A robust cloud registration method based on redundant data reduction using backpropagation neural network and shift window

Science.gov (United States)

Xin, Meiting; Li, Bing; Yan, Xiao; Chen, Lei; Wei, Xiang

2018-02-01

A robust coarse-to-fine registration method based on the backpropagation (BP) neural network and shift window technology is proposed in this study. Specifically, there are three steps: coarse alignment between the model data and measured data, data simplification based on the BP neural network and point reservation in the contour region of point clouds, and fine registration with the reweighted iterative closest point algorithm. In the process of rough alignment, the initial rotation matrix and the translation vector between the two datasets are obtained. After performing subsequent simplification operations, the number of points can be reduced greatly. Therefore, the time and space complexity of the accurate registration can be significantly reduced. The experimental results show that the proposed method improves the computational efficiency without loss of accuracy.

PCANet-Based Structural Representation for Nonrigid Multimodal Medical Image Registration

Directory of Open Access Journals (Sweden)

Xingxing Zhu

2018-05-01

Full Text Available Nonrigid multimodal image registration remains a challenging task in medical image processing and analysis. The structural representation (SR-based registration methods have attracted much attention recently. However, the existing SR methods cannot provide satisfactory registration accuracy due to the utilization of hand-designed features for structural representation. To address this problem, the structural representation method based on the improved version of the simple deep learning network named PCANet is proposed for medical image registration. In the proposed method, PCANet is firstly trained on numerous medical images to learn convolution kernels for this network. Then, a pair of input medical images to be registered is processed by the learned PCANet. The features extracted by various layers in the PCANet are fused to produce multilevel features. The structural representation images are constructed for two input images based on nonlinear transformation of these multilevel features. The Euclidean distance between structural representation images is calculated and used as the similarity metrics. The objective function defined by the similarity metrics is optimized by L-BFGS method to obtain parameters of the free-form deformation (FFD model. Extensive experiments on simulated and real multimodal image datasets show that compared with the state-of-the-art registration methods, such as modality-independent neighborhood descriptor (MIND, normalized mutual information (NMI, Weber local descriptor (WLD, and the sum of squared differences on entropy images (ESSD, the proposed method provides better registration performance in terms of target registration error (TRE and subjective human vision.

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

Science.gov (United States)

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

2010-01-01

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

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

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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.

A Remote Registration Based on MIDAS

Science.gov (United States)

JIN, Xin

2017-04-01

We often need for software registration to protect the interests of the software developers. This article narrated one kind of software long-distance registration technology. The registration method is: place the registration information in a database table, after the procedure starts in check table registration information, if it has registered then the procedure may the normal operation; Otherwise, the customer must input the sequence number and registers through the network on the long-distance server. If it registers successfully, then records the registration information in the database table. This remote registration method can protect the rights of software developers.

Advanced methods for image registration applied to JET videos

Energy Technology Data Exchange (ETDEWEB)

Craciunescu, Teddy, E-mail: teddy.craciunescu@jet.uk [EURATOM-MEdC Association, NILPRP, Bucharest (Romania); Murari, Andrea [Consorzio RFX, Associazione EURATOM-ENEA per la Fusione, Padova (Italy); Gelfusa, Michela [Associazione EURATOM-ENEA – University of Rome “Tor Vergata”, Roma (Italy); Tiseanu, Ion; Zoita, Vasile [EURATOM-MEdC Association, NILPRP, Bucharest (Romania); Arnoux, Gilles [EURATOM/CCFE Fusion Association, Culham Science Centre, Abingdon, Oxon (United Kingdom)

2015-10-15

Graphical abstract: - Highlights: • Development of an image registration method for JET IR and fast visible cameras. • Method based on SIFT descriptors and coherent point drift points set registration technique. • Method able to deal with extremely noisy images and very low luminosity images. • Computation time compatible with the inter-shot analysis. - Abstract: The last years have witnessed a significant increase in the use of digital cameras on JET. They are routinely applied for imaging in the IR and visible spectral regions. One of the main technical difficulties in interpreting the data of camera based diagnostics is the presence of movements of the field of view. Small movements occur due to machine shaking during normal pulses while large ones may arise during disruptions. Some cameras show a correlation of image movement with change of magnetic field strength. For deriving unaltered information from the videos and for allowing correct interpretation an image registration method, based on highly distinctive scale invariant feature transform (SIFT) descriptors and on the coherent point drift (CPD) points set registration technique, has been developed. The algorithm incorporates a complex procedure for rejecting outliers. The method has been applied for vibrations correction to videos collected by the JET wide angle infrared camera and for the correction of spurious rotations in the case of the JET fast visible camera (which is equipped with an image intensifier). The method has proved to be able to deal with the images provided by this camera frequently characterized by low contrast and a high level of blurring and noise.

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)

Computing homography with RANSAC algorithm: a novel method of registration

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Li, Xiaowei; Liu, Yue; Wang, Yongtian; Yan, Dayuan

2005-02-01

An AR (Augmented Reality) system can integrate computer-generated objects with the image sequences of real world scenes in either an off-line or a real-time way. Registration, or camera pose estimation, is one of the key techniques to determine its performance. The registration methods can be classified as model-based and move-matching. The former approach can accomplish relatively accurate registration results, but it requires the precise model of the scene, which is hard to be obtained. The latter approach carries out registration by computing the ego-motion of the camera. Because it does not require the prior-knowledge of the scene, its registration results sometimes turn out to be less accurate. When the model defined is as simple as a plane, a mixed method is introduced to take advantages of the virtues of the two methods mentioned above. Although unexpected objects often occlude this plane in an AR system, one can still try to detect corresponding points with a contract-expand method, while this will import erroneous correspondences. Computing homography with RANSAC algorithm is used to overcome such shortcomings. Using the robustly estimated homography resulted from RANSAC, the camera projective matrix can be recovered and thus registration is accomplished even when the markers are lost in the scene.

Accuracy of radiotherapy dose calculations based on cone-beam CT: comparison of deformable registration and image correction based methods

Science.gov (United States)

Marchant, T. E.; Joshi, K. D.; Moore, C. J.

2018-03-01

Radiotherapy dose calculations based on cone-beam CT (CBCT) images can be inaccurate due to unreliable Hounsfield units (HU) in the CBCT. Deformable image registration of planning CT images to CBCT, and direct correction of CBCT image values are two methods proposed to allow heterogeneity corrected dose calculations based on CBCT. In this paper we compare the accuracy and robustness of these two approaches. CBCT images for 44 patients were used including pelvis, lung and head & neck sites. CBCT HU were corrected using a ‘shading correction’ algorithm and via deformable registration of planning CT to CBCT using either Elastix or Niftyreg. Radiotherapy dose distributions were re-calculated with heterogeneity correction based on the corrected CBCT and several relevant dose metrics for target and OAR volumes were calculated. Accuracy of CBCT based dose metrics was determined using an ‘override ratio’ method where the ratio of the dose metric to that calculated on a bulk-density assigned version of the same image is assumed to be constant for each patient, allowing comparison to the patient’s planning CT as a gold standard. Similar performance is achieved by shading corrected CBCT and both deformable registration algorithms, with mean and standard deviation of dose metric error less than 1% for all sites studied. For lung images, use of deformed CT leads to slightly larger standard deviation of dose metric error than shading corrected CBCT with more dose metric errors greater than 2% observed (7% versus 1%).

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.

PARALLEL AND ADAPTIVE UNIFORM-DISTRIBUTED REGISTRATION METHOD FOR CHANG’E-1 LUNAR REMOTE SENSED IMAGERY

Directory of Open Access Journals (Sweden)

X. Ning

2012-08-01

To resolve the above-mentioned registration difficulties, a parallel and adaptive uniform-distributed registration method for CE-1 lunar remote sensed imagery is proposed in this paper. Based on 6 pairs of randomly selected images, both the standard SIFT algorithm and the parallel and adaptive uniform-distributed registration method were executed, the versatility and effectiveness were assessed. The experimental results indicate that: by applying the parallel and adaptive uniform-distributed registration method, the efficiency of CE-1 lunar remote sensed imagery registration were increased dramatically. Therefore, the proposed method in the paper could acquire uniform-distributed registration results more effectively, the registration difficulties including difficult to obtain results, time-consuming, non-uniform distribution could be successfully solved.

A 3D global-to-local deformable mesh model based registration and anatomy-constrained segmentation method for image guided prostate radiotherapy

International Nuclear Information System (INIS)

Zhou Jinghao; Kim, Sung; Jabbour, Salma; Goyal, Sharad; Haffty, Bruce; Chen, Ting; Levinson, Lydia; Metaxas, Dimitris; Yue, Ning J.

2010-01-01

Purpose: In the external beam radiation treatment of prostate cancers, successful implementation of adaptive radiotherapy and conformal radiation dose delivery is highly dependent on precise and expeditious segmentation and registration of the prostate volume between the simulation and the treatment images. The purpose of this study is to develop a novel, fast, and accurate segmentation and registration method to increase the computational efficiency to meet the restricted clinical treatment time requirement in image guided radiotherapy. Methods: The method developed in this study used soft tissues to capture the transformation between the 3D planning CT (pCT) images and 3D cone-beam CT (CBCT) treatment images. The method incorporated a global-to-local deformable mesh model based registration framework as well as an automatic anatomy-constrained robust active shape model (ACRASM) based segmentation algorithm in the 3D CBCT images. The global registration was based on the mutual information method, and the local registration was to minimize the Euclidian distance of the corresponding nodal points from the global transformation of deformable mesh models, which implicitly used the information of the segmented target volume. The method was applied on six data sets of prostate cancer patients. Target volumes delineated by the same radiation oncologist on the pCT and CBCT were chosen as the benchmarks and were compared to the segmented and registered results. The distance-based and the volume-based estimators were used to quantitatively evaluate the results of segmentation and registration. Results: The ACRASM segmentation algorithm was compared to the original active shape model (ASM) algorithm by evaluating the values of the distance-based estimators. With respect to the corresponding benchmarks, the mean distance ranged from -0.85 to 0.84 mm for ACRASM and from -1.44 to 1.17 mm for ASM. The mean absolute distance ranged from 1.77 to 3.07 mm for ACRASM and from 2.45 to

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

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Hu, Jinyan; Li, Li; Yang, Yunfeng

2017-06-01

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

A fast inverse consistent deformable image registration method based on symmetric optical flow computation

International Nuclear Information System (INIS)

Yang Deshan; Li Hua; Low, Daniel A; Deasy, Joseph O; Naqa, Issam El

2008-01-01

Deformable image registration is widely used in various radiation therapy applications including daily treatment planning adaptation to map planned tissue or dose to changing anatomy. In this work, a simple and efficient inverse consistency deformable registration method is proposed with aims of higher registration accuracy and faster convergence speed. Instead of registering image I to a second image J, the two images are symmetrically deformed toward one another in multiple passes, until both deformed images are matched and correct registration is therefore achieved. In each pass, a delta motion field is computed by minimizing a symmetric optical flow system cost function using modified optical flow algorithms. The images are then further deformed with the delta motion field in the positive and negative directions respectively, and then used for the next pass. The magnitude of the delta motion field is forced to be less than 0.4 voxel for every pass in order to guarantee smoothness and invertibility for the two overall motion fields that are accumulating the delta motion fields in both positive and negative directions, respectively. The final motion fields to register the original images I and J, in either direction, are calculated by inverting one overall motion field and combining the inversion result with the other overall motion field. The final motion fields are inversely consistent and this is ensured by the symmetric way that registration is carried out. The proposed method is demonstrated with phantom images, artificially deformed patient images and 4D-CT images. Our results suggest that the proposed method is able to improve the overall accuracy (reducing registration error by 30% or more, compared to the original and inversely inconsistent optical flow algorithms), reduce the inverse consistency error (by 95% or more) and increase the convergence rate (by 100% or more). The overall computation speed may slightly decrease, or increase in most cases

Comparison of time-series registration methods in breast dynamic infrared imaging

Science.gov (United States)

Riyahi-Alam, S.; Agostini, V.; Molinari, F.; Knaflitz, M.

2015-03-01

Automated motion reduction in dynamic infrared imaging is on demand in clinical applications, since movement disarranges time-temperature series of each pixel, thus originating thermal artifacts that might bias the clinical decision. All previously proposed registration methods are feature based algorithms requiring manual intervention. The aim of this work is to optimize the registration strategy specifically for Breast Dynamic Infrared Imaging and to make it user-independent. We implemented and evaluated 3 different 3D time-series registration methods: 1. Linear affine, 2. Non-linear Bspline, 3. Demons applied to 12 datasets of healthy breast thermal images. The results are evaluated through normalized mutual information with average values of 0.70 ±0.03, 0.74 ±0.03 and 0.81 ±0.09 (out of 1) for Affine, Bspline and Demons registration, respectively, as well as breast boundary overlap and Jacobian determinant of the deformation field. The statistical analysis of the results showed that symmetric diffeomorphic Demons' registration method outperforms also with the best breast alignment and non-negative Jacobian values which guarantee image similarity and anatomical consistency of the transformation, due to homologous forces enforcing the pixel geometric disparities to be shortened on all the frames. We propose Demons' registration as an effective technique for time-series dynamic infrared registration, to stabilize the local temperature oscillation.

A method of acoustic wave registration and determination their generation region

International Nuclear Information System (INIS)

Kozin, I.D.; Marchenko, M.V.

1998-01-01

Here is presented a method of acoustic wave registration with using of a synchronous LF broadcasting system. This method of detection and determination of underground nuclear explosion location is based on a registration of ionospheric disturbances induced by acoustic waves at the region of LF sign al reflection. The measuring complex created in the institute of the Ionosphere /1/ allows to register amplitude-frequency characteristics of composite signal from synchronous broadcasting net

Registration methods for pulmonary image analysis integration of morphological and physiological knowledge

CERN Document Server

Schmidt-Richberg, Alexander

2014-01-01

Various applications in the field of pulmonary image analysis require a registration of CT images of the lung. For example, a registration-based estimation of the breathing motion is employed to increase the accuracy of dose distribution in radiotherapy. Alexander Schmidt-Richberg develops methods to explicitly model morphological and physiological knowledge about respiration in algorithms for the registration of thoracic CT images. The author focusses on two lung-specific issues: on the one hand, the alignment of the interlobular fissures and on the other hand, the estimation of sliding motion at the lung boundaries. He shows that by explicitly considering these aspects based on a segmentation of the respective structure, registration accuracy can be significantly improved.

Fast time-of-flight camera based surface registration for radiotherapy patient positioning

International Nuclear Information System (INIS)

Placht, Simon; Stancanello, Joseph; Schaller, Christian; Balda, Michael; Angelopoulou, Elli

2012-01-01

Purpose: This work introduces a rigid registration framework for patient positioning in radiotherapy, based on real-time surface acquisition by a time-of-flight (ToF) camera. Dynamic properties of the system are also investigated for future gating/tracking strategies. Methods: A novel preregistration algorithm, based on translation and rotation-invariant features representing surface structures, was developed. Using these features, corresponding three-dimensional points were computed in order to determine initial registration parameters. These parameters became a robust input to an accelerated version of the iterative closest point (ICP) algorithm for the fine-tuning of the registration result. Distance calibration and Kalman filtering were used to compensate for ToF-camera dependent noise. Additionally, the advantage of using the feature based preregistration over an ''ICP only'' strategy was evaluated, as well as the robustness of the rigid-transformation-based method to deformation. Results: The proposed surface registration method was validated using phantom data. A mean target registration error (TRE) for translations and rotations of 1.62 ± 1.08 mm and 0.07 deg. ± 0.05 deg., respectively, was achieved. There was a temporal delay of about 65 ms in the registration output, which can be seen as negligible considering the dynamics of biological systems. Feature based preregistration allowed for accurate and robust registrations even at very large initial displacements. Deformations affected the accuracy of the results, necessitating particular care in cases of deformed surfaces. Conclusions: The proposed solution is able to solve surface registration problems with an accuracy suitable for radiotherapy cases where external surfaces offer primary or complementary information to patient positioning. The system shows promising dynamic properties for its use in gating/tracking applications. The overall system is competitive with commonly-used surface registration

A spatiotemporal-based scheme for efficient registration-based segmentation of thoracic 4-D MRI.

Science.gov (United States)

Yang, Y; Van Reeth, E; Poh, C L; Tan, C H; Tham, I W K

2014-05-01

Dynamic three-dimensional (3-D) (four-dimensional, 4-D) magnetic resonance (MR) imaging is gaining importance in the study of pulmonary motion for respiratory diseases and pulmonary tumor motion for radiotherapy. To perform quantitative analysis using 4-D MR images, segmentation of anatomical structures such as the lung and pulmonary tumor is required. Manual segmentation of entire thoracic 4-D MRI data that typically contains many 3-D volumes acquired over several breathing cycles is extremely tedious, time consuming, and suffers high user variability. This requires the development of new automated segmentation schemes for 4-D MRI data segmentation. Registration-based segmentation technique that uses automatic registration methods for segmentation has been shown to be an accurate method to segment structures for 4-D data series. However, directly applying registration-based segmentation to segment 4-D MRI series lacks efficiency. Here we propose an automated 4-D registration-based segmentation scheme that is based on spatiotemporal information for the segmentation of thoracic 4-D MR lung images. The proposed scheme saved up to 95% of computation amount while achieving comparable accurate segmentations compared to directly applying registration-based segmentation to 4-D dataset. The scheme facilitates rapid 3-D/4-D visualization of the lung and tumor motion and potentially the tracking of tumor during radiation delivery.

An efficient strategy based on an individualized selection of registration methods. Application to the coregistration of MR and SPECT images in neuro-oncology

International Nuclear Information System (INIS)

Tacchella, Jean-Marc; Lefort, Muriel; Habert, Marie-Odile; Yeni, Nathanaëlle; Kas, Aurélie; Frouin, Frédérique; Roullot, Elodie; Cohen, Mike-Ely; Guillevin, Rémy; Petrirena, Grégorio; Delattre, Jean-Yves

2014-01-01

An efficient registration strategy is described that aims to help solve delicate medical imaging registration problems. It consists of running several registration methods for each dataset and selecting the best one for each specific dataset, according to an evaluation criterion. Finally, the quality of the registration results, obtained with the best method, is visually scored by an expert as excellent, correct or poor. The strategy was applied to coregister Technetium-99m Sestamibi SPECT and MRI data in the framework of a follow-up protocol in patients with high grade gliomas receiving antiangiogenic therapy. To adapt the strategy to this clinical context, a robust semi-automatic evaluation criterion based on the physiological uptake of the Sestamibi tracer was defined. A panel of eighteen multimodal registration algorithms issued from BrainVisa, SPM or AIR software environments was systematically applied to the clinical database composed of sixty-two datasets. According to the expert visual validation, this new strategy provides 85% excellent registrations, 12% correct ones and only 3% poor ones. These results compare favorably to the ones obtained by the globally most efficient registration method over the whole database, for which only 61% of excellent registration results have been reported. Thus the registration strategy in its current implementation proves to be suitable for clinical application. (paper)

Unified voxel- and tensor-based morphometry (UVTBM) using registration confidence.

Science.gov (United States)

Khan, Ali R; Wang, Lei; Beg, Mirza Faisal

2015-01-01

Voxel-based morphometry (VBM) and tensor-based morphometry (TBM) both rely on spatial normalization to a template and yet have different requirements for the level of registration accuracy. VBM requires only global alignment of brain structures, with limited degrees of freedom in transformation, whereas TBM performs best when the registration is highly deformable and can achieve higher registration accuracy. In addition, the registration accuracy varies over the whole brain, with higher accuracy typically observed in subcortical areas and lower accuracy seen in cortical areas. Hence, even the determinant of Jacobian of registration maps is spatially varying in their accuracy, and combining these with VBM by direct multiplication introduces errors in VBM maps where the registration is inaccurate. We propose a unified approach to combining these 2 morphometry methods that is motivated by these differing requirements for registration and our interest in harnessing the advantages of both. Our novel method uses local estimates of registration confidence to determine how to weight the influence of VBM- and TBM-like approaches. Results are shown on healthy and mild Alzheimer's subjects (N = 150) investigating age and group differences, and potential of differential diagnosis is shown on a set of Alzheimer's disease (N = 34) and frontotemporal dementia (N = 30) patients compared against controls (N = 14). These show that the group differences detected by our proposed approach are more descriptive than those detected from VBM, Jacobian-modulated VBM, and TBM separately, hence leveraging the advantages of both approaches in a unified framework. Copyright © 2015 Elsevier Inc. All rights reserved.

Evaluation of registration methods on thoracic CT

DEFF Research Database (Denmark)

Murphy, K.; van Ginneken, B.; Reinhardt, J.

2011-01-01

method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing......EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intra-patient thoracic CT image pairs. Evaluation of non-rigid registration techniques is a non trivial task....... This article details the organisation of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed....

Combining morphometric evidence from multiple registration methods using dempster-shafer theory

Science.gov (United States)

Rajagopalan, Vidya; Wyatt, Christopher

2010-03-01

In tensor-based morphometry (TBM) group-wise differences in brain structure are measured using high degreeof- freedom registration and some form of statistical test. However, it is known that TBM results are sensitive to both the registration method and statistical test used. Given the lack of an objective model of group variation is it difficult to determine a best registration method for TBM. The use of statistical tests is also problematic given the corrections required for multiple testing and the notorius difficulty selecting and intepreting signigance values. This paper presents an approach to address both of these issues by combining multiple registration methods using Dempster-Shafer Evidence theory to produce belief maps of categorical changes between groups. This approach is applied to the comparison brain morphometry in aging, a typical application of TBM, using the determinant of the Jacobian as a measure of volume change. We show that the Dempster-Shafer combination produces a unique and easy to interpret belief map of regional changes between and within groups without the complications associated with hypothesis testing.

FEM-based evaluation of deformable image registration for radiation therapy

International Nuclear Information System (INIS)

Zhong Hualiang; Peters, Terry; Siebers, Jeffrey V

2007-01-01

This paper presents a new concept to automatically detect the neighborhood in an image where deformable registration is mis-performing. Specifically, the displacement vector field (DVF) from a deformable image registration is substituted into a finite-element-based elastic framework to calculate unbalanced energy in each element. The value of the derived energy indicates the quality of the DVF in its neighborhood. The new voxel-based evaluation approach is compared with three other validation criteria: landmark measurement, a finite element approach and visual comparison, for deformable registrations performed with the optical-flow-based 'demons' algorithm as well as thin-plate spline interpolation. This analysis was performed on three pairs of prostate CT images. The results of the analysis show that the four criteria give mutually comparable quantitative assessments on the six registration instances. As an objective concept, the unbalanced energy presents no requirement on boundary constraints in its calculation, different from traditional mechanical modeling. This method is automatic, and at voxel level suitable to evaluate deformable registration in a clinical setting

Automatic registration method for multisensor datasets adopted for dimensional measurements on cutting tools

International Nuclear Information System (INIS)

Shaw, L; Mehari, F; Weckenmann, A; Ettl, S; Häusler, G

2013-01-01

Multisensor systems with optical 3D sensors are frequently employed to capture complete surface information by measuring workpieces from different views. During coarse and fine registration the resulting datasets are afterward transformed into one common coordinate system. Automatic fine registration methods are well established in dimensional metrology, whereas there is a deficit in automatic coarse registration methods. The advantage of a fully automatic registration procedure is twofold: it enables a fast and contact-free alignment and further a flexible application to datasets of any kind of optical 3D sensor. In this paper, an algorithm adapted for a robust automatic coarse registration is presented. The method was originally developed for the field of object reconstruction or localization. It is based on a segmentation of planes in the datasets to calculate the transformation parameters. The rotation is defined by the normals of three corresponding segmented planes of two overlapping datasets, while the translation is calculated via the intersection point of the segmented planes. First results have shown that the translation is strongly shape dependent: 3D data of objects with non-orthogonal planar flanks cannot be registered with the current method. In the novel supplement for the algorithm, the translation is additionally calculated via the distance between centroids of corresponding segmented planes, which results in more than one option for the transformation. A newly introduced measure considering the distance between the datasets after coarse registration evaluates the best possible transformation. Results of the robust automatic registration method are presented on the example of datasets taken from a cutting tool with a fringe-projection system and a focus-variation system. The successful application in dimensional metrology is proven with evaluations of shape parameters based on the registered datasets of a calibrated workpiece. (paper)

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

International Nuclear Information System (INIS)

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

2012-01-01

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

3D-2D registration in endovascular image-guided surgery: evaluation of state-of-the-art methods on cerebral angiograms.

Science.gov (United States)

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

2018-02-01

Image guidance for minimally invasive surgery is based on spatial co-registration and fusion of 3D pre-interventional images and treatment plans with the 2D live intra-interventional images. The spatial co-registration or 3D-2D registration is the key enabling technology; however, the performance of state-of-the-art automated methods is rather unclear as they have not been assessed under the same test conditions. Herein we perform a quantitative and comparative evaluation of ten state-of-the-art methods for 3D-2D registration on a public dataset of clinical angiograms. Image database consisted of 3D and 2D angiograms of 25 patients undergoing treatment for cerebral aneurysms or arteriovenous malformations. On each of the datasets, highly accurate "gold-standard" registrations of 3D and 2D images were established based on patient-attached fiducial markers. The database was used to rigorously evaluate ten state-of-the-art 3D-2D registration methods, namely two intensity-, two gradient-, three feature-based and three hybrid methods, both for registration of 3D pre-interventional image to monoplane or biplane 2D images. Intensity-based methods were most accurate in all tests (0.3 mm). One of the hybrid methods was most robust with 98.75% of successful registrations (SR) and capture range of 18 mm for registrations of 3D to biplane 2D angiograms. In general, registration accuracy was similar whether registration of 3D image was performed onto mono- or biplanar 2D images; however, the SR was substantially lower in case of 3D to monoplane 2D registration. Two feature-based and two hybrid methods had clinically feasible execution times in the order of a second. Performance of methods seems to fall below expectations in terms of robustness in case of registration of 3D to monoplane 2D images, while translation into clinical image guidance systems seems readily feasible for methods that perform registration of the 3D pre-interventional image onto biplanar intra

Comparison of demons deformable registration-based methods for texture analysis of serial thoracic CT scans

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Cunliffe, Alexandra R.; Al-Hallaq, Hania A.; Fei, Xianhan M.; Tuohy, Rachel E.; Armato, Samuel G.

2013-02-01

To determine how 19 image texture features may be altered by three image registration methods, "normal" baseline and follow-up computed tomography (CT) scans from 27 patients were analyzed. Nineteen texture feature values were calculated in over 1,000 32x32-pixel regions of interest (ROIs) randomly placed in each baseline scan. All three methods used demons registration to map baseline scan ROIs to anatomically matched locations in the corresponding transformed follow-up scan. For the first method, the follow-up scan transformation was subsampled to achieve a voxel size identical to that of the baseline scan. For the second method, the follow-up scan was transformed through affine registration to achieve global alignment with the baseline scan. For the third method, the follow-up scan was directly deformed to the baseline scan using demons deformable registration. Feature values in matched ROIs were compared using Bland- Altman 95% limits of agreement. For each feature, the range spanned by the 95% limits was normalized to the mean feature value to obtain the normalized range of agreement, nRoA. Wilcoxon signed-rank tests were used to compare nRoA values across features for the three methods. Significance for individual tests was adjusted using the Bonferroni method. nRoA was significantly smaller for affine-registered scans than for the resampled scans (p=0.003), indicating lower feature value variability between baseline and follow-up scan ROIs using this method. For both of these methods, however, nRoA was significantly higher than when feature values were calculated directly on demons-deformed followup scans (p<0.001). Across features and methods, nRoA values remained below 26%.

Fast time-of-flight camera based surface registration for radiotherapy patient positioning.

Science.gov (United States)

Placht, Simon; Stancanello, Joseph; Schaller, Christian; Balda, Michael; Angelopoulou, Elli

2012-01-01

This work introduces a rigid registration framework for patient positioning in radiotherapy, based on real-time surface acquisition by a time-of-flight (ToF) camera. Dynamic properties of the system are also investigated for future gating/tracking strategies. A novel preregistration algorithm, based on translation and rotation-invariant features representing surface structures, was developed. Using these features, corresponding three-dimensional points were computed in order to determine initial registration parameters. These parameters became a robust input to an accelerated version of the iterative closest point (ICP) algorithm for the fine-tuning of the registration result. Distance calibration and Kalman filtering were used to compensate for ToF-camera dependent noise. Additionally, the advantage of using the feature based preregistration over an "ICP only" strategy was evaluated, as well as the robustness of the rigid-transformation-based method to deformation. The proposed surface registration method was validated using phantom data. A mean target registration error (TRE) for translations and rotations of 1.62 ± 1.08 mm and 0.07° ± 0.05°, respectively, was achieved. There was a temporal delay of about 65 ms in the registration output, which can be seen as negligible considering the dynamics of biological systems. Feature based preregistration allowed for accurate and robust registrations even at very large initial displacements. Deformations affected the accuracy of the results, necessitating particular care in cases of deformed surfaces. The proposed solution is able to solve surface registration problems with an accuracy suitable for radiotherapy cases where external surfaces offer primary or complementary information to patient positioning. The system shows promising dynamic properties for its use in gating/tracking applications. The overall system is competitive with commonly-used surface registration technologies. Its main benefit is the

Intensity-based hierarchical elastic registration using approximating splines.

Science.gov (United States)

Serifovic-Trbalic, Amira; Demirovic, Damir; Cattin, Philippe C

2014-01-01

We introduce a new hierarchical approach for elastic medical image registration using approximating splines. In order to obtain the dense deformation field, we employ Gaussian elastic body splines (GEBS) that incorporate anisotropic landmark errors and rotation information. Since the GEBS approach is based on a physical model in form of analytical solutions of the Navier equation, it can very well cope with the local as well as global deformations present in the images by varying the standard deviation of the Gaussian forces. The proposed GEBS approximating model is integrated into the elastic hierarchical image registration framework, which decomposes a nonrigid registration problem into numerous local rigid transformations. The approximating GEBS registration scheme incorporates anisotropic landmark errors as well as rotation information. The anisotropic landmark localization uncertainties can be estimated directly from the image data, and in this case, they represent the minimal stochastic localization error, i.e., the Cramér-Rao bound. The rotation information of each landmark obtained from the hierarchical procedure is transposed in an additional angular landmark, doubling the number of landmarks in the GEBS model. The modified hierarchical registration using the approximating GEBS model is applied to register 161 image pairs from a digital mammogram database. The obtained results are very encouraging, and the proposed approach significantly improved all registrations comparing the mean-square error in relation to approximating TPS with the rotation information. On artificially deformed breast images, the newly proposed method performed better than the state-of-the-art registration algorithm introduced by Rueckert et al. (IEEE Trans Med Imaging 18:712-721, 1999). The average error per breast tissue pixel was less than 2.23 pixels compared to 2.46 pixels for Rueckert's method. The proposed hierarchical elastic image registration approach incorporates the GEBS

Method of automatic image registration of three-dimensional range of archaeological restoration

International Nuclear Information System (INIS)

Garcia, O.; Perez, M.; Morales, N.

2012-01-01

We propose an automatic registration system for reconstruction of various positions of a large object based on a static structured light pattern. The system combines the technology of stereo vision, structured light pattern, the positioning system of the vision sensor and an algorithm that simplifies the process of finding correspondence for the modeling of large objects. A new structured light pattern based on Kautz sequence is proposed, using this pattern as static implement a proposed new registration method. (Author)

Overlay improvement by exposure map based mask registration optimization

Science.gov (United States)

Shi, Irene; Guo, Eric; Chen, Ming; Lu, Max; Li, Gordon; Li, Rivan; Tian, Eric

2015-03-01

Along with the increased miniaturization of semiconductor electronic devices, the design rules of advanced semiconductor devices shrink dramatically. [1] One of the main challenges of lithography step is the layer-to-layer overlay control. Furthermore, DPT (Double Patterning Technology) has been adapted for the advanced technology node like 28nm and 14nm, corresponding overlay budget becomes even tighter. [2][3] After the in-die mask registration (pattern placement) measurement is introduced, with the model analysis of a KLA SOV (sources of variation) tool, it's observed that registration difference between masks is a significant error source of wafer layer-to-layer overlay at 28nm process. [4][5] Mask registration optimization would highly improve wafer overlay performance accordingly. It was reported that a laser based registration control (RegC) process could be applied after the pattern generation or after pellicle mounting and allowed fine tuning of the mask registration. [6] In this paper we propose a novel method of mask registration correction, which can be applied before mask writing based on mask exposure map, considering the factors of mask chip layout, writing sequence, and pattern density distribution. Our experiment data show if pattern density on the mask keeps at a low level, in-die mask registration residue error in 3sigma could be always under 5nm whatever blank type and related writer POSCOR (position correction) file was applied; it proves random error induced by material or equipment would occupy relatively fixed error budget as an error source of mask registration. On the real production, comparing the mask registration difference through critical production layers, it could be revealed that registration residue error of line space layers with higher pattern density is always much larger than the one of contact hole layers with lower pattern density. Additionally, the mask registration difference between layers with similar pattern density

Study on Huizhou architecture of point cloud registration based on optimized ICP algorithm

Science.gov (United States)

Zhang, Runmei; Wu, Yulu; Zhang, Guangbin; Zhou, Wei; Tao, Yuqian

2018-03-01

In view of the current point cloud registration software has high hardware requirements, heavy workload and moltiple interactive definition, the source of software with better processing effect is not open, a two--step registration method based on normal vector distribution feature and coarse feature based iterative closest point (ICP) algorithm is proposed in this paper. This method combines fast point feature histogram (FPFH) algorithm, define the adjacency region of point cloud and the calculation model of the distribution of normal vectors, setting up the local coordinate system for each key point, and obtaining the transformation matrix to finish rough registration, the rough registration results of two stations are accurately registered by using the ICP algorithm. Experimental results show that, compared with the traditional ICP algorithm, the method used in this paper has obvious time and precision advantages for large amount of point clouds.

Development and validation of a CT-3D rotational angiography registration method for AVM radiosurgery

International Nuclear Information System (INIS)

Stancanello, Joseph; Cavedon, Carlo; Francescon, Paolo; Cerveri, Pietro; Ferrigno, Giancarlo; Colombo, Federico; Perini, Stefano

2004-01-01

In this paper a novel technique is proposed and validated for radiosurgery treatment planning of arteriovenous malformations (AVMs). The technique was developed for frameless radiosurgery by means of the CyberKnife, a nonisocentric, linac-based system which allows highly conformed isodose surfaces to be obtained, while also being valid for other treatment strategies. The technique is based on registration between computed tomography (CT) and three-dimensional rotational angiography (3DRA). Tests were initially performed on the effectiveness of the correction method for distortion offered by the angiographic system. These results determined the registration technique that was ultimately chosen. For CT-3DRA registration, a twelve-parameter affine transformation was selected, based on a mutual information maximization algorithm. The robustness of the algorithm was tested by attempting to register data sets increasingly distant from each other, both in translation and rotation. Registration accuracy was estimated by means of the 'full circle consistency test'. A registration quality index (expressed in millimeters) based on these results was also defined. A hybrid subtraction between CT and 3DRA is proposed in order to improve 3D reconstruction. Preprocessing improved the ability of the algorithm to find an acceptable solution to the registration process. The robustness tests showed that data sets must be manually prealigned within approximately 15 mm and 20 deg. with respect to all three directions simultaneously. Results of the consistency test showed agreement between the quality index and registration accuracy stated by visual inspection in 20 good and 10 artificially worsened registration processes. The quality index showed values smaller than the maximum voxel size (mean 0.8 mm compared to 2 mm) for all successful registrations, while it resulted in much greater values (mean 20 mm) for unsuccessful registrations. Once registered, the two data sets can be used for

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.

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.

Performance Evaluation and Optimal Management of Distance-Based Registration Using a Semi-Markov Process

Directory of Open Access Journals (Sweden)

Jae Joon Suh

2017-01-01

Full Text Available We consider the distance-based registration (DBR which is a kind of dynamic location registration scheme in a mobile communication network. In the DBR, the location of a mobile station (MS is updated when it enters a base station more than or equal to a specified distance away from the base station where the location registration for the MS was done last. In this study, we first investigate the existing performance-evaluation methods on the DBR with implicit registration (DBIR presented to improve the performance of the DBR and point out some problems of the evaluation methods. We propose a new performance-evaluation method for the DBIR scheme using a semi-Markov process (SMP which can resolve the controversial issues of the existing methods. The numerical results obtained with the proposed SMP model are compared with those from previous models. It is shown that the SMP model should be considered to get an accurate performance of the DBIR scheme.

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

Directory of Open Access Journals (Sweden)

Nasreddine Taleb

2010-09-01

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

A rigid image registration based on the nonsubsampled contourlet transform and genetic algorithms.

Science.gov (United States)

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

2010-01-01

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

Convex Hull Aided Registration Method (CHARM).

Science.gov (United States)

Fan, Jingfan; Yang, Jian; Zhao, Yitian; Ai, Danni; Liu, Yonghuai; Wang, Ge; Wang, Yongtian

2017-09-01

Non-rigid registration finds many applications such as photogrammetry, motion tracking, model retrieval, and object recognition. In this paper we propose a novel convex hull aided registration method (CHARM) to match two point sets subject to a non-rigid transformation. First, two convex hulls are extracted from the source and target respectively. Then, all points of the point sets are projected onto the reference plane through each triangular facet of the hulls. From these projections, invariant features are extracted and matched optimally. The matched feature point pairs are mapped back onto the triangular facets of the convex hulls to remove outliers that are outside any relevant triangular facet. The rigid transformation from the source to the target is robustly estimated by the random sample consensus (RANSAC) scheme through minimizing the distance between the matched feature point pairs. Finally, these feature points are utilized as the control points to achieve non-rigid deformation in the form of thin-plate spline of the entire source point set towards the target one. The experimental results based on both synthetic and real data show that the proposed algorithm outperforms several state-of-the-art ones with respect to sampling, rotational angle, and data noise. In addition, the proposed CHARM algorithm also shows higher computational efficiency compared to these methods.

The Pose Estimation of Mobile Robot Based on Improved Point Cloud Registration

Directory of Open Access Journals (Sweden)

Yanzi Miao

2016-03-01

Full Text Available Due to GPS restrictions, an inertial sensor is usually used to estimate the location of indoor mobile robots. However, it is difficult to achieve high-accuracy localization and control by inertial sensors alone. In this paper, a new method is proposed to estimate an indoor mobile robot pose with six degrees of freedom based on an improved 3D-Normal Distributions Transform algorithm (3D-NDT. First, point cloud data are captured by a Kinect sensor and segmented according to the distance to the robot. After the segmentation, the input point cloud data are processed by the Approximate Voxel Grid Filter algorithm in different sized voxel grids. Second, the initial registration and precise registration are performed respectively according to the distance to the sensor. The most distant point cloud data use the 3D-Normal Distributions Transform algorithm (3D-NDT with large-sized voxel grids for initial registration, based on the transformation matrix from the odometry method. The closest point cloud data use the 3D-NDT algorithm with small-sized voxel grids for precise registration. After the registrations above, a final transformation matrix is obtained and coordinated. Based on this transformation matrix, the pose estimation problem of the indoor mobile robot is solved. Test results show that this method can obtain accurate robot pose estimation and has better robustness.

[Manufacture method and clinical application of minimally invasive dental implant guide template based on registration technology].

Science.gov (United States)

Lin, Zeming; He, Bingwei; Chen, Jiang; D u, Zhibin; Zheng, Jingyi; Li, Yanqin

2012-08-01

To guide doctors in precisely positioning surgical operation, a new production method of minimally invasive implant guide template was presented. The mandible of patient was scanned by CT scanner, and three-dimensional jaw bone model was constructed based on CT images data The professional dental implant software Simplant was used to simulate the plant based on the three-dimensional CT model to determine the location and depth of implants. In the same time, the dental plaster models were scanned by stereo vision system to build the oral mucosa model. Next, curvature registration technology was used to fuse the oral mucosa model and the CT model, then the designed position of implant in the oral mucosa could be determined. The minimally invasive implant guide template was designed in 3-Matic software according to the design position of implant and the oral mucosa model. Finally, the template was produced by rapid prototyping. The three-dimensional registration technology was useful to fuse the CT data and the dental plaster data, and the template was accurate that could provide the doctors a guidance in the actual planting without cut-off mucosa. The guide template which fabricated by comprehensive utilization of three-dimensional registration, Simplant simulation and rapid prototyping positioning are accurate and can achieve the minimally invasive and accuracy implant surgery, this technique is worthy of clinical use.

[Non-rigid medical image registration based on mutual information and thin-plate spline].

Science.gov (United States)

Cao, Guo-gang; Luo, Li-min

2009-01-01

To get precise and complete details, the contrast in different images is needed in medical diagnosis and computer assisted treatment. The image registration is the basis of contrast, but the regular rigid registration does not satisfy the clinic requirements. A non-rigid medical image registration method based on mutual information and thin-plate spline was present. Firstly, registering two images globally based on mutual information; secondly, dividing reference image and global-registered image into blocks and registering them; then getting the thin-plate spline transformation according to the shift of blocks' center; finally, applying the transformation to the global-registered image. The results show that the method is more precise than the global rigid registration based on mutual information and it reduces the complexity of getting control points and satisfy the clinic requirements better by getting control points of the thin-plate transformation automatically.

3D Rigid Registration by Cylindrical Phase Correlation Method

Czech Academy of Sciences Publication Activity Database

Bican, Jakub; Flusser, Jan

2009-01-01

Roč. 30, č. 10 (2009), s. 914-921 ISSN 0167-8655 R&D Projects: GA MŠk 1M0572; GA ČR GA102/08/1593 Grant - others:GAUK(CZ) 48908 Institutional research plan: CEZ:AV0Z10750506 Keywords : 3D registration * correlation methods * Image registration Subject RIV: BD - Theory of Information Impact factor: 1.303, year: 2009 http://library.utia.cas.cz/separaty/2009/ZOI/bican-3d digit registration by cylindrical phase correlation method.pdf

Fast Rotation-Free Feature-Based Image Registration Using Improved N-SIFT and GMM-Based Parallel Optimization.

Science.gov (United States)

Yu, Dongdong; Yang, Feng; Yang, Caiyun; Leng, Chengcai; Cao, Jian; Wang, Yining; Tian, Jie

2016-08-01

Image registration is a key problem in a variety of applications, such as computer vision, medical image processing, pattern recognition, etc., while the application of registration is limited by time consumption and the accuracy in the case of large pose differences. Aimed at these two kinds of problems, we propose a fast rotation-free feature-based rigid registration method based on our proposed accelerated-NSIFT and GMM registration-based parallel optimization (PO-GMMREG). Our method is accelerated by using the GPU/CUDA programming and preserving only the location information without constructing the descriptor of each interest point, while its robustness to missing correspondences and outliers is improved by converting the interest point matching to Gaussian mixture model alignment. The accuracy in the case of large pose differences is settled by our proposed PO-GMMREG algorithm by constructing a set of initial transformations. Experimental results demonstrate that our proposed algorithm can fast rigidly register 3-D medical images and is reliable for aligning 3-D scans even when they exhibit a poor initialization.

Wavelet based free-form deformations for nonrigid registration

Science.gov (United States)

Sun, Wei; Niessen, Wiro J.; Klein, Stefan

2014-03-01

In nonrigid registration, deformations may take place on the coarse and fine scales. For the conventional B-splines based free-form deformation (FFD) registration, these coarse- and fine-scale deformations are all represented by basis functions of a single scale. Meanwhile, wavelets have been proposed as a signal representation suitable for multi-scale problems. Wavelet analysis leads to a unique decomposition of a signal into its coarse- and fine-scale components. Potentially, this could therefore be useful for image registration. In this work, we investigate whether a wavelet-based FFD model has advantages for nonrigid image registration. We use a B-splines based wavelet, as defined by Cai and Wang.1 This wavelet is expressed as a linear combination of B-spline basis functions. Derived from the original B-spline function, this wavelet is smooth, differentiable, and compactly supported. The basis functions of this wavelet are orthogonal across scales in Sobolev space. This wavelet was previously used for registration in computer vision, in 2D optical flow problems,2 but it was not compared with the conventional B-spline FFD in medical image registration problems. An advantage of choosing this B-splines based wavelet model is that the space of allowable deformation is exactly equivalent to that of the traditional B-spline. The wavelet transformation is essentially a (linear) reparameterization of the B-spline transformation model. Experiments on 10 CT lung and 18 T1-weighted MRI brain datasets show that wavelet based registration leads to smoother deformation fields than traditional B-splines based registration, while achieving better accuracy.

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.

A NEW IMAGE REGISTRATION METHOD FOR GREY IMAGES

Institute of Scientific and Technical Information of China (English)

Nie Xuan; Zhao Rongchun; Jiang Zetao

2004-01-01

The proposed algorithm relies on a group of new formulas for calculating tangent slope so as to address angle feature of edge curves of image. It can utilize tangent angle features to estimate automatically and fully the rotation parameters of geometric transform and enable rough matching of images with huge rotation difference. After angle compensation, it can search for matching point sets by correlation criterion, then calculate parameters of affine transform, enable higher-precision emendation of rotation and transferring. Finally, it fulfills precise matching for images with relax-tense iteration method. Compared with the registration approach based on wavelet direction-angle features, the matching algorithm with tangent feature of image edge is more robust and realizes precise registration of various images. Furthermore, it is also helpful in graphics matching.

Image registration assessment in radiotherapy image guidance based on control chart monitoring.

Science.gov (United States)

Xia, Wenyao; Breen, Stephen L

2018-04-01

Image guidance with cone beam computed tomography in radiotherapy can guarantee the precision and accuracy of patient positioning prior to treatment delivery. During the image guidance process, operators need to take great effort to evaluate the image guidance quality before correcting a patient's position. This work proposes an image registration assessment method based on control chart monitoring to reduce the effort taken by the operator. According to the control chart plotted by daily registration scores of each patient, the proposed method can quickly detect both alignment errors and image quality inconsistency. Therefore, the proposed method can provide a clear guideline for the operators to identify unacceptable image quality and unacceptable image registration with minimal effort. Experimental results demonstrate that by using control charts from a clinical database of 10 patients undergoing prostate radiotherapy, the proposed method can quickly identify out-of-control signals and find special cause of out-of-control registration events.

AUTOMATED FEATURE BASED TLS DATA REGISTRATION FOR 3D BUILDING MODELING

OpenAIRE

K. Kitamura; N. Kochi; S. Kaneko

2012-01-01

In this paper we present a novel method for the registration of point cloud data obtained using terrestrial laser scanner (TLS). The final goal of our investigation is the automated reconstruction of CAD drawings and the 3D modeling of objects surveyed by TLS. Because objects are scanned from multiple positions, individual point cloud need to be registered to the same coordinate system. We propose in this paper an automated feature based registration procedure. Our proposed method does not re...

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

Reducing uncertainties in volumetric image based deformable organ registration

International Nuclear Information System (INIS)

Liang, J.; Yan, D.

2003-01-01

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

FPFH-based graph matching for 3D point cloud registration

Science.gov (United States)

Zhao, Jiapeng; Li, Chen; Tian, Lihua; Zhu, Jihua

2018-04-01

Correspondence detection is a vital step in point cloud registration and it can help getting a reliable initial alignment. In this paper, we put forward an advanced point feature-based graph matching algorithm to solve the initial alignment problem of rigid 3D point cloud registration with partial overlap. Specifically, Fast Point Feature Histograms are used to determine the initial possible correspondences firstly. Next, a new objective function is provided to make the graph matching more suitable for partially overlapping point cloud. The objective function is optimized by the simulated annealing algorithm for final group of correct correspondences. Finally, we present a novel set partitioning method which can transform the NP-hard optimization problem into a O(n3)-solvable one. Experiments on the Stanford and UWA public data sets indicates that our method can obtain better result in terms of both accuracy and time cost compared with other point cloud registration methods.

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

Directory of Open Access Journals (Sweden)

Feng Yang

2013-06-01

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

High-Precision Registration of Point Clouds Based on Sphere Feature Constraints

Directory of Open Access Journals (Sweden)

Junhui Huang

2016-12-01

Full Text Available Point cloud registration is a key process in multi-view 3D measurements. Its precision affects the measurement precision directly. However, in the case of the point clouds with non-overlapping areas or curvature invariant surface, it is difficult to achieve a high precision. A high precision registration method based on sphere feature constraint is presented to overcome the difficulty in the paper. Some known sphere features with constraints are used to construct virtual overlapping areas. The virtual overlapping areas provide more accurate corresponding point pairs and reduce the influence of noise. Then the transformation parameters between the registered point clouds are solved by an optimization method with weight function. In that case, the impact of large noise in point clouds can be reduced and a high precision registration is achieved. Simulation and experiments validate the proposed method.

Improving alignment in Tract-based spatial statistics: evaluation and optimization of image registration.

Science.gov (United States)

de Groot, Marius; Vernooij, Meike W; Klein, Stefan; Ikram, M Arfan; Vos, Frans M; Smith, Stephen M; Niessen, Wiro J; Andersson, Jesper L R

2013-08-01

Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS establishes spatial correspondence using a combination of nonlinear registration and a "skeleton projection" that may break topological consistency of the transformed brain images. We therefore investigated feasibility of replacing the two-stage registration-projection procedure in TBSS with a single, regularized, high-dimensional registration. To optimize registration parameters and to evaluate registration performance in diffusion MRI, we designed an evaluation framework that uses native space probabilistic tractography for 23 white matter tracts, and quantifies tract similarity across subjects in standard space. We optimized parameters for two registration algorithms on two diffusion datasets of different quality. We investigated reproducibility of the evaluation framework, and of the optimized registration algorithms. Next, we compared registration performance of the regularized registration methods and TBSS. Finally, feasibility and effect of incorporating the improved registration in TBSS were evaluated in an example study. The evaluation framework was highly reproducible for both algorithms (R(2) 0.993; 0.931). The optimal registration parameters depended on the quality of the dataset in a graded and predictable manner. At optimal parameters, both algorithms outperformed the registration of TBSS, showing feasibility of adopting such approaches in TBSS. This was further confirmed in the example experiment. Copyright © 2013 Elsevier Inc. All rights reserved.

Development and application of pulmonary structure-function registration methods: towards pulmonary image-guidance tools for improved airway targeted therapies and outcomes

Science.gov (United States)

Guo, Fumin; Pike, Damien; Svenningsen, Sarah; Coxson, Harvey O.; Drozd, John J.; Yuan, Jing; Fenster, Aaron; Parraga, Grace

2014-03-01

Objectives: We aimed to develop a way to rapidly generate multi-modality (MRI-CT) pulmonary imaging structurefunction maps using novel non-rigid image registration methods. This objective is part of our overarching goal to provide an image processing pipeline to generate pulmonary structure-function maps and guide airway-targeted therapies. Methods: Anatomical 1H and functional 3He MRI were acquired in 5 healthy asymptomatic ex-smokers and 7 ex-smokers with chronic obstructive pulmonary disease (COPD) at inspiration breath-hold. Thoracic CT was performed within ten minutes of MRI using the same breath-hold volume. Landmark-based affine registration methods previously validated for imaging of COPD, was based on corresponding fiducial markers located in both CT and 1H MRI coronal slices and compared with shape-based CT-MRI non-rigid registration. Shape-based CT-MRI registration was developed by first identifying the shapes of the lung cavities manually, and then registering the two shapes using affine and thin-plate spline algorithms. We compared registration accuracy using the fiducial localization error (FLE) and target registration error (TRE). Results: For landmark-based registration, the TRE was 8.4±5.3 mm for whole lung and 7.8±4.6 mm for the R and L lungs registered independently (p=0.4). For shape-based registration, the TRE was 8.0±4.6 mm for whole lung as compared to 6.9±4.4 mm for the R and L lung registered independently and this difference was significant (p=0.01). The difference for shape-based (6.9±4.4 mm) and landmark-based R and L lung registration (7.8±4.6 mm) was also significant (p=.04) Conclusion: Shape-based registration TRE was significantly improved compared to landmark-based registration when considering L and R lungs independently.

Automatic Registration Method for Fusion of ZY-1-02C Satellite Images

Directory of Open Access Journals (Sweden)

Qi Chen

2013-12-01

Full Text Available Automatic image registration (AIR has been widely studied in the fields of medical imaging, computer vision, and remote sensing. In various cases, such as image fusion, high registration accuracy should be achieved to meet application requirements. For satellite images, the large image size and unstable positioning accuracy resulting from the limited manufacturing technology of charge-coupled device, focal plane distortion, and unrecorded spacecraft jitter lead to difficulty in obtaining agreeable corresponding points for registration using only area-based matching or feature-based matching. In this situation, a coarse-to-fine matching strategy integrating two types of algorithms is proven feasible and effective. In this paper, an AIR method for application to the fusion of ZY-1-02C satellite imagery is proposed. First, the images are geometrically corrected. Coarse matching, based on scale invariant feature transform, is performed for the subsampled corrected images, and a rough global estimation is made with the matching results. Harris feature points are then extracted, and the coordinates of the corresponding points are calculated according to the global estimation results. Precise matching is conducted, based on normalized cross correlation and least squares matching. As complex image distortion cannot be precisely estimated, a local estimation using the structure of triangulated irregular network is applied to eliminate the false matches. Finally, image resampling is conducted, based on local affine transformation, to achieve high-precision registration. Experiments with ZY-1-02C datasets demonstrate that the accuracy of the proposed method meets the requirements of fusion application, and its efficiency is also suitable for the commercial operation of the automatic satellite data process system.

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

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

Learning-Based Approaches to Deformable Image Registration

NARCIS (Netherlands)

Münzing, SEA

2014-01-01

Accurate registration of images is an important and often crucial step in many areas of image processing and analysis, yet it is only used in a small percentage of possible applications. Automated registration methods are not considered to be sufficiently robust to handle complex deformations and

COMPARISON OF VOLUMETRIC REGISTRATION ALGORITHMS FOR TENSOR-BASED MORPHOMETRY.

Science.gov (United States)

Villalon, Julio; Joshi, Anand A; Toga, Arthur W; Thompson, Paul M

2011-01-01

Nonlinear registration of brain MRI scans is often used to quantify morphological differences associated with disease or genetic factors. Recently, surface-guided fully 3D volumetric registrations have been developed that combine intensity-guided volume registrations with cortical surface constraints. In this paper, we compare one such algorithm to two popular high-dimensional volumetric registration methods: large-deformation viscous fluid registration, formulated in a Riemannian framework, and the diffeomorphic "Demons" algorithm. We performed an objective morphometric comparison, by using a large MRI dataset from 340 young adult twin subjects to examine 3D patterns of correlations in anatomical volumes. Surface-constrained volume registration gave greater effect sizes for detecting morphometric associations near the cortex, while the other two approaches gave greater effects sizes subcortically. These findings suggest novel ways to combine the advantages of multiple methods in the future.

Registration-based Reconstruction of Four-dimensional Cone Beam Computed Tomography

DEFF Research Database (Denmark)

Christoffersen, Christian; Hansen, David Christoffer; Poulsen, Per Rugaard

2013-01-01

We present a new method for reconstruction of four-dimensional (4D) cone beam computed tomography from an undersampled set of X-ray projections. The novelty of the proposed method lies in utilizing optical flow based registration to facilitate that each temporal phase is reconstructed from the full...

AN AUTOMATIC OPTICAL AND SAR IMAGE REGISTRATION METHOD USING ITERATIVE MULTI-LEVEL AND REFINEMENT MODEL

Directory of Open Access Journals (Sweden)

C. Xu

2016-06-01

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

2.5D Multi-View Gait Recognition Based on Point Cloud Registration

Science.gov (United States)

Tang, Jin; Luo, Jian; Tjahjadi, Tardi; Gao, Yan

2014-01-01

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM. PMID:24686727

An Improved InSAR Image Co-Registration Method for Pairs with Relatively Big Distortions or Large Incoherent Areas

Directory of Open Access Journals (Sweden)

Zhenwei Chen

2016-09-01

Full Text Available Co-registration is one of the most important steps in interferometric synthetic aperture radar (InSAR data processing. The standard offset-measurement method based on cross-correlating uniformly distributed patches takes no account of specific geometric transformation between images or characteristics of ground scatterers. Hence, it is inefficient and difficult to obtain satisfying co-registration results for image pairs with relatively big distortion or large incoherent areas. Given this, an improved co-registration strategy is proposed in this paper which takes both the geometric features and image content into consideration. Firstly, some geometric transformations including scale, flip, rotation, and shear between images were eliminated based on the geometrical information, and the initial co-registration polynomial was obtained. Then the registration points were automatically detected by integrating the signal-to-clutter-ratio (SCR thresholds and the amplitude information, and a further co-registration process was performed to refine the polynomial. Several comparison experiments were carried out using 2 TerraSAR-X data from the Hong Kong airport and 21 PALSAR data from the Donghai Bridge. Experiment results demonstrate that the proposed method brings accuracy and efficiency improvements for co-registration and processing abilities in the cases of big distortion between images or large incoherent areas in the images. For most co-registrations, the proposed method can enhance the reliability and applicability of co-registration and thus promote the automation to a higher level.

An Improved InSAR Image Co-Registration Method for Pairs with Relatively Big Distortions or Large Incoherent Areas.

Science.gov (United States)

Chen, Zhenwei; Zhang, Lei; Zhang, Guo

2016-09-17

Co-registration is one of the most important steps in interferometric synthetic aperture radar (InSAR) data processing. The standard offset-measurement method based on cross-correlating uniformly distributed patches takes no account of specific geometric transformation between images or characteristics of ground scatterers. Hence, it is inefficient and difficult to obtain satisfying co-registration results for image pairs with relatively big distortion or large incoherent areas. Given this, an improved co-registration strategy is proposed in this paper which takes both the geometric features and image content into consideration. Firstly, some geometric transformations including scale, flip, rotation, and shear between images were eliminated based on the geometrical information, and the initial co-registration polynomial was obtained. Then the registration points were automatically detected by integrating the signal-to-clutter-ratio (SCR) thresholds and the amplitude information, and a further co-registration process was performed to refine the polynomial. Several comparison experiments were carried out using 2 TerraSAR-X data from the Hong Kong airport and 21 PALSAR data from the Donghai Bridge. Experiment results demonstrate that the proposed method brings accuracy and efficiency improvements for co-registration and processing abilities in the cases of big distortion between images or large incoherent areas in the images. For most co-registrations, the proposed method can enhance the reliability and applicability of co-registration and thus promote the automation to a higher level.

Evaluation of registration methods on thoracic CT : the EMPIRE10 challenge

NARCIS (Netherlands)

Murphy, K.; Ginneken, van B.; Reinhardt, J.M.; Kabus, S.; Ding, K.; Deng, Xiang; Cao, K.; Du, K.; Christensen, G.E.; Garcia, V.; Vercauteren, T.; Ayache, N.; Commowick, O.; Malandain, G.; Glocker, B.; Paragios, N.; Navab, N.; Gorbunova, V.; Sporring, J.; Bruijne, de M.; Han, Xiao; Heinrich, M.P.; Schnabel, J.A.; Jenkinson, M.; Lorenz, C.; Modat, M.; McClelland, J.R.; Ourselin, S.; Muenzing, S.E.A.; Viergever, M.A.; Nigris, De D.; Collins, D.L.; Arbel, T.; Peroni, M.; Li, R.; Sharp, G.; Schmidt-Richberg, A.; Ehrhardt, J.; Werner, R.; Smeets, D.; Loeckx, D.; Song, G.; Tustison, N.; Avants, B.; Gee, J.C.; Staring, M.; Klein, S.; Stoel, B.C.; Urschler, M.; Werlberger, M.; Vandemeulebroucke, J.; Rit, S.; Sarrut, D.; Pluim, J.P.W.

2011-01-01

EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This

Motion tracking in the liver: Validation of a method based on 4D ultrasound using a nonrigid registration technique

Energy Technology Data Exchange (ETDEWEB)

Vijayan, Sinara, E-mail: sinara.vijayan@ntnu.no [Norwegian University of Science and Technology, 7491 Trondheim (Norway); Klein, Stefan [Norwegian University of Science and Technology, 7491 Trondheim, Norway and Biomedical Imaging Group Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC, 3000 CA Rotterdam (Netherlands); Hofstad, Erlend Fagertun; Langø, Thomas [SINTEF, Department Medical Technology, 7465 Trondheim (Norway); Lindseth, Frank [Norwegian University of Science and Technology, 7491 Trondheim, Norway and SINTEF, Department Medical Technology, 7465 Trondheim (Norway); Ystgaard, Brynjulf [Department of Surgery, St. Olavs Hospital, 7030 Trondheim (Norway)

2014-08-15

Purpose: Treatments like radiotherapy and focused ultrasound in the abdomen require accurate motion tracking, in order to optimize dosage delivery to the target and minimize damage to critical structures and healthy tissues around the target. 4D ultrasound is a promising modality for motion tracking during such treatments. In this study, the authors evaluate the accuracy of motion tracking in the liver based on deformable registration of 4D ultrasound images. Methods: The offline analysis was performed using a nonrigid registration algorithm that was specifically designed for motion estimation from dynamic imaging data. The method registers the entire 4D image data sequence in a groupwise optimization fashion, thus avoiding a bias toward a specifically chosen reference time point. Three healthy volunteers were scanned over several breathing cycles (12 s) from three different positions and angles on the abdomen; a total of nine 4D scans for the three volunteers. Well-defined anatomic landmarks were manually annotated in all 96 time frames for assessment of the automatic algorithm. The error of the automatic motion estimation method was compared with interobserver variability. The authors also performed experiments to investigate the influence of parameters defining the deformation field flexibility and evaluated how well the method performed with a lower temporal resolution in order to establish the minimum frame rate required for accurate motion estimation. Results: The registration method estimated liver motion with an error of 1 mm (75% percentile over all datasets), which was lower than the interobserver variability of 1.4 mm. The results were only slightly dependent on the degrees of freedom of the deformation model. The registration error increased to 2.8 mm with an eight times lower temporal resolution. Conclusions: The authors conclude that the methodology was able to accurately track the motion of the liver in the 4D ultrasound data. The authors believe

A three-dimensional correlation method for registration of medical images in radiology

Energy Technology Data Exchange (ETDEWEB)

Georgiou, Michalakis; Sfakianakis, George N [Department of Radiology, University of Miami, Jackson Memorial Hospital, Miami, FL 33136 (United States); Nagel, Joachim H [Institute of Biomedical Engineering, University of Stuttgart, Stuttgart 70174 (Germany)

1999-12-31

The availability of methods to register multi-modality images in order to `fuse` them to correlate their information is increasingly becoming an important requirement for various diagnostic and therapeutic procedures. A variety of image registration methods have been developed but they remain limited to specific clinical applications. Assuming rigid body transformation, two images can be registered if their differences are calculated in terms of translation, rotation and scaling. This paper describes the development and testing of a new correlation based approach for three-dimensional image registration. First, the scaling factors introduced by the imaging devices are calculated and compensated for. Then, the two images become translation invariant by computing their three-dimensional Fourier magnitude spectra. Subsequently, spherical coordinate transformation is performed and then the three-dimensional rotation is computed using a novice approach referred to as {sup p}olar Shells{sup .} The method of polar shells maps the three angles of rotation into one rotation and two translations of a two-dimensional function and then proceeds to calculate them using appropriate transformations based on the Fourier invariance properties. A basic assumption in the method is that the three-dimensional rotation is constrained to one large and two relatively small angles. This assumption is generally satisfied in normal clinical settings. The new three-dimensional image registration method was tested with simulations using computer generated phantom data as well as actual clinical data. Performance analysis and accuracy evaluation of the method using computer simulations yielded errors in the sub-pixel range. (authors) 6 refs., 3 figs.

A three-dimensional correlation method for registration of medical images in radiology

International Nuclear Information System (INIS)

Georgiou, Michalakis; Sfakianakis, George N.; Nagel, Joachim H.

1998-01-01

The availability of methods to register multi-modality images in order to 'fuse' them to correlate their information is increasingly becoming an important requirement for various diagnostic and therapeutic procedures. A variety of image registration methods have been developed but they remain limited to specific clinical applications. Assuming rigid body transformation, two images can be registered if their differences are calculated in terms of translation, rotation and scaling. This paper describes the development and testing of a new correlation based approach for three-dimensional image registration. First, the scaling factors introduced by the imaging devices are calculated and compensated for. Then, the two images become translation invariant by computing their three-dimensional Fourier magnitude spectra. Subsequently, spherical coordinate transformation is performed and then the three-dimensional rotation is computed using a novice approach referred to as p olar Shells . The method of polar shells maps the three angles of rotation into one rotation and two translations of a two-dimensional function and then proceeds to calculate them using appropriate transformations based on the Fourier invariance properties. A basic assumption in the method is that the three-dimensional rotation is constrained to one large and two relatively small angles. This assumption is generally satisfied in normal clinical settings. The new three-dimensional image registration method was tested with simulations using computer generated phantom data as well as actual clinical data. Performance analysis and accuracy evaluation of the method using computer simulations yielded errors in the sub-pixel range. (authors)

Automatic registration using implicit shape representations: applications in intraoperative 3D rotational angiography to preoperative CTA registration

International Nuclear Information System (INIS)

Subramanian, Navneeth; Pichon, Eric; Solomon, Stephen B.

2009-01-01

A solution for automatic registration of 3D rotational angiography (XA) to CT/MR of the liver. Targeted for use in treatment planning of liver interventions. A shape-based approach to registration is proposed that does not require specification of landmarks nor is it prone to local minima like purely intensity-based registration methods. Through the use of vessel characteristics, accurate registration is possible even in the presence of deformations induced by catheters and respiratory motion. Registration was performed on eight pairs of multiphase CT angiography and 3D rotational digital angiography datasets. Quantitative validation of the registration accuracy using vessel landmarks was performed on these datasets. The validation study showed that the method has a registration error of 9.41±4.13 mm. In addition, the computation time is well below 60 s making it attractive for clinical application. A new method for fully automatic 3DXA to CT/MR image registration was developed and found to be efficient and accurate using clinically realistic datasets. (orig.)

SU-E-J-89: Deformable Registration Method Using B-TPS in Radiotherapy.

Science.gov (United States)

Xie, Y

2012-06-01

A novel deformable registration method for four-dimensional computed tomography (4DCT) images is developed in radiation therapy. The proposed method combines the thin plate spline (TPS) and B-spline together to achieve high accuracy and high efficiency. The method consists of two steps. First, TPS is used as a global registration method to deform large unfit regions in the moving image to match counterpart in the reference image. Then B-spline is used for local registration, the previous deformed moving image is further deformed to match the reference image more accurately. Two clinical CT image sets, including one pair of lung and one pair of liver, are simulated using the proposed algorithm, which results in a tremendous improvement in both run-time and registration quality, compared with the conventional methods solely using either TPS or B-spline. The proposed method can combine the efficiency of TPS and the accuracy of B-spline, performing good adaptively and robust in registration of clinical 4DCT image. © 2012 American Association of Physicists in Medicine.

Biometric Authorization and Registration Systems and Methods

National Research Council Canada - National Science Library

Caulfield, H

2002-01-01

Biometric authorization and registration systems and methods are disclosed. In one embodiment, the system preferably comprises a firearm that includes a biometric authorization system, a plurality of training computers, and a server...

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......Non-rigid image registration techniques are widely used in medical imaging applications. Due to high computational complexities of these techniques, finding appropriate registration method to both reduce the computation burden and increase the registration accuracy has become an intense area...... 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 on registration of real lung CT images, with expert annotated landmarks, show...

A Bayesian nonrigid registration method to enhance intraoperative target definition in image-guided prostate procedures through uncertainty characterization

International Nuclear Information System (INIS)

Pursley, Jennifer; Risholm, Petter; Fedorov, Andriy; Tuncali, Kemal; Fennessy, Fiona M.; Wells, William M. III; Tempany, Clare M.; Cormack, Robert A.

2012-01-01

Purpose: This study introduces a probabilistic nonrigid registration method for use in image-guided prostate brachytherapy. Intraoperative imaging for prostate procedures, usually transrectal ultrasound (TRUS), is typically inferior to diagnostic-quality imaging of the pelvis such as endorectal magnetic resonance imaging (MRI). MR images contain superior detail of the prostate boundaries and provide substructure features not otherwise visible. Previous efforts to register diagnostic prostate images with the intraoperative coordinate system have been deterministic and did not offer a measure of the registration uncertainty. The authors developed a Bayesian registration method to estimate the posterior distribution on deformations and provide a case-specific measure of the associated registration uncertainty. Methods: The authors adapted a biomechanical-based probabilistic nonrigid method to register diagnostic to intraoperative images by aligning a physician's segmentations of the prostate in the two images. The posterior distribution was characterized with a Markov Chain Monte Carlo method; the maximum a posteriori deformation and the associated uncertainty were estimated from the collection of deformation samples drawn from the posterior distribution. The authors validated the registration method using a dataset created from ten patients with MRI-guided prostate biopsies who had both diagnostic and intraprocedural 3 Tesla MRI scans. The accuracy and precision of the estimated posterior distribution on deformations were evaluated from two predictive distance distributions: between the deformed central zone-peripheral zone (CZ-PZ) interface and the physician-labeled interface, and based on physician-defined landmarks. Geometric margins on the registration of the prostate's peripheral zone were determined from the posterior predictive distance to the CZ-PZ interface separately for the base, mid-gland, and apical regions of the prostate. Results: The authors observed

Comparison of carina-based versus bony anatomy-based registration for setup verification in esophageal cancer radiotherapy.

Science.gov (United States)

Machiels, Mélanie; Jin, Peng; van Gurp, Christianne H; van Hooft, Jeanin E; Alderliesten, Tanja; Hulshof, Maarten C C M

2018-03-21

To investigate the feasibility and geometric accuracy of carina-based registration for CBCT-guided setup verification in esophageal cancer IGRT, compared with current practice bony anatomy-based registration. Included were 24 esophageal cancer patients with 65 implanted fiducial markers, visible on planning CTs and follow-up CBCTs. All available CBCT scans (n = 236) were rigidly registered to the planning CT with respect to the bony anatomy and the carina. Target coverage was visually inspected and marker position variation was quantified relative to both registration approaches; the variation of systematic (Σ) and random errors (σ) was estimated. Automatic carina-based registration was feasible in 94.9% of the CBCT scans, with an adequate target coverage in 91.1% compared to 100% after bony anatomy-based registration. Overall, Σ (σ) in the LR/CC/AP direction was 2.9(2.4)/4.1(2.4)/2.2(1.8) mm using the bony anatomy registration compared to 3.3(3.0)/3.6(2.6)/3.9(3.1) mm for the carina. Mid-thoracic placed markers showed a non-significant but smaller Σ in CC and AP direction when using the carina-based registration. Compared with a bony anatomy-based registration, carina-based registration for esophageal cancer IGRT results in inadequate target coverage in 8.9% of cases. Furthermore, large Σ and σ, requiring larger anisotropic margins, were seen after carina-based registration. Only for tumors entirely confined to the mid-thoracic region the carina-based registration might be slightly favorable.

Feature-Based Retinal Image Registration Using D-Saddle Feature

Directory of Open Access Journals (Sweden)

Roziana Ramli

2017-01-01

Full Text Available Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%, Harris-PIIFD (4%, H-M (16%, and Saddle (16%. Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle.

Contour Propagation Using Feature-Based Deformable Registration for Lung Cancer

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Yuhan Yang

2013-01-01

Full Text Available Accurate target delineation of CT image is a critical step in radiotherapy treatment planning. This paper describes a novel strategy for automatic contour propagation, based on deformable registration, for CT images of lung cancer. The proposed strategy starts with a manual-delineated contour in one slice of a 3D CT image. By means of feature-based deformable registration, the initial contour in other slices of the image can be propagated automatically, and then refined by active contour approach. Three algorithms are employed in the strategy: the Speeded-Up Robust Features (SURF, Thin-Plate Spline (TPS, and an adapted active contour (Snake, used to refine and modify the initial contours. Five pulmonary cancer cases with about 400 slices and 1000 contours have been used to verify the proposed strategy. Experiments demonstrate that the proposed strategy can improve the segmentation performance in the pulmonary CT images. Jaccard similarity (JS mean is about 0.88 and the maximum of Hausdorff distance (HD is about 90%. In addition, delineation time has been considerably reduced. The proposed feature-based deformable registration method in the automatic contour propagation improves the delineation efficiency significantly.

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

International Nuclear Information System (INIS)

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

2013-01-01

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

Normalized mutual information based PET-MR registration using K-Means clustering and shading correction

NARCIS (Netherlands)

Knops, Z.F.; Maintz, J.B.A.; Viergever, M.A.; Pluim, J.P.W.; Gee, J.C.; Maintz, J.B.A.; Vannier, M.W.

2003-01-01

A method for the efficient re-binning and shading based correction of intensity distributions of the images prior to normalized mutual information based registration is presented. Our intensity distribution re-binning method is based on the K-means clustering algorithm as opposed to the generally

A comparative study of surface- and volume-based techniques for the automatic registration between CT and SPECT brain images

International Nuclear Information System (INIS)

Kagadis, George C.; Delibasis, Konstantinos K.; Matsopoulos, George K.; Mouravliansky, Nikolaos A.; Asvestas, Pantelis A.; Nikiforidis, George C.

2002-01-01

Image registration of multimodality images is an essential task in numerous applications in three-dimensional medical image processing. Medical diagnosis can benefit from the complementary information in different modality images. Surface-based registration techniques, while still widely used, were succeeded by volume-based registration algorithms that appear to be theoretically advantageous in terms of reliability and accuracy. Several applications of such algorithms for the registration of CT-MRI, CT-PET, MRI-PET, and SPECT-MRI images have emerged in the literature, using local optimization techniques for the matching of images. Our purpose in this work is the development of automatic techniques for the registration of real CT and SPECT images, based on either surface- or volume-based algorithms. Optimization is achieved using genetic algorithms that are known for their robustness. The two techniques are compared against a well-established method, the Iterative Closest Point--ICP. The correlation coefficient was employed as an independent measure of spatial match, to produce unbiased results. The repeated measures ANOVA indicates the significant impact of the choice of registration method on the magnitude of the correlation (F=4.968, p=0.0396). The volume-based method achieves an average correlation coefficient value of 0.454 with a standard deviation of 0.0395, as opposed to an average of 0.380 with a standard deviation of 0.0603 achieved by the surface-based method and an average of 0.396 with a standard deviation equal to 0.0353 achieved by ICP. The volume-based technique performs significantly better compared to both ICP (p<0.05, Neuman Keuls test) and the surface-based technique (p<0.05, Neuman-Keuls test). Surface-based registration and ICP do not differ significantly in performance

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

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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.

Registration of 3-dimensional facial photographs for clinical use.

Science.gov (United States)

Maal, Thomas J J; van Loon, Bram; Plooij, Joanneke M; Rangel, Frits; Ettema, Anke M; Borstlap, Wilfred A; Bergé, Stefaan J

2010-10-01

To objectively evaluate treatment outcomes in oral and maxillofacial surgery, pre- and post-treatment 3-dimensional (3D) photographs of the patient's face can be registered. For clinical use, it is of great importance that this registration process is accurate (photographs were captured at 3 different times: baseline (T(0)), after 1 minute (T(1)), and 3 weeks later (T(2)). Furthermore, a 3D photograph of the volunteer laughing (T(L)) was acquired to investigate the effect of facial expression. Two different registration methods were used to register the photographs acquired at all different times: surface-based registration and reference-based registration. Within the surface-based registration, 2 different software packages (Maxilim [Medicim NV, Mechelen, Belgium] and 3dMD Patient [3dMD LLC, Atlanta, GA]) were used to register the 3D photographs acquired at the various times. The surface-based registration process was repeated with the preprocessed photographs. Reference-based registration (Maxilim) was performed twice by 2 observers investigating the inter- and intraobserver error. The mean registration errors are small for the 3D photographs at rest (0.39 mm for T(0)-T(1) and 0.52 mm for T(0)-T(2)). The mean registration error increased to 1.2 mm for the registration between the 3D photographs acquired at T(0) and T(L). The mean registration error for the reference-based method was 1.0 mm for T(0)-T(1), 1.1 mm for T(0)-T(2), and 1.5 mm for T(0) and T(L). The mean registration errors for the preprocessed photographs were even smaller (0.30 mm for T(0)-T(1), 0.42 mm for T(0)-T(2), and 1.2 mm for T(0) and T(L)). Furthermore, a strong correlation between the results of both software packages used for surface-based registration was found. The intra- and interobserver error for the reference-based registration method was found to be 1.2 and 1.0 mm, respectively. Surface-based registration is an accurate method to compare 3D photographs of the same individual at

Multimodal image registration based on binary gradient angle descriptor.

Science.gov (United States)

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

2017-12-01

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

Neural network-based feature point descriptors for registration of optical and SAR images

Science.gov (United States)

Abulkhanov, Dmitry; Konovalenko, Ivan; Nikolaev, Dmitry; Savchik, Alexey; Shvets, Evgeny; Sidorchuk, Dmitry

2018-04-01

Registration of images of different nature is an important technique used in image fusion, change detection, efficient information representation and other problems of computer vision. Solving this task using feature-based approaches is usually more complex than registration of several optical images because traditional feature descriptors (SIFT, SURF, etc.) perform poorly when images have different nature. In this paper we consider the problem of registration of SAR and optical images. We train neural network to build feature point descriptors and use RANSAC algorithm to align found matches. Experimental results are presented that confirm the method's effectiveness.

SU-F-J-96: Comparison of Frame-Based and Mutual Information Registration Techniques for CT and MR Image Sets

Energy Technology Data Exchange (ETDEWEB)

Popple, R; Bredel, M; Brezovich, I; Dobelbower, M; Fisher, W; Fiveash, J; Guthrie, B; Riley, K; Wu, X [The University of Alabama at Birmingham, Birmingham, AL (United States)

2016-06-15

Purpose: To compare the accuracy of CT-MR registration using a mutual information method with registration using a frame-based localizer box. Methods: Ten patients having the Leksell head frame and scanned with a modality specific localizer box were imported into the treatment planning system. The fiducial rods of the localizer box were contoured on both the MR and CT scans. The skull was contoured on the CT images. The MR and CT images were registered by two methods. The frame-based method used the transformation that minimized the mean square distance of the centroids of the contours of the fiducial rods from a mathematical model of the localizer. The mutual information method used automated image registration tools in the TPS and was restricted to a volume-of-interest defined by the skull contours with a 5 mm margin. For each case, the two registrations were adjusted by two evaluation teams, each comprised of an experienced radiation oncologist and neurosurgeon, to optimize alignment in the region of the brainstem. The teams were blinded to the registration method. Results: The mean adjustment was 0.4 mm (range 0 to 2 mm) and 0.2 mm (range 0 to 1 mm) for the frame and mutual information methods, respectively. The median difference between the frame and mutual information registrations was 0.3 mm, but was not statistically significant using the Wilcoxon signed rank test (p=0.37). Conclusion: The difference between frame and mutual information registration techniques was neither statistically significant nor, for most applications, clinically important. These results suggest that mutual information is equivalent to frame-based image registration for radiosurgery. Work is ongoing to add additional evaluators and to assess the differences between evaluators.

A spline-based non-linear diffeomorphism for multimodal prostate registration.

Science.gov (United States)

Mitra, Jhimli; Kato, Zoltan; Martí, Robert; Oliver, Arnau; Lladó, Xavier; Sidibé, Désiré; Ghose, Soumya; Vilanova, Joan C; Comet, Josep; Meriaudeau, Fabrice

2012-08-01

This paper presents a novel method for non-rigid registration of transrectal ultrasound and magnetic resonance prostate images based on a non-linear regularized framework of point correspondences obtained from a statistical measure of shape-contexts. The segmented prostate shapes are represented by shape-contexts and the Bhattacharyya distance between the shape representations is used to find the point correspondences between the 2D fixed and moving images. The registration method involves parametric estimation of the non-linear diffeomorphism between the multimodal images and has its basis in solving a set of non-linear equations of thin-plate splines. The solution is obtained as the least-squares solution of an over-determined system of non-linear equations constructed by integrating a set of non-linear functions over the fixed and moving images. However, this may not result in clinically acceptable transformations of the anatomical targets. Therefore, the regularized bending energy of the thin-plate splines along with the localization error of established correspondences should be included in the system of equations. The registration accuracies of the proposed method are evaluated in 20 pairs of prostate mid-gland ultrasound and magnetic resonance images. The results obtained in terms of Dice similarity coefficient show an average of 0.980±0.004, average 95% Hausdorff distance of 1.63±0.48 mm and mean target registration and target localization errors of 1.60±1.17 mm and 0.15±0.12 mm respectively. Copyright © 2012 Elsevier B.V. All rights reserved.

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

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.

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.

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.

TH-CD-206-09: Learning-Based MRI-CT Prostate Registration Using Spare Patch-Deformation Dictionary

International Nuclear Information System (INIS)

Yang, X; Jani, A; Rossi, P; Mao, H; Curran, W; Liu, T

2016-01-01

Purpose: To enable MRI-guided prostate radiotherapy, MRI-CT deformable registration is required to map the MRI-defined tumor and key organ contours onto the CT images. Due to the intrinsic differences in grey-level intensity characteristics between MRI and CT images, the integration of MRI into CT-based radiotherapy is very challenging. We are developing a learning-based registration approach to address this technical challenge. Methods: We propose to estimate the deformation between MRI and CT images in a patch-wise fashion by using the sparse representation technique. Specifically, we assume that two image patches should follow the same deformation if their patch-wise appearance patterns are similar. We first extract a set of key points in the new CT image. Then, for each key point, we adaptively construct a coupled dictionary from the training MRI-CT images, where each coupled element includes both appearance and deformation of the same image patch. After calculating the sparse coefficients in representing the patch appearance of each key point based on the constructed dictionary, we can predict the deformation for this point by applying the same sparse coefficients to the respective deformations in the dictionary. Results: This registration technique was validated with 10 prostate-cancer patients’ data and its performance was compared with the commonly used free-form-deformation-based registration. Several landmarks in both images were identified to evaluate the accuracy of our approach. Overall, the averaged target registration error of the intensity-based registration and the proposed method was 3.8±0.4 mm and 1.9±0.3 mm, respectively. Conclusion: We have developed a novel prostate MR-CT registration approach based on patch-deformation dictionary, demonstrated its clinical feasibility, and validated its accuracy. This technique will either reduce or compensate for the effect of patient-specific treatment variation measured during the course of

TH-CD-206-09: Learning-Based MRI-CT Prostate Registration Using Spare Patch-Deformation Dictionary

Energy Technology Data Exchange (ETDEWEB)

Yang, X; Jani, A; Rossi, P; Mao, H; Curran, W; Liu, T [Emory University, Atlanta, GA (United States)

2016-06-15

Purpose: To enable MRI-guided prostate radiotherapy, MRI-CT deformable registration is required to map the MRI-defined tumor and key organ contours onto the CT images. Due to the intrinsic differences in grey-level intensity characteristics between MRI and CT images, the integration of MRI into CT-based radiotherapy is very challenging. We are developing a learning-based registration approach to address this technical challenge. Methods: We propose to estimate the deformation between MRI and CT images in a patch-wise fashion by using the sparse representation technique. Specifically, we assume that two image patches should follow the same deformation if their patch-wise appearance patterns are similar. We first extract a set of key points in the new CT image. Then, for each key point, we adaptively construct a coupled dictionary from the training MRI-CT images, where each coupled element includes both appearance and deformation of the same image patch. After calculating the sparse coefficients in representing the patch appearance of each key point based on the constructed dictionary, we can predict the deformation for this point by applying the same sparse coefficients to the respective deformations in the dictionary. Results: This registration technique was validated with 10 prostate-cancer patients’ data and its performance was compared with the commonly used free-form-deformation-based registration. Several landmarks in both images were identified to evaluate the accuracy of our approach. Overall, the averaged target registration error of the intensity-based registration and the proposed method was 3.8±0.4 mm and 1.9±0.3 mm, respectively. Conclusion: We have developed a novel prostate MR-CT registration approach based on patch-deformation dictionary, demonstrated its clinical feasibility, and validated its accuracy. This technique will either reduce or compensate for the effect of patient-specific treatment variation measured during the course of

Image registration with uncertainty analysis

Science.gov (United States)

Simonson, Katherine M [Cedar Crest, NM

2011-03-22

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

Methods for registration laser scanner point clouds in forest stands

International Nuclear Information System (INIS)

Bienert, A.; Pech, K.; Maas, H.-G.

2011-01-01

Laser scanning is a fast and efficient 3-D measurement technique to capture surface points describing the geometry of a complex object in an accurate and reliable way. Besides airborne laser scanning, terrestrial laser scanning finds growing interest for forestry applications. These two different recording platforms show large differences in resolution, recording area and scan viewing direction. Using both datasets for a combined point cloud analysis may yield advantages because of their largely complementary information. In this paper, methods will be presented to automatically register airborne and terrestrial laser scanner point clouds of a forest stand. In a first step, tree detection is performed in both datasets in an automatic manner. In a second step, corresponding tree positions are determined using RANSAC. Finally, the geometric transformation is performed, divided in a coarse and fine registration. After a coarse registration, the fine registration is done in an iterative manner (ICP) using the point clouds itself. The methods are tested and validated with a dataset of a forest stand. The presented registration results provide accuracies which fulfill the forestry requirements [de

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.

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.

Real-time CT-video registration for continuous endoscopic guidance

Science.gov (United States)

Merritt, Scott A.; Rai, Lav; Higgins, William E.

2006-03-01

Previous research has shown that CT-image-based guidance could be useful for the bronchoscopic assessment of lung cancer. This research drew upon the registration of bronchoscopic video images to CT-based endoluminal renderings of the airway tree. The proposed methods either were restricted to discrete single-frame registration, which took several seconds to complete, or required non-real-time buffering and processing of video sequences. We have devised a fast 2D/3D image registration method that performs single-frame CT-Video registration in under 1/15th of a second. This allows the method to be used for real-time registration at full video frame rates without significantly altering the physician's behavior. The method achieves its speed through a gradient-based optimization method that allows most of the computation to be performed off-line. During live registration, the optimization iteratively steps toward the locally optimal viewpoint at which a CT-based endoluminal view is most similar to a current bronchoscopic video frame. After an initial registration to begin the process (generally done in the trachea for bronchoscopy), subsequent registrations are performed in real-time on each incoming video frame. As each new bronchoscopic video frame becomes available, the current optimization is initialized using the previous frame's optimization result, allowing continuous guidance to proceed without manual re-initialization. Tests were performed using both synthetic and pre-recorded bronchoscopic video. The results show that the method is robust to initialization errors, that registration accuracy is high, and that continuous registration can proceed on real-time video at >15 frames per sec. with minimal user-intervention.

Registration of FA and T1-weighted MRI data of healthy human brain based on template matching and normalized cross-correlation.

Science.gov (United States)

Malinsky, Milos; Peter, Roman; Hodneland, Erlend; Lundervold, Astri J; Lundervold, Arvid; Jan, Jiri

2013-08-01

In this work, we propose a new approach for three-dimensional registration of MR fractional anisotropy images with T1-weighted anatomy images of human brain. From the clinical point of view, this accurate coregistration allows precise detection of nerve fibers that is essential in neuroscience. A template matching algorithm combined with normalized cross-correlation was used for this registration task. To show the suitability of the proposed method, it was compared with the normalized mutual information-based B-spline registration provided by the Elastix software library, considered a reference method. We also propose a general framework for the evaluation of robustness and reliability of both registration methods. Both registration methods were tested by four evaluation criteria on a dataset consisting of 74 healthy subjects. The template matching algorithm has shown more reliable results than the reference method in registration of the MR fractional anisotropy and T1 anatomy image data. Significant differences were observed in the regions splenium of corpus callosum and genu of corpus callosum, considered very important areas of brain connectivity. We demonstrate that, in this registration task, the currently used mutual information-based parametric registration can be replaced by more accurate local template matching utilizing the normalized cross-correlation similarity measure.

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.

Qualitative Improvement Methods Through Analysis of Inquiry Contents for Cancer Registration

Science.gov (United States)

Boo, Yoo-Kyung; Lim, Hyun-Sook; Kim, Jung-Eun; Kim, Kyoung-Beom; Won, Young-Joo

2017-06-25

Background: In Korea, the national cancer database was constructed after the initiation of the national cancer registration project in 1980, and the annual national cancer registration report has been published every year since 2005. Consequently, data management must begin even at the stage of data collection in order to ensure quality. Objectives: To determine the suitability of cancer registries’ inquiry tools through the inquiry analysis of the Korea Central Cancer Registry (KCCR), and identify the needs to improve the quality of cancer registration. Methods: Results of 721 inquiries to the KCCR from 2000 to 2014 were analyzed by inquiry year, question type, and medical institution characteristics. Using Stata version 14.1, descriptive analysis was performed to identify general participant characteristics, and chi-square analysis was applied to investigate significant differences in distribution characteristics by factors affecting the quality of cancer registration data. Results: The number of inquiries increased in 2005–2009. During this period, there were various changes, including the addition of cancer registration items such as brain tumors and guideline updates. Of the inquirers, 65.3% worked at hospitals in metropolitan cities and 60.89% of hospitals had 601–1000 beds. Tertiary hospitals had the highest number of inquiries (64.91%), and the highest number of questions by type were 353 (48.96%) for histological codes, 92 (12.76%) for primary sites, and 76 (10.54%) for reportable. Conclusions: A cancer registration inquiry system is an effective method when not confident about codes during cancer registration, or when confronting cancer cases in which previous clinical knowledge or information on the cancer registration guidelines are insufficient. Creative Commons Attribution License

Interactive initialization of 2D/3D rigid registration

Energy Technology Data Exchange (ETDEWEB)

Gong, Ren Hui; Güler, Özgür [The Sheikh Zayed Institute for Pediatric Surgical Innovation, Children' s National Medical Center, Washington, DC 20010 (United States); Kürklüoglu, Mustafa [Department of Cardiac Surgery, Children' s National Medical Center, Washington, DC 20010 (United States); Lovejoy, John [Department of Orthopaedic Surgery and Sports Medicine, Children' s National Medical Center, Washington, DC 20010 (United States); Yaniv, Ziv, E-mail: ZYaniv@childrensnational.org [The Sheikh Zayed Institute for Pediatric Surgical Innovation, Children' s National Medical Center, Washington, DC 20010 and Departments of Pediatrics and Radiology, George Washington University, Washington, DC 20037 (United States)

2013-12-15

Purpose: Registration is one of the key technical components in an image-guided navigation system. A large number of 2D/3D registration algorithms have been previously proposed, but have not been able to transition into clinical practice. The authors identify the primary reason for the lack of adoption with the prerequisite for a sufficiently accurate initial transformation, mean target registration error of about 10 mm or less. In this paper, the authors present two interactive initialization approaches that provide the desired accuracy for x-ray/MR and x-ray/CT registration in the operating room setting. Methods: The authors have developed two interactive registration methods based on visual alignment of a preoperative image, MR, or CT to intraoperative x-rays. In the first approach, the operator uses a gesture based interface to align a volume rendering of the preoperative image to multiple x-rays. The second approach uses a tracked tool available as part of a navigation system. Preoperatively, a virtual replica of the tool is positioned next to the anatomical structures visible in the volumetric data. Intraoperatively, the physical tool is positioned in a similar manner and subsequently used to align a volume rendering to the x-ray images using an augmented reality (AR) approach. Both methods were assessed using three publicly available reference data sets for 2D/3D registration evaluation. Results: In the authors' experiments, the authors show that for x-ray/MR registration, the gesture based method resulted in a mean target registration error (mTRE) of 9.3 ± 5.0 mm with an average interaction time of 146.3 ± 73.0 s, and the AR-based method had mTREs of 7.2 ± 3.2 mm with interaction times of 44 ± 32 s. For x-ray/CT registration, the gesture based method resulted in a mTRE of 7.4 ± 5.0 mm with an average interaction time of 132.1 ± 66.4 s, and the AR-based method had mTREs of 8.3 ± 5.0 mm with interaction times of 58 ± 52 s. Conclusions: Based on

Interactive initialization of 2D/3D rigid registration

International Nuclear Information System (INIS)

Gong, Ren Hui; Güler, Özgür; Kürklüoglu, Mustafa; Lovejoy, John; Yaniv, Ziv

2013-01-01

Purpose: Registration is one of the key technical components in an image-guided navigation system. A large number of 2D/3D registration algorithms have been previously proposed, but have not been able to transition into clinical practice. The authors identify the primary reason for the lack of adoption with the prerequisite for a sufficiently accurate initial transformation, mean target registration error of about 10 mm or less. In this paper, the authors present two interactive initialization approaches that provide the desired accuracy for x-ray/MR and x-ray/CT registration in the operating room setting. Methods: The authors have developed two interactive registration methods based on visual alignment of a preoperative image, MR, or CT to intraoperative x-rays. In the first approach, the operator uses a gesture based interface to align a volume rendering of the preoperative image to multiple x-rays. The second approach uses a tracked tool available as part of a navigation system. Preoperatively, a virtual replica of the tool is positioned next to the anatomical structures visible in the volumetric data. Intraoperatively, the physical tool is positioned in a similar manner and subsequently used to align a volume rendering to the x-ray images using an augmented reality (AR) approach. Both methods were assessed using three publicly available reference data sets for 2D/3D registration evaluation. Results: In the authors' experiments, the authors show that for x-ray/MR registration, the gesture based method resulted in a mean target registration error (mTRE) of 9.3 ± 5.0 mm with an average interaction time of 146.3 ± 73.0 s, and the AR-based method had mTREs of 7.2 ± 3.2 mm with interaction times of 44 ± 32 s. For x-ray/CT registration, the gesture based method resulted in a mTRE of 7.4 ± 5.0 mm with an average interaction time of 132.1 ± 66.4 s, and the AR-based method had mTREs of 8.3 ± 5.0 mm with interaction times of 58 ± 52 s. Conclusions: Based on the

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

Science.gov (United States)

2014-01-01

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

Note: A simple image processing based fiducial auto-alignment method for sample registration.

Science.gov (United States)

Robertson, Wesley D; Porto, Lucas R; Ip, Candice J X; Nantel, Megan K T; Tellkamp, Friedjof; Lu, Yinfei; Miller, R J Dwayne

2015-08-01

A simple method for the location and auto-alignment of sample fiducials for sample registration using widely available MATLAB/LabVIEW software is demonstrated. The method is robust, easily implemented, and applicable to a wide variety of experiment types for improved reproducibility and increased setup speed. The software uses image processing to locate and measure the diameter and center point of circular fiducials for distance self-calibration and iterative alignment and can be used with most imaging systems. The method is demonstrated to be fast and reliable in locating and aligning sample fiducials, provided here by a nanofabricated array, with accuracy within the optical resolution of the imaging system. The software was further demonstrated to register, load, and sample the dynamically wetted array.

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.

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.

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...

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

International Nuclear Information System (INIS)

Samavati, Navid; Velec, Michael; Brock, Kristy

2015-01-01

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

Projection-slice theorem based 2D-3D registration

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van der Bom, M. J.; Pluim, J. P. W.; Homan, R.; Timmer, J.; Bartels, L. W.

2007-03-01

In X-ray guided procedures, the surgeon or interventionalist is dependent on his or her knowledge of the patient's specific anatomy and the projection images acquired during the procedure by a rotational X-ray source. Unfortunately, these X-ray projections fail to give information on the patient's anatomy in the dimension along the projection axis. It would be very profitable to provide the surgeon or interventionalist with a 3D insight of the patient's anatomy that is directly linked to the X-ray images acquired during the procedure. In this paper we present a new robust 2D-3D registration method based on the Projection-Slice Theorem. This theorem gives us a relation between the pre-operative 3D data set and the interventional projection images. Registration is performed by minimizing a translation invariant similarity measure that is applied to the Fourier transforms of the images. The method was tested by performing multiple exhaustive searches on phantom data of the Circle of Willis and on a post-mortem human skull. Validation was performed visually by comparing the test projections to the ones that corresponded to the minimal value of the similarity measure. The Projection-Slice Theorem Based method was shown to be very effective and robust, and provides capture ranges up to 62 degrees. Experiments have shown that the method is capable of retrieving similar results when translations are applied to the projection images.

A spatial registration method for navigation system combining O-arm with spinal surgery robot

Science.gov (United States)

Bai, H.; Song, G. L.; Zhao, Y. W.; Liu, X. Z.; Jiang, Y. X.

2018-05-01

The minimally invasive surgery in spinal surgery has become increasingly popular in recent years as it reduces the chances of complications during post-operation. However, the procedure of spinal surgery is complicated and the surgical vision of minimally invasive surgery is limited. In order to increase the quality of percutaneous pedicle screw placement, the O-arm that is a mobile intraoperative imaging system is used to assist surgery. The robot navigation system combined with O-arm is also increasing, with the extensive use of O-arm. One of the major problems in the surgical navigation system is to associate the patient space with the intra-operation image space. This study proposes a spatial registration method of spinal surgical robot navigation system, which uses the O-arm to scan a calibration phantom with metal calibration spheres. First, the metal artifacts were reduced in the CT slices and then the circles in the images based on the moments invariant could be identified. Further, the position of the calibration sphere in the image space was obtained. Moreover, the registration matrix is obtained based on the ICP algorithm. Finally, the position error is calculated to verify the feasibility and accuracy of the registration method.

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.

Validation for 2D/3D registration II: The comparison of intensity- and gradient-based merit functions using a new gold standard data set

International Nuclear Information System (INIS)

Gendrin, Christelle; Markelj, Primoz; Pawiro, Supriyanto Ardjo; Spoerk, Jakob; Bloch, Christoph; Weber, Christoph; Figl, Michael; Bergmann, Helmar; Birkfellner, Wolfgang; Likar, Bostjan; Pernus, Franjo

2011-01-01

Purpose: A new gold standard data set for validation of 2D/3D registration based on a porcine cadaver head with attached fiducial markers was presented in the first part of this article. The advantage of this new phantom is the large amount of soft tissue, which simulates realistic conditions for registration. This article tests the performance of intensity- and gradient-based algorithms for 2D/3D registration using the new phantom data set. Methods: Intensity-based methods with four merit functions, namely, cross correlation, rank correlation, correlation ratio, and mutual information (MI), and two gradient-based algorithms, the backprojection gradient-based (BGB) registration method and the reconstruction gradient-based (RGB) registration method, were compared. Four volumes consisting of CBCT with two fields of view, 64 slice multidetector CT, and magnetic resonance-T1 weighted images were registered to a pair of kV x-ray images and a pair of MV images. A standardized evaluation methodology was employed. Targets were evenly spread over the volumes and 250 starting positions of the 3D volumes with initial displacements of up to 25 mm from the gold standard position were calculated. After the registration, the displacement from the gold standard was retrieved and the root mean square (RMS), mean, and standard deviation mean target registration errors (mTREs) over 250 registrations were derived. Additionally, the following merit properties were computed: Accuracy, capture range, number of minima, risk of nonconvergence, and distinctiveness of optimum for better comparison of the robustness of each merit. Results: Among the merit functions used for the intensity-based method, MI reached the best accuracy with an RMS mTRE down to 1.30 mm. Furthermore, it was the only merit function that could accurately register the CT to the kV x rays with the presence of tissue deformation. As for the gradient-based methods, BGB and RGB methods achieved subvoxel accuracy (RMS m

A method of 2D/3D registration of a statistical mouse atlas with a planar X-ray projection and an optical photo.

Science.gov (United States)

Wang, Hongkai; Stout, David B; Chatziioannou, Arion F

2013-05-01

The development of sophisticated and high throughput whole body small animal imaging technologies has created a need for improved image analysis and increased automation. The registration of a digital mouse atlas to individual images is a prerequisite for automated organ segmentation and uptake quantification. This paper presents a fully-automatic method for registering a statistical mouse atlas with individual subjects based on an anterior-posterior X-ray projection and a lateral optical photo of the mouse silhouette. The mouse atlas was trained as a statistical shape model based on 83 organ-segmented micro-CT images. For registration, a hierarchical approach is applied which first registers high contrast organs, and then estimates low contrast organs based on the registered high contrast organs. To register the high contrast organs, a 2D-registration-back-projection strategy is used that deforms the 3D atlas based on the 2D registrations of the atlas projections. For validation, this method was evaluated using 55 subjects of preclinical mouse studies. The results showed that this method can compensate for moderate variations of animal postures and organ anatomy. Two different metrics, the Dice coefficient and the average surface distance, were used to assess the registration accuracy of major organs. The Dice coefficients vary from 0.31 ± 0.16 for the spleen to 0.88 ± 0.03 for the whole body, and the average surface distance varies from 0.54 ± 0.06 mm for the lungs to 0.85 ± 0.10mm for the skin. The method was compared with a direct 3D deformation optimization (without 2D-registration-back-projection) and a single-subject atlas registration (instead of using the statistical atlas). The comparison revealed that the 2D-registration-back-projection strategy significantly improved the registration accuracy, and the use of the statistical mouse atlas led to more plausible organ shapes than the single-subject atlas. This method was also tested with shoulder

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

and segmentations by minimizing a weighted sum of the Euclidean distance between centerlines and the dissimilarity between segmentations. The method was evaluated in 28 carotid arteries from eight patients and six healthy volunteers. First, the automated US lumen segmentation method was validated and optimized......-and-point-based method, a registration using only the centerlines and a registration using manual US lumen segmentations. Registration accuracy was measured in terms of the mean surface distance between manual US segmentations and the registered MRI segmentations. The average mean surface distance was 0.78 ± 0.34 mm...

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

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

Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets

Science.gov (United States)

Ge, Xuming

2017-08-01

The coarse registration of point clouds from urban building scenes has become a key topic in applications of terrestrial laser scanning technology. Sampling-based algorithms in the random sample consensus (RANSAC) model have emerged as mainstream solutions to address coarse registration problems. In this paper, we propose a novel combined solution to automatically align two markerless point clouds from building scenes. Firstly, the method segments non-ground points from ground points. Secondly, the proposed method detects feature points from each cross section and then obtains semantic keypoints by connecting feature points with specific rules. Finally, the detected semantic keypoints from two point clouds act as inputs to a modified 4PCS algorithm. Examples are presented and the results compared with those of K-4PCS to demonstrate the main contributions of the proposed method, which are the extension of the original 4PCS to handle heavy datasets and the use of semantic keypoints to improve K-4PCS in relation to registration accuracy and computational efficiency.

Prostate multimodality image registration based on B-splines and quadrature local energy.

Science.gov (United States)

Mitra, Jhimli; Martí, Robert; Oliver, Arnau; Lladó, Xavier; Ghose, Soumya; Vilanova, Joan C; Meriaudeau, Fabrice

2012-05-01

Needle biopsy of the prostate is guided by Transrectal Ultrasound (TRUS) imaging. The TRUS images do not provide proper spatial localization of malignant tissues due to the poor sensitivity of TRUS to visualize early malignancy. Magnetic Resonance Imaging (MRI) has been shown to be sensitive for the detection of early stage malignancy, and therefore, a novel 2D deformable registration method that overlays pre-biopsy MRI onto TRUS images has been proposed. The registration method involves B-spline deformations with Normalized Mutual Information (NMI) as the similarity measure computed from the texture images obtained from the amplitude responses of the directional quadrature filter pairs. Registration accuracy of the proposed method is evaluated by computing the Dice Similarity coefficient (DSC) and 95% Hausdorff Distance (HD) values for 20 patients prostate mid-gland slices and Target Registration Error (TRE) for 18 patients only where homologous structures are visible in both the TRUS and transformed MR images. The proposed method and B-splines using NMI computed from intensities provide average TRE values of 2.64 ± 1.37 and 4.43 ± 2.77 mm respectively. Our method shows statistically significant improvement in TRE when compared with B-spline using NMI computed from intensities with Student's t test p = 0.02. The proposed method shows 1.18 times improvement over thin-plate splines registration with average TRE of 3.11 ± 2.18 mm. The mean DSC and the mean 95% HD values obtained with the proposed method of B-spline with NMI computed from texture are 0.943 ± 0.039 and 4.75 ± 2.40 mm respectively. The texture energy computed from the quadrature filter pairs provides better registration accuracy for multimodal images than raw intensities. Low TRE values of the proposed registration method add to the feasibility of it being used during TRUS-guided biopsy.

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.

Registration of Laser Scanning Point Clouds: A Review

Science.gov (United States)

Cheng, Liang; Chen, Song; Xu, Hao; Wu, Yang; Li, Manchun

2018-01-01

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.

AUTOMATIC GLOBAL REGISTRATION BETWEEN AIRBORNE LIDAR DATA AND REMOTE SENSING IMAGE BASED ON STRAIGHT LINE FEATURES

Directory of Open Access Journals (Sweden)

Z. Q. Liu

2018-04-01

Full Text Available An automatic global registration approach for point clouds and remote sensing image based on straight line features is proposed which is insensitive to rotational and scale transformation. First, the building ridge lines and contour lines in point clouds are automatically detected as registration primitives by integrating region growth and topology identification. Second, the collinear condition equation is selected as registration transformation function which is based on rotation matrix described by unit quaternion. The similarity measure is established according to the distance between the corresponding straight line features from point clouds and the image in the same reference coordinate system. Finally, an iterative Hough transform is adopted to simultaneously estimate the parameters and obtain correspondence between registration primitives. Experimental results prove the proposed method is valid and the spectral information is useful for the following classification processing.

Control over structure-specific flexibility improves anatomical accuracy for point-based deformable registration in bladder cancer radiotherapy

International Nuclear Information System (INIS)

Wognum, S.; Chai, X.; Hulshof, M. C. C. M.; Bel, A.; Bondar, L.; Zolnay, A. G.; Hoogeman, M. S.

2013-01-01

Purpose: Future developments in image guided adaptive radiotherapy (IGART) for bladder cancer require accurate deformable image registration techniques for the precise assessment of tumor and bladder motion and deformation that occur as a result of large bladder volume changes during the course of radiotherapy treatment. The aim was to employ an extended version of a point-based deformable registration algorithm that allows control over tissue-specific flexibility in combination with the authors’ unique patient dataset, in order to overcome two major challenges of bladder cancer registration, i.e., the difficulty in accounting for the difference in flexibility between the bladder wall and tumor and the lack of visible anatomical landmarks for validation. Methods: The registration algorithm used in the current study is an extension of the symmetric-thin plate splines-robust point matching (S-TPS-RPM) algorithm, a symmetric feature-based registration method. The S-TPS-RPM algorithm has been previously extended to allow control over the degree of flexibility of different structures via a weight parameter. The extended weighted S-TPS-RPM algorithm was tested and validated on CT data (planning- and four to five repeat-CTs) of five urinary bladder cancer patients who received lipiodol injections before radiotherapy. The performance of the weighted S-TPS-RPM method, applied to bladder and tumor structures simultaneously, was compared with a previous version of the S-TPS-RPM algorithm applied to bladder wall structure alone and with a simultaneous nonweighted S-TPS-RPM registration of the bladder and tumor structures. Performance was assessed in terms of anatomical and geometric accuracy. The anatomical accuracy was calculated as the residual distance error (RDE) of the lipiodol markers and the geometric accuracy was determined by the surface distance, surface coverage, and inverse consistency errors. Optimal parameter values for the flexibility and bladder weight

Control over structure-specific flexibility improves anatomical accuracy for point-based deformable registration in bladder cancer radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Wognum, S.; Chai, X.; Hulshof, M. C. C. M.; Bel, A. [Department of Radiotherapy, Academic Medical Center, Meiberdreef 9, 1105 AZ Amsterdam (Netherlands); Bondar, L.; Zolnay, A. G.; Hoogeman, M. S. [Department of Radiation Oncology, Daniel den Hoed Cancer Center, Erasmus Medical Center, Groene Hilledijk 301, 3075 EA Rotterdam (Netherlands)

2013-02-15

Purpose: Future developments in image guided adaptive radiotherapy (IGART) for bladder cancer require accurate deformable image registration techniques for the precise assessment of tumor and bladder motion and deformation that occur as a result of large bladder volume changes during the course of radiotherapy treatment. The aim was to employ an extended version of a point-based deformable registration algorithm that allows control over tissue-specific flexibility in combination with the authors' unique patient dataset, in order to overcome two major challenges of bladder cancer registration, i.e., the difficulty in accounting for the difference in flexibility between the bladder wall and tumor and the lack of visible anatomical landmarks for validation. Methods: The registration algorithm used in the current study is an extension of the symmetric-thin plate splines-robust point matching (S-TPS-RPM) algorithm, a symmetric feature-based registration method. The S-TPS-RPM algorithm has been previously extended to allow control over the degree of flexibility of different structures via a weight parameter. The extended weighted S-TPS-RPM algorithm was tested and validated on CT data (planning- and four to five repeat-CTs) of five urinary bladder cancer patients who received lipiodol injections before radiotherapy. The performance of the weighted S-TPS-RPM method, applied to bladder and tumor structures simultaneously, was compared with a previous version of the S-TPS-RPM algorithm applied to bladder wall structure alone and with a simultaneous nonweighted S-TPS-RPM registration of the bladder and tumor structures. Performance was assessed in terms of anatomical and geometric accuracy. The anatomical accuracy was calculated as the residual distance error (RDE) of the lipiodol markers and the geometric accuracy was determined by the surface distance, surface coverage, and inverse consistency errors. Optimal parameter values for the flexibility and bladder weight

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).

Quantifying brain development in early childhood using segmentation and registration

Science.gov (United States)

Aljabar, P.; Bhatia, K. K.; Murgasova, M.; Hajnal, J. V.; Boardman, J. P.; Srinivasan, L.; Rutherford, M. A.; Dyet, L. E.; Edwards, A. D.; Rueckert, D.

2007-03-01

In this work we obtain estimates of tissue growth using longitudinal data comprising MR brain images of 25 preterm children scanned at one and two years. The growth estimates are obtained using segmentation and registration based methods. The segmentation approach used an expectation maximisation (EM) method to classify tissue types and the registration approach used tensor based morphometry (TBM) applied to a free form deformation (FFD) model. The two methods show very good agreement indicating that the registration and segmentation approaches can be used interchangeably. The advantage of the registration based method, however, is that it can provide more local estimates of tissue growth. This is the first longitudinal study of growth in early childhood, previous longitudinal studies have focused on later periods during childhood.

Improving alignment in Tract-based spatial statistics: evaluation and optimization of image registration

NARCIS (Netherlands)

de Groot, Marius; Vernooij, Meike W.; Klein, Stefan; Ikram, M. Arfan; Vos, Frans M.; Smith, Stephen M.; Niessen, Wiro J.; Andersson, Jesper L. R.

2013-01-01

Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS

Improving alignment in Tract-based spatial statistics : Evaluation and optimization of image registration

NARCIS (Netherlands)

De Groot, M.; Vernooij, M.W.; Klein, S.; Arfan Ikram, M.; Vos, F.M.; Smith, S.M.; Niessen, W.J.; Andersson, J.L.R.

2013-01-01

Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS

Workplace-based assessment for vocational registration of international medical graduates.

Science.gov (United States)

Lillis, Steven; Van Dyk, Valencia

2014-01-01

Medical regulatory authorities need efficient and effective methods of ensuring the competence of immigrating international medical graduates (IMGs). Not all IMGs who apply for specialist vocational registration will have directly comparable qualifications to those usually accepted. As general licensure examinations are inappropriate for these doctors, workplace-based assessment (WBA) techniques would appear to provide a solution. However, there is little published data on such outcomes. All cases of WBA (n = 81) used for vocational registration of IMGs in New Zealand between 2008 and 2013 were collated and analyzed. The successful completion rate of IMGs through the pathway was 87%. The majority (64%) undertook the year of supervised practice and the final assessment in a provincial center. For those unsuccessful in the pathway, inadequate clinical knowledge was the most common deficit found, followed by poor clinical reasoning. A WBA approach for assessing readiness of IMGs for vocational registration is feasible. The constructivist theoretical perspective of WBA has particular advantages in assessing the standard of practice for experienced practitioners working in narrow scopes than traditional methods of assessment. The majority of IMGs undertook both the clinical year and the assessment in provincial hospitals, thus providing a workforce for underserved areas. © 2014 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

A new method for real-time co-registration of 3D coronary angiography and intravascular ultrasound or optical coherence tomography

Energy Technology Data Exchange (ETDEWEB)

Carlier, Stéphane, E-mail: sgcarlier@hotmail.com [Department of Cardiology, Universitair Ziekenhuis - UZ Brussel, Brussels (Belgium); Department of Cardiology, Erasme University Hospital, Université Libre de Bruxelles (ULB), Brussels (Belgium); Didday, Rich [INDEC Medical Systems Inc., Santa Clara, CA (United States); Slots, Tristan [Pie Medical Imaging BV, Maastricht (Netherlands); Kayaert, Peter; Sonck, Jeroen [Department of Cardiology, Universitair Ziekenhuis - UZ Brussel, Brussels (Belgium); El-Mourad, Mike; Preumont, Nicolas [Department of Cardiology, Erasme University Hospital, Université Libre de Bruxelles (ULB), Brussels (Belgium); Schoors, Dany; Van Camp, Guy [Department of Cardiology, Universitair Ziekenhuis - UZ Brussel, Brussels (Belgium)

2014-06-15

We present a new clinically practical method for online co-registration of 3D quantitative coronary angiography (QCA) and intravascular ultrasound (IVUS) or optical coherence tomography (OCT). The workflow is based on two modified commercially available software packages. Reconstruction steps are explained and compared to previously available methods. The feasibility for different clinical scenarios is illustrated. The co-registration appears accurate, robust and induced a minimal delay on the normal cath lab activities. This new method is based on the 3D angiographic reconstruction of the catheter path and does not require operator’s identification of landmarks to establish the image synchronization.

A new method for real-time co-registration of 3D coronary angiography and intravascular ultrasound or optical coherence tomography

International Nuclear Information System (INIS)

Carlier, Stéphane; Didday, Rich; Slots, Tristan; Kayaert, Peter; Sonck, Jeroen; El-Mourad, Mike; Preumont, Nicolas; Schoors, Dany; Van Camp, Guy

2014-01-01

We present a new clinically practical method for online co-registration of 3D quantitative coronary angiography (QCA) and intravascular ultrasound (IVUS) or optical coherence tomography (OCT). The workflow is based on two modified commercially available software packages. Reconstruction steps are explained and compared to previously available methods. The feasibility for different clinical scenarios is illustrated. The co-registration appears accurate, robust and induced a minimal delay on the normal cath lab activities. This new method is based on the 3D angiographic reconstruction of the catheter path and does not require operator’s identification of landmarks to establish the image synchronization

Registration-Based Range-Dependence Compensation for Bistatic STAP Radars

Directory of Open Access Journals (Sweden)

Lapierre Fabian D

2005-01-01

Full Text Available We address the problem of detecting slow-moving targets using space-time adaptive processing (STAP radar. Determining the optimum weights at each range requires data snapshots at neighboring ranges. However, in virtually all configurations, snapshot statistics are range dependent, meaning that snapshots are nonstationary with respect to range. This results in poor performance. In this paper, we propose a new compensation method based on registration of clutter ridges and designed to work on a single realization of the stochastic snapshot at each range. The method has been successfully tested on simulated, stochastic snapshots. An evaluation of performance is presented.

Efficient Variational Approaches for Deformable Registration of Images

Directory of Open Access Journals (Sweden)

Mehmet Ali Akinlar

2012-01-01

Full Text Available Dirichlet, anisotropic, and Huber regularization terms are presented for efficient registration of deformable images. Image registration, an ill-posed optimization problem, is solved using a gradient-descent-based method and some fundamental theorems in calculus of variations. Euler-Lagrange equations with homogeneous Neumann boundary conditions are obtained. These equations are discretized by multigrid and finite difference numerical techniques. The method is applied to the registration of brain MR images of size 65×65. Computational results indicate that the presented method is quite fast and efficient in the registration of deformable medical images.

Registration-based segmentation with articulated model from multipostural magnetic resonance images for hand bone motion animation.

Science.gov (United States)

Chen, Hsin-Chen; Jou, I-Ming; Wang, Chien-Kuo; Su, Fong-Chin; Sun, Yung-Nien

2010-06-01

The quantitative measurements of hand bones, including volume, surface, orientation, and position are essential in investigating hand kinematics. Moreover, within the measurement stage, bone segmentation is the most important step due to its certain influences on measuring accuracy. Since hand bones are small and tubular in shape, magnetic resonance (MR) imaging is prone to artifacts such as nonuniform intensity and fuzzy boundaries. Thus, greater detail is required for improving segmentation accuracy. The authors then propose using a novel registration-based method on an articulated hand model to segment hand bones from multipostural MR images. The proposed method consists of the model construction and registration-based segmentation stages. Given a reference postural image, the first stage requires construction of a drivable reference model characterized by hand bone shapes, intensity patterns, and articulated joint mechanism. By applying the reference model to the second stage, the authors initially design a model-based registration pursuant to intensity distribution similarity, MR bone intensity properties, and constraints of model geometry to align the reference model to target bone regions of the given postural image. The authors then refine the resulting surface to improve the superimposition between the registered reference model and target bone boundaries. For each subject, given a reference postural image, the proposed method can automatically segment all hand bones from all other postural images. Compared to the ground truth from two experts, the resulting surface image had an average margin of error within 1 mm (mm) only. In addition, the proposed method showed good agreement on the overlap of bone segmentations by dice similarity coefficient and also demonstrated better segmentation results than conventional methods. The proposed registration-based segmentation method can successfully overcome drawbacks caused by inherent artifacts in MR images and

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...

A Novel Technique for Prealignment in Multimodality Medical Image Registration

Directory of Open Access Journals (Sweden)

Wu Zhou

2014-01-01

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

Control over structure-specific flexibility improves anatomical accuracy for point-based deformable registration in bladder cancer radiotherapy.

Science.gov (United States)

Wognum, S; Bondar, L; Zolnay, A G; Chai, X; Hulshof, M C C M; Hoogeman, M S; Bel, A

2013-02-01

Future developments in image guided adaptive radiotherapy (IGART) for bladder cancer require accurate deformable image registration techniques for the precise assessment of tumor and bladder motion and deformation that occur as a result of large bladder volume changes during the course of radiotherapy treatment. The aim was to employ an extended version of a point-based deformable registration algorithm that allows control over tissue-specific flexibility in combination with the authors' unique patient dataset, in order to overcome two major challenges of bladder cancer registration, i.e., the difficulty in accounting for the difference in flexibility between the bladder wall and tumor and the lack of visible anatomical landmarks for validation. The registration algorithm used in the current study is an extension of the symmetric-thin plate splines-robust point matching (S-TPS-RPM) algorithm, a symmetric feature-based registration method. The S-TPS-RPM algorithm has been previously extended to allow control over the degree of flexibility of different structures via a weight parameter. The extended weighted S-TPS-RPM algorithm was tested and validated on CT data (planning- and four to five repeat-CTs) of five urinary bladder cancer patients who received lipiodol injections before radiotherapy. The performance of the weighted S-TPS-RPM method, applied to bladder and tumor structures simultaneously, was compared with a previous version of the S-TPS-RPM algorithm applied to bladder wall structure alone and with a simultaneous nonweighted S-TPS-RPM registration of the bladder and tumor structures. Performance was assessed in terms of anatomical and geometric accuracy. The anatomical accuracy was calculated as the residual distance error (RDE) of the lipiodol markers and the geometric accuracy was determined by the surface distance, surface coverage, and inverse consistency errors. Optimal parameter values for the flexibility and bladder weight parameters were determined

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.

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

Energy Technology Data Exchange (ETDEWEB)

Morrow, Natalya V.; Lawton, Colleen A. [Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin (United States); Qi, X. Sharon [Department of Radiation Oncology, University of Colorado Denver, Denver, Colorado (United States); Li, X. Allen, E-mail: ali@mcw.edu [Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin (United States)

2012-04-01

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.

A new method for real-time co-registration of 3D coronary angiography and intravascular ultrasound or optical coherence tomography.

Science.gov (United States)

Carlier, Stéphane; Didday, Rich; Slots, Tristan; Kayaert, Peter; Sonck, Jeroen; El-Mourad, Mike; Preumont, Nicolas; Schoors, Dany; Van Camp, Guy

2014-06-01

We present a new clinically practical method for online co-registration of 3D quantitative coronary angiography (QCA) and intravascular ultrasound (IVUS) or optical coherence tomography (OCT). The workflow is based on two modified commercially available software packages. Reconstruction steps are explained and compared to previously available methods. The feasibility for different clinical scenarios is illustrated. The co-registration appears accurate, robust and induced a minimal delay on the normal cath lab activities. This new method is based on the 3D angiographic reconstruction of the catheter path and does not require operator's identification of landmarks to establish the image synchronization. Copyright © 2014 Elsevier Inc. All rights reserved.

Image registration method for medical image sequences

Science.gov (United States)

Gee, Timothy F.; Goddard, James S.

2013-03-26

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

Improving Intensity-Based Lung CT Registration Accuracy Utilizing Vascular Information

Directory of Open Access Journals (Sweden)

Kunlin Cao

2012-01-01

Full Text Available Accurate pulmonary image registration is a challenging problem when the lungs have a deformation with large distance. In this work, we present a nonrigid volumetric registration algorithm to track lung motion between a pair of intrasubject CT images acquired at different inflation levels and introduce a new vesselness similarity cost that improves intensity-only registration. Volumetric CT datasets from six human subjects were used in this study. The performance of four intensity-only registration algorithms was compared with and without adding the vesselness similarity cost function. Matching accuracy was evaluated using landmarks, vessel tree, and fissure planes. The Jacobian determinant of the transformation was used to reveal the deformation pattern of local parenchymal tissue. The average matching error for intensity-only registration methods was on the order of 1 mm at landmarks and 1.5 mm on fissure planes. After adding the vesselness preserving cost function, the landmark and fissure positioning errors decreased approximately by 25% and 30%, respectively. The vesselness cost function effectively helped improve the registration accuracy in regions near thoracic cage and near the diaphragm for all the intensity-only registration algorithms tested and also helped produce more consistent and more reliable patterns of regional tissue deformation.

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....

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

Scan-based volume animation driven by locally adaptive articulated registrations.

Science.gov (United States)

Rhee, Taehyun; Lewis, J P; Neumann, Ulrich; Nayak, Krishna S

2011-03-01

This paper describes a complete system to create anatomically accurate example-based volume deformation and animation of articulated body regions, starting from multiple in vivo volume scans of a specific individual. In order to solve the correspondence problem across volume scans, a template volume is registered to each sample. The wide range of pose variations is first approximated by volume blend deformation (VBD), providing proper initialization of the articulated subject in different poses. A novel registration method is presented to efficiently reduce the computation cost while avoiding strong local minima inherent in complex articulated body volume registration. The algorithm highly constrains the degrees of freedom and search space involved in the nonlinear optimization, using hierarchical volume structures and locally constrained deformation based on the biharmonic clamped spline. Our registration step establishes a correspondence across scans, allowing a data-driven deformation approach in the volume domain. The results provide an occlusion-free person-specific 3D human body model, asymptotically accurate inner tissue deformations, and realistic volume animation of articulated movements driven by standard joint control estimated from the actual skeleton. Our approach also addresses the practical issues arising in using scans from living subjects. The robustness of our algorithms is tested by their applications on the hand, probably the most complex articulated region in the body, and the knee, a frequent subject area for medical imaging due to injuries. © 2011 IEEE

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

Energy Technology Data Exchange (ETDEWEB)

Yip, Stephen, E-mail: syip@lroc.harvard.edu; Chen, Aileen B.; Berbeco, Ross [Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115 (United States); Aerts, Hugo J. W. L. [Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115 and Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115 (United States)

2014-11-01

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

Efficient point cloud data processing in shipbuilding: Reformative component extraction method and registration method

Directory of Open Access Journals (Sweden)

Jingyu Sun

2014-07-01

Full Text Available To survive in the current shipbuilding industry, it is of vital importance for shipyards to have the ship components’ accuracy evaluated efficiently during most of the manufacturing steps. Evaluating components’ accuracy by comparing each component’s point cloud data scanned by laser scanners and the ship’s design data formatted in CAD cannot be processed efficiently when (1 extract components from point cloud data include irregular obstacles endogenously, or when (2 registration of the two data sets have no clear direction setting. This paper presents reformative point cloud data processing methods to solve these problems. K-d tree construction of the point cloud data fastens a neighbor searching of each point. Region growing method performed on the neighbor points of the seed point extracts the continuous part of the component, while curved surface fitting and B-spline curved line fitting at the edge of the continuous part recognize the neighbor domains of the same component divided by obstacles’ shadows. The ICP (Iterative Closest Point algorithm conducts a registration of the two sets of data after the proper registration’s direction is decided by principal component analysis. By experiments conducted at the shipyard, 200 curved shell plates are extracted from the scanned point cloud data, and registrations are conducted between them and the designed CAD data using the proposed methods for an accuracy evaluation. Results show that the methods proposed in this paper support the accuracy evaluation targeted point cloud data processing efficiently in practice.

Prostate CT segmentation method based on nonrigid registration in ultrasound-guided CT-based HDR prostate brachytherapy

Energy Technology Data Exchange (ETDEWEB)

Yang, Xiaofeng, E-mail: xyang43@emory.edu; Rossi, Peter; Ogunleye, Tomi; Marcus, David M.; Jani, Ashesh B.; Curran, Walter J.; Liu, Tian [Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia 30322 (United States); Mao, Hui [Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia 30322 (United States)

2014-11-01

Purpose: The technological advances in real-time ultrasound image guidance for high-dose-rate (HDR) prostate brachytherapy have placed this treatment modality at the forefront of innovation in cancer radiotherapy. Prostate HDR treatment often involves placing the HDR catheters (needles) into the prostate gland under the transrectal ultrasound (TRUS) guidance, then generating a radiation treatment plan based on CT prostate images, and subsequently delivering high dose of radiation through these catheters. The main challenge for this HDR procedure is to accurately segment the prostate volume in the CT images for the radiation treatment planning. In this study, the authors propose a novel approach that integrates the prostate volume from 3D TRUS images into the treatment planning CT images to provide an accurate prostate delineation for prostate HDR treatment. Methods: The authors’ approach requires acquisition of 3D TRUS prostate images in the operating room right after the HDR catheters are inserted, which takes 1–3 min. These TRUS images are used to create prostate contours. The HDR catheters are reconstructed from the intraoperative TRUS and postoperative CT images, and subsequently used as landmarks for the TRUS–CT image fusion. After TRUS–CT fusion, the TRUS-based prostate volume is deformed to the CT images for treatment planning. This method was first validated with a prostate-phantom study. In addition, a pilot study of ten patients undergoing HDR prostate brachytherapy was conducted to test its clinical feasibility. The accuracy of their approach was assessed through the locations of three implanted fiducial (gold) markers, as well as T2-weighted MR prostate images of patients. Results: For the phantom study, the target registration error (TRE) of gold-markers was 0.41 ± 0.11 mm. For the ten patients, the TRE of gold markers was 1.18 ± 0.26 mm; the prostate volume difference between the authors’ approach and the MRI-based volume was 7.28% ± 0

Prostate CT segmentation method based on nonrigid registration in ultrasound-guided CT-based HDR prostate brachytherapy

Science.gov (United States)

Yang, Xiaofeng; Rossi, Peter; Ogunleye, Tomi; Marcus, David M.; Jani, Ashesh B.; Mao, Hui; Curran, Walter J.; Liu, Tian

2014-01-01

Purpose: The technological advances in real-time ultrasound image guidance for high-dose-rate (HDR) prostate brachytherapy have placed this treatment modality at the forefront of innovation in cancer radiotherapy. Prostate HDR treatment often involves placing the HDR catheters (needles) into the prostate gland under the transrectal ultrasound (TRUS) guidance, then generating a radiation treatment plan based on CT prostate images, and subsequently delivering high dose of radiation through these catheters. The main challenge for this HDR procedure is to accurately segment the prostate volume in the CT images for the radiation treatment planning. In this study, the authors propose a novel approach that integrates the prostate volume from 3D TRUS images into the treatment planning CT images to provide an accurate prostate delineation for prostate HDR treatment. Methods: The authors’ approach requires acquisition of 3D TRUS prostate images in the operating room right after the HDR catheters are inserted, which takes 1–3 min. These TRUS images are used to create prostate contours. The HDR catheters are reconstructed from the intraoperative TRUS and postoperative CT images, and subsequently used as landmarks for the TRUS–CT image fusion. After TRUS–CT fusion, the TRUS-based prostate volume is deformed to the CT images for treatment planning. This method was first validated with a prostate-phantom study. In addition, a pilot study of ten patients undergoing HDR prostate brachytherapy was conducted to test its clinical feasibility. The accuracy of their approach was assessed through the locations of three implanted fiducial (gold) markers, as well as T2-weighted MR prostate images of patients. Results: For the phantom study, the target registration error (TRE) of gold-markers was 0.41 ± 0.11 mm. For the ten patients, the TRE of gold markers was 1.18 ± 0.26 mm; the prostate volume difference between the authors’ approach and the MRI-based volume was 7.28% ± 0

Registration-based Bone Morphometry for Shape Analysis of the Bones of the Human Wrist

Science.gov (United States)

Joshi, Anand A.; Leahy, Richard M.; Badawi, Ramsey D.; Chaudhari, Abhijit J.

2015-01-01

We present a method that quantifies point-wise changes in surface morphology of the bones of the human wrist. The proposed method, referred to as Registration-based Bone Morphometry (RBM), consists of two steps: an atlas selection step and an atlas warping step. The atlas for individual wrist bones was selected based on the shortest l2 distance to the ensemble of wrist bones from a database of a healthy population of subjects. The selected atlas was then warped to the corresponding bones of individuals in the population using a non-linear registration method based on regularized l2 distance minimization. The displacement field thus calculated showed local differences in bone shape that then were used for the analysis of group differences. Our results indicate that RBM has potential to provide a standardized approach to shape analysis of bones of the human wrist. We demonstrate the performance of RBM for examining group differences in wrist bone shapes based on sex and between those of the right and left wrists in healthy individuals. We also present data to show the application of RBM for tracking bone erosion status in rheumatoid arthritis. PMID:26353369

Background suppression of infrared small target image based on inter-frame registration

Science.gov (United States)

Ye, Xiubo; Xue, Bindang

2018-04-01

We propose a multi-frame background suppression method for remote infrared small target detection. Inter-frame information is necessary when the heavy background clutters make it difficult to distinguish real targets and false alarms. A registration procedure based on points matching in image patches is used to compensate the local deformation of background. Then the target can be separated by background subtraction. Experiments show our method serves as an effective preliminary of target detection.

Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks

NARCIS (Netherlands)

Eppenhof, K.A.J.; Pluim, J.P.W.

2018-01-01

Error estimation in nonlinear medical image registration is a nontrivial problem that is important for validation of registration methods. We propose a supervised method for estimation of registration errors in nonlinear registration of three-dimensional (3-D) images. The method is based on a 3-D

Image Registration-Based Bolt Loosening Detection of Steel Joints

Science.gov (United States)

2018-01-01

Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require extensive sensor deployment, which limit their abilities to cost-effectively detect loosened bolts in a large number of steel joints. Recently, computer vision-based structural health monitoring (SHM) technologies have demonstrated great potential for damage detection due to the benefits of being low cost, easy to deploy, and contactless. In this study, we propose a vision-based non-contact bolt loosening detection method that uses a consumer-grade digital camera. Two images of the monitored steel joint are first collected during different inspection periods and then aligned through two image registration processes. If the bolt experiences rotation between inspections, it will introduce differential features in the registration errors, serving as a good indicator for bolt loosening detection. The performance and robustness of this approach have been validated through a series of experimental investigations using three laboratory setups including a gusset plate on a cross frame, a column flange, and a girder web. The bolt loosening detection results are presented for easy interpretation such that informed decisions can be made about the detected loosened bolts. PMID:29597264

Image Registration-Based Bolt Loosening Detection of Steel Joints.

Science.gov (United States)

Kong, Xiangxiong; Li, Jian

2018-03-28

Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require extensive sensor deployment, which limit their abilities to cost-effectively detect loosened bolts in a large number of steel joints. Recently, computer vision-based structural health monitoring (SHM) technologies have demonstrated great potential for damage detection due to the benefits of being low cost, easy to deploy, and contactless. In this study, we propose a vision-based non-contact bolt loosening detection method that uses a consumer-grade digital camera. Two images of the monitored steel joint are first collected during different inspection periods and then aligned through two image registration processes. If the bolt experiences rotation between inspections, it will introduce differential features in the registration errors, serving as a good indicator for bolt loosening detection. The performance and robustness of this approach have been validated through a series of experimental investigations using three laboratory setups including a gusset plate on a cross frame, a column flange, and a girder web. The bolt loosening detection results are presented for easy interpretation such that informed decisions can be made about the detected loosened bolts.

Preliminary experience with a novel method of three-dimensional co-registration of prostate cancer digital histology and in vivo multiparametric MRI.

Science.gov (United States)

Orczyk, C; Rusinek, H; Rosenkrantz, A B; Mikheev, A; Deng, F-M; Melamed, J; Taneja, S S

2013-12-01

To assess a novel method of three-dimensional (3D) co-registration of prostate cancer digital histology and in-vivo multiparametric magnetic resonance imaging (mpMRI) image sets for clinical usefulness. A software platform was developed to achieve 3D co-registration. This software was prospectively applied to three patients who underwent radical prostatectomy. Data comprised in-vivo mpMRI [T2-weighted, dynamic contrast-enhanced weighted images (DCE); apparent diffusion coefficient (ADC)], ex-vivo T2-weighted imaging, 3D-rebuilt pathological specimen, and digital histology. Internal landmarks from zonal anatomy served as reference points for assessing co-registration accuracy and precision. Applying a method of deformable transformation based on 22 internal landmarks, a 1.6 mm accuracy was reached to align T2-weighted images and the 3D-rebuilt pathological specimen, an improvement over rigid transformation of 32% (p = 0.003). The 22 zonal anatomy landmarks were more accurately mapped using deformable transformation than rigid transformation (p = 0.0008). An automatic method based on mutual information, enabled automation of the process and to include perfusion and diffusion MRI images. Evaluation of co-registration accuracy using the volume overlap index (Dice index) met clinically relevant requirements, ranging from 0.81-0.96 for sequences tested. Ex-vivo images of the specimen did not significantly improve co-registration accuracy. This preliminary analysis suggests that deformable transformation based on zonal anatomy landmarks is accurate in the co-registration of mpMRI and histology. Including diffusion and perfusion sequences in the same 3D space as histology is essential further clinical information. The ability to localize cancer in 3D space may improve targeting for image-guided biopsy, focal therapy, and disease quantification in surveillance protocols. Copyright © 2013 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Spherical Demons: Fast Surface Registration

Science.gov (United States)

Yeo, B.T. Thomas; Sabuncu, Mert; Vercauteren, Tom; Ayache, Nicholas; Fischl, Bruce; Golland, Polina

2009-01-01

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently implemented on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, the resulting registration is diffeomorphic and fast – registration of two cortical mesh models with more than 100k nodes takes less than 5 minutes, comparable to the fastest surface registration algorithms. Moreover, the accuracy of our method compares favorably to the popular FreeSurfer registration algorithm. We validate the technique in two different settings: (1) parcellation in a set of in-vivo cortical surfaces and (2) Brodmann area localization in ex-vivo cortical surfaces. PMID:18979813

4D-CT Lung registration using anatomy-based multi-level multi-resolution optical flow analysis and thin-plate splines.

Science.gov (United States)

Min, Yugang; Neylon, John; Shah, Amish; Meeks, Sanford; Lee, Percy; Kupelian, Patrick; Santhanam, Anand P

2014-09-01

The accuracy of 4D-CT registration is limited by inconsistent Hounsfield unit (HU) values in the 4D-CT data from one respiratory phase to another and lower image contrast for lung substructures. This paper presents an optical flow and thin-plate spline (TPS)-based 4D-CT registration method to account for these limitations. The use of unified HU values on multiple anatomy levels (e.g., the lung contour, blood vessels, and parenchyma) accounts for registration errors by inconsistent landmark HU value. While 3D multi-resolution optical flow analysis registers each anatomical level, TPS is employed for propagating the results from one anatomical level to another ultimately leading to the 4D-CT registration. 4D-CT registration was validated using target registration error (TRE), inverse consistency error (ICE) metrics, and a statistical image comparison using Gamma criteria of 1 % intensity difference in 2 mm(3) window range. Validation results showed that the proposed method was able to register CT lung datasets with TRE and ICE values <3 mm. In addition, the average number of voxel that failed the Gamma criteria was <3 %, which supports the clinical applicability of the propose registration mechanism. The proposed 4D-CT registration computes the volumetric lung deformations within clinically viable accuracy.

MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

Science.gov (United States)

Fei, Baowei; Yang, Xiaofeng; Nye, Jonathon A.; Aarsvold, John N.; Raghunath, Nivedita; Cervo, Morgan; Stark, Rebecca; Meltzer, Carolyn C.; Votaw, John R.

2012-01-01

Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [11C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR

MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

Energy Technology Data Exchange (ETDEWEB)

Fei, Baowei, E-mail: bfei@emory.edu [Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road Northeast, Atlanta, Georgia 30329 (United States); Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30322 (United States); Department of Mathematics and Computer Sciences, Emory University, Atlanta, Georgia 30322 (United States); Yang, Xiaofeng; Nye, Jonathon A.; Raghunath, Nivedita; Votaw, John R. [Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 (United States); Aarsvold, John N. [Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 (United States); Nuclear Medicine Service, Atlanta Veterans Affairs Medical Center, Atlanta, Georgia 30033 (United States); Cervo, Morgan; Stark, Rebecca [The Medical Physics Graduate Program in the George W. Woodruff School, Georgia Institute of Technology, Atlanta, Georgia 30332 (United States); Meltzer, Carolyn C. [Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 (United States); Department of Neurology and Department of Psychiatry and Behavior Sciences, Emory University School of Medicine, Atlanta, Georgia 30322 (United States)

2012-10-15

Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [{sup 11}C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR/PET.

MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

International Nuclear Information System (INIS)

Fei, Baowei; Yang, Xiaofeng; Nye, Jonathon A.; Raghunath, Nivedita; Votaw, John R.; Aarsvold, John N.; Cervo, Morgan; Stark, Rebecca; Meltzer, Carolyn C.

2012-01-01

Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with ["1"1C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR/PET.

SU-E-J-95: Towards Optimum Boundary Conditions for Biomechanical Model Based Deformable Registration Using Intensity Based Image Matching for Prostate Correlative Pathology.

Science.gov (United States)

Samavati, N; McGrath, D M; Lee, J; van der Kwast, T; Jewett, M; Mã Nard, C; Pluim, J P W; Brock, K K

2012-06-01

Deformable registration of histology to MRI is an essential tool to validate in vivo prostate cancer imaging. However, direct registration of histology to in vivo MR is prone to error due to geometric differences between the tissue sections and the in vivo imaging planes. To increase the accuracy of registration, an ex vivo high resolution MRI is acquired to compensate for the direct registration difficulties. A novel intensity-based deformable registration algorithm based on local variation in image intensities is proposed to register the histology to ex vivo MRI of prostatectomy specimens. Four sets of ex vivo MR and whole mount pathology images from four patients were used to investigate and validate the technique. In addition, 9 synthetically deformed ex vivo MR images were used. The standard deviation in local windows within the images was calculated to generate intermediate images based on both MR and histology. The intermediate images were registered using the Drop package (Munich, Germany). To further increase the accuracy, a final refinement of the registration was performed using Drop with a finer B-spline rid. The registration parameters were tuned to achieve a visually acceptable registration. Magnitude of Differences (MOD) and Angular Error (AE) were used to validate the synthetic data, and the Target Registration Error (TRE) of manually indicated landmarks was used for the clinical data. MOD of 0.6mm and AE of 8.3 degrees showed the efficacy of using intermediate images, compared to 0.8mm and 10.0 degrees achieved with Drop without the intermediate images. The average mean±std TRE among the four patients was 1.0±0.6 mm using the proposed method compared to 1.6±1.1 mm using Elastix (Utrecht, The Netherlands). An intensity-based deformable registration algorithm which uses intermediate images was evaluated on prostatectomy specimens and synthetically deformed clinical data, indicating improvement in overall accuracy and robustness. OICR, Terry Fox

Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications

Directory of Open Access Journals (Sweden)

Byeong Hak Kim

2017-12-01

Full Text Available 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.

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

2017-01-01

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. PMID:29280970

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.

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

Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment

Science.gov (United States)

Unger, Jakob; Sun, Tianchen; Chen, Yi-Ling; Phipps, Jennifer E.; Bold, Richard J.; Darrow, Morgan A.; Ma, Kwan-Liu; Marcu, Laura

2018-01-01

An important step in establishing the diagnostic potential for emerging optical imaging techniques is accurate registration between imaging data and the corresponding tissue histopathology typically used as gold standard in clinical diagnostics. We present a method to precisely register data acquired with a point-scanning spectroscopic imaging technique from fresh surgical tissue specimen blocks with corresponding histological sections. Using a visible aiming beam to augment point-scanning multispectral time-resolved fluorescence spectroscopy on video images, we evaluate two different markers for the registration with histology: fiducial markers using a 405-nm CW laser and the tissue block's outer shape characteristics. We compare the registration performance with benchmark methods using either the fiducial markers or the outer shape characteristics alone to a hybrid method using both feature types. The hybrid method was found to perform best reaching an average error of 0.78±0.67 mm. This method provides a profound framework to validate diagnostical abilities of optical fiber-based techniques and furthermore enables the application of supervised machine learning techniques to automate tissue characterization.

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

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

NARCIS (Netherlands)

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

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

Skull registration for prone patient position using tracked ultrasound

Science.gov (United States)

Underwood, Grace; Ungi, Tamas; Baum, Zachary; Lasso, Andras; Kronreif, Gernot; Fichtinger, Gabor

2017-03-01

PURPOSE: Tracked navigation has become prevalent in neurosurgery. Problems with registration of a patient and a preoperative image arise when the patient is in a prone position. Surfaces accessible to optical tracking on the back of the head are unreliable for registration. We investigated the accuracy of surface-based registration using points accessible through tracked ultrasound. Using ultrasound allows access to bone surfaces that are not available through optical tracking. Tracked ultrasound could eliminate the need to work (i) under the table for registration and (ii) adjust the tracker between surgery and registration. In addition, tracked ultrasound could provide a non-invasive method in comparison to an alternative method of registration involving screw implantation. METHODS: A phantom study was performed to test the feasibility of tracked ultrasound for registration. An initial registration was performed to partially align the pre-operative computer tomography data and skull phantom. The initial registration was performed by an anatomical landmark registration. Surface points accessible by tracked ultrasound were collected and used to perform an Iterative Closest Point Algorithm. RESULTS: When the surface registration was compared to a ground truth landmark registration, the average TRE was found to be 1.6+/-0.1mm and the average distance of points off the skull surface was 0.6+/-0.1mm. CONCLUSION: The use of tracked ultrasound is feasible for registration of patients in prone position and eliminates the need to perform registration under the table. The translational component of error found was minimal. Therefore, the amount of TRE in registration is due to a rotational component of error.

Tissue Feature-Based and Segmented Deformable Image Registration for Improved Modeling of Shear Movement of Lungs

International Nuclear Information System (INIS)

Xie Yaoqin; Chao Ming; Xing Lei

2009-01-01

Purpose: To report a tissue feature-based image registration strategy with explicit inclusion of the differential motions of thoracic structures. Methods and Materials: The proposed technique started with auto-identification of a number of corresponding points with distinct tissue features. The tissue feature points were found by using the scale-invariant feature transform method. The control point pairs were then sorted into different 'colors' according to the organs in which they resided and used to model the involved organs individually. A thin-plate spline method was used to register a structure characterized by the control points with a given 'color.' The proposed technique was applied to study a digital phantom case and 3 lung and 3 liver cancer patients. Results: For the phantom case, a comparison with the conventional thin-plate spline method showed that the registration accuracy was markedly improved when the differential motions of the lung and chest wall were taken into account. On average, the registration error and standard deviation of the 15 points against the known ground truth were reduced from 3.0 to 0.5 mm and from 1.5 to 0.2 mm, respectively, when the new method was used. A similar level of improvement was achieved for the clinical cases. Conclusion: The results of our study have shown that the segmented deformable approach provides a natural and logical solution to model the discontinuous organ motions and greatly improves the accuracy and robustness of deformable registration.

Reducing Interpolation Artifacts for Mutual Information Based Image Registration

Science.gov (United States)

Soleimani, H.; Khosravifard, M.A.

2011-01-01

Medical image registration methods which use mutual information as similarity measure have been improved in recent decades. Mutual Information is a basic concept of Information theory which indicates the dependency of two random variables (or two images). In order to evaluate the mutual information of two images their joint probability distribution is required. Several interpolation methods, such as Partial Volume (PV) and bilinear, are used to estimate joint probability distribution. Both of these two methods yield some artifacts on mutual information function. Partial Volume-Hanning window (PVH) and Generalized Partial Volume (GPV) methods are introduced to remove such artifacts. In this paper we show that the acceptable performance of these methods is not due to their kernel function. It's because of the number of pixels which incorporate in interpolation. Since using more pixels requires more complex and time consuming interpolation process, we propose a new interpolation method which uses only four pixels (the same as PV and bilinear interpolations) and removes most of the artifacts. Experimental results of the registration of Computed Tomography (CT) images show superiority of the proposed scheme. PMID:22606673

ACIR: automatic cochlea image registration

Science.gov (United States)

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

2017-02-01

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

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.

Automated Coarse Registration of Point Clouds in 3d Urban Scenes Using Voxel Based Plane Constraint

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Xu, Y.; Boerner, R.; Yao, W.; Hoegner, L.; Stilla, U.

2017-09-01

For obtaining a full coverage of 3D scans in a large-scale urban area, the registration between point clouds acquired via terrestrial laser scanning (TLS) is normally mandatory. However, due to the complex urban environment, the automatic registration of different scans is still a challenging problem. In this work, we propose an automatic marker free method for fast and coarse registration between point clouds using the geometric constrains of planar patches under a voxel structure. Our proposed method consists of four major steps: the voxelization of the point cloud, the approximation of planar patches, the matching of corresponding patches, and the estimation of transformation parameters. In the voxelization step, the point cloud of each scan is organized with a 3D voxel structure, by which the entire point cloud is partitioned into small individual patches. In the following step, we represent points of each voxel with the approximated plane function, and select those patches resembling planar surfaces. Afterwards, for matching the corresponding patches, a RANSAC-based strategy is applied. Among all the planar patches of a scan, we randomly select a planar patches set of three planar surfaces, in order to build a coordinate frame via their normal vectors and their intersection points. The transformation parameters between scans are calculated from these two coordinate frames. The planar patches set with its transformation parameters owning the largest number of coplanar patches are identified as the optimal candidate set for estimating the correct transformation parameters. The experimental results using TLS datasets of different scenes reveal that our proposed method can be both effective and efficient for the coarse registration task. Especially, for the fast orientation between scans, our proposed method can achieve a registration error of less than around 2 degrees using the testing datasets, and much more efficient than the classical baseline methods.

32 CFR 1615.1 - Registration.

Science.gov (United States)

2010-07-01

... registration card or other method of registration prescribed by the Director of Selective Service by a person... the records (master computer file) of the Selective Service System. Registration is completed when... Director include completing a Selective Service Registration Card at a classified Post Office, registration...

Estimation of Completeness of Cancer Registration for Patients Referred to Shiraz Selected Centers through a Two Source Capture Re-capture Method, 2009 Data.

Science.gov (United States)

Sharifian, Roxana; SedaghatNia, Mohammad Hossein; Nematolahi, Mohtram; Zare, Najaf; Barzegari, Saeed

2015-01-01

Cancer has important social consequences with cancer registration as the basis of moving towards prevention. The present study aimed to estimate the completeness of registration of the ten most common cancers in patients referred to selected hospitals in Shiraz, Iran by using capture-recapture method. This cross-sectional analytical study was performed in 2014 based on the data of 2009, on a total of 4,388 registered cancer patients. After cleaning data from two sources, using capture-recapture common findings were identified. Then, the percentage of the completeness of cancer registration was estimated using Chapman and Chao methods. Finally, the effects of demographic and treatment variables on the completeness of cancer registration were investigated. The results showed that the percentages of completeness of cancer registration in the selected hospitals of Shiraz were 58.6% and 58.4%, and influenced by different variables. The age group between 40-49 years old was the highest represented and for the age group under 20 years old was the lowest for cancer registration. Breast cancer had the highest registration level and after that, thyroid and lung cancers, while colorectal cancer had the lowest registration level. According to the results, the number of cancers registered was very few and it seems that factors like inadequate knowledge of some doctors, imprecise diagnosis about the types of cancer, incorrectly filled out medical documents, and lack of sufficient accuracy in recording data on the computer cause errors and defects in cancer registration. This suggests a necessity to educate and teach doctors and other medical workers about the methods of documenting information related to cancer and also conduct additional measures to improve the cancer registration system.

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.)

Multi-feature-based plaque characterization in ex vivo MRI trained by registration to 3D histology

International Nuclear Information System (INIS)

Van Engelen, Arna; Niessen, Wiro J; Klein, Stefan; De Bruijne, Marleen; Groen, Harald C; Wentzel, Jolanda J; Verhagen, Hence JM; Lugt, Aad van der

2012-01-01

We present a new method for automated characterization of atherosclerotic plaque composition in ex vivo MRI. It uses MRI intensities as well as four other types of features: smoothed, gradient magnitude and Laplacian images at several scales, and the distances to the lumen and outer vessel wall. The ground truth for fibrous, necrotic and calcified tissue was provided by histology and μCT in 12 carotid plaque specimens. Semi-automatic registration of a 3D stack of histological slices and μCT images to MRI allowed for 3D rotations and in-plane deformations of histology. By basing voxelwise classification on different combinations of features, we evaluated their relative importance. To establish whether training by 3D registration yields different results than training by 2D registration, we determined plaque composition using (1) a 2D slice-based registration approach for three manually selected MRI and histology slices per specimen, and (2) an approach that uses only the three corresponding MRI slices from the 3D-registered volumes. Voxelwise classification accuracy was best when all features were used (73.3 ± 6.3%) and was significantly better than when only original intensities and distance features were used (Friedman, p < 0.05). Although 2D registration or selection of three slices from the 3D set slightly decreased accuracy, these differences were non-significant. (paper)

Real-time estimation of FLE for point-based registration

Science.gov (United States)

Wiles, Andrew D.; Peters, Terry M.

2009-02-01

In image-guide surgery, optimizing the accuracy in localizing the surgical tools within the virtual reality environment or 3D image is vitally important, significant effort has been spent reducing the measurement errors at the point of interest or target. This target registration error (TRE) is often defined by a root-mean-square statistic which reduces the vector data to a single term that can be minimized. However, lost in the data reduction is the directionality of the error which, can be modelled using a 3D covariance matrix. Recently, we developed a set of expressions that modeled the TRE statistics for point-based registrations as a function of the fiducial marker geometry, target location and the fiducial localizer error (FLE). Unfortunately, these expressions are only as good as the definition of the FLE. In order to close the gap, we have subsequently developed a closed form expression that estimates the FLE as a function of the estimated fiducial registration error (FRE, the error between the measured fiducials and the best fit locations of those fiducials). The FRE covariance matrix is estimated using a sliding window technique and used as input into the closed form expression to estimate the FLE. The estimated FLE can then used to estimate the TRE which, can be given to the surgeon to permit the procedure to be designed such that the errors associated with the point-based registrations are minimized.

On developing B-spline registration algorithms for multi-core processors

International Nuclear Information System (INIS)

Shackleford, J A; Kandasamy, N; Sharp, G C

2010-01-01

Spline-based deformable registration methods are quite popular within the medical-imaging community due to their flexibility and robustness. However, they require a large amount of computing time to obtain adequate results. This paper makes two contributions towards accelerating B-spline-based registration. First, we propose a grid-alignment scheme and associated data structures that greatly reduce the complexity of the registration algorithm. Based on this grid-alignment scheme, we then develop highly data parallel designs for B-spline registration within the stream-processing model, suitable for implementation on multi-core processors such as graphics processing units (GPUs). Particular attention is focused on an optimal method for performing analytic gradient computations in a data parallel fashion. CPU and GPU versions are validated for execution time and registration quality. Performance results on large images show that our GPU algorithm achieves a speedup of 15 times over the single-threaded CPU implementation whereas our multi-core CPU algorithm achieves a speedup of 8 times over the single-threaded implementation. The CPU and GPU versions achieve near-identical registration quality in terms of RMS differences between the generated vector fields.

Spectral embedding-based registration (SERg) for multimodal fusion of prostate histology and MRI

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Hwuang, Eileen; Rusu, Mirabela; Karthigeyan, Sudha; Agner, Shannon C.; Sparks, Rachel; Shih, Natalie; Tomaszewski, John E.; Rosen, Mark; Feldman, Michael; Madabhushi, Anant

2014-03-01

Multi-modal image registration is needed to align medical images collected from different protocols or imaging sources, thereby allowing the mapping of complementary information between images. One challenge of multimodal image registration is that typical similarity measures rely on statistical correlations between image intensities to determine anatomical alignment. The use of alternate image representations could allow for mapping of intensities into a space or representation such that the multimodal images appear more similar, thus facilitating their co-registration. In this work, we present a spectral embedding based registration (SERg) method that uses non-linearly embedded representations obtained from independent components of statistical texture maps of the original images to facilitate multimodal image registration. Our methodology comprises the following main steps: 1) image-derived textural representation of the original images, 2) dimensionality reduction using independent component analysis (ICA), 3) spectral embedding to generate the alternate representations, and 4) image registration. The rationale behind our approach is that SERg yields embedded representations that can allow for very different looking images to appear more similar, thereby facilitating improved co-registration. Statistical texture features are derived from the image intensities and then reduced to a smaller set by using independent component analysis to remove redundant information. Spectral embedding generates a new representation by eigendecomposition from which only the most important eigenvectors are selected. This helps to accentuate areas of salience based on modality-invariant structural information and therefore better identifies corresponding regions in both the template and target images. The spirit behind SERg is that image registration driven by these areas of salience and correspondence should improve alignment accuracy. In this work, SERg is implemented using Demons

Feature-based US to CT registration of the aortic root

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Lang, Pencilla; Chen, Elvis C. S.; Guiraudon, Gerard M.; Jones, Doug L.; Bainbridge, Daniel; Chu, Michael W.; Drangova, Maria; Hata, Noby; Jain, Ameet; Peters, Terry M.

2011-03-01

A feature-based registration was developed to align biplane and tracked ultrasound images of the aortic root with a preoperative CT volume. In transcatheter aortic valve replacement, a prosthetic valve is inserted into the aortic annulus via a catheter. Poor anatomical visualization of the aortic root region can result in incorrect positioning, leading to significant morbidity and mortality. Registration of pre-operative CT to transesophageal ultrasound and fluoroscopy images is a major step towards providing augmented image guidance for this procedure. The proposed registration approach uses an iterative closest point algorithm to register a surface mesh generated from CT to 3D US points reconstructed from a single biplane US acquisition, or multiple tracked US images. The use of a single simultaneous acquisition biplane image eliminates reconstruction error introduced by cardiac gating and TEE probe tracking, creating potential for real-time intra-operative registration. A simple initialization procedure is used to minimize changes to operating room workflow. The algorithm is tested on images acquired from excised porcine hearts. Results demonstrate a clinically acceptable accuracy of 2.6mm and 5mm for tracked US to CT and biplane US to CT registration respectively.

Automatic intensity-based 3D-to-2D registration of CT volume and dual-energy digital radiography for the detection of cardiac calcification

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Chen, Xiang; Gilkeson, Robert; Fei, Baowei

2007-03-01

We are investigating three-dimensional (3D) to two-dimensional (2D) registration methods for computed tomography (CT) and dual-energy digital radiography (DR) for the detection of coronary artery calcification. CT is an established tool for the diagnosis of coronary artery diseases (CADs). Dual-energy digital radiography could be a cost-effective alternative for screening coronary artery calcification. In order to utilize CT as the "gold standard" to evaluate the ability of DR images for the detection and localization of calcium, we developed an automatic intensity-based 3D-to-2D registration method for 3D CT volumes and 2D DR images. To generate digital rendering radiographs (DRR) from the CT volumes, we developed three projection methods, i.e. Gaussian-weighted projection, threshold-based projection, and average-based projection. We tested normalized cross correlation (NCC) and normalized mutual information (NMI) as similarity measurement. We used the Downhill Simplex method as the search strategy. Simulated projection images from CT were fused with the corresponding DR images to evaluate the localization of cardiac calcification. The registration method was evaluated by digital phantoms, physical phantoms, and clinical data sets. The results from the digital phantoms show that the success rate is 100% with mean errors of less 0.8 mm and 0.2 degree for both NCC and NMI. The registration accuracy of the physical phantoms is 0.34 +/- 0.27 mm. Color overlay and 3D visualization of the clinical data show that the two images are registered well. This is consistent with the improvement of the NMI values from 0.20 +/- 0.03 to 0.25 +/- 0.03 after registration. The automatic 3D-to-2D registration method is accurate and robust and may provide a useful tool to evaluate the dual-energy DR images for the detection of coronary artery calcification.

Comparison of template registration methods for multi-site meta-analysis of brain morphometry

Science.gov (United States)

Faskowitz, Joshua; de Zubicaray, Greig I.; McMahon, Katie L.; Wright, Margaret J.; Thompson, Paul M.; Jahanshad, Neda

2016-03-01

Neuroimaging consortia such as ENIGMA can significantly improve power to discover factors that affect the human brain by pooling statistical inferences across cohorts to draw generalized conclusions from populations around the world. Voxelwise analyses such as tensor-based morphometry also allow an unbiased search for effects throughout the brain. Even so, such consortium-based analyses are limited by a lack of high-powered methods to harmonize voxelwise information across study populations and scanners. While the simplest approach may be to map all images to a single standard space, the benefits of cohort-specific templates have long been established. Here we studied methods to pool voxel-wise data across sites using templates customized for each cohort but providing a meaningful common space across all studies for voxelwise comparisons. As non-linear 3D MRI registrations represent mappings between images at millimeter resolution, we need to consider the reliability of these mappings. To evaluate these mappings, we calculated test-retest statistics on the volumetric maps of expansion and contraction. Further, we created study-specific brain templates for ten T1-weighted MRI datasets, and a common space from four study-specific templates. We evaluated the efficacy of using a two-step registration framework versus a single standard space. We found that the two-step framework more reliably mapped subjects to a common space.

Optimization of PET-MR Registrations for Nonhuman Primates Using Mutual Information Measures: A Multi-Transform Method (MTM)

Science.gov (United States)

Sandiego, Christine M.; Weinzimmer, David; Carson, Richard E.

2012-01-01

An important step in PET brain kinetic analysis is the registration of functional data to an anatomical MR image. Typically, PET-MR registrations in nonhuman primate neuroreceptor studies used PET images acquired early post-injection, (e.g., 0–10 min) to closely resemble the subject’s MR image. However, a substantial fraction of these registrations (~25%) fail due to the differences in kinetics and distribution for various radiotracer studies and conditions (e.g., blocking studies). The Multi-Transform Method (MTM) was developed to improve the success of registrations between PET and MR images. Two algorithms were evaluated, MTM-I and MTM-II. The approach involves creating multiple transformations by registering PET images of different time intervals, from a dynamic study, to a single reference (i.e., MR image) (MTM-I) or to multiple reference images (i.e., MR and PET images pre-registered to the MR) (MTM-II). Normalized mutual information was used to compute similarity between the transformed PET images and the reference image(s) to choose the optimal transformation. This final transformation is used to map the dynamic dataset into the animal’s anatomical MR space, required for kinetic analysis. The chosen transformed from MTM-I and MTM-II were evaluated using visual rating scores to assess the quality of spatial alignment between the resliced PET and reference. One hundred twenty PET datasets involving eleven different tracers from 3 different scanners were used to evaluate the MTM algorithms. Studies were performed with baboons and rhesus monkeys on the HR+, HRRT, and Focus-220. Successful transformations increased from 77.5%, 85.8%, to 96.7% using the 0–10 min method, MTM-I, and MTM-II, respectively, based on visual rating scores. The Multi-Transform Methods proved to be a robust technique for PET-MR registrations for a wide range of PET studies. PMID:22926293

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.

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.

Performance evaluation of grid-enabled registration algorithms using bronze-standards

CERN Document Server

Glatard, T; Montagnat, J

2006-01-01

Evaluating registration algorithms is difficult due to the lack of gold standard in most clinical procedures. The bronze standard is a real-data based statistical method providing an alternative registration reference through a computationally intensive image database registration procedure. We propose in this paper an efficient implementation of this method through a grid-interfaced workflow enactor enabling the concurrent processing of hundreds of image registrations in a couple of hours only. The performances of two different grid infrastructures were compared. We computed the accuracy of 4 different rigid registration algorithms on longitudinal MRI images of brain tumors. Results showed an average subvoxel accuracy of 0.4 mm and 0.15 degrees in rotation.

Accelerated gradient-based free form deformable registration for online adaptive radiotherapy

International Nuclear Information System (INIS)

Yu, Gang; Yang, Guanyu; Shu, Huazhong; Li, Baosheng; Liang, Yueqiang; Yin, Yong; Li, Dengwang

2015-01-01

The registration of planning fan-beam computed tomography (FBCT) and daily cone-beam CT (CBCT) is a crucial step in adaptive radiation therapy. The current intensity-based registration algorithms, such as Demons, may fail when they are used to register FBCT and CBCT, because the CT numbers in CBCT cannot exactly correspond to the electron densities. In this paper, we investigated the effects of CBCT intensity inaccuracy on the registration accuracy and developed an accurate gradient-based free form deformation algorithm (GFFD). GFFD distinguishes itself from other free form deformable registration algorithms by (a) measuring the similarity using the 3D gradient vector fields to avoid the effect of inconsistent intensities between the two modalities; (b) accommodating image sampling anisotropy using the local polynomial approximation-intersection of confidence intervals (LPA-ICI) algorithm to ensure a smooth and continuous displacement field; and (c) introducing a ‘bi-directional’ force along with an adaptive force strength adjustment to accelerate the convergence process. It is expected that such a strategy can decrease the effect of the inconsistent intensities between the two modalities, thus improving the registration accuracy and robustness. Moreover, for clinical application, the algorithm was implemented by graphics processing units (GPU) through OpenCL framework. The registration time of the GFFD algorithm for each set of CT data ranges from 8 to 13 s. The applications of on-line adaptive image-guided radiation therapy, including auto-propagation of contours, aperture-optimization and dose volume histogram (DVH) in the course of radiation therapy were also studied by in-house-developed software. (paper)

A Matlab user interface for the statistically assisted fluid registration algorithm and tensor-based morphometry

Science.gov (United States)

Yepes-Calderon, Fernando; Brun, Caroline; Sant, Nishita; Thompson, Paul; Lepore, Natasha

2015-01-01

Tensor-Based Morphometry (TBM) is an increasingly popular method for group analysis of brain MRI data. The main steps in the analysis consist of a nonlinear registration to align each individual scan to a common space, and a subsequent statistical analysis to determine morphometric differences, or difference in fiber structure between groups. Recently, we implemented the Statistically-Assisted Fluid Registration Algorithm or SAFIRA,1 which is designed for tracking morphometric differences among populations. To this end, SAFIRA allows the inclusion of statistical priors extracted from the populations being studied as regularizers in the registration. This flexibility and degree of sophistication limit the tool to expert use, even more so considering that SAFIRA was initially implemented in command line mode. Here, we introduce a new, intuitive, easy to use, Matlab-based graphical user interface for SAFIRA's multivariate TBM. The interface also generates different choices for the TBM statistics, including both the traditional univariate statistics on the Jacobian matrix, and comparison of the full deformation tensors.2 This software will be freely disseminated to the neuroimaging research community.

Survey of Non-Rigid Registration Tools in Medicine.

Science.gov (United States)

Keszei, András P; Berkels, Benjamin; Deserno, Thomas M

2017-02-01

We catalogue available software solutions for non-rigid image registration to support scientists in selecting suitable tools for specific medical registration purposes. Registration tools were identified using non-systematic search in Pubmed, Web of Science, IEEE Xplore® Digital Library, Google Scholar, and through references in identified sources (n = 22). Exclusions are due to unavailability or inappropriateness. The remaining (n = 18) tools were classified by (i) access and technology, (ii) interfaces and application, (iii) living community, (iv) supported file formats, and (v) types of registration methodologies emphasizing the similarity measures implemented. Out of the 18 tools, (i) 12 are open source, 8 are released under a permissive free license, which imposes the least restrictions on the use and further development of the tool, 8 provide graphical processing unit (GPU) support; (ii) 7 are built on software platforms, 5 were developed for brain image registration; (iii) 6 are under active development but only 3 have had their last update in 2015 or 2016; (iv) 16 support the Analyze format, while 7 file formats can be read with only one of the tools; and (v) 6 provide multiple registration methods and 6 provide landmark-based registration methods. Based on open source, licensing, GPU support, active community, several file formats, algorithms, and similarity measures, the tools Elastics and Plastimatch are chosen for the platform ITK and without platform requirements, respectively. Researchers in medical image analysis already have a large choice of registration tools freely available. However, the most recently published algorithms may not be included in the tools, yet.

[Affine transformation-based automatic registration for peripheral digital subtraction angiography (DSA)].

Science.gov (United States)

Kong, Gang; Dai, Dao-Qing; Zou, Lu-Min

2008-07-01

In order to remove the artifacts of peripheral digital subtraction angiography (DSA), an affine transformation-based automatic image registration algorithm is introduced here. The whole process is described as follows: First, rectangle feature templates are constructed with their centers of the extracted Harris corners in the mask, and motion vectors of the central feature points are estimated using template matching technology with the similarity measure of maximum histogram energy. And then the optimal parameters of the affine transformation are calculated with the matrix singular value decomposition (SVD) method. Finally, bilinear intensity interpolation is taken to the mask according to the specific affine transformation. More than 30 peripheral DSA registrations are performed with the presented algorithm, and as the result, moving artifacts of the images are removed with sub-pixel precision, and the time consumption is less enough to satisfy the clinical requirements. Experimental results show the efficiency and robustness of the algorithm.

Modified method for registration of particle deflection by bent crystal

International Nuclear Information System (INIS)

Afanas'ev, S.V.; Kovalenko, A.D.; Kuznetsov, V.N.; Romanov, S.V.; Sajfulin, Sh.Z.; Taratin, A.M.; Volkov, V.I.; Voevodin, M.A.; Bojko, V.V.

2003-01-01

The modified method for registration of particle deflection by a bent crystal was proposed and studied at the external proton beam of the Nuclotron. The telescope of scintillation counters was placed at the angle that was smaller than a crystal bending angle. The count dependence of the telescope on the crystal orientation was formed by the particles, which passed in channeling states only some part of the crystal length. Two maximums were observed in the dependencies due to particles captured into the channeling states on the crystal surface and in the crystal volume. This allows one to obtain, using the telescope and high-intensity beams, useful data about the particle channeling and the crystal, which usually demands more complicated registration by means of the coordinate detectors

Image-based RSA: Roentgen stereophotogrammetric analysis based on 2D-3D image registration.

Science.gov (United States)

de Bruin, P W; Kaptein, B L; Stoel, B C; Reiber, J H C; Rozing, P M; Valstar, E R

2008-01-01

Image-based Roentgen stereophotogrammetric analysis (IBRSA) integrates 2D-3D image registration and conventional RSA. Instead of radiopaque RSA bone markers, IBRSA uses 3D CT data, from which digitally reconstructed radiographs (DRRs) are generated. Using 2D-3D image registration, the 3D pose of the CT is iteratively adjusted such that the generated DRRs resemble the 2D RSA images as closely as possible, according to an image matching metric. Effectively, by registering all 2D follow-up moments to the same 3D CT, the CT volume functions as common ground. In two experiments, using RSA and using a micromanipulator as gold standard, IBRSA has been validated on cadaveric and sawbone scapula radiographs, and good matching results have been achieved. The accuracy was: |mu |RSA but higher than in vivo standard RSA. Because IBRSA does not require radiopaque markers, it adds functionality to the RSA method by opening new directions and possibilities for research, such as dynamic analyses using fluoroscopy on subjects without markers and computer navigation applications.

Evaluation of body-wise and organ-wise registrations for abdominal organs

Science.gov (United States)

Xu, Zhoubing; Panjwani, Sahil A.; Lee, Christopher P.; Burke, Ryan P.; Baucom, Rebeccah B.; Poulose, Benjamin K.; Abramson, Richard G.; Landman, Bennett A.

2016-03-01

Identifying cross-sectional and longitudinal correspondence in the abdomen on computed tomography (CT) scans is necessary for quantitatively tracking change and understanding population characteristics, yet abdominal image registration is a challenging problem. The key difficulty in solving this problem is huge variations in organ dimensions and shapes across subjects. The current standard registration method uses the global or body-wise registration technique, which is based on the global topology for alignment. This method (although producing decent results) has substantial influence of outliers, thus leaving room for significant improvement. Here, we study a new image registration approach using local (organ-wise registration) by first creating organ-specific bounding boxes and then using these regions of interest (ROIs) for aligning references to target. Based on Dice Similarity Coefficient (DSC), Mean Surface Distance (MSD) and Hausdorff Distance (HD), the organ-wise approach is demonstrated to have significantly better results by minimizing the distorting effects of organ variations. This paper compares exclusively the two registration methods by providing novel quantitative and qualitative comparison data and is a subset of the more comprehensive problem of improving the multi-atlas segmentation by using organ normalization.

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)

Hierarchical patch-based co-registration of differently stained histopathology slides

Science.gov (United States)

Yigitsoy, Mehmet; Schmidt, Günter

2017-03-01

Over the past decades, digital pathology has emerged as an alternative way of looking at the tissue at subcellular level. It enables multiplexed analysis of different cell types at micron level. Information about cell types can be extracted by staining sections of a tissue block using different markers. However, robust fusion of structural and functional information from different stains is necessary for reproducible multiplexed analysis. Such a fusion can be obtained via image co-registration by establishing spatial correspondences between tissue sections. Spatial correspondences can then be used to transfer various statistics about cell types between sections. However, the multi-modal nature of images and sparse distribution of interesting cell types pose several challenges for the registration of differently stained tissue sections. In this work, we propose a co-registration framework that efficiently addresses such challenges. We present a hierarchical patch-based registration of intensity normalized tissue sections. Preliminary experiments demonstrate the potential of the proposed technique for the fusion of multi-modal information from differently stained digital histopathology sections.

Usefulness of the classification technique of cerebral artery for 2D/3D registration

International Nuclear Information System (INIS)

Takemura, Akihiro; Suzuki, Masayuki; Kikuchi, Yuzo; Okumura, Yusuke; Harauchi, Hajime

2007-01-01

Several papers have proposed 2D/3D registration methods of the cerebral artery using magnetic resonance angiography (MRA) and digital subtraction angiography (DSA). Since differences between vessels in a DSA image and MRA volume data cause registration failure, we previously proposed a method to extract vessels from MRA volume data using a technique based on classification of the cerebral artery. In this paper, we evaluated the usefulness of this classification technique by evaluating the reliability of this 2D/3D registration method. This classification method divides the cerebral artery in MRA volume data into 12 segments. According to the results of the classification, structures corresponding to vessels on a DSA image can then be extracted. We applied the 2D/3D registration with/without classification to 16 pairs of MRA volume data and DSA images obtained from six patients. The registration results were scored into four levels (Excellent, Good, Fair and Poor). The rates of successful registration (>fair) were 37.5% for registration without classification and 81.3% for that with classification. These findings suggested that there was a low percentage of incorrectly extracted voxels and we could facilitate reliable registration. Thus, the classification technique was shown to be useful for feature-based 2D/3D registration. (author)

A semi-automated 2D/3D marker-based registration algorithm modelling prostate shrinkage during radiotherapy for prostate cancer

International Nuclear Information System (INIS)

Budiharto, Tom; Slagmolen, Pieter; Hermans, Jeroen; Maes, Frederik; Verstraete, Jan; Heuvel, Frank Van den; Depuydt, Tom; Oyen, Raymond; Haustermans, Karin

2009-01-01

Background and purpose: Currently, most available patient alignment tools based on implanted markers use manual marker matching and rigid registration transformations to measure the needed translational shifts. To quantify the particular effect of prostate gland shrinkage, implanted gold markers were tracked during a course of radiotherapy including an isotropic scaling factor to model prostate shrinkage. Materials and methods: Eight patients with prostate cancer had gold markers implanted transrectally and seven were treated with (neo) adjuvant androgen deprivation therapy. After patient alignment to skin tattoos, orthogonal electronic portal images (EPIs) were taken. A semi-automated 2D/3D marker-based registration was performed to calculate the necessary couch shifts. The registration consists of a rigid transformation combined with an isotropic scaling to model prostate shrinkage. Results: The inclusion of an isotropic shrinkage model in the registration algorithm cancelled the corresponding increase in registration error. The mean scaling factor was 0.89 ± 0.09. For all but two patients, a decrease of the isotropic scaling factor during treatment was observed. However, there was almost no difference in the translation offset between the manual matching of the EPIs to the digitally reconstructed radiographs and the semi-automated 2D/3D registration. A decrease in the intermarker distance was found correlating with prostate shrinkage rather than with random marker migration. Conclusions: Inclusion of shrinkage in the registration process reduces registration errors during a course of radiotherapy. Nevertheless, this did not lead to a clinically significant change in the proposed table translations when compared to translations obtained with manual marker matching without a scaling correction

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)

Sulcal set optimization for cortical surface registration.

Science.gov (United States)

Joshi, Anand A; Pantazis, Dimitrios; Li, Quanzheng; Damasio, Hanna; Shattuck, David W; Toga, Arthur W; Leahy, Richard M

2010-04-15

Flat mapping based cortical surface registration constrained by manually traced sulcal curves has been widely used for inter subject comparisons of neuroanatomical data. Even for an experienced neuroanatomist, manual sulcal tracing can be quite time consuming, with the cost increasing with the number of sulcal curves used for registration. We present a method for estimation of an optimal subset of size N(C) from N possible candidate sulcal curves that minimizes a mean squared error metric over all combinations of N(C) curves. The resulting procedure allows us to estimate a subset with a reduced number of curves to be traced as part of the registration procedure leading to optimal use of manual labeling effort for registration. To minimize the error metric we analyze the correlation structure of the errors in the sulcal curves by modeling them as a multivariate Gaussian distribution. For a given subset of sulci used as constraints in surface registration, the proposed model estimates registration error based on the correlation structure of the sulcal errors. The optimal subset of constraint curves consists of the N(C) sulci that jointly minimize the estimated error variance for the subset of unconstrained curves conditioned on the N(C) constraint curves. The optimal subsets of sulci are presented and the estimated and actual registration errors for these subsets are computed. Copyright 2009 Elsevier Inc. All rights reserved.

Preconditioned stochastic gradient descent optimisation for monomodal image registration

NARCIS (Netherlands)

Klein, S.; Staring, M.; Andersson, J.P.; Pluim, J.P.W.; Fichtinger, G.; Martel, A.; Peters, T.

2011-01-01

We present a stochastic optimisation method for intensity-based monomodal image registration. The method is based on a Robbins-Monro stochastic gradient descent method with adaptive step size estimation, and adds a preconditioning matrix. The derivation of the pre-conditioner is based on the

Three-dimensional registration methods for multi-modal magnetic resonance neuroimages

International Nuclear Information System (INIS)

Triantafyllou, C.

2001-08-01

In this thesis, image alignment techniques are developed and evaluated for applications in neuroimaging. In particular, the problem of combining cross-sequence MRI (Magnetic Resonance Imaging) intra-subject scans is considered. The challenge in this case is to find topographically uniform mappings in order to register (find a mapping between) low resolution echo-planar images and their high resolution structural counterparts. Such an approach enables us to effectually fuse, in a clinically useful way, information across scans. This dissertation devises a new framework by which this may be achieved, involving appropriate optimisation of the required mapping functions, which turn out to be non-linear and high-dimensional in nature. Novel ways to constrain and regularise these functions to enhance the computational speed of the process and the accuracy of the solution are also studied. The algorithms, whose characteristics are demonstrated for this specific application should be fully generalisable to other medical imaging modalities and potentially, other areas of image processing. To begin with, some existing registration methods are reviewed, followed by the introduction of an automated global 3-D registration method. Its performance is investigated on extracted cortical and ventricular surfaces by utilising the principles of the chamfer matching approach. Evaluations on synthetic and real data-sets, are performed to show that removal of global image differences is possible in principle, although the true accuracy of the method depends on the type of geometrical distortions present. These results also reveal that this class of algorithm is unable to solve more localised variations and higher order magnetic field distortions between the images. These facts motivate the development of a high-dimensional 3-D registration method capable of effecting a one-to-one correspondence by capturing the localised differences. This method was seen to account not only for

[Medical image elastic registration smoothed by unconstrained optimized thin-plate spline].

Science.gov (United States)

Zhang, Yu; Li, Shuxiang; Chen, Wufan; Liu, Zhexing

2003-12-01

Elastic registration of medical image is an important subject in medical image processing. Previous work has concentrated on selecting the corresponding landmarks manually and then using thin-plate spline interpolating to gain the elastic transformation. However, the landmarks extraction is always prone to error, which will influence the registration results. Localizing the landmarks manually is also difficult and time-consuming. We the optimization theory to improve the thin-plate spline interpolation, and based on it, used an automatic method to extract the landmarks. Combining these two steps, we have proposed an automatic, exact and robust registration method and have gained satisfactory registration results.

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

cancer. These changes cannot be modeled by the conventional rigid landmark transformation method. In the current study, we found that the deformable anatomic template registration method, based on continuum-mechanics models of deformation, successfully described these large anatomic shape changes before and after ICT. These promising modeling results indicate that realistic registration of the cumulative dose distribution to the organs (or targets) of interest for radiation therapy of cervical cancers is achievable

Behaviors study of image registration algorithms in image guided radiation therapy

International Nuclear Information System (INIS)

Zou Lian; Hou Qing

2008-01-01

Objective: Study the behaviors of image registration algorithms, and analyze the elements which influence the performance of image registrations. Methods: Pre-known corresponding coordinates were appointed for reference image and moving image, and then the influence of region of interest (ROI) selection, transformation function initial parameters and coupled parameter spaces on registration results were studied with a software platform developed in home. Results: Region of interest selection had a manifest influence on registration performance. An improperly chosen ROI resulted in a bad registration. Transformation function initial parameters selection based on pre-known information could improve the accuracy of image registration. Coupled parameter spaces would enhance the dependence of image registration algorithm on ROI selection. Conclusions: It is necessary for clinic IGRT to obtain a ROI selection strategy (depending on specific commercial software) correlated to tumor sites. Three suggestions for image registration technique developers are automatic selection of the initial parameters of transformation function based on pre-known information, developing specific image registration algorithm for specific image feature, and assembling real-time image registration algorithms according to tumor sites selected by software user. (authors)

One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI

International Nuclear Information System (INIS)

Arabi, Hossein; Zaidi, Habib

2016-01-01

The outcome of a detailed assessment of various strategies for atlas-based whole-body bone segmentation from magnetic resonance imaging (MRI) was exploited to select the optimal parameters and setting, with the aim of proposing a novel one-registration multi-atlas (ORMA) pseudo-CT generation approach. The proposed approach consists of only one online registration between the target and reference images, regardless of the number of atlas images (N), while for the remaining atlas images, the pre-computed transformation matrices to the reference image are used to align them to the target image. The performance characteristics of the proposed method were evaluated and compared with conventional atlas-based attenuation map generation strategies (direct registration of the entire atlas images followed by voxel-wise weighting (VWW) and arithmetic averaging atlas fusion). To this end, four different positron emission tomography (PET) attenuation maps were generated via arithmetic averaging and VWW scheme using both direct registration and ORMA approaches as well as the 3-class attenuation map obtained from the Philips Ingenuity TF PET/MRI scanner commonly used in the clinical setting. The evaluation was performed based on the accuracy of extracted whole-body bones by the different attenuation maps and by quantitative analysis of resulting PET images compared to CT-based attenuation-corrected PET images serving as reference. The comparison of validation metrics regarding the accuracy of extracted bone using the different techniques demonstrated the superiority of the VWW atlas fusion algorithm achieving a Dice similarity measure of 0.82 ± 0.04 compared to arithmetic averaging atlas fusion (0.60 ± 0.02), which uses conventional direct registration. Application of the ORMA approach modestly compromised the accuracy, yielding a Dice similarity measure of 0.76 ± 0.05 for ORMA-VWW and 0.55 ± 0.03 for ORMA-averaging. The results of quantitative PET analysis followed the same

One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI

Energy Technology Data Exchange (ETDEWEB)

Arabi, Hossein [Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Geneva 4 (Switzerland); Zaidi, Habib [Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Geneva 4 (Switzerland); Geneva University, Geneva Neuroscience Center, Geneva (Switzerland); University of Groningen, University Medical Center Groningen, Department of Nuclear Medicine and Molecular Imaging, Groningen (Netherlands); University of Southern Denmark, Department of Nuclear Medicine, Odense (Denmark)

2016-10-15

The outcome of a detailed assessment of various strategies for atlas-based whole-body bone segmentation from magnetic resonance imaging (MRI) was exploited to select the optimal parameters and setting, with the aim of proposing a novel one-registration multi-atlas (ORMA) pseudo-CT generation approach. The proposed approach consists of only one online registration between the target and reference images, regardless of the number of atlas images (N), while for the remaining atlas images, the pre-computed transformation matrices to the reference image are used to align them to the target image. The performance characteristics of the proposed method were evaluated and compared with conventional atlas-based attenuation map generation strategies (direct registration of the entire atlas images followed by voxel-wise weighting (VWW) and arithmetic averaging atlas fusion). To this end, four different positron emission tomography (PET) attenuation maps were generated via arithmetic averaging and VWW scheme using both direct registration and ORMA approaches as well as the 3-class attenuation map obtained from the Philips Ingenuity TF PET/MRI scanner commonly used in the clinical setting. The evaluation was performed based on the accuracy of extracted whole-body bones by the different attenuation maps and by quantitative analysis of resulting PET images compared to CT-based attenuation-corrected PET images serving as reference. The comparison of validation metrics regarding the accuracy of extracted bone using the different techniques demonstrated the superiority of the VWW atlas fusion algorithm achieving a Dice similarity measure of 0.82 ± 0.04 compared to arithmetic averaging atlas fusion (0.60 ± 0.02), which uses conventional direct registration. Application of the ORMA approach modestly compromised the accuracy, yielding a Dice similarity measure of 0.76 ± 0.05 for ORMA-VWW and 0.55 ± 0.03 for ORMA-averaging. The results of quantitative PET analysis followed the same

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...

Gaussian Process Interpolation for Uncertainty Estimation in Image Registration

Science.gov (United States)

Wachinger, Christian; Golland, Polina; Reuter, Martin; Wells, William

2014-01-01

Intensity-based image registration requires resampling images on a common grid to evaluate the similarity function. The uncertainty of interpolation varies across the image, depending on the location of resampled points relative to the base grid. We propose to perform Bayesian inference with Gaussian processes, where the covariance matrix of the Gaussian process posterior distribution estimates the uncertainty in interpolation. The Gaussian process replaces a single image with a distribution over images that we integrate into a generative model for registration. Marginalization over resampled images leads to a new similarity measure that includes the uncertainty of the interpolation. We demonstrate that our approach increases the registration accuracy and propose an efficient approximation scheme that enables seamless integration with existing registration methods. PMID:25333127

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.

Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks.

Science.gov (United States)

Eppenhof, Koen A J; Pluim, Josien P W

2018-04-01

Error estimation in nonlinear medical image registration is a nontrivial problem that is important for validation of registration methods. We propose a supervised method for estimation of registration errors in nonlinear registration of three-dimensional (3-D) images. The method is based on a 3-D convolutional neural network that learns to estimate registration errors from a pair of image patches. By applying the network to patches centered around every voxel, we construct registration error maps. The network is trained using a set of representative images that have been synthetically transformed to construct a set of image pairs with known deformations. The method is evaluated on deformable registrations of inhale-exhale pairs of thoracic CT scans. Using ground truth target registration errors on manually annotated landmarks, we evaluate the method's ability to estimate local registration errors. Estimation of full domain error maps is evaluated using a gold standard approach. The two evaluation approaches show that we can train the network to robustly estimate registration errors in a predetermined range, with subvoxel accuracy. We achieved a root-mean-square deviation of 0.51 mm from gold standard registration errors and of 0.66 mm from ground truth landmark registration errors.

Accuracy evaluation of initialization-free registration for intraoperative 3D-navigation

International Nuclear Information System (INIS)

Diakov, Georgi; Freysinger, Wolfgang

2007-01-01

Purpose An initialization-free approach for perioperative registration in functional endoscopic sinus surgery (FESS) is sought. The quality of surgical navigation relies on registration accuracy of preoperative images to the patient. Although landmark-based registration is fast, it is prone to human operator errors. This study evaluates the accuracy of two well-known methods for segmentation of the occipital bone from CT-images for use in surgical 3D-navigation. Method The occipital bone was segmented for registration without pre-defined correspondences, with the iterative closest point algorithm (ICP). The thresholding plus marching cubes segmentation (TMCS), and the deformable model segmentation (DMS) were compared quantitatively by overlaying the areas of the segmentations in cross-sectional slices, and visually by displaying the pointwise distances between the segmentations in a three-dimensional distance map relative to an expert manual segmentation, taken as a ''ground truth''. Results Excellent correspondence between the two methods was achieved; the results showed, however, that the TMCS is closer to the ''ground truth''. This is due to the sub-voxel accuracy of the marching cubes algorithm by definition, and the sensitivity of the DMS method to the choice of parameters. The DMS approach, as a gradient-based method, is insensitive to the thresholding initialization. For noisy images and soft tissue delineation a gradient-based method, like the deformable model, performs better. Both methods correspond within minute differences less than 4%. Conclusion These results will allow further minimization of human interaction in the planning phase for intraoperative 3D-navigation, by allowing to automatically create surface patches for registration purposes, ultimately allowing to build an initialization-free, fully automatic registration procedure for navigated Ear-, Nose-, Throat- (ENT) surgery. (orig.)

[Accurate 3D free-form registration between fan-beam CT and cone-beam CT].

Science.gov (United States)

Liang, Yueqiang; Xu, Hongbing; Li, Baosheng; Li, Hongsheng; Yang, Fujun

2012-06-01

Because the X-ray scatters, the CT numbers in cone-beam CT cannot exactly correspond to the electron densities. This, therefore, results in registration error when the intensity-based registration algorithm is used to register planning fan-beam CT and cone-beam CT. In order to reduce the registration error, we have developed an accurate gradient-based registration algorithm. The gradient-based deformable registration problem is described as a minimization of energy functional. Through the calculus of variations and Gauss-Seidel finite difference method, we derived the iterative formula of the deformable registration. The algorithm was implemented by GPU through OpenCL framework, with which the registration time was greatly reduced. Our experimental results showed that the proposed gradient-based registration algorithm could register more accurately the clinical cone-beam CT and fan-beam CT images compared with the intensity-based algorithm. The GPU-accelerated algorithm meets the real-time requirement in the online adaptive radiotherapy.

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.

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

Energy Technology Data Exchange (ETDEWEB)

Khalvati, Farzad, E-mail: farzad.khalvati@uwaterloo.ca; Tizhoosh, Hamid R. [Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1 (Canada); Salmanpour, Aryan; Rahnamayan, Shahryar [Department of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Ontario L1H 7K4 (Canada); Rodrigues, George [Department of Radiation Oncology, London Regional Cancer Program, London, Ontario N6C 2R6, Canada and Department of Epidemiology/Biostatistics, University of Western Ontario, London, Ontario N6A 3K7 (Canada)

2013-12-15

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

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

International Nuclear Information System (INIS)

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

2013-01-01

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

Increasing the automation of a 2D-3D registration system.

Science.gov (United States)

Varnavas, Andreas; Carrell, Tom; Penney, Graeme

2013-02-01

Routine clinical use of 2D-3D registration algorithms for Image Guided Surgery remains limited. A key aspect for routine clinical use of this technology is its degree of automation, i.e., the amount of necessary knowledgeable interaction between the clinicians and the registration system. Current image-based registration approaches usually require knowledgeable manual interaction during two stages: for initial pose estimation and for verification of produced results. We propose four novel techniques, particularly suited to vertebra-based registration systems, which can significantly automate both of the above stages. Two of these techniques are based upon the intraoperative "insertion" of a virtual fiducial marker into the preoperative data. The remaining two techniques use the final registration similarity value between multiple CT vertebrae and a single fluoroscopy vertebra. The proposed methods were evaluated with data from 31 operations (31 CT scans, 419 fluoroscopy images). Results show these methods can remove the need for manual vertebra identification during initial pose estimation, and were also very effective for result verification, producing a combined true positive rate of 100% and false positive rate equal to zero. This large decrease in required knowledgeable interaction is an important contribution aiming to enable more widespread use of 2D-3D registration technology.

Marker Registration Technique for Handwritten Text Marker in Augmented Reality Applications

Science.gov (United States)

Thanaborvornwiwat, N.; Patanukhom, K.

2018-04-01

Marker registration is a fundamental process to estimate camera poses in marker-based Augmented Reality (AR) systems. We developed AR system that creates correspondence virtual objects on handwritten text markers. This paper presents a new method for registration that is robust for low-content text markers, variation of camera poses, and variation of handwritten styles. The proposed method uses Maximally Stable Extremal Regions (MSER) and polygon simplification for a feature point extraction. The experiment shows that we need to extract only five feature points per image which can provide the best registration results. An exhaustive search is used to find the best matching pattern of the feature points in two images. We also compared performance of the proposed method to some existing registration methods and found that the proposed method can provide better accuracy and time efficiency.

Improving efficiency of multi-modality registration of brain scans based on mutual information

International Nuclear Information System (INIS)

Thurfjell, L.; Lau, Y.; Hutton, B.; Westmead Hospital, Sydney, NSW; University of Technology, Sydney, NSW

1999-01-01

Full text: One approach for multi-modality registration uses a similarity measure based on mutual information (MI) of voxel intensities. MI measures the statistical dependence between two images by comparing the joint probability distribution (approximated by the 2D joint histogram), with the distribution in the case of complete independence (approximated from the I D histograms). The MI measure reaches a maximum when the images are aligned. The purpose of the current work was to investigate if the registration process could be accelerated through subsampling, i.e. by using only a subset of all voxels for the calculations. The behaviour of the MI measure at different subsampling factors was studied. It was observed that subsampling caused MI to exhibit multiple local maxima unless it was accompanied by a reduction in the number of bins used for the histograms. However, too few bins in the histograms made the peak of the MI measure broader. It was therefore concluded that a coarse-to-fine subsampling procedure, followed by a corresponding increase in the number of bins in the histogram, would be the best choice. The method was validated on SPET-MRI data from seven healthy volunteers. Using a 64:1, 32:1 and 16:1 subsampling scheme with a corresponding bin size of 24, 32 and 48, the new method converged in an average time of 2.5 min as compared to 46 min for the original method (PC Pentium 200). The average absolute differences were 0.24, 0.34, 0.30 mm translation and 0.58, 0.41, 0.66 degrees rotation. We conclude that the suggested scheme renders the registration method sufficiently rapid for routine use in the clinical setting

SU-C-207B-06: Comparison of Registration Methods for Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Riyahi, S; Choi, W; Bhooshan, N; Tan, S; Zhang, H; Lu, W [University of Maryland School of Medicine, Baltimore, MD (United States)

2016-06-15

Purpose: To compare linear and deformable registration methods for evaluation of tumor response to Chemoradiation therapy (CRT) in patients with esophageal cancer. Methods: Linear and multi-resolution BSpline deformable registration were performed on Pre-Post-CRT CT/PET images of 20 patients with esophageal cancer. For both registration methods, we registered CT using Mean Square Error (MSE) metric, however to register PET we used transformation obtained using Mutual Information (MI) from the same CT due to being multi-modality. Similarity of Warped-CT/PET was quantitatively evaluated using Normalized Mutual Information and plausibility of DF was assessed using inverse consistency Error. To evaluate tumor response four groups of tumor features were examined: (1) Conventional PET/CT e.g. SUV, diameter (2) Clinical parameters e.g. TNM stage, histology (3)spatial-temporal PET features that describe intensity, texture and geometry of tumor (4)all features combined. Dominant features were identified using 10-fold cross-validation and Support Vector Machine (SVM) was deployed for tumor response prediction while the accuracy was evaluated by ROC Area Under Curve (AUC). Results: Average and standard deviation of Normalized mutual information for deformable registration using MSE was 0.2±0.054 and for linear registration was 0.1±0.026, showing higher NMI for deformable registration. Likewise for MI metric, deformable registration had 0.13±0.035 comparing to linear counterpart with 0.12±0.037. Inverse consistency error for deformable registration for MSE metric was 4.65±2.49 and for linear was 1.32±2.3 showing smaller value for linear registration. The same conclusion was obtained for MI in terms of inverse consistency error. AUC for both linear and deformable registration was 1 showing no absolute difference in terms of response evaluation. Conclusion: Deformable registration showed better NMI comparing to linear registration, however inverse consistency of

Validation of non-rigid point-set registration methods using a porcine bladder pelvic phantom

Science.gov (United States)

Zakariaee, Roja; Hamarneh, Ghassan; Brown, Colin J.; Spadinger, Ingrid

2016-01-01

The problem of accurate dose accumulation in fractionated radiotherapy treatment for highly deformable organs, such as bladder, has garnered increasing interest over the past few years. However, more research is required in order to find a robust and efficient solution and to increase the accuracy over the current methods. The purpose of this study was to evaluate the feasibility and accuracy of utilizing non-rigid (affine or deformable) point-set registration in accumulating dose in bladder of different sizes and shapes. A pelvic phantom was built to house an ex vivo porcine bladder with fiducial landmarks adhered onto its surface. Four different volume fillings of the bladder were used (90, 180, 360 and 480 cc). The performance of MATLAB implementations of five different methods were compared, in aligning the bladder contour point-sets. The approaches evaluated were coherent point drift (CPD), gaussian mixture model, shape context, thin-plate spline robust point matching (TPS-RPM) and finite iterative closest point (ICP-finite). The evaluation metrics included registration runtime, target registration error (TRE), root-mean-square error (RMS) and Hausdorff distance (HD). The reference (source) dataset was alternated through all four points-sets, in order to study the effect of reference volume on the registration outcomes. While all deformable algorithms provided reasonable registration results, CPD provided the best TRE values (6.4 mm), and TPS-RPM yielded the best mean RMS and HD values (1.4 and 6.8 mm, respectively). ICP-finite was the fastest technique and TPS-RPM, the slowest.

Validation of non-rigid point-set registration methods using a porcine bladder pelvic phantom

International Nuclear Information System (INIS)

Zakariaee, Roja; Hamarneh, Ghassan; Brown, Colin J; Spadinger, Ingrid

2016-01-01

The problem of accurate dose accumulation in fractionated radiotherapy treatment for highly deformable organs, such as bladder, has garnered increasing interest over the past few years. However, more research is required in order to find a robust and efficient solution and to increase the accuracy over the current methods. The purpose of this study was to evaluate the feasibility and accuracy of utilizing non-rigid (affine or deformable) point-set registration in accumulating dose in bladder of different sizes and shapes. A pelvic phantom was built to house an ex vivo porcine bladder with fiducial landmarks adhered onto its surface. Four different volume fillings of the bladder were used (90, 180, 360 and 480 cc). The performance of MATLAB implementations of five different methods were compared, in aligning the bladder contour point-sets. The approaches evaluated were coherent point drift (CPD), gaussian mixture model, shape context, thin-plate spline robust point matching (TPS-RPM) and finite iterative closest point (ICP-finite). The evaluation metrics included registration runtime, target registration error (TRE), root-mean-square error (RMS) and Hausdorff distance (HD). The reference (source) dataset was alternated through all four points-sets, in order to study the effect of reference volume on the registration outcomes. While all deformable algorithms provided reasonable registration results, CPD provided the best TRE values (6.4 mm), and TPS-RPM yielded the best mean RMS and HD values (1.4 and 6.8 mm, respectively). ICP-finite was the fastest technique and TPS-RPM, the slowest. (paper)

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

Energy Technology Data Exchange (ETDEWEB)

Mitrović, Uroš [Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, Ljubljana 1000, Slovenia and Cosylab, Control System Laboratory, Teslova ulica 30, Ljubljana 1000 (Slovenia); Pernuš, Franjo [Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, Ljubljana 1000 (Slovenia); Likar, Boštjan; Špiclin, Žiga, E-mail: ziga.spiclin@fe.uni-lj.si [Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, Ljubljana 1000, Slovenia and Sensum, Computer Vision Systems, Tehnološki Park 21, Ljubljana 1000 (Slovenia)

2015-11-15

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

First clinical experience with a multiple region of interest registration and correction method in radiotherapy of head-and-neck cancer patients

International Nuclear Information System (INIS)

Beek, Suzanne van; Kranen, Simon van; Mencarelli, Angelo; Remeijer, Peter; Rasch, Coen; Herk, Marcel van; Sonke, Jan-Jakob

2010-01-01

Purpose: To discuss the first clinical experience with a multiple region of interest (mROI) registration and correction method for high-precision radiotherapy of head-and-neck cancer patients. Materials and methods: 12-13 3D rectangular-shaped ROIs were automatically placed around bony structures on the planning CT scans (n = 50 patients) which were individually registered to subsequent CBCT scans. mROI registration was used to quantify global and local setup errors. The time required to perform the mROI registration was compared with that of a previously used single-ROI method. The number of scans with residual local setup error exceeding 5 mm/5 deg. (warnings) was scored together with the frequency ROIs exceeding these limits for three or more consecutive imaging fractions (systematic errors). Results: In 40% of the CBCT scans, one or more ROI-registrations exceeded the 5 mm/5 deg.. Most warnings were seen in ROI 'hyoid', 31% of the rotation warnings and 14% of the translation warnings. Systematic errors lead to 52 consults of the treating physician. The preparation and registration time was similar for both registration methods. Conclusions: The mROI registration method is easy to use with little extra workload, provides additional information on local setup errors, and helps to select patients for re-planning.

Spline-based image-to-volume registration for three-dimensional electron microscopy

International Nuclear Information System (INIS)

Jonic, S.; Sorzano, C.O.S.; Thevenaz, P.; El-Bez, C.; De Carlo, S.; Unser, M.

2005-01-01

This paper presents an algorithm based on a continuous framework for a posteriori angular and translational assignment in three-dimensional electron microscopy (3DEM) of single particles. Our algorithm can be used advantageously to refine the assignment of standard quantized-parameter methods by registering the images to a reference 3D particle model. We achieve the registration by employing a gradient-based iterative minimization of a least-squares measure of dissimilarity between an image and a projection of the volume in the Fourier transform (FT) domain. We compute the FT of the projection using the central-slice theorem (CST). To compute the gradient accurately, we take advantage of a cubic B-spline model of the data in the frequency domain. To improve the robustness of the algorithm, we weight the cost function in the FT domain and apply a 'mixed' strategy for the assignment based on the minimum value of the cost function at registration for several different initializations. We validate our algorithm in a fully controlled simulation environment. We show that the mixed strategy improves the assignment accuracy; on our data, the quality of the angular and translational assignment was better than 2 voxel (i.e., 6.54 A). We also test the performance of our algorithm on real EM data. We conclude that our algorithm outperforms a standard projection-matching refinement in terms of both consistency of 3D reconstructions and speed

The skill of surface registration in CT-based navigation system for total hip arthroplasty

International Nuclear Information System (INIS)

Hananouchi, T.; Sugano, N.; Nishii, T.; Miki, H.; Sakai, T.; Yoshikawa, H.; Iwana, D.; Yamamura, M.; Nakamura, N.

2007-01-01

Surface registration of the CT-based navigation system, which is a matching between computational and real spatial spaces, is a key step to guarantee the accuracy of navigation. However, it has not been well described how the accuracy is affected by the registration skill of surgeon. Here, we reported the difference of the registration error between eight surgeons with the experience of navigation and six apprentice surgeons. A cadaveric pelvic model with an acetabular cup was made to measure the skill and learning curve of registration. After surface registration, two cup angles (inclination and anteversion) were recorded in the navigation system and the variance of these cup angles in ten trials were compared between the experienced surgeons and apprentices. In addition, we investigated whether the accuracy of registration by the apprentices was improved by visual information on how to take the surface points. The results showed that there was statistically significant difference in the accuracy of registration between the two groups. The accuracy of the second ten trials after getting the visual information showed great improvements. (orig.)

A novel 3D volumetric voxel registration technique for volume-view-guided image registration of multiple imaging modalities

International Nuclear Information System (INIS)

Li Guang; Xie Huchen; Ning, Holly; Capala, Jacek; Arora, Barbara C.; Coleman, C. Norman; Camphausen, Kevin; Miller, Robert W.

2005-01-01

Purpose: To provide more clinically useful image registration with improved accuracy and reduced time, a novel technique of three-dimensional (3D) volumetric voxel registration of multimodality images is developed. Methods and Materials: This technique can register up to four concurrent images from multimodalities with volume view guidance. Various visualization effects can be applied, facilitating global and internal voxel registration. Fourteen computed tomography/magnetic resonance (CT/MR) image sets and two computed tomography/positron emission tomography (CT/PET) image sets are used. For comparison, an automatic registration technique using maximization of mutual information (MMI) and a three-orthogonal-planar (3P) registration technique are used. Results: Visually sensitive registration criteria for CT/MR and CT/PET have been established, including the homogeneity of color distribution. Based on the registration results of 14 CT/MR images, the 3D voxel technique is in excellent agreement with the automatic MMI technique and is indicatory of a global positioning error (defined as the means and standard deviations of the error distribution) using the 3P pixel technique: 1.8 deg ± 1.2 deg in rotation and 2.0 ± 1.3 (voxel unit) in translation. To the best of our knowledge, this is the first time that such positioning error has been addressed. Conclusion: This novel 3D voxel technique establishes volume-view-guided image registration of up to four modalities. It improves registration accuracy with reduced time, compared with the 3P pixel technique. This article suggests that any interactive and automatic registration should be safeguarded using the 3D voxel technique

A Comparison of FFD-based Nonrigid Registration and AAMs Applied to Myocardial Perfusion MRI

DEFF Research Database (Denmark)

Ólafsdóttir, Hildur; Stegmann, Mikkel Bille; Ersbøll, Bjarne Kjær

2006-01-01

-form deformations (FFDs). AAMs are known to be much faster than nonrigid registration algorithms. On the other hand nonrigid registration algorithms are independent of a training set as required to build an AAM. To obtain a further comparison of the two methods, they are both applied to automatically register multi...

Serial volumetric registration of pulmonary CT studies

Science.gov (United States)

Silva, José Silvestre; Silva, Augusto; Sousa Santos, Beatriz

2008-03-01

Detailed morphological analysis of pulmonary structures and tissue, provided by modern CT scanners, is of utmost importance as in the case of oncological applications both for diagnosis, treatment, and follow-up. In this case, a patient may go through several tomographic studies throughout a period of time originating volumetric sets of image data that must be appropriately registered in order to track suspicious radiological findings. The structures or regions of interest may change their position or shape in CT exams acquired at different moments, due to postural, physiologic or pathologic changes, so, the exams should be registered before any follow-up information can be extracted. Postural mismatching throughout time is practically impossible to avoid being particularly evident when imaging is performed at the limiting spatial resolution. In this paper, we propose a method for intra-patient registration of pulmonary CT studies, to assist in the management of the oncological pathology. Our method takes advantage of prior segmentation work. In the first step, the pulmonary segmentation is performed where trachea and main bronchi are identified. Then, the registration method proceeds with a longitudinal alignment based on morphological features of the lungs, such as the position of the carina, the pulmonary areas, the centers of mass and the pulmonary trans-axial principal axis. The final step corresponds to the trans-axial registration of the corresponding pulmonary masked regions. This is accomplished by a pairwise sectional registration process driven by an iterative search of the affine transformation parameters leading to optimal similarity metrics. Results with several cases of intra-patient, intra-modality registration, up to 7 time points, show that this method provides accurate registration which is needed for quantitative tracking of lesions and the development of image fusion strategies that may effectively assist the follow-up process.

Position tracking of moving liver lesion based on real-time registration between 2D ultrasound and 3D preoperative images

International Nuclear Information System (INIS)

Weon, Chijun; Hyun Nam, Woo; Lee, Duhgoon; Ra, Jong Beom; Lee, Jae Young

2015-01-01

Purpose: Registration between 2D ultrasound (US) and 3D preoperative magnetic resonance (MR) (or computed tomography, CT) images has been studied recently for US-guided intervention. However, the existing techniques have some limits, either in the registration speed or the performance. The purpose of this work is to develop a real-time and fully automatic registration system between two intermodal images of the liver, and subsequently an indirect lesion positioning/tracking algorithm based on the registration result, for image-guided interventions. Methods: The proposed position tracking system consists of three stages. In the preoperative stage, the authors acquire several 3D preoperative MR (or CT) images at different respiratory phases. Based on the transformations obtained from nonrigid registration of the acquired 3D images, they then generate a 4D preoperative image along the respiratory phase. In the intraoperative preparatory stage, they properly attach a 3D US transducer to the patient’s body and fix its pose using a holding mechanism. They then acquire a couple of respiratory-controlled 3D US images. Via the rigid registration of these US images to the 3D preoperative images in the 4D image, the pose information of the fixed-pose 3D US transducer is determined with respect to the preoperative image coordinates. As feature(s) to use for the rigid registration, they may choose either internal liver vessels or the inferior vena cava. Since the latter is especially useful in patients with a diffuse liver disease, the authors newly propose using it. In the intraoperative real-time stage, they acquire 2D US images in real-time from the fixed-pose transducer. For each US image, they select candidates for its corresponding 2D preoperative slice from the 4D preoperative MR (or CT) image, based on the predetermined pose information of the transducer. The correct corresponding image is then found among those candidates via real-time 2D registration based on a

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

Science.gov (United States)

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

2007-03-01

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

3D non-rigid surface-based MR-TRUS registration for image-guided prostate biopsy

Science.gov (United States)

Sun, Yue; Qiu, Wu; Romagnoli, Cesare; Fenster, Aaron

2014-03-01

Two dimensional (2D) transrectal ultrasound (TRUS) guided prostate biopsy is the standard approach for definitive diagnosis of prostate cancer (PCa). However, due to the lack of image contrast of prostate tumors needed to clearly visualize early-stage PCa, prostate biopsy often results in false negatives, requiring repeat biopsies. Magnetic Resonance Imaging (MRI) has been considered to be a promising imaging modality for noninvasive identification of PCa, since it can provide a high sensitivity and specificity for the detection of early stage PCa. Our main objective is to develop and validate a registration method of 3D MR-TRUS images, allowing generation of volumetric 3D maps of targets identified in 3D MR images to be biopsied using 3D TRUS images. Our registration method first makes use of an initial rigid registration of 3D MR images to 3D TRUS images using 6 manually placed approximately corresponding landmarks in each image. Following the manual initialization, two prostate surfaces are segmented from 3D MR and TRUS images and then non-rigidly registered using a thin-plate spline (TPS) algorithm. The registration accuracy was evaluated using 4 patient images by measuring target registration error (TRE) of manually identified corresponding intrinsic fiducials (calcifications and/or cysts) in the prostates. Experimental results show that the proposed method yielded an overall mean TRE of 2.05 mm, which is favorably comparable to a clinical requirement for an error of less than 2.5 mm.

A Markov Random Field Groupwise Registration Framework for Face Recognition.

Science.gov (United States)

Liao, Shu; Shen, Dinggang; Chung, Albert C S

2014-04-01

In this paper, we propose a new framework for tackling face recognition problem. The face recognition problem is formulated as groupwise deformable image registration and feature matching problem. The main contributions of the proposed method lie in the following aspects: (1) Each pixel in a facial image is represented by an anatomical signature obtained from its corresponding most salient scale local region determined by the survival exponential entropy (SEE) information theoretic measure. (2) Based on the anatomical signature calculated from each pixel, a novel Markov random field based groupwise registration framework is proposed to formulate the face recognition problem as a feature guided deformable image registration problem. The similarity between different facial images are measured on the nonlinear Riemannian manifold based on the deformable transformations. (3) The proposed method does not suffer from the generalizability problem which exists commonly in learning based algorithms. The proposed method has been extensively evaluated on four publicly available databases: FERET, CAS-PEAL-R1, FRGC ver 2.0, and the LFW. It is also compared with several state-of-the-art face recognition approaches, and experimental results demonstrate that the proposed method consistently achieves the highest recognition rates among all the methods under comparison.

In-die photomask registration and overlay metrology with PROVE using 2D correlation methods

Science.gov (United States)

Seidel, D.; Arnz, M.; Beyer, D.

2011-11-01

According to the ITRS roadmap, semiconductor industry drives the 193nm lithography to its limits, using techniques like double exposure, double patterning, mask-source optimization and inverse lithography. For photomask metrology this translates to full in-die measurement capability for registration and critical dimension together with challenging specifications for repeatability and accuracy. Especially, overlay becomes more and more critical and must be ensured on every die. For this, Carl Zeiss SMS has developed the next generation photomask registration and overlay metrology tool PROVE® which serves the 32nm node and below and which is already well established in the market. PROVE® features highly stable hardware components for the stage and environmental control. To ensure in-die measurement capability, sophisticated image analysis methods based on 2D correlations have been developed. In this paper we demonstrate the in-die capability of PROVE® and present corresponding measurement results for shortterm and long-term measurements as well as the attainable accuracy for feature sizes down to 85nm using different illumination modes and mask types. Standard measurement methods based on threshold criteria are compared with the new 2D correlation methods to demonstrate the performance gain of the latter. In addition, mask-to-mask overlay results of typical box-in-frame structures down to 200nm feature size are presented. It is shown, that from overlay measurements a reproducibility budget can be derived that takes into account stage, image analysis and global effects like mask loading and environmental control. The parts of the budget are quantified from measurement results to identify critical error contributions and to focus on the corresponding improvement strategies.

A MRI-CT prostate registration using sparse representation technique

Science.gov (United States)

Yang, Xiaofeng; Jani, Ashesh B.; Rossi, Peter J.; Mao, Hui; Curran, Walter J.; Liu, Tian

2016-03-01

Purpose: To develop a new MRI-CT prostate registration using patch-based deformation prediction framework to improve MRI-guided prostate radiotherapy by incorporating multiparametric MRI into planning CT images. Methods: The main contribution is to estimate the deformation between prostate MRI and CT images in a patch-wise fashion by using the sparse representation technique. We assume that two image patches should follow the same deformation if their patch-wise appearance patterns are similar. Specifically, there are two stages in our proposed framework, i.e., the training stage and the application stage. In the training stage, each prostate MR images are carefully registered to the corresponding CT images and all training MR and CT images are carefully registered to a selected CT template. Thus, we obtain the dense deformation field for each training MR and CT image. In the application stage, for registering a new subject MR image with the same subject CT image, we first select a small number of key points at the distinctive regions of this subject CT image. Then, for each key point in the subject CT image, we extract the image patch, centered at the underlying key point. Then, we adaptively construct the coupled dictionary for the underlying point where each atom in the dictionary consists of image patches and the respective deformations obtained from training pair-wise MRI-CT images. Next, the subject image patch can be sparsely represented by a linear combination of training image patches in the dictionary, where we apply the same sparse coefficients to the respective deformations in the dictionary to predict the deformation for the subject MR image patch. After we repeat the same procedure for each subject CT key point, we use B-splines to interpolate a dense deformation field, which is used as the initialization to allow the registration algorithm estimating the remaining small segment of deformations from MRI to CT image. Results: Our MRI-CT registration

Vision based tunnel inspection using non-rigid registration

Science.gov (United States)

Badshah, Amir; Ullah, Shan; Shahzad, Danish

2015-04-01

Growing numbers of long tunnels across the globe has increased the need for safety measurements and inspections of tunnels in these days. To avoid serious damages, tunnel inspection is highly recommended at regular intervals of time to find any deformations or cracks at the right time. While following the stringent safety and tunnel accessibility standards, conventional geodetic surveying using techniques of civil engineering and other manual and mechanical methods are time consuming and results in troublesome of routine life. An automatic tunnel inspection by image processing techniques using non rigid registration has been proposed. There are many other image processing methods used for image registration purposes. Most of the processes are operation of images in its spatial domain like finding edges and corners by Harris edge detection method. These methods are quite time consuming and fail for some or other reasons like for blurred or images with noise. Due to use of image features directly by these methods in the process, are known by the group, correlation by image features. The other method is featureless correlation, in which the images are converted into its frequency domain and then correlated with each other. The shift in spatial domain is the same as in frequency domain, but the processing is order faster than in spatial domain. In the proposed method modified normalized phase correlation has been used to find any shift between two images. As pre pre-processing the tunnel images i.e. reference and template are divided into small patches. All these relative patches are registered by the proposed modified normalized phase correlation. By the application of the proposed algorithm we get the pixel movement of the images. And then these pixels shifts are converted to measuring units like mm, cm etc. After the complete process if there is any shift in the tunnel at described points are located.

Determination of optimal ultrasound planes for the initialisation of image registration during endoscopic ultrasound-guided procedures.

Science.gov (United States)

Bonmati, Ester; Hu, Yipeng; Gibson, Eli; Uribarri, Laura; Keane, Geri; Gurusami, Kurinchi; Davidson, Brian; Pereira, Stephen P; Clarkson, Matthew J; Barratt, Dean C

2018-06-01

Navigation of endoscopic ultrasound (EUS)-guided procedures of the upper gastrointestinal (GI) system can be technically challenging due to the small fields-of-view of ultrasound and optical devices, as well as the anatomical variability and limited number of orienting landmarks during navigation. Co-registration of an EUS device and a pre-procedure 3D image can enhance the ability to navigate. However, the fidelity of this contextual information depends on the accuracy of registration. The purpose of this study was to develop and test the feasibility of a simulation-based planning method for pre-selecting patient-specific EUS-visible anatomical landmark locations to maximise the accuracy and robustness of a feature-based multimodality registration method. A registration approach was adopted in which landmarks are registered to anatomical structures segmented from the pre-procedure volume. The predicted target registration errors (TREs) of EUS-CT registration were estimated using simulated visible anatomical landmarks and a Monte Carlo simulation of landmark localisation error. The optimal planes were selected based on the 90th percentile of TREs, which provide a robust and more accurate EUS-CT registration initialisation. The method was evaluated by comparing the accuracy and robustness of registrations initialised using optimised planes versus non-optimised planes using manually segmented CT images and simulated ([Formula: see text]) or retrospective clinical ([Formula: see text]) EUS landmarks. The results show a lower 90th percentile TRE when registration is initialised using the optimised planes compared with a non-optimised initialisation approach (p value [Formula: see text]). The proposed simulation-based method to find optimised EUS planes and landmarks for EUS-guided procedures may have the potential to improve registration accuracy. Further work will investigate applying the technique in a clinical setting.

Monoplane 3D-2D registration of cerebral angiograms based on multi-objective stratified optimization

Science.gov (United States)

Aksoy, T.; Špiclin, Ž.; Pernuš, F.; Unal, G.

2017-12-01

Registration of 3D pre-interventional to 2D intra-interventional medical images has an increasingly important role in surgical planning, navigation and treatment, because it enables the physician to co-locate depth information given by pre-interventional 3D images with the live information in intra-interventional 2D images such as x-ray. Most tasks during image-guided interventions are carried out under a monoplane x-ray, which is a highly ill-posed problem for state-of-the-art 3D to 2D registration methods. To address the problem of rigid 3D-2D monoplane registration we propose a novel multi-objective stratified parameter optimization, wherein a small set of high-magnitude intensity gradients are matched between the 3D and 2D images. The stratified parameter optimization matches rotation templates to depth templates, first sampled from projected 3D gradients and second from the 2D image gradients, so as to recover 3D rigid-body rotations and out-of-plane translation. The objective for matching was the gradient magnitude correlation coefficient, which is invariant to in-plane translation. The in-plane translations are then found by locating the maximum of the gradient phase correlation between the best matching pair of rotation and depth templates. On twenty pairs of 3D and 2D images of ten patients undergoing cerebral endovascular image-guided intervention the 3D to monoplane 2D registration experiments were setup with a rather high range of initial mean target registration error from 0 to 100 mm. The proposed method effectively reduced the registration error to below 2 mm, which was further refined by a fast iterative method and resulted in a high final registration accuracy (0.40 mm) and high success rate (> 96%). Taking into account a fast execution time below 10 s, the observed performance of the proposed method shows a high potential for application into clinical image-guidance systems.

A Comparative Study of Registration Methods for RGB-D Video of Static Scenes

Directory of Open Access Journals (Sweden)

Vicente Morell-Gimenez

2014-05-01

Full Text Available The use of RGB-D sensors for mapping and recognition tasks in robotics or, in general, for virtual reconstruction has increased in recent years. The key aspect of these kinds of sensors is that they provide both depth and color information using the same device. In this paper, we present a comparative analysis of the most important methods used in the literature for the registration of subsequent RGB-D video frames in static scenarios. The analysis begins by explaining the characteristics of the registration problem, dividing it into two representative applications: scene modeling and object reconstruction. Then, a detailed experimentation is carried out to determine the behavior of the different methods depending on the application. For both applications, we used standard datasets and a new one built for object reconstruction.

Global Registration of 3D LiDAR Point Clouds Based on Scene Features: Application to Structured Environments

Directory of Open Access Journals (Sweden)

Julia Sanchez

2017-09-01

Full Text Available Acquiring 3D data with LiDAR systems involves scanning multiple scenes from different points of view. In actual systems, the ICP algorithm (Iterative Closest Point is commonly used to register the acquired point clouds together to form a unique one. However, this method faces local minima issues and often needs a coarse initial alignment to converge to the optimum. This paper develops a new method for registration adapted to indoor environments and based on structure priors of such scenes. Our method works without odometric data or physical targets. The rotation and translation of the rigid transformation are computed separately, using, respectively, the Gaussian image of the point clouds and a correlation of histograms. To evaluate our algorithm on challenging registration cases, two datasets were acquired and are available for comparison with other methods online. The evaluation of our algorithm on four datasets against six existing methods shows that the proposed method is more robust against sampling and scene complexity. Moreover, the time performances enable a real-time implementation.

TH-EF-BRA-03: Assessment of Data-Driven Respiratory Motion-Compensation Methods for 4D-CBCT Image Registration and Reconstruction Using Clinical Datasets

Energy Technology Data Exchange (ETDEWEB)

Riblett, MJ; Weiss, E; Hugo, GD [Virginia Commonwealth University, Richmond, VA (United States); Christensen, GE [University of Iowa, Iowa City, IA (United States)

2016-06-15

Purpose: To evaluate the performance of a 4D-CBCT registration and reconstruction method that corrects for respiratory motion and enhances image quality under clinically relevant conditions. Methods: Building on previous work, which tested feasibility of a motion-compensation workflow using image datasets superior to clinical acquisitions, this study assesses workflow performance under clinical conditions in terms of image quality improvement. Evaluated workflows utilized a combination of groupwise deformable image registration (DIR) and image reconstruction. Four-dimensional cone beam CT (4D-CBCT) FDK reconstructions were registered to either mean or respiratory phase reference frame images to model respiratory motion. The resulting 4D transformation was used to deform projection data during the FDK backprojection operation to create a motion-compensated reconstruction. To simulate clinically realistic conditions, superior quality projection datasets were sampled using a phase-binned striding method. Tissue interface sharpness (TIS) was defined as the slope of a sigmoid curve fit to the lung-diaphragm boundary or to the carina tissue-airway boundary when no diaphragm was discernable. Image quality improvement was assessed in 19 clinical cases by evaluating mitigation of view-aliasing artifacts, tissue interface sharpness recovery, and noise reduction. Results: For clinical datasets, evaluated average TIS recovery relative to base 4D-CBCT reconstructions was observed to be 87% using fixed-frame registration alone; 87% using fixed-frame with motion-compensated reconstruction; 92% using mean-frame registration alone; and 90% using mean-frame with motion-compensated reconstruction. Soft tissue noise was reduced on average by 43% and 44% for the fixed-frame registration and registration with motion-compensation methods, respectively, and by 40% and 42% for the corresponding mean-frame methods. Considerable reductions in view aliasing artifacts were observed for each

TH-EF-BRA-03: Assessment of Data-Driven Respiratory Motion-Compensation Methods for 4D-CBCT Image Registration and Reconstruction Using Clinical Datasets

International Nuclear Information System (INIS)

Riblett, MJ; Weiss, E; Hugo, GD; Christensen, GE

2016-01-01

Purpose: To evaluate the performance of a 4D-CBCT registration and reconstruction method that corrects for respiratory motion and enhances image quality under clinically relevant conditions. Methods: Building on previous work, which tested feasibility of a motion-compensation workflow using image datasets superior to clinical acquisitions, this study assesses workflow performance under clinical conditions in terms of image quality improvement. Evaluated workflows utilized a combination of groupwise deformable image registration (DIR) and image reconstruction. Four-dimensional cone beam CT (4D-CBCT) FDK reconstructions were registered to either mean or respiratory phase reference frame images to model respiratory motion. The resulting 4D transformation was used to deform projection data during the FDK backprojection operation to create a motion-compensated reconstruction. To simulate clinically realistic conditions, superior quality projection datasets were sampled using a phase-binned striding method. Tissue interface sharpness (TIS) was defined as the slope of a sigmoid curve fit to the lung-diaphragm boundary or to the carina tissue-airway boundary when no diaphragm was discernable. Image quality improvement was assessed in 19 clinical cases by evaluating mitigation of view-aliasing artifacts, tissue interface sharpness recovery, and noise reduction. Results: For clinical datasets, evaluated average TIS recovery relative to base 4D-CBCT reconstructions was observed to be 87% using fixed-frame registration alone; 87% using fixed-frame with motion-compensated reconstruction; 92% using mean-frame registration alone; and 90% using mean-frame with motion-compensated reconstruction. Soft tissue noise was reduced on average by 43% and 44% for the fixed-frame registration and registration with motion-compensation methods, respectively, and by 40% and 42% for the corresponding mean-frame methods. Considerable reductions in view aliasing artifacts were observed for each

“Abstractive description” of land registration system based on the theory of “public confidence”

Directory of Open Access Journals (Sweden)

Nasrini Tabatabai Hesari

2014-10-01

Full Text Available the system of land registration is protective formalism that is formed based on the theory of “public confidence”. This theory presumes that what reflected by the land registration offices is based on the legal fact. This theory, which provides legal stability and security in transactions, is manifested in three guiding principles including “mirror principle”, “curtain principle” and “insurance principle”, and offers an “abstractive description” to a land registration system. This character has different effects on diverse legal systems and can be studied for both positive and negative systems.

Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets.

Science.gov (United States)

Scharfe, Michael; Pielot, Rainer; Schreiber, Falk

2010-01-11

Solving bioinformatics tasks often requires extensive computational power. Recent trends in processor architecture combine multiple cores into a single chip to improve overall performance. The Cell Broadband Engine (CBE), a heterogeneous multi-core processor, provides power-efficient and cost-effective high-performance computing. One application area is image analysis and visualisation, in particular registration of 2D cross-sections into 3D image datasets. Such techniques can be used to put different image modalities into spatial correspondence, for example, 2D images of histological cuts into morphological 3D frameworks. We evaluate the CBE-driven PlayStation 3 as a high performance, cost-effective computing platform by adapting a multimodal alignment procedure to several characteristic hardware properties. The optimisations are based on partitioning, vectorisation, branch reducing and loop unrolling techniques with special attention to 32-bit multiplies and limited local storage on the computing units. We show how a typical image analysis and visualisation problem, the multimodal registration of 2D cross-sections and 3D datasets, benefits from the multi-core based implementation of the alignment algorithm. We discuss several CBE-based optimisation methods and compare our results to standard solutions. More information and the source code are available from http://cbe.ipk-gatersleben.de. The results demonstrate that the CBE processor in a PlayStation 3 accelerates computational intensive multimodal registration, which is of great importance in biological/medical image processing. The PlayStation 3 as a low cost CBE-based platform offers an efficient option to conventional hardware to solve computational problems in image processing and bioinformatics.

3D Part-Based Sparse Tracker with Automatic Synchronization and Registration

KAUST Repository

Bibi, Adel Aamer; Zhang, Tianzhu; Ghanem, Bernard

2016-01-01

In this paper, we present a part-based sparse tracker in a particle filter framework where both the motion and appearance model are formulated in 3D. The motion model is adaptive and directed according to a simple yet powerful occlusion handling paradigm, which is intrinsically fused in the motion model. Also, since 3D trackers are sensitive to synchronization and registration noise in the RGB and depth streams, we propose automated methods to solve these two issues. Extensive experiments are conducted on a popular RGBD tracking benchmark, which demonstrate that our tracker can achieve superior results, outperforming many other recent and state-of-the-art RGBD trackers.

3D Part-Based Sparse Tracker with Automatic Synchronization and Registration

KAUST Repository

Bibi, Adel Aamer

2016-12-13

In this paper, we present a part-based sparse tracker in a particle filter framework where both the motion and appearance model are formulated in 3D. The motion model is adaptive and directed according to a simple yet powerful occlusion handling paradigm, which is intrinsically fused in the motion model. Also, since 3D trackers are sensitive to synchronization and registration noise in the RGB and depth streams, we propose automated methods to solve these two issues. Extensive experiments are conducted on a popular RGBD tracking benchmark, which demonstrate that our tracker can achieve superior results, outperforming many other recent and state-of-the-art RGBD trackers.

A block matching-based registration algorithm for localization of locally advanced lung tumors

Energy Technology Data Exchange (ETDEWEB)

Robertson, Scott P.; Weiss, Elisabeth; Hugo, Geoffrey D., E-mail: gdhugo@vcu.edu [Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, 23298 (United States)

2014-04-15

Purpose: To implement and evaluate a block matching-based registration (BMR) algorithm for locally advanced lung tumor localization during image-guided radiotherapy. Methods: Small (1 cm{sup 3}), nonoverlapping image subvolumes (“blocks”) were automatically identified on the planning image to cover the tumor surface using a measure of the local intensity gradient. Blocks were independently and automatically registered to the on-treatment image using a rigid transform. To improve speed and robustness, registrations were performed iteratively from coarse to fine image resolution. At each resolution, all block displacements having a near-maximum similarity score were stored. From this list, a single displacement vector for each block was iteratively selected which maximized the consistency of displacement vectors across immediately neighboring blocks. These selected displacements were regularized using a median filter before proceeding to registrations at finer image resolutions. After evaluating all image resolutions, the global rigid transform of the on-treatment image was computed using a Procrustes analysis, providing the couch shift for patient setup correction. This algorithm was evaluated for 18 locally advanced lung cancer patients, each with 4–7 weekly on-treatment computed tomography scans having physician-delineated gross tumor volumes. Volume overlap (VO) and border displacement errors (BDE) were calculated relative to the nominal physician-identified targets to establish residual error after registration. Results: Implementation of multiresolution registration improved block matching accuracy by 39% compared to registration using only the full resolution images. By also considering multiple potential displacements per block, initial errors were reduced by 65%. Using the final implementation of the BMR algorithm, VO was significantly improved from 77% ± 21% (range: 0%–100%) in the initial bony alignment to 91% ± 8% (range: 56%–100%;p < 0

A block matching-based registration algorithm for localization of locally advanced lung tumors

International Nuclear Information System (INIS)

Robertson, Scott P.; Weiss, Elisabeth; Hugo, Geoffrey D.

2014-01-01

Purpose: To implement and evaluate a block matching-based registration (BMR) algorithm for locally advanced lung tumor localization during image-guided radiotherapy. Methods: Small (1 cm 3 ), nonoverlapping image subvolumes (“blocks”) were automatically identified on the planning image to cover the tumor surface using a measure of the local intensity gradient. Blocks were independently and automatically registered to the on-treatment image using a rigid transform. To improve speed and robustness, registrations were performed iteratively from coarse to fine image resolution. At each resolution, all block displacements having a near-maximum similarity score were stored. From this list, a single displacement vector for each block was iteratively selected which maximized the consistency of displacement vectors across immediately neighboring blocks. These selected displacements were regularized using a median filter before proceeding to registrations at finer image resolutions. After evaluating all image resolutions, the global rigid transform of the on-treatment image was computed using a Procrustes analysis, providing the couch shift for patient setup correction. This algorithm was evaluated for 18 locally advanced lung cancer patients, each with 4–7 weekly on-treatment computed tomography scans having physician-delineated gross tumor volumes. Volume overlap (VO) and border displacement errors (BDE) were calculated relative to the nominal physician-identified targets to establish residual error after registration. Results: Implementation of multiresolution registration improved block matching accuracy by 39% compared to registration using only the full resolution images. By also considering multiple potential displacements per block, initial errors were reduced by 65%. Using the final implementation of the BMR algorithm, VO was significantly improved from 77% ± 21% (range: 0%–100%) in the initial bony alignment to 91% ± 8% (range: 56%–100%;p < 0.001). Left

A novel Gravity-FREAK feature extraction and Gravity-KLT tracking registration algorithm based on iPhone MEMS mobile sensor in mobile environment.

Science.gov (United States)

Hong, Zhiling; Lin, Fan; Xiao, Bin

2017-01-01

Based on the traditional Fast Retina Keypoint (FREAK) feature description algorithm, this paper proposed a Gravity-FREAK feature description algorithm based on Micro-electromechanical Systems (MEMS) sensor to overcome the limited computing performance and memory resources of mobile devices and further improve the reality interaction experience of clients through digital information added to the real world by augmented reality technology. The algorithm takes the gravity projection vector corresponding to the feature point as its feature orientation, which saved the time of calculating the neighborhood gray gradient of each feature point, reduced the cost of calculation and improved the accuracy of feature extraction. In the case of registration method of matching and tracking natural features, the adaptive and generic corner detection based on the Gravity-FREAK matching purification algorithm was used to eliminate abnormal matches, and Gravity Kaneda-Lucas Tracking (KLT) algorithm based on MEMS sensor can be used for the tracking registration of the targets and robustness improvement of tracking registration algorithm under mobile environment.

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.

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.

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.

Automatic vertebral identification using surface-based registration

Science.gov (United States)

Herring, Jeannette L.; Dawant, Benoit M.

2000-06-01

This work introduces an enhancement to currently existing methods of intra-operative vertebral registration by allowing the portion of the spinal column surface that correctly matches a set of physical vertebral points to be automatically selected from several possible choices. Automatic selection is made possible by the shape variations that exist among lumbar vertebrae. In our experiments, we register vertebral points representing physical space to spinal column surfaces extracted from computed tomography images. The vertebral points are taken from the posterior elements of a single vertebra to represent the region of surgical interest. The surface is extracted using an improved version of the fully automatic marching cubes algorithm, which results in a triangulated surface that contains multiple vertebrae. We find the correct portion of the surface by registering the set of physical points to multiple surface areas, including all vertebral surfaces that potentially match the physical point set. We then compute the standard deviation of the surface error for the set of points registered to each vertebral surface that is a possible match, and the registration that corresponds to the lowest standard deviation designates the correct match. We have performed our current experiments on two plastic spine phantoms and one patient.

Hospital Registration Process Reengineering Using Simulation Method

Directory of Open Access Journals (Sweden)

Qiang Su

2010-01-01

Full Text Available With increasing competition, many healthcare organizations have undergone tremendous reform in the last decade aiming to increase efficiency, decrease waste, and reshape the way that care is delivered. This study focuses on the operational efficiency improvement of hospital’s registration process. The operational efficiency related factors including the service process, queue strategy, and queue parameters were explored systematically and illustrated with a case study. Guided by the principle of business process reengineering (BPR, a simulation approach was employed for process redesign and performance optimization. As a result, the queue strategy is changed from multiple queues and multiple servers to single queue and multiple servers with a prepare queue. Furthermore, through a series of simulation experiments, the length of the prepare queue and the corresponding registration process efficiency was quantitatively evaluated and optimized.

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.

Utilization of a hybrid finite-element based registration method to quantify heterogeneous tumor response for adaptive treatment for lung cancer patients

Science.gov (United States)

Sharifi, Hoda; Zhang, Hong; Bagher-Ebadian, Hassan; Lu, Wei; Ajlouni, Munther I.; Jin, Jian-Yue; (Spring Kong, Feng-Ming; Chetty, Indrin J.; Zhong, Hualiang

2018-03-01

Tumor response to radiation treatment (RT) can be evaluated from changes in metabolic activity between two positron emission tomography (PET) images. Activity changes at individual voxels in pre-treatment PET images (PET1), however, cannot be derived until their associated PET-CT (CT1) images are appropriately registered to during-treatment PET-CT (CT2) images. This study aimed to investigate the feasibility of using deformable image registration (DIR) techniques to quantify radiation-induced metabolic changes on PET images. Five patients with non-small-cell lung cancer (NSCLC) treated with adaptive radiotherapy were considered. PET-CTs were acquired two weeks before RT and 18 fractions after the start of RT. DIR was performed from CT1 to CT2 using B-Spline and diffeomorphic Demons algorithms. The resultant displacements in the tumor region were then corrected using a hybrid finite element method (FEM). Bitmap masks generated from gross tumor volumes (GTVs) in PET1 were deformed using the four different displacement vector fields (DVFs). The conservation of total lesion glycolysis (TLG) in GTVs was used as a criterion to evaluate the quality of these registrations. The deformed masks were united to form a large mask which was then partitioned into multiple layers from center to border. The averages of SUV changes over all the layers were 1.0  ±  1.3, 1.0  ±  1.2, 0.8  ±  1.3, 1.1  ±  1.5 for the B-Spline, B-Spline  +  FEM, Demons and Demons  +  FEM algorithms, respectively. TLG changes before and after mapping using B-Spline, Demons, hybrid-B-Spline, and hybrid-Demons registrations were 20.2%, 28.3%, 8.7%, and 2.2% on average, respectively. Compared to image intensity-based DIR algorithms, the hybrid FEM modeling technique is better in preserving TLG and could be useful for evaluation of tumor response for patients with regressing tumors.

Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets

Directory of Open Access Journals (Sweden)

Pielot Rainer

2010-01-01

Full Text Available Abstract Background Solving bioinformatics tasks often requires extensive computational power. Recent trends in processor architecture combine multiple cores into a single chip to improve overall performance. The Cell Broadband Engine (CBE, a heterogeneous multi-core processor, provides power-efficient and cost-effective high-performance computing. One application area is image analysis and visualisation, in particular registration of 2D cross-sections into 3D image datasets. Such techniques can be used to put different image modalities into spatial correspondence, for example, 2D images of histological cuts into morphological 3D frameworks. Results We evaluate the CBE-driven PlayStation 3 as a high performance, cost-effective computing platform by adapting a multimodal alignment procedure to several characteristic hardware properties. The optimisations are based on partitioning, vectorisation, branch reducing and loop unrolling techniques with special attention to 32-bit multiplies and limited local storage on the computing units. We show how a typical image analysis and visualisation problem, the multimodal registration of 2D cross-sections and 3D datasets, benefits from the multi-core based implementation of the alignment algorithm. We discuss several CBE-based optimisation methods and compare our results to standard solutions. More information and the source code are available from http://cbe.ipk-gatersleben.de. Conclusions The results demonstrate that the CBE processor in a PlayStation 3 accelerates computational intensive multimodal registration, which is of great importance in biological/medical image processing. The PlayStation 3 as a low cost CBE-based platform offers an efficient option to conventional hardware to solve computational problems in image processing and bioinformatics.

Three-dimensional measurement of small inner surface profiles using feature-based 3-D panoramic registration

Science.gov (United States)

Gong, Yuanzheng; Seibel, Eric J.

2017-01-01

Rapid development in the performance of sophisticated optical components, digital image sensors, and computer abilities along with decreasing costs has enabled three-dimensional (3-D) optical measurement to replace more traditional methods in manufacturing and quality control. The advantages of 3-D optical measurement, such as noncontact, high accuracy, rapid operation, and the ability for automation, are extremely valuable for inline manufacturing. However, most of the current optical approaches are eligible for exterior instead of internal surfaces of machined parts. A 3-D optical measurement approach is proposed based on machine vision for the 3-D profile measurement of tiny complex internal surfaces, such as internally threaded holes. To capture the full topographic extent (peak to valley) of threads, a side-view commercial rigid scope is used to collect images at known camera positions and orientations. A 3-D point cloud is generated with multiview stereo vision using linear motion of the test piece, which is repeated by a rotation to form additional point clouds. Registration of these point clouds into a complete reconstruction uses a proposed automated feature-based 3-D registration algorithm. The resulting 3-D reconstruction is compared with x-ray computed tomography to validate the feasibility of our proposed method for future robotically driven industrial 3-D inspection.

Block assembly for global registration of building scans

KAUST Repository

Yan, Feilong; Nan, Liangliang; Wonka, Peter

2016-01-01

We propose a framework for global registration of building scans. The first contribution of our work is to detect and use portals (e.g., doors and windows) to improve the local registration between two scans. Our second contribution is an optimization based on a linear integer programming formulation. We abstract each scan as a block and model the blocks registration as an optimization problem that aims at maximizing the overall matching score of the entire scene. We propose an efficient solution to this optimization problem by iteratively detecting and adding local constraints. We demonstrate the effectiveness of the proposed method on buildings of various styles and that our approach is superior to the current state of the art.

Block assembly for global registration of building scans

KAUST Repository

Yan, Feilong

2016-11-11

We propose a framework for global registration of building scans. The first contribution of our work is to detect and use portals (e.g., doors and windows) to improve the local registration between two scans. Our second contribution is an optimization based on a linear integer programming formulation. We abstract each scan as a block and model the blocks registration as an optimization problem that aims at maximizing the overall matching score of the entire scene. We propose an efficient solution to this optimization problem by iteratively detecting and adding local constraints. We demonstrate the effectiveness of the proposed method on buildings of various styles and that our approach is superior to the current state of the art.

Registration performance on EUV masks using high-resolution registration metrology

Science.gov (United States)

Steinert, Steffen; Solowan, Hans-Michael; Park, Jinback; Han, Hakseung; Beyer, Dirk; Scherübl, Thomas

2016-10-01

Next-generation lithography based on EUV continues to move forward to high-volume manufacturing. Given the technical challenges and the throughput concerns a hybrid approach with 193 nm immersion lithography is expected, at least in the initial state. Due to the increasing complexity at smaller nodes a multitude of different masks, both DUV (193 nm) and EUV (13.5 nm) reticles, will then be required in the lithography process-flow. The individual registration of each mask and the resulting overlay error are of crucial importance in order to ensure proper functionality of the chips. While registration and overlay metrology on DUV masks has been the standard for decades, this has yet to be demonstrated on EUV masks. Past generations of mask registration tools were not necessarily limited in their tool stability, but in their resolution capabilities. The scope of this work is an image placement investigation of high-end EUV masks together with a registration and resolution performance qualification. For this we employ a new generation registration metrology system embedded in a production environment for full-spec EUV masks. This paper presents excellent registration performance not only on standard overlay markers but also on more sophisticated e-beam calibration patterns.

Fast free-form deformable registration via calculus of variations

International Nuclear Information System (INIS)

Lu Weiguo; Chen Mingli; Olivera, Gustavo H; Ruchala, Kenneth J; Mackie, Thomas R

2004-01-01

In this paper, we present a fully automatic, fast and accurate deformable registration technique. This technique deals with free-form deformation. It minimizes an energy functional that combines both similarity and smoothness measures. By using calculus of variations, the minimization problem was represented as a set of nonlinear elliptic partial differential equations (PDEs). A Gauss-Seidel finite difference scheme is used to iteratively solve the PDE. The registration is refined by a multi-resolution approach. The whole process is fully automatic. It takes less than 3 min to register two three-dimensional (3D) image sets of size 256 x 256 x 61 using a single 933 MHz personal computer. Extensive experiments are presented. These experiments include simulations, phantom studies and clinical image studies. Experimental results show that our model and algorithm are suited for registration of temporal images of a deformable body. The registration of inspiration and expiration phases of the lung images shows that the method is able to deal with large deformations. When applied to the daily CT images of a prostate patient, the results show that registration based on iterative refinement of displacement field is appropriate to describe the local deformations in the prostate and the rectum. Similarity measures improved significantly after the registration. The target application of this paper is for radiotherapy treatment planning and evaluation that incorporates internal organ deformation throughout the course of radiation therapy. The registration method could also be equally applied in diagnostic radiology

Image registration in gastric emptying studies

International Nuclear Information System (INIS)

Shuter, B.; Cooper, R.G.

1998-01-01

Full text: We have previously shown that image registration, based upon a two-dimensional cross-correlation (CC) of logarithmic Laplacian images (LLI), corrected motion in biliary studies in up to 90% of cases with minimal artifact. We have now applied the same technique to gastric emptying studies (GES). GES were acquired on an LFOV gamma camera over a two-hour period as 20-26 pairs of anterior-posterior frames (30 second duration and 64 x 64 matrix) for both solid and liquid components. All images were manually registered so that the solid contents of the stomach lay within an operator-drawn ROI. The anterior images of the solid component for 30 randomly selected patients were subjected to further image registration using CC of LLI, CC of raw images (Rl) (a common approach to image registration) and CC of Laplacian images (Ll). All images were aligned to the third image of the study, on which an ROI was drawn to outline the stomach. The number of images in which stomach counts appeared outside this ROI were tallied, in the original and all re-registered studies. Maximum displacements in X/Y position between images of studies registered by the LLI and Rl methods were also computed to directly compare positional accuracy. Stomachs partially exceeded the limits of the ROI in 27, 9, 53 and 54 frames (total of 710) in the original, LLI, Rl and Ll studies respectively. There were 4, 1, 6 and 7 studies with misregistered stomachs on more than 2 frames. Frames in seven Rl studies differed from the LLI studies in ) X/Y position by 3 pixels or more. Cross-correlation using LLI was the only method which improved upon the original manual registration. The Rl and Ll methods increased the number of misregistered frames. We conclude that in gastric emptying studies, as in biliary studies, object tracking by CC of LLI is the method of choice for image registration

Co-registration of optical coherence tomography and X-ray angiography in percutaneous coronary intervention

DEFF Research Database (Denmark)

Hebsgaard, Lasse; Nielsen, Troels Munck; Tu, Shengxian

2014-01-01

Background Intracoronary imaging provides accurate lesion delineation and precise measurements for sizing and positioning of coronary stents. During percutaneous coronary intervention (PCI), it may be challenging to identify corresponding segments between intracoronary imaging and angiography....... Computer based online co-registration may aid the target segment identification. Methods The DOCTOR fusion study was a prospective, single arm, observational study including patients admitted for elective PCI. Optical coherence tomography (OCT) was acquired pre-stent implantation for sizing of stents...... to the computer-based co-registration, segments of the target lesion indicated on OCT were left uncovered by stent in 14 patients (70%). Conclusion Computer based online co-registration of OCT and angiography is feasible. Frequent inaccuracies in operator based registration indicate that computer aided co...

Robust linear registration of CT images using random regression forests

Science.gov (United States)

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

2011-03-01

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

Remote Sensing Image Registration Using Multiple Image Features

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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.

Registration of retinal sequences from new video-ophthalmoscopic camera.

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Kolar, Radim; Tornow, Ralf P; Odstrcilik, Jan; Liberdova, Ivana

2016-05-20

Analysis of fast temporal changes on retinas has become an important part of diagnostic video-ophthalmology. It enables investigation of the hemodynamic processes in retinal tissue, e.g. blood-vessel diameter changes as a result of blood-pressure variation, spontaneous venous pulsation influenced by intracranial-intraocular pressure difference, blood-volume changes as a result of changes in light reflection from retinal tissue, and blood flow using laser speckle contrast imaging. For such applications, image registration of the recorded sequence must be performed. Here we use a new non-mydriatic video-ophthalmoscope for simple and fast acquisition of low SNR retinal sequences. We introduce a novel, two-step approach for fast image registration. The phase correlation in the first stage removes large eye movements. Lucas-Kanade tracking in the second stage removes small eye movements. We propose robust adaptive selection of the tracking points, which is the most important part of tracking-based approaches. We also describe a method for quantitative evaluation of the registration results, based on vascular tree intensity profiles. The achieved registration error evaluated on 23 sequences (5840 frames) is 0.78 ± 0.67 pixels inside the optic disc and 1.39 ± 0.63 pixels outside the optic disc. We compared the results with the commonly used approaches based on Lucas-Kanade tracking and scale-invariant feature transform, which achieved worse results. The proposed method can efficiently correct particular frames of retinal sequences for shift and rotation. The registration results for each frame (shift in X and Y direction and eye rotation) can also be used for eye-movement evaluation during single-spot fixation tasks.

One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI

DEFF Research Database (Denmark)

Arabi, H.; Zaidi, H.

2016-01-01

Purpose: The outcome of a detailed assessment of various strategies for atlas-based whole-body bone segmentation from magnetic resonance imaging (MRI) was exploited to select the optimal parameters and setting, with the aim of proposing a novel one-registration multi-atlas (ORMA) pseudo-CT genera......Purpose: The outcome of a detailed assessment of various strategies for atlas-based whole-body bone segmentation from magnetic resonance imaging (MRI) was exploited to select the optimal parameters and setting, with the aim of proposing a novel one-registration multi-atlas (ORMA) pseudo...... regarding the accuracy of extracted bone using the different techniques demonstrated the superiority of the VWW atlas fusion algorithm achieving a Dice similarity measure of 0.82 ± 0.04 compared to arithmetic averaging atlas fusion (0.60 ± 0.02), which uses conventional direct registration. Application...

Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra provides reduced effect of scanner for cortex volumetry with atlas-based method in healthy subjects.

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Goto, Masami; Abe, Osamu; Aoki, Shigeki; Hayashi, Naoto; Miyati, Tosiaki; Takao, Hidemasa; Iwatsubo, Takeshi; Yamashita, Fumio; Matsuda, Hiroshi; Mori, Harushi; Kunimatsu, Akira; Ino, Kenji; Yano, Keiichi; Ohtomo, Kuni

2013-07-01

This study aimed to investigate whether the effect of scanner for cortex volumetry with atlas-based method is reduced using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) normalization compared with standard normalization. Three-dimensional T1-weighted magnetic resonance images (3D-T1WIs) of 21 healthy subjects were obtained and evaluated for effect of scanner in cortex volumetry. 3D-T1WIs of the 21 subjects were obtained with five MRI systems. Imaging of each subject was performed on each of five different MRI scanners. We used the Voxel-Based Morphometry 8 tool implemented in Statistical Parametric Mapping 8 and WFU PickAtlas software (Talairach brain atlas theory). The following software default settings were used as bilateral region-of-interest labels: "Frontal Lobe," "Hippocampus," "Occipital Lobe," "Orbital Gyrus," "Parietal Lobe," "Putamen," and "Temporal Lobe." Effect of scanner for cortex volumetry using the atlas-based method was reduced with DARTEL normalization compared with standard normalization in Frontal Lobe, Occipital Lobe, Orbital Gyrus, Putamen, and Temporal Lobe; was the same in Hippocampus and Parietal Lobe; and showed no increase with DARTEL normalization for any region of interest (ROI). DARTEL normalization reduces the effect of scanner, which is a major problem in multicenter studies.

Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra provides reduced effect of scanner for cortex volumetry with atlas-based method in healthy subjects

International Nuclear Information System (INIS)

Goto, Masami; Ino, Kenji; Yano, Keiichi; Abe, Osamu; Aoki, Shigeki; Hayashi, Naoto; Miyati, Tosiaki; Takao, Hidemasa; Mori, Harushi; Kunimatsu, Akira; Ohtomo, Kuni; Iwatsubo, Takeshi; Yamashita, Fumio; Matsuda, Hiroshi

2013-01-01

This study aimed to investigate whether the effect of scanner for cortex volumetry with atlas-based method is reduced using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) normalization compared with standard normalization. Three-dimensional T1-weighted magnetic resonance images (3D-T1WIs) of 21 healthy subjects were obtained and evaluated for effect of scanner in cortex volumetry. 3D-T1WIs of the 21 subjects were obtained with five MRI systems. Imaging of each subject was performed on each of five different MRI scanners. We used the Voxel-Based Morphometry 8 tool implemented in Statistical Parametric Mapping 8 and WFU PickAtlas software (Talairach brain atlas theory). The following software default settings were used as bilateral region-of-interest labels: ''Frontal Lobe,'' ''Hippocampus,'' ''Occipital Lobe,'' ''Orbital Gyrus,'' ''Parietal Lobe,'' ''Putamen,'' and ''Temporal Lobe.'' Effect of scanner for cortex volumetry using the atlas-based method was reduced with DARTEL normalization compared with standard normalization in Frontal Lobe, Occipital Lobe, Orbital Gyrus, Putamen, and Temporal Lobe; was the same in Hippocampus and Parietal Lobe; and showed no increase with DARTEL normalization for any region of interest (ROI). DARTEL normalization reduces the effect of scanner, which is a major problem in multicenter studies. (orig.)

Toward magnetic resonance-guided electroanatomical voltage mapping for catheter ablation of scar-related ventricular tachycardia: a comparison of registration methods.

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Tao, Qian; Milles, Julien; VAN Huls VAN Taxis, Carine; Lamb, Hildo J; Reiber, Johan H C; Zeppenfeld, Katja; VAN DER Geest, Rob J

2012-01-01

Integration of preprocedural delayed enhanced magnetic resonance imaging (DE-MRI) with electroanatomical voltage mapping (EAVM) may provide additional high-resolution substrate information for catheter ablation of scar-related ventricular tachycardias (VT). Accurate and fast image integration of DE-MRI with EAVM is desirable for MR-guided ablation. Twenty-six VT patients with large transmural scar underwent catheter ablation and preprocedural DE-MRI. With different registration models and EAVM input, 3 image integration methods were evaluated and compared to the commercial registration module CartoMerge. The performance was evaluated both in terms of distance measure that describes surface matching, and correlation measure that describes actual scar correspondence. Compared to CartoMerge, the method that uses the translation-and-rotation model and high-density EAVM input resulted in a registration error of 4.32±0.69 mm as compared to 4.84 ± 1.07 (P <0.05); the method that uses the translation model and high-density EAVM input resulted in a registration error of 4.60 ± 0.65 mm (P = NS); and the method that uses the translation model and a single anatomical landmark input resulted in a registration error of 6.58 ± 1.63 mm (P < 0.05). No significant difference in scar correlation was observed between all 3 methods and CartoMerge (P = NS). During VT ablation procedures, accurate integration of EAVM and DE-MRI can be achieved using a translation registration model and a single anatomical landmark. This model allows for image integration in minimal mapping time and is likely to reduce fluoroscopy time and increase procedure efficacy. © 2011 Wiley Periodicals, Inc.

Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification

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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

Registration and three-dimensional reconstruction of autoradiographic images by the disparity analysis method

International Nuclear Information System (INIS)

Zhao, Weizhao; Ginsberg, M.; Young, T.Y.

1993-01-01

Quantitative autoradiography is a powerful radio-isotopic-imaging method for neuroscientists to study local cerebral blood flow and glucose-metabolic rate at rest, in response to physiologic activation of the visual, auditory, somatosensory, and motor systems, and in pathologic conditions. Most autoradiographic studies analyze glucose utilization and blood flow in two-dimensional (2-D) coronal sections. With modern digital computer and image-processing techniques, a large number of closely spaced coronal sections can be stacked appropriately to form a three-dimensional (3-d) image. 3-D autoradiography allows investigators to observe cerebral sections and surfaces from any viewing angle. A fundamental problem in 3-D reconstruction is the alignment (registration) of the coronal sections. A new alignment method based on disparity analysis is presented which can overcome many of the difficulties encountered by previous methods. The disparity analysis method can deal with asymmetric, damaged, or tilted coronal sections under the same general framework, and it can be used to match coronal sections of different sizes and shapes. Experimental results on alignment and 3-D reconstruction are presented

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

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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

A novel Gravity-FREAK feature extraction and Gravity-KLT tracking registration algorithm based on iPhone MEMS mobile sensor in mobile environment.

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Zhiling Hong

Full Text Available Based on the traditional Fast Retina Keypoint (FREAK feature description algorithm, this paper proposed a Gravity-FREAK feature description algorithm based on Micro-electromechanical Systems (MEMS sensor to overcome the limited computing performance and memory resources of mobile devices and further improve the reality interaction experience of clients through digital information added to the real world by augmented reality technology. The algorithm takes the gravity projection vector corresponding to the feature point as its feature orientation, which saved the time of calculating the neighborhood gray gradient of each feature point, reduced the cost of calculation and improved the accuracy of feature extraction. In the case of registration method of matching and tracking natural features, the adaptive and generic corner detection based on the Gravity-FREAK matching purification algorithm was used to eliminate abnormal matches, and Gravity Kaneda-Lucas Tracking (KLT algorithm based on MEMS sensor can be used for the tracking registration of the targets and robustness improvement of tracking registration algorithm under mobile environment.

TU-AB-202-07: A Novel Method for Registration of Mid-Treatment PET/CT Images Under Conditions of Tumor Regression for Patients with Locally Advanced Lung Cancers

Energy Technology Data Exchange (ETDEWEB)

Sharifi, Hoda [Department of Radiation Oncology, Henry Ford Health System, Detroit, MI (United States); Department of Physics, Oakland University, Rochester, MI (United States); Zhang, Hong; Jin, Jian-Yyue; Kong, Feng-Ming [Department of Radiation Oncology, GRU Cancer Center, Augusta GA (United States); Chetty, Indrin J [Department of Radiation Oncology, Henry Ford Health System, Detroit, MI (United States); Zhong, Hualiang

2016-06-15

Purpose: In PET-guided adaptive radiotherapy (RT), changes in the metabolic activity at individual voxels cannot be derived until the duringtreatment CT images are appropriately registered to pre-treatment CT images. However, deformable image registration (DIR) usually does not preserve tumor volume. This may induce errors when comparing to the target. The aim of this study was to develop a DIR-integrated mechanical modeling technique to track radiation-induced metabolic changes on PET images. Methods: Three patients with non-small cell lung cancer (NSCLC) were treated with adaptive radiotherapy under RTOG 1106. Two PET/CT image sets were acquired 2 weeks before RT and 18 fractions after the start of treatment. DIR was performed to register the during-RT CT to the pre-RT CT using a B-spline algorithm and the resultant displacements in the region of tumor were remodeled using a hybrid finite element method (FEM). Gross tumor volume (GTV) was delineated on the during-RT PET/CT image sets and deformed using the 3D deformation vector fields generated by the CT-based registrations. Metabolic tumor volume (MTV) was calculated using the pre- and during–RT image set. The quality of the PET mapping was evaluated based on the constancy of the mapped MTV and landmark comparison. Results: The B-spline-based registrations changed MTVs by 7.3%, 4.6% and −5.9% for the 3 patients and the correspondent changes for the hybrid FEM method −2.9%, 1% and 6.3%, respectively. Landmark comparisons were used to evaluate the Rigid, B-Spline, and hybrid FEM registrations with the mean errors of 10.1 ± 1.6 mm, 4.4 ± 0.4 mm, and 3.6 ± 0.4 mm for three patients. The hybrid FEM method outperforms the B-Spline-only registration for patients with tumor regression Conclusion: The hybrid FEM modeling technique improves the B-Spline registrations in tumor regions. This technique may help compare metabolic activities between two PET/CT images with regressing tumors. The author gratefully

SU-C-18A-02: Image-Based Camera Tracking: Towards Registration of Endoscopic Video to CT

International Nuclear Information System (INIS)

Ingram, S; Rao, A; Wendt, R; Castillo, R; Court, L; Yang, J; Beadle, B

2014-01-01

Purpose: Endoscopic examinations are routinely performed on head and neck and esophageal cancer patients. However, these images are underutilized for radiation therapy because there is currently no way to register them to a CT of the patient. The purpose of this work is to develop a method to track the motion of an endoscope within a structure using images from standard clinical equipment. This method will be incorporated into a broader endoscopy/CT registration framework. Methods: We developed a software algorithm to track the motion of an endoscope within an arbitrary structure. We computed frame-to-frame rotation and translation of the camera by tracking surface points across the video sequence and utilizing two-camera epipolar geometry. The resulting 3D camera path was used to recover the surrounding structure via triangulation methods. We tested this algorithm on a rigid cylindrical phantom with a pattern spray-painted on the inside. We did not constrain the motion of the endoscope while recording, and we did not constrain our measurements using the known structure of the phantom. Results: Our software algorithm can successfully track the general motion of the endoscope as it moves through the phantom. However, our preliminary data do not show a high degree of accuracy in the triangulation of 3D point locations. More rigorous data will be presented at the annual meeting. Conclusion: Image-based camera tracking is a promising method for endoscopy/CT image registration, and it requires only standard clinical equipment. It is one of two major components needed to achieve endoscopy/CT registration, the second of which is tying the camera path to absolute patient geometry. In addition to this second component, future work will focus on validating our camera tracking algorithm in the presence of clinical imaging features such as patient motion, erratic camera motion, and dynamic scene illumination

Toward efficient biomechanical-based deformable image registration of lungs for image-guided radiotherapy

Science.gov (United States)

Al-Mayah, Adil; Moseley, Joanne; Velec, Mike; Brock, Kristy

2011-08-01

Both accuracy and efficiency are critical for the implementation of biomechanical model-based deformable registration in clinical practice. The focus of this investigation is to evaluate the potential of improving the efficiency of the deformable image registration of the human lungs without loss of accuracy. Three-dimensional finite element models have been developed using image data of 14 lung cancer patients. Each model consists of two lungs, tumor and external body. Sliding of the lungs inside the chest cavity is modeled using a frictionless surface-based contact model. The effect of the type of element, finite deformation and elasticity on the accuracy and computing time is investigated. Linear and quadrilateral tetrahedral elements are used with linear and nonlinear geometric analysis. Two types of material properties are applied namely: elastic and hyperelastic. The accuracy of each of the four models is examined using a number of anatomical landmarks representing the vessels bifurcation points distributed across the lungs. The registration error is not significantly affected by the element type or linearity of analysis, with an average vector error of around 2.8 mm. The displacement differences between linear and nonlinear analysis methods are calculated for all lungs nodes and a maximum value of 3.6 mm is found in one of the nodes near the entrance of the bronchial tree into the lungs. The 95 percentile of displacement difference ranges between 0.4 and 0.8 mm. However, the time required for the analysis is reduced from 95 min in the quadratic elements nonlinear geometry model to 3.4 min in the linear element linear geometry model. Therefore using linear tetrahedral elements with linear elastic materials and linear geometry is preferable for modeling the breathing motion of lungs for image-guided radiotherapy applications.

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

Science.gov (United States)

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

2017-09-01

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

Lung tumor reproducibility with active breath control (ABC) in image-guided radiotherapy based on cone-beam computed tomography with two registration methods

International Nuclear Information System (INIS)

Wang Xin; Zhong Renming; Bai Sen; Xu Qingfeng; Zhao Yaqin; Wang Jin; Jiang Xiaoqin; Shen Yali; Xu Feng; Wei Yuquan

2011-01-01

Purpose: To study the inter- and intrafraction tumor reproducibility with active breath control (ABC) utilizing cone-beam computed tomography (CBCT), and compare validity of registration with two different regions of interest (ROI). Methods and materials: Thirty-one lung tumors in 19 patients received conventional or stereotactic body radiotherapy with ABC. During each treatment, patients had three CBCT scanned before and after online position correction and after treatment. These CBCT images were aligned to the planning CT using the gray scale registration of tumor and bony registration of the thorax, and tumor position uncertainties were then determined. Results: The interfraction systematic and random translation errors in the left-right (LR), superior-inferior (SI) and anterior-posterior (AP) directions were 3.6, 4.8, and 2.9 mm; 2.5, 4.5, and 3.5 mm, respectively, with gray scale alignment; 1.9, 4.3, 2.0 mm and 2.5, 4.4, 2.9 mm, respectively, with bony alignment. The interfraction systematic and random rotation errors with gray scale and bony alignment groups ranged from 1.4 o to 3.0 o and 0.8 o to 2.3 o , respectively. The intrafraction systematic and random errors with gray scale registration in LR, SI, AP directions were 0.9, 2.0, 1.8 mm and 1.5, 1.7, 2.9 mm, respectively, for translation; 1.5 o , 0.9 o , 1.0 o and 1.2 o , 2.2 o , 1.8 o , respectively, for rotation. The translational errors in SI direction with bony alignment were significantly larger than that of gray scale (p < 0.05). Conclusions: With CBCT guided online correction the interfraction positioning errors can be markedly reduced. The intrafraction errors were not diminished by the use of ABC. Rotation errors were not very remarkable both inter- and intrafraction. Gray scale alignment of tumor may provide a better registration in SI direction.

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

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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.

A SAR IMAGE REGISTRATION METHOD BASED ON SIFT ALGORITHM

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W. Lu

2017-09-01

Full Text Available 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.

T-Spline Based Unifying Registration Procedure for Free-Form Surface Workpieces in Intelligent CMM

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Zhenhua Han

2017-10-01

Full Text Available With the development of the modern manufacturing industry, the free-form surface is widely used in various fields, and the automatic detection of a free-form surface is an important function of future intelligent three-coordinate measuring machines (CMMs. To improve the intelligence of CMMs, a new visual system is designed based on the characteristics of CMMs. A unified model of the free-form surface is proposed based on T-splines. A discretization method of the T-spline surface formula model is proposed. Under this discretization, the position and orientation of the workpiece would be recognized by point cloud registration. A high accuracy evaluation method is proposed between the measured point cloud and the T-spline surface formula. The experimental results demonstrate that the proposed method has the potential to realize the automatic detection of different free-form surfaces and improve the intelligence of CMMs.

Registration of Space Objects

Science.gov (United States)

Schmidt-Tedd, Bernhard

2017-07-01

Space objects are subject to registration in order to allocate "jurisdiction and control" over those objects in the sovereign-free environment of outer space. This approach is similar to the registration of ships in view of the high sea and for aircrafts with respect to the international airspace. Registration is one of the basic principles of space law, starting with UN General Assembly Resolution 1721 B (XVI) of December 20, 1961, followed by Resolution 1962 (XVIII) of December 13, 1963, then formulated in Article VIII of the Outer Space Treaty of 1967 and as specified in the Registration Convention of 1975. Registration of space objects can be seen today as a principle of customary international law, relevant for each spacefaring state. Registration is divided into a national and an international level. The State Party establishes a national registry for its space objects, and those registrations have to be communicated via diplomatic channel to the UN Register of space objects. This UN Register is handled by the UN Office for Outer Space Affairs (UNOOSA) and is an open source of information for space objects worldwide. Registration is linked to the so-called launching state of the relevant space object. There might be more than one launching state for the specific launch event, but only one state actor can register a specific space object. The state of registry gains "jurisdiction and control" over the space object and therefore no double registration is permissible. Based on the established UN Space Law, registration practice was subject to some adaptions due to technical developments and legal challenges. After the privatization of the major international satellite organizations, a number of non-registrations had to be faced. The state actors reacted with the UN Registration Practice Resolution of 2007 as elaborated in the Legal Subcommittee of UNCOPUOS, the Committee for the Peaceful Use of Outer Space. In this context an UNOOSA Registration Information

Rigid 3D-3D registration of TOF MRA integrating vessel segmentation for quantification of recurrence volumes after coiling cerebral aneurysm

International Nuclear Information System (INIS)

Saering, Dennis; Forkert, Nils Daniel; Fiehler, Jens; Ries, Thorsten

2012-01-01

A fast and reproducible quantification of the recurrence volume of coiled aneurysms is required to enable a more timely evaluation of new coils. This paper presents two registration schemes for the semi-automatic quantification of aneurysm recurrence volumes based on baseline and follow-up 3D MRA TOF datasets. The quantification of shape changes requires a previous definition of corresponding structures in both datasets. For this, two different rigid registration methods have been developed and evaluated. Besides a state-of-the-art rigid registration method, a second approach integrating vessel segmentations is presented. After registration, the aneurysm recurrence volume can be calculated based on the difference image. The computed volumes were compared to manually extracted volumes. An evaluation based on 20 TOF MRA datasets (baseline and follow-up) of ten patients showed that both registration schemes are generally capable of providing sufficient registration results. Regarding the quantification of aneurysm recurrence volumes, the results suggest that the second segmentation-based registration method yields better results, while a reduction of the computation and interaction time is achieved at the same time. The proposed registration scheme incorporating vessel segmentation enables an improved quantification of recurrence volumes of coiled aneurysms with reduced computation and interaction time. (orig.)

Pulmonary nodule registration in serial CT scans based on rib anatomy and nodule template matching

International Nuclear Information System (INIS)

Shi Jiazheng; Sahiner, Berkman; Chan, H.-P.; Hadjiiski, Lubomir; Zhou, C.; Cascade, Philip N.; Bogot, Naama; Kazerooni, Ella A.; Wu, Y.-T.; Wei, J.

2007-01-01

An automated method is being developed in order to identify corresponding nodules in serial thoracic CT scans for interval change analysis. The method uses the rib centerlines as the reference for initial nodule registration. A spatially adaptive rib segmentation method first locates the regions where the ribs join the spine, which define the starting locations for rib tracking. Each rib is tracked and locally segmented by expectation-maximization. The ribs are automatically labeled, and the centerlines are estimated using skeletonization. For a given nodule in the source scan, the closest three ribs are identified. A three-dimensional (3D) rigid affine transformation guided by simplex optimization aligns the centerlines of each of the three rib pairs in the source and target CT volumes. Automatically defined control points along the centerlines of the three ribs in the source scan and the registered ribs in the target scan are used to guide an initial registration using a second 3D rigid affine transformation. A search volume of interest (VOI) is then located in the target scan. Nodule candidate locations within the search VOI are identified as regions with high Hessian responses. The initial registration is refined by searching for the maximum cross-correlation between the nodule template from the source scan and the candidate locations. The method was evaluated on 48 CT scans from 20 patients. Experienced radiologists identified 101 pairs of corresponding nodules. Three metrics were used for performance evaluation. The first metric was the Euclidean distance between the nodule centers identified by the radiologist and the computer registration, the second metric was a volume overlap measure between the nodule VOIs identified by the radiologist and the computer registration, and the third metric was the hit rate, which measures the fraction of nodules whose centroid computed by the computer registration in the target scan falls within the VOI identified by the

Multiple Vehicle Cooperative Localization with Spatial Registration Based on a Probability Hypothesis Density Filter

Directory of Open Access Journals (Sweden)

Feihu Zhang

2014-01-01

Full Text Available This paper studies the problem of multiple vehicle cooperative localization with spatial registration in the formulation of the probability hypothesis density (PHD filter. Assuming vehicles are equipped with proprioceptive and exteroceptive sensors (with biases to cooperatively localize positions, a simultaneous solution for joint spatial registration and state estimation is proposed. For this, we rely on the sequential Monte Carlo implementation of the PHD filtering. Compared to other methods, the concept of multiple vehicle cooperative localization with spatial registration is first proposed under Random Finite Set Theory. In addition, the proposed solution also addresses the challenges for multiple vehicle cooperative localization, e.g., the communication bandwidth issue and data association uncertainty. The simulation result demonstrates its reliability and feasibility in large-scale environments.

Registration of Aerial Image with Airborne LiDAR Data Based on Plücker Line

Directory of Open Access Journals (Sweden)

SHENG Qinghong

2015-07-01

Full Text Available Registration of aerial image with airborne LiDAR data is a key to feature extraction. A registration model based on Plücker line is proposed. The relative position and attitude relationship between the conjugate lines in LiDAR and image is determined based on Plücker linear equation, which describes line transformation in space, then coplanarity condition equation is established. Finally, coordinate transformation between image point and corresponding LiDAR point is achieved by the spiral movement of Plücker lines in the image. The registration model of Plücker linear coplanarity condition equation is simple, and jointly describes the rotation and translation to avoid coupling error between them, so the accuracy is approved. This research provides technical support for high-quality earth spatial information acquisition.

Evaluation of interpolation effects on upsampling and accuracy of cost functions-based optimized automatic image registration.

Science.gov (United States)

Mahmoudzadeh, Amir Pasha; Kashou, Nasser H

2013-01-01

Interpolation has become a default operation in image processing and medical imaging and is one of the important factors in the success of an intensity-based registration method. Interpolation is needed if the fractional unit of motion is not matched and located on the high resolution (HR) grid. The purpose of this work is to present a systematic evaluation of eight standard interpolation techniques (trilinear, nearest neighbor, cubic Lagrangian, quintic Lagrangian, hepatic Lagrangian, windowed Sinc, B-spline 3rd order, and B-spline 4th order) and to compare the effect of cost functions (least squares (LS), normalized mutual information (NMI), normalized cross correlation (NCC), and correlation ratio (CR)) for optimized automatic image registration (OAIR) on 3D spoiled gradient recalled (SPGR) magnetic resonance images (MRI) of the brain acquired using a 3T GE MR scanner. Subsampling was performed in the axial, sagittal, and coronal directions to emulate three low resolution datasets. Afterwards, the low resolution datasets were upsampled using different interpolation methods, and they were then compared to the high resolution data. The mean squared error, peak signal to noise, joint entropy, and cost functions were computed for quantitative assessment of the method. Magnetic resonance image scans and joint histogram were used for qualitative assessment of the method.

Evaluation of Interpolation Effects on Upsampling and Accuracy of Cost Functions-Based Optimized Automatic Image Registration

Directory of Open Access Journals (Sweden)

Amir Pasha Mahmoudzadeh

2013-01-01

Full Text Available Interpolation has become a default operation in image processing and medical imaging and is one of the important factors in the success of an intensity-based registration method. Interpolation is needed if the fractional unit of motion is not matched and located on the high resolution (HR grid. The purpose of this work is to present a systematic evaluation of eight standard interpolation techniques (trilinear, nearest neighbor, cubic Lagrangian, quintic Lagrangian, hepatic Lagrangian, windowed Sinc, B-spline 3rd order, and B-spline 4th order and to compare the effect of cost functions (least squares (LS, normalized mutual information (NMI, normalized cross correlation (NCC, and correlation ratio (CR for optimized automatic image registration (OAIR on 3D spoiled gradient recalled (SPGR magnetic resonance images (MRI of the brain acquired using a 3T GE MR scanner. Subsampling was performed in the axial, sagittal, and coronal directions to emulate three low resolution datasets. Afterwards, the low resolution datasets were upsampled using different interpolation methods, and they were then compared to the high resolution data. The mean squared error, peak signal to noise, joint entropy, and cost functions were computed for quantitative assessment of the method. Magnetic resonance image scans and joint histogram were used for qualitative assessment of the method.

Mapping of the prostate in endorectal coil-based MRI/MRSI and CT: A deformable registration and validation study

International Nuclear Information System (INIS)

Lian, J.; Xing, L.; Hunjan, S.; Dumoulin, C.; Levin, J.; Lo, A.; Watkins, R.; Rohling, K.; Giaquinto, R.; Kim, D.; Spielman, D.; Daniel, B.

2004-01-01

The endorectal coil is being increasingly used in magnetic resonance imaging (MRI) and MR spectroscopic imaging (MRSI) to obtain anatomic and metabolic images of the prostate with high signal-to-noise ratio (SNR). In practice, however, the use of endorectal probe inevitably distorts the prostate and other soft tissue organs, making the analysis and the use of the acquired image data in treatment planning difficult. The purpose of this work is to develop a deformable image registration algorithm to map the MRI/MRSI information obtained using an endorectal probe onto CT images and to verify the accuracy of the registration by phantom and patient studies. A mapping procedure involved using a thin plate spline (TPS) transformation was implemented to establish voxel-to-voxel correspondence between a reference image and a floating image with deformation. An elastic phantom with a number of implanted fiducial markers was designed for the validation of the quality of the registration. Radiographic images of the phantom were obtained before and after a series of intentionally introduced distortions. After mapping the distorted phantom to the original one, the displacements of the implanted markers were measured with respect to their ideal positions and the mean error was calculated. In patient studies, CT images of three prostate patients were acquired, followed by 3 Tesla (3 T) MR images with a rigid endorectal coil. Registration quality was estimated by the centroid position displacement and image coincidence index (CI). Phantom and patient studies show that TPS-based registration has achieved significantly higher accuracy than the previously reported method based on a rigid-body transformation and scaling. The technique should be useful to map the MR spectroscopic dataset acquired with ER probe onto the treatment planning CT dataset to guide radiotherapy planning

Automated registration of diagnostic to prediagnostic x-ray mammograms: Evaluation and comparison to radiologists' accuracy

International Nuclear Information System (INIS)

Pinto Pereira, Snehal M.; Hipwell, John H.; McCormack, Valerie A.; Tanner, Christine; Moss, Sue M.; Wilkinson, Louise S.; Khoo, Lisanne A. L.; Pagliari, Catriona; Skippage, Pippa L.; Kliger, Carole J.; Hawkes, David J.; Santos Silva, Isabel M. dos

2010-01-01

Purpose: To compare and evaluate intensity-based registration methods for computation of serial x-ray mammogram correspondence. Methods: X-ray mammograms were simulated from MRIs of 20 women using finite element methods for modeling breast compressions and employing a MRI/x-ray appearance change model. The parameter configurations of three registration methods, affine, fluid, and free-form deformation (FFD), were optimized for registering x-ray mammograms on these simulated images. Five mammography film readers independently identified landmarks (tumor, nipple, and usually two other normal features) on pairs of diagnostic and corresponding prediagnostic digitized images from 52 breast cancer cases. Landmarks were independently reidentified by each reader. Target registration errors were calculated to compare the three registration methods using the reader landmarks as a gold standard. Data were analyzed using multilevel methods. Results: Between-reader variability varied with landmark (p<0.01) and screen (p=0.03), with between-reader mean distance (mm) in point location on the diagnostic/prediagnostic images of 2.50 (95% CI 1.95, 3.15)/2.84 (2.24, 3.55) for nipples and 4.26 (3.43, 5.24)/4.76 (3.85, 5.84) for tumors. Registration accuracy was sensitive to the type of landmark and the amount of breast density. For dense breasts (≥40%), the affine and fluid methods outperformed FFD. For breasts with lower density, the affine registration surpassed both fluid and FFD. Mean accuracy (mm) of the affine registration varied between 3.16 (95% CI 2.56, 3.90) for nipple points in breasts with density 20%-39% and 5.73 (4.80, 6.84) for tumor points in breasts with density <20%. Conclusions: Affine registration accuracy was comparable to that between independent film readers. More advanced two-dimensional nonrigid registration algorithms were incapable of increasing the accuracy of image alignment when compared to affine registration.

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.

Mosaicing of single plane illumination microscopy images using groupwise registration and fast content-based image fusion

Science.gov (United States)

Preibisch, Stephan; Rohlfing, Torsten; Hasak, Michael P.; Tomancak, Pavel

2008-03-01

Single Plane Illumination Microscopy (SPIM; Huisken et al., Nature 305(5686):1007-1009, 2004) is an emerging microscopic technique that enables live imaging of large biological specimens in their entirety. By imaging the living biological sample from multiple angles SPIM has the potential to achieve isotropic resolution throughout even relatively large biological specimens. For every angle, however, only a relatively shallow section of the specimen is imaged with high resolution, whereas deeper regions appear increasingly blurred. In order to produce a single, uniformly high resolution image, we propose here an image mosaicing algorithm that combines state of the art groupwise image registration for alignment with content-based image fusion to prevent degrading of the fused image due to regional blurring of the input images. For the registration stage, we introduce an application-specific groupwise transformation model that incorporates per-image as well as groupwise transformation parameters. We also propose a new fusion algorithm based on Gaussian filters, which is substantially faster than fusion based on local image entropy. We demonstrate the performance of our mosaicing method on data acquired from living embryos of the fruit fly, Drosophila, using four and eight angle acquisitions.

Registration of Urban Aerial Image and LiDAR Based on Line Vectors

Directory of Open Access Journals (Sweden)

Qinghong Sheng

2017-09-01

Full Text Available In a traditional registration of a single aerial image with airborne light detection and ranging (LiDAR data using linear features that regard line direction as a control or linear features as constraints in the solution, lacking the constraint of linear position leads to the error propagation of the adjustment model. To solve this problem, this paper presents a line vector-based registration mode (LVR in which image rays and LiDAR lines are expressed by a line vector that integrates the line direction and the line position. A registration equation of line vector is set up by coplanar imaging rays and corresponding control lines. Three types of datasets consisting of synthetic, theInternational Society for Photogrammetry and Remote Sensing (ISPRS test project, and real aerial data are used. A group of progressive experiments is undertaken to evaluate the robustness of the LVR. Experimental results demonstrate that the integrated line direction and the line position contributes a great deal to the theoretical and real accuracies of the unknowns, as well as the stability of the adjustment model. This paper provides a new suggestion that, for a single image and LiDAR data, registration in urban areas can be accomplished by accommodating rich line features.

GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration

International Nuclear Information System (INIS)

Sharp, G C; Kandasamy, N; Singh, H; Folkert, M

2007-01-01

This paper shows how to significantly accelerate cone-beam CT reconstruction and 3D deformable image registration using the stream-processing model. We describe data-parallel designs for the Feldkamp, Davis and Kress (FDK) reconstruction algorithm, and the demons deformable registration algorithm, suitable for use on a commodity graphics processing unit. The streaming versions of these algorithms are implemented using the Brook programming environment and executed on an NVidia 8800 GPU. Performance results using CT data of a preserved swine lung indicate that the GPU-based implementations of the FDK and demons algorithms achieve a substantial speedup-up to 80 times for FDK and 70 times for demons when compared to an optimized reference implementation on a 2.8 GHz Intel processor. In addition, the accuracy of the GPU-based implementations was found to be excellent. Compared with CPU-based implementations, the RMS differences were less than 0.1 Hounsfield unit for reconstruction and less than 0.1 mm for deformable registration

A study on the theoretical and practical accuracy of conoscopic holography-based surface measurements: toward image registration in minimally invasive surgery†

Science.gov (United States)

Burgner, J.; Simpson, A. L.; Fitzpatrick, J. M.; Lathrop, R. A.; Herrell, S. D.; Miga, M. I.; Webster, R. J.

2013-01-01

Background Registered medical images can assist with surgical navigation and enable image-guided therapy delivery. In soft tissues, surface-based registration is often used and can be facilitated by laser surface scanning. Tracked conoscopic holography (which provides distance measurements) has been recently proposed as a minimally invasive way to obtain surface scans. Moving this technique from concept to clinical use requires a rigorous accuracy evaluation, which is the purpose of our paper. Methods We adapt recent non-homogeneous and anisotropic point-based registration results to provide a theoretical framework for predicting the accuracy of tracked distance measurement systems. Experiments are conducted a complex objects of defined geometry, an anthropomorphic kidney phantom and a human cadaver kidney. Results Experiments agree with model predictions, producing point RMS errors consistently holography is clinically viable; it enables minimally invasive surface scan accuracy comparable to current clinical methods that require open surgery. PMID:22761086

SU-E-J-122: The CBCT Dose Calculation Using a Patient Specific CBCT Number to Mass Density Conversion Curve Based On a Novel Image Registration and Organ Mapping Method in Head-And-Neck Radiation Therapy

International Nuclear Information System (INIS)

Zhou, J; Lasio, G; Chen, S; Zhang, B; Langen, K; Prado, K; D’Souza, W; Yi, B; Huang, J

2015-01-01

Purpose: To develop a CBCT HU correction method using a patient specific HU to mass density conversion curve based on a novel image registration and organ mapping method for head-and-neck radiation therapy. Methods: There are three steps to generate a patient specific CBCT HU to mass density conversion curve. First, we developed a novel robust image registration method based on sparseness analysis to register the planning CT (PCT) and the CBCT. Second, a novel organ mapping method was developed to transfer the organs at risk (OAR) contours from the PCT to the CBCT and corresponding mean HU values of each OAR were measured in both the PCT and CBCT volumes. Third, a set of PCT and CBCT HU to mass density conversion curves were created based on the mean HU values of OARs and the corresponding mass density of the OAR in the PCT. Then, we compared our proposed conversion curve with the traditional Catphan phantom based CBCT HU to mass density calibration curve. Both curves were input into the treatment planning system (TPS) for dose calculation. Last, the PTV and OAR doses, DVH and dose distributions of CBCT plans are compared to the original treatment plan. Results: One head-and-neck cases which contained a pair of PCT and CBCT was used. The dose differences between the PCT and CBCT plans using the proposed method are −1.33% for the mean PTV, 0.06% for PTV D95%, and −0.56% for the left neck. The dose differences between plans of PCT and CBCT corrected using the CATPhan based method are −4.39% for mean PTV, 4.07% for PTV D95%, and −2.01% for the left neck. Conclusion: The proposed CBCT HU correction method achieves better agreement with the original treatment plan compared to the traditional CATPhan based calibration method

SU-E-J-122: The CBCT Dose Calculation Using a Patient Specific CBCT Number to Mass Density Conversion Curve Based On a Novel Image Registration and Organ Mapping Method in Head-And-Neck Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Zhou, J [University of Maryland School of Medicine, Bel Air, MD (United States); Lasio, G; Chen, S; Zhang, B; Langen, K; Prado, K; D’Souza, W [University of Maryland School of Medicine, Baltimore, MD (United States); Yi, B [Univ. of Maryland School Of Medicine, Baltimore, MD (United States); Huang, J [University of Texas at Arlington, Arlington, TX (United States)

2015-06-15

Purpose: To develop a CBCT HU correction method using a patient specific HU to mass density conversion curve based on a novel image registration and organ mapping method for head-and-neck radiation therapy. Methods: There are three steps to generate a patient specific CBCT HU to mass density conversion curve. First, we developed a novel robust image registration method based on sparseness analysis to register the planning CT (PCT) and the CBCT. Second, a novel organ mapping method was developed to transfer the organs at risk (OAR) contours from the PCT to the CBCT and corresponding mean HU values of each OAR were measured in both the PCT and CBCT volumes. Third, a set of PCT and CBCT HU to mass density conversion curves were created based on the mean HU values of OARs and the corresponding mass density of the OAR in the PCT. Then, we compared our proposed conversion curve with the traditional Catphan phantom based CBCT HU to mass density calibration curve. Both curves were input into the treatment planning system (TPS) for dose calculation. Last, the PTV and OAR doses, DVH and dose distributions of CBCT plans are compared to the original treatment plan. Results: One head-and-neck cases which contained a pair of PCT and CBCT was used. The dose differences between the PCT and CBCT plans using the proposed method are −1.33% for the mean PTV, 0.06% for PTV D95%, and −0.56% for the left neck. The dose differences between plans of PCT and CBCT corrected using the CATPhan based method are −4.39% for mean PTV, 4.07% for PTV D95%, and −2.01% for the left neck. Conclusion: The proposed CBCT HU correction method achieves better agreement with the original treatment plan compared to the traditional CATPhan based calibration method.

Three dimensional image alignment, registration and fusion

International Nuclear Information System (INIS)

Treves, S.T.; Mitchell, K.D.; Habboush, I.H.

1998-01-01

Combined assessment of three dimensional anatomical and functional images (SPECT, PET, MRI, CT) is useful to determine the nature and extent of lesions in many parts of the body. Physicians principally rely on their spatial sense of mentally re-orient and overlap images obtained with different imaging modalities. Objective methods that enable easy and intuitive image registration can help the physician arrive at more optimal diagnoses and better treatment decisions. This review describes a simple, intuitive and robust image registration approach developed in our laboratory. It differs from most other registration techniques in that it allows the user to incorporate all of the available information within the images in the registration process. This method takes full advantage of the ability of knowledgeable operators to achieve image registration and fusion using an intuitive interactive visual approach. It can register images accurately and quickly without the use of elaborate mathematical modeling or optimization techniques. The method provides the operator with tools to manipulate images in three dimensions, including visual feedback techniques to assess the accuracy of registration (grids, overlays, masks, and fusion of images in different colors). Its application is not limited to brain imaging and can be applied to images from any region in the body. The overall effect is a registration algorithm that is easy to implement and can achieve accuracy on the order of one pixel

Feasibility of Multimodal Deformable Registration for Head and Neck Tumor Treatment Planning

Energy Technology Data Exchange (ETDEWEB)

Fortunati, Valerio, E-mail: v.fortunati@erasmusmc.nl [Biomedical Imaging Group Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC University Medical Center, Rotterdam (Netherlands); Verhaart, René F. [Hyperthermia Unit, Department of Radiation Oncology, Erasmus MC University Medical Center Cancer Institute, Rotterdam (Netherlands); Angeloni, Francesco [Istituto di Ricovero e Cura a Carattere Scientifico Foundation SDN for Research and High Education in Nuclear Diagnostics, Naples (Italy); Lugt, Aad van der [Department of Radiology, Erasmus MC University Medical Center, Rotterdam (Netherlands); Niessen, Wiro J. [Biomedical Imaging Group Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC University Medical Center, Rotterdam (Netherlands); Faculty of Applied Sciences, Delft University of Technology, Delft (Netherlands); Veenland, Jifke F. [Biomedical Imaging Group Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC University Medical Center, Rotterdam (Netherlands); Paulides, Margarethus M. [Hyperthermia Unit, Department of Radiation Oncology, Erasmus MC University Medical Center Cancer Institute, Rotterdam (Netherlands); Walsum, Theo van [Biomedical Imaging Group Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC University Medical Center, Rotterdam (Netherlands)

2014-09-01

Purpose: To investigate the feasibility of using deformable registration in clinical practice to fuse MR and CT images of the head and neck for treatment planning. Method and Materials: A state-of-the-art deformable registration algorithm was optimized, evaluated, and compared with rigid registration. The evaluation was based on manually annotated anatomic landmarks and regions of interest in both modalities. We also developed a multiparametric registration approach, which simultaneously aligns T1- and T2-weighted MR sequences to CT. This was evaluated and compared with single-parametric approaches. Results: Our results show that deformable registration yielded a better accuracy than rigid registration, without introducing unrealistic deformations. For deformable registration, an average landmark alignment of approximatively 1.7 mm was obtained. For all the regions of interest excluding the cerebellum and the parotids, deformable registration provided a median modified Hausdorff distance of approximatively 1 mm. Similar accuracies were obtained for the single-parameter and multiparameter approaches. Conclusions: This study demonstrates that deformable registration of head-and-neck CT and MR images is feasible, with overall a significanlty higher accuracy than for rigid registration.

Feasibility of Multimodal Deformable Registration for Head and Neck Tumor Treatment Planning

International Nuclear Information System (INIS)

Fortunati, Valerio; Verhaart, René F.; Angeloni, Francesco; Lugt, Aad van der; Niessen, Wiro J.; Veenland, Jifke F.; Paulides, Margarethus M.; Walsum, Theo van

2014-01-01

Purpose: To investigate the feasibility of using deformable registration in clinical practice to fuse MR and CT images of the head and neck for treatment planning. Method and Materials: A state-of-the-art deformable registration algorithm was optimized, evaluated, and compared with rigid registration. The evaluation was based on manually annotated anatomic landmarks and regions of interest in both modalities. We also developed a multiparametric registration approach, which simultaneously aligns T1- and T2-weighted MR sequences to CT. This was evaluated and compared with single-parametric approaches. Results: Our results show that deformable registration yielded a better accuracy than rigid registration, without introducing unrealistic deformations. For deformable registration, an average landmark alignment of approximatively 1.7 mm was obtained. For all the regions of interest excluding the cerebellum and the parotids, deformable registration provided a median modified Hausdorff distance of approximatively 1 mm. Similar accuracies were obtained for the single-parameter and multiparameter approaches. Conclusions: This study demonstrates that deformable registration of head-and-neck CT and MR images is feasible, with overall a significanlty higher accuracy than for rigid registration

Significantly reducing registration time in IGRT using graphics processing units

DEFF Research Database (Denmark)

Noe, Karsten Østergaard; Denis de Senneville, Baudouin; Tanderup, Kari

2008-01-01

respiration phases in a free breathing volunteer and 41 anatomical landmark points in each image series. The registration method used is a multi-resolution GPU implementation of the 3D Horn and Schunck algorithm. It is based on the CUDA framework from Nvidia. Results On an Intel Core 2 CPU at 2.4GHz each...... registration took 30 minutes. On an Nvidia Geforce 8800GTX GPU in the same machine this registration took 37 seconds, making the GPU version 48.7 times faster. The nine image series of different respiration phases were registered to the same reference image (full inhale). Accuracy was evaluated on landmark...

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

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

Energy Technology Data Exchange (ETDEWEB)

Chang, Yuan-Jen [Department of Management Information Systems, Central Taiwan University of Science and Technology, No. 666, Buzih Rd., Beitun District, Taichung City, Taiwan (R.O.C.) (China); Institute of Biomedical Engineering and Materials Science, Central Taiwan University of Science and Technology, No. 666, Buzih Rd., Beitun District, Taichung City, Taiwan (R.O.C.) (China); Yao, Chun-Hsu [School of Chinese Medicine, China Medical University, Taichung, Taiwan (R.O.C.) (China); Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan (R.O.C.) (China); Department of Biomedical Informatics, Asia University, Taichung, Taiwan (R.O.C.) (China); Wu, Jay [Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan (R.O.C.) (China); Hsieh, Bor-Tsung [Department of Biomedical Imaging and Radiological Sciences, Central Taiwan University of Science and Technology, Taichung, Taiwan (R.O.C.) (China); Tsang, Yuk-Wah [Department of Radiation Oncology, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi, Taiwan (R.O.C.) (China); Chen, Chin-Hsing [Department of Management Information Systems, Central Taiwan University of Science and Technology, No. 666, Buzih Rd., Beitun District, Taichung City, Taiwan (R.O.C.) (China)

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. - 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.

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.

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.)

The effect of high fidelity simulated learning methods on physiotherapy pre-registration education: a systematic review protocol.

Science.gov (United States)

Roberts, Fiona; Cooper, Kay

2017-11-01

The objective of this review is to identify if high fidelity simulated learning methods are effective in enhancing clinical/practical skills compared to usual, low fidelity simulated learning methods in pre-registration physiotherapy education.

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

NARCIS (Netherlands)

Eppenhof, Koen A.J.; Pluim, Josien P.W.; Styner, M.A.; Angelini, E.D.

2017-01-01

Error estimation in medical image registration is valuable when validating, comparing, or combining registration methods. To validate a nonlinear image registration method, ideally the registration error should be known for the entire image domain. We propose a supervised method for the estimation

2D/3D registration using a rotation-invariant cost function based on Zernike moments

Science.gov (United States)

Birkfellner, Wolfgang; Yang, Xinhui; Burgstaller, Wolfgang; Baumann, Bernard; Jacob, Augustinus L.; Niederer, Peter F.; Regazzoni, Pietro; Messmer, Peter

2004-05-01

We present a novel in-plane rotation invariant cost function for 2D/3D registration utilizing projection-invariant transformation properties and the decomposition of the X-ray nad the DRR under comparision into orhogonal Zernike moments. As a result, only five dof have to be optimized, and the number of iteration necessary for registration can be significantly reduced. Results in a phantom study show that an accuracy of approximately 0.7° and 2 mm can be achieved using this method. We conclude that reduction of coupled dof and usage of linear independent coefficients for cost function evaluation provide intersting new perspectives for the field of 2D/3D registration.

3D/2D model-to-image registration by imitation learning for cardiac procedures.

Science.gov (United States)

Toth, Daniel; Miao, Shun; Kurzendorfer, Tanja; Rinaldi, Christopher A; Liao, Rui; Mansi, Tommaso; Rhode, Kawal; Mountney, Peter

2018-05-12

In cardiac interventions, such as cardiac resynchronization therapy (CRT), image guidance can be enhanced by involving preoperative models. Multimodality 3D/2D registration for image guidance, however, remains a significant research challenge for fundamentally different image data, i.e., MR to X-ray. Registration methods must account for differences in intensity, contrast levels, resolution, dimensionality, field of view. Furthermore, same anatomical structures may not be visible in both modalities. Current approaches have focused on developing modality-specific solutions for individual clinical use cases, by introducing constraints, or identifying cross-modality information manually. Machine learning approaches have the potential to create more general registration platforms. However, training image to image methods would require large multimodal datasets and ground truth for each target application. This paper proposes a model-to-image registration approach instead, because it is common in image-guided interventions to create anatomical models for diagnosis, planning or guidance prior to procedures. An imitation learning-based method, trained on 702 datasets, is used to register preoperative models to intraoperative X-ray images. Accuracy is demonstrated on cardiac models and artificial X-rays generated from CTs. The registration error was [Formula: see text] on 1000 test cases, superior to that of manual ([Formula: see text]) and gradient-based ([Formula: see text]) registration. High robustness is shown in 19 clinical CRT cases. Besides the proposed methods feasibility in a clinical environment, evaluation has shown good accuracy and high robustness indicating that it could be applied in image-guided interventions.

Solid Mesh Registration for Radiotherapy Treatment Planning

DEFF Research Database (Denmark)

Noe, Karsten Østergaard; Sørensen, Thomas Sangild

2010-01-01

We present an algorithm for solid organ registration of pre-segmented data represented as tetrahedral meshes. Registration of the organ surface is driven by force terms based on a distance field representation of the source and reference shapes. Registration of internal morphology is achieved usi...

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.

Automated registration of diagnostic to prediagnostic x-ray mammograms: Evaluation and comparison to radiologists' accuracy

Energy Technology Data Exchange (ETDEWEB)

Pinto Pereira, Snehal M.; Hipwell, John H.; McCormack, Valerie A.; Tanner, Christine; Moss, Sue M.; Wilkinson, Louise S.; Khoo, Lisanne A. L.; Pagliari, Catriona; Skippage, Pippa L.; Kliger, Carole J.; Hawkes, David J.; Santos Silva, Isabel M. dos [Cancer Research UK Epidemiology and Genetics Group, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT (United Kingdom); Centre for Medical Image Computing, University College London, London WC1E 6BT (United Kingdom); Lifestyle and Cancer Group, International Agency for Research on Cancer, 150 cours Albert Thomas, Lyon 69008 (France); Centre for Medical Image Computing, University College London, London WC1E 6BT (United Kingdom); Cancer Screening Evaluation Unit, Institute of Cancer Research, Surrey SM2 5NG (United Kingdom); St. George' s Healthcare NHS Trust and South West London Breast Screening Service, London SW17 0QT (United Kingdom); Centre for Medical Image Computing, University College London, London WC1E 6BT (United Kingdom); Cancer Research UK Epidemiology and Genetics Group, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT (United Kingdom)

2010-09-15

Purpose: To compare and evaluate intensity-based registration methods for computation of serial x-ray mammogram correspondence. Methods: X-ray mammograms were simulated from MRIs of 20 women using finite element methods for modeling breast compressions and employing a MRI/x-ray appearance change model. The parameter configurations of three registration methods, affine, fluid, and free-form deformation (FFD), were optimized for registering x-ray mammograms on these simulated images. Five mammography film readers independently identified landmarks (tumor, nipple, and usually two other normal features) on pairs of diagnostic and corresponding prediagnostic digitized images from 52 breast cancer cases. Landmarks were independently reidentified by each reader. Target registration errors were calculated to compare the three registration methods using the reader landmarks as a gold standard. Data were analyzed using multilevel methods. Results: Between-reader variability varied with landmark (p<0.01) and screen (p=0.03), with between-reader mean distance (mm) in point location on the diagnostic/prediagnostic images of 2.50 (95% CI 1.95, 3.15)/2.84 (2.24, 3.55) for nipples and 4.26 (3.43, 5.24)/4.76 (3.85, 5.84) for tumors. Registration accuracy was sensitive to the type of landmark and the amount of breast density. For dense breasts ({>=}40%), the affine and fluid methods outperformed FFD. For breasts with lower density, the affine registration surpassed both fluid and FFD. Mean accuracy (mm) of the affine registration varied between 3.16 (95% CI 2.56, 3.90) for nipple points in breasts with density 20%-39% and 5.73 (4.80, 6.84) for tumor points in breasts with density <20%. Conclusions: Affine registration accuracy was comparable to that between independent film readers. More advanced two-dimensional nonrigid registration algorithms were incapable of increasing the accuracy of image alignment when compared to affine registration.

A national multicenter registration study. Omalizumb in children in Denmark

DEFF Research Database (Denmark)

Spangberg, Katrien; Jørgensen, Inger Merete; Agertoft, Lone

Background: In Denmark, Omalizumab is approved to treat children with severe persistent allergic asthma older than 6 years of age. No systematic registration of the efficacy in the Danish child population according to asthma symptoms or of the efficacy on co-morbid allergic symptoms exists. Results...... from a pilot study show that a broad panel of outcome measures is necessary to evaluate the efficacy of Omalizumab treatment as no change in lung function could be demonstrated Aim: To provide a standardized systematic registration in order to create a database enrolling children with severe allergic...... asthma treated with Omalizumab. Method and study design: A national multicenter registration and follow-up study based on children with clinical persistent severe allergic asthma including both retrospective and prospective registration. Inclusion criteria: • 6-18 years of age. • Severe persistent...

A Comparative Evaluation of 3 Different Free-Form Deformable Image Registration and Contour Propagation Methods for Head and Neck MRI: The Case of Parotid Changes During Radiotherapy.

Science.gov (United States)

Broggi, Sara; Scalco, Elisa; Belli, Maria Luisa; Logghe, Gerlinde; Verellen, Dirk; Moriconi, Stefano; Chiara, Anna; Palmisano, Anna; Mellone, Renata; Fiorino, Claudio; Rizzo, Giovanna

2017-06-01

To validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approaches-the commercial MIM, the open-source Elastix software, and an optimized version of it. Twelve patients with head and neck cancer previously treated with radiotherapy were considered. Deformable image registration and parotid contour propagation were evaluated by considering the magnetic resonance images acquired before and after the end of the treatment. Deformable image registration, based on free-form deformation method, and contour propagation available on MIM were compared to Elastix. Two different contour propagation approaches were implemented for Elastix software, a conventional one (DIR_Trx) and an optimized homemade version, based on mesh deformation (DIR_Mesh). The accuracy of these 3 approaches was estimated by comparing propagated to manual contours in terms of average symmetric distance, maximum symmetric distance, Dice similarity coefficient, sensitivity, and inclusiveness. A good agreement was generally found between the manual contours and the propagated ones, without differences among the 3 methods; in few critical cases with complex deformations, DIR_Mesh proved to be more accurate, having the lowest values of average symmetric distance and maximum symmetric distance and the highest value of Dice similarity coefficient, although nonsignificant. The average propagation errors with respect to the reference contours are lower than the voxel diagonal (2 mm), and Dice similarity coefficient is around 0.8 for all 3 methods. The 3 free-form deformation approaches were not significantly different in terms of deformable image registration accuracy and can be safely adopted for the registration and parotid contour propagation during radiotherapy on magnetic resonance imaging. More optimized approaches (as DIR_Mesh) could be preferable for critical

The influence of the image registration method on the adaptive radiotherapy. A proof of the principle in a selected case of prostate IMRT.

Science.gov (United States)

Berenguer, Roberto; de la Vara, Victoria; Lopez-Honrubia, Veronica; Nuñez, Ana Teresa; Rivera, Miguel; Villas, Maria Victoria; Sabater, Sebastia

2018-01-01

To analyse the influence of the image registration method on the adaptive radiotherapy of an IMRT prostate treatment, and to compare the dose accumulation according to 3 different image registration methods with the planned dose. The IMRT prostate patient was CT imaged 3 times throughout his treatment. The prostate, PTV, rectum and bladder were segmented on each CT. A Rigid, a deformable (DIR) B-spline and a DIR with landmarks registration algorithms were employed. The difference between the accumulated doses and planned doses were evaluated by the gamma index. The Dice coefficient and Hausdorff distance was used to evaluate the overlap between volumes, to quantify the quality of the registration. When comparing adaptive vs no adaptive RT, the gamma index calculation showed large differences depending on the image registration method (as much as 87.6% in the case of DIR B-spline). The quality of the registration was evaluated using an index such as the Dice coefficient. This showed that the best result was obtained with DIR with landmarks compared with the rest and it was always above 0.77, reported as a recommended minimum value for prostate studies in a multi-centre review. Apart from showing the importance of the application of an adaptive RT protocol in a particular treatment, this work shows that the election of the registration method is decisive in the result of the adaptive radiotherapy and dose accumulation. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

SU-E-J-132: Automated Segmentation with Post-Registration Atlas Selection Based On Mutual Information

International Nuclear Information System (INIS)

Ren, X; Gao, H; Sharp, G

2015-01-01

Purpose: The delineation of targets and organs-at-risk is a critical step during image-guided radiation therapy, for which manual contouring is the gold standard. However, it is often time-consuming and may suffer from intra- and inter-rater variability. The purpose of this work is to investigate the automated segmentation. Methods: The automatic segmentation here is based on mutual information (MI), with the atlas from Public Domain Database for Computational Anatomy (PDDCA) with manually drawn contours.Using dice coefficient (DC) as the quantitative measure of segmentation accuracy, we perform leave-one-out cross-validations for all PDDCA images sequentially, during which other images are registered to each chosen image and DC is computed between registered contour and ground truth. Meanwhile, six strategies, including MI, are selected to measure the image similarity, with MI to be the best. Then given a target image to be segmented and an atlas, automatic segmentation consists of: (a) the affine registration step for image positioning; (b) the active demons registration method to register the atlas to the target image; (c) the computation of MI values between the deformed atlas and the target image; (d) the weighted image fusion of three deformed atlas images with highest MI values to form the segmented contour. Results: MI was found to be the best among six studied strategies in the sense that it had the highest positive correlation between similarity measure (e.g., MI values) and DC. For automated segmentation, the weighted image fusion of three deformed atlas images with highest MI values provided the highest DC among four proposed strategies. Conclusion: MI has the highest correlation with DC, and therefore is an appropriate choice for post-registration atlas selection in atlas-based segmentation. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)

SU-E-J-132: Automated Segmentation with Post-Registration Atlas Selection Based On Mutual Information

Energy Technology Data Exchange (ETDEWEB)

Ren, X; Gao, H [Shanghai Jiao Tong University, Shanghai, Shanghai (China); Sharp, G [Massachusetts General Hospital, Boston, MA (United States)

2015-06-15

Purpose: The delineation of targets and organs-at-risk is a critical step during image-guided radiation therapy, for which manual contouring is the gold standard. However, it is often time-consuming and may suffer from intra- and inter-rater variability. The purpose of this work is to investigate the automated segmentation. Methods: The automatic segmentation here is based on mutual information (MI), with the atlas from Public Domain Database for Computational Anatomy (PDDCA) with manually drawn contours.Using dice coefficient (DC) as the quantitative measure of segmentation accuracy, we perform leave-one-out cross-validations for all PDDCA images sequentially, during which other images are registered to each chosen image and DC is computed between registered contour and ground truth. Meanwhile, six strategies, including MI, are selected to measure the image similarity, with MI to be the best. Then given a target image to be segmented and an atlas, automatic segmentation consists of: (a) the affine registration step for image positioning; (b) the active demons registration method to register the atlas to the target image; (c) the computation of MI values between the deformed atlas and the target image; (d) the weighted image fusion of three deformed atlas images with highest MI values to form the segmented contour. Results: MI was found to be the best among six studied strategies in the sense that it had the highest positive correlation between similarity measure (e.g., MI values) and DC. For automated segmentation, the weighted image fusion of three deformed atlas images with highest MI values provided the highest DC among four proposed strategies. Conclusion: MI has the highest correlation with DC, and therefore is an appropriate choice for post-registration atlas selection in atlas-based segmentation. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)

Feasibility of a novel deformable image registration technique to facilitate classification, targeting, and monitoring of tumor and normal tissue

International Nuclear Information System (INIS)

Brock, Kristy K.; Dawson, Laura A.; Sharpe, Michael B.; Moseley, Douglas J.; Jaffray, David A.

2006-01-01

Purpose: To investigate the feasibility of a biomechanical-based deformable image registration technique for the integration of multimodality imaging, image guided treatment, and response monitoring. Methods and Materials: A multiorgan deformable image registration technique based on finite element modeling (FEM) and surface projection alignment of selected regions of interest with biomechanical material and interface models has been developed. FEM also provides an inherent method for direct tracking specified regions through treatment and follow-up. Results: The technique was demonstrated on 5 liver cancer patients. Differences of up to 1 cm of motion were seen between the diaphragm and the tumor center of mass after deformable image registration of exhale and inhale CT scans. Spatial differences of 5 mm or more were observed for up to 86% of the surface of the defined tumor after deformable image registration of the computed tomography (CT) and magnetic resonance images. Up to 6.8 mm of motion was observed for the tumor after deformable image registration of the CT and cone-beam CT scan after rigid registration of the liver. Deformable registration of the CT to the follow-up CT allowed a more accurate assessment of tumor response. Conclusions: This biomechanical-based deformable image registration technique incorporates classification, targeting, and monitoring of tumor and normal tissue using one methodology

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

Registration of prone and supine CT colonography scans using correlation optimized warping and canonical correlation analysis

International Nuclear Information System (INIS)

Wang Shijun; Yao Jianhua; Liu Jiamin; Petrick, Nicholas; Van Uitert, Robert L.; Periaswamy, Senthil; Summers, Ronald M.

2009-01-01

Purpose: In computed tomographic colonography (CTC), a patient will be scanned twice--Once supine and once prone--to improve the sensitivity for polyp detection. To assist radiologists in CTC reading, in this paper we propose an automated method for colon registration from supine and prone CTC scans. Methods: We propose a new colon centerline registration method for prone and supine CTC scans using correlation optimized warping (COW) and canonical correlation analysis (CCA) based on the anatomical structure of the colon. Four anatomical salient points on the colon are first automatically distinguished. Then correlation optimized warping is applied to the segments defined by the anatomical landmarks to improve the global registration based on local correlation of segments. The COW method was modified by embedding canonical correlation analysis to allow multiple features along the colon centerline to be used in our implementation. Results: We tested the COW algorithm on a CTC data set of 39 patients with 39 polyps (19 training and 20 test cases) to verify the effectiveness of the proposed COW registration method. Experimental results on the test set show that the COW method significantly reduces the average estimation error in a polyp location between supine and prone scans by 67.6%, from 46.27±52.97 to 14.98 mm±11.41 mm, compared to the normalized distance along the colon centerline algorithm (p<0.01). Conclusions: The proposed COW algorithm is more accurate for the colon centerline registration compared to the normalized distance along the colon centerline method and the dynamic time warping method. Comparison results showed that the feature combination of z-coordinate and curvature achieved lowest registration error compared to the other feature combinations used by COW. The proposed method is tolerant to centerline errors because anatomical landmarks help prevent the propagation of errors across the entire colon centerline.

Semi-automatic registration of 3D orthodontics models from photographs

Science.gov (United States)

Destrez, Raphaël.; Treuillet, Sylvie; Lucas, Yves; Albouy-Kissi, Benjamin

2013-03-01

In orthodontics, a common practice used to diagnose and plan the treatment is the dental cast. After digitization by a CT-scan or a laser scanner, the obtained 3D surface models can feed orthodontics numerical tools for computer-aided diagnosis and treatment planning. One of the pre-processing critical steps is the 3D registration of dental arches to obtain the occlusion of these numerical models. For this task, we propose a vision based method to automatically compute the registration based on photos of patient mouth. From a set of matched singular points between two photos and the dental 3D models, the rigid transformation to apply to the mandible to be in contact with the maxillary may be computed by minimizing the reprojection errors. During a precedent study, we established the feasibility of this visual registration approach with a manual selection of singular points. This paper addresses the issue of automatic point detection. Based on a priori knowledge, histogram thresholding and edge detection are used to extract specific points in 2D images. Concurrently, curvatures information detects 3D corresponding points. To improve the quality of the final registration, we also introduce a combined optimization of the projection matrix with the 2D/3D point positions. These new developments are evaluated on real data by considering the reprojection errors and the deviation angles after registration in respect to the manual reference occlusion realized by a specialist.

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

Band co-registration modeling of LAPAN-A3/IPB multispectral imager based on satellite attitude

Science.gov (United States)

Hakim, P. R.; Syafrudin, A. H.; Utama, S.; Jayani, A. P. S.

2018-05-01

One of significant geometric distortion on images of LAPAN-A3/IPB multispectral imager is co-registration error between each color channel detector. Band co-registration distortion usually can be corrected by using several approaches, which are manual method, image matching algorithm, or sensor modeling and calibration approach. This paper develops another approach to minimize band co-registration distortion on LAPAN-A3/IPB multispectral image by using supervised modeling of image matching with respect to satellite attitude. Modeling results show that band co-registration error in across-track axis is strongly influenced by yaw angle, while error in along-track axis is fairly influenced by both pitch and roll angle. Accuracy of the models obtained is pretty good, which lies between 1-3 pixels error for each axis of each pair of band co-registration. This mean that the model can be used to correct the distorted images without the need of slower image matching algorithm, nor the laborious effort needed in manual approach and sensor calibration. Since the calculation can be executed in order of seconds, this approach can be used in real time quick-look image processing in ground station or even in satellite on-board image processing.

Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra provides reduced effect of scanner for cortex volumetry with atlas-based method in healthy subjects

Energy Technology Data Exchange (ETDEWEB)

Goto, Masami; Ino, Kenji; Yano, Keiichi [University of Tokyo Hospital, Department of Radiological Technology, Bunkyo-ku, Tokyo (Japan); Abe, Osamu [Nihon University School of Medicine, Department of Radiology, Itabashi-ku, Tokyo (Japan); Aoki, Shigeki [Juntendo University, Department of Radiology, Bunkyo-ku, Tokyo (Japan); Hayashi, Naoto [University of Tokyo Hospital, Department of Computational Diagnostic Radiology and Preventive Medicine, Bunkyo-ku, Tokyo (Japan); Miyati, Tosiaki [Kanazawa University, Graduate School of Medical Science, Kanazawa (Japan); Takao, Hidemasa; Mori, Harushi; Kunimatsu, Akira; Ohtomo, Kuni [University of Tokyo Hospital, Department of Radiology and Department of Computational Diagnostic Radiology and Preventive Medicine, Bunkyo-ku, Tokyo (Japan); Iwatsubo, Takeshi [University of Tokyo, Department of Neuropathology, Bunkyo-ku, Tokyo (Japan); Yamashita, Fumio [Iwate Medical University, Department of Radiology, Yahaba, Iwate (Japan); Matsuda, Hiroshi [Integrative Brain Imaging Center National Center of Neurology and Psychiatry, Department of Nuclear Medicine, Kodaira, Tokyo (Japan); Collaboration: Japanese Alzheimer' s Disease Neuroimaging Initiative

2013-07-15

This study aimed to investigate whether the effect of scanner for cortex volumetry with atlas-based method is reduced using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) normalization compared with standard normalization. Three-dimensional T1-weighted magnetic resonance images (3D-T1WIs) of 21 healthy subjects were obtained and evaluated for effect of scanner in cortex volumetry. 3D-T1WIs of the 21 subjects were obtained with five MRI systems. Imaging of each subject was performed on each of five different MRI scanners. We used the Voxel-Based Morphometry 8 tool implemented in Statistical Parametric Mapping 8 and WFU PickAtlas software (Talairach brain atlas theory). The following software default settings were used as bilateral region-of-interest labels: ''Frontal Lobe,'' ''Hippocampus,'' ''Occipital Lobe,'' ''Orbital Gyrus,'' ''Parietal Lobe,'' ''Putamen,'' and ''Temporal Lobe.'' Effect of scanner for cortex volumetry using the atlas-based method was reduced with DARTEL normalization compared with standard normalization in Frontal Lobe, Occipital Lobe, Orbital Gyrus, Putamen, and Temporal Lobe; was the same in Hippocampus and Parietal Lobe; and showed no increase with DARTEL normalization for any region of interest (ROI). DARTEL normalization reduces the effect of scanner, which is a major problem in multicenter studies. (orig.)

Interindividual registration and dose mapping for voxelwise population analysis of rectal toxicity in prostate cancer radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Dréan, Gaël; Acosta, Oscar, E-mail: Oscar.Acosta@univ-rennes1.fr; Simon, Antoine; Haigron, Pascal [INSERM, U1099, Rennes F-35000 (France); Université de Rennes 1, LTSI, Rennes F-35000 (France); Lafond, Caroline; Crevoisier, Renaud de [INSERM, U1099, Rennes F-35000 (France); Université de Rennes 1, LTSI, Rennes F-35000 (France); Département de Radiothérapie, Center Eugène Marquis, Rennes F-35000 (France)

2016-06-15

Purpose: Recent studies revealed a trend toward voxelwise population analysis in order to understand the local dose/toxicity relationships in prostate cancer radiotherapy. Such approaches require, however, an accurate interindividual mapping of the anatomies and 3D dose distributions toward a common coordinate system. This step is challenging due to the high interindividual variability. In this paper, the authors propose a method designed for interindividual nonrigid registration of the rectum and dose mapping for population analysis. Methods: The method is based on the computation of a normalized structural description of the rectum using a Laplacian-based model. This description takes advantage of the tubular structure of the rectum and its centerline to be embedded in a nonrigid registration-based scheme. The performances of the method were evaluated on 30 individuals treated for prostate cancer in a leave-one-out cross validation. Results: Performance was measured using classical metrics (Dice score and Hausdorff distance), along with new metrics devised to better assess dose mapping in relation with structural deformation (dose-organ overlap). Considering these scores, the proposed method outperforms intensity-based and distance maps-based registration methods. Conclusions: The proposed method allows for accurately mapping interindividual 3D dose distributions toward a single anatomical template, opening the way for further voxelwise statistical analysis.

Image registration with auto-mapped control volumes