WorldWideScience

Sample records for non-rigid image registration

  1. Optimized imaging using non-rigid registration

    International Nuclear Information System (INIS)

    Berkels, Benjamin; Binev, Peter; Blom, Douglas A.; Dahmen, Wolfgang; Sharpley, Robert C.; Vogt, Thomas

    2014-01-01

    The extraordinary improvements of modern imaging devices offer access to data with unprecedented information content. However, widely used image processing methodologies fall far short of exploiting the full breadth of information offered by numerous types of scanning probe, optical, and electron microscopies. In many applications, it is necessary to keep measurement intensities below a desired threshold. We propose a methodology for extracting an increased level of information by processing a series of data sets suffering, in particular, from high degree of spatial uncertainty caused by complex multiscale motion during the acquisition process. An important role is played by a non-rigid pixel-wise registration method that can cope with low signal-to-noise ratios. This is accompanied by formulating objective quality measures which replace human intervention and visual inspection in the processing chain. Scanning transmission electron microscopy of siliceous zeolite material exhibits the above-mentioned obstructions and therefore serves as orientation and a test of our procedures. - Highlights: • Developed a new process for extracting more information from a series of STEM images. • An objective non-rigid registration process copes with distortions. • Images of zeolite Y show retrieval of all information available from the data set. • Quantitative measures of registration quality were implemented. • Applicable to any serially acquired data, e.g. STM, AFM, STXM, etc

  2. Non-rigid image registration using bone growth model

    DEFF Research Database (Denmark)

    Bro-Nielsen, Morten; Gramkow, Claus; Kreiborg, Sven

    1997-01-01

    Non-rigid registration has traditionally used physical models like elasticity and fluids. These models are very seldom valid models of the difference between the registered images. This paper presents a non-rigid registration algorithm, which uses a model of bone growth as a model of the change b...

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

    Science.gov (United States)

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

    2007-03-01

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

  4. Non-rigid consistent registration of 2D image sequences

    International Nuclear Information System (INIS)

    Arganda-Carreras, I; Sorzano, C O S; Marabini, R; Carazo, J M; Thevenaz, P; Munoz-Barrutia, A; Ortiz-de Solorzano, C; Kybic, J

    2010-01-01

    We present a novel algorithm for the registration of 2D image sequences that combines the principles of multiresolution B-spline-based elastic registration and those of bidirectional consistent registration. In our method, consecutive triples of images are iteratively registered to gradually extend the information through the set of images of the entire sequence. The intermediate results are reused for the registration of the following triple. We choose to interpolate the images and model the deformation fields using B-spline multiresolution pyramids. Novel boundary conditions are introduced to better characterize the deformations at the boundaries. In the experimental section, we quantitatively show that our method recovers from barrel/pincushion and fish-eye deformations with subpixel error. Moreover, it is more robust against outliers-occasional strong noise and large rotations-than the state-of-the-art methods. Finally, we show that our method can be used to realign series of histological serial sections, which are often heavily distorted due to folding and tearing of the tissues.

  5. Non-rigid consistent registration of 2D image sequences

    Energy Technology Data Exchange (ETDEWEB)

    Arganda-Carreras, I; Sorzano, C O S; Marabini, R; Carazo, J M [Biocomputing Unit, National Centre for Biotechnology, CSIC, Darwin 3, Universidad Autonoma de Madrid, 28049 Madrid (Spain); Thevenaz, P [Biomedical Imaging Group, Ecole polytechnique federale de Lausanne (EPFL) (Switzerland); Munoz-Barrutia, A; Ortiz-de Solorzano, C [Cancer Imaging Laboratory, Centre for Applied Medical Research, University of Navarra, Pamplona (Spain); Kybic, J, E-mail: iarganda@cnb.csic.e [Center for Machine Perception, Czech Technical University, Prague (Czech Republic)

    2010-10-21

    We present a novel algorithm for the registration of 2D image sequences that combines the principles of multiresolution B-spline-based elastic registration and those of bidirectional consistent registration. In our method, consecutive triples of images are iteratively registered to gradually extend the information through the set of images of the entire sequence. The intermediate results are reused for the registration of the following triple. We choose to interpolate the images and model the deformation fields using B-spline multiresolution pyramids. Novel boundary conditions are introduced to better characterize the deformations at the boundaries. In the experimental section, we quantitatively show that our method recovers from barrel/pincushion and fish-eye deformations with subpixel error. Moreover, it is more robust against outliers-occasional strong noise and large rotations-than the state-of-the-art methods. Finally, we show that our method can be used to realign series of histological serial sections, which are often heavily distorted due to folding and tearing of the tissues.

  6. Robust feature estimation by non-rigid hierarchical image registration and its application in disparity measurement

    Science.gov (United States)

    Badshah, Amir; Choudhry, Aadil Jaleel; Ullah, Shan

    2017-03-01

    Industries are moving towards automation in order to increase productivity and ensure quality. Variety of electronic and electromagnetic systems are being employed to assist human operator in fast and accurate quality inspection of products. Majority of these systems are equipped with cameras and rely on diverse image processing algorithms. Information is lost in 2D image, therefore acquiring accurate 3D data from 2D images is an open issue. FAST, SURF and SIFT are well-known spatial domain techniques for features extraction and henceforth image registration to find correspondence between images. The efficiency of these methods is measured in terms of the number of perfect matches found. A novel fast and robust technique for stereo-image processing is proposed. It is based on non-rigid registration using modified normalized phase correlation. The proposed method registers two images in hierarchical fashion using quad-tree structure. The registration process works through global to local level resulting in robust matches even in presence of blur and noise. The computed matches can further be utilized to determine disparity and depth for industrial product inspection. The same can be used in driver assistance systems. The preliminary tests on Middlebury dataset produced satisfactory results. The execution time for a 413 x 370 stereo-pair is 500ms approximately on a low cost DSP.

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

    Science.gov (United States)

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

    2008-03-01

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

  8. Harmonic Auto-Regularization for Non Rigid Groupwise Registration in Cardiac Magnetic Resonance Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Sanz-Estebanez, S.; Royuela-del-Val, J.; Sevilla, T.; Revilla-Orodea, A.; Aja-Fernandez, S.; Alberola-Lopez, C.

    2016-07-01

    In this paper we present a new approach for non rigid groupwise registration of cardiac magnetic resonance images by means of free-form deformations, imposing a prior harmonic deformation assumption. The procedure proposes a primal-dual framework for solving an equality constrained minimization problem, which allows an automatic estimate of the trade-off between image fidelity and the Laplacian smoothness terms for each iteration. The method has been applied to both a 4D extended cardio-torso phantom and to a set of voluntary patients. The accuracy of the method has been measured for the synthetic experiment as the difference in modulus between the estimated displacement field and the ground truth; as for the real data, we have calculated the Dice coefficient between the contour manual delineations provided by two cardiologists at end systolic phase and those provided by them at end diastolic phase and, consequently propagated by the registration algorithm to the systolic instant. The automatic procedure turns out to be competitive in motion compensation with other methods even though their parameters have been previously set for optimal performance in different scenarios. (Author)

  9. A GPU-based symmetric non-rigid image registration method in human lung.

    Science.gov (United States)

    Haghighi, Babak; D Ellingwood, Nathan; Yin, Youbing; Hoffman, Eric A; Lin, Ching-Long

    2018-03-01

    Quantitative computed tomography (QCT) of the lungs plays an increasing role in identifying sub-phenotypes of pathologies previously lumped into broad categories such as chronic obstructive pulmonary disease and asthma. Methods for image matching and linking multiple lung volumes have proven useful in linking structure to function and in the identification of regional longitudinal changes. Here, we seek to improve the accuracy of image matching via the use of a symmetric multi-level non-rigid registration employing an inverse consistent (IC) transformation whereby images are registered both in the forward and reverse directions. To develop the symmetric method, two similarity measures, the sum of squared intensity difference (SSD) and the sum of squared tissue volume difference (SSTVD), were used. The method is based on a novel generic mathematical framework to include forward and backward transformations, simultaneously, eliminating the need to compute the inverse transformation. Two implementations were used to assess the proposed method: a two-dimensional (2-D) implementation using synthetic examples with SSD, and a multi-core CPU and graphics processing unit (GPU) implementation with SSTVD for three-dimensional (3-D) human lung datasets (six normal adults studied at total lung capacity (TLC) and functional residual capacity (FRC)). Success was evaluated in terms of the IC transformation consistency serving to link TLC to FRC. 2-D registration on synthetic images, using both symmetric and non-symmetric SSD methods, and comparison of displacement fields showed that the symmetric method gave a symmetrical grid shape and reduced IC errors, with the mean values of IC errors decreased by 37%. Results for both symmetric and non-symmetric transformations of human datasets showed that the symmetric method gave better results for IC errors in all cases, with mean values of IC errors for the symmetric method lower than the non-symmetric methods using both SSD and SSTVD

  10. Topology preserving non-rigid image registration using time-varying elasticity model for MRI brain volumes.

    Science.gov (United States)

    Ahmad, Sahar; Khan, Muhammad Faisal

    2015-12-01

    In this paper, we present a new non-rigid image registration method that imposes a topology preservation constraint on the deformation. We propose to incorporate the time varying elasticity model into the deformable image matching procedure and constrain the Jacobian determinant of the transformation over the entire image domain. The motion of elastic bodies is governed by a hyperbolic partial differential equation, generally termed as elastodynamics wave equation, which we propose to use as a deformation model. We carried out clinical image registration experiments on 3D magnetic resonance brain scans from IBSR database. The results of the proposed registration approach in terms of Kappa index and relative overlap computed over the subcortical structures were compared against the existing topology preserving non-rigid image registration methods and non topology preserving variant of our proposed registration scheme. The Jacobian determinant maps obtained with our proposed registration method were qualitatively and quantitatively analyzed. The results demonstrated that the proposed scheme provides good registration accuracy with smooth transformations, thereby guaranteeing the preservation of topology. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. A Grid service for the interactive use of a parallel non-rigid registration algorithm of medical images.

    Science.gov (United States)

    Stefanescu, R; Pennec, X; Ayache, N

    2005-01-01

    The goal of this work is to improve the usability of a non-rigid registration software for medical images. We have built a registration grid service in order to use the interactivity of a visualization workstation and the computing power of a cluster. On the user side, the system is composed of a graphical interface that interacts in a complex and fluid manner with the registration software running on a remote cluster. Although the transmission of images back and forth between the computer running the user interface and the cluster running the registration service adds to the total registration time, it provides a user-friendly way of using the registration software without heavy infrastructure investments in hospitals. The system exhibits good performances even if the user is connected to the grid service through a low throughput network such as a wireless network interface or ADSL.

  12. Non-rigid contour-to-pixel registration of photographic and quantitative light-induced fluorescence imaging of decalcified teeth

    Science.gov (United States)

    Berkels, Benjamin; Deserno, Thomas; Ehrlich, Eva E.; Fritz, Ulrike B.; Sirazitdinova, Ekaterina; Tatano, Rosalia

    2016-03-01

    Quantitative light-induced fluorescence (QLF) is widely used to assess the damage of a tooth due to decalcification. In digital photographs, decalcification appears as white spot lesions, i.e. white spots on the tooth surface. We propose a novel multimodal registration approach for the matching of digital photographs and QLF images of decalcified teeth. The registration is based on the idea of contour-to-pixel matching. Here, the curve, which represents the shape of the tooth, is extracted from the QLF image using a contour segmentation by binarization and morphological processing. This curve is aligned to the photo with a non-rigid variational registration approach. Thus, the registration problem is formulated as minimization problem with an objective function that consists of a data term and a regularizer for the deformation. To construct the data term, the photo is pointwise classified into tooth and non-tooth regions. Then, the signed distance function of the tooth region allows to measure the mismatch between curve and photo. As regularizer a higher order, linear elastic prior is used. The resulting minimization problem is solved numerically using bilinear Finite Elements for the spatial discretization and the Gauss-Newton algorithm. The evaluation is based on 150 image pairs, where an average of 5 teeth have been captured from 32 subjects. All registrations have been confirmed correctly by a dental expert. The contour-to-pixel methods can directly be used in 3D for surface-to-voxel tasks.

  13. Non-rigid Registration for Large Sets of Microscopic Images on Graphics Processors.

    Science.gov (United States)

    Ruiz, Antonio; Ujaldon, Manuel; Cooper, Lee; Huang, Kun

    2009-04-01

    Microscopic imaging is an important tool for characterizing tissue morphology and pathology. 3D reconstruction and visualization of large sample tissue structure requires registration of large sets of high-resolution images. However, the scale of this problem presents a challenge for automatic registration methods. In this paper we present a novel method for efficient automatic registration using graphics processing units (GPUs) and parallel programming. Comparing a C++ CPU implementation with Compute Unified Device Architecture (CUDA) libraries and pthreads running on GPU we achieve a speed-up factor of up to 4.11× with a single GPU and 6.68× with a GPU pair. We present execution times for a benchmark composed of two sets of large-scale images: mouse placenta (16 K × 16 K pixels) and breast cancer tumors (23 K × 62 K pixels). It takes more than 12 hours for the genetic case in C++ to register a typical sample composed of 500 consecutive slides, which was reduced to less than 2 hours using two GPUs, in addition to a very promising scalability for extending those gains easily on a large number of GPUs in a distributed system.

  14. Myocardial strain assessment by cine cardiac magnetic resonance imaging using non-rigid registration.

    Science.gov (United States)

    Tsadok, Yossi; Friedman, Zvi; Haluska, Brian A; Hoffmann, Rainer; Adam, Dan

    2016-05-01

    To evaluate a novel post-processing method for assessment of longitudinal mid-myocardial strain in standard cine cardiac magnetic resonance (CMR) imaging sequences. Cine CMR imaging and tagged cardiac magnetic resonance imaging (TMRI) were performed in 15 patients with acute myocardial infarction (AMI) and 15 healthy volunteers served as control group. A second group of 37 post-AMI patients underwent both cine CMR and late gadolinium enhancement (LGE) CMR exams. Speckle tracking echocardiography (STE) was performed in 36 of these patients. Cine CMR, TMRI and STE were analyzed to obtain longitudinal strain. LGE-CMR datasets were analyzed to evaluate scar extent. Comparison of peak systolic strain (PSS) measured from CMR and TMRI yielded a strong correlation (r=0.86, pcine CMR data. The method was found to be highly correlated with strain measurements obtained by TMRI and STE. This tool allows accurate discrimination between different transmurality states of myocardial infarction. Copyright © 2015 Elsevier Inc. All rights reserved.

  15. A new technique for noise reduction at coronary CT angiography with multi-phase data-averaging and non-rigid image registration.

    Science.gov (United States)

    Tatsugami, Fuminari; Higaki, Toru; Nakamura, Yuko; Yamagami, Takuji; Date, Shuji; Fujioka, Chikako; Kiguchi, Masao; Kihara, Yasuki; Awai, Kazuo

    2015-01-01

    To investigate the feasibility of a newly developed noise reduction technique at coronary CT angiography (CTA) that uses multi-phase data-averaging and non-rigid image registration. Sixty-five patients underwent coronary CTA with prospective ECG-triggering. The range of the phase window was set at 70-80% of the R-R interval. First, three sets of consecutive volume data at 70%, 75% and 80% of the R-R interval were prepared. Second, we applied non-rigid registration to align the 70% and 80% images to the 75% image. Finally, we performed weighted averaging of the three images and generated a de-noised image. The image noise and contrast-to-noise ratio (CNR) in the proximal coronary arteries between the conventional 75% and the de-noised images were compared. Two radiologists evaluated the image quality using a 5-point scale (1, poor; 5, excellent). On de-noised images, mean image noise was significantly lower than on conventional 75% images (18.3 HU ± 2.6 vs. 23.0 HU ± 3.3, P < 0.01) and the CNR was significantly higher (P < 0.01). The mean image quality score for conventional 75% and de-noised images was 3.9 and 4.4, respectively (P < 0.01). Our method reduces image noise and improves image quality at coronary CTA. • We introduce a new method for image noise reduction at cardiac CT. • Multiple data acquisitions of an object and their averaging yield lower noise. • Our method uses multi-phase images reconstructed from unused redundant imaging data. • It reduces image noise by averaging multi-phase images transformed by non-rigid registration. • This method achieves a 20% image noise reduction at cardiac CT.

  16. A new technique for noise reduction at coronary CT angiography with multi-phase data-averaging and non-rigid image registration

    Energy Technology Data Exchange (ETDEWEB)

    Tatsugami, Fuminari; Higaki, Toru; Nakamura, Yuko; Yamagami, Takuji; Date, Shuji; Awai, Kazuo [Hiroshima University, Department of Diagnostic Radiology, Minami-ku, Hiroshima (Japan); Fujioka, Chikako; Kiguchi, Masao [Hiroshima University, Department of Radiology, Minami-ku, Hiroshima (Japan); Kihara, Yasuki [Hiroshima University, Department of Cardiovascular Medicine, Minami-ku, Hiroshima (Japan)

    2015-01-15

    To investigate the feasibility of a newly developed noise reduction technique at coronary CT angiography (CTA) that uses multi-phase data-averaging and non-rigid image registration. Sixty-five patients underwent coronary CTA with prospective ECG-triggering. The range of the phase window was set at 70-80 % of the R-R interval. First, three sets of consecutive volume data at 70 %, 75 % and 80 % of the R-R interval were prepared. Second, we applied non-rigid registration to align the 70 % and 80 % images to the 75 % image. Finally, we performed weighted averaging of the three images and generated a de-noised image. The image noise and contrast-to-noise ratio (CNR) in the proximal coronary arteries between the conventional 75 % and the de-noised images were compared. Two radiologists evaluated the image quality using a 5-point scale (1, poor; 5, excellent). On de-noised images, mean image noise was significantly lower than on conventional 75 % images (18.3 HU ± 2.6 vs. 23.0 HU ± 3.3, P < 0.01) and the CNR was significantly higher (P < 0.01). The mean image quality score for conventional 75 % and de-noised images was 3.9 and 4.4, respectively (P < 0.01). Our method reduces image noise and improves image quality at coronary CTA. (orig.)

  17. A non-rigid registration method for mouse whole body skeleton registration

    Science.gov (United States)

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

    2010-03-01

    Micro-CT/PET imaging scanner provides a powerful tool to study tumor in small rodents in response to therapy. Accurate image registration is a necessary step to quantify the characteristics of images acquired in longitudinal studies. Small animal registration is challenging because of the very deformable body of the animal often resulting in different postures despite physical restraints. In this paper, we propose a non-rigid registration approach for the automatic registration of mouse whole body skeletons, which is based on our improved 3D shape context non-rigid registration method. The whole body skeleton registration approach has been tested on 21 pairs of mouse CT images with variations of individuals and time-instances. The experimental results demonstrated the stability and accuracy of the proposed method for automatic mouse whole body skeleton registration.

  18. Brownian Warps for Non-Rigid Registration

    DEFF Research Database (Denmark)

    Nielsen, Mads; Johansen, Peter; Jackson, Andrew D.

    2008-01-01

    prior, we formulate a Partial Differential Equation for obtaining the maximally likely warp given matching constraints derived from the images. We solve for the free boundary conditions, and the bias toward smaller areas in the finite domain setting. Furthermore, we demonstrate the technique on 2D...... images, and show that the obtained warps are also in practice source-destination symmetric and in an example on X-ray spine registration provides extrapolations from landmark point superior to those of spline solutions. Udgivelsesdato: July...

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

    Science.gov (United States)

    Park, Hyunjin; Meyer, Charles R.

    2012-10-01

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

  20. The influence of non-rigid anatomy and patient positioning on endoscopy-CT image registration in the head and neck.

    Science.gov (United States)

    Ingram, W Scott; Yang, Jinzhong; Wendt, Richard; Beadle, Beth M; Rao, Arvind; Wang, Xin A; Court, Laurence E

    2017-08-01

    To assess the influence of non-rigid anatomy and differences in patient positioning between CT acquisition and endoscopic examination on endoscopy-CT image registration in the head and neck. Radiotherapy planning CTs and 31-35 daily treatment-room CTs were acquired for nineteen patients. Diagnostic CTs were acquired for thirteen of the patients. The surfaces of the airways were segmented on all scans and triangular meshes were created to render virtual endoscopic images with a calibrated pinhole model of an endoscope. The virtual images were used to take projective measurements throughout the meshes, with reference measurements defined as those taken on the planning CTs and test measurements defined as those taken on the daily or diagnostic CTs. The influence of non-rigid anatomy was quantified by 3D distance errors between reference and test measurements on the daily CTs, and the influence of patient positioning was quantified by 3D distance errors between reference and test measurements on the diagnostic CTs. The daily CT measurements were also used to investigate the influences of camera-to-surface distance, surface angle, and the interval of time between scans. Average errors in the daily CTs were 0.36 ± 0.61 cm in the nasal cavity, 0.58 ± 0.83 cm in the naso- and oropharynx, and 0.47 ± 0.73 cm in the hypopharynx and larynx. Average errors in the diagnostic CTs in those regions were 0.52 ± 0.69 cm, 0.65 ± 0.84 cm, and 0.69 ± 0.90 cm, respectively. All CTs had errors heavily skewed towards 0, albeit with large outliers. Large camera-to-surface distances were found to increase the errors, but the angle at which the camera viewed the surface had no effect. The errors in the Day 1 and Day 15 CTs were found to be significantly smaller than those in the Day 30 CTs (P positioning have a larger influence than non-rigid anatomy on projective measurement errors. In general, these errors are largest when the camera is in the superior pharynx

  1. A field map guided approach to non-rigid registration of brain EPI to structural MRI

    Science.gov (United States)

    Gholipour, Ali; Kehtarnavaz, Nasser; Briggs, Richard W.; Gopinath, Kaundinya S.

    2007-03-01

    It is known that along the phase encoding direction the effect of magnetic field inhomogeneity causes significant spatial distortions in fast functional MRI Echo Planar Imaging (EPI). In this work, our previously developed distortion correction technique via a non-rigid registration of EPI to anatomical MRI is improved by adding information from field maps to achieve a more accurate and efficient registration. Local deformation models are used in regions of distortion artifacts instead of using a global non-rigid transformation. The use of local deformations not only enhances the efficiency of the non-rigid registration by reducing the number of deformation model parameters, but also provides constraints to avoid physically incorrect deformations in undistorted regions. The accuracy and reliability of the non-rigid registration technique is improved by using an additional high-resolution gradient echo EPI scan. In-vivo validation is performed by comparing the similarity of the low-resolution EPI to various structural MRI scans before and after applying the computed deformation models. Visual inspection of the images, as well as Mutual Information (MI) and Normalized Cross Correlation (NCC) comparisons, reveal improvements within the sub-voxel range in the moderately distorted areas but not in the signal loss regions.

  2. Robust non-rigid point set registration using student's-t mixture model.

    Directory of Open Access Journals (Sweden)

    Zhiyong Zhou

    Full Text Available The Student's-t mixture model, which is heavily tailed and more robust than the Gaussian mixture model, has recently received great attention on image processing. In this paper, we propose a robust non-rigid point set registration algorithm using the Student's-t mixture model. Specifically, first, we consider the alignment of two point sets as a probability density estimation problem and treat one point set as Student's-t mixture model centroids. Then, we fit the Student's-t mixture model centroids to the other point set which is treated as data. Finally, we get the closed-form solutions of registration parameters, leading to a computationally efficient registration algorithm. The proposed algorithm is especially effective for addressing the non-rigid point set registration problem when significant amounts of noise and outliers are present. Moreover, less registration parameters have to be set manually for our algorithm compared to the popular coherent points drift (CPD algorithm. We have compared our algorithm with other state-of-the-art registration algorithms on both 2D and 3D data with noise and outliers, where our non-rigid registration algorithm showed accurate results and outperformed the other algorithms.

  3. Non-Rigid Contour-Based Registration of Cell Nuclei in 2-D Live Cell Microscopy Images Using a Dynamic Elasticity Model.

    Science.gov (United States)

    Sorokin, Dmitry V; Peterlik, Igor; Tektonidis, Marco; Rohr, Karl; Matula, Pavel

    2018-01-01

    The analysis of the pure motion of subnuclear structures without influence of the cell nucleus motion and deformation is essential in live cell imaging. In this paper, we propose a 2-D contour-based image registration approach for compensation of nucleus motion and deformation in fluorescence microscopy time-lapse sequences. The proposed approach extends our previous approach, which uses a static elasticity model to register cell images. Compared with that scheme, the new approach employs a dynamic elasticity model for the forward simulation of nucleus motion and deformation based on the motion of its contours. The contour matching process is embedded as a constraint into the system of equations describing the elastic behavior of the nucleus. This results in better performance in terms of the registration accuracy. Our approach was successfully applied to real live cell microscopy image sequences of different types of cells including image data that was specifically designed and acquired for evaluation of cell image registration methods. An experimental comparison with the existing contour-based registration methods and an intensity-based registration method has been performed. We also studied the dependence of the results on the choice of method parameters.

  4. A deformation model for non-rigid registration of the kidney

    Science.gov (United States)

    Ong, Rowena E.; Glisson, Courtenay L.; Herrell, S. Duke; Miga, Michael I.; Galloway, Robert

    2009-02-01

    The development of an image-guided renal surgery system may aid tumor resection during partial nephrectomies. This system would require the registration of pre-operative kidney CT or MR scans to the physical kidney; however, the amount of non-rigid deformation occurring during surgery and whether it can be corrected for in an image-guided system is unknown. One possible source of non-rigid deformation is a change in pressure within the kidney: during surgery, clamping of the renal artery and vein results in a loss of perfusion, such that the subsequent cutting of the kidney and fluid outflow may cause a decrease in intrarenal pressure. In this work, we attempt to characterize the deformation due to cutting of the kidney and subsequent changes in intrarenal pressure. To accomplish this, we perfused a resected porcine kidney at a physiologically realistic pressure, clamped the renal vessels, and cut the kidney using a tracked scalpel. The resulting deformation was tracked in a CT scanner using 15-20 glass bead fiducials attached to the kidney surface. A modified form of Biot's consolidation model was used to simulate the deformation, and the accuracy was assessed by calculating the target registration error and image similarity.

  5. Constraint-based simulation for non-rigid real-time registration.

    Science.gov (United States)

    Courtecuisse, Hadrien; Peterlik, Igor; Trivisonne, Raffaella; Duriez, Christian; Cotin, Stéphane

    2014-01-01

    In this paper we propose a method to address the problem of non-rigid registration in real-time. We use Lagrange multipliers and soft sliding constraints to combine data acquired from dynamic image sequence and a biomechanical model of the structure of interest. The biomechanical model plays a role of regularization to improve the robustness and the flexibility of the registration. We apply our method to a pre-operative 3D CT scan of a porcine liver that is registered to a sequence of 2D dynamic MRI slices during the respiratory motion. The finite element simulation provides a full 3D representation (including heterogeneities such as vessels, tumor,...) of the anatomical structure in real-time.

  6. Non-rigid registration by geometry-constrained diffusion

    DEFF Research Database (Denmark)

    Andresen, Per Rønsholt; Nielsen, Mads

    1999-01-01

    are not given. We will advocate the viewpoint that the aperture and the 3D interpolation problem may be solved simultaneously by finding the simplest displacement field. This is obtained by a geometry-constrained diffusion which yields the simplest displacement field in a precise sense. The point registration...

  7. Non-rigid registration by geometry-constrained diffusion

    DEFF Research Database (Denmark)

    Andresen, Per Rønsholt; Nielsen, Mads

    2001-01-01

    by a geometry-constrained diffusion, which in a precise sense yields the simplest displacement field. The point registration obtained may be used for segmentation, growth modeling, shape analysis, or kinematic interpolation. The algorithm applies to geometrical objects of any dimensionality. We may thus keep...

  8. Improving Feature-based Non-rigid Registration for Applications in Radiotherapy

    NARCIS (Netherlands)

    E.M. Vásquez Osorio (Eliana)

    2012-01-01

    textabstractThis thesis describes the improvements of a feature-based non-rigid registration method that were essential for its application in radiotherapy. In addition, this thesis presents three practical applications of the improved method: 1) quantification of anatomical changes in 3D for head

  9. 3D full-field quantification of cell-induced large deformations in fibrillar biomaterials by combining non-rigid image registration with label-free second harmonic generation.

    Science.gov (United States)

    Jorge-Peñas, Alvaro; Bové, Hannelore; Sanen, Kathleen; Vaeyens, Marie-Mo; Steuwe, Christian; Roeffaers, Maarten; Ameloot, Marcel; Van Oosterwyck, Hans

    2017-08-01

    To advance our current understanding of cell-matrix mechanics and its importance for biomaterials development, advanced three-dimensional (3D) measurement techniques are necessary. Cell-induced deformations of the surrounding matrix are commonly derived from the displacement of embedded fiducial markers, as part of traction force microscopy (TFM) procedures. However, these fluorescent markers may alter the mechanical properties of the matrix or can be taken up by the embedded cells, and therefore influence cellular behavior and fate. In addition, the currently developed methods for calculating cell-induced deformations are generally limited to relatively small deformations, with displacement magnitudes and strains typically of the order of a few microns and less than 10% respectively. Yet, large, complex deformation fields can be expected from cells exerting tractions in fibrillar biomaterials, like collagen. To circumvent these hurdles, we present a technique for the 3D full-field quantification of large cell-generated deformations in collagen, without the need of fiducial markers. We applied non-rigid, Free Form Deformation (FFD)-based image registration to compute full-field displacements induced by MRC-5 human lung fibroblasts in a collagen type I hydrogel by solely relying on second harmonic generation (SHG) from the collagen fibrils. By executing comparative experiments, we show that comparable displacement fields can be derived from both fibrils and fluorescent beads. SHG-based fibril imaging can circumvent all described disadvantages of using fiducial markers. This approach allows measuring 3D full-field deformations under large displacement (of the order of 10 μm) and strain regimes (up to 40%). As such, it holds great promise for the study of large cell-induced deformations as an inherent component of cell-biomaterial interactions and cell-mediated biomaterial remodeling. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Non-rigid CT/CBCT to CBCT registration for online external beam radiotherapy guidance

    Science.gov (United States)

    Zachiu, Cornel; de Senneville, Baudouin Denis; Tijssen, Rob H. N.; Kotte, Alexis N. T. J.; Houweling, Antonetta C.; Kerkmeijer, Linda G. W.; Lagendijk, Jan J. W.; Moonen, Chrit T. W.; Ries, Mario

    2018-01-01

    Image-guided external beam radiotherapy (EBRT) allows radiation dose deposition with a high degree of accuracy and precision. Guidance is usually achieved by estimating the displacements, via image registration, between cone beam computed tomography (CBCT) and computed tomography (CT) images acquired at different stages of the therapy. The resulting displacements are then used to reposition the patient such that the location of the tumor at the time of treatment matches its position during planning. Moreover, ongoing research aims to use CBCT-CT image registration for online plan adaptation. However, CBCT images are usually acquired using a small number of x-ray projections and/or low beam intensities. This often leads to the images being subject to low contrast, low signal-to-noise ratio and artifacts, which ends-up hampering the image registration process. Previous studies addressed this by integrating additional image processing steps into the registration procedure. However, these steps are usually designed for particular image acquisition schemes, therefore limiting their use on a case-by-case basis. In the current study we address CT to CBCT and CBCT to CBCT registration by the means of the recently proposed EVolution registration algorithm. Contrary to previous approaches, EVolution does not require the integration of additional image processing steps in the registration scheme. Moreover, the algorithm requires a low number of input parameters, is easily parallelizable and provides an elastic deformation on a point-by-point basis. Results have shown that relative to a pure CT-based registration, the intrinsic artifacts present in typical CBCT images only have a sub-millimeter impact on the accuracy and precision of the estimated deformation. In addition, the algorithm has low computational requirements, which are compatible with online image-based guidance of EBRT treatments.

  11. Evaluation of non-rigid constrained CT/CBCT registration algorithms for delineation propagation in the context of prostate cancer radiotherapy

    Science.gov (United States)

    Rubeaux, Mathieu; Simon, Antoine; Gnep, Khemara; Colliaux, Jérémy; Acosta, Oscar; de Crevoisier, Renaud; Haigron, Pascal

    2013-03-01

    Image-Guided Radiation Therapy (IGRT) aims at increasing the precision of radiation dose delivery. In the context of prostate cancer, a planning Computed Tomography (CT) image with manually defined prostate and organs at risk (OAR) delineations is usually associated with daily Cone Beam Computed Tomography (CBCT) follow-up images. The CBCT images allow to visualize the prostate position and to reposition the patient accordingly. They also should be used to evaluate the dose received by the organs at each fraction of the treatment. To do so, the first step is a prostate and OAR segmentation on the daily CBCTs, which is very timeconsuming. To simplify this task, CT to CBCT non-rigid registration could be used in order to propagate the original CT delineations in the CBCT images. For this aim, we compared several non-rigid registration methods. They are all based on the Mutual Information (MI) similarity measure, and use a BSpline transformation model. But we add different constraints to this global scheme in order to evaluate their impact on the final results. These algorithms are investigated on two real datasets, representing a total of 70 CBCT on which a reference delineation has been realized. The evaluation is led using the Dice Similarity Coefficient (DSC) as a quality criteria. The experiments show that a rigid penalty term on the bones improves the final registration result, providing high quality propagated delineations.

  12. Distortion Correction in Fetal EPI Using Non-Rigid Registration With a Laplacian Constraint.

    Science.gov (United States)

    Kuklisova-Murgasova, Maria; Lockwood Estrin, Georgia; Nunes, Rita G; Malik, Shaihan J; Rutherford, Mary A; Rueckert, Daniel; Hajnal, Joseph V

    2018-01-01

    Geometric distortion induced by the main B0 field disrupts the consistency of fetal echo planar imaging (EPI) data, on which diffusion and functional magnetic resonance imaging is based. In this paper, we present a novel data-driven method for simultaneous motion and distortion correction of fetal EPI. A motion-corrected and reconstructed T2 weighted single shot fast spin echo (ssFSE) volume is used as a model of undistorted fetal brain anatomy. Our algorithm interleaves two registration steps: estimation of fetal motion parameters by aligning EPI slices to the model; and deformable registration of EPI slices to slices simulated from the undistorted model to estimate the distortion field. The deformable registration is regularized by a physically inspired Laplacian constraint, to model distortion induced by a source-free background B0 field. Our experiments show that distortion correction significantly improves consistency of reconstructed EPI volumes with ssFSE volumes. In addition, the estimated distortion fields are consistent with fields calculated from acquired field maps, and the Laplacian constraint is essential for estimation of plausible distortion fields. The EPI volumes reconstructed from different scans of the same subject were more consistent when the proposed method was used in comparison with EPI volumes reconstructed from data distortion corrected using a separately acquired B0 field map.

  13. Validation of TMJ osteoarthritis synthetic defect database via non-rigid registration

    Science.gov (United States)

    Paniagua, Beatriz; Pera, Juliette; Budin, Francois; Gomes, Liliane; Styner, Martin; Lucia, Cevidanes; Nguyen, Tung

    2015-03-01

    Temporomandibular joint (TMJ) disorders are a group of conditions that cause pain and dysfunction in the jaw joint and the muscles controlling jaw movement. However, diagnosis and treatment of these conditions remain controversial. To date, there is no single sign, symptom, or test that can clearly diagnose early stages of osteoarthritis (OA). Instead, the diagnosis is based on a consideration of several factors, including radiological evaluation. The current radiological diagnosis scores of TMJ pathology are subject to misdiagnosis. We believe these scores are limited by the acquisition procedures, such as oblique cuts of the CT and head positioning errors, and can lead to incorrect diagnoses of flattening of the head of the condyle, formation of osteophytes, or condylar pitting. This study consists of creating and validating a methodological framework to simulate defects in CBCT scans of known location and size, in order to create synthetic TMJ OA database. User-generated defects were created using a non-rigid deformation protocol in CBCT. All segmentation evaluation, surface distances and linear distances from the user-generated to the simulated defects showed our methodological framework to be very precise and within a voxel (0.5 mm) of magnitude. A TMJ OA synthetic database will be created next, and evaluated by expert radiologists, and this will serve to evaluate how sensitive the current radiological diagnosis tools are.

  14. Robust CPD Algorithm for Non-Rigid Point Set Registration Based on Structure Information.

    Directory of Open Access Journals (Sweden)

    Lei Peng

    Full Text Available Recently, the Coherent Point Drift (CPD algorithm has become a very popular and efficient method for point set registration. However, this method does not take into consideration the neighborhood structure information of points to find the correspondence and requires a manual assignment of the outlier ratio. Therefore, CPD is not robust for large degrees of degradation. In this paper, an improved method is proposed to overcome the two limitations of CPD. A structure descriptor, such as shape context, is used to perform the auxiliary calculation of the correspondence, and the proportion of each GMM component is adjusted by the similarity. The outlier ratio is formulated in the EM framework so that it can be automatically calculated and optimized iteratively. The experimental results on both synthetic data and real data demonstrate that the proposed method described here is more robust to deformation, noise, occlusion, and outliers than CPD and other state-of-the-art algorithms.

  15. Fast fluid registration of medical images

    DEFF Research Database (Denmark)

    Bro-Nielsen, Morten; Gramkow, Claus

    1996-01-01

    This paper offers a new fast algorithm for non-rigid viscous fluid registration of medical images that is at least an order of magnitude faster than the previous method by (Christensen et al., 1994). The core algorithm in the fluid registration method is based on a linear elastic deformation...

  16. Medical image registration for analysis

    International Nuclear Information System (INIS)

    Petrovic, V.

    2006-01-01

    Full text: Image registration techniques represent a rich family of image processing and analysis tools that aim to provide spatial correspondences across sets of medical images of similar and disparate anatomies and modalities. Image registration is a fundamental and usually the first step in medical image analysis and this paper presents a number of advanced techniques as well as demonstrates some of the advanced medical image analysis techniques they make possible. A number of both rigid and non-rigid medical image alignment algorithms of equivalent and merely consistent anatomical structures respectively are presented. The algorithms are compared in terms of their practical aims, inputs, computational complexity and level of operator (e.g. diagnostician) interaction. In particular, the focus of the methods discussion is placed on the applications and practical benefits of medical image registration. Results of medical image registration on a number of different imaging modalities and anatomies are presented demonstrating the accuracy and robustness of their application. Medical image registration is quickly becoming ubiquitous in medical imaging departments with the results of such algorithms increasingly used in complex medical image analysis and diagnostics. This paper aims to demonstrate at least part of the reason why

  17. Nonrigid registration of volumetric images using ranked order statistics

    DEFF Research Database (Denmark)

    Tennakoon, Ruwan; Bab-Hadiashar, Alireza; Cao, Zhenwei

    2014-01-01

    burden and increase the registration accuracy has become an intensive area of research. In this paper we propose a fast and accurate non-rigid registration method for intra-modality volumetric images. Our approach exploits the information provided by an order statistics based segmentation method, to find......Non-rigid image registration techniques using intensity based similarity measures are widely used in medical imaging applications. Due to high computational complexities of these techniques, particularly for volumetric images, finding appropriate registration methods to both reduce the computation...... the important regions for registration and use an appropriate sampling scheme to target those areas and reduce the registration computation time. A unique advantage of the proposed method is its ability to identify the point of diminishing returns and stop the registration process. Our experiments...

  18. Digital anthropomorphic phantoms of non-rigid human respiratory and voluntary body motion for investigating motion correction in emission imaging

    International Nuclear Information System (INIS)

    Könik, Arda; Johnson, Karen L; Dasari, Paul; Pretorius, P H; Dey, Joyoni; King, Michael A; Connolly, Caitlin M; Segars, Paul W; Lindsay, Clifford

    2014-01-01

    The development of methods for correcting patient motion in emission tomography has been receiving increased attention. Often the performance of these methods is evaluated through simulations using digital anthropomorphic phantoms, such as the commonly used extended cardiac torso (XCAT) phantom, which models both respiratory and cardiac motion based on human studies. However, non-rigid body motion, which is frequently seen in clinical studies, is not present in the standard XCAT phantom. In addition, respiratory motion in the standard phantom is limited to a single generic trend. In this work, to obtain a more realistic representation of motion, we developed a series of individual-specific XCAT phantoms, modeling non-rigid respiratory and non-rigid body motions derived from the magnetic resonance imaging (MRI) acquisitions of volunteers. Acquisitions were performed in the sagittal orientation using the Navigator methodology. Baseline (no motion) acquisitions at end-expiration were obtained at the beginning of each imaging session for each volunteer. For the body motion studies, MRI was again acquired only at end-expiration for five body motion poses (shoulder stretch, shoulder twist, lateral bend, side roll, and axial slide). For the respiratory motion studies, an MRI was acquired during free/regular breathing. The magnetic resonance slices were then retrospectively sorted into 14 amplitude-binned respiratory states, end-expiration, end-inspiration, six intermediary states during inspiration, and six during expiration using the recorded Navigator signal. XCAT phantoms were then generated based on these MRI data by interactive alignment of the organ contours of the XCAT with the MRI slices using a graphical user interface. Thus far we have created five body motion and five respiratory motion XCAT phantoms from the MRI acquisitions of six healthy volunteers (three males and three females). Non-rigid motion exhibited by the volunteers was reflected in both respiratory

  19. Performance evaluation of 2D image registration algorithms with the numeric image registration and comparison platform

    International Nuclear Information System (INIS)

    Gerganov, G.; Kuvandjiev, V.; Dimitrova, I.; Mitev, K.; Kawrakow, I.

    2012-01-01

    The objective of this work is to present the capabilities of the NUMERICS web platform for evaluation of the performance of image registration algorithms. The NUMERICS platform is a web accessible tool which provides access to dedicated numerical algorithms for registration and comparison of medical images (http://numerics.phys.uni-sofia.bg). The platform allows comparison of noisy medical images by means of different types of image comparison algorithms, which are based on statistical tests for outliers. The platform also allows 2D image registration with different techniques like Elastic Thin-Plate Spline registration, registration based on rigid transformations, affine transformations, as well as non-rigid image registration based on Mobius transformations. In this work we demonstrate how the platform can be used as a tool for evaluation of the quality of the image registration process. We demonstrate performance evaluation of a deformable image registration technique based on Mobius transformations. The transformations are applied with appropriate cost functions like: Mutual information, Correlation coefficient, Sum of Squared Differences. The accent is on the results provided by the platform to the user and their interpretation in the context of the performance evaluation of 2D image registration. The NUMERICS image registration and image comparison platform provides detailed statistical information about submitted image registration jobs and can be used to perform quantitative evaluation of the performance of different image registration techniques. (authors)

  20. Medical Image Registration and Surgery Simulation

    DEFF Research Database (Denmark)

    Bro-Nielsen, Morten

    1996-01-01

    This thesis explores the application of physical models in medical image registration and surgery simulation. The continuum models of elasticity and viscous fluids are described in detail, and this knowledge is used as a basis for most of the methods described here. Real-time deformable models......, and the use of selective matrix vector multiplication. Fluid medical image registration A new and faster algorithm for non-rigid registration using viscous fluid models is presented. This algorithm replaces the core part of the original algorithm with multi-resolution convolution using a new filter, which...... growth is also presented. Using medical knowledge about the growth processes of the mandibular bone, a registration algorithm for time sequence images of the mandible is developed. Since this registration algorithm models the actual development of the mandible, it is possible to simulate the development...

  1. Non-rigid isometric ICP: A practical registration method for the analysis and compensation of form errors in production engineering

    KAUST Repository

    Sacharow, Alexei

    2011-12-01

    The unprecedented success of the iterative closest point (ICP) method for registration in geometry processing and related fields can be attributed to its efficiency, robustness, and wide spectrum of applications. Its use is however quite limited as soon as the objects to be registered arise from each other by a transformation significantly different from a Euclidean motion. We present a novel variant of ICP, tailored for the specific needs of production engineering, which registers a triangle mesh with a second surface model of arbitrary digital representation. Our method inherits most of ICP\\'s practical advantages but is capable of detecting medium-strength bendings i.e. isometric deformations. Initially, the algorithm assigns to all vertices in the source their closest point on the target mesh and then iteratively establishes isometry, a process which, very similar to ICP, requires intermediate re-projections. A NURBS-based technique for applying the resulting deformation to arbitrary instances of the source geometry, other than the very mesh used for correspondence estimation, is described before we present numerical results on synthetic and real data to underline the viability of our approach in comparison with others. © 2011 Elsevier Ltd. All rights reserved.

  2. Mass preserving image registration

    DEFF Research Database (Denmark)

    Gorbunova, Vladlena; Sporring, Jon; Lo, Pechin Chien Pau

    2010-01-01

    The paper presents results the mass preserving image registration method in the Evaluation of Methods for Pulmonary Image Registration 2010 (EMPIRE10) Challenge. The mass preserving image registration algorithm was applied to the 20 image pairs. Registration was evaluated using four different...

  3. Remote Sensing Image Registration Using Multiple Image Features

    Directory of Open Access Journals (Sweden)

    Kun Yang

    2017-06-01

    Full Text Available Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform distance which is endowed with the intensity information is used to measure the scale space extrema. (iii To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

  4. Issues In Image Registration

    Science.gov (United States)

    Kiremidjian, Garo K.

    1987-06-01

    This paper presents a concept for automated model-based image registration. The overall approach relies on computing a correspondence between a three-dimensional (1-D) data base and a reconnaissance image in terms of an appropriate sensor model as a function of such parameters as sensor location, orientation, scale, etc. The construction of such models is illustrated for frame camera and SAR sensors. Initial (platform ephemeris) parameter estimates are refined to achieve accurate correspondence by techniques which optimally match a collection of 3-D lineal features to an edge set extracted from the image. In the case of 3-D data bases consisting of stereo imagery (such as PPDBs) 3-D lineal features are automatically generated by applying the Marr-Poggio-Grimson computational models for stereo vision. The computed 3-D data-base-to-image correspondence can be used to predict accurately the image location of any 3-D point and to develop an elevation surface model associated with the image. This also leads to establishing automated model-based image-to-image registration.

  5. Supervised quality assessment of medical image registration: application to intra-patient CT lung registration.

    Science.gov (United States)

    Muenzing, Sascha E A; van Ginneken, Bram; Murphy, Keelin; Pluim, Josien P W

    2012-12-01

    A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal multi-feature model, classifies local alignments into three quality categories: correct, poor or wrong alignment. We establish a reference registration error set as basis for training and testing of the method. It consists of image registrations obtained from different non-rigid registration algorithms and manually established point correspondences of automatically determined landmarks. We employ a set of different classifiers and evaluate the performance of the proposed image features based on the classification performance of corresponding single-feature classifiers. Feature selection is conducted to find an optimal subset of image features and the resulting multi-feature model is validated against the set of single-feature classifiers. We consider the setup generic, however, its application is demonstrated on 51 CT follow-up scan pairs of the lung. On this data, the proposed method performs with an overall classification accuracy of 90%. Copyright © 2012 Elsevier B.V. All rights reserved.

  6. Microscopic validation of whole mouse micro-metastatic tumor imaging agents using cryo-imaging and sliding organ image registration.

    Science.gov (United States)

    Liu, Yiqiao; Zhou, Bo; Qutaish, Mohammed; Wilson, David L

    2016-01-01

    We created a metastasis imaging, analysis platform consisting of software and multi-spectral cryo-imaging system suitable for evaluating emerging imaging agents targeting micro-metastatic tumor. We analyzed CREKA-Gd in MRI, followed by cryo-imaging which repeatedly sectioned and tiled microscope images of the tissue block face, providing anatomical bright field and molecular fluorescence, enabling 3D microscopic imaging of the entire mouse with single metastatic cell sensitivity. To register MRI volumes to the cryo bright field reference, we used our standard mutual information, non-rigid registration which proceeded: preprocess → affine → B-spline non-rigid 3D registration. In this report, we created two modified approaches: mask where we registered locally over a smaller rectangular solid, and sliding organ . Briefly, in sliding organ , we segmented the organ, registered the organ and body volumes separately and combined results. Though s liding organ required manual annotation, it provided the best result as a standard to measure other registration methods. Regularization parameters for standard and mask methods were optimized in a grid search. Evaluations consisted of DICE, and visual scoring of a checkerboard display. Standard had accuracy of 2 voxels in all regions except near the kidney, where there were 5 voxels sliding. After mask and sliding organ correction, kidneys sliding were within 2 voxels, and Dice overlap increased 4%-10% in mask compared to standard . Mask generated comparable results with sliding organ and allowed a semi-automatic process.

  7. COLLINARUS: collection of image-derived non-linear attributes for registration using splines

    Science.gov (United States)

    Chappelow, Jonathan; Bloch, B. Nicolas; Rofsky, Neil; Genega, Elizabeth; Lenkinski, Robert; DeWolf, William; Viswanath, Satish; Madabhushi, Anant

    2009-02-01

    We present a new method for fully automatic non-rigid registration of multimodal imagery, including structural and functional data, that utilizes multiple texutral feature images to drive an automated spline based non-linear image registration procedure. Multimodal image registration is significantly more complicated than registration of images from the same modality or protocol on account of difficulty in quantifying similarity between different structural and functional information, and also due to possible physical deformations resulting from the data acquisition process. The COFEMI technique for feature ensemble selection and combination has been previously demonstrated to improve rigid registration performance over intensity-based MI for images of dissimilar modalities with visible intensity artifacts. Hence, we present here the natural extension of feature ensembles for driving automated non-rigid image registration in our new technique termed Collection of Image-derived Non-linear Attributes for Registration Using Splines (COLLINARUS). Qualitative and quantitative evaluation of the COLLINARUS scheme is performed on several sets of real multimodal prostate images and synthetic multiprotocol brain images. Multimodal (histology and MRI) prostate image registration is performed for 6 clinical data sets comprising a total of 21 groups of in vivo structural (T2-w) MRI, functional dynamic contrast enhanced (DCE) MRI, and ex vivo WMH images with cancer present. Our method determines a non-linear transformation to align WMH with the high resolution in vivo T2-w MRI, followed by mapping of the histopathologic cancer extent onto the T2-w MRI. The cancer extent is then mapped from T2-w MRI onto DCE-MRI using the combined non-rigid and affine transformations determined by the registration. Evaluation of prostate registration is performed by comparison with the 3 time point (3TP) representation of functional DCE data, which provides an independent estimate of cancer

  8. SU-E-J-209: Verification of 3D Surface Registration Between Stereograms and CT Images

    Energy Technology Data Exchange (ETDEWEB)

    Han, T; Gifford, K [UT MD Anderson Cancer Center, Houston, TX (United States); Smith, B [MD Anderson Cancer Center, Houston, TX (United States); Salehpour, M [M.D. Anderson Cancer Center, Houston, TX (United States)

    2014-06-01

    Purpose: Stereography can provide a visualization of the skin surface for radiation therapy patients. The aim of this study was to verify the registration algorithm in a commercial image analysis software, 3dMDVultus, for the fusion of stereograms and CT images. Methods: CT and stereographic scans were acquired of a head phantom and a deformable phantom. CT images were imported in 3dMDVultus and the surface contours were generated by threshold segmentation. Stereograms were reconstructed in 3dMDVultus. The resulting surfaces were registered with Vultus algorithm and then exported to in-house registration software and compared with four algorithms: rigid, affine, non-rigid iterative closest point (ICP) and b-spline algorithm. RMS (root-mean-square residuals of the surface point distances) error between the registered CT and stereogram surfaces was calculated and analyzed. Results: For the head phantom, the maximum RMS error between registered CT surfaces to stereogram was 6.6 mm for Vultus algorithm, whereas the mean RMS error was 0.7 mm. For the deformable phantom, the maximum RMS error was 16.2 mm for Vultus algorithm, whereas the mean RMS error was 4.4 mm. Non-rigid ICP demonstrated the best registration accuracy, as the mean of RMS errors were both within 1 mm. Conclusion: The accuracy of registration algorithm in 3dMDVultus was verified and exceeded RMS of 2 mm for deformable cases. Non-rigid ICP and b-spline algorithms improve the registration accuracy for both phantoms, especially in deformable one. For those patients whose body habitus deforms during radiation therapy, more advanced nonrigid algorithms need to be used.

  9. Image registration methods: a survey

    Czech Academy of Sciences Publication Activity Database

    Zitová, Barbara; Flusser, Jan

    2003-01-01

    Roč. 21, č. 11 (2003), s. 977-1000 ISSN 0262-8856 R&D Projects: GA ČR GP102/01/P065 Institutional research plan: CEZ:AV0Z1075907 Keywords : image registration * feature detection * feature matching Subject RIV: JD - Computer Applications, Robotics Impact factor: 1.169, year: 2003 http://library.utia.cas.cz/prace/20030125.pdf

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

    Science.gov (United States)

    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.

  11. Co-registration of the BNCT treatment planning images for clinical practice

    International Nuclear Information System (INIS)

    Salli, Eero; Seppaelae, Tiina; Kankaanranta, Leena; Asikainen, Sami; Savolainen, Sauli; Koivunoro, Hanna

    2006-01-01

    We have co-registered MRI, CT and FBPA-PET images for BNCT in clinical practice. Co-registration improves the spatial accuracy of the treatment planning by enabling use of information from all the co-registered modalities. The multimodal co-registration has been implemented as a service product provided by the Imaging Center of Helsinki University Central Hospital to other departments. To increase the accuracy of co-registration and patient positioning in the head area BNCT, a patient-specific fixation mask suitable for PET, MRI and CT was developed. The goal of the fixation mask is to normalize the orientation of the patient's head and neck. Co-registration is performed at the image processing unit by using a rigid body model, mutual-information based algorithms and partly in-house developed software tools. The accuracy of co-registration is verified by comparing the locations of the external skin markers and anatomical landmarks in different modalities. After co-registration, the images are transformed and covered into a format required by the BNCT dose-planning software and set to the dose-planning unit of the hospital. So far co-registration has been done for 22 patients. The co-registration protocol has proved to be reliable and efficient. Some registration errors are seen on some patients in the neck area because the rigid-body model used in co-registration is not fully valid for the brain-neck entity. The registration accuracy in this area could likely be improved by implementing a co-registration procedure utilizing a partly non-rigid body model. (author)

  12. Correction for non-rigid movement artefacts in calcium imaging using local-global optical flow and PCA-based templates

    DEFF Research Database (Denmark)

    Brazhe, A.; Fordsmann, J.; Lauritzen, M.

    2017-01-01

    and can be non-uniform across image; (2) due to fluorescence intensity changes single template image may not be optimal for a subset of the movie frames. Methods: We address the first problem by using either a combined local/global algorithm of optical flow estimation or an original algorithm based...... a set of template images, obtained from clusters of image frames in low-dimensional PCA-based space. To allow for efficient storage of the estimated image warps, they can be represented as low-pass DCT coefficients or by other dictionary-based methods. Conclusions: The proposed pipeline for motion...... on calculation of optical flow in image patches with global regularization. Both algorithms estimate smooth optical flow fields between a current image and a template image and allow for correction of large-scale displacements by employing a multiscale pyramidal approach. The second problem is solved by using...

  13. Efficient nonrigid registration using ranked order statistics

    DEFF Research Database (Denmark)

    Tennakoon, Ruwan B.; Bab-Hadiashar, Alireza; de Bruijne, Marleen

    2013-01-01

    of research. In this paper we propose a fast and accurate non-rigid registration method for intra-modality volumetric images. Our approach exploits the information provided by an order statistics based segmentation method, to find the important regions for registration and use an appropriate sampling scheme...

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

    Directory of Open Access Journals (Sweden)

    Jingya Zhang

    Full Text Available 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

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

    Science.gov (United States)

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

    2015-01-01

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

  16. Image Registration for Stability Testing of MEMS

    Science.gov (United States)

    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.

  17. Local image registration a comparison for bilateral registration mammography

    Science.gov (United States)

    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.

  18. An Image Registration Method for Colposcopic Images

    Directory of Open Access Journals (Sweden)

    Efrén Mezura-Montes

    2013-01-01

    sequence and a division of such image into small windows. A search process is then carried out to find the window with the highest affinity in each image of the sequence and replace it with the window in the reference image. The affinity value is based on polynomial approximation of the time series computed and the search is bounded by a search radius which defines the neighborhood of each window. The proposed approach is tested in ten 310-frame real cases in two experiments: the first one to determine the best values for the window size and the search radius and the second one to compare the best obtained results with respect to four registration methods found in the specialized literature. The obtained results show a robust and competitive performance of the proposed approach with a significant lower time with respect to the compared methods.

  19. Uvod u registraciju slika / Image registration introduction

    Directory of Open Access Journals (Sweden)

    Boban P. Bondžulić

    2009-07-01

    Full Text Available U radu su dati osnovni pojmovi koji se koriste u registraciji slika, a koja je potrebna u različitim primenama obrade i analize slike. Prikazano je nekoliko primera registracije izvornih slika. Kroz jedan od primera ilustrovani su koraci koji se javljaju u registraciji. U drugom primeru, korišćenjem programskog paketa 'Matlab 7.0', ilustrovana je registracija slika iz baze multisenzorskih slika autora. Ostali primeri preuzeti su iz literature. / This paper gives the basic ideas of image registration needed in various applications of image processing and image analysis. A couple of examples of source image registration are given. The image registration steps are illustrated in one example. The 'Matlab 7.0' software is used in another example to illustrate image registration out of the author's multisensor image dataset. The other examples are taken from available literature.

  20. Edge-based correlation image registration for multispectral imaging

    Science.gov (United States)

    Nandy, Prabal [Albuquerque, NM

    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.

  1. Automated Registration Of Images From Multiple Sensors

    Science.gov (United States)

    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.

  2. 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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Hengkai Guo

    Full Text Available 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.

  4. Automatic registration of imaging mass spectrometry data to the Allen Brain Atlas transcriptome

    Science.gov (United States)

    Abdelmoula, Walid M.; Carreira, Ricardo J.; Shyti, Reinald; Balluff, Benjamin; Tolner, Else; van den Maagdenberg, Arn M. J. M.; Lelieveldt, B. P. F.; McDonnell, Liam; Dijkstra, Jouke

    2014-03-01

    Imaging Mass Spectrometry (IMS) is an emerging molecular imaging technology that provides spatially resolved information on biomolecular structures; each image pixel effectively represents a molecular mass spectrum. By combining the histological images and IMS-images, neuroanatomical structures can be distinguished based on their biomolecular features as opposed to morphological features. The combination of IMS data with spatially resolved gene expression maps of the mouse brain, as provided by the Allen Mouse Brain atlas, would enable comparative studies of spatial metabolic and gene expression patterns in life-sciences research and biomarker discovery. As such, it would be highly desirable to spatially register IMS slices to the Allen Brain Atlas (ABA). In this paper, we propose a multi-step automatic registration pipeline to register ABA histology to IMS- images. Key novelty of the method is the selection of the best reference section from the ABA, based on pre-processed histology sections. First, we extracted a hippocampus-specific geometrical feature from the given experimental histological section to initially localize it among the ABA sections. Then, feature-based linear registration is applied to the initially localized section and its two neighbors in the ABA to select the most similar reference section. A non-rigid registration yields a one-to-one mapping of the experimental IMS slice to the ABA. The pipeline was applied on 6 coronal sections from two mouse brains, showing high anatomical correspondence, demonstrating the feasibility of complementing biomolecule distributions from individual mice with the genome-wide ABA transcriptome.

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

    International Nuclear Information System (INIS)

    Brock, K; Mutic, S

    2014-01-01

    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

  6. Mid-space-independent deformable image registration.

    Science.gov (United States)

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

    2017-05-15

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

  7. A multicore based parallel image registration method.

    Science.gov (United States)

    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.

  8. Mass preserving image registration for lung CT

    DEFF Research Database (Denmark)

    Gorbunova, Vladlena; Sporring, Jon; Lo, Pechin Chien Pau

    2012-01-01

    This paper presents a mass preserving image registration algorithm for lung CT images. To account for the local change in lung tissue intensity during the breathing cycle, a tissue appearance model based on the principle of preservation of total lung mass is proposed. This model is incorporated...

  9. CT image registration in sinogram space.

    Science.gov (United States)

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

    2007-09-01

    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.

  10. Fractional Regularization Term for Variational Image Registration

    Directory of Open Access Journals (Sweden)

    Rafael Verdú-Monedero

    2009-01-01

    Full Text Available Image registration is a widely used task of image analysis with applications in many fields. Its classical formulation and current improvements are given in the spatial domain. In this paper a regularization term based on fractional order derivatives is formulated. This term is defined and implemented in the frequency domain by translating the energy functional into the frequency domain and obtaining the Euler-Lagrange equations which minimize it. The new regularization term leads to a simple formulation and design, being applicable to higher dimensions by using the corresponding multidimensional Fourier transform. The proposed regularization term allows for a real gradual transition from a diffusion registration to a curvature registration which is best suited to some applications and it is not possible in the spatial domain. Results with 3D actual images show the validity of this approach.

  11. CT image registration in sinogram space

    International Nuclear Information System (INIS)

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

    2007-01-01

    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

  12. Automated image registration for FDOPA PET studies

    International Nuclear Information System (INIS)

    Kang-Ping Lin; Sung-Cheng Huang, Dan-Chu Yu; Melega, W.; Barrio, J.R.; Phelps, M.E.

    1996-01-01

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

  13. Dynamic Non-Rigid Objects Reconstruction with a Single RGB-D Sensor

    Directory of Open Access Journals (Sweden)

    Sen Wang

    2018-03-01

    Full Text Available This paper deals with the 3D reconstruction problem for dynamic non-rigid objects with a single RGB-D sensor. It is a challenging task as we consider the almost inevitable accumulation error issue in some previous sequential fusion methods and also the possible failure of surface tracking in a long sequence. Therefore, we propose a global non-rigid registration framework and tackle the drifting problem via an explicit loop closure. Our novel scheme starts with a fusion step to get multiple partial scans from the input sequence, followed by a pairwise non-rigid registration and loop detection step to obtain correspondences between neighboring partial pieces and those pieces that form a loop. Then, we perform a global registration procedure to align all those pieces together into a consistent canonical space as guided by those matches that we have established. Finally, our proposed model-update step helps fixing potential misalignments that still exist after the global registration. Both geometric and appearance constraints are enforced during our alignment; therefore, we are able to get the recovered model with accurate geometry as well as high fidelity color maps for the mesh. Experiments on both synthetic and various real datasets have demonstrated the capability of our approach to reconstruct complete and watertight deformable objects.

  14. Registration of Large Motion Blurred Images

    Science.gov (United States)

    2016-05-09

    Large Motion Blurred Images . in IEEE Computer Vision and Pattern Recognition Workshop on Registration of Very Large Images , pp. 315-322, 2014. 2. Vijay...Seetharaman, “Efficient change detection for very large motion blurred images ,” in Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE...Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, June 2010, pp. 2392–2399. 1 [13] E. Ringaby and P.-E. Forssén, “Efficient

  15. An image registration based ultrasound probe calibration

    Science.gov (United States)

    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).

  16. Dimensional Metrology of Non-rigid Parts Without Specialized Inspection Fixtures =

    Science.gov (United States)

    Sabri, Vahid

    Quality control is an important factor for manufacturing companies looking to prosper in an era of globalization, market pressures and technological advances. Functionality and product quality cannot be guaranteed without this important aspect. Manufactured parts have deviations from their nominal (CAD) shape caused by the manufacturing process. Thus, geometric inspection is a very important element in the quality control of mechanical parts. We will focus here on the geometric inspection of non-rigid (flexible) parts which are widely used in the aeronautic and automotive industries. Non-rigid parts can have different forms in a free-state condition compared with their nominal models due to residual stress and gravity loads. To solve this problem, dedicated inspection fixtures are generally used in industry to compensate for the displacement of such parts for simulating the use state in order to perform geometric inspections. These fixtures and the installation and inspection processes are expensive and time-consuming. Our aim in this thesis is therefore to develop an inspection method which eliminates the need for specialized fixtures. This is done by acquiring a point cloud from the part in a free-state condition using a contactless measuring device such as optical scanning and comparing it with the CAD model for the deviation identification. Using a non-rigid registration method and finite element analysis, we numerically inspect the profile of a non-rigid part. To do so, a simulated displacement is performed using an improved definition of displacement boundary conditions for simulating unfixed parts. In addition, we propose a numerical method for dimensional metrology of non-rigid parts in a free-state condition based on the arc length measurement by calculating the geodesic distance using the Fast Marching Method (FMM). In this thesis, we apply our developed methods on industrial non-rigid parts with free-form surfaces simulated with different types of

  17. Image registration for remote sensing

    National Research Council Canada - National Science Library

    Le Moigne, Jacqueline; Netanyahu, Nathan S; Eastman, Roger D

    2011-01-01

    ... for environmental, political and basic science studies. The book brings together invited contributions by 36 distinguished researchers in the field to present a coherent and detailed overview of current research and practice in the application of image...

  18. Image Segmentation, Registration, Compression, and Matching

    Science.gov (United States)

    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

  19. SU-E-J-263: Dosimetric Analysis On Breast Brachytherapy Based On Deformable Image Registration

    Energy Technology Data Exchange (ETDEWEB)

    Chen, T; Nie, K; Narra, V; Zou, J; Zhang, M; Khan, A; Haffty, B; Yue, N [Rutgers - Cancer Institute of New Jersey, New Brunswick, NJ (United States)

    2014-06-01

    Purpose: To quantitatively compare and evaluate the dosimetry difference between breast brachytherapy protocols with different fractionation using deformable image registration. Methods: The accumulative dose distribution for multiple breast brachytherapy patients using four different applicators: Contura, Mammosite, Savi, and interstitial catheters, under two treatment protocols: 340cGy by 10 fractions in 5 days and 825cGy by 3 fractions in 2days has been reconstructed using a two stage deformable image registration approach. For all patients, daily CT was acquired with the same slice thickness (2.5mm). In the first stage, the daily CT images were rigidly registered to the initial planning CT using the registration module in Eclipse (Varian) to align the applicators. In the second stage, the tissues surrounding the applicator in the rigidly registered daily CT image were non-rigidly registered to the initial CT using a combination of image force and the local constraint that enforce zero normal motion on the surface of the applicator, using a software developed in house. We calculated the dose distribution in the daily CTs and deformed them using the final registration to convert into the image domain of the initial planning CT. The accumulative dose distributions were evaluated by dosimetry parameters including D90, V150 and V200, as well as DVH. Results: Dose reconstruction results showed that the two day treatment has a significant dosimetry improvement over the five day protocols. An average daily drop of D90 at 1.3% of the prescription dose has been observed on multiple brachytherapy patients. There is no significant difference on V150 and V200 between those two protocols. Conclusion: Brachytherapy with higher fractional dose and less fractions has an improved performance on being conformal to the dose distribution in the initial plan. Elongated brachytherapy treatments need to consider the dose uncertainty caused by the temporal changes of the soft tissue.

  20. Deformable image registration in radiation therapy

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Seung Jong; Kim, Si Yong [Dept. of Radiation Oncology, Virginia Commonwealth University, Richmond (United States)

    2017-06-15

    The number of imaging data sets has significantly increased during radiation treatment after introducing a diverse range of advanced techniques into the field of radiation oncology. As a consequence, there have been many studies proposing meaningful applications of imaging data set use. These applications commonly require a method to align the data sets at a reference. Deformable image registration (DIR) is a process which satisfies this requirement by locally registering image data sets into a reference image set. DIR identifies the spatial correspondence in order to minimize the differences between two or among multiple sets of images. This article describes clinical applications, validation, and algorithms of DIR techniques. Applications of DIR in radiation treatment include dose accumulation, mathematical modeling, automatic segmentation, and functional imaging. Validation methods discussed are based on anatomical landmarks, physical phantoms, digital phantoms, and per application purpose. DIR algorithms are also briefly reviewed with respect to two algorithmic components: similarity index and deformation models.

  1. SU-E-J-266: A Pitfall of a Deformable Image Registration in Lung Cancer

    International Nuclear Information System (INIS)

    Sugawara, Y; Tachibana, H; Moriya, S; Sawant, A

    2014-01-01

    Purpose: For four-dimensional (4D) planning and adaptive radiotherapy, deformable image registration (DIR) is needed and the accuracy is essential. We evaluated the accuracy of one free-downloadable DIR software library package (NiftyReg) and one commercial DIR software (MIM) in lung SBRT cancer patients. Methods: A rigid and non-rigid registrations were implemented to our in-house software. The non-rigid registration algorithm of the NiftyReg and MIM was based on the free-form deformation. The accuracy of the two software was evaluated when contoured structures to peak-inhale and peak-exhale 4DCT image data sets were measured using the dice similarity coefficient (DSC). The evaluation was performed in 20 lung SBRT patients. Results: In our visual evaluation, the eighteen cases show good agreement between the deformed structures for the peak-inhale phase and the peak-exhale phase structures (more than 0.8 DSC value). In the evaluation of the DSC in-house software, averaged DSC values of GTV and lung, heart, spinal cord, stomach and body were 0.862 and 0.979, 0.932, 0.974, 0.860, 0.998, respectively. As the Resultof the registration using the MIM program in the two cases which had less than 0.7 DSC value when analyzed using the in-house software, the DSC value were improved to 0.8. The CT images in a case with low DSC value shows the tumor was surrounded by the structure with the similar CT values, which were the chest wall or the diaphragm. Conclusion: Not only a free-downloadable DIR software but also a commercial software may provide unexpected results and there is a possibility that the results would make us misjudge the treatment planning. Therefore, we recommend that a commissioning test of any DIR software should be performed before clinical use and we should understand the characteristics of the software

  2. SU-E-J-266: A Pitfall of a Deformable Image Registration in Lung Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Sugawara, Y [The National Center for Global Health and Medicine, Shinjuku, Tokyo (Japan); Tachibana, H [The National Cancer Center Hospital East, Kashiwa, Chiba (Japan); Moriya, S [Komazawa University, Setagaya, Tokyo (Japan); Sawant, A [UT Southwestern Medical Center, Dallas, TX (United States)

    2014-06-01

    Purpose: For four-dimensional (4D) planning and adaptive radiotherapy, deformable image registration (DIR) is needed and the accuracy is essential. We evaluated the accuracy of one free-downloadable DIR software library package (NiftyReg) and one commercial DIR software (MIM) in lung SBRT cancer patients. Methods: A rigid and non-rigid registrations were implemented to our in-house software. The non-rigid registration algorithm of the NiftyReg and MIM was based on the free-form deformation. The accuracy of the two software was evaluated when contoured structures to peak-inhale and peak-exhale 4DCT image data sets were measured using the dice similarity coefficient (DSC). The evaluation was performed in 20 lung SBRT patients. Results: In our visual evaluation, the eighteen cases show good agreement between the deformed structures for the peak-inhale phase and the peak-exhale phase structures (more than 0.8 DSC value). In the evaluation of the DSC in-house software, averaged DSC values of GTV and lung, heart, spinal cord, stomach and body were 0.862 and 0.979, 0.932, 0.974, 0.860, 0.998, respectively. As the Resultof the registration using the MIM program in the two cases which had less than 0.7 DSC value when analyzed using the in-house software, the DSC value were improved to 0.8. The CT images in a case with low DSC value shows the tumor was surrounded by the structure with the similar CT values, which were the chest wall or the diaphragm. Conclusion: Not only a free-downloadable DIR software but also a commercial software may provide unexpected results and there is a possibility that the results would make us misjudge the treatment planning. Therefore, we recommend that a commissioning test of any DIR software should be performed before clinical use and we should understand the characteristics of the software.

  3. Canny edge-based deformable image registration.

    Science.gov (United States)

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

    2017-02-07

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

  4. Automated landmark-guided deformable image registration

    International Nuclear Information System (INIS)

    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. (paper)

  5. Automated landmark-guided deformable image registration.

    Science.gov (United States)

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

    2015-01-07

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

  6. Dimensionality reduction of medical image descriptors for multimodal image registration

    Directory of Open Access Journals (Sweden)

    Degen Johanna

    2015-09-01

    Full Text Available Defining similarity forms a challenging and relevant research topic in multimodal image registration. The frequently used mutual information disregards contextual information, which is shared across modalities. A recent popular approach, called modality independent neigh-bourhood descriptor, is based on local self-similarities of image patches and is therefore able to capture spatial information. This image descriptor generates vectorial representations, i.e. it is multidimensional, which results in a disadvantage in terms of computation time. In this work, we present a problem-adapted solution for dimensionality reduction, by using principal component analysis and Horn’s parallel analysis. Furthermore, the influence of dimensionality reduction in global rigid image registration is investigated. It is shown that the registration results obtained from the reduced descriptor have the same high quality in comparison to those found for the original descriptor.

  7. Deformable image registration for image guided prostate radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Cassetta, Roberto; Riboldi, Marco; Baroni, Guido [DEIB, Politecnico di Milano, Milano (Italy); Leandro, Kleber; Novaes, Paulo Eduardo [Hospital Vitoria, Santos, SP (Brazil); Goncalves, Vinicius; Sakuraba, Roberto [Hospital Israelita Albert Einstein, Sao Paulo, SP (Brazil); Fattori, Giovanni [Paul Scherrer Institute, Center for Proton Therapy, Villigen (Switzerland)

    2016-07-01

    In this study, we present a CT to CBCT deformable registration method based on the ITK library. An algorithm was developed in order to explore the soft tissue information of the CT-CBCT images to perform deformable image registration (DIR), making efforts to overcome the poor signal-to-noise ratio and HU calibration issues that limits CBCT use for treatment planning purposes. Warped CT images and contours were generated and their impact in adaptive radiotherapy was evaluated by DVH analysis for photon and proton treatments. Considerable discrepancies, related to the treatment planning dose distribution, might be found due to changes in patient’s anatomy. (author)

  8. High performance deformable image registration algorithms for manycore processors

    CERN Document Server

    Shackleford, James; Sharp, Gregory

    2013-01-01

    High Performance Deformable Image Registration Algorithms for Manycore Processors develops highly data-parallel image registration algorithms suitable for use on modern multi-core architectures, including graphics processing units (GPUs). Focusing on deformable registration, we show how to develop data-parallel versions of the registration algorithm suitable for execution on the GPU. Image registration is the process of aligning two or more images into a common coordinate frame and is a fundamental step to be able to compare or fuse data obtained from different sensor measurements. E

  9. Line scale measurement using image registration

    Directory of Open Access Journals (Sweden)

    Costa P.B.

    2013-01-01

    Full Text Available Currently, most advances in dimensional metrology might be seen by the evolution of non-contact measurement (optical measurements, in order to provide traceability for different areas that needs to calibrate microscopes or optical measure machines. In a similar way, the use of image processing techniques for the measuring of objects has been the subject of recent studies in computer vision and image metrology. In the attempt to meet the requirements and demands for high accuracy dimensional metrology with image processing techniques, this work will present the application of the image registration technique for the measurement of line scales. In the conventional calibration, the scales are measured in pre-established points, generally in intervals of 10% of the total scale length. With this application, it becomes possible to provide results for all the scale marks quickly and automatically, whereas in the conventional method it would require more time and, thus, a higher cost for the fulfillment of this measurement.

  10. Registration accuracy for MR images of the prostate using a subvolume based registration protocol

    Directory of Open Access Journals (Sweden)

    Söderström Karin

    2011-06-01

    Full Text Available Abstract Background In recent years, there has been a considerable research effort concerning the integration of magnetic resonance imaging (MRI into the external radiotherapy workflow motivated by the superior soft tissue contrast as compared to computed tomography. Image registration is a necessary step in many applications, e.g. in patient positioning and therapy response assessment with repeated imaging. In this study, we investigate the dependence between the registration accuracy and the size of the registration volume for a subvolume based rigid registration protocol for MR images of the prostate. Methods Ten patients were imaged four times each over the course of radiotherapy treatment using a T2 weighted sequence. The images were registered to each other using a mean square distance metric and a step gradient optimizer for registration volumes of different sizes. The precision of the registrations was evaluated using the center of mass distance between the manually defined prostates in the registered images. The optimal size of the registration volume was determined by minimizing the standard deviation of these distances. Results We found that prostate position was most uncertain in the anterior-posterior (AP direction using traditional full volume registration. The improvement in standard deviation of the mean center of mass distance between the prostate volumes using a registration volume optimized to the prostate was 3.9 mm (p Conclusions Repeated MR imaging of the prostate for therapy set-up or therapy assessment will both require high precision tissue registration. With a subvolume based registration the prostate registration uncertainty can be reduced down to the order of 1 mm (1 SD compared to several millimeters for registration based on the whole pelvis.

  11. Image registration for daylight adaptive optics.

    Science.gov (United States)

    Hart, Michael

    2018-03-15

    Daytime use of adaptive optics (AO) at large telescopes is hampered by shot noise from the bright sky background. Wave-front sensing may use a sodium laser guide star observed through a magneto-optical filter to suppress the background, but the laser beacon is not sensitive to overall image motion. To estimate that, laser-guided AO systems generally rely on light from the object itself, collected through the full aperture of the telescope. Daylight sets a lower limit to the brightness of an object that may be tracked at rates sufficient to overcome the image jitter. Below that limit, wave-front correction on the basis of the laser alone will yield an image that is approximately diffraction limited but that moves randomly. I describe an iterative registration algorithm that recovers high-resolution long-exposure images in this regime from a rapid series of short exposures with very low signal-to-noise ratio. The technique takes advantage of the fact that in the photon noise limit there is negligible penalty in taking short exposures, and also that once the images are recorded, it is not necessary, as in the case of an AO tracker loop, to estimate the image motion correctly and quickly on every cycle. The algorithm is likely to find application in space situational awareness, where high-resolution daytime imaging of artificial satellites is important.

  12. DOCUMENT IMAGE REGISTRATION FOR IMPOSED LAYER EXTRACTION

    Directory of Open Access Journals (Sweden)

    Surabhi Narayan

    2017-02-01

    Full Text Available Extraction of filled-in information from document images in the presence of template poses challenges due to geometrical distortion. Filled-in document image consists of null background, general information foreground and vital information imposed layer. Template document image consists of null background and general information foreground layer. In this paper a novel document image registration technique has been proposed to extract imposed layer from input document image. A convex polygon is constructed around the content of the input and the template image using convex hull. The vertices of the convex polygons of input and template are paired based on minimum Euclidean distance. Each vertex of the input convex polygon is subjected to transformation for the permutable combinations of rotation and scaling. Translation is handled by tight crop. For every transformation of the input vertices, Minimum Hausdorff distance (MHD is computed. Minimum Hausdorff distance identifies the rotation and scaling values by which the input image should be transformed to align it to the template. Since transformation is an estimation process, the components in the input image do not overlay exactly on the components in the template, therefore connected component technique is applied to extract contour boxes at word level to identify partially overlapping components. Geometrical features such as density, area and degree of overlapping are extracted and compared between partially overlapping components to identify and eliminate components common to input image and template image. The residue constitutes imposed layer. Experimental results indicate the efficacy of the proposed model with computational complexity. Experiment has been conducted on variety of filled-in forms, applications and bank cheques. Data sets have been generated as test sets for comparative analysis.

  13. Free Form Deformation-Based Image Registration Improves Accuracy of Traction Force Microscopy.

    Directory of Open Access Journals (Sweden)

    Alvaro Jorge-Peñas

    Full Text Available Traction Force Microscopy (TFM is a widespread method used to recover cellular tractions from the deformation that they cause in their surrounding substrate. Particle Image Velocimetry (PIV is commonly used to quantify the substrate's deformations, due to its simplicity and efficiency. However, PIV relies on a block-matching scheme that easily underestimates the deformations. This is especially relevant in the case of large, locally non-uniform deformations as those usually found in the vicinity of a cell's adhesions to the substrate. To overcome these limitations, we formulate the calculation of the deformation of the substrate in TFM as a non-rigid image registration process that warps the image of the unstressed material to match the image of the stressed one. In particular, we propose to use a B-spline -based Free Form Deformation (FFD algorithm that uses a connected deformable mesh to model a wide range of flexible deformations caused by cellular tractions. Our FFD approach is validated in 3D fields using synthetic (simulated data as well as with experimental data obtained using isolated endothelial cells lying on a deformable, polyacrylamide substrate. Our results show that FFD outperforms PIV providing a deformation field that allows a better recovery of the magnitude and orientation of tractions. Together, these results demonstrate the added value of the FFD algorithm for improving the accuracy of traction recovery.

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

    Science.gov (United States)

    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.

  15. An efficient similarity measure technique for medical image registration

    Indian Academy of Sciences (India)

    In this paper, an efficient similarity measure technique is proposed for medical image registration. The proposed approach is based on the Gerschgorin circles theorem. In this approach, image registration is carried out by considering Gerschgorin bounds of a covariance matrix of two compared images with normalized ...

  16. Efficient Constrained Local Model Fitting for Non-Rigid Face Alignment.

    Science.gov (United States)

    Lucey, Simon; Wang, Yang; Cox, Mark; Sridharan, Sridha; Cohn, Jeffery F

    2009-11-01

    Active appearance models (AAMs) have demonstrated great utility when being employed for non-rigid face alignment/tracking. The "simultaneous" algorithm for fitting an AAM achieves good non-rigid face registration performance, but has poor real time performance (2-3 fps). The "project-out" algorithm for fitting an AAM achieves faster than real time performance (> 200 fps) but suffers from poor generic alignment performance. In this paper we introduce an extension to a discriminative method for non-rigid face registration/tracking referred to as a constrained local model (CLM). Our proposed method is able to achieve superior performance to the "simultaneous" AAM algorithm along with real time fitting speeds (35 fps). We improve upon the canonical CLM formulation, to gain this performance, in a number of ways by employing: (i) linear SVMs as patch-experts, (ii) a simplified optimization criteria, and (iii) a composite rather than additive warp update step. Most notably, our simplified optimization criteria for fitting the CLM divides the problem of finding a single complex registration/warp displacement into that of finding N simple warp displacements. From these N simple warp displacements, a single complex warp displacement is estimated using a weighted least-squares constraint. Another major advantage of this simplified optimization lends from its ability to be parallelized, a step which we also theoretically explore in this paper. We refer to our approach for fitting the CLM as the "exhaustive local search" (ELS) algorithm. Experiments were conducted on the CMU Multi-PIE database.

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

    Science.gov (United States)

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

    2007-12-01

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

  18. Groupwise registration of MR brain images with tumors

    Science.gov (United States)

    Tang, Zhenyu; Wu, Yihong; Fan, Yong

    2017-09-01

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

  19. A Variational Approach to Video Registration with Subspace Constraints.

    Science.gov (United States)

    Garg, Ravi; Roussos, Anastasios; Agapito, Lourdes

    2013-01-01

    This paper addresses the problem of non-rigid video registration, or the computation of optical flow from a reference frame to each of the subsequent images in a sequence, when the camera views deformable objects. We exploit the high correlation between 2D trajectories of different points on the same non-rigid surface by assuming that the displacement of any point throughout the sequence can be expressed in a compact way as a linear combination of a low-rank motion basis. This subspace constraint effectively acts as a trajectory regularization term leading to temporally consistent optical flow. We formulate it as a robust soft constraint within a variational framework by penalizing flow fields that lie outside the low-rank manifold. The resulting energy functional can be decoupled into the optimization of the brightness constancy and spatial regularization terms, leading to an efficient optimization scheme. Additionally, we propose a novel optimization scheme for the case of vector valued images, based on the dualization of the data term. This allows us to extend our approach to deal with colour images which results in significant improvements on the registration results. Finally, we provide a new benchmark dataset, based on motion capture data of a flag waving in the wind, with dense ground truth optical flow for evaluation of multi-frame optical flow algorithms for non-rigid surfaces. Our experiments show that our proposed approach outperforms state of the art optical flow and dense non-rigid registration algorithms.

  20. Elastic models application for thorax image registration

    International Nuclear Information System (INIS)

    Correa Prado, Lorena S; Diaz, E Andres Valdez; Romo, Raul

    2007-01-01

    This work consist of the implementation and evaluation of elastic alignment algorithms of biomedical images, which were taken at thorax level and simulated with the 4D NCAT digital phantom. Radial Basis Functions spatial transformations (RBF), a kind of spline, which allows carrying out not only global rigid deformations but also local elastic ones were applied, using a point-matching method. The applied functions were: Thin Plate Spline (TPS), Multiquadric (MQ) Gaussian and B-Spline, which were evaluated and compared by means of calculating the Target Registration Error and similarity measures between the registered images (the squared sum of intensity differences (SSD) and correlation coefficient (CC)). In order to value the user incurred error in the point-matching and segmentation tasks, two algorithms were also designed that calculate the Fiduciary Localization Error. TPS and MQ were demonstrated to have better performance than the others. It was proved RBF represent an adequate model for approximating the thorax deformable behaviour. Validation algorithms showed the user error was not significant

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

    Science.gov (United States)

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

    2017-02-01

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

  2. Normalized Total Gradient: A New Measure for Multispectral Image Registration

    Science.gov (United States)

    Chen, Shu-Jie; Shen, Hui-Liang; Li, Chunguang; Xin, John H.

    2018-03-01

    Image registration is a fundamental issue in multispectral image processing. In filter wheel based multispectral imaging systems, the non-coplanar placement of the filters always causes the misalignment of multiple channel images. The selective characteristic of spectral response in multispectral imaging raises two challenges to image registration. First, the intensity levels of a local region may be different in individual channel images. Second, the local intensity may vary rapidly in some channel images while keeps stationary in others. Conventional multimodal measures, such as mutual information, correlation coefficient, and correlation ratio, can register images with different regional intensity levels, but will fail in the circumstance of severe local intensity variation. In this paper, a new measure, namely normalized total gradient (NTG), is proposed for multispectral image registration. The NTG is applied on the difference between two channel images. This measure is based on the key assumption (observation) that the gradient of difference image between two aligned channel images is sparser than that between two misaligned ones. A registration framework, which incorporates image pyramid and global/local optimization, is further introduced for rigid transform. Experimental results validate that the proposed method is effective for multispectral image registration and performs better than conventional methods.

  3. Multimodality image registration with software: state-of-the-art.

    Science.gov (United States)

    Slomka, Piotr J; Baum, Richard P

    2009-03-01

    Multimodality image integration of functional and anatomical data can be performed by means of dedicated hybrid imaging systems or by software image co-registration techniques. Hybrid positron emission tomography (PET)/computed tomography (CT) systems have found wide acceptance in oncological imaging, while software registration techniques have a significant role in patient-specific, cost-effective, and radiation dose-effective application of integrated imaging. Software techniques allow accurate (2-3 mm) rigid image registration of brain PET with CT and MRI. Nonlinear techniques are used in whole-body image registration, and recent developments allow for significantly accelerated computing times. Nonlinear software registration of PET with CT or MRI is required for multimodality radiation planning. Difficulties remain in the validation of nonlinear registration of soft tissue organs. The utilization of software-based multimodality image integration in a clinical environment is sometimes hindered by the lack of appropriate picture archiving and communication systems (PACS) infrastructure needed to efficiently and automatically integrate all available images into one common database. In cardiology applications, multimodality PET/single photon emission computed tomography and coronary CT angiography imaging is typically not required unless the results of one of the tests are equivocal. Software image registration is likely to be used in a complementary fashion with hybrid PET/CT or PET/magnetic resonance imaging systems. Software registration of stand-alone scans "paved the way" for the clinical application of hybrid scanners, demonstrating practical benefits of image integration before the hybrid dual-modality devices were available.

  4. Image Registration Algorithm Based on Parallax Constraint and Clustering Analysis

    Science.gov (United States)

    Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

    2018-01-01

    To resolve the problem of slow computation speed and low matching accuracy in image registration, a new image registration algorithm based on parallax constraint and clustering analysis is proposed. Firstly, Harris corner detection algorithm is used to extract the feature points of two images. Secondly, use Normalized Cross Correlation (NCC) function to perform the approximate matching of feature points, and the initial feature pair is obtained. Then, according to the parallax constraint condition, the initial feature pair is preprocessed by K-means clustering algorithm, which is used to remove the feature point pairs with obvious errors in the approximate matching process. Finally, adopt Random Sample Consensus (RANSAC) algorithm to optimize the feature points to obtain the final feature point matching result, and the fast and accurate image registration is realized. The experimental results show that the image registration algorithm proposed in this paper can improve the accuracy of the image matching while ensuring the real-time performance of the algorithm.

  5. Registration accuracy and quality of real-life images.

    Directory of Open Access Journals (Sweden)

    Wei-Yen Hsu

    Full Text Available BACKGROUND: A common registration problem for the application of consumer device is to align all the acquired image sequences into a complete scene. Image alignment requires a registration algorithm that will compensate as much as possible for geometric variability among images. However, images captured views from a real scene usually produce different distortions. Some are derived from the optic characteristics of image sensors, and others are caused by the specific scenes and objects. METHODOLOGY/PRINCIPAL FINDINGS: An image registration algorithm considering the perspective projection is proposed for the application of consumer devices in this study. It exploits a multiresolution wavelet-based method to extract significant features. An analytic differential approach is then proposed to achieve fast convergence of point matching. Finally, the registration accuracy is further refined to obtain subpixel precision by a feature-based modified Levenberg-Marquardt method. Due to its feature-based and nonlinear characteristic, it converges considerably faster than most other methods. In addition, vignette compensation and color difference adjustment are also performed to further improve the quality of registration results. CONCLUSIONS/SIGNIFICANCE: The performance of the proposed method is evaluated by testing the synthetic and real images acquired by a hand-held digital still camera and in comparison with two registration techniques in terms of the squared sum of intensity differences (SSD and correlation coefficient (CC. The results indicate that the proposed method is promising in registration accuracy and quality, which are statistically significantly better than other two approaches.

  6. Distance-Dependent Multimodal Image Registration for Agriculture Tasks

    OpenAIRE

    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-cali...

  7. The role of image registration in brain mapping

    Science.gov (United States)

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

    2008-01-01

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

  8. Image Registration Using Redundant Wavelet Transforms

    National Research Council Canada - National Science Library

    Brown, Richard

    2001-01-01

    .... 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...

  9. Registration of Large Motion Blurred CMOS Images

    Science.gov (United States)

    2017-08-28

    registration to detect changes. This algorithm includes an optimization problem that leverages the sparsity of the camera trajectory in the pose space and...rolling shutter (RS) cameras and reveal the constraints on camera motion that admit registration, change detection, and rectification. Their analysis ...deal with wide-angle systems (which most cell-phone and drone cameras are) and irregular camera trajectory . In the first part of this work, we

  10. Automatic intra-modality brain image registration method

    International Nuclear Information System (INIS)

    Whitaker, J.M.; Ardekani, B.A.; Braun, M.

    1996-01-01

    Full text: Registration of 3D images of brain of the same or different subjects has potential importance in clinical diagnosis, treatment planning and neurological research. The broad aim of our work is to produce an automatic and robust intra-modality, brain image registration algorithm for intra-subject and inter-subject studies. Our algorithm is composed of two stages. Initial alignment is achieved by finding the values of nine transformation parameters (representing translation, rotation and scale) that minimise the nonoverlapping regions of the head. This is achieved by minimisation of the sum of the exclusive OR of two binary head images, produced using the head extraction procedure described by Ardekani et al. (J Comput Assist Tomogr, 19:613-623, 1995). The initial alignment successfully determines the scale parameters and gross translation and rotation parameters. Fine alignment uses an objective function described for inter-modality registration in Ardekani et al. (ibid.). The algorithm segments one of the images to be aligned into a set of connected components using K-means clustering. Registration is achieved by minimising the K-means variance of the segmentation induced in the other image. Similarity of images of the same modality makes the method attractive for intra-modality registration. A 3D MR image, with voxel dimensions, 2x2x6 mm, was misaligned. The registered image shows visually accurate registration. The average displacement of a pixel from its correct location was measured to be 3.3 mm. The algorithm was tested on intra-subject MR images and was found to produce good qualitative results. Using the data available, the algorithm produced promising qualitative results in intra-subject registration. Further work is necessary in its application to intersubject registration, due to large variability in brain structure between subjects. Clinical evaluation of the algorithm for selected applications is required

  11. Supervised Quality Assessment Of Medical Image Registration: Application to intra-patient CT lung registration

    NARCIS (Netherlands)

    Muenzing, S.E.; Ginneken, B. van; Murphy, K.; Pluim, J.P.

    2012-01-01

    A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal

  12. Laser range scanning for image-guided neurosurgery: investigation of image-to-physical space registrations.

    Science.gov (United States)

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

    2008-04-01

    In this article a comprehensive set of registration methods is utilized to provide image-to-physical space registration for image-guided neurosurgery in a clinical study. Central to all methods is the use of textured point clouds as provided by laser range scanning technology. The objective is to perform a systematic comparison of registration methods that include both extracranial (skin marker point-based registration (PBR), and face-based surface registration) and intracranial methods (feature PBR, cortical vessel-contour registration, a combined geometry/intensity surface registration method, and a constrained form of that method to improve robustness). The platform facilitates the selection of discrete soft-tissue landmarks that appear on the patient's intraoperative cortical surface and the preoperative gadolinium-enhanced magnetic resonance (MR) image volume, i.e., true corresponding novel targets. In an 11 patient study, data were taken to allow statistical comparison among registration methods within the context of registration error. The results indicate that intraoperative face-based surface registration is statistically equivalent to traditional skin marker registration. The four intracranial registration methods were investigated and the results demonstrated a target registration error of 1.6 +/- 0.5 mm, 1.7 +/- 0.5 mm, 3.9 +/- 3.4 mm, and 2.0 +/- 0.9 mm, for feature PBR, cortical vessel-contour registration, unconstrained geometric/intensity registration, and constrained geometric/intensity registration, respectively. When analyzing the results on a per case basis, the constrained geometric/intensity registration performed best, followed by feature PBR, and finally cortical vessel-contour registration. Interestingly, the best target registration errors are similar to targeting errors reported using bone-implanted markers within the context of rigid targets. The experience in this study as with others is that brain shift can compromise extracranial

  13. System and method for image registration of multiple video streams

    Science.gov (United States)

    Dillavou, Marcus W.; Shum, Phillip Corey; Guthrie, Baron L.; Shenai, Mahesh B.; Deaton, Drew Steven; May, Matthew Benton

    2018-02-06

    Provided herein are methods and systems for image registration from multiple sources. A method for image registration includes rendering a common field of interest that reflects a presence of a plurality of elements, wherein at least one of the elements is a remote element located remotely from another of the elements and updating the common field of interest such that the presence of the at least one of the elements is registered relative to another of the elements.

  14. Infrared thermal facial image sequence registration analysis and verification

    Science.gov (United States)

    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.

  15. MR to CT registration of brains using image synthesis

    Science.gov (United States)

    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.

  16. An efficient similarity measure technique for medical image registration

    Indian Academy of Sciences (India)

    are obtained from the reference image by cropping the defective area using Adobe Photoshop. 7.0.1 software. In figure 4, row 1 shows the registration results for the image having the subacute stroke disease. The template image shows the particular affected region. In this experiment, the illu- mination (local distortion) effect ...

  17. A batch Algorithm for Implicit Non-Rigid Shape and Motion Recovery

    DEFF Research Database (Denmark)

    Bartoli, Adrien; Olsen, Søren Ingvor

    2005-01-01

    The recovery of 3D shape and camera motion for non-rigid scenes from single-camera video footage is a very important problem in computer vision. The low-rank shape model consists in regarding the deformations as linear combinations of basis shapes. Most algorithms for reconstructing the parameters...... of the subsequence, using a robust estimator incorporating a model selection criterion that detects erroneous image points. Preliminary experimental results on real and simulated data show that our algorithm deals with challenging video sequences....

  18. Image panoramic mosaicing with global and local registration

    Science.gov (United States)

    Li, Qi; Ji, Zhen; Zhang, Jihong

    2001-09-01

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

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

    Science.gov (United States)

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

    2014-07-01

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

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

    Science.gov (United States)

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

    2017-09-01

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

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

    Science.gov (United States)

    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.

  2. Remote Sensing Image Registration with Line Segments and Their Intersections

    Directory of Open Access Journals (Sweden)

    Chengjin Lyu

    2017-05-01

    Full Text Available Image registration is a basic but essential step for remote sensing image processing, and finding stable features in multitemporal images is one of the most considerable challenges in the field. The main shape contours of artificial objects (e.g., roads, buildings, farmlands, and airports can be generally described as a group of line segments, which are stable features, even in images with evident background changes (e.g., images taken before and after a disaster. In this study, a registration method that uses line segments and their intersections is proposed for multitemporal remote sensing images. First, line segments are extracted in image pyramids to unify the scales of the reference image and the test image. Then, a line descriptor based on the gradient distribution of local areas is constructed, and the segments are matched in image pyramids. Lastly, triplets of intersections of matching lines are selected to estimate affine transformation between two images. Additional corresponding intersections are provided based on the estimated transformation, and an iterative process is adopted to remove outliers. The performance of the proposed method is tested on a variety of optical remote sensing image pairs, including synthetic and real data. Compared with existing methods, our method can provide more accurate registration results, even in images with significant background changes.

  3. Improved image registration by sparse patch-based deformation estimation.

    Science.gov (United States)

    Kim, Minjeong; Wu, Guorong; Wang, Qian; Lee, Seong-Whan; Shen, Dinggang

    2015-01-15

    Despite 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 toward 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; and (4) we

  4. 3-D brain image registration using optimal morphological processing

    International Nuclear Information System (INIS)

    Loncaric, S.; Dhawan, A.P.

    1994-01-01

    The three-dimensional (3-D) registration of Magnetic Resonance (MR) and Positron Emission Tomographic (PET) images of the brain is important for analysis of the human brain and its diseases. A procedure for optimization of (3-D) morphological structuring elements, based on a genetic algorithm, is presented in the paper. The registration of the MR and PET images is done by means of a registration procedure in two major phases. In the first phase, the Iterative Principal Axis Transform (IPAR) is used for initial registration. In the second phase, the optimal shape description method based on the Morphological Signature Transform (MST) is used for final registration. The morphological processing is used to improve the accuracy of the basic IPAR method. The brain ventricle is used as a landmark for MST registration. A near-optimal structuring element obtained by means of a genetic algorithm is used in MST to describe the shape of the ventricle. The method has been tested on the set of brain images demonstrating the feasibility of approach. (author). 11 refs., 3 figs

  5. 4D ultrasound and 3D MRI registration of beating heart

    International Nuclear Information System (INIS)

    Herlambang, N.; Matsumiya, K.; Masamune, K.; Dohi, T.; Liao, H.; Tsukihara, H.; Takamoto, S.

    2007-01-01

    To realize intra-cardiac surgery without cardio-pulmonary bypass, a medical imaging technique with both high image quality and data acquisition rate that is fast enough to follow heart beat movements is required. In this research, we proposed a method that utilized the image quality of MRI and the speed of ultrasound. We developed a 4D image reconstruction method using image registration of 3D MRI and 4D ultrasound images. The registration method consists of rigid registration between 3D MRI and 3D ultrasound with the same heart beat phase, and non-rigid registration between 3D ultrasound images from different heart beat phases. Non-rigid registration was performed with B-spline based registration using variable spring model. In phantom experiment using balloon phantom, registration accuracy was less than 2 mm for total heart volume variation range of 10%. We applied our registration method on 3D MRI and 4D ultrasound images of a volunteer's beating heart data and confirmed through visual observation that heart beat pattern was well reproduced. (orig.)

  6. Free-form image registration of human cochlear μCT data using skeleton similarity as anatomical prior

    DEFF Research Database (Denmark)

    Kjer, Hans Martin; Fagertun, Jens; Vera, Sergio

    2016-01-01

    Better understanding of the anatomical variability of the human cochlear is important for the design and function of Cochlear Implants. Proper non-rigid alignment of high-resolution cochlear μCT data is a challenge for the typical cubic B-spline registration model. In this paper we study one way...... to provide a more global guidance of the alignment. The resulting registrations are evaluated using different metrics for accuracy and model behavior, and compared to the results of a registration without the prior....

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

    Science.gov (United States)

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

    2012-01-01

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

  8. Entropy-Based Block Processing for Satellite Image Registration

    Directory of Open Access Journals (Sweden)

    Ikhyun Lee

    2012-11-01

    Full Text Available Image registration is an important task in many computer vision applications such as fusion systems, 3D shape recovery and earth observation. Particularly, registering satellite images is challenging and time-consuming due to limited resources and large image size. In such scenario, state-of-the-art image registration methods such as scale-invariant feature transform (SIFT may not be suitable due to high processing time. In this paper, we propose an algorithm based on block processing via entropy to register satellite images. The performance of the proposed method is evaluated using different real images. The comparative analysis shows that it not only reduces the processing time but also enhances the accuracy.

  9. Retrospective registration of tomographic brain images

    NARCIS (Netherlands)

    Maintz, J.B.A.

    1996-01-01

    In modern clinical practice, the clinician can make use of a vast array of specialized imaging techniques supporting diagnosis and treatment. For various reasons, the same anatomy of one patient is sometimes imaged more than once, either using the same imaging apparatus (monomodal acquisition ),

  10. Efficient Method for Scalable Registration of Remote Sensing Images

    Science.gov (United States)

    Prouty, R.; LeMoigne, J.; Halem, M.

    2017-12-01

    The goal of this project is to build a prototype of a resource-efficient pipeline that will provide registration within subpixel accuracy of multitemporal Earth science data. Accurate registration of Earth-science data is imperative to proper data integration and seamless mosaicing of data from multiple times, sensors, and/or observation geometries. Modern registration methods make use of many arithmetic operations and sometimes require complete knowledge of the image domain. As such, while sensors become more advanced and are able to provide higher-resolution data, the memory resources required to properly register these data become prohibitive. The proposed pipeline employs a region of interest extraction algorithm in order to extract image subsets with high local feature density. These image subsets are then used to generate local solutions to the global registration problem. The local solutions are then 'globalized' to determine the deformation model that best solves the registration problem. The region of interest extraction and globalization routines are tested for robustness among the variety of scene-types and spectral locations provided by Earth-observing instruments such as Landsat, MODIS, or ASTER.

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

    Science.gov (United States)

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

    2017-03-01

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

  12. Diffusion tensor image registration using hybrid connectivity and tensor features.

    Science.gov (United States)

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

    2014-07-01

    Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone. Copyright © 2013 Wiley Periodicals, Inc.

  13. Morphological Feature Extraction for Automatic Registration of Multispectral Images

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  15. Diffeomorphic image registration with automatic time-step adjustment

    DEFF Research Database (Denmark)

    Pai, Akshay Sadananda Uppinakudru; Klein, S.; Sommer, Stefan Horst

    2015-01-01

    In this paper, we propose an automated Euler's time-step adjustment scheme for diffeomorphic image registration using stationary velocity fields (SVFs). The proposed variational problem aims at bounding the inverse consistency error by adaptively adjusting the number of Euler's step required to r...... accuracy as a fixed time-step scheme however at a much less computational cost....

  16. An efficient similarity measure technique for medical image registration

    Indian Academy of Sciences (India)

    The similarity measures are tested on the matrix laboratory (MATLAB) plat- form of version 7.0.4. Table 4 shows the comparison of computational time required by various image registration methods. Figures 5a and c of the size 256 × 256 pixels are considered for the calculation of computational time of various similarity ...

  17. Collocation for diffeomorphic deformations in medical image registration

    DEFF Research Database (Denmark)

    Darkner, Sune; Pai, Akshay Sadananda Uppinakudru; Liptrot, Matthew George

    2018-01-01

    Diffeomorphic deformation is a popular choice in medical image registration. A fundamental property of diffeomorphisms is in vertibility, implying that once the relation between two points A to B is found, then the relation B to A is given per definition. Consistency is a measure of a numerical...

  18. Video image stabilization and registration--plus

    Science.gov (United States)

    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.

  19. A multi-institution evaluation of deformable image registration algorithms for automatic organ delineation in adaptive head and neck radiotherapy

    Directory of Open Access Journals (Sweden)

    Hardcastle Nicholas

    2012-06-01

    Full Text Available Abstract Background Adaptive Radiotherapy aims to identify anatomical deviations during a radiotherapy course and modify the treatment plan to maintain treatment objectives. This requires regions of interest (ROIs to be defined using the most recent imaging data. This study investigates the clinical utility of using deformable image registration (DIR to automatically propagate ROIs. Methods Target (GTV and organ-at-risk (OAR ROIs were non-rigidly propagated from a planning CT scan to a per-treatment CT scan for 22 patients. Propagated ROIs were quantitatively compared with expert physician-drawn ROIs on the per-treatment scan using Dice scores and mean slicewise Hausdorff distances, and center of mass distances for GTVs. The propagated ROIs were qualitatively examined by experts and scored based on their clinical utility. Results Good agreement between the DIR-propagated ROIs and expert-drawn ROIs was observed based on the metrics used. 94% of all ROIs generated using DIR were scored as being clinically useful, requiring minimal or no edits. However, 27% (12/44 of the GTVs required major edits. Conclusion DIR was successfully used on 22 patients to propagate target and OAR structures for ART with good anatomical agreement for OARs. It is recommended that propagated target structures be thoroughly reviewed by the treating physician.

  20. A multi-institution evaluation of deformable image registration algorithms for automatic organ delineation in adaptive head and neck radiotherapy

    International Nuclear Information System (INIS)

    Hardcastle, Nicholas; Kumar, Prashant; Oechsner, Markus; Richter, Anne; Song, Shiyu; Myers, Michael; Polat, Bülent; Bzdusek, Karl; Tomé, Wolfgang A; Cannon, Donald M; Brouwer, Charlotte L; Wittendorp, Paul WH; Dogan, Nesrin; Guckenberger, Matthias; Allaire, Stéphane; Mallya, Yogish

    2012-01-01

    Adaptive Radiotherapy aims to identify anatomical deviations during a radiotherapy course and modify the treatment plan to maintain treatment objectives. This requires regions of interest (ROIs) to be defined using the most recent imaging data. This study investigates the clinical utility of using deformable image registration (DIR) to automatically propagate ROIs. Target (GTV) and organ-at-risk (OAR) ROIs were non-rigidly propagated from a planning CT scan to a per-treatment CT scan for 22 patients. Propagated ROIs were quantitatively compared with expert physician-drawn ROIs on the per-treatment scan using Dice scores and mean slicewise Hausdorff distances, and center of mass distances for GTVs. The propagated ROIs were qualitatively examined by experts and scored based on their clinical utility. Good agreement between the DIR-propagated ROIs and expert-drawn ROIs was observed based on the metrics used. 94% of all ROIs generated using DIR were scored as being clinically useful, requiring minimal or no edits. However, 27% (12/44) of the GTVs required major edits. DIR was successfully used on 22 patients to propagate target and OAR structures for ART with good anatomical agreement for OARs. It is recommended that propagated target structures be thoroughly reviewed by the treating physician

  1. Fast Registration Method for Point Clouds Using the Image Information

    Directory of Open Access Journals (Sweden)

    WANG Ruiyan

    2016-01-01

    Full Text Available On the existing laser scanners, there usually is a coaxial camera, which could capture images in the scanning site. For the laser scanners with a coaxial camera, we propose a fast registration method using the image information. Unlike the traditional registration methods that computing the rotation and translation simultaneously, our method calculates them individually. The rotation transformation between the point clouds is obtained by the knowledge of the vision geometry and the image information, while their translation is acquired by our improved ICP algorithm. In the improved ICP algorithm, only the translation vector is updated iteratively, whose input is the point clouds that removing the rotation transformation. Experimental results show that the rotation matrix obtained by the images has a high accuracy. In addition, compared with the traditional ICP algorithm, our algorithm converges faster and is easier to fall into the global optimum.

  2. Registration of Images with N-fold Dihedral Blur

    Czech Academy of Sciences Publication Activity Database

    Pedone, M.; Flusser, Jan; Heikkila, J.

    2015-01-01

    Roč. 24, č. 3 (2015), s. 1036-1045 ISSN 1057-7149 R&D Projects: GA ČR GA13-29225S; GA ČR GA15-16928S Institutional support: RVO:67985556 Keywords : Image registration * blurred image s * N-fold rotational symmetry * dihedral symmetry * phase correlation Subject RIV: JD - Computer Applications, Robotics Impact factor: 3.735, year: 2015 http://library.utia.cas.cz/separaty/2015/ZOI/flusser-0441247.pdf

  3. An accelerated image matching technique for UAV orthoimage registration

    Science.gov (United States)

    Tsai, Chung-Hsien; Lin, Yu-Ching

    2017-06-01

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

  4. Multi-stage 3D-2D registration for correction of anatomical deformation in image-guided spine surgery

    Science.gov (United States)

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

    2017-06-01

    A multi-stage image-based 3D-2D registration method is presented that maps annotations in a 3D image (e.g. point labels annotating individual vertebrae in preoperative CT) to an intraoperative radiograph in which the patient has undergone non-rigid anatomical deformation due to changes in patient positioning or due to the intervention itself. The proposed method (termed msLevelCheck) extends a previous rigid registration solution (LevelCheck) to provide an accurate mapping of vertebral labels in the presence of spinal deformation. The method employs a multi-stage series of rigid 3D-2D registrations performed on sets of automatically determined and increasingly localized sub-images, with the final stage achieving a rigid mapping for each label to yield a locally rigid yet globally deformable solution. The method was evaluated first in a phantom study in which a CT image of the spine was acquired followed by a series of 7 mobile radiographs with increasing degree of deformation applied. Second, the method was validated using a clinical data set of patients exhibiting strong spinal deformation during thoracolumbar spine surgery. Registration accuracy was assessed using projection distance error (PDE) and failure rate (PDE  >  20 mm—i.e. label registered outside vertebra). The msLevelCheck method was able to register all vertebrae accurately for all cases of deformation in the phantom study, improving the maximum PDE of the rigid method from 22.4 mm to 3.9 mm. The clinical study demonstrated the feasibility of the approach in real patient data by accurately registering all vertebral labels in each case, eliminating all instances of failure encountered in the conventional rigid method. The multi-stage approach demonstrated accurate mapping of vertebral labels in the presence of strong spinal deformation. The msLevelCheck method maintains other advantageous aspects of the original LevelCheck method (e.g. compatibility with standard clinical workflow, large

  5. Prostate MR image segmentation using 3D active appearance models

    NARCIS (Netherlands)

    Maan, Bianca; van der Heijden, Ferdinand

    2012-01-01

    This paper presents a method for automatic segmentation of the prostate from transversal T2-weighted images based on 3D Active Appearance Models (AAM). The algorithm consist of two stages. Firstly, Shape Context based non-rigid surface registration of the manual segmented images is used to obtain

  6. High-performance GPU-based rendering for real-time, rigid 2D/3D-image registration and motion prediction in radiation oncology

    Science.gov (United States)

    Spoerk, Jakob; Gendrin, Christelle; Weber, Christoph; Figl, Michael; Pawiro, Supriyanto Ardjo; Furtado, Hugo; Fabri, Daniella; Bloch, Christoph; Bergmann, Helmar; Gröller, Eduard; Birkfellner, Wolfgang

    2012-01-01

    A common problem in image-guided radiation therapy (IGRT) of lung cancer as well as other malignant diseases is the compensation of periodic and aperiodic motion during dose delivery. Modern systems for image-guided radiation oncology allow for the acquisition of cone-beam computed tomography data in the treatment room as well as the acquisition of planar radiographs during the treatment. A mid-term research goal is the compensation of tumor target volume motion by 2D/3D registration. In 2D/3D registration, spatial information on organ location is derived by an iterative comparison of perspective volume renderings, so-called digitally rendered radiographs (DRR) from computed tomography volume data, and planar reference x-rays. Currently, this rendering process is very time consuming, and real-time registration, which should at least provide data on organ position in less than a second, has not come into existence. We present two GPU-based rendering algorithms which generate a DRR of 512 × 512 pixels size from a CT dataset of 53 MB size at a pace of almost 100 Hz. This rendering rate is feasible by applying a number of algorithmic simplifications which range from alternative volume-driven rendering approaches – namely so-called wobbled splatting – to sub-sampling of the DRR-image by means of specialized raycasting techniques. Furthermore, general purpose graphics processing unit (GPGPU) programming paradigms were consequently utilized. Rendering quality and performance as well as the influence on the quality and performance of the overall registration process were measured and analyzed in detail. The results show that both methods are competitive and pave the way for fast motion compensation by rigid and possibly even non-rigid 2D/3D registration and, beyond that, adaptive filtering of motion models in IGRT. PMID:21782399

  7. Elastic image registration via rigid object motion induced deformation

    Science.gov (United States)

    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.

  8. Distance-Dependent Multimodal Image Registration for Agriculture Tasks.

    Science.gov (United States)

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

    2015-08-21

    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.

  9. A Condition Number for Non-Rigid Shape Matching

    KAUST Repository

    Ovsjanikov, Maks

    2011-08-01

    © 2011 The Author(s). Despite the large amount of work devoted in recent years to the problem of non-rigid shape matching, practical methods that can successfully be used for arbitrary pairs of shapes remain elusive. In this paper, we study the hardness of the problem of shape matching, and introduce the notion of the shape condition number, which captures the intuition that some shapes are inherently more difficult to match against than others. In particular, we make a connection between the symmetry of a given shape and the stability of any method used to match it while optimizing a given distortion measure. We analyze two commonly used classes of methods in deformable shape matching, and show that the stability of both types of techniques can be captured by the appropriate notion of a condition number. We also provide a practical way to estimate the shape condition number and show how it can be used to guide the selection of landmark correspondences between shapes. Thus we shed some light on the reasons why general shape matching remains difficult and provide a way to detect and mitigate such difficulties in practice.

  10. An efficient direct method for image registration of flat objects

    Science.gov (United States)

    Nikolaev, Dmitry; Tihonkih, Dmitrii; Makovetskii, Artyom; Voronin, Sergei

    2017-09-01

    Image alignment of rigid surfaces is a rapidly developing area of research and has many practical applications. Alignment methods can be roughly divided into two types: feature-based methods and direct methods. Known SURF and SIFT algorithms are examples of the feature-based methods. Direct methods refer to those that exploit the pixel intensities without resorting to image features and image-based deformations are general direct method to align images of deformable objects in 3D space. Nevertheless, it is not good for the registration of images of 3D rigid objects since the underlying structure cannot be directly evaluated. In the article, we propose a model that is suitable for image alignment of rigid flat objects under various illumination models. The brightness consistency assumptions used for reconstruction of optimal geometrical transformation. Computer simulation results are provided to illustrate the performance of the proposed algorithm for computing of an accordance between pixels of two images.

  11. Registration and recognition in images and videos

    CERN Document Server

    Battiato, Sebastiano; Farinella, Giovanni

    2014-01-01

    Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout. The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art  research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry, and cover both theoretical and practical aspects of real Computer Vision problems.  The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year.This edited volume contains a selection of articles covering some of the talks and tutorials held during the last editions of the school. The chapters provide an in-depth overview o...

  12. Image registration via optimization over disjoint image regions

    Energy Technology Data Exchange (ETDEWEB)

    Pitts, Todd; Hathaway, Simon; Karelitz, David B.; Sandusky, John; Laine, Mark Richard

    2018-02-06

    Technologies pertaining to registering a target image with a base image are described. In a general embodiment, the base image is selected from a set of images, and the target image is an image in the set of images that is to be registered to the base image. A set of disjoint regions of the target image is selected, and a transform to be applied to the target image is computed based on the optimization of a metric over the selected set of disjoint regions. The transform is applied to the target image so as to register the target image with the base image.

  13. Error analysis of two methods for range-images registration

    Science.gov (United States)

    Liu, Xiaoli; Yin, Yongkai; Li, Ameng; He, Dong; Peng, Xiang

    2010-08-01

    With the improvements in range image registration techniques, this paper focuses on error analysis of two registration methods being generally applied in industry metrology including the algorithm comparison, matching error, computing complexity and different application areas. One method is iterative closest points, by which beautiful matching results with little error can be achieved. However some limitations influence its application in automatic and fast metrology. The other method is based on landmarks. We also present a algorithm for registering multiple range-images with non-coding landmarks, including the landmarks' auto-identification and sub-pixel location, 3D rigid motion, point pattern matching, global iterative optimization techniques et al. The registering results by the two methods are illustrated and a thorough error analysis is performed.

  14. Use of the CT component of PET-CT to improve PET-MR registration: demonstration in soft-tissue sarcoma

    International Nuclear Information System (INIS)

    Somer, Edward J; Benatar, Nigel A; O'Doherty, Michael J; Smith, Mike A; Marsden, Paul K

    2007-01-01

    We have investigated improvements to PET-MR image registration offered by PET-CT scanning. Ten subjects with suspected soft-tissue sarcomas were scanned with an in-line PET-CT and a clinical MR scanner. PET to CT, CT to MR and PET to MR image registrations were performed using a rigid-body external marker technique and rigid and non-rigid voxel-similarity algorithms. PET-MR registration was also performed using transformations derived from the registration of CT to MR. The external marker technique gave fiducial registration errors of 2.1 mm, 5.1 mm and 5.3 mm for PET-CT, PET-MR and CT-MR registration. Target registration errors were 3.9 mm, 9.0 mm and 9.3 mm, respectively. Voxel-based algorithms were evaluated by measuring the distance between corresponding fiducials after registration. Registration errors of 6.4 mm, 14.5 mm and 9.5 mm, respectively, for PET-CT, PET-MR and CT-MR were observed for rigid-body registration while non-rigid registration gave errors of 6.8 mm, 16.3 mm and 7.6 mm for the same modality combinations. The application of rigid and non-rigid CT to MR transformations to accompanying PET data gives significantly reduced PET-MR errors of 10.0 mm and 8.5 mm, respectively. Visual comparison by two independent observers confirmed the improvement over direct PET-MR registration. We conclude that PET-MR registration can be more accurately and reliably achieved using the hybrid technique described than through direct rigid-body registration of PET to MR

  15. CO-REGISTRATION BETWEEN MULTISOURCE REMOTE-SENSING IMAGES

    Directory of Open Access Journals (Sweden)

    J. Wu

    2012-07-01

    Full Text Available Image registration is essential for geospatial information systems analysis, which usually involves integrating multitemporal and multispectral datasets from remote optical and radar sensors. An algorithm that deals with feature extraction, keypoint matching, outlier detection and image warping is experimented in this study. The methods currently available in the literature rely on techniques, such as the scale-invariant feature transform, between-edge cost minimization, normalized cross correlation, leasts-quares image matching, random sample consensus, iterated data snooping and thin-plate splines. Their basics are highlighted and encoded into a computer program. The test images are excerpts from digital files created by the multispectral SPOT-5 and Formosat-2 sensors, and by the panchromatic IKONOS and QuickBird sensors. Suburban areas, housing rooftops, the countryside and hilly plantations are studied. The co-registered images are displayed with block subimages in a criss-cross pattern. Besides the imagery, the registration accuracy is expressed by the root mean square error. Toward the end, this paper also includes a few opinions on issues that are believed to hinder a correct correspondence between diverse images.

  16. Four-dimensional deformable image registration using trajectory modeling

    International Nuclear Information System (INIS)

    Castillo, Edward; Guerrero, Thomas; Castillo, Richard; Martinez, Josue; Shenoy, Maithili

    2010-01-01

    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.

  17. Representing the dosimetric impact of deformable image registration errors

    Science.gov (United States)

    Vickress, Jason; Battista, Jerry; Barnett, Rob; Yartsev, Slav

    2017-09-01

    Deformable image registration (DIR) is emerging as a tool in radiation therapy for calculating the cumulative dose distribution across multiple fractions of treatment. Unfortunately, due to the variable nature of DIR algorithms and dependence of performance on image quality, registration errors can result in dose accumulation errors. In this study, landmarked images were used to characterize the DIR error throughout an image space and determine its impact on dosimetric analysis. Ten thoracic 4DCT images with 300 landmarks per image study matching the end-inspiration and end-expiration phases were obtained from ‘dir-labs’. DIR was performed using commercial software MIM Maestro. The range of dose uncertainty (RDU) was calculated at each landmark pair as the maximum and minimum of the doses within a sphere around the landmark in the end-expiration phase. The radius of the sphere was defined by a measure of DIR error which included either the actual DIR error, mean DIR error per study, constant errors of 2 or 5 mm, inverse consistency error, transitivity error or the distance discordance metric (DDM). The RDUs were evaluated using the magnitude of dose uncertainty (MDU) and inclusion rate (IR) of actual error lying within the predicted RDU. The RDU was calculated for 300 landmark pairs on each 4DCT study for all measures of DIR error. The most representative RDU was determined using the actual DIR error with a MDU of 2.5 Gy and IR of 97%. Across all other measures of DIR error, the DDM was most predictive with a MDU of 2.5 Gy and IR of 86%, closest to the actual DIR error. The proposed method represents the range of dosimetric uncertainty of DIR error using either landmarks at specific voxels or measures of registration accuracy throughout the volume.

  18. Image registration and fusion via picture archiving and communication system

    International Nuclear Information System (INIS)

    Gu Zhaoxiang; Jiang Maosong

    2002-01-01

    Objective: The preliminary studies of the multimodality image registration and fusion were performed using picture archiving and communication system (PACS) and image fusion software to explore the methodology. Methods: The original image volume data were acquired with Siemens Somatom Plus S CT scanner, Magneton Vision 1.5 T MR and E. CAM + dual-head coincidence SPECT, respectively. The data sets from all imaging devices were acquired, retrieved, transferred and accessed via DICOM PACS. The image fusion was performed at SPECT ICON work-station, where the medical image merge (MIM) fusion software was installed. The images were created by re-slicing original volume on the fly. The image volumes were aligned by translation and rotation of these view ports with respect to the original volume orientation. The transparency factor and contrast were adjusted in order that both volumes can be visualized in the merged images. Results: The image volume data of CT, MR and nuclear medicine were transferred, accessed and loaded via PACS successfully. The perfectly fused images of brain MR/SPECT and chest CT/ 18 F-FDG were obtained. Conclusions: The results showed that the image fusion technique using PACS was feasible and practical. Further experimentation and larger validation studies are needed to explore the full potential of the clinical use

  19. A one-bit approach for image registration

    Science.gov (United States)

    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.

  20. Learning-based deformable image registration for infant MR images in the first year of life.

    Science.gov (United States)

    Hu, Shunbo; Wei, Lifang; Gao, Yaozong; Guo, Yanrong; Wu, Guorong; Shen, Dinggang

    2017-01-01

    Many brain development studies have been devoted to investigate dynamic structural and functional changes in the first year of life. To quantitatively measure brain development in such a dynamic period, accurate image registration for different infant subjects with possible large age gap is of high demand. Although many state-of-the-art image registration methods have been proposed for young and elderly brain images, very few registration methods work for infant brain images acquired in the first year of life, because of (a) large anatomical changes due to fast brain development and (b) dynamic appearance changes due to white-matter myelination. To address these two difficulties, we propose a learning-based registration method to not only align the anatomical structures but also alleviate the appearance differences between two arbitrary infant MR images (with large age gap) by leveraging the regression forest to predict both the initial displacement vector and appearance changes. Specifically, in the training stage, two regression models are trained separately, with (a) one model learning the relationship between local image appearance (of one development phase) and its displacement toward the template (of another development phase) and (b) another model learning the local appearance changes between the two brain development phases. Then, in the testing stage, to register a new infant image to the template, we first predict both its voxel-wise displacement and appearance changes by the two learned regression models. Since such initializations can alleviate significant appearance and shape differences between new infant image and the template, it is easy to just use a conventional registration method to refine the remaining registration. We apply our proposed registration method to align 24 infant subjects at five different time points (i.e., 2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old), and achieve more accurate and robust registration

  1. Polyaffine parametrization of image registration based on geodesic flows

    DEFF Research Database (Denmark)

    Hansen, Michael Sass; Thorup, Signe Strann; Warfield, Simon K.

    2012-01-01

    Image registration based on geodesic flows has gained much popularity in recent years. We describe a novel parametrization of the velocity field in a stationary flow equation. We show that the method offers both precision, flexibility, and simplicity of evaluation. With our representation, which ...... of geodesic shooting for computational anatomy. We avoid to do warp field convolution by interpolation in a dense field, we can easily calculate warp derivatives in a reference frame of choice, and we can consequently avoid interpolation in the image space altogether....

  2. Image registration: An essential part of radiation therapy treatment planning

    International Nuclear Information System (INIS)

    Rosenman, Julian G.; Miller, Elizabeth P.; Tracton, Gregg; Cullip, Tim J.

    1998-01-01

    Purpose: We believe that a three-dimensional (3D) registration of nonplanning (diagnostic) imaging data with the planning computed tomography (CT) offers a substantial improvement in tumor target identification for many radiation therapy patients. The purpose of this article is to review and discuss our experience to date. Methods and Materials: We reviewed the charts and treatment planning records of all patients that underwent 3D radiation treatment planning in our department from June 1994 to December 1995, to learn which patients had image registration performed and why it was thought they would benefit from this approach. We also measured how much error would have been introduced into the target definition if the nonplanning imaging data had not been available and only the planning CT had been used. Results: Between June 1994 and December 1995, 106 of 246 (43%) of patients undergoing 3D treatment planning had image registration. Four reasons for performing registration were identified. First, some tumor volumes have better definition on magnetic resonance imaging (MRI) than on CT. Second, a properly contrasted diagnostic CT sometimes can show the tumor target better than can the planning CT. Third, the diagnostic CT or MR may have been preoperative, with the postoperative planning CT no longer showing the tumor. Fourth, the patient may have undergone cytoreductive chemotherapy so that the postchemotherapy planning CT no longer showed the original tumor volume. In patients in whom the planning CT did not show the tumor volume well an analysis was done to determine how the treatment plan was changed with the addition of a better tumor-defining nonplanning CT or MR. We have found that the use of this additional imaging modality changed the tumor location in the treatment plan at least 1.5 cm for half of the patients, and up to 3.0 cm for ((1)/(4)) of the patients. Conclusions: Multimodality and/or sequential imaging can substantially aid in better tumor

  3. Image registration for digital radiographic images and its applications to industrial NDT inspection

    International Nuclear Information System (INIS)

    Gao, Jianxin; Penney, G.; Wright, M.

    2006-01-01

    Radiographic images taken under different conditions of the engineering structures in service need to be aligned properly prior to performing effectively any of the automatic defect detection. To this end, image registration techniques based on intensity correlation were employed in this paper in a coarse-to-fine multilevel manner to speed up the timeconsuming calculation process. The original image with high spatial resolution was downscaled to several images, each with the resolution half of the previous level. The registration for the coarsest image pair resulted in the detection of predominant part of the displacement field associated with the image pairs, while the registration for the finest image pairs generated delicate corrections for the previously calculated displacement field. This image registration procedure was streamlined into a whole framework for digital radiographic processing for industrial NDT inspection. The aligned image sequences can be used subsequently for 3D defect assessment. Preliminary applications showed that the implementation of image registration improved considerably the effectiveness of the automatic defect detection. (orig.)

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

    International Nuclear Information System (INIS)

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

    2010-01-01

    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

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

    Science.gov (United States)

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

    2010-09-01

    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). 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 SWMI registration with

  6. Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms.

    Science.gov (United States)

    Ger, Rachel B; Yang, Jinzhong; Ding, Yao; Jacobsen, Megan C; Fuller, Clifton D; Howell, Rebecca M; Li, Heng; Jason Stafford, R; Zhou, Shouhao; Court, Laurence E

    2017-10-01

    Accurate deformable image registration is necessary for longitudinal studies. The error associated with commercial systems has been evaluated using computed tomography (CT). Several in-house algorithms have been evaluated for use with magnetic resonance imaging (MRI), but there is still relatively little information about MRI deformable image registration. This work presents an evaluation of two deformable image registration systems, one commercial (Velocity) and one in-house (demons-based algorithm), with MRI using two different metrics to quantify the registration error. The registration error was analyzed with synthetic MR images. These images were generated from interpatient and intrapatient variation models trained on 28 patients. Four synthetic post-treatment images were generated for each of four synthetic pretreatment images, resulting in 16 image registrations for both the T1- and T2-weighted images. The synthetic post-treatment images were registered to their corresponding synthetic pretreatment image. The registration error was calculated between the known deformation vector field and the generated deformation vector field from the image registration system. The registration error was also analyzed using a porcine phantom with ten implanted 0.35-mm diameter gold markers. The markers were visible on CT but not MRI. CT, T1-weighted MR, and T2-weighted MR images were taken in four different positions. The markers were contoured on the CT images and rigidly registered to their corresponding MR images. The MR images were deformably registered and the distance between the projected marker location and true marker location was measured as the registration error. The synthetic images were evaluated only on Velocity. Root mean square errors (RMSEs) of 0.76 mm in the left-right (LR) direction, 0.76 mm in the anteroposterior (AP) direction, and 0.69 mm in the superior-inferior (SI) direction were observed for the T1-weighted MR images. RMSEs of 1.1 mm in the LR

  7. Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data

    Directory of Open Access Journals (Sweden)

    Xiangyu Zhuo

    2017-04-01

    Full Text Available Recent years have witnessed the fast development of UAVs (unmanned aerial vehicles. As an alternative to traditional image acquisition methods, UAVs bridge the gap between terrestrial and airborne photogrammetry and enable flexible acquisition of high resolution images. However, the georeferencing accuracy of UAVs is still limited by the low-performance on-board GNSS and INS. This paper investigates automatic geo-registration of an individual UAV image or UAV image blocks by matching the UAV image(s with a previously taken georeferenced image, such as an individual aerial or satellite image with a height map attached or an aerial orthophoto with a DSM (digital surface model attached. As the biggest challenge for matching UAV and aerial images is in the large differences in scale and rotation, we propose a novel feature matching method for nadir or slightly tilted images. The method is comprised of a dense feature detection scheme, a one-to-many matching strategy and a global geometric verification scheme. The proposed method is able to find thousands of valid matches in cases where SIFT and ASIFT fail. Those matches can be used to geo-register the whole UAV image block towards the reference image data. When the reference images offer high georeferencing accuracy, the UAV images can also be geolocalized in a global coordinate system. A series of experiments involving different scenarios was conducted to validate the proposed method. The results demonstrate that our approach achieves not only decimeter-level registration accuracy, but also comparable global accuracy as the reference images.

  8. 3D ultrasound-CT registration of the liver using combined landmark-intensity information

    International Nuclear Information System (INIS)

    Lange, Thomas; Schlag, Peter M.; Papenberg, Nils; Heldmann, Stefan; Modersitzki, Jan; Fischer, Bernd; Lamecker, Hans

    2009-01-01

    An important issue in computer-assisted surgery of the liver is a fast and reliable transfer of preoperative resection plans to the intraoperative situation. One problem is to match the planning data, derived from preoperative CT or MR images, with 3D ultrasound images of the liver, acquired during surgery. As the liver deforms significantly in the intraoperative situation non-rigid registration is necessary. This is a particularly challenging task because pre- and intraoperative image data stem from different modalities and ultrasound images are generally very noisy. One way to overcome these problems is to incorporate prior knowledge into the registration process. We propose a method of combining anatomical landmark information with a fast non-parametric intensity registration approach. Mathematically, this leads to a constrained optimization problem. As distance measure we use the normalized gradient field which allows for multimodal image registration. A qualitative and quantitative validation on clinical liver data sets of three different patients has been performed. We used the distance of dense corresponding points on vessel center lines for quantitative validation. The combined landmark and intensity approach improves the mean and percentage of point distances above 3 mm compared to rigid and thin-plate spline registration based only on landmarks. The proposed algorithm offers the possibility to incorporate additional a priori knowledge - in terms of few landmarks - provided by a human expert into a non-rigid registration process. (orig.)

  9. An Advanced Rotation Invariant Descriptor for SAR Image Registration

    Directory of Open Access Journals (Sweden)

    Yuming Xiang

    2017-07-01

    Full Text Available The Scale-Invariant Feature Transform (SIFT algorithm and its many variants have been widely used in Synthetic Aperture Radar (SAR image registration. The SIFT-like algorithms maintain rotation invariance by assigning a dominant orientation for each keypoint, while the calculation of dominant orientation is not robust due to the effect of speckle noise in SAR imagery. In this paper, we propose an advanced local descriptor for SAR image registration to achieve rotation invariance without assigning a dominant orientation. Based on the improved intensity orders, we first divide a circular neighborhood into several sub-regions. Second, rotation-invariant ratio orientation histograms of each sub-region are proposed by accumulating the ratio values of different directions in a rotation-invariant coordinate system. The proposed descriptor is composed of the concatenation of the histograms of each sub-region. In order to increase the distinctiveness of the proposed descriptor, multiple image neighborhoods are aggregated. Experimental results on several satellite SAR images have shown an improvement in the matching performance over other state-of-the-art algorithms.

  10. Deformable Image Registration based on Similarity-Steered CNN Regression

    Science.gov (United States)

    Cao, Xiaohuan; Yang, Jianhua; Zhang, Jun; Nie, Dong; Kim, Min-jeong; Wang, Qian

    2017-01-01

    Existing deformable registration methods require exhaustively iterative optimization, along with careful parameter tuning, to estimate the deformation field between images. Although some learning-based methods have been proposed for initiating deformation estimation, they are often template-specific and not flexible in practical use. In this paper, we propose a convolutional neural network (CNN) based regression model to directly learn the complex mapping from the input image pair (i.e., a pair of template and subject) to their corresponding deformation field. Specifically, our CNN architecture is designed in a patch-based manner to learn the complex mapping from the input patch pairs to their respective deformation field. First, the equalized active-points guided sampling strategy is introduced to facilitate accurate CNN model learning upon a limited image dataset. Then, the similarity-steered CNN architecture is designed, where we propose to add the auxiliary contextual cue, i.e., the similarity between input patches, to more directly guide the learning process. Experiments on different brain image datasets demonstrate promising registration performance based on our CNN model. Furthermore, it is found that the trained CNN model from one dataset can be successfully transferred to another dataset, although brain appearances across datasets are quite variable. PMID:29250613

  11. LINE-BASED REGISTRATION OF DSM AND HYPERSPECTRAL IMAGES

    Directory of Open Access Journals (Sweden)

    J. Avbelj

    2013-04-01

    Full Text Available Data fusion techniques require a good registration of all the used datasets. In remote sensing, images are usually geo-referenced using the GPS and IMU data. However, if more precise registration is required, image processing techniques can be employed. We propose a method for multi-modal image coregistration between hyperspectral images (HSI and digital surface models (DSM. The method is divided in three parts: object and line detection of the same object in HSI and DSM, line matching and determination of transformation parameters. Homogeneous coordinates are used to implement matching and adjustment of transformation parameters. The common object in HSI and DSM are building boundaries. They have apparent change in height and material, that can be detected in DSM and HSI, respectively. Thus, before the matching and transformation parameter computation, building outlines are detected and adjusted in HSI and DSM. We test the method on a HSI and two DSM, using extracted building outbounds and for comparison also extracted lines with a line detector. The results show that estimated building boundaries provide more line assignments, than using line detector.

  12. NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data.

    Science.gov (United States)

    Pnevmatikakis, Eftychios A; Giovannucci, Andrea

    2017-11-01

    Motion correction is a challenging pre-processing problem that arises early in the analysis pipeline of calcium imaging data sequences. The motion artifacts in two-photon microscopy recordings can be non-rigid, arising from the finite time of raster scanning and non-uniform deformations of the brain medium. We introduce an algorithm for fast Non-Rigid Motion Correction (NoRMCorre) based on template matching. NoRMCorre operates by splitting the field of view (FOV) into overlapping spatial patches along all directions. The patches are registered at a sub-pixel resolution for rigid translation against a regularly updated template. The estimated alignments are subsequently up-sampled to create a smooth motion field for each frame that can efficiently approximate non-rigid artifacts in a piecewise-rigid manner. Existing approaches either do not scale well in terms of computational performance or are targeted to non-rigid artifacts arising just from the finite speed of raster scanning, and thus cannot correct for non-rigid motion observable in datasets from a large FOV. NoRMCorre can be run in an online mode resulting in comparable to or even faster than real time motion registration of streaming data. We evaluate its performance with simple yet intuitive metrics and compare against other non-rigid registration methods on simulated data and in vivo two-photon calcium imaging datasets. Open source Matlab and Python code is also made available. The proposed method and accompanying code can be useful for solving large scale image registration problems in calcium imaging, especially in the presence of non-rigid deformations. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

  13. Distance-Dependent Multimodal Image Registration for Agriculture Tasks

    Directory of Open Access Journals (Sweden)

    Ron Berenstein

    2015-08-01

    Full Text Available 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.

  14. Preparing diagnostic 3D images for image registration with planning CT images

    International Nuclear Information System (INIS)

    Tracton, Gregg S.; Miller, Elizabeth P.; Rosenman, Julian; Chang, Sha X.; Sailer, Scott; Boxwala, Azaz; Chaney, Edward L.

    1997-01-01

    Purpose: Pre-radiotherapy (pre-RT) tomographic images acquired for diagnostic purposes often contain important tumor and/or normal tissue information which is poorly defined or absent in planning CT images. Our two years of clinical experience has shown that computer-assisted 3D registration of pre-RT images with planning CT images often plays an indispensable role in accurate treatment volume definition. Often the only available format of the diagnostic images is film from which the original 3D digital data must be reconstructed. In addition, any digital data, whether reconstructed or not, must be put into a form suitable for incorporation into the treatment planning system. The purpose of this investigation was to identify all problems that must be overcome before this data is suitable for clinical use. Materials and Methods: In the past two years we have 3D-reconstructed 300 diagnostic images from film and digital sources. As a problem was discovered we built a software tool to correct it. In time we collected a large set of such tools and found that they must be applied in a specific order to achieve the correct reconstruction. Finally, a toolkit (ediScan) was built that made all these tools available in the proper manner via a pleasant yet efficient mouse-based user interface. Results: Problems we discovered included different magnifications, shifted display centers, non-parallel image planes, image planes not perpendicular to the long axis of the table-top (shearing), irregularly spaced scans, non contiguous scan volumes, multiple slices per film, different orientations for slice axes (e.g. left-right reversal), slices printed at window settings corresponding to tissues of interest for diagnostic purposes, and printing artifacts. We have learned that the specific steps to correct these problems, in order of application, are: Also, we found that fast feedback and large image capacity (at least 2000 x 2000 12-bit pixels) are essential for practical application

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

  17. Multiresolution image registration for multimodal brain images and fusion for better neurosurgical planning

    Directory of Open Access Journals (Sweden)

    Siddeshappa Nandish

    2017-12-01

    Conclusion: The end resultant fused images are validated by the radiologists and mutual information measure is used to validate registration results. It is found that CT and MRI sequence with more number of slices gave promising results. Few cases with deformation during misregistrations recorded with low mutual information of about 0.3 and which is not acceptable and few recorded with 0.6 and above mutual information during registration gives promising results.

  18. Conoscopic holography for image registration: a feasibility study

    Science.gov (United States)

    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.

  19. A new robust markerless method for automatic image-to-patient registration in image-guided neurosurgery system.

    Science.gov (United States)

    Liu, Yinlong; Song, Zhijian; Wang, Manning

    2017-12-01

    Compared with the traditional point-based registration in the image-guided neurosurgery system, surface-based registration is preferable because it does not use fiducial markers before image scanning and does not require image acquisition dedicated for navigation purposes. However, most existing surface-based registration methods must include a manual step for coarse registration, which increases the registration time and elicits some inconvenience and uncertainty. A new automatic surface-based registration method is proposed, which applies 3D surface feature description and matching algorithm to obtain point correspondences for coarse registration and uses the iterative closest point (ICP) algorithm in the last step to obtain an image-to-patient registration. Both phantom and clinical data were used to execute automatic registrations and target registration error (TRE) calculated to verify the practicality and robustness of the proposed method. In phantom experiments, the registration accuracy was stable across different downsampling resolutions (18-26 mm) and different support radii (2-6 mm). In clinical experiments, the mean TREs of two patients by registering full head surfaces were 1.30 mm and 1.85 mm. This study introduced a new robust automatic surface-based registration method based on 3D feature matching. The method achieved sufficient registration accuracy with different real-world surface regions in phantom and clinical experiments.

  20. Multi-atlas segmentation with particle-based group-wise image registration.

    Science.gov (United States)

    Lee, Joohwi; Lyu, Ilwoo; Styner, Martin

    2014-03-21

    We propose a novel multi-atlas segmentation method that employs a group-wise image registration method for the brain segmentation on rodent magnetic resonance (MR) images. The core element of the proposed segmentation is the use of a particle-guided image registration method that extends the concept of particle correspondence into the volumetric image domain. The registration method performs a group-wise image registration that simultaneously registers a set of images toward the space defined by the average of particles. The particle-guided image registration method is robust with low signal-to-noise ratio images as well as differing sizes and shapes observed in the developing rodent brain. Also, the use of an implicit common reference frame can prevent potential bias induced by the use of a single template in the segmentation process. We show that the use of a particle guided-image registration method can be naturally extended to a novel multi-atlas segmentation method and improves the registration method to explicitly use the provided template labels as an additional constraint. In the experiment, we show that our segmentation algorithm provides more accuracy with multi-atlas label fusion and stability against pair-wise image registration. The comparison with previous group-wise registration method is provided as well.

  1. Rolled fingerprint construction using MRF-based nonrigid image registration.

    Science.gov (United States)

    Kwon, Dongjin; Yun, Il Dong; Lee, Sang Uk

    2010-12-01

    This paper proposes a new rolled fingerprint construction approach incorporating a state-of-the-art nonrigid image registration method based upon a Markov random field (MRF) energy model. The proposed method finds dense correspondences between images from a rolled fingerprint sequence and warps the entire fingerprint area to synthesize a rolled fingerprint. This method can generate conceptually more accurate rolled fingerprints by preserving the geometric properties of the finger surface as opposed to ink-based rolled impressions and other existing rolled fingerprint construction methods. To verify the accuracy of the proposed method, various comparative experiments were designed to reveal differences among the rolled construction methods. The results show that the proposed method is significantly superior in various aspects compared to previous approaches.

  2. Image Registration and Optimization in the Virtual Slaughterhouse

    DEFF Research Database (Denmark)

    Vester-Christensen, Martin

    This thesis presents the development and application of algorithms for the analysis of pig carcasses. Focus is on the simulation and quality estimation of meat products produced in a Danish slaughterhouse. Computed Tomography scans of pig carcasses provide the data used in the application. Image...... analysis is applied in order to imitate some of the cutting processes found in a slaughterhouse but also to give a quantitative measure of the composition of each carcass. The basis of the algorithms is non-linear image registration. This method finds the anatomical correspondence between a reference......-invasively estimating the quality of the slaughtering products provides a very valuable tool for use in the slaughterhouse in the future....

  3. OPTICAL-TO-SAR IMAGE REGISTRATION BASED ON GAUSSIAN MIXTURE MODEL

    Directory of Open Access Journals (Sweden)

    H. Wang

    2012-07-01

    Full Text Available Image registration is a fundamental in remote sensing applications such as inter-calibration and image fusion. Compared to other multi sensor image registration problems such as optical-to-IR, the registration for SAR and optical images has its specials. Firstly, the radiometric and geometric characteristics are different between SAR and optical images. Secondly, the feature extraction methods are heavily suffered with the speckle in SAR images. Thirdly, the structural information is more useful than the point features such as corners. In this work, we proposed a novel Gaussian Mixture Model (GMM based Optical-to-SAR image registration algorithm. The feature of line support region (LSR is used to describe the structural information and the orientation attributes are added into the GMM to avoid Expectation Maximization (EM algorithm falling into local extremum in feature sets matching phase. Through the experiments it proves that our algorithm is very robust for optical-to- SAR image registration problem.

  4. Image/patient registration from (partial) projection data by the Fourier phase matching method

    International Nuclear Information System (INIS)

    Weiguo Lu; You, J.

    1999-01-01

    A technique for 2D or 3D image/patient registration, PFPM (projection based Fourier phase matching method), is proposed. This technique provides image/patient registration directly from sequential tomographic projection data. The method can also deal with image files by generating 2D Radon transforms slice by slice. The registration in projection space is done by calculating a Fourier invariant (FI) descriptor for each one-dimensional projection datum, and then registering the FI descriptor by the Fourier phase matching (FPM) method. The algorithm has been tested on both synthetic and experimental data. When dealing with translated, rotated and uniformly scaled 2D image registration, the performance of the PFPM method is comparable to that of the IFPM (image based Fourier phase matching) method in robustness, efficiency, insensitivity to the offset between images, and registration time. The advantages of the former are that subpixel resolution is feasible, and it is more insensitive to image noise due to the averaging effect of the projection acquisition. Furthermore, the PFPM method offers the ability to generalize to 3D image/patient registration and to register partial projection data. By applying patient registration directly from tomographic projection data, image reconstruction is not needed in the therapy set-up verification, thus reducing computational time and artefacts. In addition, real time registration is feasible. Registration from partial projection data meets the geometry and dose requirements in many application cases and makes dynamic set-up verification possible in tomotherapy. (author)

  5. Moving object detection by using GPS assisted image registration method

    Directory of Open Access Journals (Sweden)

    Barış Gökçe

    2016-10-01

    Full Text Available In this study, a Global Positioning System (GPS assistance based system has been developed for unmanned ground vehicles (UGV to detect moving objects along their route using a computer vision system. Before the real time application of the UGV, image models were created that represents a default background in specified horizontal positions of the specified coordinates on the route. This model is a type of feature matrix which is much smaller than the pure image matrices. The model matrices were recorded in the system database and a database relation was created between the model and its coordinate. The feature matrices of the images captured when the moment UGV arrived to the determined coordinates are compared with the models belong to present coordinate. As a result of the evaluation image frames are aligned with 2D image registration methods. The silhouettes of the objects are obtained by subtracting aligned frames. Thus, using this developed approach, there is no need for costly solutions to compensate for the noise generated by the moving camera. It was observed from the experiments that the system was able to detect the objects with 90% accuracy and it was able to run with 8% CPU loading and 0.057 s processing time per frame.

  6. Quantum Assisted Learning for Registration of MODIS Images

    Science.gov (United States)

    Pelissier, C.; Le Moigne, J.; Fekete, G.; Halem, M.

    2017-12-01

    The advent of the first large scale quantum annealer by D-Wave has led to an increased interest in quantum computing. However, the quantum annealing computer of the D-Wave is limited to either solving Quadratic Unconstrained Binary Optimization problems (QUBOs) or using the ground state sampling of an Ising system that can be produced by the D-Wave. These restrictions make it challenging to find algorithms to accelerate the computation of typical Earth Science applications. A major difficulty is that most applications have continuous real-valued parameters rather than binary. Here we present an exploratory study using the ground state sampling to train artificial neural networks (ANNs) to carry out image registration of MODIS images. The key idea to using the D-Wave to train networks is that the quantum chip behaves thermally like Boltzmann machines (BMs), and BMs are known to be successful at recognizing patterns in images. The ground state sampling of the D-Wave also depends on the dynamics of the adiabatic evolution and is subject to other non-thermal fluctuations, but the statistics are thought to be similar and ANNs tend to be robust under fluctuations. In light of this, the D-Wave ground state sampling is used to define a Boltzmann like generative model and is investigated to register MODIS images. Image intensities of MODIS images are transformed using a Discrete Cosine Transform and used to train a several layers network to learn how to align images to a reference image. The network layers consist of an initial sigmoid layer acting as a binary filter of the input followed by a strict binarization using Bernoulli sampling, and then fed into a Boltzmann machine. The output is then classified using a soft-max layer. Results are presented and discussed.

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

    Science.gov (United States)

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

    2017-09-01

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

  8. RESEARCH OF REGISTRATION APPROACHES OF THERMAL INFRARED IMAGES AND INTENSITY IMAGES OF POINT CLOUD

    Directory of Open Access Journals (Sweden)

    L. Liu

    2017-09-01

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

  9. A vascular image registration method based on network structure and circuit simulation.

    Science.gov (United States)

    Chen, Li; Lian, Yuxi; Guo, Yi; Wang, Yuanyuan; Hatsukami, Thomas S; Pimentel, Kristi; Balu, Niranjan; Yuan, Chun

    2017-05-02

    Image registration is an important research topic in the field of image processing. Applying image registration to vascular image allows multiple images to be strengthened and fused, which has practical value in disease detection, clinical assisted therapy, etc. However, it is hard to register vascular structures with high noise and large difference in an efficient and effective method. Different from common image registration methods based on area or features, which were sensitive to distortion and uncertainty in vascular structure, we proposed a novel registration method based on network structure and circuit simulation. Vessel images were transformed to graph networks and segmented to branches to reduce the calculation complexity. Weighted graph networks were then converted to circuits, in which node voltages of the circuit reflecting the vessel structures were used for node registration. The experiments in the two-dimensional and three-dimensional simulation and clinical image sets showed the success of our proposed method in registration. The proposed vascular image registration method based on network structure and circuit simulation is stable, fault tolerant and efficient, which is a useful complement to the current mainstream image registration methods.

  10. Registration accuracy in the integration of laser-scanned dental images into maxillofacial cone-beam computed tomography images.

    Science.gov (United States)

    Noh, Hoon; Nabha, Wael; Cho, Jin-Hyoung; Hwang, Hyeon-Shik

    2011-10-01

    A precision 3-dimensional (3D) head model can be fabricated by integrating a digital dental model into a maxillofacial 3D image. The integration requires accurate registration of 2 image modalities. The aims of this study were to determine the registration errors for implementation of laser-scanned dental images into cone-beam computed tomography (CBCT) scan data and to examine the influence of the registration area on the accuracy of registration. The CBCT scans were obtained from 30 adults, and the maxillofacial 3D images were reconstructed. Maxillary and mandibular dental casts were taken from the same subjects and scanned with a 3D laser scanner. The laser-scanned maxillary and mandibular dentition images were incorporated into the CBCT images of each arch in 3 ways according to the registration area: only the buccal surfaces, only the lingual surfaces, and both the buccal and lingual surfaces. Surface-based registration was performed by using an iterative closest point algorithm, and its errors were evaluated by measuring the 3D Euclidean distances between the surface points on the 2 images. The registration errors ranged from 0.27 to 0.33 mm. The mandibular arch did not show significant differences in registration errors according to the selected area for the registration. The maxillary arch, however, showed significant differences according to the registration area. When the lingual surfaces only were used for registration, the errors were greater than for the other 2 methods. The errors were least when both the buccal and lingual surfaces were used for registration. The results of this study indicate that accuracy in the integration of laser-scanned dental images into the maxillofacial CBCT images increases when a broad area is used for registration. Copyright © 2011 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

  11. THE IMAGE REGISTRATION OF FOURIER-MELLIN BASED ON THE COMBINATION OF PROJECTION AND GRADIENT PREPROCESSING

    Directory of Open Access Journals (Sweden)

    D. Gao

    2017-09-01

    Full Text Available Image registration is one of the most important applications in the field of image processing. The method of Fourier Merlin transform, which has the advantages of high precision and good robustness to change in light and shade, partial blocking, noise influence and so on, is widely used. However, not only this method can’t obtain the unique mutual power pulse function for non-parallel image pairs, even part of image pairs also can’t get the mutual power function pulse. In this paper, an image registration method based on Fourier-Mellin transformation in the view of projection-gradient preprocessing is proposed. According to the projection conformational equation, the method calculates the matrix of image projection transformation to correct the tilt image; then, gradient preprocessing and Fourier-Mellin transformation are performed on the corrected image to obtain the registration parameters. Eventually, the experiment results show that the method makes the image registration of Fourier-Mellin transformation not only applicable to the registration of the parallel image pairs, but also to the registration of non-parallel image pairs. What’s more, the better registration effect can be obtained

  12. The Image Registration of Fourier-Mellin Based on the Combination of Projection and Gradient Preprocessing

    Science.gov (United States)

    Gao, D.; Zhao, X.; Pan, X.

    2017-09-01

    Image registration is one of the most important applications in the field of image processing. The method of Fourier Merlin transform, which has the advantages of high precision and good robustness to change in light and shade, partial blocking, noise influence and so on, is widely used. However, not only this method can't obtain the unique mutual power pulse function for non-parallel image pairs, even part of image pairs also can't get the mutual power function pulse. In this paper, an image registration method based on Fourier-Mellin transformation in the view of projection-gradient preprocessing is proposed. According to the projection conformational equation, the method calculates the matrix of image projection transformation to correct the tilt image; then, gradient preprocessing and Fourier-Mellin transformation are performed on the corrected image to obtain the registration parameters. Eventually, the experiment results show that the method makes the image registration of Fourier-Mellin transformation not only applicable to the registration of the parallel image pairs, but also to the registration of non-parallel image pairs. What's more, the better registration effect can be obtained

  13. Validation of experts versus atlas-based and automatic registration methods for subthalamic nucleus targeting on MRI

    Energy Technology Data Exchange (ETDEWEB)

    Sanchez Castro, F.J.; Cuisenaire, O.; Thiran, J.P. [Ecole Polytechnique Federale de Lausanne (EPFL) (Switzerland). Signal Processing Inst.; Pollo, C. [Ecole Polytechnique Federale de Lausanne (EPFL) (Switzerland). Signal Processing Inst.; Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne (Switzerland). Dept. of Neurosurgery; Villemure, J.G. [Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne (Switzerland). Dept. of Neurosurgery

    2006-03-15

    Objects: In functional stereotactic neurosurgery, one of the cornerstones upon which the success and the operating time depends is an accurate targeting. The subthalamic nucleus (STN) is the usual target involved when applying deep brain stimulation for Parkinson's disease (PD). Unfortunately, STN is usually not clearly visible in common medical imaging modalities, which justifies the use of atlas-based segmentation techniques to infer the STN location. Materials and methods: Eight bilaterally implanted PD patients were included in this study. A three-dimensional T1-weighted sequence and inversion recovery T2-weighted coronal slices were acquired pre-operatively. We propose a methodology for the construction of a ground truth of the STN location and a scheme that allows both, to perform a comparison between different non-rigid registration algorithms and to evaluate their usability to locate the STN automatically. Results: The intra-expert variability in identifying the STN location is 1.06{+-}0.61 mm while the best non-rigid registration method gives an error of 1.80{+-}0.62 mm. On the other hand, statistical tests show that an affine registration with only 12 degrees of freedom is not enough for this application. Conclusions: Using our validation-evaluation scheme, we demonstrate that automatic STN localization is possible and accurate with non-rigid registration algorithms. (orig.)

  14. Quality Assurance of Serial 3D Image Registration, Fusion, and Segmentation

    International Nuclear Information System (INIS)

    Sharpe, Michael; Brock, Kristy K.

    2008-01-01

    Radiotherapy relies on images to plan, guide, and assess treatment. Image registration, fusion, and segmentation are integral to these processes; specifically for aiding anatomic delineation, assessing organ motion, and aligning targets with treatment beams in image-guided radiation therapy (IGRT). Future developments in image registration will also improve estimations of the actual dose delivered and quantitative assessment in patient follow-up exams. This article summarizes common and emerging technologies and reviews the role of image registration, fusion, and segmentation in radiotherapy processes. The current quality assurance practices are summarized, and implications for clinical procedures are discussed

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

    Science.gov (United States)

    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.

  16. A CNN based Hybrid approach towards automatic image registration

    Science.gov (United States)

    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

  17. Efficient nonlinear registration of 3D images using high order co-ordinate transfer functions.

    Science.gov (United States)

    Barber, D C

    1999-01-01

    There is an increasing interest in image registration for a variety of medical imaging applications. Image registration is achieved through the use of a co-ordinate transfer function (CTF) which maps voxels in one image to voxels in the other image, including in the general case changes in mapped voxel intensity. If images of the same subject are to be registered the co-ordinate transfer function needs to implement a spatial transformation consisting of a displacement and a rigid rotation. In order to achieve registration a common approach is to choose a suitable quality-of-registration measure and devise a method for the efficient generation of the parameters of the CTF which minimize this measure. For registration of images from different subjects more complex transforms are required. In general function minimization is too slow to allow the use of CTFs with more than a small number of parameters. However, provided the images are from the same modality and the CTF can be expanded in terms of an appropriate set of basis functions this paper will show how relatively complex CTFs can be used for registration. The use of increasingly complex CTFs to minimize the within group standard deviation of a set of normal single photon emission tomography brain images is used to demonstrate the improved registration of images from different subjects using CTFs of increasing complexity.

  18. A graphical user interface for automatic image registration software designed for radiotherapy treatment planning

    International Nuclear Information System (INIS)

    Rajasekar, David; Datta, Niloy R.; Gupta, Rakesh K.; Rao, Sajja

    2004-01-01

    Medical imaging forms a vital component of radiotherapy treatment planning and its evaluation. The integration of the useful data obtained from multiple imaging modalities for radiotherapy planning is achieved by image registration softwares. In radiotherapy planning systems, normally the computed tomography (CT) slices are kept as a standard upon which other modality images (magnetic resonance imaging [MRI], single photon emission computed tomography [SPECT], positron emission tomography [PET], etc.) are aligned-automatically or interactively. Following validation of successful registration, they are resampled and reformatted, as per the requirements. This paper defines the minimum requirements of automatic image registration software for 3-dimensional (3D) radiotherapy planning and describes the implementation of a suitable graphical user interface developed in Visual Basic (version 5). The automatic image registration (AIR) routines freely available from Dr. Roger P. Woods, UCLA, (USA) were used in this software. This software could be easily implemented and was easy to use for image processing suitable for radiotherapy planning systems

  19. CO-REGISTRATION AIRBORNE LIDAR POINT CLOUD DATA AND SYNCHRONOUS DIGITAL IMAGE REGISTRATION BASED ON COMBINED ADJUSTMENT

    Directory of Open Access Journals (Sweden)

    Z. H. Yang

    2016-06-01

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  1. FPGA Accelerator for Wavelet-Based Automated Global Image Registration

    Directory of Open Access Journals (Sweden)

    Baofeng Li

    2009-01-01

    Full Text Available Wavelet-based automated global image registration (WAGIR is fundamental for most remote sensing image processing algorithms and extremely computation-intensive. With more and more algorithms migrating from ground computing to onboard computing, an efficient dedicated architecture of WAGIR is desired. In this paper, a BWAGIR architecture is proposed based on a block resampling scheme. BWAGIR achieves a significant performance by pipelining computational logics, parallelizing the resampling process and the calculation of correlation coefficient and parallel memory access. A proof-of-concept implementation with 1 BWAGIR processing unit of the architecture performs at least 7.4X faster than the CL cluster system with 1 node, and at least 3.4X than the MPM massively parallel machine with 1 node. Further speedup can be achieved by parallelizing multiple BWAGIR units. The architecture with 5 units achieves a speedup of about 3X against the CL with 16 nodes and a comparative speed with the MPM with 30 nodes. More importantly, the BWAGIR architecture can be deployed onboard economically.

  2. FPGA Accelerator for Wavelet-Based Automated Global Image Registration

    Directory of Open Access Journals (Sweden)

    Li Baofeng

    2009-01-01

    Full Text Available Abstract Wavelet-based automated global image registration (WAGIR is fundamental for most remote sensing image processing algorithms and extremely computation-intensive. With more and more algorithms migrating from ground computing to onboard computing, an efficient dedicated architecture of WAGIR is desired. In this paper, a BWAGIR architecture is proposed based on a block resampling scheme. BWAGIR achieves a significant performance by pipelining computational logics, parallelizing the resampling process and the calculation of correlation coefficient and parallel memory access. A proof-of-concept implementation with 1 BWAGIR processing unit of the architecture performs at least 7.4X faster than the CL cluster system with 1 node, and at least 3.4X than the MPM massively parallel machine with 1 node. Further speedup can be achieved by parallelizing multiple BWAGIR units. The architecture with 5 units achieves a speedup of about 3X against the CL with 16 nodes and a comparative speed with the MPM with 30 nodes. More importantly, the BWAGIR architecture can be deployed onboard economically.

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

    Science.gov (United States)

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

    2016-01-01

    The field of veterinary radiation therapy (RT) has gained substantial momentum in recent decades with significant advances in conformal treatment planning, image-guided radiation therapy (IGRT), and intensity-modulated (IMRT) techniques. At the root of these advancements lie improvements in tumor imaging, image alignment (registration), target volume delineation, and identification of critical structures. Image registration has been widely used to combine information from multimodality images such as computerized tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) to improve the accuracy of radiation delivery and reliably identify tumor-bearing areas. Many different techniques have been applied in image registration. This review provides an overview of medical image registration in RT and its applications in veterinary oncology. A summary of the most commonly used approaches in human and veterinary medicine is presented along with their current use in IGRT and adaptive radiation therapy (ART). It is important to realize that registration does not guarantee that target volumes, such as the gross tumor volume (GTV), are correctly identified on the image being registered, as limitations unique to registration algorithms exist. Research involving novel registration frameworks for automatic segmentation of tumor volumes is ongoing and comparative oncology programs offer a unique opportunity to test the efficacy of proposed algorithms. © 2016 American College of Veterinary Radiology.

  4. Registration of Vibro-acoustography Images and X-ray Mammography.

    Science.gov (United States)

    Gholam Hosseini, H; Fatemi, M; Alizad, A

    2005-01-01

    Image registration has been widely used for generating more diagnostic and clinical values in medical imaging. On the other hand, inaccurate image registration and incorrect localization of region of interest risks a potential impact on patients. Vibro-acoustography (VA) is a new imaging modality that has been applied to both medical and industrial imaging. Combining unique diagnostic information of VA with other medical imaging is one of our research interests. In this work, we studied the VA and x-ray image pairs and adopted a flexible control-point selection technique for image registration. A modified second-order polynomial, which leads to a scale/rotation/translation invariant registration, was used. The results of registration were used to spatially transform the breast VA images to map with the x-ray mammography with a registration error of less than 1.65 mm. These two completely different modalities were combined to generate an image including a ratio of each image pixel value. Therefore, the proposed technique allows clinicians to maximize their insight by combining the information from x-ray mammogram and VA modalities into a single image.

  5. S-HAMMER: hierarchical attribute-guided, symmetric diffeomorphic registration for MR brain images.

    Science.gov (United States)

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

    2014-03-01

    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. Copyright © 2013 Wiley Periodicals, Inc.

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

    Science.gov (United States)

    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.

  7. Registration of eye reflection and scene images using an aspherical eye model.

    Science.gov (United States)

    Nakazawa, Atsushi; Nitschke, Christian; Nishida, Toyoaki

    2016-11-01

    This paper introduces an image registration algorithm between an eye reflection and a scene image. Although there are currently a large number of image registration algorithms, this task remains difficult due to nonlinear distortions at the eye surface and large amounts of noise, such as iris texture, eyelids, eyelashes, and their shadows. To overcome this issue, we developed an image registration method combining an aspherical eye model that simulates nonlinear distortions considering eye geometry and a two-step iterative registration strategy that obtains dense correspondence of the feature points to achieve accurate image registrations for the entire image region. We obtained a database of eye reflection and scene images featuring four subjects in indoor and outdoor scenes and compared the registration performance with different asphericity conditions. Results showed that the proposed approach can perform accurate registration with an average accuracy of 1.05 deg by using the aspherical cornea model. This work is relevant for eye image analysis in general, enabling novel applications and scenarios.

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

    Science.gov (United States)

    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.

  9. Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization.

    Science.gov (United States)

    Abdel-Basset, Mohamed; Fakhry, Ahmed E; El-Henawy, Ibrahim; Qiu, Tie; Sangaiah, Arun Kumar

    2017-11-03

    Image registration is an important aspect in medical image analysis, and kinds use in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from multi-modal like Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame for reference. Registered datasets can be used for providing information relating to the structure, function, and pathology of the organ or individual being imaged. In this paper a hybrid approach for medical images registration has been developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is effected using the versatile Particle Swarm Optimization which is developed easily with adjusted less parameter. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate the proposed model as accurate and effective, and show the posture contribution in inclusion of both statistical and spatial image data to the developed approach.

  10. Frameless image registration of X-ray CT and SPECT by volume matching

    International Nuclear Information System (INIS)

    Tanaka, Yuko; Kihara, Tomohiko; Yui, Nobuharu; Kinoshita, Fujimi; Kamimura, Yoshitsugu; Yamada, Yoshifumi.

    1998-01-01

    Image registration of functional (SPECT) and morphological (X-ray CT/MRI) images is studied in order to improve the accuracy and the quantity of the image diagnosis. We have developed a new frameless registration method of X-ray CT and SPECT image using transmission CT image acquired for absorption correction of SPECT images. This is the automated registration method and calculates the transformation matrix between the two coordinate systems of image data by the optimization method. This registration method is based on the similar physical property of X-ray CT and transmission CT image. The three-dimensional overlap of the bone region is used for image matching. We verified by a phantom test that it can provide a good result of within two millimeters error. We also evaluated visually the accuracy of the registration method by the application study of SPECT, X-ray CT, and transmission CT head images. This method can be carried out accurately without any frames. We expect this registration method becomes an efficient tool to improve image diagnosis and medical treatment. (author)

  11. A coarse-to-fine scheme for groupwise registration of multisensor images

    Directory of Open Access Journals (Sweden)

    Yinghao Li

    2016-11-01

    Full Text Available Ensemble registration is concerned with a group of images that need to be registered simultaneously. It is challenging but important for many image analysis tasks such as vehicle detection and medical image fusion. To solve this problem effectively, a novel coarse-to-fine scheme for groupwise image registration is proposed. First, in the coarse registration step, unregistered images are divided into reference image set and float image set. The images of the two sets are registered based on segmented region matching. The coarse registration results are used as an initial solution for the next step. Then, in the fine registration step, a Gaussian mixture model with a local template is used to model the joint intensity of coarse-registered images. Meanwhile, a minimum message length criterion-based method is employed to determine the unknown number of mixing components. Based on this mixture model, a maximum likelihood framework is used to register a group of images. To evaluate the performance of the proposed approach, some representative groupwise registration approaches are compared on different image data sets. The experimental results show that the proposed approach has improved performance compared to conventional approaches.

  12. A prospective comparison between auto-registration and manual registration of real-time ultrasound with MR images for percutaneous ablation or biopsy of hepatic lesions.

    Science.gov (United States)

    Cha, Dong Ik; Lee, Min Woo; Song, Kyoung Doo; Oh, Young-Taek; Jeong, Ja-Yeon; Chang, Jung-Woo; Ryu, Jiwon; Lee, Kyong Joon; Kim, Jaeil; Bang, Won-Chul; Shin, Dong Kuk; Choi, Sung Jin; Koh, Dalkwon; Seo, Bong Koo; Kim, Kyunga

    2017-06-01

    To compare the accuracy and required time for image fusion of real-time ultrasound (US) with pre-procedural magnetic resonance (MR) images between positioning auto-registration and manual registration for percutaneous radiofrequency ablation or biopsy of hepatic lesions. This prospective study was approved by the institutional review board, and all patients gave written informed consent. Twenty-two patients (male/female, n = 18/n = 4; age, 61.0 ± 7.7 years) who were referred for planning US to assess the feasibility of radiofrequency ablation (n = 21) or biopsy (n = 1) for focal hepatic lesions were included. One experienced radiologist performed the two types of image fusion methods in each patient. The performance of auto-registration and manual registration was evaluated. The accuracy of the two methods, based on measuring registration error, and the time required for image fusion for both methods were recorded using in-house software and respectively compared using the Wilcoxon signed rank test. Image fusion was successful in all patients. The registration error was not significantly different between the two methods (auto-registration: median, 3.75 mm; range, 1.0-15.8 mm vs. manual registration: median, 2.95 mm; range, 1.2-12.5 mm, p = 0.242). The time required for image fusion was significantly shorter with auto-registration than with manual registration (median, 28.5 s; range, 18-47 s, vs. median, 36.5 s; range, 14-105 s, p = 0.026). Positioning auto-registration showed promising results compared with manual registration, with similar accuracy and even shorter registration time.

  13. Mammogram CAD, hybrid registration and iconic analysis

    Science.gov (United States)

    Boucher, A.; Cloppet, F.; Vincent, N.

    2013-03-01

    This paper aims to develop a computer aided diagnosis (CAD) based on a two-step methodology to register and analyze pairs of temporal mammograms. The concept of "medical file", including all the previous medical information on a patient, enables joint analysis of different acquisitions taken at different times, and the detection of significant modifications. The developed registration method aims to superimpose at best the different anatomical structures of the breast. The registration is designed in order to get rid of deformation undergone by the acquisition process while preserving those due to breast changes indicative of malignancy. In order to reach this goal, a referent image is computed from control points based on anatomical features that are extracted automatically. Then the second image of the couple is realigned on the referent image, using a coarse-to-fine approach according to expert knowledge that allows both rigid and non-rigid transforms. The joint analysis detects the evolution between two images representing the same scene. In order to achieve this, it is important to know the registration error limits in order to adapt the observation scale. The approach used in this paper is based on an image sparse representation. Decomposed in regular patterns, the images are analyzed under a new angle. The evolution detection problem has many practical applications, especially in medical images. The CAD is evaluated using recall and precision of differences in mammograms.

  14. Fully automated registration of vibrational microspectroscopic images in histologically stained tissue sections.

    Science.gov (United States)

    Yang, Chen; Niedieker, Daniel; Grosserüschkamp, Frederik; Horn, Melanie; Tannapfel, Andrea; Kallenbach-Thieltges, Angela; Gerwert, Klaus; Mosig, Axel

    2015-11-25

    In recent years, hyperspectral microscopy techniques such as infrared or Raman microscopy have been applied successfully for diagnostic purposes. In many of the corresponding studies, it is common practice to measure one and the same sample under different types of microscopes. Any joint analysis of the two image modalities requires to overlay the images, so that identical positions in the sample are located at the same coordinate in both images. This step, commonly referred to as image registration, has typically been performed manually in the lack of established automated computational registration tools. We propose a corresponding registration algorithm that addresses this registration problem, and demonstrate the robustness of our approach in different constellations of microscopes. First, we deal with subregion registration of Fourier Transform Infrared (FTIR) microscopic images in whole-slide histopathological staining images. Second, we register FTIR imaged cores of tissue microarrays in their histopathologically stained counterparts, and finally perform registration of Coherent anti-Stokes Raman spectroscopic (CARS) images within histopathological staining images. Our validation involves a large variety of samples obtained from colon, bladder, and lung tissue on three different types of microscopes, and demonstrates that our proposed method works fully automated and highly robust in different constellations of microscopes involving diverse types of tissue samples.

  15. Active zone impact on deformation state of non-rigid pavement

    Directory of Open Access Journals (Sweden)

    Mandula Ján

    2014-06-01

    Full Text Available The paper deals with the design of non-rigid pavement, with emphasis on the effect of active zone on its deformation state. The concepts of determination of active zone are described. The results of numerical modelling of pavement laying on elastic subgrade are presented in the paper

  16. Learned Non-Rigid Object Motion is a View-Invariant Cue to Recognizing Novel Objects.

    Science.gov (United States)

    Chuang, Lewis L; Vuong, Quoc C; Bülthoff, Heinrich H

    2012-01-01

    There is evidence that observers use learned object motion to recognize objects. For instance, studies have shown that reversing the learned direction in which a rigid object rotated in depth impaired recognition accuracy. This motion reversal can be achieved by playing animation sequences of moving objects in reverse frame order. In the current study, we used this sequence-reversal manipulation to investigate whether observers encode the motion of dynamic objects in visual memory, and whether such dynamic representations are encoded in a way that is dependent on the viewing conditions. Participants first learned dynamic novel objects, presented as animation sequences. Following learning, they were then tested on their ability to recognize these learned objects when their animation sequence was shown in the same sequence order as during learning or in the reverse sequence order. In Experiment 1, we found that non-rigid motion contributed to recognition performance; that is, sequence-reversal decreased sensitivity across different tasks. In subsequent experiments, we tested the recognition of non-rigidly deforming (Experiment 2) and rigidly rotating (Experiment 3) objects across novel viewpoints. Recognition performance was affected by viewpoint changes for both experiments. Learned non-rigid motion continued to contribute to recognition performance and this benefit was the same across all viewpoint changes. By comparison, learned rigid motion did not contribute to recognition performance. These results suggest that non-rigid motion provides a source of information for recognizing dynamic objects, which is not affected by changes to viewpoint.

  17. Feasibility analysis of high resolution tissue image registration using 3-D synthetic data

    Directory of Open Access Journals (Sweden)

    Yachna Sharma

    2011-01-01

    Full Text Available Background: Registration of high-resolution tissue images is a critical step in the 3D analysis of protein expression. Because the distance between images (~4-5μm thickness of a tissue section is nearly the size of the objects of interest (~10-20μm cancer cell nucleus, a given object is often not present in both of two adjacent images. Without consistent correspondence of objects between images, registration becomes a difficult task. This work assesses the feasibility of current registration techniques for such images. Methods: We generated high resolution synthetic 3-D image data sets emulating the constraints in real data. We applied multiple registration methods to the synthetic image data sets and assessed the registration performance of three techniques (i.e., mutual information (MI, kernel density estimate (KDE method [1], and principal component analysis (PCA at various slice thicknesses (with increments of 1μm in order to quantify the limitations of each method. Results: Our analysis shows that PCA, when combined with the KDE method based on nuclei centers, aligns images corresponding to 5μm thick sections with acceptable accuracy. We also note that registration error increases rapidly with increasing distance between images, and that the choice of feature points which are conserved between slices improves performance. Conclusions: We used simulation to help select appropriate features and methods for image registration by estimating best-case-scenario errors for given data constraints in histological images. The results of this study suggest that much of the difficulty of stained tissue registration can be reduced to the problem of accurately identifying feature points, such as the center of nuclei.

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

    Science.gov (United States)

    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. Parallel Processing and Bio-inspired Computing for Biomedical Image Registration

    Directory of Open Access Journals (Sweden)

    Silviu Ioan Bejinariu

    2014-07-01

    Full Text Available Image Registration (IR is an optimization problem computing optimal parameters of a geometric transform used to overlay one or more source images to a given model by maximizing a similarity measure. In this paper the use of bio-inspired optimization algorithms in image registration is analyzed. Results obtained by means of three different algorithms are compared: Bacterial Foraging Optimization Algorithm (BFOA, Genetic Algorithm (GA and Clonal Selection Algorithm (CSA. Depending on the images type, the registration may be: area based, which is slow but more precise, and features based, which is faster. In this paper a feature based approach based on the Scale Invariant Feature Transform (SIFT is proposed. Finally, results obtained using sequential and parallel implementations on multi-core systems for area based and features based image registration are compared.

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

    Energy Technology Data Exchange (ETDEWEB)

    Kadoya, Noriyuki, E-mail: kadoya.n@rad.med.tohoku.ac.jp [Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai (Japan); Nakajima, Yujiro; Saito, Masahide [Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai (Japan); Miyabe, Yuki [Department of Radiation Oncology and Image-Applied Therapy, Kyoto University Graduate School of Medicine, Kyoto (Japan); Kurooka, Masahiko [Department of Radiation Oncology, Kanagawa Cancer Center, Yokohama (Japan); Kito, Satoshi [Department of Radiotherapy, Tokyo Metropolitan Cancer and Infectious Diseases Center, Komagome Hospital, Tokyo (Japan); Fujita, Yukio [Department of Radiation Oncology, Tokai University School of Medicine, Hachioji (Japan); Sasaki, Motoharu [Department of Radiological Technology, Tokushima University Hospital, Tokushima (Japan); Arai, Kazuhiro [Department of Radiation Physics and Technology, Southern Tohoku Proton Therapy Center, Koriyama (Japan); Tani, Kensuke [Department of Radiation Oncology, St Luke' s International Hospital, Tokyo (Japan); Yagi, Masashi [Department of Carbon Ion Radiotherapy, Osaka University Graduate School of Medicine, Suita (Japan); Wakita, Akihisa [Department of Radiation Oncology, National Cancer Center Hospital, Tokyo (Japan); Tohyama, Naoki [Department of Radiation Oncology, Tokyo Bay Advanced Imaging and Radiation Oncology Clinic Makuhari, Chiba (Japan); Jingu, Keiichi [Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai (Japan)

    2016-10-01

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

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

  2. Historical Image Registration and Land-Use Land-Cover Change Analysis

    Directory of Open Access Journals (Sweden)

    Fang-Ju Jao

    2014-12-01

    Full Text Available Historical aerial images are important to retain past ground surface information. The land-use land-cover change in the past can be identified using historical aerial images. Automatic historical image registration and stitching is essential because the historical image pose information was usually lost. In this study, the Scale Invariant Feature Transform (SIFT algorithm was used for feature extraction. Subsequently, the present study used the automatic affine transformation algorithm for historical image registration, based on SIFT features and control points. This study automatically determined image affine parameters and simultaneously transformed from an image coordinate system to a ground coordinate system. After historical aerial image registration, the land-use land-cover change was analyzed between two different years (1947 and 1975 at the Tseng Wen River estuary. Results show that sandbars and water zones were transformed into a large number of fish ponds between 1947 and 1975.

  3. Overcoming Registration Uncertainty in Image Super-Resolution: Maximize or Marginalize?

    Directory of Open Access Journals (Sweden)

    Andrew Zisserman

    2007-01-01

    Full Text Available In multiple-image super-resolution, a high-resolution image is estimated from a number of lower-resolution images. This usually involves computing the parameters of a generative imaging model (such as geometric and photometric registration, and blur and obtaining a MAP estimate by minimizing a cost function including an appropriate prior. Two alternative approaches are examined. First, both registrations and the super-resolution image are found simultaneously using a joint MAP optimization. Second, we perform Bayesian integration over the unknown image registration parameters, deriving a cost function whose only variables of interest are the pixel values of the super-resolution image. We also introduce a scheme to learn the parameters of the image prior as part of the super-resolution algorithm. We show examples on a number of real sequences including multiple stills, digital video, and DVDs of movies.

  4. Anatomical accuracy of lesion localization. Retrospective interactive rigid image registration between 18F-FDG-PET and X-ray CT

    International Nuclear Information System (INIS)

    Noemayr, A.; Roemer, W.; Kuwert, T.; Hothorn, T.; Pfahlberg, A.; Hornegger, J.; Bautz, W.

    2005-01-01

    The aim of this study was to evaluate the anatomical accuracy and reproducibility of retrospective interactive rigid image registration (RIR) between routinely archived X-ray computer tomography (CT) and positron emission tomography performed with 18 F-deoxyglucose (FDG-PET) in oncological patients. Methods: two observers registered PET and CT data obtained in 37 patients using a commercially available image fusion tool. RIR was performed separately for the thorax and the abdomen using physiological FDG uptake in several organs as a reference. One observer performed the procedure twice (01a and 01b), another person once (02). For 94 malignant lesions, clearly visible in CT and PET, the signed and absolute distances between their representation on PET and CT were measured in X-, Y-, and Z-direction with reference to a coordinate system centered in the CT representation of each lesion (X-, Y-, Z-distances). Results: the mean differences of the signed and absolute distances between 01a, 01b, and 02 did not exceed 3 mm in any dimension. The absolute X-, Y-, and Z-distances ranged between 0.57 ± 0.58 cm for 01a (X-direction) and 1.12 ± 1.28 cm for 02 (Z-direction). When averaging the absolute distances measured by 01a, 01b, and 02, the percentage of lesions misregistered by less than 1.5 cm was 91% for the X-, 88% for the Y-, and 77% for the Z-direction. The larger error of fusion determined for the remaining lesions was caused by non-rigid body transformations due to differences in breathing, arm position, or bowel movements between the two examinations. Mixed effects analysis of the signed and absolute X-, Y-, and Z-distances disclosed a significantly greater misalignment in the thorax than in the abdomen as well as axially than transaxially. Conclusion: the anatomical inaccuracy of RIR can be expected to be <1.5 cm for the majority of neoplastic foci. errors of alignment are bigger in the thorax and in Z-direction, due to non-rigid body transformations caused, e

  5. Nonrigid Image Registration for Head and Neck Cancer Radiotherapy Treatment Planning With PET/CT

    International Nuclear Information System (INIS)

    Ireland, Rob H.; Dyker, Karen E.; Barber, David C.; Wood, Steven M.; Hanney, Michael B.; Tindale, Wendy B.; Woodhouse, Neil; Hoggard, Nigel; Conway, John; Robinson, Martin H.

    2007-01-01

    Purpose: Head and neck radiotherapy planning with positron emission tomography/computed tomography (PET/CT) requires the images to be reliably registered with treatment planning CT. Acquiring PET/CT in treatment position is problematic, and in practice for some patients it may be beneficial to use diagnostic PET/CT for radiotherapy planning. Therefore, the aim of this study was first to quantify the image registration accuracy of PET/CT to radiotherapy CT and, second, to assess whether PET/CT acquired in diagnostic position can be registered to planning CT. Methods and Materials: Positron emission tomography/CT acquired in diagnostic and treatment position for five patients with head and neck cancer was registered to radiotherapy planning CT using both rigid and nonrigid image registration. The root mean squared error for each method was calculated from a set of anatomic landmarks marked by four independent observers. Results: Nonrigid and rigid registration errors for treatment position PET/CT to planning CT were 2.77 ± 0.80 mm and 4.96 ± 2.38 mm, respectively, p = 0.001. Applying the nonrigid registration to diagnostic position PET/CT produced a more accurate match to the planning CT than rigid registration of treatment position PET/CT (3.20 ± 1.22 mm and 4.96 ± 2.38 mm, respectively, p = 0.012). Conclusions: Nonrigid registration provides a more accurate registration of head and neck PET/CT to treatment planning CT than rigid registration. In addition, nonrigid registration of PET/CT acquired with patients in a standardized, diagnostic position can provide images registered to planning CT with greater accuracy than a rigid registration of PET/CT images acquired in treatment position. This may allow greater flexibility in the timing of PET/CT for head and neck cancer patients due to undergo radiotherapy

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

    Science.gov (United States)

    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.

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

    International Nuclear Information System (INIS)

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

    2014-01-01

    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. Preliminary study of lateral cerebral angiography with reverse rotation in the digital image registration and subtraction

    International Nuclear Information System (INIS)

    Shen Zhenglin; Liu Dongyang; Shen Zhenghai; Li Shuping; Zhang Ziyan; Wu Yongjuan; Liu Peijun

    2012-01-01

    Objective: Investigate the value and feasibility of image registration with reverse rotation in lateral cerebral DSA. Methods: (1) Experimental study: the target images were subtracted directly, and subtracted again after reverse rotation. Software of registration and subtraction with reverse rotation edited by the author utilizing Visual Basic. The function of the automatic angle detection by the software were evaluated to see whether it detected the angle of line. The subtraction function of DSA by the software was evaluated. (2) Clinical retrospective study: the untreated mask and target images of 15 patients with motion along vertical axis during lateral cerebral DSA were uploaded to the software. The target images were processed with and without the software to get two sets of images. (3) Evaluation: four experienced radiologists read and compared the two sets of the images,and graded their findings. Results: (1) The automatic detection by the software suggested that the target images should be rotated counterclockwise 1.3°. The subtraction result of the software was satisfactory. (2) In the 15 sets of images, there were only three sets of images deemed optimal after traditional subtraction. After reverse rotation, artifacts were significantly reduced and the image sharper. There were ten cases with significant artifacts after traditional subtraction, and those images were sharper and showed more peripheral vessels after reverse rotation. The traditional subtraction images of two sets could not be interpreted,the reverse rotation registration images reached the diagnostic quality. (3) Subjective evaluation: there were more information and less noise and distortion in the registration images with reverse rotation than in the traditional subtraction. But the image resolution decreased slightly after reverse rotation registration. Conclusion: The registration of digital angiography with reverse rotation can improve the image quality in lateral cerebral DSA

  9. Automated robust registration of grossly misregistered whole-slide images with varying stains

    Science.gov (United States)

    Litjens, G.; Safferling, K.; Grabe, N.

    2016-03-01

    Cancer diagnosis and pharmaceutical research increasingly depend on the accurate quantification of cancer biomarkers. Identification of biomarkers is usually performed through immunohistochemical staining of cancer sections on glass slides. However, combination of multiple biomarkers from a wide variety of immunohistochemically stained slides is a tedious process in traditional histopathology due to the switching of glass slides and re-identification of regions of interest by pathologists. Digital pathology now allows us to apply image registration algorithms to digitized whole-slides to align the differing immunohistochemical stains automatically. However, registration algorithms need to be robust to changes in color due to differing stains and severe changes in tissue content between slides. In this work we developed a robust registration methodology to allow for fast coarse alignment of multiple immunohistochemical stains to the base hematyoxylin and eosin stained image. We applied HSD color model conversion to obtain a less stain color dependent representation of the whole-slide images. Subsequently, optical density thresholding and connected component analysis were used to identify the relevant regions for registration. Template matching using normalized mutual information was applied to provide initial translation and rotation parameters, after which a cost function-driven affine registration was performed. The algorithm was validated using 40 slides from 10 prostate cancer patients, with landmark registration error as a metric. Median landmark registration error was around 180 microns, which indicates performance is adequate for practical application. None of the registrations failed, indicating the robustness of the algorithm.

  10. Generation of synthetic CT data using patient specific daily MR image data and image registration

    Science.gov (United States)

    Melanie Kraus, Kim; Jäkel, Oliver; Niebuhr, Nina I.; Pfaffenberger, Asja

    2017-02-01

    To fully exploit the advantages of magnetic resonance imaging (MRI) for radiotherapy (RT) treatment planning, a method is required to overcome the problem of lacking electron density information. We aim to establish and evaluate a new method for computed tomography (CT) data generation based on MRI and image registration. The thereby generated CT data is used for dose accumulation. We developed a process flow based on an initial pair of rigidly co-registered CT and T2-weighted MR image representing the same anatomical situation. Deformable image registration using anatomical landmarks is performed between the initial MRI data and daily MR images. The resulting transformation is applied to the initial CT, thus fractional CT data is generated. Furthermore, the dose for a photon intensity modulated RT (IMRT) or intensity modulated proton therapy (IMPT) plan is calculated on the generated fractional CT and accumulated on the initial CT via inverse transformation. The method is evaluated by the use of phantom CT and MRI data. Quantitative validation is performed by evaluation of the mean absolute error (MAE) between the measured and the generated CT. The effect on dose accumulation is examined by means of dose-volume parameters. One patient case is presented to demonstrate the applicability of the method introduced here. Overall, CT data derivation lead to MAEs with a median of 37.0 HU ranging from 29.9 to 66.6 HU for all investigated tissues. The accuracy of image registration showed to be limited in the case of unexpected air cavities and at tissue boundaries. The comparisons of dose distributions based on measured and generated CT data agree well with the published literature. Differences in dose volume parameters kept within 1.6% and 3.2% for photon and proton RT, respectively. The method presented here is particularly suited for application in adaptive RT in current clinical routine, since only minor additional technical equipment is required.

  11. Improving the convergence rate in affine registration of PET and SPECT brain images using histogram equalization.

    Science.gov (United States)

    Salas-Gonzalez, D; Górriz, J M; Ramírez, J; Padilla, P; Illán, I A

    2013-01-01

    A procedure to improve the convergence rate for affine registration methods of medical brain images when the images differ greatly from the template is presented. The methodology is based on a histogram matching of the source images with respect to the reference brain template before proceeding with the affine registration. The preprocessed source brain images are spatially normalized to a template using a general affine model with 12 parameters. A sum of squared differences between the source images and the template is considered as objective function, and a Gauss-Newton optimization algorithm is used to find the minimum of the cost function. Using histogram equalization as a preprocessing step improves the convergence rate in the affine registration algorithm of brain images as we show in this work using SPECT and PET brain images.

  12. A Comment on a Novel Approach for the Registration of Weak Affine Images

    Czech Academy of Sciences Publication Activity Database

    Flusser, Jan; Zitová, Barbara

    2013-01-01

    Roč. 34, č. 12 (2013), s. 1381-1385 ISSN 0167-8655 R&D Projects: GA ČR GAP103/11/1552 Keywords : Image registration * Affine transform * Affine subgroups * Weak affine transform * Area-preserving affine transform Subject RIV: JD - Computer Applications, Robotics Impact factor: 1.062, year: 2013 http://library.utia.cas.cz/separaty/2013/ZOI/flusser-a comment on a novel approach for the registration of weak affine images.pdf

  13. Nuclear medicine image registration by spatially noncoherent interferometry.

    Science.gov (United States)

    Scheiber, C; Malet, Y; Sirat, G; Grucker, D

    2000-02-01

    This article introduces a technique for obtaining high-resolution body contour data in the same coordinate frame as that of a rotating gamma camera, using a miniature range finder, the conoscope, mounted on the camera gantry. One potential application of the technique is accurate coregistration in longitudinal brain SPECT studies, using the face of the patient (or "mask"), instead of SPECT slices, to coregister subsequent acquisitions involving the brain. Conoscopic holography is an interferometry technique that relies on spatially incoherent light interference in birefringent crystals. In this study, the conoscope was used to measure the absolute distance (Z) between a light source reflected from the skin and its observation plane. This light was emitted by a 0.2-mW laser diode. A scanning system was used to image the face during SPECT acquisition. The system consisted of a motor-driven mirror (Y axis) and the gamma-camera gantry (1 profile was obtained for each rotation step, X axis). The system was calibrated to place the conoscopic measurements and SPECT slices in the same coordinate frame. Through a simple and robust calibration of the system, the SE for measurements performed on geometric shapes was less than 2 mm, i.e., less than the actual pixel size of the SPECT data. Biometric measurements of an anthropomorphic brain phantom were within 3%-5% of actual values. The mask data were used to register images of a brain phantom and of a volunteer's brain, respectively. The rigid transformation that allowed the merging of masks by visual inspection was applied to the 2 sets of SPECT slices to perform the fusion of the data. At the cost of an additional low-cost setup integrated into the gamma-camera gantry, real-time data about the surface of the head were obtained. As in all other surface-based techniques (as opposed to volume-based techniques), this method allows the match of data independently from the dataset of interest and facilitates further registration

  14. Comparison of arterial spin labeling registration strategies in the multi-center GENetic frontotemporal dementia initiative (GENFI).

    Science.gov (United States)

    Mutsaerts, Henri J M M; Petr, Jan; Thomas, David L; De Vita, Enrico; Cash, David M; van Osch, Matthias J P; Golay, Xavier; Groot, Paul F C; Ourselin, Sebastien; van Swieten, John; Laforce, Robert; Tagliavini, Fabrizio; Borroni, Barbara; Galimberti, Daniela; Rowe, James B; Graff, Caroline; Pizzini, Francesca B; Finger, Elizabeth; Sorbi, Sandro; Castelo Branco, Miguel; Rohrer, Jonathan D; Masellis, Mario; MacIntosh, Bradley J

    2018-01-01

    To compare registration strategies to align arterial spin labeling (ASL) with 3D T1-weighted (T1w) images, with the goal of reducing the between-subject variability of cerebral blood flow (CBF) images. Multi-center 3T ASL data were collected at eight sites with four different sequences in the multi-center GENetic Frontotemporal dementia Initiative (GENFI) study. In a total of 48 healthy controls, we compared the following image registration options: (I) which images to use for registration (perfusion-weighted images [PWI] to the segmented gray matter (GM) probability map (pGM) (CBF-pGM) or M0 to T1w (M0-T1w); (II) which transformation to use (rigid-body or non-rigid); and (III) whether to mask or not (no masking, M0-based FMRIB software library Brain Extraction Tool [BET] masking). In addition to visual comparison, we quantified image similarity using the Pearson correlation coefficient (CC), and used the Mann-Whitney U rank sum test. CBF-pGM outperformed M0-T1w (CC improvement 47.2% ± 22.0%; P Masking only improved the M0-T1w rigid-body registration (14.5% ± 15.5%; P = 0.007). The choice of image registration strategy impacts ASL group analyses. The non-rigid transformation is promising but requires validation. CBF-pGM rigid-body registration without masking can be used as a default strategy. In patients with expansive perfusion deficits, M0-T1w may outperform CBF-pGM in sequences with high effective spatial resolution. BET-masking only improves M0-T1w registration when the M0 image has sufficient contrast. 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:131-140. © 2017 International Society for Magnetic Resonance in Medicine.

  15. Semiautomatic registration of 3D transabdominal ultrasound images for patient repositioning during postprostatectomy radiotherapy

    International Nuclear Information System (INIS)

    Presles, Benoît; Rit, Simon; Sarrut, David; Fargier-Voiron, Marie; Liebgott, Hervé; Biston, Marie-Claude; Munoz, Alexandre; Pommier, Pascal; Lynch, Rod

    2014-01-01

    Purpose: The aim of the present work is to propose and evaluate registration algorithms of three-dimensional (3D) transabdominal (TA) ultrasound (US) images to setup postprostatectomy patients during radiation therapy. Methods: Three registration methods have been developed and evaluated to register a reference 3D-TA-US image acquired during the planning CT session and a 3D-TA-US image acquired before each treatment session. The first method (method A) uses only gray value information, whereas the second one (method B) uses only gradient information. The third one (method C) combines both sets of information. All methods restrict the comparison to a region of interest computed from the dilated reference positioning volume drawn on the reference image and use mutual information as a similarity measure. The considered geometric transformations are translations and have been optimized by using the adaptive stochastic gradient descent algorithm. Validation has been carried out using manual registration by three operators of the same set of image pairs as the algorithms. Sixty-two treatment US images of seven patients irradiated after a prostatectomy have been registered to their corresponding reference US image. The reference registration has been defined as the average of the manual registration values. Registration error has been calculated by subtracting the reference registration from the algorithm result. For each session, the method has been considered a failure if the registration error was above both the interoperator variability of the session and a global threshold of 3.0 mm. Results: All proposed registration algorithms have no systematic bias. Method B leads to the best results with mean errors of −0.6, 0.7, and −0.2 mm in left–right (LR), superior–inferior (SI), and anterior–posterior (AP) directions, respectively. With this method, the standard deviations of the mean error are of 1.7, 2.4, and 2.6 mm in LR, SI, and AP directions, respectively

  16. Image registration under illumination variations using region-based confidence weighted M-estimators.

    Science.gov (United States)

    Fouad, Mohamed M; Dansereau, Richard M; Whitehead, Anthony D

    2012-03-01

    We present an image registration model for image sets with arbitrarily shaped local illumination variations between images. Any nongeometric variations tend to degrade the geometric registration precision and impact subsequent processing. Traditional image registration approaches do not typically account for changes and movement of light sources, which result in interimage illumination differences with arbitrary shape. In addition, these approaches typically use a least-square estimator that is sensitive to outliers, where interimage illumination variations are often large enough to act as outliers. In this paper, we propose an image registration approach that compensates for arbitrarily shaped interimage illumination variations, which are processed using robust M -estimators tuned to that region. Each M-estimator for each illumination region has a distinct cost function by which small and large interimage residuals are unevenly penalized. Since the segmentation of the interimage illumination variations may not be perfect, a segmentation confidence weighting is also imposed to reduce the negative effect of mis-segmentation around illumination region boundaries. The proposed approach is cast in an iterative coarse-to-fine framework, which allows a convergence rate similar to competing intensity-based image registration approaches. The overall proposed approach is presented in a general framework, but experimental results use the bisquare M-estimator with region segmentation confidence weighting. A nearly tenfold improvement in subpixel registration precision is seen with the proposed technique when convergence is attained, as compared with competing techniques using both simulated and real data sets with interimage illumination variations.

  17. The plant virus microscope image registration method based on mismatches removing.

    Science.gov (United States)

    Wei, Lifang; Zhou, Shucheng; Dong, Heng; Mao, Qianzhuo; Lin, Jiaxiang; Chen, Riqing

    2016-01-01

    The electron microscopy is one of the major means to observe the virus. The view of virus microscope images is limited by making specimen and the size of the camera's view field. To solve this problem, the virus sample is produced into multi-slice for information fusion and image registration techniques are applied to obtain large field and whole sections. Image registration techniques have been developed in the past decades for increasing the camera's field of view. Nevertheless, these approaches typically work in batch mode and rely on motorized microscopes. Alternatively, the methods are conceived just to provide visually pleasant registration for high overlap ratio image sequence. This work presents a method for virus microscope image registration acquired with detailed visual information and subpixel accuracy, even when overlap ratio of image sequence is 10% or less. The method proposed focus on the correspondence set and interimage transformation. A mismatch removal strategy is proposed by the spatial consistency and the components of keypoint to enrich the correspondence set. And the translation model parameter as well as tonal inhomogeneities is corrected by the hierarchical estimation and model select. In the experiments performed, we tested different registration approaches and virus images, confirming that the translation model is not always stationary, despite the fact that the images of the sample come from the same sequence. The mismatch removal strategy makes building registration of virus microscope images at subpixel accuracy easier and optional parameters for building registration according to the hierarchical estimation and model select strategies make the proposed method high precision and reliable for low overlap ratio image sequence. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Calculation of the confidence intervals for transformation parameters in the registration of medical images.

    Science.gov (United States)

    Bansal, Ravi; Staib, Lawrence H; Laine, Andrew F; Xu, Dongrong; Liu, Jun; Posecion, Lainie F; Peterson, Bradley S

    2009-04-01

    Images from different individuals typically cannot be registered precisely because anatomical features within the images differ across the people imaged and because the current methods for image registration have inherent technological limitations that interfere with perfect registration. Quantifying the inevitable error in image registration is therefore of crucial importance in assessing the effects that image misregistration may have on subsequent analyses in an imaging study. We have developed a mathematical framework for quantifying errors in registration by computing the confidence intervals of the estimated parameters (3 translations, 3 rotations, and 1 global scale) for the similarity transformation. The presence of noise in images and the variability in anatomy across individuals ensures that estimated registration parameters are always random variables. We assume a functional relation among intensities across voxels in the images, and we use the theory of nonlinear, least-squares estimation to show that the parameters are multivariate Gaussian distributed. We then use the covariance matrix of this distribution to compute the confidence intervals of the transformation parameters. These confidence intervals provide a quantitative assessment of the registration error across the images. Because transformation parameters are nonlinearly related to the coordinates of landmark points in the brain, we subsequently show that the coordinates of those landmark points are also multivariate Gaussian distributed. Using these distributions, we then compute the confidence intervals of the coordinates for landmark points in the image. Each of these confidence intervals in turn provides a quantitative assessment of the registration error at a particular landmark point. Because our method is computationally intensive, however, its current implementation is limited to assessing the error of the parameters in the similarity transformation across images. We assessed the

  19. Semiautomatic registration of digital histopathology images to in vivo MR images in molded and unmolded prostates.

    Science.gov (United States)

    Starobinets, Olga; Guo, Richard; Simko, Jeffry P; Kuchinsky, Kyle; Kurhanewicz, John; Carroll, Peter R; Greene, Kirsten L; Noworolski, Susan M

    2014-05-01

    To evaluate a semiautomatic software-based method of registering in vivo prostate MR images to digital histopathology images using two approaches: (i) in which the prostates were molded to simulate distortion due to the endorectal imaging coil before fixation, and (ii) in which the prostates were not molded. T2-weighted MR images and digitized whole-mount histopathology images were acquired for 26 patients with biopsy-confirmed prostate cancer who underwent radical prostatectomy. Ten excised prostates were molded before fixation. A semiautomatic method was used to align MR images to histopathology. Percent overlap between MR and histopathology images, as well as distances between corresponding anatomical landmarks were calculated and used to evaluate the registration technique for molded and unmolded cases. The software successfully morphed histology-based prostate images into corresponding MR images. Percent overlap improved from 80.4 ± 5.8% before morphing to 99.7 ± 0.62% post morphing. Molded prostates had a smaller distance between landmarks (1.91 ± 0.75 mm) versus unmolded (2.34 ± 0.68 mm), P < 0.08. Molding a prostate before fixation provided a better alignment of internal structures within the prostate, but this did not reach statistical significance. Software-based morphing allowed for nearly complete overlap between the pathology slides and the MR images. Copyright © 2013 Wiley Periodicals, Inc.

  20. Carrier for registration of optical images and holographic information

    International Nuclear Information System (INIS)

    Andries, A.; Bivol, V.; Iovu, M.

    2000-01-01

    The invention relates to the field of registration of optical information including the holographic one and may be used in the holography, cinematography, micro- and optical electronics, computer engineering. Summary of the invention consists in, that in the carrier containing a dielectric substrate on which there are placed in sequence the first electrode, photoinjection substrate, registration substrate of the chalcogenide vitreous semiconductor and the second electrode, the photoinjection substrate is fabricated of the monocrystalline germanium of the n-type conductivity and the relation of the registration substrate conductivity, during illumination to the photoinjection substrate conductivity in darkness is 0,001. The technical result consists in increasing the carrier photosensibility and in diffraction effectiveness of the information registered on the carrier

  1. Registration of vehicle based panoramic image and LiDAR point cloud

    Science.gov (United States)

    Chen, Changjun; Cao, Liang; Xie, Hong; Zhuo, Xiangyu

    2013-10-01

    Higher quality surface information would be got when data from optical images and LiDAR were integrated, owing to the fact that optical images and LiDAR point cloud have unique characteristics that make them preferable in many applications. While most previous works focus on registration of pinhole perspective cameras to 2D or 3D LiDAR data. In this paper, a method for the registration of vehicle based panoramic image and LiDAR point cloud is proposed. Using the translation among panoramic image, single CCD image, laser scanner and Position and Orientation System (POS) along with the GPS/IMU data, precise co-registration between the panoramic image and the LiDAR point cloud in the world system is achieved. Results are presented under a real world data set collected by a new developed Mobile Mapping System (MMS) integrated with a high resolution panoramic camera, two laser scanners and a POS.

  2. Landmark Optimization Using Local Curvature for Point-Based Nonlinear Rodent Brain Image Registration

    Directory of Open Access Journals (Sweden)

    Yutong Liu

    2012-01-01

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

  3. MREG V1.1 : a multi-scale image registration algorithm for SAR applications.

    Energy Technology Data Exchange (ETDEWEB)

    Eichel, Paul H.

    2013-08-01

    MREG V1.1 is the sixth generation SAR image registration algorithm developed by the Signal Processing&Technology Department for Synthetic Aperture Radar applications. Like its predecessor algorithm REGI, it employs a powerful iterative multi-scale paradigm to achieve the competing goals of sub-pixel registration accuracy and the ability to handle large initial offsets. Since it is not model based, it allows for high fidelity tracking of spatially varying terrain-induced misregistration. Since it does not rely on image domain phase, it is equally adept at coherent and noncoherent image registration. This document provides a brief history of the registration processors developed by Dept. 5962 leading up to MREG V1.1, a full description of the signal processing steps involved in the algorithm, and a user's manual with application specific recommendations for CCD, TwoColor MultiView, and SAR stereoscopy.

  4. IMAGE REGISTRATION OF HIGH-RESOLUTION UAV DATA: THE NEW HYPARE ALGORITHM

    Directory of Open Access Journals (Sweden)

    T. Bahr

    2013-08-01

    Full Text Available 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™.

  5. Automatic localization of landmark sets in head CT images with regression forests for image registration initialization

    Science.gov (United States)

    Zhang, Dongqing; Liu, Yuan; Noble, Jack H.; Dawant, Benoit M.

    2016-03-01

    Cochlear Implants (CIs) are electrode arrays that are surgically inserted into the cochlea. Individual contacts stimulate frequency-mapped nerve endings thus replacing the natural electro-mechanical transduction mechanism. CIs are programmed post-operatively by audiologists but this is currently done using behavioral tests without imaging information that permits relating electrode position to inner ear anatomy. We have recently developed a series of image processing steps that permit the segmentation of the inner ear anatomy and the localization of individual contacts. We have proposed a new programming strategy that uses this information and we have shown in a study with 68 participants that 78% of long term recipients preferred the programming parameters determined with this new strategy. A limiting factor to the large scale evaluation and deployment of our technique is the amount of user interaction still required in some of the steps used in our sequence of image processing algorithms. One such step is the rough registration of an atlas to target volumes prior to the use of automated intensity-based algorithms when the target volumes have very different fields of view and orientations. In this paper we propose a solution to this problem. It relies on a random forest-based approach to automatically localize a series of landmarks. Our results obtained from 83 images with 132 registration tasks show that automatic initialization of an intensity-based algorithm proves to be a reliable technique to replace the manual step.

  6. Deformable registration of the planning image (kVCT) and the daily images (MVCT) for adaptive radiation therapy

    International Nuclear Information System (INIS)

    Lu Weiguo; Olivera, Gustavo H; Chen, Quan; Ruchala, Kenneth J; Haimerl, Jason; Meeks, Sanford L; Langen, Katja M; Kupelian, Patrick A

    2006-01-01

    The incorporation of daily images into the radiotherapy process leads to adaptive radiation therapy (ART), in which the treatment is evaluated periodically and the plan is adaptively modified for the remaining course of radiotherapy. Deformable registration between the planning image and the daily images is a key component of ART. In this paper, we report our researches on deformable registration between the planning kVCT and the daily MVCT image sets. The method is based on a fast intensity-based free-form deformable registration technique. Considering the noise and contrast resolution differences between the kVCT and the MVCT, an 'edge-preserving smoothing' is applied to the MVCT image prior to the deformable registration process. We retrospectively studied daily MVCT images from commercial TomoTherapy machines from different clinical centers. The data set includes five head-neck cases, one pelvis case, two lung cases and one prostate case. Each case has one kVCT image and 20-40 MVCT images. We registered the MVCT images with their corresponding kVCT image. The similarity measures and visual inspections of contour matches by physicians validated this technique. The applications of deformable registration in ART, including 'deformable dose accumulation', 'automatic re-contouring' and 'tumour growth/regression evaluation' throughout the course of radiotherapy are also studied

  7. An embedded system for image segmentation and multimodal registration in noninvasive skin cancer screening.

    Science.gov (United States)

    Diaz, Silvana; Soto, Javier E; Inostroza, Fabian; Godoy, Sebastian E; Figueroa, Miguel

    2017-07-01

    We present a heterogeneous architecture for image registration and multimodal segmentation on an embedded system for noninvasive skin cancer screening. The architecture combines Otsu thresholding and the random walker algorithm to perform image segmentation, and features a hardware implementation of the Harris corner detection algorithm to perform region-of-interest detection and image registration. Running on a Xilinx XC7Z020 reconfigurable system-on-a-chip, our prototype computes the initial segmentation of a 400×400-pixel region of interest in the visible spectrum in 12.1 seconds, and registers infrared images against this region at 540 frames per second, while consuming 1.9W.

  8. A level-set approach to joint image segmentation and registration with application to CT lung imaging.

    Science.gov (United States)

    Swierczynski, Piotr; Papież, Bartłomiej W; Schnabel, Julia A; Macdonald, Colin

    2018-04-01

    Automated analysis of structural imaging such as lung Computed Tomography (CT) plays an increasingly important role in medical imaging applications. Despite significant progress in the development of image registration and segmentation methods, lung registration and segmentation remain a challenging task. In this paper, we present a novel image registration and segmentation approach, for which we develop a new mathematical formulation to jointly segment and register three-dimensional lung CT volumes. The new algorithm is based on a level-set formulation, which merges a classic Chan-Vese segmentation with the active dense displacement field estimation. Combining registration with segmentation has two key advantages: it allows to eliminate the problem of initializing surface based segmentation methods, and to incorporate prior knowledge into the registration in a mathematically justified manner, while remaining computationally attractive. We evaluate our framework on a publicly available lung CT data set to demonstrate the properties of the new formulation. The presented results show the improved accuracy for our joint segmentation and registration algorithm when compared to registration and segmentation performed separately. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  9. Fast Parallel Image Registration on CPU and GPU for Diagnostic Classification of Alzheimer's Disease

    Directory of Open Access Journals (Sweden)

    Denis P Shamonin

    2014-01-01

    Full Text Available Nonrigid image registration is an important, but time-consuming taskin medical image analysis. In typical neuroimaging studies, multipleimage registrations are performed, i.e. for atlas-based segmentationor template construction. Faster image registration routines wouldtherefore be beneficial.In this paper we explore acceleration of the image registrationpackage elastix by a combination of several techniques: iparallelization on the CPU, to speed up the cost function derivativecalculation; ii parallelization on the GPU building on andextending the OpenCL framework from ITKv4, to speed up the Gaussianpyramid computation and the image resampling step; iii exploitationof certain properties of the B-spline transformation model; ivfurther software optimizations.The accelerated registration tool is employed in a study ondiagnostic classification of Alzheimer's disease and cognitivelynormal controls based on T1-weighted MRI. We selected 299participants from the publicly available Alzheimer's DiseaseNeuroimaging Initiative database. Classification is performed with asupport vector machine based on gray matter volumes as a marker foratrophy. We evaluated two types of strategies (voxel-wise andregion-wise that heavily rely on nonrigid image registration.Parallelization and optimization resulted in an acceleration factorof 4-5x on an 8-core machine. Using OpenCL a speedup factor of ~2was realized for computation of the Gaussian pyramids, and 15-60 forthe resampling step, for larger images. The voxel-wise and theregion-wise classification methods had an area under thereceiver operator characteristic curve of 88% and 90%,respectively, both for standard and accelerated registration.We conclude that the image registration package elastix wassubstantially accelerated, with nearly identical results to thenon-optimized version. The new functionality will become availablein the next release of elastix as open source under the BSD license.

  10. Adaptive mesh generation for image registration and segmentation

    DEFF Research Database (Denmark)

    Fogtmann, Mads; Larsen, Rasmus

    2013-01-01

    measure. The method was tested on a T1 weighted MR volume of an adult brain and showed a 66% reduction in the number of mesh vertices compared to a red-subdivision strategy. The deformation capability of the mesh was tested by registration to five additional T1-weighted MR volumes....

  11. Nonrigid registration with free-form deformation model of multilevel uniform cubic B-splines: application to image registration and distortion correction of spectral image cubes.

    Science.gov (United States)

    Eckhard, Timo; Eckhard, Jia; Valero, Eva M; Nieves, Juan Luis

    2014-06-10

    In spectral imaging, spatial and spectral information of an image scene are combined. There exist several technologies that allow the acquisition of this kind of data. Depending on the optical components used in the spectral imaging systems, misalignment between image channels can occur. Further, the projection of some systems deviates from that of a perfect optical lens system enough that a distortion of scene content in the images becomes apparent to the observer. Correcting distortion and misalignment can be complicated for spectral image data if they are different at each image channel. In this work, we propose an image registration and distortion correction scheme for spectral image cubes that is based on a free-form deformation model of uniform cubic B-splines with multilevel grid refinement. This scheme is adaptive with respect to image size, degree of misalignment, and degree of distortion, and in that sense is superior to previous approaches. We support our proposed scheme with empirical data from a Bragg-grating-based hyperspectral imager, for which a registration accuracy of approximately one pixel was achieved.

  12. REGISTRATION OF LASER SCANNING POINT CLOUDS AND AERIAL IMAGES USING EITHER ARTIFICIAL OR NATURAL TIE FEATURES

    Directory of Open Access Journals (Sweden)

    P. Rönnholm

    2012-07-01

    Full Text Available 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

  13. Real-time non-rigid target tracking for ultrasound-guided clinical interventions

    Science.gov (United States)

    Zachiu, C.; Ries, M.; Ramaekers, P.; Guey, J.-L.; Moonen, C. T. W.; de Senneville, B. Denis

    2017-10-01

    Biological motion is a problem for non- or mini-invasive interventions when conducted in mobile/deformable organs due to the targeted pathology moving/deforming with the organ. This may lead to high miss rates and/or incomplete treatment of the pathology. Therefore, real-time tracking of the target anatomy during the intervention would be beneficial for such applications. Since the aforementioned interventions are often conducted under B-mode ultrasound (US) guidance, target tracking can be achieved via image registration, by comparing the acquired US images to a separate image established as positional reference. However, such US images are intrinsically altered by speckle noise, introducing incoherent gray-level intensity variations. This may prove problematic for existing intensity-based registration methods. In the current study we address US-based target tracking by employing the recently proposed EVolution registration algorithm. The method is, by construction, robust to transient gray-level intensities. Instead of directly matching image intensities, EVolution aligns similar contrast patterns in the images. Moreover, the displacement is computed by evaluating a matching criterion for image sub-regions rather than on a point-by-point basis, which typically provides more robust motion estimates. However, unlike similar previously published approaches, which assume rigid displacements in the image sub-regions, the EVolution algorithm integrates the matching criterion in a global functional, allowing the estimation of an elastic dense deformation. The approach was validated for soft tissue tracking under free-breathing conditions on the abdomen of seven healthy volunteers. Contact echography was performed on all volunteers, while three of the volunteers also underwent standoff echography. Each of the two modalities is predominantly specific to a particular type of non- or mini-invasive clinical intervention. The method demonstrated on average an accuracy of

  14. Automatic kidney segmentation in CT images based on multi-atlas image registration.

    Science.gov (United States)

    Yang, Guanyu; Gu, Jinjin; Chen, Yang; Liu, Wangyan; Tang, Lijun; Shu, Huazhong; Toumoulin, Christine

    2014-01-01

    Kidney segmentation is an important step for computer-aided diagnosis or treatment in urology. In this paper, we present an automatic method based on multi-atlas image registration for kidney segmentation. The method mainly relies on a two-step framework to obtain coarse-to-fine segmentation results. In the first step, down-sampled patient image is registered with a set of low-resolution atlas images. A coarse kidney segmentation result is generated to locate the left and right kidneys. In the second step, the left and right kidneys are cropped from original images and aligned with another set of high-resolution atlas images to obtain the final results respectively. Segmentation results from 14 CT angiographic (CTA) images show that our proposed method can segment the kidneys with a high accuracy. The average Dice similarity coefficient and surface-to-surface distance between segmentation results and reference standard are 0.952 and 0.913mm. Furthermore, the kidney segmentation in CT urography (CTU) and CTA images of 12 patients were performed to show the feasibility of our method in CTU images.

  15. Development of a hardware-based registration system for the multimodal medical images by USB cameras

    International Nuclear Information System (INIS)

    Iwata, Michiaki; Minato, Kotaro; Watabe, Hiroshi; Koshino, Kazuhiro; Yamamoto, Akihide; Iida, Hidehiro

    2009-01-01

    There are several medical imaging scanners and each modality has different aspect for visualizing inside of human body. By combining these images, diagnostic accuracy could be improved, and therefore, several attempts for multimodal image registration have been implemented. One popular approach is to use hybrid image scanners such as positron emission tomography (PET)/CT and single photon emission computed tomography (SPECT)/CT. However, these hybrid scanners are expensive and not fully available. We developed multimodal image registration system with universal serial bus (USB) cameras, which is inexpensive and applicable to any combinations of existed conventional imaging scanners. The multiple USB cameras will determine the three dimensional positions of a patient while scanning. Using information of these positions and rigid body transformation, the acquired image is registered to the common coordinate which is shared with another scanner. For each scanner, reference marker is attached on gantry of the scanner. For observing the reference marker's position by the USB cameras, the location of the USB cameras can be arbitrary. In order to validate the system, we scanned a cardiac phantom with different positions by PET and MRI scanners. Using this system, images from PET and MRI were visually aligned, and good correlations between PET and MRI images were obtained after the registration. The results suggest this system can be inexpensively used for multimodal image registrations. (author)

  16. Algorithm of sub-pixel image registration based on Harris corner and SIFT descriptor

    Science.gov (United States)

    Zhu, Jianguo; Fan, Guihua

    2014-09-01

    Since multi-cameras images involve much differences in spatial characteristics and spectral characteristics, so it is full of difficulties in the image registration. According to the different characteristics of the multi-cameras images, this paper proposed a new algorithm of sub-pixel image registration based on Harris corner and Scale Invariant Features Transform (SIFT) descriptor. The algorithm consists of three procedures: feature detection, pixel-level registration and sub-pixel-level registration. Firstly, the Harris algorithm was selected to extract the feature corners and determine the main direction of the Harris corners. Secondly, the SIFT descriptor was chose to describe the key points. Then, feature points acquired on matching by the two-way nearest neighbor algorithm. Finally, in the sub-pixel-level registration process, we carry out interpolation in the neighborhood of the pixel-level matching points. Then the pixel-level registration is taken once again. The experimental results show that, the proposed algorithm is accurate, efficient, and retains the rotational invariance of the SIFT descriptor. What's more, processing speed is significantly increased.

  17. Automated analysis of small animal PET studies through deformable registration to an atlas

    International Nuclear Information System (INIS)

    Gutierrez, Daniel F.; Zaidi, Habib

    2012-01-01

    This work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model. A non-rigid registration technique is used to put into correspondence relevant anatomical regions of rodent CT images from combined PET/CT studies to corresponding CT images of the Digimouse anatomical mouse model. The latter provides a pre-segmented atlas consisting of 21 anatomical regions suitable for automated quantitative analysis. Image registration is performed using a package based on the Insight Toolkit allowing the implementation of various image registration algorithms. The optimal parameters obtained for deformable registration were applied to simulated and experimental mouse PET/CT studies. The accuracy of the image registration procedure was assessed by segmenting mouse CT images into seven regions: brain, lungs, heart, kidneys, bladder, skeleton and the rest of the body. This was accomplished prior to image registration using a semi-automated algorithm. Each mouse segmentation was transformed using the parameters obtained during CT to CT image registration. The resulting segmentation was compared with the original Digimouse atlas to quantify image registration accuracy using established metrics such as the Dice coefficient and Hausdorff distance. PET images were then transformed using the same technique and automated quantitative analysis of tracer uptake performed. The Dice coefficient and Hausdorff distance show fair to excellent agreement and a mean registration mismatch distance of about 6 mm. The results demonstrate good quantification accuracy in most of the regions, especially the brain, but not in the bladder, as expected. Normalized mean activity estimates were preserved between the reference and automated quantification techniques with relative errors below 10 % in most of the organs considered. The proposed automated quantification technique is

  18. A method for accurate spatial registration of PET images and histopathology slices.

    Science.gov (United States)

    Puri, Tanuj; Chalkidou, Anastasia; Henley-Smith, Rhonda; Roy, Arunabha; Barber, Paul R; Guerrero-Urbano, Teresa; Oakley, Richard; Simo, Ricard; Jeannon, Jean-Pierre; McGurk, Mark; Odell, Edward W; O'Doherty, Michael J; Marsden, Paul K

    2015-12-01

    Accurate alignment between histopathology slices and positron emission tomography (PET) images is important for radiopharmaceutical validation studies. Limited data is available on the registration accuracy that can be achieved between PET and histopathology slices acquired under routine pathology conditions where slices may be non-parallel, non-contiguously cut and of standard block size. The purpose of this study was to demonstrate a method for aligning PET images and histopathology slices acquired from patients with laryngeal cancer and to assess the registration accuracy obtained under these conditions. Six subjects with laryngeal cancer underwent a (64)Cu-copper-II-diacetyl-bis(N4-methylthiosemicarbazone) ((64)Cu-ATSM) PET computed tomography (CT) scan prior to total laryngectomy. Sea urchin spines were inserted into the pathology specimen to act as fiducial markers. The specimen was fixed in formalin, as per standard histopathology operating procedures, and was then CT scanned and cut into millimetre-thick tissue slices. A subset of the tissue slices that included both tumour and fiducial markers was taken and embedded in paraffin blocks. Subsequently, microtome sectioning and haematoxylin and eosin staining were performed to produce 5-μm-thick tissue sections for microscopic digitisation. A series of rigid registration procedures was performed between the different imaging modalities (PET; in vivo CT-i.e. the CT component of the PET-CT; ex vivo CT; histology slices) with the ex vivo CT serving as the reference image. In vivo and ex vivo CTs were registered using landmark-based registration. Histopathology and ex vivo CT images were aligned using the sea urchin spines with additional anatomical landmarks where available. Registration errors were estimated using a leave-one-out strategy for in vivo to ex vivo CT and were estimated from the RMS landmark accuracy for histopathology to ex vivo CT. The mean ± SD accuracy for registration of the in vivo to ex

  19. Image registration using stationary velocity fields parameterized by norm-minimizing Wendland kernel

    DEFF Research Database (Denmark)

    Pai, Akshay Sadananda Uppinakudru; Sommer, Stefan Horst; Sørensen, Lauge

    Interpolating kernels are crucial to solving a stationary velocity field (SVF) based image registration problem. This is because, velocity fields need to be computed in non-integer locations during integration. The regularity in the solution to the SVF registration problem is controlled by the re...... that Wendland SVF based measures separate (Alzheimer's disease v/s normal controls) better than both B-Spline SVFs (pamygdala) and B-Spline freeform deformation (pamygdala and cortical gray matter)....

  20. Improving oncoplastic breast tumor bed localization for radiotherapy planning using image registration algorithms

    Science.gov (United States)

    Wodzinski, Marek; Skalski, Andrzej; Ciepiela, Izabela; Kuszewski, Tomasz; Kedzierawski, Piotr; Gajda, Janusz

    2018-02-01

    Knowledge about tumor bed localization and its shape analysis is a crucial factor for preventing irradiation of healthy tissues during supportive radiotherapy and as a result, cancer recurrence. The localization process is especially hard for tumors placed nearby soft tissues, which undergo complex, nonrigid deformations. Among them, breast cancer can be considered as the most representative example. A natural approach to improving tumor bed localization is the use of image registration algorithms. However, this involves two unusual aspects which are not common in typical medical image registration: the real deformation field is discontinuous, and there is no direct correspondence between the cancer and its bed in the source and the target 3D images respectively. The tumor no longer exists during radiotherapy planning. Therefore, a traditional evaluation approach based on known, smooth deformations and target registration error are not directly applicable. In this work, we propose alternative artificial deformations which model the tumor bed creation process. We perform a comprehensive evaluation of the most commonly used deformable registration algorithms: B-Splines free form deformations (B-Splines FFD), different variants of the Demons and TV-L1 optical flow. The evaluation procedure includes quantitative assessment of the dedicated artificial deformations, target registration error calculation, 3D contour propagation and medical experts visual judgment. The results demonstrate that the currently, practically applied image registration (rigid registration and B-Splines FFD) are not able to correctly reconstruct discontinuous deformation fields. We show that the symmetric Demons provide the most accurate soft tissues alignment in terms of the ability to reconstruct the deformation field, target registration error and relative tumor volume change, while B-Splines FFD and TV-L1 optical flow are not an appropriate choice for the breast tumor bed localization problem

  1. Wavelet Compressed PCA Models for Real-Time Image Registration in Augmented Reality Applications

    OpenAIRE

    Christopher Cooper; Kent Wise; John Cooper; Makarand Deo

    2015-01-01

    The use of augmented reality (AR) has shown great promise in enhancing medical training and diagnostics via interactive simulations. This paper presents a novel method to perform accurate and inexpensive image registration (IR) utilizing a pre-constructed database of reference objects in conjunction with a principal component analysis (PCA) model. In addition, a wavelet compression algorithm is utilized to enhance the speed of the registration process. The proposed method is used to perform r...

  2. ACCURATE ESTIMATION OF ORIENTATION PARAMETERS OF UAV IMAGES THROUGH IMAGE REGISTRATION WITH AERIAL OBLIQUE IMAGERY

    Directory of Open Access Journals (Sweden)

    F. A. Onyango

    2017-05-01

    Full Text Available Unmanned Aerial Vehicles (UAVs have gained popularity in acquiring geotagged, low cost and high resolution images. However, the images acquired by UAV-borne cameras often have poor georeferencing information, because of the low quality on-board Global Navigation Satellite System (GNSS receiver. In addition, lightweight UAVs have a limited payload capacity to host a high quality on-board Inertial Measurement Unit (IMU. Thus, orientation parameters of images acquired by UAV-borne cameras may not be very accurate. Poorly georeferenced UAV images can be correctly oriented using accurately oriented airborne images capturing a similar scene by finding correspondences between the images. This is not a trivial task considering the image pairs have huge variations in scale, perspective and illumination conditions. This paper presents a procedure to successfully register UAV and aerial oblique imagery. The proposed procedure implements the use of the AKAZE interest operator for feature extraction in both images. Brute force is implemented to find putative correspondences and later on Lowe’s ratio test (Lowe, 2004 is used to discard a significant number of wrong matches. In order to filter out the remaining mismatches, the putative correspondences are used in the computation of multiple homographies, which aid in the reduction of outliers significantly. In order to increase the number and improve the quality of correspondences, the impact of pre-processing the images using the Wallis filter (Wallis, 1974 is investigated. This paper presents the test results of different scenarios and the respective accuracies compared to a manual registration of the finally computed fundamental and essential matrices that encode the orientation parameters of the UAV images with respect to the aerial images.

  3. Impact of Computed Tomography Image Quality on Image-Guided Radiation Therapy Based on Soft Tissue Registration

    International Nuclear Information System (INIS)

    Morrow, Natalya V.; Lawton, Colleen A.; Qi, X. Sharon; Li, X. Allen

    2012-01-01

    Purpose: In image-guided radiation therapy (IGRT), different computed tomography (CT) modalities with varying image quality are being used to correct for interfractional variations in patient set-up and anatomy changes, thereby reducing clinical target volume to the planning target volume (CTV-to-PTV) margins. We explore how CT image quality affects patient repositioning and CTV-to-PTV margins in soft tissue registration-based IGRT for prostate cancer patients. Methods and Materials: Four CT-based IGRT modalities used for prostate RT were considered in this study: MV fan beam CT (MVFBCT) (Tomotherapy), MV cone beam CT (MVCBCT) (MVision; Siemens), kV fan beam CT (kVFBCT) (CTVision, Siemens), and kV cone beam CT (kVCBCT) (Synergy; Elekta). Daily shifts were determined by manual registration to achieve the best soft tissue agreement. Effect of image quality on patient repositioning was determined by statistical analysis of daily shifts for 136 patients (34 per modality). Inter- and intraobserver variability of soft tissue registration was evaluated based on the registration of a representative scan for each CT modality with its corresponding planning scan. Results: Superior image quality with the kVFBCT resulted in reduced uncertainty in soft tissue registration during IGRT compared with other image modalities for IGRT. The largest interobserver variations of soft tissue registration were 1.1 mm, 2.5 mm, 2.6 mm, and 3.2 mm for kVFBCT, kVCBCT, MVFBCT, and MVCBCT, respectively. Conclusions: Image quality adversely affects the reproducibility of soft tissue-based registration for IGRT and necessitates a careful consideration of residual uncertainties in determining different CTV-to-PTV margins for IGRT using different image modalities.

  4. Registration of in vivo MR to histology of rodent brains using blockface imaging

    Science.gov (United States)

    Uberti, Mariano; Liu, Yutong; Dou, Huanyu; Mosley, R. Lee; Gendelman, Howard E.; Boska, Michael

    2009-02-01

    Registration of MRI to histopathological sections can enhance bioimaging validation for use in pathobiologic, diagnostic, and therapeutic evaluations. However, commonly used registration methods fall short of this goal due to tissue shrinkage and tearing after brain extraction and preparation. In attempts to overcome these limitations we developed a software toolbox using 3D blockface imaging as the common space of reference. This toolbox includes a semi-automatic brain extraction technique using constraint level sets (CLS), 3D reconstruction methods for the blockface and MR volume, and a 2D warping technique using thin-plate splines with landmark optimization. Using this toolbox, the rodent brain volume is first extracted from the whole head MRI using CLS. The blockface volume is reconstructed followed by 3D brain MRI registration to the blockface volume to correct the global deformations due to brain extraction and fixation. Finally, registered MRI and histological slices are warped to corresponding blockface images to correct slice specific deformations. The CLS brain extraction technique was validated by comparing manual results showing 94% overlap. The image warping technique was validated by calculating target registration error (TRE). Results showed a registration accuracy of a TRE < 1 pixel. Lastly, the registration method and the software tools developed were used to validate cell migration in murine human immunodeficiency virus type one encephalitis.

  5. Progressive attenuation fields: Fast 2D-3D image registration without precomputation

    International Nuclear Information System (INIS)

    Rohlfing, Torsten; Russakoff, Daniel B.; Denzler, Joachim; Mori, Kensaku; Maurer, Calvin R. Jr.

    2005-01-01

    Computation of digitally reconstructed radiograph (DRR) images is the rate-limiting step in most current intensity-based algorithms for the registration of three-dimensional (3D) images to two-dimensional (2D) projection images. This paper introduces and evaluates the progressive attenuation field (PAF), which is a new method to speed up DRR computation. A PAF is closely related to an attenuation field (AF). A major difference is that a PAF is constructed on the fly as the registration proceeds; it does not require any precomputation time, nor does it make any prior assumptions of the patient pose or limit the permissible range of patient motion. A PAF effectively acts as a cache memory for projection values once they are computed, rather than as a lookup table for precomputed projections like standard AFs. We use a cylindrical attenuation field parametrization, which is better suited for many medical applications of 2D-3D registration than the usual two-plane parametrization. The computed attenuation values are stored in a hash table for time-efficient storage and access. Using clinical gold-standard spine image data sets from five patients, we demonstrate consistent speedups of intensity-based 2D-3D image registration using PAF DRRs by a factor of 10 over conventional ray casting DRRs with no decrease of registration accuracy or robustness

  6. The utilization of consistency metrics for error analysis in deformable image registration

    International Nuclear Information System (INIS)

    Bender, Edward T; Tome, Wolfgang A

    2009-01-01

    The aim of this study was to investigate the utility of consistency metrics, such as inverse consistency, in contour-based deformable registration error analysis. Four images were acquired of the same phantom that has experienced varying levels of deformation. The deformations were simulated with deformable image registration. Using calculated deformation maps, the inconsistencies within the algorithm were investigated. This can be done, for example, by calculating deformation maps both in forward and reverse directions and applying them subsequently to an image. If the algorithm is not inverse consistent, then this final image will not be the same as the original, as it should be. Other consistency tests were done, for example by comparing different algorithms or by applying the deformation maps to a circular set of multiple deformations, whereby the original and final images are in fact the same. The resulting composite deformation map in this case contains a combination of the errors within those maps, because if error free, the resulting deformation map should be zero everywhere. We have termed this the generalized inverse consistency error map (Σ-vector(x-vector)). The correlation between the consistency metrics and registration error varied considerably depending on the registration algorithm and type of consistency metric. There was also a trend for the actual registration error to be larger than the consistency metrics. A disadvantage of these techniques is that good performance in these consistency checks is a necessary but not sufficient condition for an accurate deformation method.

  7. End-to-end unsupervised deformable image registration with a convolutional neural network

    NARCIS (Netherlands)

    de Vos, Bob D.; Berendsen, Floris; Viergever, Max A.; Staring, Marius; Išgum, Ivana

    2017-01-01

    In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial

  8. Evaluating automatic registration of UAV imagery using multi-temporal ortho images

    Science.gov (United States)

    Saur, Günter; Krüger, Wolfgang

    2016-10-01

    Accurate geo-registration of acquired imagery is an important task when using unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. As an example, change detection needs accurately geo-registered images for selecting and comparing co-located images taken at different points in time. One challenge using small UAVs lies in the instable flight behavior and using low-weight cameras. Thus, there is a need to stabilize and register the UAV imagery by image processing methods since using only direct approaches based on positional information coming from a GPS and attitude and acceleration measured by an inertial measurement unit (IMU) are not accurate enough. In order to improve this direct geo-registration (or "pre-registration"), image matching techniques are applied to align the UAV imagery to geo-registered reference images. The main challenge consists in matching images taken from different sensors at different day time and seasons. In this paper, we present evaluation methods for measuring the performance of image registration algorithms w.r.t. multi-temporal input data. They are based on augmenting a set of aligned image pairs by synthetic pre-registrations to an evaluation data set including truth transformations. The evaluation characteristics are based on quantiles of transformation residuals at certain control points. For a test site, video frames of a UAV mission and several ortho images from a period of 12 years are collected and synthetic pre-registrations corresponding to real flight parameters and registration errors are computed. Two algorithms A1 and A2 based on extracting key-points with a floating point descriptor (A1) and a binary descriptor (A2) are applied to the evaluation data set. As evaluation result, the algorithm A1 turned out to perform better than A2. Using affine or Helmert transformation types, both algorithms perform better than in the projective case. Furthermore, the evaluation classifies the ortho images w

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

    Science.gov (United States)

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

    2012-08-01

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

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  11. Mjolnir: extending HAMMER using a diffusion transformation model and histogram equalization for deformable image registration.

    Science.gov (United States)

    Ellingsen, Lotta M; Prince, Jerry L

    2009-01-01

    Image registration is a crucial step in many medical image analysis procedures such as image fusion, surgical planning, segmentation and labeling, and shape comparison in population or longitudinal studies. A new approach to volumetric intersubject deformable image registration is presented. The method, called Mjolnir, is an extension of the highly successful method HAMMER. New image features in order to better localize points of correspondence between the two images are introduced as well as a novel approach to generate a dense displacement field based upon the weighted diffusion of automatically derived feature correspondences. An extensive validation of the algorithm was performed on T1-weighted SPGR MR brain images from the NIREP evaluation database. The results were compared with results generated by HAMMER and are shown to yield significant improvements in cortical alignment as well as reduced computation time.

  12. Balancing dose and image registration accuracy for cone beam tomosynthesis (CBTS) for breast patient setup

    International Nuclear Information System (INIS)

    Winey, B. A.; Zygmanski, P.; Cormack, R. A.; Lyatskaya, Y.

    2010-01-01

    Purpose: To balance dose reduction and image registration accuracy in breast setup imaging. In particular, the authors demonstrate the relationship between scan angle and dose delivery for cone beam tomosynthesis (CBTS) when employed for setup verification of breast cancer patients with surgical clips. Methods: The dose measurements were performed in a female torso phantom for varying scan angles of CBTS. Setup accuracy was measured using three registration methods: Clip centroid localization accuracy and the accuracy of two semiautomatic registration algorithms. The dose to the organs outside of the ipsilateral breast and registration accuracy information were compared to determine the optimal scan angle for CBTS for breast patient setup verification. Isocenter positions at the center of the patient and at the breast-chest wall interface were considered. Results: Image registration accuracy was within 1 mm for the CBTS scan angles θ above 20 deg. for some scenarios and as large as 80 deg. for the worst case, depending on the imaged breast and registration algorithm. Registration accuracy was highest based on clip centroid localization. For left and right breast imaging with the isocenter at the chest wall, the dose to the contralateral side of the patient was very low (<0.5 cGy) for all scan angles considered. For central isocenter location, the optimal scan angles were 30 deg. - 50 deg. for the left breast imaging and 40 deg. - 50 deg. for the right breast imaging, with the difference due to the geometric asymmetry of the current clinical imaging system. Conclusions: The optimal scan angles for CBTS imaging were found to be between 10 deg. and 50 deg., depending on the isocenter location and ipsilateral breast. Use of the isocenter at the breast-chest wall locations always resulted in greater accuracy of image registration (<1 mm) at smaller angles (10 deg. - 20 deg.) and at lower doses (<0.1 cGy) to the contralateral organs. For chest wall isocenters, doses

  13. Estimating the 4D respiratory lung motion by spatiotemporal registration and super-resolution image reconstruction.

    Science.gov (United States)

    Wu, Guorong; Wang, Qian; Lian, Jun; Shen, Dinggang

    2013-03-01

    One of the main challenges in lung cancer radiation therapy is how to reduce the treatment margin but accommodate the geometric uncertainty of moving tumor. 4D-CT is able to provide the full range of motion information for the lung and tumor. However, accurate estimation of lung motion with respect to the respiratory phase is difficult due to various challenges in image registration, e.g., motion artifacts and large interslice thickness in 4D-CT. Meanwhile, the temporal coherence across respiration phases is usually not guaranteed in the conventional registration methods which consider each phase image in 4D-CT independently. To address these challenges, the authors present a unified approach to estimate the respiratory lung motion with two iterative steps. First, the authors propose a novel spatiotemporal registration algorithm to align all phase images of 4D-CT (in low-resolution) to a high-resolution group-mean image in the common space. The temporal coherence of registration is maintained by a set of temporal fibers that delineate temporal correspondences across different respiratory phases. Second, a super-resolution technique is utilized to build the high-resolution group-mean image with more anatomical details than any individual phase image, thus largely alleviating the registration uncertainty especially in correspondence detection. In particular, the authors use the concept of sparse representation to keep the group-mean image as sharp as possible. The performance of our 4D motion estimation method has been extensively evaluated on both the simulated datasets and real lung 4D-CT datasets. In all experiments, our method achieves more accurate and consistent results in lung motion estimation than all other state-of-the-art approaches under comparison. The authors have proposed a novel spatiotemporal registration method to estimate the lung motion in 4D-CT. Promising results have been obtained, which indicates the high applicability of our method in clinical

  14. Rapid block matching based nonlinear registration on GPU for image guided radiation therapy

    Science.gov (United States)

    Wang, An; Disher, Brandon; Carnes, Greg; Peters, Terry M.

    2010-02-01

    To compensate for non-uniform deformation due to patient motion within and between fractions in image guided radiation therapy, a block matching technique was adapted and implemented on a standard graphics processing unit (GPU) to determine the displacement vector field that maps the nonlinear transformation between successive CT images. Normalized cross correlation (NCC) was chosen as the similarity metric for the matching step, with regularization of the displacement vector field being performed by Gaussian smoothing. A multi-resolution framework was adopted to further improve the performance of the algorithm. The nonlinear registration algorithm was first applied to estimate the intrafractional motion from 4D lung CT images. It was also used to calculate the inter-fractional organ deformation between planning CT (PCT) and Daily Cone Beam CT (CBCT) images of thorax. For both experiments, manual landmark-based evaluation was performed to quantify the registration performance. In 4D CT registration, the mean TRE of 5 cases was 1.75 mm. In PCT-CBCT registration, the TRE of one case was 2.26mm. Compared to the CPU-based AtamaiWarp program, our GPU-based implementation achieves comparable registration accuracy and is ~25 times faster. The results highlight the potential utility of our algorithm for online adaptive radiation treatment.

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

    CERN Document Server

    Glatard, T; Pennec, X

    2006-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Silja Kiriyanthan

    2016-01-01

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

  17. 3D registration of surfaces for change detection in medical images

    Science.gov (United States)

    Fisher, Elizabeth; van der Stelt, Paul F.; Dunn, Stanley M.

    1997-04-01

    Spatial registration of data sets is essential for quantifying changes that take place over time in cases where the position of a patient with respect to the sensor has been altered. Changes within the region of interest can be problematic for automatic methods of registration. This research addresses the problem of automatic 3D registration of surfaces derived from serial, single-modality images for the purpose of quantifying changes over time. The registration algorithm utilizes motion-invariant, curvature- based geometric properties to derive an approximation to an initial rigid transformation to align two image sets. Following the initial registration, changed portions of the surface are detected and excluded before refining the transformation parameters. The performance of the algorithm was tested using simulation experiments. To quantitatively assess the registration, random noise at various levels, known rigid motion transformations, and analytically-defined volume changes were applied to the initial surface data acquired from models of teeth. These simulation experiments demonstrated that the calculated transformation parameters were accurate to within 1.2 percent of the total applied rotation and 2.9 percent of the total applied translation, even at the highest applied noise levels and simulated wear values.

  18. Design of an Image Motion Compenstaion (IMC Algorithm for Image Registration of the Communication, Ocean, Meteorolotical Satellite (COMS-1

    Directory of Open Access Journals (Sweden)

    Taek Seo Jung

    2006-03-01

    Full Text Available This paper presents an Image Motion Compensation (IMC algorithm for the Korea's Communication, Ocean, and Meteorological Satellite (COMS-1. An IMC algorithm is a priority component of image registration in Image Navigation and Registration (INR system to locate and register radiometric image data. Due to various perturbations, a satellite has orbit and attitude errors with respect to a reference motion. These errors cause depointing of the imager aiming direction, and in consequence cause image distortions. To correct the depointing of the imager aiming direction, a compensation algorithm is designed by adapting different equations from those used for the GOES satellites. The capability of the algorithm is compared with that of existing algorithm applied to the GOES's INR system. The algorithm developed in this paper improves pointing accuracy by 40%, and efficiently compensates the depointings of the imager aiming direction.

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

    Science.gov (United States)

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

    2010-11-01

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

  20. Analysis of Point Based Image Registration Errors With Applications in Single Molecule Microscopy.

    Science.gov (United States)

    Cohen, E A K; Ober, R J

    2013-12-15

    We present an asymptotic treatment of errors involved in point-based image registration where control point (CP) localization is subject to heteroscedastic noise; a suitable model for image registration in fluorescence microscopy. Assuming an affine transform, CPs are used to solve a multivariate regression problem. With measurement errors existing for both sets of CPs this is an errors-in-variable problem and linear least squares is inappropriate; the correct method being generalized least squares. To allow for point dependent errors the equivalence of a generalized maximum likelihood and heteroscedastic generalized least squares model is achieved allowing previously published asymptotic results to be extended to image registration. For a particularly useful model of heteroscedastic noise where covariance matrices are scalar multiples of a known matrix (including the case where covariance matrices are multiples of the identity) we provide closed form solutions to estimators and derive their distribution. We consider the target registration error (TRE) and define a new measure called the localization registration error (LRE) believed to be useful, especially in microscopy registration experiments. Assuming Gaussianity of the CP localization errors, it is shown that the asymptotic distribution for the TRE and LRE are themselves Gaussian and the parameterized distributions are derived. Results are successfully applied to registration in single molecule microscopy to derive the key dependence of the TRE and LRE variance on the number of CPs and their associated photon counts. Simulations show asymptotic results are robust for low CP numbers and non-Gaussianity. The method presented here is shown to outperform GLS on real imaging data.

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

    Science.gov (United States)

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

    2003-08-21

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

  2. A study on applying image dictionary to inner organ registration

    International Nuclear Information System (INIS)

    Matsuno, Takamichi; Asai, Takeshi; Iwata, Takuya; Hontani, Hidekata

    2010-01-01

    In this article, we report on selecting image features that are useful for registering organ surface in medical image based on image dictionary constructed for the organ. Here, the image dictionary denotes a basis set, which is non-orthogonal and over-complete one and is designed to represent images of the target organ. We propose a method that refers to a combination of the basis obtained for reconstructing a given image in order to estimate the location of the target organ. (author)

  3. Contrast-enhanced magnetic resonance angiography in carotid artery disease: does automated image registration improve image quality?

    International Nuclear Information System (INIS)

    Menke, Jan; Larsen, Joerg

    2009-01-01

    Contrast-enhanced magnetic resonance angiography (MRA) is a noninvasive imaging alternative to digital subtraction angiography (DSA) for patients with carotid artery disease. In DSA, image quality can be improved by shifting the mask image if the patient has moved during angiography. This study investigated whether such image registration may also help to improve the image quality of carotid MRA. Data from 370 carotid MRA examinations of patients likely to have carotid artery disease were prospectively collected. The standard nonregistered MRAs were compared to automatically linear, affine and warp registered MRA by using three image quality parameters: the vessel detection probability (VDP) in maximum intensity projection (MIP) images, contrast-to-noise ratio (CNR) in MIP images, and contrast-to-noise ratio in three-dimensional image volumes. A body shift of less than 1 mm occurred in 96.2% of cases. Analysis of variance revealed no significant influence of image registration and body shift on image quality (p > 0.05). In conclusion, standard contrast-enhanced carotid MRA usually requires no image registration to improve image quality and is generally robust against any naturally occurring body shift. (orig.)

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

    Science.gov (United States)

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

    2015-12-01

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

  5. Non-rigid 3D shape classification using Bag-of-Feature techniques

    OpenAIRE

    Tabia, Hedi; Colot, Olivier; Daoudi, Mohamed; Vandeborre, Jean-Philippe

    2011-01-01

    International audience; In this paper, we present a new method for 3D-shape categorization using Bag-of-Feature techniques (BoF). This method is based on vector quantization of invariant descriptors of 3D-object patches. We analyze the performance of two well-known classifiers: the Naïve Bayes and the SVM. The results show the effectiveness of our approach and prove that the method is robust to non-rigid and deformable shapes, in which the class of transformations may be very wide due to the ...

  6. A strategy for multimodal deformable image registration to integrate PET/MR into radiotherapy treatment planning

    International Nuclear Information System (INIS)

    Leibfarth, Sara; Moennich, David; Thorwarth, Daniela; Welz, Stefan; Siegel, Christine; Zips, Daniel; Schwenzer, Nina; Holger Schmidt, Holger

    2013-01-01

    Background: Combined positron emission tomography (PET)/magnetic resonance imaging (MRI) is highly promising for biologically individualized radiotherapy (RT). Hence, the purpose of this work was to develop an accurate and robust registration strategy to integrate combined PET/MR data into RT treatment planning. Material and methods: Eight patient datasets consisting of an FDG PET/computed tomography (CT) and a subsequently acquired PET/MR of the head and neck (HN) region were available. Registration strategies were developed based on CT and MR data only, whereas the PET components were fused with the resulting deformation field. Following a rigid registration, deformable registration was performed with a transform parametrized by B-splines. Three different optimization metrics were investigated: global mutual information (GMI), GMI combined with a bending energy penalty (BEP) for regularization (GMI + BEP) and localized mutual information with BEP (LMI + BEP). Different quantitative registration quality measures were developed, including volumetric overlap and mean distance measures for structures segmented on CT and MR as well as anatomical landmark distances. Moreover, the local registration quality in the tumor region was assessed by the normalized cross correlation (NCC) of the two PET datasets. Results: LMI + BEP yielded the most robust and accurate registration results. For GMI, GMI + BEP and LMI + BEP, mean landmark distances (standard deviations) were 23.9 mm (15.5 mm), 4.8 mm (4.0 mm) and 3.0 mm (1.0 mm), and mean NCC values (standard deviations) were 0.29 (0.29), 0.84 (0.14) and 0.88 (0.06), respectively. Conclusion: Accurate and robust multimodal deformable image registration of CT and MR in the HN region can be performed using a B-spline parametrized transform and LMI + BEP as optimization metric. With this strategy, biologically individualized RT based on combined PET/MRI in terms of dose painting is possible

  7. Anisotropic multi-scale fluid registration: evaluation in magnetic resonance breast imaging

    International Nuclear Information System (INIS)

    Crum, W R; Tanner, C; Hawkes, D J

    2005-01-01

    Registration using models of compressible viscous fluids has not found the general application of some other techniques (e.g., free-form-deformation (FFD)) despite its ability to model large diffeomorphic deformations. We report on a multi-resolution fluid registration algorithm which improves on previous work by (a) directly solving the Navier-Stokes equation at the resolution of the images (b) accommodating image sampling anisotropy using semi-coarsening and implicit smoothing in a full multi-grid (FMG) solver and (c) exploiting the inherent multi-resolution nature of FMG to implement a multi-scale approach. Evaluation is on five magnetic resonance (MR) breast images subject to six biomechanical deformation fields over 11 multi-resolution schemes. Quantitative assessment is by tissue overlaps and target registration errors and by registering using the known correspondences rather than image features to validate the fluid model. Context is given by comparison with a validated FFD algorithm and by application to images of volunteers subjected to large applied deformation. The results show that fluid registration of 3D breast MR images to sub-voxel accuracy is possible in minutes on a 1.6 GHz Linux-based Athlon processor with coarse solutions obtainable in a few tens of seconds. Accuracy and computation time are comparable to FFD techniques validated for this application

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

    Science.gov (United States)

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

    2016-04-01

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

  9. The use of an image registration technique in the urban growth monitoring

    Science.gov (United States)

    Parada, N. D. J. (Principal Investigator); Foresti, C.; Deoliveira, M. D. L. N.; Niero, M.; Parreira, E. M. D. M. F.

    1984-01-01

    The use of an image registration program in the studies of urban growth is described. This program permits a quick identification of growing areas with the overlap of the same scene in different periods, and with the use of adequate filters. The city of Brasilia, Brazil, is selected for the test area. The dynamics of Brasilia urban growth are analyzed with the overlap of scenes dated June 1973, 1978 and 1983. The results showed the utilization of the image registration technique for the monitoring of dynamic urban growth.

  10. MatchGUI: A Graphical MATLAB-Based Tool for Automatic Image Co-Registration

    Science.gov (United States)

    Ansar, Adnan I.

    2011-01-01

    MatchGUI software, based on MATLAB, automatically matches two images and displays the match result by superimposing one image on the other. A slider bar allows focus to shift between the two images. There are tools for zoom, auto-crop to overlap region, and basic image markup. Given a pair of ortho-rectified images (focused primarily on Mars orbital imagery for now), this software automatically co-registers the imagery so that corresponding image pixels are aligned. MatchGUI requires minimal user input, and performs a registration over scale and inplane rotation fully automatically

  11. The Accuracy of ADC Measurements in Liver Is Improved by a Tailored and Computationally Efficient Local-Rigid Registration Algorithm.

    Science.gov (United States)

    Ragheb, Hossein; Thacker, Neil A; Guyader, Jean-Marie; Klein, Stefan; deSouza, Nandita M; Jackson, Alan

    2015-01-01

    This study describes post-processing methodologies to reduce the effects of physiological motion in measurements of apparent diffusion coefficient (ADC) in the liver. The aims of the study are to improve the accuracy of ADC measurements in liver disease to support quantitative clinical characterisation and reduce the number of patients required for sequential studies of disease progression and therapeutic effects. Two motion correction methods are compared, one based on non-rigid registration (NRA) using freely available open source algorithms and the other a local-rigid registration (LRA) specifically designed for use with diffusion weighted magnetic resonance (DW-MR) data. Performance of these methods is evaluated using metrics computed from regional ADC histograms on abdominal image slices from healthy volunteers. While the non-rigid registration method has the advantages of being applicable on the whole volume and in a fully automatic fashion, the local-rigid registration method is faster while maintaining the integrity of the biological structures essential for analysis of tissue heterogeneity. Our findings also indicate that the averaging commonly applied to DW-MR images as part of the acquisition protocol should be avoided if possible.

  12. a SAR Image Registration Method Based on Sift Algorithm

    Science.gov (United States)

    Lu, W.; Yue, X.; Zhao, Y.; Han, C.

    2017-09-01

    In order to improve the stability and rapidity of synthetic aperture radar (SAR) images matching, an effective method was presented. Firstly, the adaptive smoothing filtering was employed for image denoising in image processing based on Wallis filtering to avoid the follow-up noise is amplified. Secondly, feature points were extracted by a simplified SIFT algorithm. Finally, the exact matching of the images was achieved with these points. Compared with the existing methods, it not only maintains the richness of features, but a-lso reduces the noise of the image. The simulation results show that the proposed algorithm can achieve better matching effect.

  13. Topology-guided deformable registration with local importance preservation for biomedical images

    Science.gov (United States)

    Zheng, Chaojie; Wang, Xiuying; Zeng, Shan; Zhou, Jianlong; Yin, Yong; Feng, Dagan; Fulham, Michael

    2018-01-01

    The demons registration (DR) model is well recognized for its deformation capability. However, it might lead to misregistration due to erroneous diffusion direction when there are no overlaps between corresponding regions. We propose a novel registration energy function, introducing topology energy, and incorporating a local energy function into the DR in a progressive registration scheme, to address these shortcomings. The topology energy that is derived from the topological information of the images serves as a direction inference to guide diffusion transformation to retain the merits of DR. The local energy constrains the deformation disparity of neighbouring pixels to maintain important local texture and density features. The energy function is minimized in a progressive scheme steered by a topology tree graph and we refer to it as topology-guided deformable registration (TDR). We validated our TDR on 20 pairs of synthetic images with Gaussian noise, 20 phantom PET images with artificial deformations and 12 pairs of clinical PET-CT studies. We compared it to three methods: (1) free-form deformation registration method, (2) energy-based DR and (3) multi-resolution DR. The experimental results show that our TDR outperformed the other three methods in regard to structural correspondence and preservation of the local important information including texture and density, while retaining global correspondence.

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

    Energy Technology Data Exchange (ETDEWEB)

    Klooster, R. van ' t; Staring, M.; Reiber, J. H. C.; Lelieveldt, B. P. F.; Geest, R. J. van der, E-mail: rvdgeest@lumc.nl [Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC Leiden (Netherlands); Klein, S. [Department of Radiology and Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam 3015 GE (Netherlands); Kwee, R. M.; Kooi, M. E. [Department of Radiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht 6202 AZ (Netherlands)

    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

  15. Biomechanical Model for Computing Deformations for Whole-Body Image Registration: A Meshless Approach

    Science.gov (United States)

    Li, Mao; Miller, Karol; Joldes, Grand Roman; Kikinis, Ron; Wittek, Adam

    2016-01-01

    Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time consuming. Meshless discretisation has been proposed to solve this challenge. However, applications so far have been limited to 2-D models and computing single organ deformations. In this study, 3-D comprehensive patient-specific non-linear biomechanical models implemented using Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithms are applied to predict a 3-D deformation field for whole-body image registration. Unlike a conventional approach which requires dividing (segmenting) the image into non-overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the Fuzzy C-Means (FCM) algorithm without the image segmentation. Verification indicates that the deformations predicted using the proposed meshless approach are for practical purposes the same as those obtained using the previously validated finite element models. To quantitatively evaluate the accuracy of the predicted deformations, we determined the spatial misalignment between the registered (i.e. source images warped using the predicted deformations) and target images by computing the edge-based Hausdorff distance. The Hausdorff distance-based evaluation determines that our meshless models led to successful registration of the vast majority of the image features. PMID:26791945

  16. Image Navigation and Registration Performance Assessment Tool Set for the GOES-R Advanced Baseline Imager and Geostationary Lightning Mapper

    Science.gov (United States)

    De Luccia, Frank J.; Houchin, Scott; Porter, Brian C.; Graybill, Justin; Haas, Evan; Johnson, Patrick D.; Isaacson, Peter J.; Reth, Alan D.

    2016-01-01

    The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. For ABI, these metrics are the 3-sigma errors in navigation (NAV), channel-to-channel registration (CCR), frame-to-frame registration (FFR), swath-to-swath registration (SSR), and within frame registration (WIFR) for the Level 1B image products. For GLM, the single metric of interest is the 3-sigma error in the navigation of background images (GLM NAV) used by the system to navigate lightning strikes. 3-sigma errors are estimates of the 99.73rd percentile of the errors accumulated over a 24-hour data collection period. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24-hour evaluation period. Another aspect of the IPATS design that vastly reduces execution time is the off-line propagation of Landsat based truth images to the fixed grid coordinates system for each of the three GOES-R satellite locations, operational East and West and initial checkout locations. This paper describes the algorithmic design and implementation of IPATS and provides preliminary test results.

  17. Automatic registration of panoramic image sequence and mobile laser scanning data using semantic features

    Science.gov (United States)

    Li, Jianping; Yang, Bisheng; Chen, Chi; Huang, Ronggang; Dong, Zhen; Xiao, Wen

    2018-02-01

    Inaccurate exterior orientation parameters (EoPs) between sensors obtained by pre-calibration leads to failure of registration between panoramic image sequence and mobile laser scanning data. To address this challenge, this paper proposes an automatic registration method based on semantic features extracted from panoramic images and point clouds. Firstly, accurate rotation parameters between the panoramic camera and the laser scanner are estimated using GPS and IMU aided structure from motion (SfM). The initial EoPs of panoramic images are obtained at the same time. Secondly, vehicles in panoramic images are extracted by the Faster-RCNN as candidate primitives to be matched with potential corresponding primitives in point clouds according to the initial EoPs. Finally, translation between the panoramic camera and the laser scanner is refined by maximizing the overlapping area of corresponding primitive pairs based on the Particle Swarm Optimization (PSO), resulting in a finer registration between panoramic image sequences and point clouds. Two challenging urban scenes were experimented to assess the proposed method, and the final registration errors of these two scenes were both less than three pixels, which demonstrates a high level of automation, robustness and accuracy.

  18. Segmentation and registration duality from echographic images by use of physiological and morphological knowledge

    International Nuclear Information System (INIS)

    Ionescu, G.

    1998-01-01

    Echographic imaging could potentially play a major role in the field of Computer Assisted Surgery (CAS). For doctors and surgeons to make full use of tool in planning and executing surgical operations, they also need user-friendly automatic software based on fast, precise and reliable algorithms. The main goal of this thesis is to take advantage of the segmentation/registration duality to extract the relevant information from echo graphical images. This information will allow the precise and automatic registration both of anatomical structures contained in the pre-operative model and of per-operative data contained in echographic images. The result of registration will be further to guide a computer-assisted tool. In the first part we propose different methods for filtering, segmentation and calibration of echographic images. The development of fast, precise and reliable algorithms is emphasized. The second part concerns the segmentation-registration duality and the corrections of elastic deformations of soft tissues. High-level segmentation algorithms for echographic images were developed. They are based on results of low -level segmentation, a priori anatomical knowledge as well as on information provided by the pre-operative model. The third part deals with detailed descriptions of applications and interpretation of results. The cases studied include: screwing inside the vertebral pedicles, ilio-sacral screwing, prostatic radiotherapy and puncture of pericardial effusion. Future developments for this approach are discussed. (author)

  19. Mesh-to-raster region-of-interest-based nonrigid registration of multimodal images.

    Science.gov (United States)

    Tatano, Rosalia; Berkels, Benjamin; Deserno, Thomas M

    2017-10-01

    Region of interest (RoI) alignment in medical images plays a crucial role in diagnostics, procedure planning, treatment, and follow-up. Frequently, a model is represented as triangulated mesh while the patient data is provided from computed axial tomography scanners as pixel or voxel data. Previously, we presented a 2-D method for curve-to-pixel registration. This paper contributes (i) a general mesh-to-raster framework to register RoIs in multimodal images; (ii) a 3-D surface-to-voxel application, and (iii) a comprehensive quantitative evaluation in 2-D using ground truth (GT) provided by the simultaneous truth and performance level estimation (STAPLE) method. The registration is formulated as a minimization problem, where the objective consists of a data term, which involves the signed distance function of the RoI from the reference image and a higher order elastic regularizer for the deformation. The evaluation is based on quantitative light-induced fluoroscopy (QLF) and digital photography (DP) of decalcified teeth. STAPLE is computed on 150 image pairs from 32 subjects, each showing one corresponding tooth in both modalities. The RoI in each image is manually marked by three experts (900 curves in total). In the QLF-DP setting, our approach significantly outperforms the mutual information-based registration algorithm implemented with the Insight Segmentation and Registration Toolkit and Elastix.

  20. Nonlinear mechanics of non-rigid origami: an efficient computational approach.

    Science.gov (United States)

    Liu, K; Paulino, G H

    2017-10-01

    Origami-inspired designs possess attractive applications to science and engineering (e.g. deployable, self-assembling, adaptable systems). The special geometric arrangement of panels and creases gives rise to unique mechanical properties of origami, such as reconfigurability, making origami designs well suited for tunable structures. Although often being ignored, origami structures exhibit additional soft modes beyond rigid folding due to the flexibility of thin sheets that further influence their behaviour. Actual behaviour of origami structures usually involves significant geometric nonlinearity, which amplifies the influence of additional soft modes. To investigate the nonlinear mechanics of origami structures with deformable panels, we present a structural engineering approach for simulating the nonlinear response of non-rigid origami structures. In this paper, we propose a fully nonlinear, displacement-based implicit formulation for performing static/quasi-static analyses of non-rigid origami structures based on 'bar-and-hinge' models. The formulation itself leads to an efficient and robust numerical implementation. Agreement between real models and numerical simulations demonstrates the ability of the proposed approach to capture key features of origami behaviour.

  1. Nonlinear mechanics of non-rigid origami: an efficient computational approach

    Science.gov (United States)

    Liu, K.; Paulino, G. H.

    2017-10-01

    Origami-inspired designs possess attractive applications to science and engineering (e.g. deployable, self-assembling, adaptable systems). The special geometric arrangement of panels and creases gives rise to unique mechanical properties of origami, such as reconfigurability, making origami designs well suited for tunable structures. Although often being ignored, origami structures exhibit additional soft modes beyond rigid folding due to the flexibility of thin sheets that further influence their behaviour. Actual behaviour of origami structures usually involves significant geometric nonlinearity, which amplifies the influence of additional soft modes. To investigate the nonlinear mechanics of origami structures with deformable panels, we present a structural engineering approach for simulating the nonlinear response of non-rigid origami structures. In this paper, we propose a fully nonlinear, displacement-based implicit formulation for performing static/quasi-static analyses of non-rigid origami structures based on `bar-and-hinge' models. The formulation itself leads to an efficient and robust numerical implementation. Agreement between real models and numerical simulations demonstrates the ability of the proposed approach to capture key features of origami behaviour.

  2. Registration of clinical volumes to beams-eye-view images for real-time tracking

    Energy Technology Data Exchange (ETDEWEB)

    Bryant, Jonathan H.; Rottmann, Joerg; Lewis, John H.; Mishra, Pankaj; Berbeco, Ross I., E-mail: rberbeco@lroc.harvard.edu [Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115 (United States); Keall, Paul J. [Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, New South Wales 2006 (Australia)

    2014-12-15

    Purpose: The authors combine the registration of 2D beam’s eye view (BEV) images and 3D planning computed tomography (CT) images, with relative, markerless tumor tracking to provide automatic absolute tracking of physician defined volumes such as the gross tumor volume (GTV). Methods: During treatment of lung SBRT cases, BEV images were continuously acquired with an electronic portal imaging device (EPID) operating in cine mode. For absolute registration of physician-defined volumes, an intensity based 2D/3D registration to the planning CT was performed using the end-of-exhale (EoE) phase of the four dimensional computed tomography (4DCT). The volume was converted from Hounsfield units into electron density by a calibration curve and digitally reconstructed radiographs (DRRs) were generated for each beam geometry. Using normalized cross correlation between the DRR and an EoE BEV image, the best in-plane rigid transformation was found. The transformation was applied to physician-defined contours in the planning CT, mapping them into the EPID image domain. A robust multiregion method of relative markerless lung tumor tracking quantified deviations from the EoE position. Results: The success of 2D/3D registration was demonstrated at the EoE breathing phase. By registering at this phase and then employing a separate technique for relative tracking, the authors are able to successfully track target volumes in the BEV images throughout the entire treatment delivery. Conclusions: Through the combination of EPID/4DCT registration and relative tracking, a necessary step toward the clinical implementation of BEV tracking has been completed. The knowledge of tumor volumes relative to the treatment field is important for future applications like real-time motion management, adaptive radiotherapy, and delivered dose calculations.

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

    Science.gov (United States)

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

    2012-06-01

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

  4. Understanding geological processes: Visualization of rigid and non-rigid transformations

    Science.gov (United States)

    Shipley, T. F.; Atit, K.; Manduca, C. A.; Ormand, C. J.; Resnick, I.; Tikoff, B.

    2012-12-01

    Visualizations are used in the geological sciences to support reasoning about structures and events. Research in cognitive sciences offers insights into the range of skills of different users, and ultimately how visualizations might support different users. To understand the range of skills needed to reason about earth processes we have developed a program of research that is grounded in the geosciences' careful description of the spatial and spatiotemporal patterns associated with earth processes. In particular, we are pursuing a research program that identifies specific spatial skills and investigates whether and how they are related to each other. For this study, we focus on a specific question: Is there an important distinction in the geosciences between rigid and non-rigid deformation? To study a general spatial thinking skill we employed displays with non-geological objects that had been altered by rigid change (rotation), and two types of non-rigid change ("brittle" (or discontinuous) and "ductile" (or continuous) deformation). Disciplinary scientists (geosciences and chemistry faculty), and novices (non-science faculty and undergraduate psychology students) answered questions that required them to visualize the appearance of the object before the change. In one study, geologists and chemists were found to be superior to non-science faculty in reasoning about rigid rotations (e.g., what an object would look like from a different perspective). Geologists were superior to chemists in reasoning about brittle deformations (e.g., what an object looked like before it was broken - here the object was a word cut into many fragments displaced in different directions). This finding is consistent with two hypotheses: 1) Experts are good at visualizing the types of changes required for their domain; and 2) Visualization of rigid and non-rigid changes are not the same skill. An additional important finding is that there was a broad range of skill in both rigid and non-rigid

  5. PCA-based groupwise image registration for quantitative MRI

    NARCIS (Netherlands)

    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.

    2016-01-01

    Quantitative magnetic resonance imaging (qMRI) is a technique for estimating quantitative tissue properties, such as the T5 and T2 relaxation times, apparent diffusion coefficient (ADC), and various perfusion measures. This estimation is achieved by acquiring multiple images with different

  6. Influence of Acquisition Mode of Cone-beam Computed Tomography on Accuracy of Image Registration for Image-guided Radiotherapy.

    Science.gov (United States)

    Taniguchi, Takuya; Hara, Takanori; Shimozato, Tomohiro; Shiraki, Katsuhiko; Ohono, Kousei; Maejima, Ryousyuu

    2017-01-01

    Half scan can acquire images at the 200° rotation in image-guided radiation treatment using cone-beam CT and is useful to evaluate the influence of the half-scan-imaging start angle and imaging direction on image registration accuracy. The half-scan-imaging start angle is changed from 180° to 340° in the clockwise direction and from 180° to 20° in the counter clockwise direction to calculate the registration error. As a result, registration errors between -0.37 mm and 0.27 mm in the left and right directions occur because of the difference in the imaging start angle and approximately 0.3° in the gantry rotation direction because of the difference in the imaging direction. Because half scan does not have data for 360° rotation, depending on the subject structure, inconsistency of opposing data can lower reconstruction accuracy and cause a verification error. In addition, in image acquisition during rotation, the slower the shutter speed is, the more the actual gantry angle and angle information of the image are apart, which is considered the cause of rotation errors. Although these errors are very minute, it is thought that there is no influence on the treatment effect, but these errors are considered an evaluation item indispensable for ensuring the accuracy of high-precision radiation treatment. In addition, these errors need to be considered for ensuring the quality of high-precision radiation treatment.

  7. Markerless laser registration in image-guided oral and maxillofacial surgery.

    Science.gov (United States)

    Marmulla, Rüdiger; Lüth, Tim; Mühling, Joachim; Hassfeld, Stefan

    2004-07-01

    The use of registration markers in computer-assisted surgery is combined with high logistic costs and efforts. Markerless patient registration using laser scan surface registration techniques is a new challenging method. The present study was performed to evaluate the clinical accuracy in finding defined target points within the surgical site after markerless patient registration in image-guided oral and maxillofacial surgery. Twenty consecutive patients with different cranial diseases were scheduled for computer-assisted surgery. Data set alignment between the surgical site and the computed tomography (CT) data set was performed by markerless laser scan surface registration of the patient's face. Intraoral rigidly attached registration markers were used as target points, which had to be detected by an infrared pointer. The Surgical Segment Navigator SSN++ has been used for all procedures. SSN++ is an investigative product based on the SSN system that had previously been developed by the presenting authors with the support of Carl Zeiss (Oberkochen, Germany). SSN++ is connected to a Polaris infrared camera (Northern Digital, Waterloo, Ontario, Canada) and to a Minolta VI 900 3D digitizer (Tokyo, Japan) for high-resolution laser scanning. Minimal differences in shape between the laser scan surface and the surface generated from the CT data set could be detected. Nevertheless, high-resolution laser scan of the skin surface allows for a precise patient registration (mean deviation 1.1 mm, maximum deviation 1.8 mm). Radiation load, logistic costs, and efforts arising from the planning of computer-assisted surgery of the head can be reduced because native (markerless) CT data sets can be used for laser scan-based surface registration.

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

    Science.gov (United States)

    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.

  9. Rigid registration of CT, MR and cryosection images using a GLCM framework

    DEFF Research Database (Denmark)

    Bro-Nielsen, Morten; Grimson, E.; Mosges, R.

    1997-01-01

    The majority of the available rigid registration measures are based on a 2-dimensional histogram of corresponding grey-values in the registered images. This paper shows that these features are similar to a family of texture measures based on grey level co-occurrence matrices (GLCM). Features from...

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

    NARCIS (Netherlands)

    K. Pirpinia (Kleopatra); P.A.N. Bosman (Peter); J.-J. Sonke (Jan-Jakob); M. van Herk (Marcel); T. Alderliesten (Tanja)

    2016-01-01

    textabstractDeformable 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

  11. Spatially varying Riemannian elasticity regularization: Application to thoracic CT registration in image-guided radiotherapy

    DEFF Research Database (Denmark)

    Bjerre, Troels; Hansen, Mads Fogtmann; Aznar, M.

    2012-01-01

    For deformable registration of computed tomography (CT) scans in image guided radiation therapy (IGRT) we apply Riemannian elasticity regularization. We explore the use of spatially varying elasticity parameters to encourage bone rigidity and local tissue volume change only in the gross tumor...

  12. Nonrigid image registration using multi-scale 3D convolutional neural networks

    NARCIS (Netherlands)

    Sokooti, Hessam; de Vos, Bob; Berendsen, Floris; Lelieveldt, Boudewijn P.F.; Išgum, Ivana; Staring, Marius

    2017-01-01

    In this paper we propose a method to solve nonrigid image registration through a learning approach, instead of via iterative optimization of a predefined dissimilarity metric. We design a Convolutional Neural Network (CNN) architecture that, in contrast to all other work, directly estimates the

  13. Multimodal Registration and Fusion for 3D Thermal Imaging

    Directory of Open Access Journals (Sweden)

    Moulay A. Akhloufi

    2015-01-01

    Full Text Available 3D vision is an area of computer vision that has attracted a lot of research interest and has been widely studied. In recent years we witness an increasing interest from the industrial community. This interest is driven by the recent advances in 3D technologies, which enable high precision measurements at an affordable cost. With 3D vision techniques we can conduct advanced manufactured parts inspections and metrology analysis. However, we are not able to detect subsurface defects. This kind of detection is achieved by other techniques, like infrared thermography. In this work, we present a new registration framework for 3D and thermal infrared multimodal fusion. The resulting fused data can be used for advanced 3D inspection in Nondestructive Testing and Evaluation (NDT&E applications. The fusion permits the simultaneous visible surface and subsurface inspections to be conducted in the same process. Experimental tests were conducted with different materials. The obtained results are promising and show how these new techniques can be used efficiently in a combined NDT&E-Metrology analysis of manufactured parts, in areas such as aerospace and automotive.

  14. TU-CD-BRA-01: A Novel 3D Registration Method for Multiparametric Radiological Images

    International Nuclear Information System (INIS)

    Akhbardeh, A; Parekth, VS; Jacobs, MA

    2015-01-01

    Purpose: Multiparametric and multimodality radiological imaging methods, such as, magnetic resonance imaging(MRI), computed tomography(CT), and positron emission tomography(PET), provide multiple types of tissue contrast and anatomical information for clinical diagnosis. However, these radiological modalities are acquired using very different technical parameters, e.g.,field of view(FOV), matrix size, and scan planes, which, can lead to challenges in registering the different data sets. Therefore, we developed a hybrid registration method based on 3D wavelet transformation and 3D interpolations that performs 3D resampling and rotation of the target radiological images without loss of information Methods: T1-weighted, T2-weighted, diffusion-weighted-imaging(DWI), dynamic-contrast-enhanced(DCE) MRI and PET/CT were used in the registration algorithm from breast and prostate data at 3T MRI and multimodality(PET/CT) cases. The hybrid registration scheme consists of several steps to reslice and match each modality using a combination of 3D wavelets, interpolations, and affine registration steps. First, orthogonal reslicing is performed to equalize FOV, matrix sizes and the number of slices using wavelet transformation. Second, angular resampling of the target data is performed to match the reference data. Finally, using optimized angles from resampling, 3D registration is performed using similarity transformation(scaling and translation) between the reference and resliced target volume is performed. After registration, the mean-square-error(MSE) and Dice Similarity(DS) between the reference and registered target volumes were calculated. Results: The 3D registration method registered synthetic and clinical data with significant improvement(p<0.05) of overlap between anatomical structures. After transforming and deforming the synthetic data, the MSE and Dice similarity were 0.12 and 0.99. The average improvement of the MSE in breast was 62%(0.27 to 0.10) and prostate was

  15. An Image-Based Approach for the Co-Registration of Multi-Temporal UAV Image Datasets

    Directory of Open Access Journals (Sweden)

    Irene Aicardi

    2016-09-01

    Full Text Available During the past years, UAVs (Unmanned Aerial Vehicles became very popular as low-cost image acquisition platforms since they allow for high resolution and repetitive flights in a flexible way. One application is to monitor dynamic scenes. However, the fully automatic co-registration of the acquired multi-temporal data still remains an open issue. Most UAVs are not able to provide accurate direct image georeferencing and the co-registration process is mostly performed with the manual introduction of ground control points (GCPs, which is time consuming, costly and sometimes not possible at all. A new technique to automate the co-registration of multi-temporal high resolution image blocks without the use of GCPs is investigated in this paper. The image orientation is initially performed on a reference epoch and the registration of the following datasets is achieved including some anchor images from the reference data. The interior and exterior orientation parameters of the anchor images are then fixed in order to constrain the Bundle Block Adjustment of the slave epoch to be aligned with the reference one. The study involved the use of two different datasets acquired over a construction site and a post-earthquake damaged area. Different tests have been performed to assess the registration procedure using both a manual and an automatic approach for the selection of anchor images. The tests have shown that the procedure provides results comparable to the traditional GCP-based strategy and both the manual and automatic selection of the anchor images can provide reliable results.

  16. Study on Low Illumination Simultaneous Polarization Image Registration Based on Improved SURF Algorithm

    Science.gov (United States)

    Zhang, Wanjun; Yang, Xu

    2017-12-01

    Registration of simultaneous polarization images is the premise of subsequent image fusion operations. However, in the process of shooting all-weather, the polarized camera exposure time need to be kept unchanged, sometimes polarization images under low illumination conditions due to too dark result in SURF algorithm can not extract feature points, thus unable to complete the registration, therefore this paper proposes an improved SURF algorithm. Firstly, the luminance operator is used to improve overall brightness of low illumination image, and then create integral image, using Hession matrix to extract the points of interest to get the main direction of characteristic points, calculate Haar wavelet response in X and Y directions to get the SURF descriptor information, then use the RANSAC function to make precise matching, the function can eliminate wrong matching points and improve accuracy rate. And finally resume the brightness of the polarized image after registration, the effect of the polarized image is not affected. Results show that the improved SURF algorithm can be applied well under low illumination conditions.

  17. An Image Registration Based Technique for Noninvasive Vascular Elastography

    OpenAIRE

    Valizadeh, Sina; Makkiabadi, Bahador; Mirbagheri, Alireza; Soozande, Mehdi; Manwar, Rayyan; Mozaffarzadeh, Moein; Nasiriavanaki, Mohammadreza

    2018-01-01

    Non-invasive vascular elastography is an emerging technique in vascular tissue imaging. During the past decades, several techniques have been suggested to estimate the tissue elasticity by measuring the displacement of the Carotid vessel wall. Cross correlation-based methods are the most prevalent approaches to measure the strain exerted in the wall vessel by the blood pressure. In the case of a low pressure, the displacement is too small to be apparent in ultrasound imaging, especially in th...

  18. Multiscale deformable registration for dual-energy x-ray imaging

    Energy Technology Data Exchange (ETDEWEB)

    Gang, G. J.; Varon, C. A.; Kashani, H.; Richard, S.; Paul, N. S.; Van Metter, R.; Yorkston, J.; Siewerdsen, J. H. [Ontario Cancer Institute, Princess Margaret Hospital, Toronto, Ontario M5G 2M9 (Canada) and Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5G 2M9 (Canada); Ontario Cancer Institute, Princess Margaret Hospital, Toronto, Ontario M5G 2M9 (Canada); Ontario Cancer Institute, Princess Margaret Hospital, Toronto, Ontario M5G 2M9 (Canada) and Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 2M9 (Canada); Department of Medical Imaging, University Health Network, Toronto, Ontario M5G 2M9 (Canada); Carestream Health Inc., Rochester, New York 14650 (United States); Ontario Cancer Institute, Princess Margaret Hospital, Toronto, Ontario M5G 2M9, Canada and Institute of Biomaterials and Biomedical Engineering, and Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 2M9 (Canada)

    2009-02-15

    Dual-energy (DE) imaging of the chest improves the conspicuity of subtle lung nodules through the removal of overlying anatomical noise. Recent work has shown double-shot DE imaging (i.e., successive acquisition of low- and high-energy projections) to provide detective quantum efficiency, spectral separation (and therefore contrast), and radiation dose superior to single-shot DE imaging configurations (e.g., with a CR cassette). However, the temporal separation between high-energy (HE) and low-energy (LE) image acquisition can result in motion artifacts in the DE images, reducing image quality and diminishing diagnostic performance. This has motivated the development of a deformable registration technique that aligns the HE image onto the LE image before DE decomposition. The algorithm reported here operates in multiple passes at progressively smaller scales and increasing resolution. The first pass addresses large-scale motion by means of mutual information optimization, while successive passes (2-4) correct misregistration at finer scales by means of normalized cross correlation. Evaluation of registration performance in 129 patients imaged using an experimental DE imaging prototype demonstrated a statistically significant improvement in image alignment. Specific to the cardiac region, the registration algorithm was found to outperform a simple cardiac-gating system designed to trigger both HE and LE exposures during diastole. Modulation transfer function (MTF) analysis reveals additional advantages in DE image quality in terms of noise reduction and edge enhancement. This algorithm could offer an important tool in enhancing DE image quality and potentially improving diagnostic performance.

  19. Multiscale deformable registration for dual-energy x-ray imaging.

    Science.gov (United States)

    Gang, G J; Varon, C A; Kashani, H; Richard, S; Paul, N S; Van Metter, R; Yorkston, J; Siewerdsen, J H

    2009-02-01

    Dual-energy (DE) imaging of the chest improves the conspicuity of subtle lung nodules through the removal of overlying anatomical noise. Recent work has shown double-shot DE imaging (i.e., successive acquisition of low- and high-energy projections) to provide detective quantum efficiency, spectral separation (and therefore contrast), and radiation dose superior to single-shot DE imaging configurations (e.g., with a CR cassette). However, the temporal separation between high-energy (HE) and low-energy (LE) image acquisition can result in motion artifacts in the DE images, reducing image quality and diminishing diagnostic performance. This has motivated the development of a deformable registration technique that aligns the HE image onto the LE image before DE decomposition. The algorithm reported here operates in multiple passes at progressively smaller scales and increasing resolution. The first pass addresses large-scale motion by means of mutual information optimization, while successive passes (2-4) correct misregistration at finer scales by means of normalized cross correlation. Evaluation of registration performance in 129 patients imaged using an experimental DE imaging prototype demonstrated a statistically significant improvement in image alignment. Specific to the cardiac region, the registration algorithm was found to outperform a simple cardiac-gating system designed to trigger both HE and LE exposures during diastole. Modulation transfer function (MTF) analysis reveals additional advantages in DE image quality in terms of noise reduction and edge enhancement. This algorithm could offer an important tool in enhancing DE image quality and potentially improving diagnostic performance.

  20. Physics-based elastic image registration using splines and including landmark localization uncertainties.

    Science.gov (United States)

    Wörz, Stefan; Rohr, Karl

    2006-01-01

    We introduce an elastic registration approach which is based on a physical deformation model and uses Gaussian elastic body splines (GEBS). We formulate an extended energy functional related to the Navier equation under Gaussian forces which also includes landmark localization uncertainties. These uncertainties are characterized by weight matrices representing anisotropic errors. Since the approach is based on a physical deformation model, cross-effects in elastic deformations can be taken into account. Moreover, we have a free parameter to control the locality of the transformation for improved registration of local geometric image differences. We demonstrate the applicability of our scheme based on 3D CT images from the Truth Cube experiment, 2D MR images of the brain, as well as 2D gel electrophoresis images. It turns out that the new scheme achieves more accurate results compared to previous approaches.

  1. Weight preserving image registration for monitoring disease progression in lung CT.

    Science.gov (United States)

    Gorbunova, Vladlena; Lol, Pechin; Ashraf, Haseem; Dirksen, Asger; Nielsen, Mads; de Bruijne, Marleen

    2008-01-01

    We present a new image registration based method for monitoring regional disease progression in longitudinal image studies of lung disease. A free-form image registration technique is used to match a baseline 3D CT lung scan onto a following scan. Areas with lower intensity in the following scan compared with intensities in the deformed baseline image indicate local loss of lung tissue that is associated with progression of emphysema. To account for differences in lung intensity owing to differences in the inspiration level in the two scans rather than disease progression, we propose to adjust the density of lung tissue with respect to local expansion or compression such that the total weight of the lungs is preserved during deformation. Our method provides a good estimation of regional destruction of lung tissue for subjects with a significant difference in inspiration level between CT scans and may result in a more sensitive measure of disease progression than standard quantitative CT measures.

  2. Weight preserving image registration for monitoring disease progression in lung CT

    DEFF Research Database (Denmark)

    Gorbunova, Vladlena; Lo, Pechin Chien Pau; Haseem, Ashraf

    2008-01-01

    compared with intensities in the deformed baseline image indicate local loss of lung tissue that is associated with progression of emphysema. To account for differences in lung intensity owing to differences in the inspiration level in the two scans rather than disease progression, we propose to adjust......We present a new image registration based method for monitoring regional disease progression in longitudinal image studies of lung disease. A free-form image registration technique is used to match a baseline 3D CT lung scan onto a following scan. Areas with lower intensity in the following scan...... the density of lung tissue with respect to local expansion or compression such that the total weight of the lungs is preserved during deformation. Our method provides a good estimation of regional destruction of lung tissue for subjects with a significant difference in inspiration level between CT scans...

  3. Accuracy of rigid CT-FDG-PET image registration of the liver

    International Nuclear Information System (INIS)

    Dalen, J A van; Vogel, W; Huisman, H J; Oyen, W J G; Jager, G J; Karssemeijer, N

    2004-01-01

    Diagnostic and surgical strategies could benefit from accurate localization of liver malignancies via CT-FDG-PET image registration. However, registration uncertainty occurs due to protocol differences in data-acquisition, the limited spatial resolution of positron emission tomography (PET) and the low uptake of 18 F-fluorodeoxyglucose (FDG) in normal liver tissue. To assess this uncertainty, methods were presented to estimate registration precision and systematic bias. A semi-automatic, organ-focused method was investigated to minimize the uncertainty well beyond the typical uncertainty of 5-10 mm obtained by commonly available methods. By restricting registration to the liver region and by isolating the liver on computed tomography (CT) from surrounding structures using a thresholding technique, registration was achieved using the mutual information-based method as implemented in insight toolkit (ITK). CT and FDG-PET images of 10 patients with liver metastases were registered rigidly a number of times. Results of the organ-focused method were compared to results of three commonly available methods (a manual, a landmark-based and a 'standard' mutual information-based method) where no dedicated image processing was performed. The proposed method outperformed the other methods with a precision (mean ± s.d.) of 2.5 ± 1.3 mm and a bias of 1.9 mm with a 95% CI of [1.0, 2.8] mm. Unlike the commonly available methods, our approach allows for robust CT-FDG-PET registration of the liver, with an accuracy better than the spatial resolution of the PET scanner that was used

  4. Automatic block-matching registration to improve lung tumor localization during image-guided radiotherapy

    Science.gov (United States)

    Robertson, Scott Patrick

    To improve relatively poor outcomes for locally-advanced lung cancer patients, many current efforts are dedicated to minimizing uncertainties in radiotherapy. This enables the isotoxic delivery of escalated tumor doses, leading to better local tumor control. The current dissertation specifically addresses inter-fractional uncertainties resulting from patient setup variability. An automatic block-matching registration (BMR) algorithm is implemented and evaluated for the purpose of directly localizing advanced-stage lung tumors during image-guided radiation therapy. In this algorithm, small image sub-volumes, termed "blocks", are automatically identified on the tumor surface in an initial planning computed tomography (CT) image. Each block is independently and automatically registered to daily images acquired immediately prior to each treatment fraction. To improve the accuracy and robustness of BMR, this algorithm incorporates multi-resolution pyramid registration, regularization with a median filter, and a new multiple-candidate-registrations technique. The result of block-matching is a sparse displacement vector field that models local tissue deformations near the tumor surface. The distribution of displacement vectors is aggregated to obtain the final tumor registration, corresponding to the treatment couch shift for patient setup correction. Compared to existing rigid and deformable registration algorithms, the final BMR algorithm significantly improves the overlap between target volumes from the planning CT and registered daily images. Furthermore, BMR results in the smallest treatment margins for the given study population. However, despite these improvements, large residual target localization errors were noted, indicating that purely rigid couch shifts cannot correct for all sources of inter-fractional variability. Further reductions in treatment uncertainties may require the combination of high-quality target localization and adaptive radiotherapy.

  5. Development of a QA Phantom for online image registration and resultant couch shifts

    International Nuclear Information System (INIS)

    Arumugam, S.; Jameson, M.G.; Holloway, L.C.

    2010-01-01

    Full text: Purpose Recently our centre purchased an Elekta-Synergy accelerator with kV-CBCT and a hexapod couch attachment. This system allows six degrees of freedom for couch lOp shifts, based on registration of on line imaging. We designed and built a phantom in our centre to test the accuracy and precision of this system. The goal of this project was to investigate the accuracy and practical utilisation of this phantom. Method The phantom was constructed from perspex sheets and high density dental putty (Fig. I). Five high density regions (three small regions to simulate prostate seeds and two larger regions to simulate boney anatomy) were incorporated to test the manual and automatic registrations within the software. The phantom was utilised to test the accuracy and precision of repositioning with the hexapod couch and imaging system. To achieve this, the phantom was placed on the couch at known orientations and the shifts were quantified using the registration of verification and reference image data sets. True shifts and those predicted by the software were compared. Results The geometrical accuracy of the phantom was verified with measurements of the CT scan to be with I mm of the intended geometry. The image registration and resultant couch shifts were found to be accurate within I mm and 0.5 degrees. The phantom was found to be practical and easy to use. Conclusion The presented phantom provides a less expensive and effective alternative to commercially available systems for verifying imaging registration and corresponding six degrees of freedom couch shifts. (author)

  6. An Automatic and Novel SAR Image Registration Algorithm: A Case Study of the Chinese GF-3 Satellite

    Directory of Open Access Journals (Sweden)

    Yuming Xiang

    2018-02-01

    Full Text Available The Chinese GF-3 satellite launched in August 2016 is a Synthetic Aperture Radar (SAR satellite that has the largest number of imaging modes in the world. It achieves a free switch in the spotlight, stripmap, scanSAR, wave, global observation and other imaging modes. In order to further utilize GF-3 SAR images, an automatic and fast image registration procedure needs to be done. In this paper, we propose a novel image registration technique for GF-3 images of different imaging modes. The proposed algorithm consists of two stages: coarse registration and fine registration. In the first stage, we combine an adaptive sampling method with the SAR-SIFT algorithm to efficiently eliminate obvious translation, rotation and scale differences between the reference and sensed images. In the second stage, uniformly-distributed control points are extracted, then the fast normalized cross-correlation of an improved phase congruency model is utilized as a new similarity metric to match the reference image and the coarse-registered image in a local search region. Moreover, a selection strategy is used to remove outliers. Experimental results on several GF-3 SAR images of different imaging modes show that the proposed algorithm gives a robust, efficient and precise registration performance, compared with other state-of-the-art algorithms for SAR image registration.

  7. An Automatic and Novel SAR Image Registration Algorithm: A Case Study of the Chinese GF-3 Satellite.

    Science.gov (United States)

    Xiang, Yuming; Wang, Feng; You, Hongjian

    2018-02-24

    The Chinese GF-3 satellite launched in August 2016 is a Synthetic Aperture Radar (SAR) satellite that has the largest number of imaging modes in the world. It achieves a free switch in the spotlight, stripmap, scanSAR, wave, global observation and other imaging modes. In order to further utilize GF-3 SAR images, an automatic and fast image registration procedure needs to be done. In this paper, we propose a novel image registration technique for GF-3 images of different imaging modes. The proposed algorithm consists of two stages: coarse registration and fine registration. In the first stage, we combine an adaptive sampling method with the SAR-SIFT algorithm to efficiently eliminate obvious translation, rotation and scale differences between the reference and sensed images. In the second stage, uniformly-distributed control points are extracted, then the fast normalized cross-correlation of an improved phase congruency model is utilized as a new similarity metric to match the reference image and the coarse-registered image in a local search region. Moreover, a selection strategy is used to remove outliers. Experimental results on several GF-3 SAR images of different imaging modes show that the proposed algorithm gives a robust, efficient and precise registration performance, compared with other state-of-the-art algorithms for SAR image registration.

  8. Simulating Deformations of MR Brain Images for Validation of Atlas-based Segmentation and Registration Algorithms

    OpenAIRE

    Xue, Zhong; Shen, Dinggang; Karacali, Bilge; Stern, Joshua; Rottenberg, David; Davatzikos, Christos

    2006-01-01

    Simulated deformations and images can act as the gold standard for evaluating various template-based image segmentation and registration algorithms. Traditional deformable simulation methods, such as the use of analytic deformation fields or the displacement of landmarks followed by some form of interpolation, are often unable to construct rich (complex) and/or realistic deformations of anatomical organs. This paper presents new methods aiming to automatically simulate realistic inter- and in...

  9. Iterative multi-atlas-based multi-image segmentation with tree-based registration.

    Science.gov (United States)

    Jia, Hongjun; Yap, Pew-Thian; Shen, Dinggang

    2012-01-02

    In this paper, we present a multi-atlas-based framework for accurate, consistent and simultaneous segmentation of a group of target images. Multi-atlas-based segmentation algorithms consider concurrently complementary information from multiple atlases to produce optimal segmentation outcomes. However, the accuracy of these algorithms relies heavily on the precise alignment of the atlases with the target image. In particular, the commonly used pairwise registration may result in inaccurate alignment especially between images with large shape differences. Additionally, when segmenting a group of target images, most current methods consider these images independently with disregard of their correlation, thus resulting in inconsistent segmentations of the same structures across different target images. We propose two novel strategies to address these limitations: 1) a novel tree-based groupwise registration method for concurrent alignment of both the atlases and the target images, and 2) an iterative groupwise segmentation method for simultaneous consideration of segmentation information propagated from all available images, including the atlases and other newly segmented target images. Evaluation based on various datasets indicates that the proposed multi-atlas-based multi-image segmentation (MABMIS) framework yields substantial improvements in terms of consistency and accuracy over methods that do not consider the group of target images holistically. Copyright © 2011 Elsevier Inc. All rights reserved.

  10. Computer assisted determination of acetabular cup orientation using 2D-3D image registration

    International Nuclear Information System (INIS)

    Zheng, Guoyan; Zhang, Xuan

    2010-01-01

    2D-3D image-based registration methods have been developed to measure acetabular cup orientation after total hip arthroplasty (THA). These methods require registration of both the prosthesis and the CT images to 2D radiographs and compute implant position with respect to a reference. The application of these methods is limited in clinical practice due to two limitations: (1) the requirement of a computer-aided design (CAD) model of the prosthesis, which may be unavailable due to the proprietary concerns of the manufacturer, and (2) the requirement of either multiple radiographs or radiograph-specific calibration, usually unavailable for retrospective studies. In this paper, we propose a new method to address these limitations. A new formulation for determination of post-operative cup orientation, which couples a radiographic measurement with 2D-3D image matching, was developed. In our formulation, the radiographic measurement can be obtained with known methods so that the challenge lies in the 2D-3D image matching. To solve this problem, a hybrid 2D-3D registration scheme combining a landmark-to-ray 2D-3D alignment with a robust intensity-based 2D-3D registration was used. The hybrid 2D-3D registration scheme allows computing both the post-operative cup orientation with respect to an anatomical reference and the pelvic tilt and rotation with respect to the X-ray imaging table/plate. The method was validated using 2D adult cadaver hips. Using the hybrid 2D-3D registration scheme, our method showed a mean accuracy of 1.0 ± 0.7 (range from 0.1 to 2.0 ) for inclination and 1.7 ± 1.2 (range from 0.0 to 3.9 ) for anteversion, taking the measurements from post-operative CT images as ground truths. Our new solution formulation and the hybrid 2D-3D registration scheme facilitate estimation of post-operative cup orientation and measurement of pelvic tilt and rotation. (orig.)

  11. Clinical assessment of SPECT/CT co-registration image fusion

    International Nuclear Information System (INIS)

    Zhou Wen; Luan Zhaosheng; Peng Yong

    2004-01-01

    Objective: Study the methodology of the SPECT/CT co-registration image fusion, and Assessment the Clinical application value. Method: 172 patients who underwent SPECT/CT image fusion during 2001-2003 were studied, 119 men, 53 women. 51 patients underwent 18FDG image +CT, 26 patients underwent 99m Tc-RBC Liver pool image +CT, 43 patients underwent 99mTc-MDP Bone image +CT, 18 patients underwent 99m Tc-MAA Lung perfusion image +CT. The machine is Millium VG SPECT of GE Company. All patients have been taken three steps image: X-ray survey, X-ray transmission and nuclear emission image (Including planer imaging, SPECT or 18 F-FDG of dual head camera) without changing the position of the patients. We reconstruct the emission image with X-ray map and do reconstruction, 18FDG with COSEM and 99mTc with OSEM. Then combine the transmission image and the reconstructed emission image. We use different process parameters in deferent image methods. The accurate rate of SPECT/CT image fusion were statistics, and compare their accurate with that of single nuclear emission image. Results: The nuclear image which have been reconstructed by X-ray attenuation and OSEM are apparent better than pre-reconstructed. The post-reconstructed emission images have no scatter lines around the organs. The outline between different issues is more clear than before. The validity of All post-reconstructed images is better than pre-reconstructed. SPECT/CT image fusion make localization have worthy bases. 138 patients, the accuracy of SPECT/CT image fusion is 91.3% (126/138), whereas 60(88.2%) were found through SPECT/CT image fusion, There are significant difference between them(P 99m Tc- RBC-SPECT +CT image fusion, but 21 of them were inspected by emission image. In BONE 99m Tc -MDP-SPECT +CT image fusion, 4 patients' removed bone(1-6 months after surgery) and their relay with normal bone had activity, their morphologic and density in CT were different from normal bones. 11 of 20 patients who could

  12. Detection of patient setup errors with a portal image - DRR registration software application.

    Science.gov (United States)

    Sutherland, Kenneth; Ishikawa, Masayori; Bengua, Gerard; Ito, Yoichi M; Miyamoto, Yoshiko; Shirato, Hiroki

    2011-02-18

    The purpose of this study was to evaluate a custom portal image - digitally reconstructed radiograph (DRR) registration software application. The software works by transforming the portal image into the coordinate space of the DRR image using three control points placed on each image by the user, and displaying the fused image. In order to test statistically that the software actually improves setup error estimation, an intra- and interobserver phantom study was performed. Portal images of anthropomorphic thoracic and pelvis phantoms with virtually placed irradiation fields at known setup errors were prepared. A group of five doctors was first asked to estimate the setup errors by examining the portal and DRR image side-by-side, not using the software. A second group of four technicians then estimated the same set of images using the registration software. These two groups of human subjects were then compared with an auto-registration feature of the software, which is based on the mutual information between the portal and DRR images. For the thoracic case, the average distance between the actual setup error and the estimated error was 4.3 ± 3.0 mm for doctors using the side-by-side method, 2.1 ± 2.4 mm for technicians using the registration method, and 0.8 ± 0.4mm for the automatic algorithm. For the pelvis case, the average distance between the actual setup error and estimated error was 2.0 ± 0.5 mm for the doctors using the side-by-side method, 2.5 ± 0.4 mm for technicians using the registration method, and 2.0 ± 1.0 mm for the automatic algorithm. The ability of humans to estimate offset values improved statistically using our software for the chest phantom that we tested. Setup error estimation was further improved using our automatic error estimation algorithm. Estimations were not statistically different for the pelvis case. Consistency improved using the software for both the chest and pelvis phantoms. We also tested the automatic algorithm with a

  13. Registration and analysis for images couple : application to mammograms

    OpenAIRE

    Boucher, Arnaud

    2014-01-01

    Advisor: Nicole Vincent. Date and location of PhD thesis defense: 10 January 2013, University of Paris Descartes In this thesis, the problem addressed is the development of a computer-aided diagnosis system (CAD) based on conjoint analysis of several images, and therefore on the comparison of these medical images. The particularity of our approach is to look for evolutions or aberrant new tissues in a given set, rather than attempting to characterize, with a strong a priori, the type of ti...

  14. Fast and Robust Registration of Multimodal Remote Sensing Images via Dense Orientated Gradient Feature

    Science.gov (United States)

    Ye, Y.

    2017-09-01

    This paper presents a fast and robust method for the registration of multimodal remote sensing data (e.g., optical, LiDAR, SAR and map). The proposed method is based on the hypothesis that structural similarity between images is preserved across different modalities. In the definition of the proposed method, we first develop a pixel-wise feature descriptor named Dense Orientated Gradient Histogram (DOGH), which can be computed effectively at every pixel and is robust to non-linear intensity differences between images. Then a fast similarity metric based on DOGH is built in frequency domain using the Fast Fourier Transform (FFT) technique. Finally, a template matching scheme is applied to detect tie points between images. Experimental results on different types of multimodal remote sensing images show that the proposed similarity metric has the superior matching performance and computational efficiency than the state-of-the-art methods. Moreover, based on the proposed similarity metric, we also design a fast and robust automatic registration system for multimodal images. This system has been evaluated using a pair of very large SAR and optical images (more than 20000 × 20000 pixels). Experimental results show that our system outperforms the two popular commercial software systems (i.e. ENVI and ERDAS) in both registration accuracy and computational efficiency.

  15. Automatic Registration of Low Altitude UAV Sequent Images and Laser Point Clouds

    Directory of Open Access Journals (Sweden)

    CHEN Chi

    2015-05-01

    Full Text Available It is proposed that a novel registration method for automatic co-registration of unmanned aerial vehicle (UAV images sequence and laser point clouds. Firstly, contours of building roofs are extracted from the images sequence and laser point clouds using marked point process and local salient region detection, respectively. The contours from each data are matched via back-project proximity. Secondly, the exterior orientations of the images are recovered using a linear solver based on the contours corner pairs followed by a co-planar optimization which is implicated by the matched lines form contours pairs. Finally, the exterior orientation parameters of images are further optimized by matching 3D points generated from images sequence and laser point clouds using an iterative near the point (ICP algorithm with relative movement threshold constraint. Experiments are undertaken to check the validity and effectiveness of the proposed method. The results show that the proposed method achieves high-precision co-registration of low-altitude UAV image sequence and laser points cloud robustly. The accuracy of the co-produced DOMs meets 1:500 scale standards.

  16. Visible and infrared image registration algorithm based on NSCT and gradient mirroring

    Science.gov (United States)

    Huang, Qingqing; Gao, Qiong; Yang, Jian; Chen, Jiansheng; Song, Zhanjie

    2014-11-01

    Multi-sensor image registration is an important part of the remote sensing image processing. The gray property of the same object would have large differences in infrared and visible imaging mode, so it could get less matching points by using traditional SIFT algorithm directly in registration. However, NSCT decomposition can represent the structural information of the image very well and extract more SIFT feature points in its high frequency decomposed image. In addition, traditional SIFT descriptors' gradient is affected by gray contrast, which could get less feature matching points during the similarity search in the matching procedure. Gradient mirroring (GM) is a method that can modify the direction of the feature points, which can reduce the contrast impact on the similarity matching. Therefore, a novel method combining NSCT and GM is proposed in this article. The experiments prove that, comparing with the traditional SIFT algorithm, the new method can get more matching points, better distributing and higher matching rate in infrared and visible image registration.

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

    Science.gov (United States)

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

    2017-02-01

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

  18. Technical Note: DIRART- A software suite for deformable image registration and adaptive radiotherapy research

    International Nuclear Information System (INIS)

    Yang Deshan; Brame, Scott; El Naqa, Issam; Aditya, Apte; Wu Yu; Murty Goddu, S.; Mutic, Sasa; Deasy, Joseph O.; Low, Daniel A.

    2011-01-01

    Purpose: Recent years have witnessed tremendous progress in image guide radiotherapy technology and a growing interest in the possibilities for adapting treatment planning and delivery over the course of treatment. One obstacle faced by the research community has been the lack of a comprehensive open-source software toolkit dedicated for adaptive radiotherapy (ART). To address this need, the authors have developed a software suite called the Deformable Image Registration and Adaptive Radiotherapy Toolkit (DIRART). Methods: DIRART is an open-source toolkit developed in MATLAB. It is designed in an object-oriented style with focus on user-friendliness, features, and flexibility. It contains four classes of DIR algorithms, including the newer inverse consistency algorithms to provide consistent displacement vector field in both directions. It also contains common ART functions, an integrated graphical user interface, a variety of visualization and image-processing features, dose metric analysis functions, and interface routines. These interface routines make DIRART a powerful complement to the Computational Environment for Radiotherapy Research (CERR) and popular image-processing toolkits such as ITK. Results: DIRART provides a set of image processing/registration algorithms and postprocessing functions to facilitate the development and testing of DIR algorithms. It also offers a good amount of options for DIR results visualization, evaluation, and validation. Conclusions: By exchanging data with treatment planning systems via DICOM-RT files and CERR, and by bringing image registration algorithms closer to radiotherapy applications, DIRART is potentially a convenient and flexible platform that may facilitate ART and DIR research.

  19. FAST AND ROBUST REGISTRATION OF MULTIMODAL REMOTE SENSING IMAGES VIA DENSE ORIENTATED GRADIENT FEATURE

    Directory of Open Access Journals (Sweden)

    Y. Ye

    2017-09-01

    Full Text Available This paper presents a fast and robust method for the registration of multimodal remote sensing data (e.g., optical, LiDAR, SAR and map. The proposed method is based on the hypothesis that structural similarity between images is preserved across different modalities. In the definition of the proposed method, we first develop a pixel-wise feature descriptor named Dense Orientated Gradient Histogram (DOGH, which can be computed effectively at every pixel and is robust to non-linear intensity differences between images. Then a fast similarity metric based on DOGH is built in frequency domain using the Fast Fourier Transform (FFT technique. Finally, a template matching scheme is applied to detect tie points between images. Experimental results on different types of multimodal remote sensing images show that the proposed similarity metric has the superior matching performance and computational efficiency than the state-of-the-art methods. Moreover, based on the proposed similarity metric, we also design a fast and robust automatic registration system for multimodal images. This system has been evaluated using a pair of very large SAR and optical images (more than 20000 × 20000 pixels. Experimental results show that our system outperforms the two popular commercial software systems (i.e. ENVI and ERDAS in both registration accuracy and computational efficiency.

  20. Medical Image Registration by means of a Bio-Inspired Optimization Strategy

    Directory of Open Access Journals (Sweden)

    Hariton Costin

    2012-07-01

    Full Text Available Medical imaging mainly treats and processes missing, ambiguous, complementary, redundant and distorted data. Biomedical image registration is the process of geometric overlaying or alignment of two or more 2D/3D images of the same scene, taken at different time slots, from different angles, and/or by different acquisition systems. In medical practice, it is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Technically, image registration implies a complex optimization of different parameters, performed at local or/and global levels. Local optimization methods frequently fail because functions of the involved metrics with respect to transformation parameters are generally nonconvex and irregular. Therefore, global methods are often required, at least at the beginning of the procedure. In this paper, a new evolutionary and bio-inspired approach -- bacterial foraging optimization -- is adapted for single-slice to 3-D PET and CT multimodal image registration. Preliminary results of optimizing the normalized mutual information similarity metric validated the efficacy of the proposed method by using a freely available medical image database.

  1. Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

    Science.gov (United States)

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

    2014-02-01

    In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.

  2. MO-F-BRA-04: Voxel-Based Statistical Analysis of Deformable Image Registration Error via a Finite Element Method.

    Science.gov (United States)

    Li, S; Lu, M; Kim, J; Glide-Hurst, C; Chetty, I; Zhong, H

    2012-06-01

    Purpose Clinical implementation of adaptive treatment planning is limited by the lack of quantitative tools to assess deformable image registration errors (R-ERR). The purpose of this study was to develop a method, using finite element modeling (FEM), to estimate registration errors based on mechanical changes resulting from them. Methods An experimental platform to quantify the correlation between registration errors and their mechanical consequences was developed as follows: diaphragm deformation was simulated on the CT images in patients with lung cancer using a finite element method (FEM). The simulated displacement vector fields (F-DVF) were used to warp each CT image to generate a FEM image. B-Spline based (Elastix) registrations were performed from reference to FEM images to generate a registration DVF (R-DVF). The F- DVF was subtracted from R-DVF. The magnitude of the difference vector was defined as the registration error, which is a consequence of mechanically unbalanced energy (UE), computed using 'in-house-developed' FEM software. A nonlinear regression model was used based on imaging voxel data and the analysis considered clustered voxel data within images. Results A regression model analysis showed that UE was significantly correlated with registration error, DVF and the product of registration error and DVF respectively with R̂2=0.73 (R=0.854). The association was verified independently using 40 tracked landmarks. A linear function between the means of UE values and R- DVF*R-ERR has been established. The mean registration error (N=8) was 0.9 mm. 85.4% of voxels fit this model within one standard deviation. Conclusions An encouraging relationship between UE and registration error has been found. These experimental results suggest the feasibility of UE as a valuable tool for evaluating registration errors, thus supporting 4D and adaptive radiotherapy. The research was supported by NIH/NCI R01CA140341. © 2012 American Association of Physicists in

  3. Volume preserving image registration via a post-processing stage

    NARCIS (Netherlands)

    Hameeteman, R.; Veenland, J.F.; Niessen, W.J.

    2006-01-01

    In this paper a method to remove the divergence from a vector field is presented. When applied to a displacement field, this will remove all local compression and expansion. The method can be used as a post-processing step for (unconstrained) registered images, when volume changes in the deformation

  4. Automatic image fusion of real-time ultrasound with computed tomography images: a prospective comparison between two auto-registration methods.

    Science.gov (United States)

    Cha, Dong Ik; Lee, Min Woo; Kim, Ah Yeong; Kang, Tae Wook; Oh, Young-Taek; Jeong, Ja-Yeon; Chang, Jung-Woo; Ryu, Jiwon; Lee, Kyong Joon; Kim, Jaeil; Bang, Won-Chul; Shin, Dong Kuk; Choi, Sung Jin; Koh, Dalkwon; Seo, Bong Koo; Kim, Kyunga

    2017-11-01

    Background A major drawback of conventional manual image fusion is that the process may be complex, especially for less-experienced operators. Recently, two automatic image fusion techniques called Positioning and Sweeping auto-registration have been developed. Purpose To compare the accuracy and required time for image fusion of real-time ultrasonography (US) and computed tomography (CT) images between Positioning and Sweeping auto-registration. Material and Methods Eighteen consecutive patients referred for planning US for radiofrequency ablation or biopsy for focal hepatic lesions were enrolled. Image fusion using both auto-registration methods was performed for each patient. Registration error, time required for image fusion, and number of point locks used were compared using the Wilcoxon signed rank test. Results Image fusion was successful in all patients. Positioning auto-registration was significantly faster than Sweeping auto-registration for both initial (median, 11 s [range, 3-16 s] vs. 32 s [range, 21-38 s]; P auto-registration was significantly higher for initial image fusion (median, 38.8 mm [range, 16.0-84.6 mm] vs. 18.2 mm [6.7-73.4 mm]; P = 0.029), but not for complete image fusion (median, 4.75 mm [range, 1.7-9.9 mm] vs. 5.8 mm [range, 2.0-13.0 mm]; P = 0.338]. Number of point locks required to refine the initially fused images was significantly higher with Positioning auto-registration (median, 2 [range, 2-3] vs. 1 [range, 1-2]; P = 0.012]. Conclusion Positioning auto-registration offers faster image fusion between real-time US and pre-procedural CT images than Sweeping auto-registration. The final registration error is similar between the two methods.

  5. Reduction by Lie Group Symmetries in Diffeomorphic Image Registration and Deformation Modelling

    Directory of Open Access Journals (Sweden)

    Stefan Sommer

    2015-05-01

    Full Text Available We survey the role of reduction by symmetry in the large deformation diffeomorphic metric mapping framework for registration of a variety of data types (landmarks, curves, surfaces, images and higher-order derivative data. Particle relabelling symmetry allows the equations of motion to be reduced to the Lie algebra allowing the equations to be written purely in terms of the Eulerian velocity field. As a second use of symmetry, the infinite dimensional problem of finding correspondences between objects can be reduced for a range of concrete data types, resulting in compact representations of shape and spatial structure. Using reduction by symmetry, we describe these models in a common theoretical framework that draws on links between the registration problem and geometric mechanics. We outline these constructions and further cases where reduction by symmetry promises new approaches to the registration of complex data types.

  6. Interactive, multi-modality image registrations for combined MRI/MRSI-planned HDR prostate brachytherapy

    Directory of Open Access Journals (Sweden)

    Galen Reed

    2011-03-01

    Full Text Available Purpose: This study presents the steps and criteria involved in the series of image registrations used clinically during the planning and dose delivery of focal high dose-rate (HDR brachytherapy of the prostate. Material and methods: Three imaging modalities – Magnetic Resonance Imaging (MRI, Magnetic Resonance Spectroscopic Imaging (MRSI, and Computed Tomography (CT – were used at different steps during the process. MRSI is used for identification of dominant intraprosatic lesions (DIL. A series of rigid and nonrigid transformations were applied to the data to correct for endorectal-coil-induced deformations and for alignment with the planning CT. Mutual information was calculated as a morphing metric. An inverse planning optimization algorithm was applied to boost dose to the DIL while providing protection to the urethra, penile bulb, rectum, and bladder. Six prostate cancer patients were treated using this protocol. Results: The morphing algorithm successfully modeled the probe-induced prostatic distortion. Mutual information calculated between the morphed images and images acquired without the endorectal probe showed a significant (p = 0.0071 increase to that calculated between the unmorphed images and images acquired without the endorectal probe. Both mutual information and visual inspection serve as effective diagnostics of image morphing. The entire procedure adds less than thirty minutes to the treatment planning. Conclusion: This work demonstrates the utility of image transformations and registrations to HDR brachytherapy of prostate cancer.

  7. Exploiting Measurement Uncertainty Estimation in Evaluation of GOES-R ABI Image Navigation Accuracy Using Image Registration Techniques

    Science.gov (United States)

    Haas, Evan; DeLuccia, Frank

    2016-01-01

    In evaluating GOES-R Advanced Baseline Imager (ABI) image navigation quality, upsampled sub-images of ABI images are translated against downsampled Landsat 8 images of localized, high contrast earth scenes to determine the translations in the East-West and North-South directions that provide maximum correlation. The native Landsat resolution is much finer than that of ABI, and Landsat navigation accuracy is much better than ABI required navigation accuracy and expected performance. Therefore, Landsat images are considered to provide ground truth for comparison with ABI images, and the translations of ABI sub-images that produce maximum correlation with Landsat localized images are interpreted as ABI navigation errors. The measured local navigation errors from registration of numerous sub-images with the Landsat images are averaged to provide a statistically reliable measurement of the overall navigation error of the ABI image. The dispersion of the local navigation errors is also of great interest, since ABI navigation requirements are specified as bounds on the 99.73rd percentile of the magnitudes of per pixel navigation errors. However, the measurement uncertainty inherent in the use of image registration techniques tends to broaden the dispersion in measured local navigation errors, masking the true navigation performance of the ABI system. We have devised a novel and simple method for estimating the magnitude of the measurement uncertainty in registration error for any pair of images of the same earth scene. We use these measurement uncertainty estimates to filter out the higher quality measurements of local navigation error for inclusion in statistics. In so doing, we substantially reduce the dispersion in measured local navigation errors, thereby better approximating the true navigation performance of the ABI system.

  8. Non-Rigid Object Contour Tracking via a Novel Supervised Level Set Model.

    Science.gov (United States)

    Sun, Xin; Yao, Hongxun; Zhang, Shengping; Li, Dong

    2015-11-01

    We present a novel approach to non-rigid objects contour tracking in this paper based on a supervised level set model (SLSM). In contrast to most existing trackers that use bounding box to specify the tracked target, the proposed method extracts the accurate contours of the target as tracking output, which achieves better description of the non-rigid objects while reduces background pollution to the target model. Moreover, conventional level set models only emphasize the regional intensity consistency and consider no priors. Differently, the curve evolution of the proposed SLSM is object-oriented and supervised by the specific knowledge of the targets we want to track. Therefore, the SLSM can ensure a more accurate convergence to the exact targets in tracking applications. In particular, we firstly construct the appearance model for the target in an online boosting manner due to its strong discriminative power between the object and the background. Then, the learnt target model is incorporated to model the probabilities of the level set contour by a Bayesian manner, leading the curve converge to the candidate region with maximum likelihood of being the target. Finally, the accurate target region qualifies the samples fed to the boosting procedure as well as the target model prepared for the next time step. We firstly describe the proposed mechanism of two-phase SLSM for single target tracking, then give its generalized multi-phase version for dealing with multi-target tracking cases. Positive decrease rate is used to adjust the learning pace over time, enabling tracking to continue under partial and total occlusion. Experimental results on a number of challenging sequences validate the effectiveness of the proposed method.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  11. Myocardial deformation from tagged MRI in hypertrophic cardiomyopathy using an efficient registration strategy

    Science.gov (United States)

    Piella, G.; De Craene, M.; Oubel, E.; Larrabide, I.; Huguet, M.; Bijnens, B. H.; Frangi, A. F.

    2009-02-01

    This paper combines different parallelization strategies for speeding up motion and deformation computation by non-rigid registration of a sequence of images. The registration is performed in a two-level acceleration approach: (1) parallelization of each registration process using MPI and/or threads, and (2) distribution of the sequential registrations over a cluster. On a 24-node double quad-core Intel Xeon (2.66 GHz CPU, 16 GB RAM) cluster, the method is demonstrated to efficiently compute the deformation of a cardiac sequence reducing the computation time from more than 3 hours to a couple of minutes (for low downsampled images). It is shown that the distribution of the sequential registrations over the cluster together with the parallelization of each pairwise registration by multithreading lowers the computation time towards values compatible with clinical requirements (a few minutes per patient). The combination of MPI and multithreading is only advantageous for large input data sizes. Performances are assessed for the specific scenario of aligning cardiac sequences of taggedMagnetic Resonance (tMR) images, with the aim of comparing strain in healthy subjects and hypertrophic cardiomyopathy (HCM) patients. In particular, we compared the distribution of systolic strain in both populations. On average, HCM patients showed lower average values of strain with larger deviation due to the coexistence of regions with impaired deformation and regions with normal deformation.

  12. Quantitative evaluation of an image registration method for a NIPAM gel dosimeter

    International Nuclear Information System (INIS)

    Chang, Yuan-Jen; Yao, Chun-Hsu; Wu, Jay; Hsieh, Bor-Tsung; Tsang, Yuk-Wah; Chen, Chin-Hsing

    2015-01-01

    One of the problems in obtaining quality results is image registration when a gel dosimeter is used in conjunction with optical computed tomography (CT). This study proposes a passive alignment mechanism to obtain a precisely measured dose map. A holder plate with two pin–hole pairs is placed on the gel container cap. These two pin–hole pairs attach the gel container to the vertical shaft and can be precisely aligned with the rotation center of the vertical shaft at any time. Accordingly, a better reconstructed image quality is obtained. After obtaining a precisely measured dose map, the scale invariant feature transform (SIFT)-flow algorithm is utilized as an image registration method to align the treatment plan software (TPS) image with the measured dose map image. The results show that the gamma pass rate for the single-field irradiation increases from 83.39% to 94.03% when the algorithm is applied. And the gamma pass rate for the five-field irradiation treatment plan increases from 87.36% to 94.34%. The translation, scaling, and rotation occurring in the dose map image constructed using an optical CT scanner are also aligned with those in the TPS image using the SIFT-flow algorithm. Accordingly, improved gamma comparison results and a higher gamma pass rate are obtained. - Highlights: • A passive alignment method for obtaining a precisely measured dose map is developed. • The SIFT-flow algorithm is adopted as an image registration method for the gel dosimeter. • The SIFT-flow algorithm increases the gamma pass rate from 83.39% to 94.03% for the single-field irradiation. • The SIFT-flow algorithm increases the gamma pass rate from 87.36% to 94.34% for the five-field irradiation. • The translation, scaling, and rotation in the measured dose map image are aligned with those in the TPS image using the SIFT-flow algorithm

  13. Three-dimensional image registration as a tool for forensic odontology: a preliminary investigation.

    Science.gov (United States)

    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.

  14. 3D registration method based on scattered point cloud from B-model ultrasound image

    Science.gov (United States)

    Hu, Lei; Xu, Xiaojun; Wang, Lifeng; Guo, Na; Xie, Feng

    2017-01-01

    The paper proposes a registration method on 3D point cloud of the bone tissue surface extracted by B-mode ultrasound image and the CT model . The B-mode ultrasound is used to get two-dimensional images of the femur tissue . The binocular stereo vision tracker is used to obtain spatial position and orientation of the optical positioning device fixed on the ultrasound probe. The combining of the two kind of data generates 3D point cloud of the bone tissue surface. The pixel coordinates of the bone surface are automatically obtained from ultrasound image using an improved local phase symmetry (phase symmetry, PS) . The mapping of the pixel coordinates on the ultrasound image and 3D space is obtained through a series of calibration methods. In order to detect the effect of registration, six markers are implanted on a complete fresh pig femoral .The actual coordinates of the marks are measured with two methods. The first method is to get the coordinates with measuring tools under a coordinate system. The second is to measure the coordinates of the markers in the CT model registered with 3D point cloud using the ICP registration algorithm under the same coordinate system. Ten registration experiments are carried out in the same way. Error results are obtained by comparing the two sets of mark point coordinates obtained by two different methods. The results is that a minimum error is 1.34mm, the maximum error is 3.22mm,and the average error of 2.52mm; ICP registration algorithm calculates the average error of 0.89mm and a standard deviation of 0.62mm.This evaluation standards of registration accuracy is different from the average error obtained by the ICP registration algorithm. It can be intuitive to show the error caused by the operation of clinical doctors. Reference to the accuracy requirements of different operation in the Department of orthopedics, the method can be apply to the bone reduction and the anterior cruciate ligament surgery.

  15. Experiment and numerical simulation on the characteristics of fluid–structure interactions of non-rigid airships

    Directory of Open Access Journals (Sweden)

    Xiaocui Wu

    2015-11-01

    Full Text Available Fluid–structure interaction is an important issue for non-rigid airships with inflated envelopes. In this study, a wind tunnel test is conducted, and a loosely coupled procedure is correspondingly established for numerical simulation based on computational fluid dynamics and nonlinear finite element analysis methods. The typical results of the numerical simulation and wind tunnel experiment, including the overall lift and deformation, are in good agreement with each other. The results obtained indicate that the effect of fluid–structure interaction is noticeable and should be considered for non-rigid airships. Flow-induced deformation can further intensify the upward lift force and pitching moment, which can lead to a large deformation. Under a wind speed of 15 m/s, the lift force of the non-rigid model is increased to approximately 60% compared with that of the rigid model under a high angle of attack.

  16. Image Registration of Cochlear µCT Data Using Heat Distribution Similarity

    DEFF Research Database (Denmark)

    Kjer, Hans Martin; Vera, Sergio; Fagertun, Jens

    2015-01-01

    Better understanding of the anatomical variability of the human cochlear is important for the design and function of Cochlear Implants. Good non-rigid alignment of high-resolution cochlear μCT data is a challenging task. In this paper we study the use of heat distribution similarity between sampl...

  17. A complete software application for automatic registration of x-ray mammography and magnetic resonance images

    International Nuclear Information System (INIS)

    Solves-Llorens, J. A.; Rupérez, M. J.; Monserrat, C.; Feliu, E.; García, M.; Lloret, M.

    2014-01-01

    Purpose: This work presents a complete and automatic software application to aid radiologists in breast cancer diagnosis. The application is a fully automated method that performs a complete registration of magnetic resonance (MR) images and x-ray (XR) images in both directions (from MR to XR and from XR to MR) and for both x-ray mammograms, craniocaudal (CC), and mediolateral oblique (MLO). This new approximation allows radiologists to mark points in the MR images and, without any manual intervention, it provides their corresponding points in both types of XR mammograms and vice versa. Methods: The application automatically segments magnetic resonance images and x-ray images using the C-Means method and the Otsu method, respectively. It compresses the magnetic resonance images in both directions, CC and MLO, using a biomechanical model of the breast that distinguishes the specific biomechanical behavior of each one of its three tissues (skin, fat, and glandular tissue) separately. It makes a projection of both compressions and registers them with the original XR images using affine transformations and nonrigid registration methods. Results: The application has been validated by two expert radiologists. This was carried out through a quantitative validation on 14 data sets in which the Euclidean distance between points marked by the radiologists and the corresponding points obtained by the application were measured. The results showed a mean error of 4.2 ± 1.9 mm for the MRI to CC registration, 4.8 ± 1.3 mm for the MRI to MLO registration, and 4.1 ± 1.3 mm for the CC and MLO to MRI registration. Conclusions: A complete software application that automatically registers XR and MR images of the breast has been implemented. The application permits radiologists to estimate the position of a lesion that is suspected of being a tumor in an imaging modality based on its position in another different modality with a clinically acceptable error. The results show that the

  18. A complete software application for automatic registration of x-ray mammography and magnetic resonance images

    Energy Technology Data Exchange (ETDEWEB)

    Solves-Llorens, J. A.; Rupérez, M. J., E-mail: mjruperez@labhuman.i3bh.es; Monserrat, C. [LabHuman, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia (Spain); Feliu, E.; García, M. [Hospital Clínica Benidorm, Avda. Alfonso Puchades, 8, 03501 Benidorm (Alicante) (Spain); Lloret, M. [Hospital Universitari y Politècnic La Fe, Bulevar Sur, 46026 Valencia (Spain)

    2014-08-15

    Purpose: This work presents a complete and automatic software application to aid radiologists in breast cancer diagnosis. The application is a fully automated method that performs a complete registration of magnetic resonance (MR) images and x-ray (XR) images in both directions (from MR to XR and from XR to MR) and for both x-ray mammograms, craniocaudal (CC), and mediolateral oblique (MLO). This new approximation allows radiologists to mark points in the MR images and, without any manual intervention, it provides their corresponding points in both types of XR mammograms and vice versa. Methods: The application automatically segments magnetic resonance images and x-ray images using the C-Means method and the Otsu method, respectively. It compresses the magnetic resonance images in both directions, CC and MLO, using a biomechanical model of the breast that distinguishes the specific biomechanical behavior of each one of its three tissues (skin, fat, and glandular tissue) separately. It makes a projection of both compressions and registers them with the original XR images using affine transformations and nonrigid registration methods. Results: The application has been validated by two expert radiologists. This was carried out through a quantitative validation on 14 data sets in which the Euclidean distance between points marked by the radiologists and the corresponding points obtained by the application were measured. The results showed a mean error of 4.2 ± 1.9 mm for the MRI to CC registration, 4.8 ± 1.3 mm for the MRI to MLO registration, and 4.1 ± 1.3 mm for the CC and MLO to MRI registration. Conclusions: A complete software application that automatically registers XR and MR images of the breast has been implemented. The application permits radiologists to estimate the position of a lesion that is suspected of being a tumor in an imaging modality based on its position in another different modality with a clinically acceptable error. The results show that the

  19. Line-Based Registration of Panoramic Images and LiDAR Point Clouds for Mobile Mapping

    Directory of Open Access Journals (Sweden)

    Tingting Cui

    2016-12-01

    Full Text Available For multi-sensor integrated systems, such as the mobile mapping system (MMS, data fusion at sensor-level, i.e., the 2D-3D registration between an optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and a LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for the panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and the LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, with the latter being more reliable.

  20. A reference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study archive

    International Nuclear Information System (INIS)

    Castillo, Richard; Castillo, Edward; Wood, Abbie M; Ludwig, Michelle S; Guerrero, Thomas; Fuentes, David; Ahmad, Moiz

    2013-01-01

    Landmark point-pairs provide a strategy to assess deformable image registration (DIR) accuracy in terms of the spatial registration of the underlying anatomy depicted in medical images. In this study, we propose to augment a publicly available database (www.dir-lab.com) of medical images with large sets of manually identified anatomic feature pairs between breath-hold computed tomography (BH-CT) images for DIR spatial accuracy evaluation. Ten BH-CT image pairs were randomly selected from the COPDgene study cases. Each patient had received CT imaging of the entire thorax in the supine position at one-fourth dose normal expiration and maximum effort full dose inspiration. Using dedicated in-house software, an imaging expert manually identified large sets of anatomic feature pairs between images. Estimates of inter- and intra-observer spatial variation in feature localization were determined by repeat measurements of multiple observers over subsets of randomly selected features. 7298 anatomic landmark features were manually paired between the 10 sets of images. Quantity of feature pairs per case ranged from 447 to 1172. Average 3D Euclidean landmark displacements varied substantially among cases, ranging from 12.29 (SD: 6.39) to 30.90 (SD: 14.05) mm. Repeat registration of uniformly sampled subsets of 150 landmarks for each case yielded estimates of observer localization error, which ranged in average from 0.58 (SD: 0.87) to 1.06 (SD: 2.38) mm for each case. The additions to the online web database (www.dir-lab.com) described in this work will broaden the applicability of the reference data, providing a freely available common dataset for targeted critical evaluation of DIR spatial accuracy performance in multiple clinical settings. Estimates of observer variance in feature localization suggest consistent spatial accuracy for all observers across both four-dimensional CT and COPDgene patient cohorts. (paper)

  1. Deformable image registration for geometrical evaluation of DIBH radiotherapy treatment of lung cancer patients

    DEFF Research Database (Denmark)

    Ottosson, Wiviann; Lykkegaard Andersen, J. A.; Borrisova, S.

    2014-01-01

    Respiration and anatomical variation during radiotherapy (RT) of lung cancer yield dosimetric uncertainties of the delivered dose, possibly affecting the clinical outcome if not corrected for. Adaptive radiotherapy (ART), based on deformable image registration (DIR) and Deep-Inspiration-Breath-Hold...... (DIBH) gating can potentially improve the accuracy of RT. Purpose: The objective was to investigate the performance of contour propagation on repeated CT and Cone Beam CT (CBCT) images in DIBH compared to images acquired in free breathing (FB), using a recently released DIR software. Method: Three...

  2. Numerical calculation of the stress-strain state of non-rigid pavements, renovated by cold recycling technology

    Directory of Open Access Journals (Sweden)

    Світлана Михайлівна Талах

    2017-01-01

    Full Text Available The problem of improving the scientific basis to determine the stress-strain state of non-rigid pavements, renovated by cold recycling technology, is considered. The results of numerical calculation of stress-strain state of non-rigid pavements in the section of road Kyv-Kovel (297 + 700 km - 302 + 400 km are given using automated calculation software complex of thin-walled spatial structures (KARTPK. The real state of the road section through 8.5 years after the renovation is analyzed

  3. Deformable image registration with local rigidity constraints for cone-beam CT-guided spine surgery

    Science.gov (United States)

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

    2014-07-01

    Image-guided spine surgery (IGSS) is associated with reduced co-morbidity and improved surgical outcome. However, precise localization of target anatomy and adjacent nerves and vessels relative to planning information (e.g., device trajectories) can be challenged by anatomical deformation. Rigid registration alone fails to account for deformation associated with changes in spine curvature, and conventional deformable registration fails to account for rigidity of the vertebrae, causing unrealistic distortions in the registered image that can confound high-precision surgery. We developed and evaluated a deformable registration method capable of preserving rigidity of bones while resolving the deformation of surrounding soft tissue. The method aligns preoperative CT to intraoperative cone-beam CT (CBCT) using free-form deformation (FFD) with constraints on rigid body motion imposed according to a simple intensity threshold of bone intensities. The constraints enforced three 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 (uFFD) and Demons registration. 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

  4. a Robust Registration Algorithm for Point Clouds from Uav Images for Change Detection

    Science.gov (United States)

    Al-Rawabdeh, A.; Al-Gurrani, H.; Al-Durgham, K.; Detchev, I.; He, F.; El-Sheimy, N.; Habib, A.

    2016-06-01

    Landslides are among the major threats to urban landscape and manmade infrastructure. They often cause economic losses, property damages, and loss of lives. Temporal monitoring data of landslides from different epochs empowers the evaluation of landslide progression. Alignment of overlapping surfaces from two or more epochs is crucial for the proper analysis of landslide dynamics. The traditional methods for point-cloud-based landslide monitoring rely on using a variation of the Iterative Closest Point (ICP) registration procedure to align any reconstructed surfaces from different epochs to a common reference frame. However, sometimes the ICP-based registration can fail or may not provide sufficient accuracy. For example, point clouds from different epochs might fit to local minima due to lack of geometrical variability within the data. Also, manual interaction is required to exclude any non-stable areas from the registration process. In this paper, a robust image-based registration method is introduced for the simultaneous evaluation of all registration parameters. This includes the Interior Orientation Parameters (IOPs) of the camera and the Exterior Orientation Parameters (EOPs) of the involved images from all available observation epochs via a bundle block adjustment with self-calibration. Next, a semi-global dense matching technique is implemented to generate dense 3D point clouds for each epoch using the images captured in a particular epoch separately. The normal distances between any two consecutive point clouds can then be readily computed, because the point clouds are already effectively co-registered. A low-cost DJI Phantom II Unmanned Aerial Vehicle (UAV) was customised and used in this research for temporal data collection over an active soil creep area in Lethbridge, Alberta, Canada. The customisation included adding a GPS logger and a Large-Field-Of-View (LFOV) action camera which facilitated capturing high-resolution geo-tagged images in two epochs

  5. Bayesian estimation of regularization and atlas building in diffeomorphic image registration.

    Science.gov (United States)

    Zhang, Miaomiao; Singh, Nikhil; Fletcher, P Thomas

    2013-01-01

    This paper presents a generative Bayesian model for diffeomorphic image registration and atlas building. We develop an atlas estimation procedure that simultaneously estimates the parameters controlling the smoothness of the diffeomorphic transformations. To achieve this, we introduce a Monte Carlo Expectation Maximization algorithm, where the expectation step is approximated via Hamiltonian Monte Carlo sampling on the manifold of diffeomorphisms. An added benefit of this stochastic approach is that it can successfully solve difficult registration problems involving large deformations, where direct geodesic optimization fails. Using synthetic data generated from the forward model with known parameters, we demonstrate the ability of our model to successfully recover the atlas and regularization parameters. We also demonstrate the effectiveness of the proposed method in the atlas estimation problem for 3D brain images.

  6. Evaluation of the mutual information cost function for registration of SPET and MRI images of the brain

    International Nuclear Information System (INIS)

    Taleb, M.; McKay, E.

    1999-01-01

    Full text: Any strategy for image registration requires some method (a cost function) by which two images may be compared The mutual information (MI) between images is one such cost function. MI measures the structural similarity between pairs of gray-scale images and performs cross-modality image registration with minimal image pre-processing. This project compares the performance of MI vs the sum of absolute differences (SAD) 'gold standard' in monomodality image registration problems. It also examines the precision of cross-modality registration based on MI, using a human observer to decide whether registration is accurate. Thirteen paired brain SPET scans were registered using SAD as a cost function. Registration was repeated using MI and differences from the SAD results were recorded. Ten paired MRI and SPET brain scans registered using the MI cost function. Registration was repeated three times for each pair, varying the SPET position or orientation each time. Comparing MI to SAD, the median values of translation error were 2.85, 4.63 and 2.56 mm in the x, y and z axis and 0.5 j , 1.1 j and 1.0 j around the x, y and z axis respectively. For the cross-modality problems, the mean standard deviation (MSD) observed in x, y and z positioning was 0.18, 0.28 and 0.16 mm respectively. The MSD of orientation was 5.35 j , 1.95 j and 2.48 j around the x, y and z axis respectively. MI performed as well as SAD for monomodality registration. Unlike SAD, MI is also useful for cross-modality image registration tasks, producing visually acceptable results with minimal preprocessing

  7. An automatic high precision registration method between large area aerial images and aerial light detection and ranging data

    Science.gov (United States)

    Du, Q.; Xie, D.; Sun, Y.

    2015-06-01

    The integration of digital aerial photogrammetry and Light Detetion And Ranging (LiDAR) is an inevitable trend in Surveying and Mapping field. We calculate the external orientation elements of images which identical with LiDAR coordinate to realize automatic high precision registration between aerial images and LiDAR data. There are two ways to calculate orientation elements. One is single image spatial resection using image matching 3D points that registered to LiDAR. The other one is Position and Orientation System (POS) data supported aerotriangulation. The high precision registration points are selected as Ground Control Points (GCPs) instead of measuring GCPs manually during aerotriangulation. The registration experiments indicate that the method which registering aerial images and LiDAR points has a great advantage in higher automation and precision compare with manual registration.

  8. Quantitative evaluation of an image registration method for a NIPAM gel dosimeter

    Science.gov (United States)

    Chang, Yuan-Jen; Yao, Chun-Hsu; Wu, Jay; Hsieh, Bor-Tsung; Tsang, Yuk-Wah; Chen, Chin-Hsing

    2015-06-01

    One of the problems in obtaining quality results is image registration when a gel dosimeter is used in conjunction with optical computed tomography (CT). This study proposes a passive alignment mechanism to obtain a precisely measured dose map. A holder plate with two pin-hole pairs is placed on the gel container cap. These two pin-hole pairs attach the gel container to the vertical shaft and can be precisely aligned with the rotation center of the vertical shaft at any time. Accordingly, a better reconstructed image quality is obtained. After obtaining a precisely measured dose map, the scale invariant feature transform (SIFT)-flow algorithm is utilized as an image registration method to align the treatment plan software (TPS) image with the measured dose map image. The results show that the gamma pass rate for the single-field irradiation increases from 83.39% to 94.03% when the algorithm is applied. And the gamma pass rate for the five-field irradiation treatment plan increases from 87.36% to 94.34%. The translation, scaling, and rotation occurring in the dose map image constructed using an optical CT scanner are also aligned with those in the TPS image using the SIFT-flow algorithm. Accordingly, improved gamma comparison results and a higher gamma pass rate are obtained.

  9. Shadow and feature recognition aids for rapid image geo-registration in UAV vision system architectures

    Science.gov (United States)

    Baer, Wolfgang; Kölsch, Mathias

    2009-05-01

    The problem of real-time image geo-referencing is encountered in all vision based cognitive systems. In this paper we present a model-image feedback approach to this problem and show how it can be applied to image exploitation from Unmanned Arial Vehicle (UAV) vision systems. By calculating reference images from a known terrain database, using a novel ray trace algorithm, we are able to eliminate foreshortening, elevation, and lighting distortions, introduce registration aids and reduce the geo-referencing problem to a linear transformation search over the two dimensional image space. A method for shadow calculation that maintains real-time performance is also presented. The paper then discusses the implementation of our model-image feedback approach in the Perspective View Nascent Technology (PVNT) software package and provides sample results from UAV mission control and target mensuration experiments conducted at China Lake and Camp Roberts, California.

  10. Volumetric Image Guidance Using Carina vs Spine as Registration Landmarks for Conventionally Fractionated Lung Radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Lavoie, Caroline; Higgins, Jane; Bissonnette, Jean-Pierre [Department of Radiation Oncology, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, M5G 2M9 (Canada); Le, Lisa W. [Department of Biostatistics, Princess Margaret Hospital, Toronto, Ontario, M5G 2M9 (Canada); Sun, Alexander; Brade, Anthony; Hope, Andrew; Cho, John [Department of Radiation Oncology, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, M5G 2M9 (Canada); Bezjak, Andrea, E-mail: andrea.bezjak@rmp.uhn.on.ca [Department of Radiation Oncology, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, M5G 2M9 (Canada)

    2012-12-01

    Purpose: To compare the relative accuracy of 2 image guided radiation therapy methods using carina vs spine as landmarks and then to identify which landmark is superior relative to tumor coverage. Methods and Materials: For 98 lung patients, 2596 daily image-guidance cone-beam computed tomography scans were analyzed. Tattoos were used for initial patient alignment; then, spine and carina registrations were performed independently. A separate analysis assessed the adequacy of gross tumor volume, internal target volume, and planning target volume coverage on cone-beam computed tomography using the initial, middle, and final fractions of radiation therapy. Coverage was recorded for primary tumor (T), nodes (N), and combined target (T+N). Three scenarios were compared: tattoos alignment, spine registration, and carina registration. Results: Spine and carina registrations identified setup errors {>=}5 mm in 35% and 46% of fractions, respectively. The mean vector difference between spine and carina matching had a magnitude of 3.3 mm. Spine and carina improved combined target coverage, compared with tattoos, in 50% and 34% (spine) to 54% and 46% (carina) of the first and final fractions, respectively. Carina matching showed greater combined target coverage in 17% and 23% of fractions for the first and final fractions, respectively; with spine matching, this was only observed in 4% (first) and 6% (final) of fractions. Carina matching provided superior nodes coverage at the end of radiation compared with spine matching (P=.0006), without compromising primary tumor coverage. Conclusion: Frequent patient setup errors occur in locally advanced lung cancer patients. Spine and carina registrations improved combined target coverage throughout the treatment course, but carina matching provided superior combined target coverage.

  11. Volumetric Image Guidance Using Carina vs Spine as Registration Landmarks for Conventionally Fractionated Lung Radiotherapy

    International Nuclear Information System (INIS)

    Lavoie, Caroline; Higgins, Jane; Bissonnette, Jean-Pierre; Le, Lisa W.; Sun, Alexander; Brade, Anthony; Hope, Andrew; Cho, John; Bezjak, Andrea

    2012-01-01

    Purpose: To compare the relative accuracy of 2 image guided radiation therapy methods using carina vs spine as landmarks and then to identify which landmark is superior relative to tumor coverage. Methods and Materials: For 98 lung patients, 2596 daily image-guidance cone-beam computed tomography scans were analyzed. Tattoos were used for initial patient alignment; then, spine and carina registrations were performed independently. A separate analysis assessed the adequacy of gross tumor volume, internal target volume, and planning target volume coverage on cone-beam computed tomography using the initial, middle, and final fractions of radiation therapy. Coverage was recorded for primary tumor (T), nodes (N), and combined target (T+N). Three scenarios were compared: tattoos alignment, spine registration, and carina registration. Results: Spine and carina registrations identified setup errors ≥5 mm in 35% and 46% of fractions, respectively. The mean vector difference between spine and carina matching had a magnitude of 3.3 mm. Spine and carina improved combined target coverage, compared with tattoos, in 50% and 34% (spine) to 54% and 46% (carina) of the first and final fractions, respectively. Carina matching showed greater combined target coverage in 17% and 23% of fractions for the first and final fractions, respectively; with spine matching, this was only observed in 4% (first) and 6% (final) of fractions. Carina matching provided superior nodes coverage at the end of radiation compared with spine matching (P=.0006), without compromising primary tumor coverage. Conclusion: Frequent patient setup errors occur in locally advanced lung cancer patients. Spine and carina registrations improved combined target coverage throughout the treatment course, but carina matching provided superior combined target coverage.

  12. Post-operative assessment in Deep Brain Stimulation based on multimodal images: registration workflow and validation

    Science.gov (United States)

    Lalys, Florent; Haegelen, Claire; Abadie, Alexandre; Jannin, Pierre

    2009-02-01

    Object Movement disorders in Parkinson disease patients may require functional surgery, when medical therapy isn't effective. In Deep Brain Stimulation (DBS) electrodes are implanted within the brain to stimulate deep structures such as SubThalamic Nucleus (STN). This paper describes successive steps for constructing a digital Atlas gathering patient's location of electrodes and contacts for post operative assessment. Materials and Method 12 patients who had undergone bilateral STN DBS have participated to the study. Contacts on post-operative CT scans were automatically localized, based on black artefacts. For each patient, post operative CT images were rigidly registered to pre operative MR images. Then, pre operative MR images were registered to a MR template (super-resolution Collin27 average MRI template). This last registration was the combination of global affine, local affine and local non linear registrations, respectively. Four different studies were performed in order to validate the MR patient to template registration process, based on anatomical landmarks and clinical scores (i.e., Unified Parkinson's disease rating Scale). Visualisation software was developed for displaying into the template images the stimulated contacts represented as cylinders with a colour code related to the improvement of the UPDRS. Results The automatic contact localization algorithm was successful for all the patients. Validation studies for the registration process gave a placement error of 1.4 +/- 0.2 mm and coherence with UPDRS scores. Conclusion The developed visualization tool allows post-operative assessment for previous interventions. Correlation with additional clinical scores will certainly permit to learn more about DBS and to better understand clinical side-effects.

  13. [Landmark-based automatic registration of serial cross-sectional images of Chinese digital human using Photoshop and Matlab software].

    Science.gov (United States)

    Su, Xiu-yun; Pei, Guo-xian; Yu, Bin; Hu, Yan-ling; Li, Jin; Huang, Qian; Li, Xu; Zhang, Yuan-zhi

    2007-12-01

    This paper describes automatic registration of the serial cross-sectional images of Chinese digital human by projective registration method based on the landmarks using the commercially available software Photoshop and Matlab. During cadaver embedment for acquisition of the Chinese digital human images, 4 rods were placed parallel to the vertical axis of the frozen cadaver to allow orientation. Projective distortion of the rod positions on the cross-sectional images was inevitable due to even slight changes of the relative position of the camera. The original cross-sectional images were first processed using Photoshop software firstly to obtain the images of the orientation rods, and the centroid coordinate of every rod image was acquired with Matlab software. With the average coordinate value of the rods as the fiducial point, two-dimensional projective transformation coefficient of each image was determined. Projective transformation was then carried out and projective distortion from each original serial image was eliminated. The rectified cross-sectional images were again processed using Photoshop to obtain the image of the first orientation rod, the coordinate value of first rod image was calculated using Matlab software, and the cross-sectional images were cut into images of the same size according to the first rod spatial coordinate, to achieve automatic registration of the serial cross-sectional images. sing Photoshop and Matlab softwares, projective transformation can accurately accomplish the image registration for the serial images with simpler calculation processes and easier computer processing.

  14. Three-dimensional registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation.

    Science.gov (United States)

    Prabhu, David; Mehanna, Emile; Gargesha, Madhusudhana; Brandt, Eric; Wen, Di; van Ditzhuijzen, Nienke S; Chamie, Daniel; Yamamoto, Hirosada; Fujino, Yusuke; Alian, Ali; Patel, Jaymin; Costa, Marco; Bezerra, Hiram G; Wilson, David L

    2016-04-01

    Evidence suggests high-resolution, high-contrast, [Formula: see text] intravascular optical coherence tomography (IVOCT) can distinguish plaque types, but further validation is needed, especially for automated plaque characterization. We developed experimental and three-dimensional (3-D) registration methods to provide validation of IVOCT pullback volumes using microscopic, color, and fluorescent cryo-image volumes with optional registered cryo-histology. A specialized registration method matched IVOCT pullback images acquired in the catheter reference frame to a true 3-D cryo-image volume. Briefly, an 11-parameter registration model including a polynomial virtual catheter was initialized within the cryo-image volume, and perpendicular images were extracted, mimicking IVOCT image acquisition. Virtual catheter parameters were optimized to maximize cryo and IVOCT lumen overlap. Multiple assessments suggested that the registration error was better than the [Formula: see text] spacing between IVOCT image frames. Tests on a digital synthetic phantom gave a registration error of only [Formula: see text] (signed distance). Visual assessment of randomly presented nearby frames suggested registration accuracy within 1 IVOCT frame interval ([Formula: see text]). This would eliminate potential misinterpretations confronted by the typical histological approaches to validation, with estimated 1-mm errors. The method can be used to create annotated datasets and automated plaque classification methods and can be extended to other intravascular imaging modalities.

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

    Science.gov (United States)

    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

  16. Second-order optimization of mutual information for real-time image registration.

    Science.gov (United States)

    Dame, Amaury; Marchand, Eric

    2012-09-01

    In this paper, we present a direct image registration approach that uses mutual information (MI) as a metric for alignment. The proposed approach is robust and gives an accurate estimation of a set of 2-D motion parameters in real time. MI is a measure of the quantity of information shared by signals. Although it has the ability to perform robust alignment with illumination changes, multimodality, and partial occlusions, few works have proposed MI-based applications related to spatiotemporal image registration or object tracking in image sequences because of some optimization problems, which we will explain. In this paper, we propose a new optimization method that is adapted to the MI cost function and gives a practical solution for real-time tracking. We show that by refining the computation of the Hessian matrix and using a specific optimization approach, the registration results are far more robust and accurate than the existing solutions, with the computation also being cheaper. A new approach is also proposed to speed up the computation of the derivatives and keep the same optimization efficiency. To validate the advantages of the proposed approach, several experiments are performed.

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

    Energy Technology Data Exchange (ETDEWEB)

    Caldas-Magalhaes, Joana, E-mail: J.CaldasMagalhaes@umcutrecht.nl [Department of Radiotherapy, University Medical Center Utrecht (Netherlands); Kasperts, Nicolien [Department of Radiotherapy, University Medical Center Utrecht (Netherlands); Kooij, Nina [Department of Pathology, University Medical Center Utrecht (Netherlands); Berg, Cornelis A.T. van den; Terhaard, Chris H.J.; Raaijmakers, Cornelis P.J.; Philippens, Marielle E.P. [Department of Radiotherapy, University Medical Center Utrecht (Netherlands)

    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

  18. Validation of imaging with pathology in laryngeal cancer: accuracy of the registration methodology.

    Science.gov (United States)

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

    2012-02-01

    To investigate the feasibility and accuracy of an automated method to validate gross tumor volume (GTV) delineations with pathology in laryngeal and hypopharyngeal cancer. High-resolution computed tomography (CT(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&E) sections were obtained. A pathologist delineated tumor tissue in the H&E sections (GTV(PATH)). An automatic three-dimensional (3D) reconstruction of the specimen was performed, and the CT(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. 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(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(PATH) by 85%, 88%, and 77%, respectively. The tumor delineation inaccuracies exceeded the registration error in all the imaging modalities. 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 anatomical and functional imaging techniques for GTV delineation is possible in

  19. 3D/3D registration of coronary CTA and biplane XA reconstructions for improved image guidance

    Energy Technology Data Exchange (ETDEWEB)

    Dibildox, Gerardo, E-mail: g.dibildox@erasmusmc.nl; Baka, Nora; Walsum, Theo van [Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus Medical Center, 3015 GE Rotterdam (Netherlands); Punt, Mark; Aben, Jean-Paul [Pie Medical Imaging, 6227 AJ Maastricht (Netherlands); Schultz, Carl [Department of Cardiology, Erasmus Medical Center, 3015 GE Rotterdam (Netherlands); Niessen, Wiro [Quantitative Imaging Group, Faculty of Applied Sciences, Delft University of Technology, 2628 CJ Delft, The Netherlands and Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus Medical Center, 3015 GE Rotterdam (Netherlands)

    2014-09-15

    Purpose: The authors aim to improve image guidance during percutaneous coronary interventions of chronic total occlusions (CTO) by providing information obtained from computed tomography angiography (CTA) to the cardiac interventionist. To this end, the authors investigate a method to register a 3D CTA model to biplane reconstructions. Methods: The authors developed a method for registering preoperative coronary CTA with intraoperative biplane x-ray angiography (XA) images via 3D models of the coronary arteries. The models are extracted from the CTA and biplane XA images, and are temporally aligned based on CTA reconstruction phase and XA ECG signals. Rigid spatial alignment is achieved with a robust probabilistic point set registration approach using Gaussian mixture models (GMMs). This approach is extended by including orientation in the Gaussian mixtures and by weighting bifurcation points. The method is evaluated on retrospectively acquired coronary CTA datasets of 23 CTO patients for which biplane XA images are available. Results: The Gaussian mixture model approach achieved a median registration accuracy of 1.7 mm. The extended GMM approach including orientation was not significantly different (P > 0.1) but did improve robustness with regards to the initialization of the 3D models. Conclusions: The authors demonstrated that the GMM approach can effectively be applied to register CTA to biplane XA images for the purpose of improving image guidance in percutaneous coronary interventions.

  20. Automatic registration of Iphone images to LASER point clouds of the urban structures using shape features

    Science.gov (United States)

    Sirmacek, B.; Lindenbergh, R. C.; Menenti, M.

    2013-10-01

    Fusion of 3D airborne laser (LIDAR) data and terrestrial optical imagery can be applied in 3D urban modeling and model up-dating. The most challenging aspect of the fusion procedure is registering the terrestrial optical images on the LIDAR point clouds. In this article, we propose an approach for registering these two different data from different sensor sources. As we use iPhone camera images which are taken in front of the interested urban structure by the application user and the high resolution LIDAR point clouds of the acquired by an airborne laser sensor. After finding the photo capturing position and orientation from the iPhone photograph metafile, we automatically select the area of interest in the point cloud and transform it into a range image which has only grayscale intensity levels according to the distance from the image acquisition position. We benefit from local features for registering the iPhone image to the generated range image. In this article, we have applied the registration process based on local feature extraction and graph matching. Finally, the registration result is used for facade texture mapping on the 3D building surface mesh which is generated from the LIDAR point cloud. Our experimental results indicate possible usage of the proposed algorithm framework for 3D urban map updating and enhancing purposes.

  1. Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications.

    Science.gov (United States)

    Kim, Byeong Hak; Kim, Min Young; Chae, You Seong

    2017-12-27

    Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC.

  2. An Orthogonal Learning Differential Evolution Algorithm for Remote Sensing Image Registration

    Directory of Open Access Journals (Sweden)

    Wenping Ma

    2014-01-01

    Full Text Available We introduce an area-based method for remote sensing image registration. We use orthogonal learning differential evolution algorithm to optimize the similarity metric between the reference image and the target image. Many local and global methods have been used to achieve the optimal similarity metric in the last few years. Because remote sensing images are usually influenced by large distortions and high noise, local methods will fail in some cases. For this reason, global methods are often required. The orthogonal learning (OL strategy is efficient when searching in complex problem spaces. In addition, it can discover more useful information via orthogonal experimental design (OED. Differential evolution (DE is a heuristic algorithm. It has shown to be efficient in solving the remote sensing image registration problem. So orthogonal learning differential evolution algorithm (OLDE is efficient for many optimization problems. The OLDE method uses the OL strategy to guide the DE algorithm to discover more useful information. Experiments show that the OLDE method is more robust and efficient for registering remote sensing images.

  3. Automated, non-linear registration between 3-dimensional brain map and medical head image

    International Nuclear Information System (INIS)

    Mizuta, Shinobu; Urayama, Shin-ichi; Zoroofi, R.A.; Uyama, Chikao

    1998-01-01

    In this paper, we propose an automated, non-linear registration method between 3-dimensional medical head image and brain map in order to efficiently extract the regions of interest. In our method, input 3-dimensional image is registered into a reference image extracted from a brain map. The problems to be solved are automated, non-linear image matching procedure, and cost function which represents the similarity between two images. Non-linear matching is carried out by dividing the input image into connected partial regions, transforming the partial regions preserving connectivity among the adjacent images, evaluating the image similarity between the transformed regions of the input image and the correspondent regions of the reference image, and iteratively searching the optimal transformation of the partial regions. In order to measure the voxelwise similarity of multi-modal images, a cost function is introduced, which is based on the mutual information. Some experiments using MR images presented the effectiveness of the proposed method. (author)

  4. THE PROGRAM OF NON-RIGID PAVEMENT COMPUTER-AIDED CALCULATION УКРРДО 15

    Directory of Open Access Journals (Sweden)

    I. Musiienko

    2016-06-01

    Full Text Available A program for computer-aided calculation of non-rigid road coating UKRRDO 15 is considered in the given article. The program interface, a set of input data and the results of calculations are considered in detail. Calculations are performed for three strength criteria: permissible elastic deflection, shear strength of subsoil and monolithic layers resistance of tensile bending.

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

    Science.gov (United States)

    Sun, Kaiqiong; Udupa, Jayaram K; Odhner, Dewey; Tong, Yubing; Zhao, Liming; Torigian, Drew A

    2016-03-01

    In an attempt to overcome several hurdles that exist in organ segmentation approaches, the authors previously described a general automatic anatomy recognition (AAR) methodology for segmenting all major organs in multiple body regions body-wide [J. K. Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18(5), 752-771 (2014)]. That approach utilized fuzzy modeling strategies, a hierarchical organization of organs, and divided the segmentation task into a recognition step to localize organs which was then followed by a delineation step to demarcate the boundary of organs. It achieved speed and accuracy without employing image/object registration which is commonly utilized in many reported methods, particularly atlas-based. In this paper, our aim is to study how registration may influence performance of the AAR approach. By tightly coupling the recognition and delineation steps, by performing registration in the hierarchical order of the organs, and through several object-specific refinements, the authors demonstrate that improved accuracy for recognition and delineation can be achieved by judicial use of image/object registration. The presented approach consists of three processes: model building, hierarchical recognition, and delineation. Labeled binary images for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The hierarchical relation and mean location relation between different organs are captured in the model. The gray intensity distributions of the corresponding regions of the organ in the original image are also recorded 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 connectedness delineation method is then employed to obtain the final segmentation result of organs with seed

  6. GPU accelerated generation of digitally reconstructed radiographs for 2-D/3-D image registration.

    Science.gov (United States)

    Dorgham, Osama M; Laycock, Stephen D; Fisher, Mark H

    2012-09-01

    Recent advances in programming languages for graphics processing units (GPUs) provide developers with a convenient way of implementing applications which can be executed on the CPU and GPU interchangeably. GPUs are becoming relatively cheap, powerful, and widely available hardware components, which can be used to perform intensive calculations. The last decade of hardware performance developments shows that GPU-based computation is progressing significantly faster than CPU-based computation, particularly if one considers the execution of highly parallelisable algorithms. Future predictions illustrate that this trend is likely to continue. In this paper, we introduce a way of accelerating 2-D/3-D image registration by developing a hybrid system which executes on the CPU and utilizes the GPU for parallelizing the generation of digitally reconstructed radiographs (DRRs). Based on the advancements of the GPU over the CPU, it is timely to exploit the benefits of many-core GPU technology by developing algorithms for DRR generation. Although some previous work has investigated the rendering of DRRs using the GPU, this paper investigates approximations which reduce the computational overhead while still maintaining a quality consistent with that needed for 2-D/3-D registration with sufficient accuracy to be clinically acceptable in certain applications of radiation oncology. Furthermore, by comparing implementations of 2-D/3-D registration on the CPU and GPU, we investigate current performance and propose an optimal framework for PC implementations addressing the rigid registration problem. Using this framework, we are able to render DRR images from a 256×256×133 CT volume in ~24 ms using an NVidia GeForce 8800 GTX and in ~2 ms using NVidia GeForce GTX 580. In addition to applications requiring fast automatic patient setup, these levels of performance suggest image-guided radiation therapy at video frame rates is technically feasible using relatively low cost PC

  7. Brain Arteries Movement Detection With Gated Gradient Echo Sequence: Standardization, Registration and Subtraction of Serial Magnetic Resonance Images

    National Research Council Canada - National Science Library

    Ionescu, Razvan

    2001-01-01

    In order to make evident pulsing brain arteries movements associated with heart activity, intramodality MR registration and subtraction has to be used to detect small differences between serial MR brain images...

  8. Reduction of Cone-Beam CT scan time without compromising the accuracy of the image registration in IGRT

    DEFF Research Database (Denmark)

    Westberg, Jonas; Jensen, Henrik R; Bertelsen, Anders

    2010-01-01

    In modern radiotherapy accelerators are equipped with 3D cone-beam CT (CBCT) which is used to verify patient position before treatment. The verification is based on an image registration between the CBCT acquired just before treatment and the CT scan made for the treatment planning. The purpose...... of this study is to minimise the scan time of the CBCT without compromising the accuracy of the image registration in IGRT....

  9. Geo-registration of Unprofessional and Weakly-related Image and Precision Evaluation

    Directory of Open Access Journals (Sweden)

    LIU Yingzhen

    2015-09-01

    Full Text Available The 3D geo-spatial model built by unprofessional and weakly-related image is a significant source of geo-spatial information. The unprofessional and weakly-related image cannot be useful geo-spatial information until be geo-registered with accurate geo-spatial orientation and location. In this paper, we present an automatic geo-registration using the coordination acquired by real-time GPS module. We calculate 2D and 3D spatial transformation parameters based on the spatial similarity between the image location in the geo-spatial coordination system and in the 3D reconstruction coordination system. Because of the poor precision of GPS information and especially the unstability of elevation measurement, we use RANSAC algorithm to get rid of outliers. In the experiment, we compare the geo-registered image positions to their differential GPS coordinates. The errors of translation, rotation and scaling are evaluated quantitively and the causes of bad result are analyzed. The experiment demonstrates that this geo-registration method can get a precise result with enough images.

  10. Least median of squares filtering of locally optimal point matches for compressible flow image registration

    International Nuclear Information System (INIS)

    Castillo, Edward; Guerrero, Thomas; Castillo, Richard; White, Benjamin; Rojo, Javier

    2012-01-01

    Compressible flow based image registration operates under the assumption that the mass of the imaged material is conserved from one image to the next. Depending on how the mass conservation assumption is modeled, the performance of existing compressible flow methods is limited by factors such as image quality, noise, large magnitude voxel displacements, and computational requirements. The Least Median of Squares Filtered Compressible Flow (LFC) method introduced here is based on a localized, nonlinear least squares, compressible flow model that describes the displacement of a single voxel that lends itself to a simple grid search (block matching) optimization strategy. Spatially inaccurate grid search point matches, corresponding to erroneous local minimizers of the nonlinear compressible flow model, are removed by a novel filtering approach based on least median of squares fitting and the forward search outlier detection method. The spatial accuracy of the method is measured using ten thoracic CT image sets and large samples of expert determined landmarks (available at www.dir-lab.com). The LFC method produces an average error within the intra-observer error on eight of the ten cases, indicating that the method is capable of achieving a high spatial accuracy for thoracic CT registration. (paper)

  11. Multiphase joint segmentation-registration and object tracking for layered images.

    Science.gov (United States)

    Chen, Ping-Feng; Krim, Hamid; Mendoza, Olga L

    2010-07-01

    In this paper we propose to jointly segment and register objects of interest in layered images. Layered imaging refers to imageries taken from different perspectives and possibly by different sensors. Registration and segmentation are therefore the two main tasks which contribute to the bottom level, data alignment, of the multisensor data fusion hierarchical structures. Most exploitations of two layered images assumed that scanners are at very high altitudes and that only one transformation ties the two images. Our data are however taken at mid-range and therefore requires segmentation to assist us examining different object regions in a divide-and-conquer fashion. Our approach is a combination of multiphase active contour method with a joint segmentation-registration technique (which we called MPJSR) carried out in a local moving window prior to a global optimization. To further address layered video sequences and tracking objects in frames, we propose a simple adaptation of optical flow calculations along the active contours in a pair of layered image sequences. The experimental results show that the whole integrated algorithm is able to delineate the objects of interest, align them for a pair of layered frames and keep track of the objects over time.

  12. Applications of deformable image registration: Automatic segmentation and adaptive radiation therapy

    Science.gov (United States)

    Morcos, Marc

    The contents of this thesis are best divided into two components: (i) evaluation of atlas-based segmentation and deformable contour propagation and (ii) adaptive radiation therapy using deformable electron density mapping. The first component of this thesis involves the evaluation of two commercial deformable registration systems with respect to automatic segmentation techniques. Overall, the techniques revealed that manual modifications would be required if the structures were to be used for treatment planning. The automatic segmentation methods utilized by both commercial products serve as an excellent starting point for contouring process and also reduce inter- and intra-physician variability when contouring. In the second component, we developed a framework for dose accumulation adaptive radiation therapy. By registering the planning computed tomography (CT) images to the weekly cone-beam computed tomography (CBCT) images, we were able to produce modified CBCT images which possessed CT Hounsfield units; this was achieved by using deformable image registration. Dose distributions were recalculated onto the modified CBCT images and then compared to the planned dose distributions. Results indicated that deformable electron density mapping is a feasible technique to allow dose distributions to be recalculated on pre-treatment CBCT scans.

  13. Nonrigid Registration of Brain Tumor Resection MR Images Based on Joint Saliency Map and Keypoint Clustering

    Directory of Open Access Journals (Sweden)

    Binjie Qin

    2009-12-01

    Full Text Available This paper proposes a novel global-to-local nonrigid brain MR image registration to compensate for the brain shift and the unmatchable outliers caused by the tumor resection. The mutual information between the corresponding salient structures, which are enhanced by the joint saliency map (JSM, is maximized to achieve a global rigid registration of the two images. Being detected and clustered at the paired contiguous matching areas in the globally registered images, the paired pools of DoG keypoints in combination with the JSM provide a useful cluster-to-cluster correspondence to guide the local control-point correspondence detection and the outlier keypoint rejection. Lastly, a quasi-inverse consistent deformation is smoothly approximated to locally register brain images through the mapping the clustered control points by compact support radial basis functions. The 2D implementation of the method can model the brain shift in brain tumor resection MR images, though the theory holds for the 3D case.

  14. Spatial correlations of trabecular bone microdamage with local stresses and strains using rigid image registration.

    Science.gov (United States)

    Nagaraja, Srinidhi; Skrinjar, Oskar; Guldberg, Robert E

    2011-06-01

    Although microdamage is known to accumulate in trabecular bone with overloading and aging, the tissue-level stresses and strains associated with local bone failure are not well known. Local correlation of microdamage with microstructural stresses and strains requires methods to accurately register histological sections with micro-computed tomography (micro-CT) based finite element models. In addition, the resolution of correlation (i.e., grid size) selected for analysis may affect the observed results. Therefore, an automated, repeatable, and accurate image registration algorithm was developed to determine the range of local stresses and strains associated with microdamage initiation. Using a two-dimensional rigid registration algorithm, bone structures from histology and micro-CT imaging were aligned. Once aligned, microdamaged regions were spatially correlated with local stresses and strains obtained from micro-CT based finite element analysis. Using this more sophisticated registration technique, we were able to analyze the effects of varying spatial grid resolution on local stresses and strains initiating microdamage. The results indicated that grid refinement to the individual pixel level (pixel-by-pixel method) more precisely defined the range of microdamage initiation compared to manually selected individual damaged and undamaged trabeculae. Using the pixel-by-pixel method, we confirmed that trabecular bone from younger cows sustained higher local strains prior to microdamage initiation compared to older bone.

  15. Automatic registration of terrestrial point clouds based on panoramic reflectance images and efficient BaySAC

    Science.gov (United States)

    Kang, Zhizhong

    2013-10-01

    This paper presents a new approach to automatic registration of terrestrial laser scanning (TLS) point clouds utilizing a novel robust estimation method by an efficient BaySAC (BAYes SAmpling Consensus). The proposed method directly generates reflectance images from 3D point clouds, and then using SIFT algorithm extracts keypoints to identify corresponding image points. The 3D corresponding points, from which transformation parameters between point clouds are computed, are acquired by mapping the 2D ones onto the point cloud. To remove false accepted correspondences, we implement a conditional sampling method to select the n data points with the highest inlier probabilities as a hypothesis set and update the inlier probabilities of each data point using simplified Bayes' rule for the purpose of improving the computation efficiency. The prior probability is estimated by the verification of the distance invariance between correspondences. The proposed approach is tested on four data sets acquired by three different scanners. The results show that, comparing with the performance of RANSAC, BaySAC leads to less iterations and cheaper computation cost when the hypothesis set is contaminated with more outliers. The registration results also indicate that, the proposed algorithm can achieve high registration accuracy on all experimental datasets.

  16. Modreg: A Modular Framework for RGB-D Image Acquisition and 3D Object Model Registration

    Directory of Open Access Journals (Sweden)

    Kornuta Tomasz

    2017-09-01

    Full Text Available RGB-D sensors became a standard in robotic applications requiring object recognition, such as object grasping and manipulation. A typical object recognition system relies on matching of features extracted from RGB-D images retrieved from the robot sensors with the features of the object models. In this paper we present ModReg: a system for registration of 3D models of objects. The system consists of a modular software associated with a multi-camera setup supplemented with an additional pattern projector, used for the registration of high-resolution RGB-D images. The objects are placed on a fiducial board with two dot patterns enabling extraction of masks of the placed objects and estimation of their initial poses. The acquired dense point clouds constituting subsequent object views undergo pairwise registration and at the end are optimized with a graph-based technique derived from SLAM. The combination of all those elements resulted in a system able to generate consistent 3D models of objects.

  17. Image registration and analysis for quantitative myocardial perfusion: application to dynamic circular cardiac CT

    Science.gov (United States)

    Isola, A. A.; Schmitt, H.; van Stevendaal, U.; Begemann, P. G.; Coulon, P.; Boussel, L.; Grass, M.

    2011-09-01

    Large area detector computed tomography systems with fast rotating gantries enable volumetric dynamic cardiac perfusion studies. Prospectively, ECG-triggered acquisitions limit the data acquisition to a predefined cardiac phase and thereby reduce x-ray dose and limit motion artefacts. Even in the case of highly accurate prospective triggering and stable heart rate, spatial misalignment of the cardiac volumes acquired and reconstructed per cardiac cycle may occur due to small motion pattern variations from cycle to cycle. These misalignments reduce the accuracy of the quantitative analysis of myocardial perfusion parameters on a per voxel basis. An image-based solution to this problem is elastic 3D image registration of dynamic volume sequences with variable contrast, as it is introduced in this contribution. After circular cone-beam CT reconstruction of cardiac volumes covering large areas of the myocardial tissue, the complete series is aligned with respect to a chosen reference volume. The results of the registration process and the perfusion analysis with and without registration are evaluated quantitatively in this paper. The spatial alignment leads to improved quantification of myocardial perfusion for three different pig data sets.

  18. Tensor-based morphometry with stationary velocity field diffeomorphic registration: application to ADNI.

    Science.gov (United States)

    Bossa, Matias; Zacur, Ernesto; Olmos, Salvador

    2010-07-01

    Tensor-based morphometry (TBM) is an analysis technique where anatomical information is characterized by means of the spatial transformations mapping a customized template with the observed images. Therefore, accurate inter-subject non-rigid registration is an essential prerequisite for both template estimation and image warping. Subsequent statistical analysis on the spatial transformations is performed to highlight voxel-wise differences. Most of previous TBM studies did not explore the influence of the registration parameters, such as the parameters defining the deformation and the regularization models. In this work performance evaluation of TBM using stationary velocity field (SVF) diffeomorphic registration was performed in a subset of subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) study. A wide range of values of the registration parameters that define the transformation smoothness and the balance between image matching and regularization were explored in the evaluation. The proposed methodology provided brain atrophy maps with very detailed anatomical resolution and with a high significance level compared with results recently published on the same data set using a non-linear elastic registration method. Copyright (c) 2010 Elsevier Inc. All rights reserved.

  19. Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification

    Directory of Open Access Journals (Sweden)

    Xiaoyang Zhao

    2018-04-01

    Full Text Available In recent years, digital frame cameras have been increasingly used for remote sensing applications. However, it is always a challenge to align or register images captured with different cameras or different imaging sensor units. In this research, a novel registration method was proposed. Coarse registration was first applied to approximately align the sensed and reference images. Window selection was then used to reduce the search space and a histogram specification was applied to optimize the grayscale similarity between the images. After comparisons with other commonly-used detectors, the fast corner detector, FAST (Features from Accelerated Segment Test, was selected to extract the feature points. The matching point pairs were then detected between the images, the outliers were eliminated, and geometric transformation was performed. The appropriate window size was searched and set to one-tenth of the image width. The images that were acquired by a two-camera system, a camera with five imaging sensors, and a camera with replaceable filters mounted on a manned aircraft, an unmanned aerial vehicle, and a ground-based platform, respectively, were used to evaluate the performance of the proposed method. The image analysis results showed that, through the appropriate window selection and histogram specification, the number of correctly matched point pairs had increased by 11.30 times, and that the correct matching rate had increased by 36%, compared with the results based on FAST alone. The root mean square error (RMSE in the x and y directions was generally within 0.5 pixels. In comparison with the binary robust invariant scalable keypoints (BRISK, curvature scale space (CSS, Harris, speed up robust features (SURF, and commercial software ERDAS and ENVI, this method resulted in larger numbers of correct matching pairs and smaller, more consistent RMSE. Furthermore, it was not necessary to choose any tie control points manually before registration

  20. 2D imaging and 3D sensing data acquisition and mutual registration for painting conservation

    Science.gov (United States)

    Fontana, Raffaella; Gambino, Maria Chiara; Greco, Marinella; Marras, Luciano; Pampaloni, Enrico M.; Pelagotti, Anna; Pezzati, Luca; Poggi, Pasquale

    2005-01-01

    We describe the application of 2D and 3D data acquisition and mutual registration to the conservation of paintings. RGB color image acquisition, IR and UV fluorescence imaging, together with the more recent hyperspectral imaging (32 bands) are among the most useful techniques in this field. They generally are meant to provide information on the painting materials, on the employed techniques and on the object state of conservation. However, only when the various images are perfectly registered on each other and on the 3D model, no ambiguity is possible and safe conclusions may be drawn. We present the integration of 2D and 3D measurements carried out on two different paintings: "Madonna of the Yarnwinder" by Leonardo da Vinci, and "Portrait of Lionello d'Este", by Pisanello, both painted in the XV century.

  1. A generalized framework unifying image registration and respiratory motion models and incorporating image reconstruction, for partial image data or full images

    Science.gov (United States)

    McClelland, Jamie R.; Modat, Marc; Arridge, Simon; Grimes, Helen; D'Souza, Derek; Thomas, David; O' Connell, Dylan; Low, Daniel A.; Kaza, Evangelia; Collins, David J.; Leach, Martin O.; Hawkes, David J.

    2017-06-01

    Surrogate-driven respiratory motion models relate the motion of the internal anatomy to easily acquired respiratory surrogate signals, such as the motion of the skin surface. They are usually built by first using image registration to determine the motion from a number of dynamic images, and then fitting a correspondence model relating the motion to the surrogate signals. In this paper we present a generalized framework that unifies the image registration and correspondence model fitting into a single optimization. This allows the use of ‘partial’ imaging data, such as individual slices, projections, or k-space data, where it would not be possible to determine the motion from an individual frame of data. Motion compensated image reconstruction can also be incorporated using an iterative approach, so that both the motion and a motion-free image can be estimated from the partial image data. The framework has been applied to real 4DCT, Cine CT, multi-slice CT, and multi-slice MR data, as well as simulated datasets from a computer phantom. This includes the use of a super-resolution reconstruction method for the multi-slice MR data. Good results were obtained for all datasets, including quantitative results for the 4DCT and phantom datasets where the ground truth motion was known or could be estimated.

  2. Intersection-based registration of slice stacks to form 3D images of the human fetal brain

    DEFF Research Database (Denmark)

    Kim, Kio; Hansen, Mads Fogtmann; Habas, Piotr

    2008-01-01

    Clinical fetal MR imaging of the brain commonly makes use of fast 2D acquisitions of multiple sets of approximately orthogonal 2D slices. We and others have previously proposed an iterative slice-to-volume registration process to recover a geometrically consistent 3D image. However...... of the approach applied to simulated data and clinically acquired fetal images....

  3. Infrared and visible images registration with adaptable local-global feature integration for rail inspection

    Science.gov (United States)

    Tang, Chaoqing; Tian, Gui Yun; Chen, Xiaotian; Wu, Jianbo; Li, Kongjing; Meng, Hongying

    2017-12-01

    Active thermography provides infrared images that contain sub-surface defect information, while visible images only reveal surface information. Mapping infrared information to visible images offers more comprehensive visualization for decision-making in rail inspection. However, the common information for registration is limited due to different modalities in both local and global level. For example, rail track which has low temperature contrast reveals rich details in visible images, but turns blurry in the infrared counterparts. This paper proposes a registration algorithm called Edge-Guided Speeded-Up-Robust-Features (EG-SURF) to address this issue. Rather than sequentially integrating local and global information in matching stage which suffered from buckets effect, this algorithm adaptively integrates local and global information into a descriptor to gather more common information before matching. This adaptability consists of two facets, an adaptable weighting factor between local and global information, and an adaptable main direction accuracy. The local information is extracted using SURF while the global information is represented by shape context from edges. Meanwhile, in shape context generation process, edges are weighted according to local scale and decomposed into bins using a vector decomposition manner to provide more accurate descriptor. The proposed algorithm is qualitatively and quantitatively validated using eddy current pulsed thermography scene in the experiments. In comparison with other algorithms, better performance has been achieved.

  4. Deformable Registration of Biomedical Images using 2D Hidden Markov Models.

    Science.gov (United States)

    Shenoy, Renuka; Shih, Min-Chi; Rose, Kenneth

    2016-07-18

    Robust registration of unimodal and multimodal images is a key task in biomedical image analysis, and is often utilized as an initial step on which subsequent analysis techniques critically depend. We propose a novel probabilistic framework, based on a variant of the 2D hidden Markov model, namely the turbo hidden Markov model, to capture the deformation between pairs of images. The HMM is tailored to capture spatial transformations across images via state transitions, and modalityspecific data costs via emission probabilities. The method is derived for the unimodal setting (where simpler matching metrics may be used) as well as the multimodal setting, where different modalities may provide very different representations for a given class of objects, necessitating the use of advanced similarity measures. We utilize a rich model with hundreds of model parameters to describe the deformation relationships across such modalities. We also introduce a local edge-adaptive constraint to allow for varying degrees of smoothness between object boundaries and homogeneous regions. The parameters of the described method are estimated in a principled manner from training data via maximum likelihood learning, and the deformation is subsequently estimated using an efficient dynamic programming algorithm. Experimental results demonstrate the improved performance of the proposed approach over state-ofthe- art deformable registration techniques, on both unimodal and multimodal biomedical datasets.

  5. Slice-to-volume deformable registration: efficient one-shot consensus between plane selection and in-plane deformation.

    Science.gov (United States)

    Ferrante, Enzo; Fecamp, Vivien; Paragios, Nikos

    2015-06-01

    This paper introduces a novel decomposed graphical model to deal with slice-to-volume registration in the context of medical images and image-guided surgeries. We present a new non-rigid slice-to-volume registration method whose main contribution is the ability to decouple the plane selection and the in-plane deformation parts of the transformation--through two distinct graphs--toward reducing the complexity of the model while being able to obtain simultaneously the solution for both of them. To this end, the plane selection process is expressed as a local graph-labeling problem endowed with planarity satisfaction constraints, which is then directly linked with the deformable part through the data registration likelihoods. The resulting model is modular with respect to the image metric, can cope with arbitrary in-plane regularization terms and inherits excellent properties in terms of computational efficiency. The proof of concept for the proposed formulation is done using cardiac MR sequences of a beating heart (an artificially generated 2D temporal sequence is extracted using real data with known ground truth) as well as multimodal brain images involving ultrasound and computed tomography images. We achieve state-of-the-art results while decreasing the computational time when we compare with another method based on similar techniques. We confirm that graphical models and discrete optimization techniques are suitable to solve non-rigid slice-to-volume registration problems. Moreover, we show that decoupling the graphical model and labeling it using two lower-dimensional label spaces, we can achieve state-of-the-art results while substantially reducing the complexity of our method and moving the approach close to real clinical applications once considered in the context of modern parallel architectures.

  6. A phantom study of the accuracy of CT, MR and PET image registrations with a block matching-based algorithm.

    Science.gov (United States)

    Isambert, A; Bonniaud, G; Lavielle, F; Malandain, G; Lefkopoulos, D

    2008-12-01

    The aim of the present study was to quantitatively assess the performance of a block matching-based automatic registration algorithm integrated within the commercial treatment planning system designated ISOgray from Dosisoft. The accuracy of the process was evaluated by a phantom study on computed tomography (CT), magnetic resonance (MR) and positron emission tomography (PET) images. Two phantoms were used to carry out this study: the cylindrical Jaszczak phantom and the anthropomorphic Liqui-Phil Head Phantom (the Phantom Laboratory), containing fillable spheres. External fiducial markers were used to quantify the accuracy of 41 CT/CT, MR/CT and PET/CT automatic registrations with images of the rotated and tilted phantoms. The study first showed that a cylindrical phantom was not adapted for the evaluation of the performance of a block matching-based registration software. Secondly, the Liqui-Phil Head Phantom study showed that the algorithm was able to perform automatic registrations of CT/CT and MR/CT images with differences of up to 40 degrees in phantom rotation and of up to 20-30 degrees for PET/CT with accuracy below the image voxel size. The study showed that the block matching-based automatic registration software under investigation was robust, reliable and yielded very satisfactory results. This phantom-based test can be integrated into a periodical quality assurance process and used for any commissioning of image registration software for radiation therapy.

  7. Investigation of six-degree-of-freedom image registration between planning and cone beam computed tomography in esophageal cancer

    International Nuclear Information System (INIS)

    Li Jiancheng; Pan Jianji; Hu Cairong; Wang Xiaoliang; Cheng Wenfang; Zhao Yunhui

    2010-01-01

    Objective: To explore six-degree-of-freedom (6-DF) registration methods between planning and cone beam computed tomography (CBCT) during image-guided radiation therapy (IGRT) in esophageal cancer. Methods: Thirty pairs of CBCT images acquired before radiation and the corresponding planning computed tomography (CT) images of esophageal cancer were selected for further investigation. Registration markers for 6-DF image registration were determined and contoured in those images. The results of registration as well as time cost were compared among different registration methods of bone match, gray value match, manual match, and bone plus manual match. Results: Contouring bone and spinal canal posterior to the target volume of esophageal carcinoma as registration marker could make 6-DF registration quick and precise. Compared with manual match, set-up errors of v rotation in bone plus manual match (-0.55 degree vs.-0.88 degree, t=2.55, P=0.020), of x-axis and v rotation in bone match (0.12 mm vs.-2.33 mm, t=5.75, P=0.000; -0.35 degree vs. -0.88 degree, t=3.00, P=0.007), and of x-axis and w rotation in gray value match (7.20 mm vs. -2.33 mm, t=3.10, P=0.006; -0.10 degree vs. -0.59 degree, t=2.81, P =0.011) were significantly different. Compared with manual match, the coincidence rate of bone plus manual match was the highest (85.55%), followed by bone match and gray value match (74.45% and 74.45%). The time cost of each registration method from longest to shortest was: 6.00 -10.00 minutes for manual match, 1.00 - 5.00 minutes for bone plus manual match, 0.75 - 1.50 minutes for gray value match, and 0.50 - 0.83 minutes for bone match. Conclusions: Registration marker is useful for image registration of CBCT and planning CT in patients with esophageal cancer. Bone plus manual match may be the best registration method considering both registration time and accuracy. (authors)

  8. Image-based dose planning of intracavitary brachytherapy: registration of serial-imaging studies using deformable anatomic templates

    International Nuclear Information System (INIS)

    Christensen, Gary E.; Carlson, Blake; Chao, K.S. Clifford; Yin Pen; Grigsby, Perry W.; Nguyen, Kim; Dempsey, James F; Lerma, Fritz A.; Bae, Kyongtae T.; Vannier, Michael W.; Williamson, Jeffrey F.

    2001-01-01

    Purpose: To demonstrate that high-dimensional voxel-to-voxel transformations, derived from continuum mechanics models of the underlying pelvic tissues, can be used to register computed tomography (CT) serial examinations into a single anatomic frame of reference for cumulative dose calculations. Methods and Materials: Three patients with locally advanced cervix cancer were treated with CT-compatible intracavitary (ICT) applicators. Each patient underwent five volumetric CT examinations: before initiating treatment, and immediately before and after the first and second ICT insertions, respectively. Each serial examination was rigidly registered to the patient's first ICT examination by aligning the bony anatomy. Detailed nonrigid alignment for organs (or targets) of interest was subsequently achieved by deforming the CT exams as a viscous-fluid, described by the Navier-Stokes equation, until the coincidence with the corresponding targets on CT image was maximized. In cases where ICT insertion induced very large and topologically complex rearrangements of pelvic organs, e.g., extreme uterine canal reorientation following tandem insertion, a viscous-fluid-landmark transformation was used to produce an initial registration. Results: For all three patients, reasonable registrations for organs (or targets) of interest were achieved. Fluid-landmark initialization was required in 4 of the 11 registrations. Relative to the best rigid bony landmark alignment, the viscous-fluid registration resulted in average soft-tissue displacements from 2.8 to 28.1 mm, and improved organ coincidence from the range of 5.2% to 72.2% to the range of 90.6% to 100%. Compared to the viscous-fluid transformation, global registration of bony anatomy mismatched 5% or more of the contoured organ volumes by 15-25 mm. Conclusion: Pelvic soft-tissue structures undergo large deformations and displacements during the external-beam and multiple-ICT course of radiation therapy for locally advanced cervix

  9. Improving fluid registration through white matter segmentation in a twin study design

    Science.gov (United States)

    Chou, Yi-Yu; Lepore, Natasha; Brun, Caroline; Barysheva, Marina; McMahon, Katie; de Zubicaray, Greig I.; Wright, Margaret J.; Toga, Arthur W.; Thompson, Paul M.

    2010-03-01

    Robust and automatic non-rigid registration depends on many parameters that have not yet been systematically explored. Here we determined how tissue classification influences non-linear fluid registration of brain MRI. Twin data is ideal for studying this question, as volumetric correlations between corresponding brain regions that are under genetic control should be higher in monozygotic twins (MZ) who share 100% of their genes when compared to dizygotic twins (DZ) who share half their genes on average. When these substructure volumes are quantified using tensor-based morphometry, improved registration can be defined based on which method gives higher MZ twin correlations when compared to DZs, as registration errors tend to deplete these correlations. In a study of 92 subjects, higher effect sizes were found in cumulative distribution functions derived from statistical maps when performing tissue classification before fluid registration, versus fluidly registering the raw images. This gives empirical evidence in favor of pre-segmenting images for tensor-based morphometry.

  10. Evaluation of Deformable Image Registration Methods for Dose Monitoring in Head and Neck Radiotherapy

    Directory of Open Access Journals (Sweden)

    Bastien Rigaud

    2015-01-01

    Full Text Available In the context of head and neck cancer (HNC adaptive radiation therapy (ART, the two purposes of the study were to compare the performance of multiple deformable image registration (DIR methods and to quantify their impact for dose accumulation, in healthy structures. Fifteen HNC patients had a planning computed tomography (CT0 and weekly CTs during the 7 weeks of intensity-modulated radiation therapy (IMRT. Ten DIR approaches using different registration methods (demons or B-spline free form deformation (FFD, preprocessing, and similarity metrics were tested. Two observers identified 14 landmarks (LM on each CT-scan to compute LM registration error. The cumulated doses estimated by each method were compared. The two most effective DIR methods were the demons and the FFD, with both the mutual information (MI metric and the filtered CTs. The corresponding LM registration accuracy (precision was 2.44 mm (1.30 mm and 2.54 mm (1.33 mm, respectively. The corresponding LM estimated cumulated dose accuracy (dose precision was 0.85 Gy (0.93 Gy and 0.88 Gy (0.95 Gy, respectively. The mean uncertainty (difference between maximal and minimal dose considering all the 10 methods to estimate the cumulated mean dose to the parotid gland (PG was 4.03 Gy (SD = 2.27 Gy, range: 1.06–8.91 Gy.

  11. Deformable Image Registration of Liver With Consideration of Lung Sliding Motion

    International Nuclear Information System (INIS)

    Xie, Yaoqin; Chao, Ming; Xiong, Guanglei

    2011-01-01

    Purpose: A feature based deformable registration model with sliding transformation was developed in the upper abdominal region for liver cancer. Methods: A two-step thin-plate spline (bi-TPS) algorithm was implemented to deformably register the liver organ. The first TPS registration was performed to exclusively quantify the sliding displacement component. A manual segmentation of the thoracic and abdominal cavity was performed as a priori knowledge. Tissue feature points were automatically identified inside the segmented contour on the images. The scale invariant feature transform method was utilized to match feature points that served as landmarks for the subsequent TPS registration to derive the sliding displacement vector field. To a good approximation, only motion along superior/inferior (SI) direction of voxels on each slice was averaged to obtain the sliding displacement for each slice. A second TPS transformation, as the last step, was carried out to obtain the local deformation field. Manual identification of bifurcation on liver, together with the manual segmentation of liver organ, was employed as a ''ground truth'' for assessing the algorithm's performance. Results: The proposed two-step TPS was assessed with six liver patients. The average error of liver bifurcation between manual identification and calculation for these patients was less than 1.8 mm. The residual errors between manual contour and propagated contour of liver organ using the algorithm fell in the range between 2.1 and 2.8 mm. An index of Dice similarity coefficient (DSC) between manual contour and calculated contour for liver tumor was 93.6% compared with 71.2% from the conventional TPS calculation. Conclusions: A high accuracy (∼2 mm) of the two-step feature based TPS registration algorithm was achievable for registering the liver organ. The discontinuous motion in the upper abdominal region was properly taken into consideration. Clinical implementation of the algorithm will find

  12. WE-AB-BRA-12: Virtual Endoscope Tracking for Endoscopy-CT Image Registration

    International Nuclear Information System (INIS)

    Ingram, W; Rao, A; Wendt, R; Court, L; Yang, J; Beadle, B

    2015-01-01

    Purpose: The use of endoscopy in radiotherapy will remain limited until we can register endoscopic video to CT using standard clinical equipment. In this phantom study we tested a registration method using virtual endoscopy to measure CT-space positions from endoscopic video. Methods: Our phantom is a contorted clay cylinder with 2-mm-diameter markers in the luminal surface. These markers are visible on both CT and endoscopic video. Virtual endoscope images were rendered from a polygonal mesh created by segmenting the phantom’s luminal surface on CT. We tested registration accuracy by tracking the endoscope’s 6-degree-of-freedom coordinates frame-to-frame in a video recorded as it moved through the phantom, and using these coordinates to measure CT-space positions of markers visible in the final frame. To track the endoscope we used the Nelder-Mead method to search for coordinates that render the virtual frame most similar to the next recorded frame. We measured the endoscope’s initial-frame coordinates using a set of visible markers, and for image similarity we used a combination of mutual information and gradient alignment. CT-space marker positions were measured by projecting their final-frame pixel addresses through the virtual endoscope to intersect with the mesh. Registration error was quantified as the distance between this intersection and the marker’s manually-selected CT-space position. Results: Tracking succeeded for 6 of 8 videos, for which the mean registration error was 4.8±3.5mm (24 measurements total). The mean error in the axial direction (3.1±3.3mm) was larger than in the sagittal or coronal directions (2.0±2.3mm, 1.7±1.6mm). In the other 2 videos, the virtual endoscope got stuck in a false minimum. Conclusion: Our method can successfully track the position and orientation of an endoscope, and it provides accurate spatial mapping from endoscopic video to CT. This method will serve as a foundation for an endoscopy-CT registration

  13. Tracking Regional Tissue Volume and Function Change in Lung Using Image Registration

    Directory of Open Access Journals (Sweden)

    Kunlin Cao

    2012-01-01

    Full Text Available We have previously demonstrated the 24-hour redistribution and reabsorption of bronchoalveolar lavage (BAL fluid delivered to the lung during a bronchoscopic procedure in normal volunteers. In this work we utilize image-matching procedures to correlate fluid redistribution and reabsorption to changes in regional lung function. Lung CT datasets from six human subjects were used in this study. Each subject was scanned at four time points before and after BAL procedure. Image registration was performed to align images at different time points and different inflation levels. The resulting dense displacement fields were utilized to track tissue volume changes and reveal deformation patterns of local parenchymal tissue quantitatively. The registration accuracy was assessed by measuring landmark matching errors, which were on the order of 1 mm. The results show that quantitative-assessed fluid volume agreed well with bronchoscopist-reported unretrieved BAL volume in the whole lungs (squared linear correlation coefficient was 0.81. The average difference of lung tissue volume at baseline and after 24 hours was around 2%, which indicates that BAL fluid in the lungs was almost absorbed after 24 hours. Regional lung-function changes correlated with the presence of BAL fluid, and regional function returned to baseline as the fluid was reabsorbed.

  14. Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms.

    Science.gov (United States)

    Xue, Zhong; Shen, Dinggang; Karacali, Bilge; Stern, Joshua; Rottenberg, David; Davatzikos, Christos

    2006-11-15

    Simulated deformations and images can act as the gold standard for evaluating various template-based image segmentation and registration algorithms. Traditional deformable simulation methods, such as the use of analytic deformation fields or the displacement of landmarks followed by some form of interpolation, are often unable to construct rich (complex) and/or realistic deformations of anatomical organs. This paper presents new methods aiming to automatically simulate realistic inter- and intra-individual deformations. The paper first describes a statistical approach to capturing inter-individual variability of high-deformation fields from a number of examples (training samples). In this approach, Wavelet-Packet Transform (WPT) of the training deformations and their Jacobians, in conjunction with a Markov random field (MRF) spatial regularization, are used to capture both coarse and fine characteristics of the training deformations in a statistical fashion. Simulated deformations can then be constructed by randomly sampling the resultant statistical distribution in an unconstrained or a landmark-constrained fashion. The paper also describes a model for generating tissue atrophy or growth in order to simulate intra-individual brain deformations. Several sets of simulated deformation fields and respective images are generated, which can be used in the future for systematic and extensive validation studies of automated atlas-based segmentation and deformable registration methods. The code and simulated data are available through our Web site.

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Yang, F; Meyer, J; Sandison, G [Department of Radiation Oncology, University of Washington Medical Center, Seattle, WA (United States)

    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.

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

    Science.gov (United States)

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

    2007-04-01

    Image guidance has become a pre-requisite for hypofractionated radiotherapy where the applied dose per fraction is increased. Particularly in stereotactic body radiotherapy (SBRT) for lung tumours, one has to account for set-up errors and intrafraction tumour motion. In our feasibility study, we compared digitally reconstructed radiographs (DRRs) of lung lesions with MV portal images (PIs) to obtain the displacement of the tumour before irradiation. The verification of the tumour position was performed by rigid intensity based registration and three different merit functions such as the sum of squared pixel intensity differences, normalized cross correlation and normalized mutual information. The registration process then provided a translation vector that defines the displacement of the target in order to align the tumour with the isocentre. To evaluate the registration algorithms, 163 test images were created and subsequently, a lung phantom containing an 8 cm3 tumour was built. In a further step, the registration process was applied on patient data, containing 38 tumours in 113 fractions. To potentially improve registration outcome, two filter types (histogram equalization and display equalization) were applied and their impact on the registration process was evaluated. Generated test images showed an increase in successful registrations when applying a histogram equalization filter whereas the lung phantom study proved the accuracy of the selected algorithms, i.e. deviations of the calculated translation vector for all test algorithms were below 1 mm. For clinical patient data, successful registrations occurred in about 59% of anterior-posterior (AP) and 46% of lateral projections, respectively. When patients with a clinical target volume smaller than 10 cm3 were excluded, successful registrations go up to 90% in AP and 50% in lateral projection. In addition, a reliable identification of the tumour position was found to be difficult for clinical target volumes

  18. Tracking lung tissue motion and expansion/compression with inverse consistent image registration and spirometry

    International Nuclear Information System (INIS)

    Christensen, Gary E.; Song, Joo Hyun; Lu, Wei; Naqa, Issam El; Low, Daniel A.

    2007-01-01

    Breathing motion is one of the major limiting factors for reducing dose and irradiation of normal tissue for conventional conformal radiotherapy. This paper describes a relationship between tracking lung motion using spirometry data and image registration of consecutive CT image volumes collected from a multislice CT scanner over multiple breathing periods. Temporal CT sequences from 5 individuals were analyzed in this study. The couch was moved from 11 to 14 different positions to image the entire lung. At each couch position, 15 image volumes were collected over approximately 3 breathing periods. It is assumed that the expansion and contraction of lung tissue can be modeled as an elastic material. Furthermore, it is assumed that the deformation of the lung is small over one-fifth of a breathing period and therefore the motion of the lung can be adequately modeled using a small deformation linear elastic model. The small deformation inverse consistent linear elastic image registration algorithm is therefore well suited for this problem and was used to register consecutive image scans. The pointwise expansion and compression of lung tissue was measured by computing the Jacobian of the transformations used to register the images. The logarithm of the Jacobian was computed so that expansion and compression of the lung were scaled equally. The log-Jacobian was computed at each voxel in the volume to produce a map of the local expansion and compression of the lung during the breathing period. These log-Jacobian images demonstrate that the lung does not expand uniformly during the breathing period, but rather expands and contracts locally at different rates during inhalation and exhalation. The log-Jacobian numbers were averaged over a cross section of the lung to produce an estimate of the average expansion or compression from one time point to the next and compared to the air flow rate measured by spirometry. In four out of five individuals, the average log

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

    International Nuclear Information System (INIS)

    Shi Yonghong; Liao Shu; Shen Dinggang

    2011-01-01

    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

  20. Unbiased group-wise image registration: applications in brain fiber tract atlas construction and functional connectivity analysis.

    Science.gov (United States)

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

    2011-10-01

    We propose an unbiased implicit-reference group-wise (IRG) image registration method and demonstrate its applications in the construction of a brain white matter fiber tract atlas and the analysis of resting-state functional MRI (fMRI) connectivity. Most image registration techniques pair-wise align images to a selected reference image and group analyses are performed in the reference space, which may produce bias. The proposed method jointly estimates transformations, with an elastic deformation model, registering all images to an implicit reference corresponding to the group average. The unbiased registration is applied to build a fiber tract atlas by registering a group of diffusion tensor images. Compared to reference-based registration, the IRG registration improves the fiber track overlap within the group. After applying the method in the fMRI connectivity analysis, results suggest a general improvement in functional connectivity maps at a group level in terms of larger cluster size and higher average t-scores.

  1. A fast, accurate, and automatic 2D-3D image registration for image-guided cranial radiosurgery

    International Nuclear Information System (INIS)

    Fu Dongshan; Kuduvalli, Gopinath

    2008-01-01

    The authors developed a fast and accurate two-dimensional (2D)-three-dimensional (3D) image registration method to perform precise initial patient setup and frequent detection and correction for patient movement during image-guided cranial radiosurgery treatment. In this method, an approximate geometric relationship is first established to decompose a 3D rigid transformation in the 3D patient coordinate into in-plane transformations and out-of-plane rotations in two orthogonal 2D projections. Digitally reconstructed radiographs are generated offline from a preoperative computed tomography volume prior to treatment and used as the reference for patient position. A multiphase framework is designed to register the digitally reconstructed radiographs with the x-ray images periodically acquired during patient setup and treatment. The registration in each projection is performed independently; the results in the two projections are then combined and converted to a 3D rigid transformation by 2D-3D geometric backprojection. The in-plane transformation and the out-of-plane rotation are estimated using different search methods, including multiresolution matching, steepest descent minimization, and one-dimensional search. Two similarity measures, optimized pattern intensity and sum of squared difference, are applied at different registration phases to optimize accuracy and computation speed. Various experiments on an anthropomorphic head-and-neck phantom showed that, using fiducial registration as a gold standard, the registration errors were 0.33±0.16 mm (s.d.) in overall translation and 0.29 deg. ±0.11 deg. (s.d.) in overall rotation. The total targeting errors were 0.34±0.16 mm (s.d.), 0.40±0.2 mm (s.d.), and 0.51±0.26 mm (s.d.) for the targets at the distances of 2, 6, and 10 cm from the rotation center, respectively. The computation time was less than 3 s on a computer with an Intel Pentium 3.0 GHz dual processor

  2. Aid in the detection of myocardial perfusion abnormality utilizing SPECT atlas and images registration: preliminary results

    Energy Technology Data Exchange (ETDEWEB)

    Padua, Rodrigo Donizete Santana de [Universidade de Sao Paulo (USP), Ribeirao Preto, SP (Brazil). Faculdade de Medicina. Div. de Cardiologia]. E-mail: rodrigo_dsp@hcrp.fmrp.usp.br; Oliveira, Lucas Ferrari de [Universidade Federal de Pelotas (UFPel), RS (Brazil). Inst. de Fisica e Matematica. Dept. de Tecnologia da Informacao; Marques, Paulo Mazzoncini de Azevedo [Universidade de Sao Paulo (USP), Ribeirao Preto, SP (Brazil). Faculdade de Medicina. Centro de Ciencias das Imagens e Fisica Medica; Groote, Jean-Jacques Georges Soares de [Instituto de Ensino Superior COC, Ribeirao Preto, SP (Brazil). Lab. of Artifical Intelligence and Applications; Castro, Adelson Antonio de [Universidade de Sao Paulo (USP), Ribeirao Preto, SP, (Brazil). Faculdade de Medicina; Ana, Lauro Wichert [Universidade de Sao Paulo (USP), Ribeirao Preto, SP (Brazil). Faculdade de Medicina. Centro de Ciencias das Imagens e Fisica Medica; Simoes, Marcus Vinicius [Universidade de Sao Paulo (USP), Ribeirao Preto, SP, (Brazil). Faculdade de Medicina. Divisao de Cardiologia

    2008-11-15

    To develop an atlas of myocardial perfusion scintigraphy and evaluating its applicability in computer-aided detection of myocardial perfusion defects in patients with ischemic heart disease. The atlas was created with rest-stress myocardial perfusion scintigraphic images of 20 patients of both genders with low probability of coronary artery disease and considered as normal by two experienced observers. Techniques of image registration and mathematical operations on images were utilized for obtaining template images depicting mean myocardial uptake and standard deviation for each gender and physiological condition. Myocardial perfusion scintigraphy images of one male and one female patient were aligned with the corresponding atlas template image, and voxels with myocardial uptake rates two standard deviations below the mean voxel value of the respective region in the atlas template image were highlighted on the tomographic sections and confirmed as perfusion defects by both observe. The present study demonstrated the creation of an atlas of myocardial perfusion scintigraphy with promising results of this tool as an aid in the detection of myocardial perfusion defects. However, further prospective validation with a more representative sample is recommended. (author)

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

    Science.gov (United States)

    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. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Consistency in electronic portal imaging registration in prostate cancer radiation treatment verification

    Directory of Open Access Journals (Sweden)

    Liu Mitchell C

    2006-09-01

    Full Text Available Abstract Background A protocol of electronic portal imaging (EPI registration for the verification of radiation treatment fields has been implemented at our institution. A template is generated using the reference images, which is then registered with the EPI for treatment verification. This study examines interobserver consistency among trained radiation therapists in the registration and verification of external beam radiotherapy (EBRT for patients with prostate cancer. Materials and methods 20 consecutive patients with prostate cancer undergoing EBRT were analyzed. The EPIs from the initial 10 fractions were registered independently by 6 trained radiation therapist observers. For each fraction, an anterior-posterior (AP or PA and left lateral (Lat EPIs were generated and registered with the reference images. Two measures of displacement for the AP EPI in the superior-inferior (SI and right left (RL directions and two measures of displacement for the Lat EPI in the AP and SI directions were prospectively recorded. A total of 2400 images and 4800 measures were analyzed. Means and standard deviations, as well as systematic and random errors were calculated for each observer. Differences between observers were compared using the chi-square test. Variance components analysis was used to evaluate how much variance is attributed to the observers. Time trends were estimated using repeated measures analysis. Results Inter-observer variation expressed as the standard deviation of the six observers' measurements within each image were 0.7, 1.0, 1.7 and 1.4 mm for APLR, APSI, LatAP and LatSI respectively. Variance components analysis showed that the variation attributed to the observers was small compared to variation due to the images. On repeated measure analysis, time trends were apparent only for the APLR and LatSI measurements. Their magnitude however was small. Conclusion No clinically important systematic observer effect or time trends were

  5. Planning, guidance, and quality assurance of pelvic screw placement using deformable image registration

    Science.gov (United States)

    Goerres, J.; Uneri, A.; Jacobson, M.; Ramsay, B.; De Silva, T.; Ketcha, M.; Han, R.; Manbachi, A.; Vogt, S.; Kleinszig, G.; Wolinsky, J.-P.; Osgood, G.; Siewerdsen, J. H.

    2017-12-01

    Percutaneous pelvic screw placement is challenging due to narrow bone corridors surrounded by vulnerable structures and difficult visual interpretation of complex anatomical shapes in 2D x-ray projection images. To address these challenges, a system for planning, guidance, and quality assurance (QA) is presented, providing functionality analogous to surgical navigation, but based on robust 3D-2D image registration techniques using fluoroscopy images already acquired in routine workflow. Two novel aspects of the system are investigated: automatic planning of pelvic screw trajectories and the ability to account for deformation of surgical devices (K-wire deflection). Atlas-based registration is used to calculate a patient-specific plan of screw trajectories in preoperative CT. 3D-2D registration aligns the patient to CT within the projective geometry of intraoperative fluoroscopy. Deformable known-component registration (dKC-Reg) localizes the surgical device, and the combination of plan and device location is used to provide guidance and QA. A leave-one-out analysis evaluated the accuracy of automatic planning, and a cadaver experiment compared the accuracy of dKC-Reg to rigid approaches (e.g. optical tracking). Surgical plans conformed within the bone cortex by 3-4 mm for the narrowest corridor (superior pubic ramus) and  >5 mm for the widest corridor (tear drop). The dKC-Reg algorithm localized the K-wire tip within 1.1 mm and 1.4° and was consistently more accurate than rigid-body tracking (errors up to 9 mm). The system was shown to automatically compute reliable screw trajectories and accurately localize deformed surgical devices (K-wires). Such capability could improve guidance and QA in orthopaedic surgery, where workflow is impeded by manual planning, conventional tool trackers add complexity and cost, rigid tool assumptions are often inaccurate, and qualitative interpretation of complex anatomy from 2D projections is prone to trial

  6. Augmented reality with image registration, vision correction and sunlight readability via liquid crystal devices.

    Science.gov (United States)

    Wang, Yu-Jen; Chen, Po-Ju; Liang, Xiao; Lin, Yi-Hsin

    2017-03-27

    Augmented reality (AR), which use computer-aided projected information to augment our sense, has important impact on human life, especially for the elder people. However, there are three major challenges regarding the optical system in the AR system, which are registration, vision correction, and readability under strong ambient light. Here, we solve three challenges simultaneously for the first time using two liquid crystal (LC) lenses and polarizer-free attenuator integrated in optical-see-through AR system. One of the LC lens is used to electrically adjust the position of the projected virtual image which is so-called registration. The other LC lens with larger aperture and polarization independent characteristic is in charge of vision correction, such as myopia and presbyopia. The linearity of lens powers of two LC lenses is also discussed. The readability of virtual images under strong ambient light is solved by electrically switchable transmittance of the LC attenuator originating from light scattering and light absorption. The concept demonstrated in this paper could be further extended to other electro-optical devices as long as the devices exhibit the capability of phase modulations and amplitude modulations.

  7. Automated registration of freehand B-mode ultrasound and magnetic resonance imaging of the carotid arteries based on geometric features

    DEFF Research Database (Denmark)

    Carvalho, Diego D. B.; Arias Lorza, Andres Mauricio; Niessen, Wiro J.

    2017-01-01

    An automated method for registering B-mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid arteries is proposed. The registration uses geometric features, namely, lumen centerlines and lumen segmentations, which are extracted fully automatically from the images after manual an...

  8. 3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes.

    Science.gov (United States)

    Zhong, Zichun; Guo, Xiaohu; Cai, Yiqi; Yang, Yin; Wang, Jing; Jia, Xun; Mao, Weihua

    2016-01-01

    By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT) scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs) are specified at the mesh vertices to drive the deformation of original CT images. Digitally reconstructed radiographs (DRRs) of the deformed anatomy are generated and compared with corresponding 2D projections. DVFs are optimized to minimize the objective function including differences between DRRs and projections and the regularity. To further accelerate the above 3D-2D registration, a procedure to obtain good initial deformations by deforming the volume surface to match 2D body boundary on projections has been developed. This complete method is evaluated quantitatively by using several digital phantoms and data from head and neck cancer patients. The feature-based nonuniform meshing method leads to better results than either uniform orthogonal grid or uniform tetrahedral meshes.

  9. 3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes

    Directory of Open Access Journals (Sweden)

    Zichun Zhong

    2016-01-01

    Full Text Available By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs are specified at the mesh vertices to drive the deformation of original CT images. Digitally reconstructed radiographs (DRRs of the deformed anatomy are generated and compared with corresponding 2D projections. DVFs are optimized to minimize the objective function including differences between DRRs and projections and the regularity. To further accelerate the above 3D-2D registration, a procedure to obtain good initial deformations by deforming the volume surface to match 2D body boundary on projections has been developed. This complete method is evaluated quantitatively by using several digital phantoms and data from head and neck cancer patients. The feature-based nonuniform meshing method leads to better results than either uniform orthogonal grid or uniform tetrahedral meshes.

  10. COMPARISON AND CO-REGISTRATION OF DEMS GENERATED FROM HiRISE AND CTX IMAGES

    Directory of Open Access Journals (Sweden)

    Y. Wang

    2016-06-01

    Full Text Available Images from two sensors, the High-Resolution Imaging Science Experiment (HiRISE and the Context Camera (CTX, both on-board the Mars Reconnaissance Orbiter (MRO, were used to generate high-quality DEMs (Digital Elevation Models of the Martian surface. However, there were discrepancies between the DEMs generated from the images acquired by these two sensors due to various reasons, such as variations in boresight alignment between the two sensors during the flight in the complex environment. This paper presents a systematic investigation of the discrepancies between the DEMs generated from the HiRISE and CTX images. A combined adjustment algorithm is presented for the co-registration of HiRISE and CTX DEMs. Experimental analysis was carried out using the HiRISE and CTX images collected at the Mars Rover landing site and several other typical regions. The results indicated that there were systematic offsets between the HiRISE and CTX DEMs in the longitude and latitude directions. However, the offset in the altitude was less obvious. After combined adjustment, the offsets were eliminated and the HiRISE and CTX DEMs were co-registered to each other. The presented research is of significance for the synergistic use of HiRISE and CTX images for precision Mars topographic mapping.

  11. Band registration of tuneable frame format hyperspectral UAV imagers in complex scenes

    Science.gov (United States)

    Honkavaara, Eija; Rosnell, Tomi; Oliveira, Raquel; Tommaselli, Antonio

    2017-12-01

    A recent revolution in miniaturised sensor technology has provided markets with novel hyperspectral imagers operating in the frame format principle. In the case of unmanned aerial vehicle (UAV) based remote sensing, the frame format technology is highly attractive in comparison to the commonly utilised pushbroom scanning technology, because it offers better stability and the possibility to capture stereoscopic data sets, bringing an opportunity for 3D hyperspectral object reconstruction. Tuneable filters are one of the approaches for capturing multi- or hyperspectral frame images. The individual bands are not aligned when operating a sensor based on tuneable filters from a mobile platform, such as UAV, because the full spectrum recording is carried out in the time-sequential principle. The objective of this investigation was to study the aspects of band registration of an imager based on tuneable filters and to develop a rigorous and efficient approach for band registration in complex 3D scenes, such as forests. The method first determines the orientations of selected reference bands and reconstructs the 3D scene using structure-from-motion and dense image matching technologies. The bands, without orientation, are then matched to the oriented bands accounting the 3D scene to provide exterior orientations, and afterwards, hyperspectral orthomosaics, or hyperspectral point clouds, are calculated. The uncertainty aspects of the novel approach were studied. An empirical assessment was carried out in a forested environment using hyperspectral images captured with a hyperspectral 2D frame format camera, based on a tuneable Fabry-Pérot interferometer (FPI) on board a multicopter and supported by a high spatial resolution consumer colour camera. A theoretical assessment showed that the method was capable of providing band registration accuracy better than 0.5-pixel size. The empirical assessment proved the performance and showed that, with the novel method, most parts of

  12. Dental non-linear image registration and collection method with 3D reconstruction and change detection

    Science.gov (United States)

    Rahmes, Mark; Fagan, Dean; Lemieux, George

    2017-03-01

    The capability of a software algorithm to automatically align same-patient dental bitewing and panoramic x-rays over time is complicated by differences in collection perspectives. We successfully used image correlation with an affine transform for each pixel to discover common image borders, followed by a non-linear homography perspective adjustment to closely align the images. However, significant improvements in image registration could be realized if images were collected from the same perspective, thus facilitating change analysis. The perspective differences due to current dental image collection devices are so significant that straightforward change analysis is not possible. To address this, a new custom dental tray could be used to provide the standard reference needed for consistent positioning of a patient's mouth. Similar to sports mouth guards, the dental tray could be fabricated in standard sizes from plastic and use integrated electronics that have been miniaturized. In addition, the x-ray source needs to be consistently positioned in order to collect images with similar angles and scales. Solving this pose correction is similar to solving for collection angle in aerial imagery for change detection. A standard collection system would provide a method for consistent source positioning using real-time sensor position feedback from a digital x-ray image reference. Automated, robotic sensor positioning could replace manual adjustments. Given an image set from a standard collection, a disparity map between images can be created using parallax from overlapping viewpoints to enable change detection. This perspective data can be rectified and used to create a three-dimensional dental model reconstruction.

  13. Behaviors of cost functions in image registration between 201Tl brain tumor single-photon emission computed tomography and magnetic resonance images

    International Nuclear Information System (INIS)

    Soma, Tsutomu; Takaki, Akihiro; Teraoka, Satomi; Ishikawa, Yasushi; Murase, Kenya; Koizumi, Kiyoshi

    2008-01-01

    We studied the behaviors of cost functions in the registration of thallium-201 ( 201 Tl) brain tumor single-photon emission computed tomography (SPECT) and magnetic resonance (MR) images, as the similarity index of image positioning. A marker for image registration [technetium-99m ( 99m Tc) point source] was attached at three sites on the heads of 13 patients with brain tumor, from whom 42 sets of 99m Tc- 201 Tl SPECT (the dual-isotope acquisition) and MR images were obtained. The 201 Tl SPECT and MR images were manually registered according to the markers. From the positions where the two images were registered, the position of the 201 Tl SPECT was moved to examine the behaviors of the three cost functions, i.e., ratio image uniformity (RIU), mutual information (MI), and normalized MI (NMI). The cost functions MI and NMI reached the maximum at positions adjacent to those where the SPECT and MR images were manually registered. As for the accuracy of image registration in terms of the cost functions MI and NMI, on average, the images were accurately registered within 3 deg of rotation around the X-, Y-, and Z-axes, and within 1.5 mm (within 2 pixels), 3 mm (within 3 pixels), and 4 mm (within 1 slice) of translation to the X-, Y-, and Z-axes, respectively. In terms of rotation around the Z-axis, the cost function RIU reached the minimum at positions where the manual registration of the two images was substantially inadequate. The MI and NMI were suitable cost functions in the registration of 201 Tl SPECT and MR images. The behavior of the RIU, in contrast, was unstable, being unsuitable as an index of image registration. (author)

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

    International Nuclear Information System (INIS)

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

    2007-01-01

    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 Δx vertical bar ±σ=3.3 mm±1.0 mm and vertical bar Δy vertical bar ±σ=3.6 mm±1.0 mm. These values were substantially improved upon applying fiducial fusion based on external landmark points: vertical bar Δx vertical bar ±σ=0.7 mm±0.8 mm and vertical bar Δy vertical bar ±σ=0.3 mm±1.7 mm. We also noted that differences found for each of the fusion methods were similar for

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

    Science.gov (United States)

    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.

  16. SU-F-I-50: Finite Element-Based Deformable Image Registration of Lung and Heart

    Energy Technology Data Exchange (ETDEWEB)

    Penjweini, R [University of Pennsylvania, Philadelphia, Pennsylvania (United States); Kim, M [University of Pennsylvania, Philadelphia, PA (United States); Zhu, T [University Pennsylvania, Philadelphia, PA (United States)

    2016-06-15

    Purpose: Photodynamic therapy (PDT) is used after surgical resection to treat the microscopic disease for malignant pleural mesothelioma and to increase survival rates. Although accurate light delivery is imperative to PDT efficacy, the deformation of the pleural volume during the surgery impacts the delivered light dose. To facilitate treatment planning, we use a finite-element-based (FEM) deformable image registration to quantify the anatomical variation of lung and heart volumes between CT pre-(or post-) surgery and surface contours obtained during PDT using an infrared camera-based navigation system (NDI). Methods: NDI is used during PDT to obtain the information of the cumulative light fluence on every cavity surface point that is being treated. A wand, comprised of a modified endotrachial tube filled with Intralipid and an optical fiber inside the tube, is used to deliver the light during PDT. The position of the treatment is tracked using an attachment with nine reflective passive markers that are seen by the NDI system. Then, the position points are plotted as three-dimensional volume of the pleural cavity using Matlab and Meshlab. A series of computed tomography (CT) scans of the lungs and heart, in the same patient, are also acquired before and after the surgery. The NDI and CT contours are imported into COMSOL Multiphysics, where the FEM-based deformable image registration is obtained. The NDI and CT contours acquired during and post-PDT are considered as the reference, and the Pre-PDT CT contours are used as the target, which will be deformed. Results: Anatomical variation of the lung and heart volumes, taken at different times from different imaging devices, was determined by using our model. The resulting three-dimensional deformation map along x, y and z-axes was obtained. Conclusion: Our model fuses images acquired by different modalities and provides insights into the variation in anatomical structures over time.

  17. A LAGRANGIAN GAUSS-NEWTON-KRYLOV SOLVER FOR MASS- AND INTENSITY-PRESERVING DIFFEOMORPHIC IMAGE REGISTRATION.

    Science.gov (United States)

    Mang, Andreas; Ruthotto, Lars

    2017-01-01

    We present an efficient solver for diffeomorphic image registration problems in the framework of Large Deformations Diffeomorphic Metric Mappings (LDDMM). We use an optimal control formulation, in which the velocity field of a hyperbolic PDE needs to be found such that the distance between the final state of the system (the transformed/transported template image) and the observation (the reference image) is minimized. Our solver supports both stationary and non-stationary (i.e., transient or time-