based Similarity Metrics and Normalized Mutual Information

    PubMed Central

    Yang, Feng; Ding, Mingyue; Zhang, Xuming; Wu, Yi; Hu, Jiani

    2013-01-01

    Non-rigid multi-modal image registration plays an important role in medical image processing and analysis. Existing image registration methods based on similarity metrics such as mutual information (MI) and sum of squared differences (SSD) cannot achieve either high registration accuracy or high registration efficiency. To address this problem, we propose a novel two phase non-rigid multi-modal image registration method by combining Weber local descriptor (WLD) based similarity metrics with the normalized mutual information (NMI) using the diffeomorphic free-form deformation (FFD) model. The first phase aims at recovering the large deformation component using the WLD based non-local SSD (wldNSSD) or weighted structural similarity (wldWSSIM). Based on the output of the former phase, the second phase is focused on getting accurate transformation parameters related to the small deformation using the NMI. Extensive experiments on T1, T2 and PD weighted MR images demonstrate that the proposed wldNSSD-NMI or wldWSSIM-NMI method outperforms the registration methods based on the NMI, the conditional mutual information (CMI), the SSD on entropy images (ESSD) and the ESSD-NMI in terms of registration accuracy and computation efficiency. PMID:23765270

  15. Acceptance test of a commercially available software for automatic image registration of computed tomography (CT), magnetic resonance imaging (MRI) and 99mTc-methoxyisobutylisonitrile (MIBI) single-photon emission computed tomography (SPECT) brain images.

    PubMed

    Loi, Gianfranco; Dominietto, Marco; Manfredda, Irene; Mones, Eleonora; Carriero, Alessandro; Inglese, Eugenio; Krengli, Marco; Brambilla, Marco

    2008-09-01

    This note describes a method to characterize the performances of image fusion software (Syntegra) with respect to accuracy and robustness. Computed tomography (CT), magnetic resonance imaging (MRI), and single-photon emission computed tomography (SPECT) studies were acquired from two phantoms and 10 patients. Image registration was performed independently by two couples composed of one radiotherapist and one physicist by means of superposition of anatomic landmarks. Each couple performed jointly and saved the registration. The two solutions were averaged to obtain the gold standard registration. A new set of estimators was defined to identify translation and rotation errors in the coordinate axes, independently from point position in image field of view (FOV). Algorithms evaluated were local correlation (LC) for CT-MRI, normalized mutual information (MI) for CT-MRI, and CT-SPECT registrations. To evaluate accuracy, estimator values were compared to limiting values for the algorithms employed, both in phantoms and in patients. To evaluate robustness, different alignments between images taken from a sample patient were produced and registration errors determined. LC algorithm resulted accurate in CT-MRI registrations in phantoms, but exceeded limiting values in 3 of 10 patients. MI algorithm resulted accurate in CT-MRI and CT-SPECT registrations in phantoms; limiting values were exceeded in one case in CT-MRI and never reached in CT-SPECT registrations. Thus, the evaluation of robustness was restricted to the algorithm of MI both for CT-MRI and CT-SPECT registrations. The algorithm of MI proved to be robust: limiting values were not exceeded with translation perturbations up to 2.5 cm, rotation perturbations up to 10 degrees and roto-translational perturbation up to 3 cm and 5 degrees. PMID:17549564

  16. Iterative edge- and wavelet-based image registration of AVHRR and GOES satellite imagery

    NASA Technical Reports Server (NTRS)

    LeMoigne, Jacqueline; El-Saleous, Nazmi; Vermote, Eric

    1997-01-01

    Most automatic registration methods are either correlation-based, feature-based, or a combination of both. Examples of features which can be utilized for automatic image registration are edges, regions, corners, or wavelet-extracted features. In this paper, we describe two proposed approaches, based on edge or edge-like features, which are very appropriate to highlight regions of interest such as coastlines. The two iterative methods utilize the Normalized Cross-Correlation of edge and wavelet features and are applied to such problems as image-to-map registration, landmarking, and channel-to-channel co-registration, utilizing test data, AVHRR data, as well as GOES image data.

  17. Automatic quantitative evaluation of image registration techniques with the ɛ dissimilarity criterion in the case of retinal images

    NASA Astrophysics Data System (ADS)

    Gavet, Yann; Fernandes, Mathieu; Pinoli, Jean-Charles

    2011-07-01

    In human retina observation (with non mydriatic optical microscopes), a registration process is often employed to enlarge the field of view. For the ophthalmologist, this is a way to spare time browsing all the images. A lot of techniques have been proposed to perform this registration process, and indeed, its good evaluation is a question that can be raised. This article presents the use of the ɛ dissimilarity criterion to evaluate and compare some classical featurebased image registration techniques. The problem of retina images registration is employed as an example, but it could also be used in other applications. The images are first segmented and these segmentations are registered. The good quality of this registration is evaluated with the dissimilarity criterion for 25 pairs of images with a manual selection of control points. This study can be useful in order to choose the type of registration method and to evaluate the results of a new one.

  18. D Point Cloud Model Colorization by Dense Registration of Digital Images

    NASA Astrophysics Data System (ADS)

    Crombez, N.; Caron, G.; Mouaddib, E.

    2015-02-01

    Architectural heritage is a historic and artistic property which has to be protected, preserved, restored and must be shown to the public. Modern tools like 3D laser scanners are more and more used in heritage documentation. Most of the time, the 3D laser scanner is completed by a digital camera which is used to enrich the accurate geometric informations with the scanned objects colors. However, the photometric quality of the acquired point clouds is generally rather low because of several problems presented below. We propose an accurate method for registering digital images acquired from any viewpoints on point clouds which is a crucial step for a good colorization by colors projection. We express this image-to-geometry registration as a pose estimation problem. The camera pose is computed using the entire images intensities under a photometric visual and virtual servoing (VVS) framework. The camera extrinsic and intrinsic parameters are automatically estimated. Because we estimates the intrinsic parameters we do not need any informations about the camera which took the used digital image. Finally, when the point cloud model and the digital image are correctly registered, we project the 3D model in the digital image frame and assign new colors to the visible points. The performance of the approach is proven in simulation and real experiments on indoor and outdoor datasets of the cathedral of Amiens, which highlight the success of our method, leading to point clouds with better photometric quality and resolution.

  19. An automated deformable image registration evaluation of confidence tool.

    PubMed

    Kirby, Neil; Chen, Josephine; Kim, Hojin; Morin, Olivier; Nie, Ke; Pouliot, Jean

    2016-04-21

    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. PMID:27025957

  20. 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.

  1. 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. PMID:19041946

  2. Effect of GOES-R Image Navigation and Registration Errors on Atmospheric Motion Vectors

    NASA Technical Reports Server (NTRS)

    Jedlovec, Gary

    2008-01-01

    High temporal frequency imagery from geostationary satellites allows for the continuous monitoring of rapidly changing atmospheric constituents such as smoke, dust, water vapor and clouds. The image sequences are often used to quantify the displacement of image features such as water vapor and clouds to produce atmospheric motion vectors (AMVs) which are used as diagnostic tools and also assimilated into numerical weather forecast models. The basic principle behind the determination of AMVs is the calculation of the physical displacement of features from one image (time) to the next. This process assumes that the features being tracked do not change as a function of time, usually requiring the use of short time interval imagery to minimize substantial change in size and shape of the features being tracked. High spatial resolution imagery also is required for reliable feature identification. While these image resolution and temporal sampling requirements often provide major drivers for space-based instrument design requirements, accurate image navigation and registration, INn (between a sequence of images), is also critical to the derivation of useful AMVs. In this paper and poster to be presented at the conference, the image navigation and registration (INR) accuracy expected for the Advanced Baseline Imager (ABI) on the GOES-R series of satellites will be discussed in light of its impact on AMV accuracy. Significant satellite platform and modeling enhancements are planned which should significantly improve INn performance of the GOES-R instruments. Some of these improvements have been demonstrated for the GOES-13 satellite which was launched in summer of 2006. An analysis of GOES-13 INR data, from the special satellite check out period, will be used in the assessment.

  3. MO-C-17A-11: A Segmentation and Point Matching Enhanced Deformable Image Registration Method for Dose Accumulation Between HDR CT Images

    SciTech Connect

    Zhen, X; Chen, H; Zhou, L; Yan, H; Jiang, S; Jia, X; Gu, X; Mell, L; Yashar, C; Cervino, L

    2014-06-15

    Purpose: To propose and validate a novel and accurate deformable image registration (DIR) scheme to facilitate dose accumulation among treatment fractions of high-dose-rate (HDR) gynecological brachytherapy. Method: We have developed a method to adapt DIR algorithms to gynecologic anatomies with HDR applicators by incorporating a segmentation step and a point-matching step into an existing DIR framework. In the segmentation step, random walks algorithm is used to accurately segment and remove the applicator region (AR) in the HDR CT image. A semi-automatic seed point generation approach is developed to obtain the incremented foreground and background point sets to feed the random walks algorithm. In the subsequent point-matching step, a feature-based thin-plate spline-robust point matching (TPS-RPM) algorithm is employed for AR surface point matching. With the resulting mapping, a DVF characteristic of the deformation between the two AR surfaces is generated by B-spline approximation, which serves as the initial DVF for the following Demons DIR between the two AR-free HDR CT images. Finally, the calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. Results: The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative results as well as the visual inspection of the DIR indicate that our proposed method can suppress the interference of the applicator with the DIR algorithm, and accurately register HDR CT images as well as deform and add interfractional HDR doses. Conclusions: We have developed a novel and robust DIR scheme that can perform registration between HDR gynecological CT images and yield accurate registration results. This new DIR scheme has potential for accurate interfractional HDR dose accumulation. This work is supported in part by the National Natural ScienceFoundation of China (no 30970866 and no

  4. Multi-channel millimeter wave image registration and segmentation for concealed object detection

    NASA Astrophysics Data System (ADS)

    Lee, Dong-Su; Yeom, Seokwon; Son, Jung-Young; Kim, Shin-Hwan

    2010-04-01

    We address an image registration and segmentation method to detect concealed objects captured by passive millimeter wave (MMW) imaging. Passive MMW imaging can create interpretable imagery on the objects concealed under clothing. Due to the penetrating property of the MMW imaging, the MMW imaging system is often employed for the security and defense system. In this paper, we utilize a multi-channel PMMW imaging system operating at the 8 mm regime with linear polarization. Image registration and segmentation are performed to detect concealed objects under clothing. The registration is preceded to align different channel images by means of geometric feature extraction and a matching process. The Linde-Buzo-Gray (LBG) vector quantization with multi-channel information is adopted to segment the concealed object from the body area. In the experiment, the automated image registration and segmentation are performed with various concealed objects including a metal axe and a liquid container.

  5. [Clinical trial requests of indigenous diagnostic imaging ultrasound devices in first-time registration application].

    PubMed

    Guo, Zhaojun; Cao, Guofang; Tao, Kan

    2012-11-01

    This article introduces the clinical requests of indigenous diagnostic imaging ultrasound devices in first-time registration application and the clinical trial requests in Technical Review Guidance of Ultrasound Imaging Diagnostic Devices (category III) Registration and puts forward some questions of the guidance's implementation. It is hoped to help concerned people. PMID:23461122

  6. 3D-2D registration of cerebral angiograms: a method and evaluation on clinical images.

    PubMed

    Mitrovic, Uroš; Špiclin, Žiga; Likar, Boštjan; Pernuš, Franjo

    2013-08-01

    Endovascular image-guided interventions (EIGI) involve navigation of a catheter through the vasculature followed by application of treatment at the site of anomaly using live 2D projection images for guidance. 3D images acquired prior to EIGI are used to quantify the vascular anomaly and plan the intervention. If fused with the information of live 2D images they can also facilitate navigation and treatment. For this purpose 3D-2D image registration is required. Although several 3D-2D registration methods for EIGI achieve registration accuracy below 1 mm, their clinical application is still limited by insufficient robustness or reliability. In this paper, we propose a 3D-2D registration method based on matching a 3D vasculature model to intensity gradients of live 2D images. To objectively validate 3D-2D registration methods, we acquired a clinical image database of 10 patients undergoing cerebral EIGI and established "gold standard" registrations by aligning fiducial markers in 3D and 2D images. The proposed method had mean registration accuracy below 0.65 mm, which was comparable to tested state-of-the-art methods, and execution time below 1 s. With the highest rate of successful registrations and the highest capture range the proposed method was the most robust and thus a good candidate for application in EIGI. PMID:23649179

  7. Multiscale registration of planning CT and daily cone beam CT images for adaptive radiation therapy

    SciTech Connect

    Paquin, Dana; Levy, Doron; Xing Lei

    2009-01-15

    Adaptive radiation therapy (ART) is the incorporation of daily images in the radiotherapy treatment process so that the treatment plan can be evaluated and modified to maximize the amount of radiation dose to the tumor while minimizing the amount of radiation delivered to healthy tissue. Registration of planning images with daily images is thus an important component of ART. In this article, the authors report their research on multiscale registration of planning computed tomography (CT) images with daily cone beam CT (CBCT) images. The multiscale algorithm is based on the hierarchical multiscale image decomposition of E. Tadmor, S. Nezzar, and L. Vese [Multiscale Model. Simul. 2(4), pp. 554-579 (2004)]. Registration is achieved by decomposing the images to be registered into a series of scales using the (BV, L{sup 2}) decomposition and initially registering the coarsest scales of the image using a landmark-based registration algorithm. The resulting transformation is then used as a starting point to deformably register the next coarse scales with one another. This procedure is iterated at each stage using the transformation computed by the previous scale registration as the starting point for the current registration. The authors present the results of studies of rectum, head-neck, and prostate CT-CBCT registration, and validate their registration method quantitatively using synthetic results in which the exact transformations our known, and qualitatively using clinical deformations in which the exact results are not known.

  8. Characterization of endothelial function in the brachial artery via affine registration of ultrasonographic image sequences

    NASA Astrophysics Data System (ADS)

    Lamata, Pablo; Laclaustra, Martin; Frangi, Alejandro F.

    2003-05-01

    The assessment and characterization of the endothelial function is a current research topic as it may play an important role in the diagnosis of cardiovascular diseases. Flow mediated dilatation may be used to investigate endothelial function, and B-mode ultrasonography is a cheap and non-invasive way to assess the vasodilation response. Computerized analysis techniques are very desirable to give higher accuracy and objectivity to the measurements. A new method is presented that solves some limitations of existing methods, which in general depend on accurate edge detection of the arterial wall. This method is based on a global image analysis strategy. The arterial vasodilation between two frames is modeled by a superposition of a rigid motion model and a stretching perpendicular to the artery. Both transformation models are recovered using an image registration algorithm based on normalized mutual information and a multi-resolution search framework. Temporal continuity of in the variation of the registration parameters is enforced with a Kalman filter, since the dilation process is known to be a gradual and continuous physiological phenomenon. The proposed method presents a negligible bias when compared with manual assessment. It also eliminates artifacts introduced by patient and probe motion, thus improving the accuracy of the measurements. Finally, it is also robust to typical problems of ultrasound, like speckle noise and poor image quality.

  9. Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

    NASA Astrophysics Data System (ADS)

    Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.

    2014-03-01

    This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

  10. 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. PMID:25350234

  11. 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.

  12. Research based on the SoPC platform of feature-based image registration

    NASA Astrophysics Data System (ADS)

    Shi, Yue-dong; Wang, Zhi-hui

    2015-12-01

    This paper focuses on the study of implementing feature-based image registration by System on a Programmable Chip (SoPC) hardware platform. We solidify the image registration algorithm on the FPGA chip, in which embedded soft core processor Nios II can speed up the image processing system. In this way, we can make image registration technology get rid of the PC. And, consequently, this kind of technology will be got an extensive use. The experiment result indicates that our system shows stable performance, particularly in terms of matching processing which noise immunity is good. And feature points of images show a reasonable distribution.

  13. Groupwise consistent image registration: a crucial step for the construction of a standardized near infrared hyper-spectral teeth database

    NASA Astrophysics Data System (ADS)

    Špiclin, Žiga; Usenik, Peter; Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

    2011-03-01

    Construction of a standardized near infrared (NIR) hyper-spectral teeth database is a first step in the development of a reliable diagnostic tool for quantification and early detection of dental diseases. The standardized diffuse reflectance hyper-spectral database was constructed by imaging 12 extracted human teeth with natural lesions of various degrees in the spectral range from 900 to 1700 nm with spectral resolution of 10 nm. Additionally, all the teeth were imaged by X-ray and digital color camera. The color and X-ray teeth images were presented to the expert for localization and classification of the dental diseases, thereby obtaining a dental disease gold standard. Accurate transfer of the dental disease gold standard to the NIR images was achieved by image registration in a groupwise manner, taking advantage of the multichannel image information and promoting image edges as the features for the improvement of spatial correspondence detection. By the presented fully automatic multi-modal groupwise registration method, images of new teeth samples can be accurately and reliably registered and then added to the standardized NIR hyper-spectral teeth database. Adding more samples increases the biological and patho-physiological variability of the NIR hyper-spectral teeth database and can importantly contribute to the objective assessment of the sensitivity and specificity of multivariate image analysis techniques used for the detection of dental diseases. Such assessment is essential for the development and validation of reliable qualitative and especially quantitative diagnostic tools based on NIR spectroscopy.

  14. Fast, Accurate and Precise Mid-Sagittal Plane Location in 3D MR Images of the Brain

    NASA Astrophysics Data System (ADS)

    Bergo, Felipe P. G.; Falcão, Alexandre X.; Yasuda, Clarissa L.; Ruppert, Guilherme C. S.

    Extraction of the mid-sagittal plane (MSP) is a key step for brain image registration and asymmetry analysis. We present a fast MSP extraction method for 3D MR images, based on automatic segmentation of the brain and on heuristic maximization of the cerebro-spinal fluid within the MSP. The method is robust to severe anatomical asymmetries between the hemispheres, caused by surgical procedures and lesions. The method is also accurate with respect to MSP delineations done by a specialist. The method was evaluated on 64 MR images (36 pathological, 20 healthy, 8 synthetic), and it found a precise and accurate approximation of the MSP in all of them with a mean time of 60.0 seconds per image, mean angular variation within a same image (precision) of 1.26o and mean angular difference from specialist delineations (accuracy) of 1.64o.

  15. Inter-slice bidirectional registration-based segmentation of the prostate gland in MR and CT image sequences

    SciTech Connect

    Khalvati, Farzad Tizhoosh, Hamid R.; Salmanpour, Aryan; Rahnamayan, Shahryar; Rodrigues, George

    2013-12-15

    Purpose: Accurate segmentation and volume estimation of the prostate gland in magnetic resonance (MR) and computed tomography (CT) images are necessary steps in diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semiautomated segmentation of individual slices in T2-weighted MR and CT image sequences. Methods: The proposedInter-Slice Bidirectional Registration-based Segmentation (iBRS) algorithm relies on interslice image registration of volume data to segment the prostate gland without the use of an anatomical atlas. It requires the user to mark only three slices in a given volume dataset, i.e., the first, middle, and last slices. Next, the proposed algorithm uses a registration algorithm to autosegment the remaining slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid techniques). Results: The results with the proposed technique were compared with manual marking using prostate MR and CT images from 117 patients. Manual marking was performed by an expert user for all 117 patients. The median accuracies for individual slices measured using the Dice similarity coefficient (DSC) were 92% and 91% for MR and CT images, respectively. The iBRS algorithm was also evaluated regarding user variability, which confirmed that the algorithm was robust to interuser variability when marking the prostate gland. Conclusions: The proposed algorithm exploits the interslice data redundancy of the images in a volume dataset of MR and CT images and eliminates the need for an atlas, minimizing the computational cost while producing highly accurate results which are robust to interuser variability.

  16. Fast interactive registration tool for reproducible multi-spectral imaging for wound healing and treatment evaluation

    NASA Astrophysics Data System (ADS)

    Noordmans, Herke J.; de Roode, Rowland; Verdaasdonk, Rudolf

    2007-02-01

    Multi-spectral images of human tissue taken in-vivo often contain image alignment problems as patients have difficulty in retaining their posture during the acquisition time of 20 seconds. Previously, it has been attempted to correct motion errors with image registration software developed for MR or CT data but these algorithms have been proven to be too slow and erroneous for practical use with multi-spectral images. A new software package has been developed which allows the user to play a decisive role in the registration process as the user can monitor the progress of the registration continuously and force it in the right direction when it starts to fail. The software efficiently exploits videocard hardware to gain speed and to provide a perfect subvoxel correspondence between registration field and display. An 8 bit graphic card was used to efficiently register and resample 12 bit images using the hardware interpolation modes present on the graphic card. To show the feasibility of this new registration process, the software was applied in clinical practice evaluating the dosimetry for psoriasis and KTP laser treatment. The microscopic differences between images of normal skin and skin exposed to UV light proved that an affine registration step including zooming and slanting is critical for a subsequent elastic match to have success. The combination of user interactive registration software with optimal addressing the potentials of PC video card hardware greatly improves the speed of multi spectral image registration.

  17. Accurate registration of peri-implant soft tissues to create an optimal emergence profile

    PubMed Central

    Alshiddi, Ibraheem Fahad; Dent, D. Clin

    2015-01-01

    One of the challenges in restoring anterior space with implant restoration is maintaining the natural looking of peri-implant area. This case report presents a clinical procedure to create the soft tissue emergence profile for anterior maxillary teeth. A 49-year-old male presented with missing right maxillary lateral incisor. A provisional restoration was inserted 1 week after implant placement. Area of the provisional restoration related to the gingival tissue (transmucosal area) was adjusted to create an optimum emergence profile. Two months later, an indirect method was used to accurately transfer the soft peri-implant tissues to the master cast. This clinical technique minimizes surgical procedure and avoids the possibility of soft tissue collapsing that may occur during the impression procedure. PMID:25821365

  18. Elastic registration of prostate MR images based on state estimation of dynamical systems

    NASA Astrophysics Data System (ADS)

    Marami, Bahram; Ghoul, Suha; Sirouspour, Shahin; Capson, David W.; Davidson, Sean R. H.; Trachtenberg, John; Fenster, Aaron

    2014-03-01

    Magnetic resonance imaging (MRI) is being increasingly used for image-guided biopsy and focal therapy of prostate cancer. A combined rigid and deformable registration technique is proposed to register pre-treatment diagnostic 3T magnetic resonance (MR) images, with the identified target tumor(s), to the intra-treatment 1.5T MR images. The pre-treatment 3T images are acquired with patients in strictly supine position using an endorectal coil, while 1.5T images are obtained intra-operatively just before insertion of the ablation needle with patients in the lithotomy position. An intensity-based registration routine rigidly aligns two images in which the transformation parameters is initialized using three pairs of manually selected approximate corresponding points. The rigid registration is followed by a deformable registration algorithm employing a generic dynamic linear elastic deformation model discretized by the finite element method (FEM). The model is used in a classical state estimation framework to estimate the deformation of the prostate based on a similarity metric between pre- and intra-treatment images. Registration results using 10 sets of prostate MR images showed that the proposed method can significantly improve registration accuracy in terms of target registration error (TRE) for all prostate substructures. The root mean square (RMS) TRE of 46 manually identified fiducial points was found to be 2.40+/-1.20 mm, 2.51+/-1.20 mm, and 2.28+/-1.22mm for the whole gland (WG), central gland (CG), and peripheral zone (PZ), respectively after deformable registration. These values are improved from 3.15+/-1.60 mm, 3.09+/-1.50 mm, and 3.20+/-1.73mm in the WG, CG and PZ, respectively resulted from rigid registration. Registration results are also evaluated based on the Dice similarity coefficient (DSC), mean absolute surface distances (MAD) and maximum absolute surface distances (MAXD) of the WG and CG in the prostate images.

  19. GOES I/M image navigation and registration

    NASA Technical Reports Server (NTRS)

    Fiorello, J. L., Jr.; Oh, I. H.; Kelly, K. A.; Ranne, L.

    1989-01-01

    Image Navigation and Registration (INR) is the system that will be used on future Geostationary Operational Environmental Satellite (GOES) missions to locate and register radiometric imagery data. It consists of a semiclosed loop system with a ground-based segment that generates coefficients to perform image motion compensation (IMC). The IMC coefficients are uplinked to the satellite-based segment, where they are used to adjust the displacement of the imagery data due to movement of the imaging instrument line-of-sight. The flight dynamics aspects of the INR system is discussed in terms of the attitude and orbit determination, attitude pointing, and attitude and orbit control needed to perform INR. The modeling used in the determination of orbit and attitude is discussed, along with the method of on-orbit control used in the INR system, and various factors that affect stability. Also discussed are potential error sources inherent in the INR system and the operational methods of compensating for these errors.

  20. Knee osteoarthritis image registration: data from the Osteoarthritis Initiative

    NASA Astrophysics Data System (ADS)

    Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Treviño, Victor; Tamez-Peña, José G.

    2015-03-01

    Knee osteoarthritis is a very common disease, in early stages, changes in joint structures are shown, some of the most common symptoms are; formation of osteophytes, cartilage degradation and joint space reduction, among others. Based on a joint space reduction measurement, Kellgren-Lawrence grading scale, is a very extensive used tool to asses radiological OA knee x-ray images, based on information obtained from these assessments, the objective of this work is to correlate the Kellgren-Lawrence score to the bilateral asymmetry between knees. Using public data from the Osteoarthritis initiative (OAI), a set of images with different Kellgren-Lawrencescores were used to determine a relationship of Kellgren-Lawrence score and the bilateral asymmetry, in order to measure the asymmetry between the knees, the right knee was registered to match the left knee, then a series of similarity metrics, mutual information, correlation, and mean squared error where computed to correlate the deformation (mismatch) of the knees to the Kellgren-Lawrence score. Radiological information was evaluated and scored by OAI radiologist groups. The results of the study suggest an association between Radiological Kellgren-Lawrence score and image registration metrics, mutual information and correlation is higher in the early stages, and mean squared error is higher in advanced stages. This association can be helpful to develop a computer aided grading tool.

  1. Robust Deformable Image Registration using Prior Shape Information for Atlas to Patient Registration

    PubMed Central

    Ellingsen, Lotta M.; Chintalapani, Gouthami; Taylor, Russell H.; Prince, Jerry L.

    2009-01-01

    Statistical atlases enable the individualization of atlas information for patient specific applications such as surgical planning. In this paper, a statistical atlas comprising a point distribution model defined on the vertices of a tetrahedral mesh is registered to a subject’s computed tomography scan of the human pelvis. The approach consists of a volumetric deformable registration method augmented to maintain the topology of the atlas mesh after deformation as well as incorporating the dominant three-dimensional shape modes in the atlas. Experimental results demonstrate that incorporation of the statistical shape atlas helps to stabilize the registration and improves robustness and registration accuracy. PMID:19515532

  2. Automatic Masking for Robust 3D-2D Image Registration in Image-Guided Spine Surgery

    PubMed Central

    Ketcha, M. D.; De Silva, T.; Uneri, A.; Kleinszig, G.; Vogt, S.; Wolinsky, J.-P.; Siewerdsen, J. H.

    2016-01-01

    During spinal neurosurgery, patient-specific information, planning, and annotation such as vertebral labels can be mapped from preoperative 3D CT to intraoperative 2D radiographs via image-based 3D-2D registration. Such registration has been shown to provide a potentially valuable means of decision support in target localization as well as quality assurance of the surgical product. However, robust registration can be challenged by mismatch in image content between the preoperative CT and intraoperative radiographs, arising, for example, from anatomical deformation or the presence of surgical tools within the radiograph. In this work, we develop and evaluate methods for automatically mitigating the effect of content mismatch by leveraging the surgical planning data to assign greater weight to anatomical regions known to be reliable for registration and vital to the surgical task while removing problematic regions that are highly deformable or often occluded by surgical tools. We investigated two approaches to assigning variable weight (i.e., "masking") to image content and/or the similarity metric: (1) masking the preoperative 3D CT ("volumetric masking"); and (2) masking within the 2D similarity metric calculation ("projection masking"). The accuracy of registration was evaluated in terms of projection distance error (PDE) in 61 cases selected from an IRB-approved clinical study. The best performing of the masking techniques was found to reduce the rate of gross failure (PDE > 20 mm) from 11.48% to 5.57% in this challenging retrospective data set. These approaches provided robustness to content mismatch and eliminated distinct failure modes of registration. Such improvement was gained without additional workflow and has motivated incorporation of the masking methods within a system under development for prospective clinical studies.

  3. Evaluation of a robotic arm for echocardiography to X-ray image registration during cardiac catheterization procedures.

    PubMed

    Ma, Yingliang; Penney, Graeme P; Bos, Dennis; Frissen, Peter; de Fockert, George; King, Andy; Gao, Gang; Yao, Cheng; Totman, John; Ginks, Matthew; Rinaldi, C; Razavi, Reza; Rhode, Kawal S

    2009-01-01

    We present an initial evaluation of a robotic arm for positioning a 3D echo probe during cardiac catheterization procedures. By tracking the robotic arm, X-ray table and X-ray C-arm, we are able to register the 3D echo images with live 2D X-ray images. In addition, we can also use tracking data from the robotic arm combined with system calibrations to create extended field of view 3D echo images. Both these features can be used for roadmapping to guide cardiac catheterization procedures. We have carried out a validation experiment of our registration method using a cross-wire phantom. Results show our method to be accurate to 3.5 mm. We have successfully demonstrated the creation of the extended field of view data on 2 healthy volunteers and the registration of echo and X-ray data on 1 patient undergoing a pacing study. PMID:19964867

  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

    2016-03-01

    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. PMID:26777133

  5. Optimizing nonrigid registration performance between volumetric true 3D ultrasound images in image-guided neurosurgery

    NASA Astrophysics Data System (ADS)

    Ji, Songbai; Fan, Xiaoyao; Roberts, David W.; Hartov, Alex; Paulsen, Keith D.

    2011-03-01

    Compensating for brain shift as surgery progresses is important to ensure sufficient accuracy in patient-to-image registration in the operating room (OR) for reliable neuronavigation. Ultrasound has emerged as an important and practical imaging technique for brain shift compensation either by itself or through computational modeling that estimates whole-brain deformation. Using volumetric true 3D ultrasound (3DUS), it is possible to nonrigidly (e.g., based on B-splines) register two temporally different 3DUS images directly to generate feature displacement maps for data assimilation in the biomechanical model. Because of a large amount of data and number of degrees-of-freedom (DOFs) involved, however, a significant computational cost may be required that can adversely influence the clinical feasibility of the technique for efficiently generating model-updated MR (uMR) in the OR. This paper parametrically investigates three B-splines registration parameters and their influence on the computational cost and registration accuracy: number of grid nodes along each direction, floating image volume down-sampling rate, and number of iterations. A simulated rigid body displacement field was employed as a ground-truth against which the accuracy of displacements generated from the B-splines nonrigid registration was compared. A set of optimal parameters was then determined empirically that result in a registration computational cost of less than 1 min and a sub-millimetric accuracy in displacement measurement. These resulting parameters were further applied to a clinical surgery case to demonstrate their practical use. Our results indicate that the optimal set of parameters result in sufficient accuracy and computational efficiency in model computation, which is important for future application of the overall biomechanical modeling to generate uMR for image-guidance in the OR.

  6. MRI-SPECT image registration using multiple MR pulse sequences to examine osteoarthritis of the knee

    NASA Astrophysics Data System (ADS)

    Lynch, John A.; Peterfy, Charles G.; White, David L.; Hawkins, Randall A.; Genant, Harry K.

    1999-05-01

    We have examined whether automated image registration can be used to combine metabolic information from SPECT knee scans with anatomical information from MRI. Ten patients, at risk of developing OA due to meniscal surgery, were examined. 99mTc methyldiphosphonate SPECT, T2-weighted fast spin echo (FSE) MRI, and T1-weighted, 3D fat-suppressed gradient recalled echo (SPGR) MRI images were obtained. Registration was performed using normalized mutual information. For each patient, FSE data was registered to SPGR data, providing a composite MRI image with each voxel represented by two intensities (ISPGR, IFSE). Modifications to the registration algorithm were made to allow registration of SPECT data (one intensity per voxel) to composite MRI data (2 intensities per voxel). Registration sources was assessed by visual inspection of uptake localization over expected anatomical locations, and the absence of uptake over unlikely sites. Three patients were discarded from SPECT-MRI registration tests since they had metallic artifacts that prevented co-registration of MR data. Registration of SPECT to SPGR or FSE data alone proved unreliable, with less than 50% of attempts succeeding. The modified algorithm, treating co-registered SPGR and FSE data as a two-value-per-voxel image, proved most reliable, allowing registration of all patients with no metallic artifacts on MRI.

  7. A Novel Ultrasound-Based Registration for Image-Guided Laparoscopic Liver Ablation.

    PubMed

    Fusaglia, Matteo; Tinguely, Pascale; Banz, Vanessa; Weber, Stefan; Lu, Huanxiang

    2016-08-01

    Background Patient-to-image registration is a core process of image-guided surgery (IGS) systems. We present a novel registration approach for application in laparoscopic liver surgery, which reconstructs in real time an intraoperative volume of the underlying intrahepatic vessels through an ultrasound (US) sweep process. Methods An existing IGS system for an open liver procedure was adapted, with suitable instrument tracking for laparoscopic equipment. Registration accuracy was evaluated on a realistic phantom by computing the target registration error (TRE) for 5 intrahepatic tumors. The registration work flow was evaluated by computing the time required for performing the registration. Additionally, a scheme for intraoperative accuracy assessment by visual overlay of the US image with preoperative image data was evaluated. Results The proposed registration method achieved an average TRE of 7.2 mm in the left lobe and 9.7 mm in the right lobe. The average time required for performing the registration was 12 minutes. A positive correlation was found between the intraoperative accuracy assessment and the obtained TREs. Conclusions The registration accuracy of the proposed method is adequate for laparoscopic intrahepatic tumor targeting. The presented approach is feasible and fast and may, therefore, not be disruptive to the current surgical work flow. PMID:26969718

  8. S-HAMMER: Hierarchical Attribute-Guided, Symmetric Diffeomorphic Registration for MR Brain Images

    PubMed Central

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Shen, Dinggang

    2013-01-01

    Deformable registration has been widely used in neuroscience studies for spatial normalization of brain images onto the standard space. Because of possible large anatomical differences across different individual brains, registration performance could be limited when trying to estimate a single directed deformation pathway, i.e., either from template to subject or from subject to template. Symmetric image registration, however, offers an effective way to simultaneously deform template and subject images toward each other until they meet at the middle point. Although some intensity-based registration algorithms have nicely incorporated this concept of symmetric deformation, the pointwise intensity matching between two images may not necessarily imply the matching of correct anatomical correspondences. Based on HAMMER registration algorithm (Shen and Davatzikos, [2002]: IEEE Trans Med Imaging 21:1421–1439), we integrate the strategies of hierarchical attribute matching and symmetric diffeomorphic deformation to build a new symmetric-diffeomorphic HAMMER registration algorithm, called as S-HAMMER. The performance of S-HAMMER has been extensively compared with 14 state-of-the-art nonrigid registration algorithms evaluated in (Klein et al., [2009]: NeuroImage 46:786–802) by using real brain images in LPBA40, IBSR18, CUMC12, and MGH10 datasets. In addition, the registration performance of S-HAMMER, by comparison with other methods, is also demonstrated on both elderly MR brain images (>70 years old) and the simulated brain images with ground-truth deformation fields. In all experiments, our proposed method achieves the best registration performance over all other registration methods, indicating the high applicability of our method in future neuroscience and clinical applications. PMID:23283836

  9. Landmark-driven parameter optimization for non-linear image registration

    NASA Astrophysics Data System (ADS)

    Schmidt-Richberg, Alexander; Werner, René; Ehrhardt, Jan; Wolf, Jan-Christoph; Handels, Heinz

    2011-03-01

    Image registration is one of the most common research areas in medical image processing. It is required for example for image fusion, motion estimation, patient positioning, or generation of medical atlases. In most intensity-based registration approaches, parameters have to be determined, most commonly a parameter indicating to which extend the transformation is required to be smooth. Its optimal value depends on multiple factors like the application and the occurrence of noise in the images, and may therefore vary from case to case. Moreover, multi-scale approaches are commonly applied on registration problems and demand for further adjustment of the parameters. In this paper, we present a landmark-based approach for automatic parameter optimization in non-linear intensity-based image registration. In a first step, corresponding landmarks are automatically detected in the images to match. The landmark-based target registration error (TRE), which is shown to be a valid metric for quantifying registration accuracy, is then used to optimize the parameter choice during the registration process. The approach is evaluated for the registration of lungs based on 22 thoracic 4D CT data sets. Experiments show that the TRE can be reduced on average by 0.07 mm using automatic parameter optimization.

  10. Deformable registration for image-guided spine surgery: preserving rigid body vertebral morphology in free-form transformations

    NASA Astrophysics Data System (ADS)

    Reaungamornrat, S.; Wang, A. S.; Uneri, A.; Otake, Y.; Zhao, Z.; Khanna, A. J.; Siewerdsen, J. H.

    2014-03-01

    Purpose: Deformable registration of preoperative and intraoperative images facilitates accurate localization of target and critical anatomy in image-guided spine surgery. However, conventional deformable registration fails to preserve the morphology of rigid bone anatomy and can impart distortions that confound high-precision intervention. We propose a constrained registration method that preserves rigid morphology while allowing deformation of surrounding soft tissues. Method: The registration method aligns preoperative 3D CT to intraoperative cone-beam CT (CBCT) using free-form deformation (FFD) with penalties on rigid body motion imposed according to a simple intensity threshold. The penalties enforced 3 properties of a rigid transformation - namely, constraints on affinity (AC), orthogonality (OC), and properness (PC). The method also incorporated an injectivity constraint (IC) to preserve topology. Physical experiments (involving phantoms, an ovine spine, and a human cadaver) as well as digital simulations were performed to evaluate the sensitivity to registration parameters, preservation of rigid body morphology, and overall registration accuracy of constrained FFD in comparison to conventional unconstrained FFD (denoted uFFD) and Demons registration. Result: FFD with orthogonality and injectivity constraints (denoted FFD+OC+IC) demonstrated improved performance compared to uFFD and Demons. Affinity and properness constraints offered little or no additional improvement. The FFD+OC+IC method preserved rigid body morphology at near-ideal values of zero dilatation (D = 0.05, compared to 0.39 and 0.56 for uFFD and Demons, respectively) and shear (S = 0.08, compared to 0.36 and 0.44 for uFFD and Demons, respectively). Target registration error (TRE) was similarly improved for FFD+OC+IC (0.7 mm), compared to 1.4 and 1.8 mm for uFFD and Demons. Results were validated in human cadaver studies using CT and CBCT images, with FFD+OC+IC providing excellent preservation

  11. Co-Registration Airborne LIDAR Point Cloud Data and Synchronous Digital Image Registration Based on Combined Adjustment

    NASA Astrophysics Data System (ADS)

    Yang, Z. H.; Zhang, Y. S.; Zheng, T.; Lai, W. B.; Zou, Z. R.; Zou, B.

    2016-06-01

    Aim at the problem of co-registration airborne laser point cloud data with the synchronous digital image, this paper proposed a registration method based on combined adjustment. By integrating tie point, point cloud data with elevation constraint pseudo observations, using the principle of least-squares adjustment to solve the corrections of exterior orientation elements of each image, high-precision registration results can be obtained. In order to ensure the reliability of the tie point, and the effectiveness of pseudo observations, this paper proposed a point cloud data constrain SIFT matching and optimizing method, can ensure that the tie points are located on flat terrain area. Experiments with the airborne laser point cloud data and its synchronous digital image, there are about 43 pixels error in image space using the original POS data. If only considering the bore-sight of POS system, there are still 1.3 pixels error in image space. The proposed method regards the corrections of the exterior orientation elements of each image as unknowns and the errors are reduced to 0.15 pixels.

  12. Comparative study of multimodal intra-subject image registration methods on a publicly available database

    NASA Astrophysics Data System (ADS)

    Miri, Mohammad Saleh; Ghayoor, Ali; Johnson, Hans J.; Sonka, Milan

    2016-03-01

    This work reports on a comparative study between five manual and automated methods for intra-subject pair-wise registration of images from different modalities. The study includes a variety of inter-modal image registrations (MR-CT, PET-CT, PET-MR) utilizing different methods including two manual point-based techniques using rigid and similarity transformations, one automated point-based approach based on Iterative Closest Point (ICP) algorithm, and two automated intensity-based methods using mutual information (MI) and normalized mutual information (NMI). These techniques were employed for inter-modal registration of brain images of 9 subjects from a publicly available dataset, and the results were evaluated qualitatively via checkerboard images and quantitatively using root mean square error and MI criteria. In addition, for each inter-modal registration, a paired t-test was performed on the quantitative results in order to find any significant difference between the results of the studied registration techniques.

  13. Assessing 3D tunnel position in ACL reconstruction using a novel single image 3D-2D registration

    NASA Astrophysics Data System (ADS)

    Kang, X.; Yau, W. P.; Otake, Y.; Cheung, P. Y. S.; Hu, Y.; Taylor, R. H.

    2012-02-01

    The routinely used procedure for evaluating tunnel positions following anterior cruciate ligament (ACL) reconstructions based on standard X-ray images is known to pose difficulties in terms of obtaining accurate measures, especially in providing three-dimensional tunnel positions. This is largely due to the variability in individual knee joint pose relative to X-ray plates. Accurate results were reported using postoperative CT. However, its extensive usage in clinical routine is hampered by its major requirement of having CT scans of individual patients, which is not available for most ACL reconstructions. These difficulties are addressed through the proposed method, which aligns a knee model to X-ray images using our novel single-image 3D-2D registration method and then estimates the 3D tunnel position. In the proposed method, the alignment is achieved by using a novel contour-based 3D-2D registration method wherein image contours are treated as a set of oriented points. However, instead of using some form of orientation weighting function and multiplying it with a distance function, we formulate the 3D-2D registration as a probability density estimation using a mixture of von Mises-Fisher-Gaussian (vMFG) distributions and solve it through an expectation maximization (EM) algorithm. Compared with the ground-truth established from postoperative CT, our registration method in an experiment using a plastic phantom showed accurate results with errors of (-0.43°+/-1.19°, 0.45°+/-2.17°, 0.23°+/-1.05°) and (0.03+/-0.55, -0.03+/-0.54, -2.73+/-1.64) mm. As for the entry point of the ACL tunnel, one of the key measurements, it was obtained with high accuracy of 0.53+/-0.30 mm distance errors.

  14. Applications of digital image processing techniques to problems of data registration and correlation

    NASA Technical Reports Server (NTRS)

    Green, W. B.

    1978-01-01

    An overview is presented of the evolution of the computer configuration at JPL's Image Processing Laboratory (IPL). The development of techniques for the geometric transformation of digital imagery is discussed and consideration is given to automated and semiautomated image registration, and the registration of imaging and nonimaging data. The increasing complexity of image processing tasks at IPL is illustrated with examples of various applications from the planetary program and earth resources activities. It is noted that the registration of existing geocoded data bases with Landsat imagery will continue to be important if the Landsat data is to be of genuine use to the user community.

  15. Deformable image registration of CT images for automatic contour propagation in radiation therapy.

    PubMed

    Wu, Qian; Cao, Ruifen; Pei, Xi; Jia, Jing; Hu, Liqin

    2015-01-01

    Radiotherapy treatment plan may be replanned due the changes of tumors and organs at risk (OARs) during the treatment. Deformable image registration (DIR) based Computed Tomography (CT) contour propagation in the routine clinical setting is expected to reduce time needed for necessary manual tumors and OARs delineations and increase the efficiency of replanning. In this study, a DIR method was developed for CT contour propagation. Prior structure delineations were incorporated into Demons DIR, which was represented by adding an intensity matching term of the delineated tissues pairs to the energy function of Demons. The performance of our DIR was evaluated with five clinical head-and-neck and five lung cancer cases. The experimental results verified the improved accuracy of the proposed registration method compared with conventional registration and Demons DIR. PMID:26405859

  16. An image acquisition and registration strategy for the fusion of hyperpolarized helium-3 MRI and x-ray CT images of the lung

    NASA Astrophysics Data System (ADS)

    Ireland, Rob H.; Woodhouse, Neil; Hoggard, Nigel; Swinscoe, James A.; Foran, Bernadette H.; Hatton, Matthew Q.; Wild, Jim M.

    2008-11-01

    The purpose of this ethics committee approved prospective study was to evaluate an image acquisition and registration protocol for hyperpolarized helium-3 magnetic resonance imaging (3He-MRI) and x-ray computed tomography. Nine patients with non-small cell lung cancer (NSCLC) gave written informed consent to undergo a free-breathing CT, an inspiration breath-hold CT and a 3D ventilation 3He-MRI in CT position using an elliptical birdcage radiofrequency (RF) body coil. 3He-MRI to CT image fusion was performed using a rigid registration algorithm which was assessed by two observers using anatomical landmarks and a percentage volume overlap coefficient. Registration of 3He-MRI to breath-hold CT was more accurate than to free-breathing CT; overlap 82.9 ± 4.2% versus 59.8 ± 9.0% (p < 0.001) and mean landmark error 0.75 ± 0.24 cm versus 1.25 ± 0.60 cm (p = 0.002). Image registration is significantly improved by using an imaging protocol that enables both 3He-MRI and CT to be acquired with similar breath holds and body position through the use of a birdcage 3He-MRI body RF coil and an inspiration breath-hold CT. Fusion of 3He-MRI to CT may be useful for the assessment of patients with lung diseases.

  17. A Combined Method for Segmentation and Registration for an Advanced and Progressive Evaluation of Thermal Images

    PubMed Central

    Barcelos, Emilio Z.; Caminhas, Walmir M.; Ribeiro, Eraldo; Pimenta, Eduardo M.; Palhares, Reinaldo M.

    2014-01-01

    In this paper, a method that combines image analysis techniques, such as segmentation and registration, is proposed for an advanced and progressive evaluation of thermograms. The method is applied for the prevention of muscle injury in high-performance athletes, in collaboration with a Brazilian professional soccer club. The goal is to produce information on spatio-temporal variations of thermograms favoring the investigation of the athletes' conditions along the competition. The proposed method improves on current practice by providing a means for automatically detecting adaptive body-shaped regions of interest, instead of the manual selection of simple shapes. Specifically, our approach combines the optimization features in Otsu's method with a correction factor and post-processing techniques, enhancing thermal-image segmentation when compared to other methods. Additional contributions resulting from the combination of the segmentation and registration steps of our approach are the progressive analyses of thermograms in a unique spatial coordinate system and the accurate extraction of measurements and isotherms. PMID:25414972

  18. A comparison of seven methods of within-subjects rigid-body pedobarographic image registration.

    PubMed

    Pataky, Todd C; Goulermas, John Y; Crompton, Robin H

    2008-10-20

    Image registration, the process of transforming images such that homologous structures optimally overlap, provides the pre-processing foundation for pixel-level functional image analysis. The purpose of this study was to compare the performances of seven methods of within-subjects pedobarographic image registration: (1) manual, (2) principal axes, (3) centre of pressure trajectory, (4) mean squared error, (5) probability-weighted variance, (6) mutual information, and (7) exclusive OR. We assumed that foot-contact geometry changes were negligibly small trial-to-trial and thus that a rigid-body transformation could yield optimum registration performance. Thirty image pairs were randomly selected from our laboratory database and were registered using each method. To compensate for inter-rater variability, the mean registration parameters across 10 raters were taken as representative of manual registration. Registration performance was assessed using four dissimilarity metrics (#4-7 above). One-way MANOVA found significant differences between the methods (p<0.001). Bonferroni post-hoc tests revealed that the centre of pressure method performed the poorest (p<0.001) and that the principal axes method tended to perform more poorly than remaining methods (p<0.070). Average manual registration was not different from the remaining methods (p=1.000). The results suggest that a variety of linear registration methods are appropriate for within-subjects pedobarographic images, and that manual image registration is a viable alternative to algorithmic registration when parameters are averaged across raters. The latter finding, in particular, may be useful for cases of image peculiarities resulting from outlier trials or from experimental manipulations that induce substantial changes in contact area or pressure profile geometry. PMID:18790481

  19. Registration of knee joint surfaces for the in vivo study of joint injuries based on magnetic resonance imaging

    NASA Astrophysics Data System (ADS)

    Cheng, Rita W. T.; Habib, Ayman F.; Frayne, Richard; Ronsky, Janet L.

    2006-03-01

    In-vivo quantitative assessments of joint conditions and health status can help to increase understanding of the pathology of osteoarthritis, a degenerative joint disease that affects a large population each year. Magnetic resonance imaging (MRI) provides a non-invasive and accurate means to assess and monitor joint properties, and has become widely used for diagnosis and biomechanics studies. Quantitative analyses and comparisons of MR datasets require accurate alignment of anatomical structures, thus image registration becomes a necessary procedure for these applications. This research focuses on developing a registration technique for MR knee joint surfaces to allow quantitative study of joint injuries and health status. It introduces a novel idea of translating techniques originally developed for geographic data in the field of photogrammetry and remote sensing to register 3D MR data. The proposed algorithm works with surfaces that are represented by randomly distributed points with no requirement of known correspondences. The algorithm performs matching locally by identifying corresponding surface elements, and solves for the transformation parameters relating the surfaces by minimizing normal distances between them. This technique was used in three applications to: 1) register temporal MR data to verify the feasibility of the algorithm to help monitor diseases, 2) quantify patellar movement with respect to the femur based on the transformation parameters, and 3) quantify changes in contact area locations between the patellar and femoral cartilage at different knee flexion angles. The results indicate accurate registration and the proposed algorithm can be applied for in-vivo study of joint injuries with MRI.

  20. Robust image registration for functional magnetic resonance imaging of the brain.

    PubMed

    Hsu, C C; Wu, M T; Lee, C

    2001-09-01

    Motion-related artifacts are still a major problem in data analysis of functional magnetic resonance imaging (FMRI) studies of brain activation. However, the traditional image registration algorithm is prone to inaccuracy when there are residual variations owing to counting statistics, partial volume effects or biological variation. In particular, susceptibility artifacts usually result in remarkable signal intensity variance, and they can mislead the estimation of motion parameters. In this study, Two robust estimation algorithms for the registration of FMRI images are described. The first estimation algorithm was based on the Newton method and used Tukey's biweight objective function. The second estimation algorithm was based on the Levenberg-Marquardt technique and used a skipped mean objective function. The robust M-estimators can suppress the effects of the outliers by scaling down their error magnitudes or completely rejecting outliers using a weighting function. The proposed registration methods consisted of the following steps: fast segmentation of the brain region from noisy background as a preprocessing step; pre-registration of the volume centroids to provide a good initial estimation; and two robust estimation algorithms and a voxel sampling technique to find the affine transformation parameters. The accuracy of the algorithms was within 0.5 mm in translation and within 0.5 degrees in rotation. For the FMRI data sets, the performance of the algorithms was visually compared with the AIR 2.0 software, which is a software for image registration, using colour-coded statistical mapping by the Kolmogorov-Smirov method. Experimental results showed, that the algorithms provided significant improvement in correcting motion-related artifacts and can enhance the detection of real brain activation. PMID:11712647

  1. PET/CT image registration: Preliminary tests for its application to clinical dosimetry in radiotherapy

    SciTech Connect

    Banos-Capilla, M. C.; Garcia, M. A.; Bea, J.; Pla, C.; Larrea, L.; Lopez, E.

    2007-06-15

    The quality of dosimetry in radiotherapy treatment requires the accurate delimitation of the gross tumor volume. This can be achieved by complementing the anatomical detail provided by CT images through fusion with other imaging modalities that provide additional metabolic and physiological information. Therefore, use of multiple imaging modalities for radiotherapy treatment planning requires an accurate image registration method. This work describes tests carried out on a Discovery LS positron emission/computed tomography (PET/CT) system by General Electric Medical Systems (GEMS), for its later use to obtain images to delimit the target in radiotherapy treatment. Several phantoms have been used to verify image correlation, in combination with fiducial markers, which were used as a system of external landmarks. We analyzed the geometrical accuracy of two different fusion methods with the images obtained with these phantoms. We first studied the fusion method used by the PET/CT system by GEMS (hardware fusion) on the basis that there is satisfactory coincidence between the reconstruction centers in CT and PET systems; and secondly the fiducial fusion, a registration method, by means of least-squares fitting algorithm of a landmark points system. The study concluded with the verification of the centroid position of some phantom components in both imaging modalities. Centroids were estimated through a calculation similar to center-of-mass, weighted by the value of the CT number and the uptake intensity in PET. The mean deviations found for the hardware fusion method were: vertical bar {delta}x vertical bar {+-}{sigma}=3.3 mm{+-}1.0 mm and vertical bar {delta}y vertical bar {+-}{sigma}=3.6 mm{+-}1.0 mm. These values were substantially improved upon applying fiducial fusion based on external landmark points: vertical bar {delta}x vertical bar {+-}{sigma}=0.7 mm{+-}0.8 mm and vertical bar {delta}y vertical bar {+-}{sigma}=0.3 mm{+-}1.7 mm. We also noted that differences

  2. Analytic differential approach for robust registration of rat brain histological images.

    PubMed

    Hsu, Wei-Yen

    2011-06-01

    Image registration is an important topic in medical image analysis. It is usually used to reconstruct 3D structure of tissues from a series of microscopic images. However, a variety of inherent factors may result in great differences between acquired slices during imaging even if they are adjacent. The common differences include the color difference and geometry discrepancy, which make the registration problem a difficult challenge. In this study, we propose a robust registration method to automatically reconstruct 3D volume data of the rat brain. It mainly consists of three procedures, including multiscale wavelet-based feature extraction, analytic robust point matching (ARPM), and registration refinement with feature-based modified Levenberg-Marquardt algorithm (FMLM). The product of gradient moduli in multi-scales is used to decide if extracted feature points are true according to the characteristic that features could exist in multiscale. The ARPM registration algorithm is proposed to speedily accomplish the registration of two point sets with different size by simultaneously evaluating the spatial correspondence and geometrical transformation. In addition, a FMLM method is also proposed to further refine registration results and achieve subpixel accuracy. The FMLM method converges much faster than most other methods due to its feature-based and nonlinear characteristic. The performance of proposed method is evaluated by comparing it with well-known thin-plate spline robust point matching (TPS-RPM) algorithm. The results indicate that ARPM-FMLM algorithm is a robust and fast method in image registration. PMID:20945464

  3. Hyperspectral Imaging for Cancer Surgical Margin Delineation: Registration of Hyperspectral and Histological Images

    PubMed Central

    Lu, Guolan; Halig, Luma; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

    2014-01-01

    The determination of tumor margins during surgical resection remains a challenging task. A complete removal of malignant tissue and conservation of healthy tissue is important for the preservation of organ function, patient satisfaction, and quality of life. Visual inspection and palpation is not sufficient for discriminating between malignant and normal tissue types. Hyperspectral imaging (HSI) technology has the potential to noninvasively delineate surgical tumor margin and can be used as an intra-operative visual aid tool. Since histological images provide the ground truth of cancer margins, it is necessary to warp the cancer regions in ex vivo histological images back to in vivo hyperspectral images in order to validate the tumor margins detected by HSI and to optimize the imaging parameters. In this paper, principal component analysis (PCA) is utilized to extract the principle component bands of the HSI images, which is then used to register HSI images with the corresponding histological image. Affine registration is chosen to model the global transformation. A B-spline free form deformation (FFD) method is used to model the local non-rigid deformation. Registration experiment was performed on animal hyperspectral and histological images. Experimental results from animals demonstrated the feasibility of the hyperspectral imaging method for cancer margin detection. PMID:25328640

  4. Hyperspectral imaging for cancer surgical margin delineation: registration of hyperspectral and histological images

    NASA Astrophysics Data System (ADS)

    Lu, Guolan; Halig, Luma; Wang, Dongsheng; Chen, Zhuo G.; Fei, Baowei

    2014-03-01

    The determination of tumor margins during surgical resection remains a challenging task. A complete removal of malignant tissue and conservation of healthy tissue is important for the preservation of organ function, patient satisfaction, and quality of life. Visual inspection and palpation is not sufficient for discriminating between malignant and normal tissue types. Hyperspectral imaging (HSI) technology has the potential to noninvasively delineate surgical tumor margin and can be used as an intra-operative visual aid tool. Since histological images provide the ground truth of cancer margins, it is necessary to warp the cancer regions in ex vivo histological images back to in vivo hyperspectral images in order to validate the tumor margins detected by HSI and to optimize the imaging parameters. In this paper, principal component analysis (PCA) is utilized to extract the principle component bands of the HSI images, which is then used to register HSI images with the corresponding histological image. Affine registration is chosen to model the global transformation. A B-spline free form deformation (FFD) method is used to model the local non-rigid deformation. Registration experiment was performed on animal hyperspectral and histological images. Experimental results from animals demonstrated the feasibility of the hyperspectral imaging method for cancer margin detection.

  5. Hyperspectral Imaging for Cancer Surgical Margin Delineation: Registration of Hyperspectral and Histological Images.

    PubMed

    Lu, Guolan; Halig, Luma; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

    2014-03-12

    The determination of tumor margins during surgical resection remains a challenging task. A complete removal of malignant tissue and conservation of healthy tissue is important for the preservation of organ function, patient satisfaction, and quality of life. Visual inspection and palpation is not sufficient for discriminating between malignant and normal tissue types. Hyperspectral imaging (HSI) technology has the potential to noninvasively delineate surgical tumor margin and can be used as an intra-operative visual aid tool. Since histological images provide the ground truth of cancer margins, it is necessary to warp the cancer regions in ex vivo histological images back to in vivo hyperspectral images in order to validate the tumor margins detected by HSI and to optimize the imaging parameters. In this paper, principal component analysis (PCA) is utilized to extract the principle component bands of the HSI images, which is then used to register HSI images with the corresponding histological image. Affine registration is chosen to model the global transformation. A B-spline free form deformation (FFD) method is used to model the local non-rigid deformation. Registration experiment was performed on animal hyperspectral and histological images. Experimental results from animals demonstrated the feasibility of the hyperspectral imaging method for cancer margin detection. PMID:25328640

  6. Analyzing in situ gene expression in the mouse brain with image registration, feature extraction and block clustering

    PubMed Central

    Jagalur, Manjunatha; Pal, Chris; Learned-Miller, Erik; Zoeller, R Thomas; Kulp, David

    2007-01-01

    Background Many important high throughput projects use in situ hybridization and may require the analysis of images of spatial cross sections of organisms taken with cellular level resolution. Projects creating gene expression atlases at unprecedented scales for the embryonic fruit fly as well as the embryonic and adult mouse already involve the analysis of hundreds of thousands of high resolution experimental images mapping mRNA expression patterns. Challenges include accurate registration of highly deformed tissues, associating cells with known anatomical regions, and identifying groups of genes whose expression is coordinately regulated with respect to both concentration and spatial location. Solutions to these and other challenges will lead to a richer understanding of the complex system aspects of gene regulation in heterogeneous tissue. Results We present an end-to-end approach for processing raw in situ expression imagery and performing subsequent analysis. We use a non-linear, information theoretic based image registration technique specifically adapted for mapping expression images to anatomical annotations and a method for extracting expression information within an anatomical region. Our method consists of coarse registration, fine registration, and expression feature extraction steps. From this we obtain a matrix for expression characteristics with rows corresponding to genes and columns corresponding to anatomical sub-structures. We perform matrix block cluster analysis using a novel row-column mixture model and we relate clustered patterns to Gene Ontology (GO) annotations. Conclusion Resulting registrations suggest that our method is robust over intensity levels and shape variations in ISH imagery. Functional enrichment studies from both simple analysis and block clustering indicate that gene relationships consistent with biological knowledge of neuronal gene functions can be extracted from large ISH image databases such as the Allen Brain Atlas [1

  7. SU-E-J-112: Intensity-Based Pulmonary Image Registration: An Evaluation Study

    SciTech Connect

    Yang, F; Meyer, J; Sandison, G

    2015-06-15

    Purpose: Accurate alignment of thoracic CT images is essential for dose tracking and to safely implement adaptive radiotherapy in lung cancers. At the same time it is challenging given the highly elastic nature of lung tissue deformations. The objective of this study was to assess the performances of three state-of-art intensity-based algorithms in terms of their ability to register thoracic CT images subject to affine, barrel, and sinusoid transformation. Methods: Intensity similarity measures of the evaluated algorithms contained sum-of-squared difference (SSD), local mutual information (LMI), and residual complexity (RC). Five thoracic CT scans obtained from the EMPIRE10 challenge database were included and served as reference images. Each CT dataset was distorted by realistic affine, barrel, and sinusoid transformations. Registration performances of the three algorithms were evaluated for each distortion type in terms of intensity root mean square error (IRMSE) between the reference and registered images in the lung regions. Results: For affine distortions, the three algorithms differed significantly in registration of thoracic images both visually and nominally in terms of IRMSE with a mean of 0.011 for SSD, 0.039 for RC, and 0.026 for LMI (p<0.01; Kruskal-Wallis test). For barrel distortion, the three algorithms showed nominally no significant difference in terms of IRMSE with a mean of 0.026 for SSD, 0.086 for RC, and 0.054 for LMI (p=0.16) . A significant difference was seen for sinusoid distorted thoracic CT data with mean lung IRMSE of 0.039 for SSD, 0.092 for RC, and 0.035 for LMI (p=0.02). Conclusion: Pulmonary deformations might vary to a large extent in nature in a daily clinical setting due to factors ranging from anatomy variations to respiratory motion to image quality. It can be appreciated from the results of the present study that the suitability of application of a particular algorithm for pulmonary image registration is deformation-dependent.

  8. Sequential and Automatic Image-Sequence Registration of Road Areas Monitored from a Hovering Helicopter

    PubMed Central

    Nejadasl, Fatemeh Karimi.; Lindenbergh, Roderik.

    2014-01-01

    In this paper, we propose an automatic and sequential method for the registration of an image sequence of a road area without ignoring scene-induced motion. This method contributes to a larger work, aiming at vehicle tracking. A typical image sequence is recorded from a helicopter hovering above the freeway. The demand for automation is inevitable due to the large number of images and continuous changes in the traffic situation and weather conditions. A framework is designed and implemented for this purpose. The registration errors are removed in a sequential way based on two homography assumptions. First, an approximate registration is obtained, which is efficiently refined in a second step, using a restricted search area. The results of the stabilization framework are demonstrated on an image sequence consisting of 1500 images and show that our method allows a registration between arbitrary images in the sequence with a geometric error of zero in pixel accuracy. PMID:25198006

  9. Investigation of uncertainties in image registration of cone beam CT to CT on an image-guided radiotherapy system

    NASA Astrophysics Data System (ADS)

    Sykes, J. R.; Brettle, D. S.; Magee, D. R.; Thwaites, D. I.

    2009-12-01

    Methods of measuring uncertainties in rigid body image registration of fan beam computed tomography (FBCT) to cone beam CT (CBCT) have been developed for automatic image registration algorithms in a commercial image guidance system (Synergy, Elekta, UK). The relationships between image registration uncertainty and both imaging dose and image resolution have been investigated with an anthropomorphic skull phantom and further measurements performed with patient images of the head. A new metric of target registration error is proposed. The metric calculates the mean distance traversed by a set of equi-spaced points on the surface of a 5 cm sphere, centred at the isocentre when transformed by the residual error of registration. Studies aimed at giving practical guidance on the use of the Synergy automated image registration, including choice of algorithm and use of the Clipbox are reported. The chamfer-matching algorithm was found to be highly robust to the increased noise induced by low-dose acquisitions. This would allow the imaging dose to be reduced from the current clinical norm of 2 mGy to 0.2 mGy without a clinically significant loss of accuracy. A study of the effect of FBCT slice thickness/spacing and CBCT voxel size showed that 2.5 mm and 1 mm, respectively, gave acceptable image registration performance. Registration failures were highly infrequent if the misalignment was typical of normal clinical set-up errors and these were easily identified. The standard deviation of translational registration errors, measured with patient images, was 0.5 mm on the surface of a 5 cm sphere centred on the treatment centre. The chamfer algorithm is suitable for routine clinical use with minimal need for close inspection of image misalignment.

  10. Investigation of uncertainties in image registration of cone beam CT to CT on an image-guided radiotherapy system.

    PubMed

    Sykes, J R; Brettle, D S; Magee, D R; Thwaites, D I

    2009-12-21

    Methods of measuring uncertainties in rigid body image registration of fan beam computed tomography (FBCT) to cone beam CT (CBCT) have been developed for automatic image registration algorithms in a commercial image guidance system (Synergy, Elekta, UK). The relationships between image registration uncertainty and both imaging dose and image resolution have been investigated with an anthropomorphic skull phantom and further measurements performed with patient images of the head. A new metric of target registration error is proposed. The metric calculates the mean distance traversed by a set of equi-spaced points on the surface of a 5 cm sphere, centred at the isocentre when transformed by the residual error of registration. Studies aimed at giving practical guidance on the use of the Synergy automated image registration, including choice of algorithm and use of the Clipbox are reported. The chamfer-matching algorithm was found to be highly robust to the increased noise induced by low-dose acquisitions. This would allow the imaging dose to be reduced from the current clinical norm of 2 mGy to 0.2 mGy without a clinically significant loss of accuracy. A study of the effect of FBCT slice thickness/spacing and CBCT voxel size showed that 2.5 mm and 1 mm, respectively, gave acceptable image registration performance. Registration failures were highly infrequent if the misalignment was typical of normal clinical set-up errors and these were easily identified. The standard deviation of translational registration errors, measured with patient images, was 0.5 mm on the surface of a 5 cm sphere centred on the treatment centre. The chamfer algorithm is suitable for routine clinical use with minimal need for close inspection of image misalignment. PMID:19926913

  11. Free-form deformation based non-rigid registration on breast cancer MR imaging

    NASA Astrophysics Data System (ADS)

    Zhang, Liangbin; Suo, Shiteng; Lu, Xuesong; Li, Yuehua; Chen, Li; Zhang, Su

    2013-07-01

    High-Intensity Focused Ultrasound treatment combined with magnetic resonance technology (MRI-guided HIFU, MRgHIFU) can protect the thermal ablation without harming the surrounding tissue by using MRI for target positioning, where image registration plays an important role in the implementation of precise treatment. In this paper, we apply three-dimension free-form deformation non-rigid registration on treatment plan amendments and tracking of breast cancer. Free-form deformation based and demons based non-rigid registration are respectively employed on breast cancer MR imaging required at different times before and after for comparison. The results of the experiments show that the registration performed on the breast tumor image data with slight and larger deformation is effective, and the mutual information of the ROI increased from 1.49 before registration to 1.53.

  12. A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images

    NASA Astrophysics Data System (ADS)

    Zhen, Xin; Chen, Haibin; Yan, Hao; Zhou, Linghong; Mell, Loren K.; Yashar, Catheryn M.; Jiang, Steve; Jia, Xun; Gu, Xuejun; Cervino, Laura

    2015-04-01

    Deformable image registration (DIR) of fractional high-dose-rate (HDR) CT images is challenging due to the presence of applicators in the brachytherapy image. Point-to-point correspondence fails because of the undesired deformation vector fields (DVF) propagated from the applicator region (AR) to the surrounding tissues, which can potentially introduce significant DIR errors in dose mapping. This paper proposes a novel segmentation and point-matching enhanced efficient DIR (named SPEED) scheme to facilitate dose accumulation among HDR treatment fractions. In SPEED, a semi-automatic seed point generation approach is developed to obtain the incremented fore/background point sets to feed the random walks algorithm, which is used to segment and remove the AR, leaving empty AR cavities in the HDR CT images. A feature-based ‘thin-plate-spline robust point matching’ algorithm is then employed for AR cavity surface points matching. With the resulting mapping, a DVF defining on each voxel is estimated by B-spline approximation, which serves as the initial DVF for the subsequent Demons-based DIR between the AR-free HDR CT images. The calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative analysis and visual inspection of the DIR results indicate that SPEED can suppress the impact of applicator on DIR, and accurately register HDR CT images as well as deform and add interfractional HDR doses.

  13. A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images.

    PubMed

    Zhen, Xin; Chen, Haibin; Yan, Hao; Zhou, Linghong; Mell, Loren K; Yashar, Catheryn M; Jiang, Steve; Jia, Xun; Gu, Xuejun; Cervino, Laura

    2015-04-01

    Deformable image registration (DIR) of fractional high-dose-rate (HDR) CT images is challenging due to the presence of applicators in the brachytherapy image. Point-to-point correspondence fails because of the undesired deformation vector fields (DVF) propagated from the applicator region (AR) to the surrounding tissues, which can potentially introduce significant DIR errors in dose mapping. This paper proposes a novel segmentation and point-matching enhanced efficient DIR (named SPEED) scheme to facilitate dose accumulation among HDR treatment fractions. In SPEED, a semi-automatic seed point generation approach is developed to obtain the incremented fore/background point sets to feed the random walks algorithm, which is used to segment and remove the AR, leaving empty AR cavities in the HDR CT images. A feature-based 'thin-plate-spline robust point matching' algorithm is then employed for AR cavity surface points matching. With the resulting mapping, a DVF defining on each voxel is estimated by B-spline approximation, which serves as the initial DVF for the subsequent Demons-based DIR between the AR-free HDR CT images. The calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative analysis and visual inspection of the DIR results indicate that SPEED can suppress the impact of applicator on DIR, and accurately register HDR CT images as well as deform and add interfractional HDR doses. PMID:25790059

  14. Registration of a synthetic aperture radar image to Thematic Mapper imagery for remote sensing applications

    NASA Technical Reports Server (NTRS)

    Yao, S. S.; Gilbert, J. R.

    1984-01-01

    Multiple Thematic Mapper multitemporal acquisitions from Landsat and one synthetic-aperture radar acquisition from Seasat have been precisely registered using Johnson Space Center registration processors. The registered images have been output in the Universal Transverse Mercator projection. The procedure to accomplish such disparate data processing tasks and the registration accuracy evaluation are discussed.

  15. Automatic Registration of Terrestrial Laser Scanning Point Clouds using Panoramic Reflectance Images

    PubMed Central

    Kang, Zhizhong; Li, Jonathan; Zhang, Liqiang; Zhao, Qile; Zlatanova, Sisi

    2009-01-01

    This paper presents a new approach to the automatic registration of terrestrial laser scanning (TLS) point clouds using panoramic reflectance images. The approach follows a two-step procedure that includes both pair-wise registration and global registration. The pair-wise registration consists of image matching (pixel-to-pixel correspondence) and point cloud registration (point-to-point correspondence), as the correspondence between the image and the point cloud (pixel-to-point) is inherent to the reflectance images. False correspondences are removed by a geometric invariance check. The pixel-to-point correspondence and the computation of the rigid transformation parameters (RTPs) are integrated into an iterative process that allows for the pair-wise registration to be optimised. The global registration of all point clouds is obtained by a bundle adjustment using a circular self-closure constraint. Our approach is tested with both indoor and outdoor scenes acquired by a FARO LS 880 laser scanner with an angular resolution of 0.036° and 0.045°, respectively. The results show that the pair-wise and global registration accuracies are of millimetre and centimetre orders, respectively, and that the process is fully automatic and converges quickly. PMID:22574036

  16. Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration

    PubMed Central

    Xiao, Jinjun; Li, Min; Zhang, Haipeng

    2015-01-01

    This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance. The contributions can be divided into three parts. Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects. Secondly, the robust similarity measure is proposed based on adaptive principal component. Finally, the robust registration algorithm is derived based on the RAPCA. The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images. PMID:25960739

  17. Optimal landmarks selection and fiducial marker placement for minimal target registration error in image-guided neurosurgery

    NASA Astrophysics Data System (ADS)

    Shamir, Reuben R.; Joskowicz, Leo; Shoshan, Yigal

    2009-02-01

    We describe a new framework and method for the optimal selection of anatomical landmarks and optimal placement of fiducial markers in image-guided neurosurgery. The method allows the surgeon to optimally plan the markers locations on routine diagnostic images before preoperative imaging and to intraoperatively select the fiducial markers and the anatomical landmarks that minimize the Target Registration Error (TRE). The optimal fiducial marker configuration selection is performed by the surgeon on the diagnostic image following the target selection based on a visual Estimated TRE (E-TRE) map. The E-TRE map is automatically updated when the surgeon interactively adds and deletes candidate markers and targets. The method takes the guesswork out of the registration process, provides a reliable localization uncertainty error for navigation, and can reduce the localization error without additional imaging and hardware. Our clinical experiments on five patients who underwent brain surgery with a navigation system show that optimizing one marker location and the anatomical landmarks configuration reduces the average TRE from 4.7mm to 3.2mm, with a maximum improvement of 4mm. The reduction of the target registration error has the potential to support safer and more accurate minimally invasive neurosurgical procedures.

  18. IR and visual image registration based on mutual information and PSO-Powell algorithm

    NASA Astrophysics Data System (ADS)

    Zhuang, Youwen; Gao, Kun; Miu, Xianghu

    2014-11-01

    Infrared and visual image registration has a wide application in the fields of remote sensing and military. Mutual information (MI) has proved effective and successful in infrared and visual image registration process. To find the most appropriate registration parameters, optimal algorithms, such as Particle Swarm Optimization (PSO) algorithm or Powell search method, are often used. The PSO algorithm has strong global search ability and search speed is fast at the beginning, while the weakness is low search performance in late search stage. In image registration process, it often takes a lot of time to do useless search and solution's precision is low. Powell search method has strong local search ability. However, the search performance and time is more sensitive to initial values. In image registration, it is often obstructed by local maximum and gets wrong results. In this paper, a novel hybrid algorithm, which combined PSO algorithm and Powell search method, is proposed. It combines both advantages that avoiding obstruction caused by local maximum and having higher precision. Firstly, using PSO algorithm gets a registration parameter which is close to global minimum. Based on the result in last stage, the Powell search method is used to find more precision registration parameter. The experimental result shows that the algorithm can effectively correct the scale, rotation and translation additional optimal algorithm. It can be a good solution to register infrared difference of two images and has a greater performance on time and precision than traditional and visible images.

  19. Dense registration of CHRIS-Proba and Ikonos images using multi-dimensional mutual information maximization

    NASA Astrophysics Data System (ADS)

    Cariou, Claude; Chehdi, Kacem

    2013-10-01

    We investigate the potential of multidimensional mutual information for the registration of multi-spectral remote sensing images. We devise a gradient flow algorithm which iteratively maximizes the multidimensional mutual information with respect to a differentiable displacement map, accounting for partial derivatives of the multivariate joint distribution and the multivariate marginal of the float image with respect to each variable of the mutual information derivative. The resulting terms are shown to weight the band specific gradients of the warp image, and we propose in addition to compute them with a method based on the k-nearest neighbours. We apply our method to the registration of Ikonos and CHRIS-Proba images over the region of Baabdat, Lebanon, for purposes of cedar pines detection. A comparison between (crossed) single band and multi-band registration results obtained shows that using the multidimensional mutual information brings a significant gain in positional accuracy and is suitable for multispectral remote sensing image registration.

  20. Speckle reduction in optical coherence tomography imaging by affine-motion image registration

    NASA Astrophysics Data System (ADS)

    Alonso-Caneiro, David; Read, Scott A.; Collins, Michael J.

    2011-11-01

    Signal-degrading speckle is one factor that can reduce the quality of optical coherence tomography images. We demonstrate the use of a hierarchical model-based motion estimation processing scheme based on an affine-motion model to reduce speckle in optical coherence tomography imaging, by image registration and the averaging of multiple B-scans. The proposed technique is evaluated against other methods available in the literature. The results from a set of retinal images show the benefit of the proposed technique, which provides an improvement in signal-to-noise ratio of the square root of the number of averaged images, leading to clearer visual information in the averaged image. The benefits of the proposed technique are also explored in the case of ocular anterior segment imaging.

  1. Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features

    PubMed Central

    Mousavi Kahaki, Seyed Mostafa; Nordin, Md Jan; Ashtari, Amir H.; J. Zahra, Sophia

    2016-01-01

    An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics—such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient—are insufficient for achieving adequate results under different image deformations. Thus, new descriptor’s similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence. PMID:26985996

  2. dPIRPLE: A Joint Estimation Framework for Deformable Registration and Penalized-Likelihood CT Image Reconstruction using Prior Images

    PubMed Central

    Dang, H.; Wang, A. S.; Sussman, Marc S.; Siewerdsen, J. H.; Stayman, J. W.

    2014-01-01

    Sequential imaging studies are conducted in many clinical scenarios. Prior images from previous studies contain a great deal of patient-specific anatomical information and can be used in conjunction with subsequent imaging acquisitions to maintain image quality while enabling radiation dose reduction (e.g., through sparse angular sampling, reduction in fluence, etc.). However, patient motion between images in such sequences results in misregistration between the prior image and current anatomy. Existing prior-image-based approaches often include only a simple rigid registration step that can be insufficient for capturing complex anatomical motion, introducing detrimental effects in subsequent image reconstruction. In this work, we propose a joint framework that estimates the 3D deformation between an unregistered prior image and the current anatomy (based on a subsequent data acquisition) and reconstructs the current anatomical image using a model-based reconstruction approach that includes regularization based on the deformed prior image. This framework is referred to as deformable prior image registration, penalized-likelihood estimation (dPIRPLE). Central to this framework is the inclusion of a 3D B-spline-based free-form-deformation model into the joint registration-reconstruction objective function. The proposed framework is solved using a maximization strategy whereby alternating updates to the registration parameters and image estimates are applied allowing for improvements in both the registration and reconstruction throughout the optimization process. Cadaver experiments were conducted on a cone-beam CT testbench emulating a lung nodule surveillance scenario. Superior reconstruction accuracy and image quality were demonstrated using the dPIRPLE algorithm as compared to more traditional reconstruction methods including filtered backprojection, penalized-likelihood estimation (PLE), prior image penalized-likelihood estimation (PIPLE) without registration

  3. Learning statistical correlation for fast prostate registration in image-guided radiotherapy

    SciTech Connect

    Shi Yonghong; Liao Shu; Shen Dinggang

    2011-11-15

    Purpose: In adaptive radiation therapy of prostate cancer, fast and accurate registration between the planning image and treatment images of the patient is of essential importance. With the authors' recently developed deformable surface model, prostate boundaries in each treatment image can be rapidly segmented and their correspondences (or relative deformations) to the prostate boundaries in the planning image are also established automatically. However, the dense correspondences on the nonboundary regions, which are important especially for transforming the treatment plan designed in the planning image space to each treatment image space, are remained unresolved. This paper presents a novel approach to learn the statistical correlation between deformations of prostate boundary and nonboundary regions, for rapidly estimating deformations of the nonboundary regions when given the deformations of the prostate boundary at a new treatment image. Methods: The main contributions of the proposed method lie in the following aspects. First, the statistical deformation correlation will be learned from both current patient and other training patients, and further updated adaptively during the radiotherapy. Specifically, in the initial treatment stage when the number of treatment images collected from the current patient is small, the statistical deformation correlation is mainly learned from other training patients. As more treatment images are collected from the current patient, the patient-specific information will play a more important role in learning patient-specific statistical deformation correlation to effectively reflect prostate deformation of the current patient during the treatment. Eventually, only the patient-specific statistical deformation correlation is used to estimate dense correspondences when a sufficient number of treatment images have been acquired from the current patient. Second, the statistical deformation correlation will be learned by using a

  4. Exploiting Image Registration for Automated Resonance Assignment in NMR

    PubMed Central

    Strickland, Madeleine; Stephens, Thomas; Liu, Jian; Tjandra, Nico

    2015-01-01

    Summary Analysis of protein NMR data involves the assignment of resonance peaks in a number of multidimensional data sets. To establish resonance assignment a three-dimensional search is used to match a pair of common variables, such as chemical shifts of the same spin system, in different NMR spectra. We show that by displaying the variables to be compared in two-dimensional plots the process can be simplified. Moreover, by utilizing a fast Fourier transform (FFT) cross-correlation algorithm, more common to the field of image registration or pattern matching, we can automate this process. Here, we use sequential NMR backbone assignment as an example to show that the combination of correlation plots and segmented pattern matching establishes fast backbone assignment in fifteen proteins of varying sizes. For example, the 265-residue RalBP1 protein was 95.4% correctly assigned in 10 seconds. The same concept can be applied to any multidimensional NMR data set where analysis comprises the comparison of two variables. This modular and robust approach offers high efficiency with excellent computational scalability and could be easily incorporated into existing assignment software. PMID:25828257

  5. Accurate reconstruction of hyperspectral images from compressive sensing measurements

    NASA Astrophysics Data System (ADS)

    Greer, John B.; Flake, J. C.

    2013-05-01

    The emerging field of Compressive Sensing (CS) provides a new way to capture data by shifting the heaviest burden of data collection from the sensor to the computer on the user-end. This new means of sensing requires fewer measurements for a given amount of information than traditional sensors. We investigate the efficacy of CS for capturing HyperSpectral Imagery (HSI) remotely. We also introduce a new family of algorithms for constructing HSI from CS measurements with Split Bregman Iteration [Goldstein and Osher,2009]. These algorithms combine spatial Total Variation (TV) with smoothing in the spectral dimension. We examine models for three different CS sensors: the Coded Aperture Snapshot Spectral Imager-Single Disperser (CASSI-SD) [Wagadarikar et al.,2008] and Dual Disperser (CASSI-DD) [Gehm et al.,2007] cameras, and a hypothetical random sensing model closer to CS theory, but not necessarily implementable with existing technology. We simulate the capture of remotely sensed images by applying the sensor forward models to well-known HSI scenes - an AVIRIS image of Cuprite, Nevada and the HYMAP Urban image. To measure accuracy of the CS models, we compare the scenes constructed with our new algorithm to the original AVIRIS and HYMAP cubes. The results demonstrate the possibility of accurately sensing HSI remotely with significantly fewer measurements than standard hyperspectral cameras.

  6. Multi-modal image registration based on gradient orientations of minimal uncertainty.

    PubMed

    De Nigris, Dante; Collins, D Louis; Arbel, Tal

    2012-12-01

    In this paper, we propose a new multi-scale technique for multi-modal image registration based on the alignment of selected gradient orientations of reduced uncertainty. We show how the registration robustness and accuracy can be improved by restricting the evaluation of gradient orientation alignment to locations where the uncertainty of fixed image gradient orientations is minimal, which we formally demonstrate correspond to locations of high gradient magnitude. We also embed a computationally efficient technique for estimating the gradient orientations of the transformed moving image (rather than resampling pixel intensities and recomputing image gradients). We have applied our method to different rigid multi-modal registration contexts. Our approach outperforms mutual information and other competing metrics in the context of rigid multi-modal brain registration, where we show sub-millimeter accuracy with cases obtained from the retrospective image registration evaluation project. Furthermore, our approach shows significant improvements over standard methods in the highly challenging clinical context of image guided neurosurgery, where we demonstrate misregistration of less than 2 mm with relation to expert selected landmarks for the registration of pre-operative brain magnetic resonance images to intra-operative ultrasound images. PMID:22987509

  7. SU-E-J-248: Comparative Study of Two Image Registration for Image-Guided Radiation Therapy in Esophageal Cancer

    SciTech Connect

    Shang, K; Wang, J; Liu, D; Li, R; Cao, Y; Chi, Z

    2014-06-01

    Purpose: Image-guided radiation therapy (IGRT) is one of the major treatment of esophageal cancer. Gray value registration and bone registration are two kinds of image registration, the purpose of this work is to compare which one is more suitable for esophageal cancer patients. Methods: Twenty three esophageal patients were treated by Elekta Synergy, CBCT images were acquired and automatically registered to planning kilovoltage CT scans according to gray value or bone registration. The setup errors were measured in the X, Y and Z axis, respectively. Two kinds of setup errors were analysed by matching T test statistical method. Results: Four hundred and five groups of CBCT images were available and the systematic and random setup errors (cm) in X, Y, Z directions were 0.35, 0.63, 0.29 and 0.31, 0.53, 0.21 with gray value registration, while 0.37, 0.64, 0.26 and 0.32, 0.55, 0.20 with bone registration, respectively. Compared with bone registration and gray value registration, the setup errors in X and Z axis have significant differences. In Y axis, both measurement comparison results of T value is 0.256 (P value > 0.05); In X axis, the T value is 5.287(P value < 0.05); In Z axis, the T value is −5.138 (P value < 0.05). Conclusion: Gray value registration is recommended in image-guided radiotherapy for esophageal cancer and the other thoracic tumors. Manual registration could be applied when it is necessary. Bone registration is more suitable for the head tumor and pelvic tumor department where composed of redundant interconnected and immobile bone tissue.

  8. A nonlinear biomechanical model based registration method for aligning prone and supine MR breast images.

    PubMed

    Han, Lianghao; Hipwell, John H; Eiben, Björn; Barratt, Dean; Modat, Marc; Ourselin, Sebastien; Hawkes, David J

    2014-03-01

    Preoperative diagnostic magnetic resonance (MR) breast images can provide good contrast between different tissues and 3-D information about suspicious tissues. Aligning preoperative diagnostic MR images with a patient in the theatre during breast conserving surgery could assist surgeons in achieving the complete excision of cancer with sufficient margins. Typically, preoperative diagnostic MR breast images of a patient are obtained in the prone position, while surgery is performed in the supine position. The significant shape change of breasts between these two positions due to gravity loading, external forces and related constraints makes the alignment task extremely difficult. Our previous studies have shown that either nonrigid intensity-based image registration or biomechanical modelling alone are limited in their ability to capture such a large deformation. To tackle this problem, we proposed in this paper a nonlinear biomechanical model-based image registration method with a simultaneous optimization procedure for both the material parameters of breast tissues and the direction of the gravitational force. First, finite element (FE) based biomechanical modelling is used to estimate a physically plausible deformation of the pectoral muscle and the major deformation of breast tissues due to gravity loading. Then, nonrigid intensity-based image registration is employed to recover the remaining deformation that FE analyses do not capture due to the simplifications and approximations of biomechanical models and the uncertainties of external forces and constraints. We assess the registration performance of the proposed method using the target registration error of skin fiducial markers and the Dice similarity coefficient (DSC) of fibroglandular tissues. The registration results on prone and supine MR image pairs are compared with those from two alternative nonrigid registration methods for five breasts. Overall, the proposed algorithm achieved the best registration

  9. Group-wise feature-based registration of CT and ultrasound images of spine

    NASA Astrophysics Data System (ADS)

    Rasoulian, Abtin; Mousavi, Parvin; Hedjazi Moghari, Mehdi; Foroughi, Pezhman; Abolmaesumi, Purang

    2010-02-01

    Registration of pre-operative CT and freehand intra-operative ultrasound of lumbar spine could aid surgeons in the spinal needle injection which is a common procedure for pain management. Patients are always in a supine position during the CT scan, and in the prone or sitting position during the intervention. This leads to a difference in the spinal curvature between the two imaging modalities, which means a single rigid registration cannot be used for all of the lumbar vertebrae. In this work, a method for group-wise registration of pre-operative CT and intra-operative freehand 2-D ultrasound images of the lumbar spine is presented. The approach utilizes a pointbased registration technique based on the unscented Kalman filter, taking as input segmented vertebrae surfaces in both CT and ultrasound data. Ultrasound images are automatically segmented using a dynamic programming approach, while the CT images are semi-automatically segmented using thresholding. Since the curvature of the spine is different between the pre-operative and the intra-operative data, the registration approach is designed to simultaneously align individual groups of points segmented from each vertebra in the two imaging modalities. A biomechanical model is used to constrain the vertebrae transformation parameters during the registration and to ensure convergence. The mean target registration error achieved for individual vertebrae on five spine phantoms generated from CT data of patients, is 2.47 mm with standard deviation of 1.14 mm.

  10. 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. PMID:25882735

  11. 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.

  12. Joint image registration and fusion method with a gradient strength regularization

    NASA Astrophysics Data System (ADS)

    Lidong, Huang; Wei, Zhao; Jun, Wang

    2015-05-01

    Image registration is an essential process for image fusion, and fusion performance can be used to evaluate registration accuracy. We propose a maximum likelihood (ML) approach to joint image registration and fusion instead of treating them as two independent processes in the conventional way. To improve the visual quality of a fused image, a gradient strength (GS) regularization is introduced in the cost function of ML. The GS of the fused image is controllable by setting the target GS value in the regularization term. This is useful because a larger target GS brings a clearer fused image and a smaller target GS makes the fused image smoother and thus restrains noise. Hence, the subjective quality of the fused image can be improved whether the source images are polluted by noise or not. We can obtain the fused image and registration parameters successively by minimizing the cost function using an iterative optimization method. Experimental results show that our method is effective with transformation, rotation, and scale parameters in the range of [-2.0, 2.0] pixel, [-1.1 deg, 1.1 deg], and [0.95, 1.05], respectively, and variances of noise smaller than 300. It also demonstrated that our method yields a more visual pleasing fused image and higher registration accuracy compared with a state-of-the-art algorithm.

  13. A simple device for high-precision head image registration: Preliminary performance and accuracy tests

    SciTech Connect

    Pallotta, Stefania

    2007-05-15

    The purpose of this paper is to present a new device for multimodal head study registration and to examine its performance in preliminary tests. The device consists of a system of eight markers fixed to mobile carbon pipes and bars which can be easily mounted on the patient's head using the ear canals and the nasal bridge. Four graduated scales fixed to the rigid support allow examiners to find the same device position on the patient's head during different acquisitions. The markers can be filled with appropriate substances for visualisation in computed tomography (CT), magnetic resonance, single photon emission computer tomography (SPECT) and positron emission tomography images. The device's rigidity and its position reproducibility were measured in 15 repeated CT acquisitions of the Alderson Rando anthropomorphic phantom and in two SPECT studies of a patient. The proposed system displays good rigidity and reproducibility characteristics. A relocation accuracy of less than 1,5 mm was found in more than 90% of the results. The registration parameters obtained using such a device were compared to those obtained using fiducial markers fixed on phantom and patient heads, resulting in differences of less than 1 deg. and 1 mm for rotation and translation parameters, respectively. Residual differences between fiducial marker coordinates in reference and in registered studies were less than 1 mm in more than 90% of the results, proving that the device performed as accurately as noninvasive stereotactic devices. Finally, an example of multimodal employment of the proposed device is reported.

  14. 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. PMID:20425983

  15. Lung iodine mapping by subtraction with image registration allowing for tissue sliding

    NASA Astrophysics Data System (ADS)

    Mohr, Brian; Brink, Monique; Oostveen, Luuk J.; Schuijf, Joanne D.; Prokop, Mathias

    2016-03-01

    Pulmonary embolism is a fairly common and serious entity, so rapid diagnosis and treatment has a significant impact on morbidity and mortality rates. Iodine maps representing tissue perfusion enhancement are commonly generated by dual-energy CT acquisitions to provide information about the effect of the embolism on pulmonary perfusion. Alternatively, the iodine map can be generated by subtracting pre- from post-contrast CT scans as previously reported. Although accurate image registration is essential, subtraction has the advantage of a higher signal-to-noise ratio and suppression of bone. This paper presents an improvement over the previously reported registration algorithm. Significantly, allowance for sliding motion at tissue boundaries is included in the regularization. Pre- and post-contrast helical CT scans were acquired for thirty subjects using a Toshiba Aquilion ONE scanner. Ten of these subjects were designated for algorithm development, while the remaining twenty were reserved for qualitative clinical evaluation. Quantitative evaluation was performed against the previously reported method and using publicly available data for comparison against other methods. Comparison of 100 landmarks in seven datasets shows no change in the mean Euclidean error of 0.48 mm, compared to the previous method. Evaluation in the publicly available DIR-Lab data with 300 annotations results in a mean Euclidean error of 1.17 mm in the ten 4DCT cases and 3.37 mm in the ten COPDGene cases. Clinical evaluation on a sliding scale from 1 (excellent) to 5 (non-diagnostic) indicates a slight, but non-significant, improvement in registration adequacy from 3.1 to 2.9.

  16. 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. PMID

  17. 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

  18. Phantom study and accuracy evaluation of an image-to-world registration approach used with electro-magnetic tracking system for neurosurgery

    NASA Astrophysics Data System (ADS)

    Li, Senhu; Sarment, David

    2015-12-01

    Minimally invasive neurosurgery needs intraoperative imaging updates and high efficient image guide system to facilitate the procedure. An automatic image guided system utilized with a compact and mobile intraoperative CT imager was introduced in this work. A tracking frame that can be easily attached onto the commercially available skull clamp was designed. With known geometry of fiducial and tracking sensor arranged on this rigid frame that was fabricated through high precision 3D printing, not only was an accurate, fully automatic registration method developed in a simple and less-costly approach, but also it helped in estimating the errors from fiducial localization in image space through image processing, and in patient space through the calibration of tracking frame. Our phantom study shows the fiducial registration error as 0.348+/-0.028mm, comparing the manual registration error as 1.976+/-0.778mm. The system in this study provided a robust and accurate image-to-patient registration without interruption of routine surgical workflow and any user interactions involved through the neurosurgery.

  19. Image registration system in the Landsat-D production environment

    NASA Technical Reports Server (NTRS)

    Kiss, P.; Arnold, P.; Goldstine, J.

    1981-01-01

    It is the purpose of the considered system to take pieces of imagery, called control point chips (CPC), whose geodetic location has been previously determined and stored, and locate their position in later imagery of the same area. The registration processes are carried out partially on a DEC VAX 780 computer and partially on a Floating Point Systems Array Processor. Typically sets of 20 control points are processed at a time. To process these as sets, and to optimize the use of both machines, operations are grouped into loops instead of a sequential processing for each point. Attention is given to cloud cover assessment, enhancement, correlation techniques, pixel registration, and subpixel registration.

  20. Direct estimation of nonrigid registrations with image-based self-occlusion reasoning.

    PubMed

    Gay-Bellile, Vincent; Bartoli, Adrien; Sayd, Patrick

    2010-01-01

    The registration problem for images of a deforming surface has been well studied. External occlusions are usually well handled. In 2D image-based registration, self-occlusions are more challenging. Consequently, the surface is usually assumed to be only slightly self-occluding. This paper is about image-based nonrigid registration with self-occlusion reasoning. A specific framework explicitly modeling self-occlusions is proposed. It is combined with an intensity-based, "direct" data term for registration. Self-occlusions are detected as shrinkage areas in the 2D warp. Experimental results on several challenging data sets show that our approach successfully registers images with self-occlusions while effectively detecting the self-occluded regions. PMID:19926901

  1. A hybrid biomechanical model-based image registration method for sliding objects

    NASA Astrophysics Data System (ADS)

    Han, Lianghao; Hawkes, David; Barratt, Dean

    2014-03-01

    The sliding motion between two anatomic structures, such as lung against chest wall, liver against surrounding tissues, produces a discontinuous displacement field between their boundaries. Capturing the sliding motion is quite challenging for intensity-based image registration methods in which a smoothness condition has commonly been applied to ensure the deformation consistency of neighborhood voxels. Such a smoothness constraint contradicts motion physiology at the boundaries of these anatomic structures. Although various regularisation schemes have been developed to handle sliding motion under the framework of non-rigid intensity-based image registration, the recovered displacement field may still not be physically plausible. In this study, a new framework that incorporates a patient-specific biomechanical model with a non-rigid image registration scheme for motion estimation of sliding objects has been developed. The patient-specific model provides the motion estimation with an explicit simulation of sliding motion, while the subsequent non-rigid image registration compensates for smaller residuals of the deformation due to the inaccuracy of the physical model. The algorithm was tested against the results of the published literature using 4D CT data from 10 lung cancer patients. The target registration error (TRE) of 3000 landmarks with the proposed method (1.37+/-0.89 mm) was significantly lower than that with the popular B-spline based free form deformation (FFD) registration (4.5+/-3.9 mm), and was smaller than that using the B-spline based FFD registration with the sliding constraint (1.66+/-1.14 mm) or using the B-spline based FFD registration on segmented lungs (1.47+/-1.1 mm). A paired t-test showed that the improvement of registration performance with the proposed method was significant (p<0.01). The propose method also achieved the best registration performance on the landmarks near lung surfaces. Since biomechanical models captured most of the lung

  2. Three-dimensional elastic image registration based on strain energy minimization: application to prostate magnetic resonance imaging.

    PubMed

    Zhang, Bao; Arola, Dwayne D; Roys, Steve; Gullapalli, Rao P

    2011-08-01

    The use of magnetic resonance (MR) imaging in conjunction with an endorectal coil is currently the clinical standard for the diagnosis of prostate cancer because of the increased sensitivity and specificity of this approach. However, imaging in this manner provides images and spectra of the prostate in the deformed state because of the insertion of the endorectal coil. Such deformation may lead to uncertainties in the localization of prostate cancer during therapy. We propose a novel 3-D elastic registration procedure that is based on the minimization of a physically motivated strain energy function that requires the identification of similar features (points, curves, or surfaces) in the source and target images. The Gauss-Seidel method was used in the numerical implementation of the registration algorithm. The registration procedure was validated on synthetic digital images, MR images from prostate phantom, and MR images obtained on patients. The registration error, assessed by averaging the displacement of a fiducial landmark in the target to its corresponding point in the registered image, was 0.2 ± 0.1 pixels on synthetic images. On the prostate phantom and patient data, the registration errors were 1.0 ± 0.6 pixels (0.6 ± 0.4 mm) and 1.8 ± 0.7 pixels (1.1 ± 0.4 mm), respectively. Registration also improved image similarity (normalized cross-correlation) from 0.72 ± 0.10 to 0.96 ± 0.03 on patient data. Registration results on digital images, phantom, and prostate data in vivo demonstrate that the registration procedure can be used to significantly improve both the accuracy of localized therapies such as brachytherapy or external beam therapy and can be valuable in the longitudinal follow-up of patients after therapy. PMID:20552248

  3. Optic disc boundary segmentation from diffeomorphic demons registration of monocular fundus image sequences versus 3D visualization of stereo fundus image pairs for automated early stage glaucoma assessment

    NASA Astrophysics Data System (ADS)

    Gatti, Vijay; Hill, Jason; Mitra, Sunanda; Nutter, Brian

    2014-03-01

    Despite the current availability in resource-rich regions of advanced technologies in scanning and 3-D imaging in current ophthalmology practice, world-wide screening tests for early detection and progression of glaucoma still consist of a variety of simple tools, including fundus image-based parameters such as CDR (cup to disc diameter ratio) and CAR (cup to disc area ratio), especially in resource -poor regions. Reliable automated computation of the relevant parameters from fundus image sequences requires robust non-rigid registration and segmentation techniques. Recent research work demonstrated that proper non-rigid registration of multi-view monocular fundus image sequences could result in acceptable segmentation of cup boundaries for automated computation of CAR and CDR. This research work introduces a composite diffeomorphic demons registration algorithm for segmentation of cup boundaries from a sequence of monocular images and compares the resulting CAR and CDR values with those computed manually by experts and from 3-D visualization of stereo pairs. Our preliminary results show that the automated computation of CDR and CAR from composite diffeomorphic segmentation of monocular image sequences yield values comparable with those from the other two techniques and thus may provide global healthcare with a cost-effective yet accurate tool for management of glaucoma in its early stage.

  4. Evolving generalized Voronoi diagrams for accurate cellular image segmentation.

    PubMed

    Yu, Weimiao; Lee, Hwee Kuan; Hariharan, Srivats; Bu, Wenyu; Ahmed, Sohail

    2010-04-01

    Analyzing cellular morphologies on a cell-by-cell basis is vital for drug discovery, cell biology, and many other biological studies. Interactions between cells in their culture environments cause cells to touch each other in acquired microscopy images. Because of this phenomenon, cell segmentation is a challenging task, especially when the cells are of similar brightness and of highly variable shapes. The concept of topological dependence and the maximum common boundary (MCB) algorithm are presented in our previous work (Yu et al., Cytometry Part A 2009;75A:289-297). However, the MCB algorithm suffers a few shortcomings, such as low computational efficiency and difficulties in generalizing to higher dimensions. To overcome these limitations, we present the evolving generalized Voronoi diagram (EGVD) algorithm. Utilizing image intensity and geometric information, EGVD preserves topological dependence easily in both 2D and 3D images, such that touching cells can be segmented satisfactorily. A systematic comparison with other methods demonstrates that EGVD is accurate and much more efficient. PMID:20169588

  5. Mutual information image registration based on improved bee evolutionary genetic algorithm

    NASA Astrophysics Data System (AD