Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132.

PubMed

Brock, Kristy K; Mutic, Sasa; McNutt, Todd R; Li, Hua; Kessler, Marc L

2017-07-01

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes. © 2017 American Association of Physicists in Medicine.

Medical image registration based on normalized multidimensional mutual information

NASA Astrophysics Data System (ADS)

Li, Qi; Ji, Hongbing; Tong, Ming

2009-10-01

Registration of medical images is an essential research topic in medical image processing and applications, and especially a preliminary and key step for multimodality image fusion. This paper offers a solution to medical image registration based on normalized multi-dimensional mutual information. Firstly, affine transformation with translational and rotational parameters is applied to the floating image. Then ordinal features are extracted by ordinal filters with different orientations to represent spatial information in medical images. Integrating ordinal features with pixel intensities, the normalized multi-dimensional mutual information is defined as similarity criterion to register multimodality images. Finally the immune algorithm is used to search registration parameters. The experimental results demonstrate the effectiveness of the proposed registration scheme.

A fast image registration approach of neural activities in light-sheet fluorescence microscopy images

NASA Astrophysics Data System (ADS)

Meng, Hui; Hui, Hui; Hu, Chaoen; Yang, Xin; Tian, Jie

2017-03-01

The ability of fast and single-neuron resolution imaging of neural activities enables light-sheet fluorescence microscopy (LSFM) as a powerful imaging technique in functional neural connection applications. The state-of-art LSFM imaging system can record the neuronal activities of entire brain for small animal, such as zebrafish or C. elegans at single-neuron resolution. However, the stimulated and spontaneous movements in animal brain result in inconsistent neuron positions during recording process. It is time consuming to register the acquired large-scale images with conventional method. In this work, we address the problem of fast registration of neural positions in stacks of LSFM images. This is necessary to register brain structures and activities. To achieve fast registration of neural activities, we present a rigid registration architecture by implementation of Graphics Processing Unit (GPU). In this approach, the image stacks were preprocessed on GPU by mean stretching to reduce the computation effort. The present image was registered to the previous image stack that considered as reference. A fast Fourier transform (FFT) algorithm was used for calculating the shift of the image stack. The calculations for image registration were performed in different threads while the preparation functionality was refactored and called only once by the master thread. We implemented our registration algorithm on NVIDIA Quadro K4200 GPU under Compute Unified Device Architecture (CUDA) programming environment. The experimental results showed that the registration computation can speed-up to 550ms for a full high-resolution brain image. Our approach also has potential to be used for other dynamic image registrations in biomedical applications.

Non-rigid ultrasound image registration using generalized relaxation labeling process

NASA Astrophysics Data System (ADS)

Lee, Jong-Ha; Seong, Yeong Kyeong; Park, MoonHo; Woo, Kyoung-Gu; Ku, Jeonghun; Park, Hee-Jun

2013-03-01

This research proposes a novel non-rigid registration method for ultrasound images. The most predominant anatomical features in medical images are tissue boundaries, which appear as edges. In ultrasound images, however, other features can be identified as well due to the specular reflections that appear as bright lines superimposed on the ideal edge location. In this work, an image's local phase information (via the frequency domain) is used to find the ideal edge location. The generalized relaxation labeling process is then formulated to align the feature points extracted from the ideal edge location. In this work, the original relaxation labeling method was generalized by taking n compatibility coefficient values to improve non-rigid registration performance. This contextual information combined with a relaxation labeling process is used to search for a correspondence. Then the transformation is calculated by the thin plate spline (TPS) model. These two processes are iterated until the optimal correspondence and transformation are found. We have tested our proposed method and the state-of-the-art algorithms with synthetic data and bladder ultrasound images of in vivo human subjects. Experiments show that the proposed method improves registration performance significantly, as compared to other state-of-the-art non-rigid registration algorithms.

Range image registration based on hash map and moth-flame optimization

NASA Astrophysics Data System (ADS)

Zou, Li; Ge, Baozhen; Chen, Lei

2018-03-01

Over the past decade, evolutionary algorithms (EAs) have been introduced to solve range image registration problems because of their robustness and high precision. However, EA-based range image registration algorithms are time-consuming. To reduce the computational time, an EA-based range image registration algorithm using hash map and moth-flame optimization is proposed. In this registration algorithm, a hash map is used to avoid over-exploitation in registration process. Additionally, we present a search equation that is better at exploration and a restart mechanism to avoid being trapped in local minima. We compare the proposed registration algorithm with the registration algorithms using moth-flame optimization and several state-of-the-art EA-based registration algorithms. The experimental results show that the proposed algorithm has a lower computational cost than other algorithms and achieves similar registration precision.

Joint image registration and fusion method with a gradient strength regularization

NASA Astrophysics Data System (ADS)

Lidong, Huang; Wei, Zhao; Jun, Wang

2015-05-01

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

Fast interactive elastic registration of 12-bit multi-spectral images with subvoxel accuracy using display hardware

NASA Astrophysics Data System (ADS)

Noordmans, Herke Jan; de Roode, Rowland; Verdaasdonk, Rudolf

2007-03-01

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

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

NASA Astrophysics Data System (ADS)

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

2007-02-01

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

Image Processing Of Images From Peripheral-Artery Digital Subtraction Angiography (DSA) Studies

NASA Astrophysics Data System (ADS)

Wilson, David L.; Tarbox, Lawrence R.; Cist, David B.; Faul, David D.

1988-06-01

A system is being developed to test the possibility of doing peripheral, digital subtraction angiography (DSA) with a single contrast injection using a moving gantry system. Given repositioning errors that occur between the mask and contrast-containing images, factors affecting the success of subtractions following image registration have been investigated theoretically and experimentally. For a 1 mm gantry displacement, parallax and geometric image distortion (pin-cushion) both give subtraction errors following registration that are approximately 25% of the error resulting from no registration. Image processing techniques improve the subtractions. The geometric distortion effect is reduced using a piece-wise, 8 parameter unwarping method. Plots of image similarity measures versus pixel shift are well behaved and well fit by a parabola, leading to the development of an iterative, automatic registration algorithm that uses parabolic prediction of the new minimum. The registration algorithm converges quickly (less than 1 second on a MicroVAX) and is relatively immune to the region of interest (ROI) selected.

Image navigation and registration performance assessment tool set for the GOES-R Advanced Baseline Imager and Geostationary Lightning Mapper

NASA Astrophysics Data System (ADS)

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

2016-05-01

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. For ABI, these metrics are the 3-sigma errors in navigation (NAV), channel-to-channel registration (CCR), frame-to-frame registration (FFR), swath-to-swath registration (SSR), and within frame registration (WIFR) for the Level 1B image products. For GLM, the single metric of interest is the 3-sigma error in the navigation of background images (GLM NAV) used by the system to navigate lightning strikes. 3-sigma errors are estimates of the 99. 73rd percentile of the errors accumulated over a 24 hour data collection period. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24 hour evaluation period. Another aspect of the IPATS design that vastly reduces execution time is the off-line propagation of Landsat based truth images to the fixed grid coordinates system for each of the three GOES-R satellite locations, operational East and West and initial checkout locations. This paper describes the algorithmic design and implementation of IPATS and provides preliminary test results.

Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for the GOES-R Advanced Baseline Imager and Geostationary Lightning Mapper

NASA Technical Reports Server (NTRS)

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

2016-01-01

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

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

NASA Technical Reports Server (NTRS)

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

2016-01-01

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

Image Registration of High-Resolution Uav Data: the New Hypare Algorithm

NASA Astrophysics Data System (ADS)

Bahr, T.; Jin, X.; Lasica, R.; Giessel, D.

2013-08-01

Unmanned aerial vehicles play an important role in the present-day civilian and military intelligence. Equipped with a variety of sensors, such as SAR imaging modes, E/O- and IR sensor technology, they are due to their agility suitable for many applications. Hence, the necessity arises to use fusion technologies and to develop them continuously. Here an exact image-to-image registration is essential. It serves as the basis for important image processing operations such as georeferencing, change detection, and data fusion. Therefore we developed the Hybrid Powered Auto-Registration Engine (HyPARE). HyPARE combines all available spatial reference information with a number of image registration approaches to improve the accuracy, performance, and automation of tie point generation and image registration. We demonstrate this approach by the registration of 39 still images from a high-resolution image stream, acquired with a Aeryon Photo3S™ camera on an Aeryon Scout micro-UAV™.

Geometric registration of remotely sensed data with SAMIR

NASA Astrophysics Data System (ADS)

Gianinetto, Marco; Barazzetti, Luigi; Dini, Luigi; Fusiello, Andrea; Toldo, Roberto

2015-06-01

The commercial market offers several software packages for the registration of remotely sensed data through standard one-to-one image matching. Although very rapid and simple, this strategy does not take into consideration all the interconnections among the images of a multi-temporal data set. This paper presents a new scientific software, called Satellite Automatic Multi-Image Registration (SAMIR), able to extend the traditional registration approach towards multi-image global processing. Tests carried out with high-resolution optical (IKONOS) and high-resolution radar (COSMO-SkyMed) data showed that SAMIR can improve the registration phase with a more rigorous and robust workflow without initial approximations, user's interaction or limitation in spatial/spectral data size. The validation highlighted a sub-pixel accuracy in image co-registration for the considered imaging technologies, including optical and radar imagery.

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

NASA Astrophysics Data System (ADS)

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.

Influence of image registration on ADC images computed from free-breathing diffusion MRIs of the abdomen

NASA Astrophysics Data System (ADS)

Guyader, Jean-Marie; Bernardin, Livia; Douglas, Naomi H. M.; Poot, Dirk H. J.; Niessen, Wiro J.; Klein, Stefan

2014-03-01

The apparent diffusion coefficient (ADC) is an imaging biomarker providing quantitative information on the diffusion of water in biological tissues. This measurement could be of relevance in oncology drug development, but it suffers from a lack of reliability. ADC images are computed by applying a voxelwise exponential fitting to multiple diffusion-weighted MR images (DW-MRIs) acquired with different diffusion gradients. In the abdomen, respiratory motion induces misalignments in the datasets, creating visible artefacts and inducing errors in the ADC maps. We propose a multistep post-acquisition motion compensation pipeline based on 3D non-rigid registrations. It corrects for motion within each image and brings all DW-MRIs to a common image space. The method is evaluated on 10 datasets of free-breathing abdominal DW-MRIs acquired from healthy volunteers. Regions of interest (ROIs) are segmented in the right part of the abdomen and measurements are compared in the three following cases: no image processing, Gaussian blurring of the raw DW-MRIs and registration. Results show that both blurring and registration improve the visual quality of ADC images, but compared to blurring, registration yields visually sharper images. Measurement uncertainty is reduced both by registration and blurring. For homogeneous ROIs, blurring and registration result in similar median ADCs, which are lower than without processing. In a ROI at the interface between liver and kidney, registration and blurring yield different median ADCs, suggesting that uncorrected motion introduces a bias. Our work indicates that averaging procedures on the scanner should be avoided, as they remove the opportunity to perform motion correction.

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

NASA Technical Reports Server (NTRS)

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

2016-01-01

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

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

PubMed

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

2016-01-01

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

Implementation of Remote 3-Dimensional Image Guided Radiation Therapy Quality Assurance for Radiation Therapy Oncology Group Clinical Trials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui Yunfeng; Galvin, James M.; Radiation Therapy Oncology Group, American College of Radiology, Philadelphia, Pennsylvania

2013-01-01

Purpose: To report the process and initial experience of remote credentialing of three-dimensional (3D) image guided radiation therapy (IGRT) as part of the quality assurance (QA) of submitted data for Radiation Therapy Oncology Group (RTOG) clinical trials; and to identify major issues resulting from this process and analyze the review results on patient positioning shifts. Methods and Materials: Image guided radiation therapy datasets including in-room positioning CT scans and daily shifts applied were submitted through the Image Guided Therapy QA Center from institutions for the IGRT credentialing process, as required by various RTOG trials. A centralized virtual environment is establishedmore » at the RTOG Core Laboratory, containing analysis tools and database infrastructure for remote review by the Physics Principal Investigators of each protocol. The appropriateness of IGRT technique and volumetric image registration accuracy were evaluated. Registration accuracy was verified by repeat registration with a third-party registration software system. With the accumulated review results, registration differences between those obtained by the Physics Principal Investigators and from the institutions were analyzed for different imaging sites, shift directions, and imaging modalities. Results: The remote review process was successfully carried out for 87 3D cases (out of 137 total cases, including 2-dimensional and 3D) during 2010. Frequent errors in submitted IGRT data and challenges in the review of image registration for some special cases were identified. Workarounds for these issues were developed. The average differences of registration results between reviewers and institutions ranged between 2 mm and 3 mm. Large discrepancies in the superior-inferior direction were found for megavoltage CT cases, owing to low spatial resolution in this direction for most megavoltage CT cases. Conclusion: This first experience indicated that remote review for 3D IGRT as part of QA for RTOG clinical trials is feasible and effective. The magnitude of registration discrepancy between institution and reviewer was presented, and the major issues were investigated to further improve this remote evaluation process.« less

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eichel, Paul H.

2013-08-01

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

Registration of heat capacity mapping mission day and night images

NASA Technical Reports Server (NTRS)

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

1982-01-01

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

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

PubMed

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.

Image Navigation and Registration Performance Assessment Evaluation Tools for GOES-R ABI and GLM

NASA Technical Reports Server (NTRS)

Houchin, Scott; Porter, Brian; Graybill, Justin; Slingerland, Philip

2017-01-01

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24 hour evaluation period. This paper describes the software design and implementation of IPATS and provides preliminary test results.

Non-rigid multi-frame registration of cell nuclei in live cell fluorescence microscopy image data.

PubMed

Tektonidis, Marco; Kim, Il-Han; Chen, Yi-Chun M; Eils, Roland; Spector, David L; Rohr, Karl

2015-01-01

The analysis of the motion of subcellular particles in live cell microscopy images is essential for understanding biological processes within cells. For accurate quantification of the particle motion, compensation of the motion and deformation of the cell nucleus is required. We introduce a non-rigid multi-frame registration approach for live cell fluorescence microscopy image data. Compared to existing approaches using pairwise registration, our approach exploits information from multiple consecutive images simultaneously to improve the registration accuracy. We present three intensity-based variants of the multi-frame registration approach and we investigate two different temporal weighting schemes. The approach has been successfully applied to synthetic and live cell microscopy image sequences, and an experimental comparison with non-rigid pairwise registration has been carried out. Copyright © 2014 Elsevier B.V. All rights reserved.

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

PubMed

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 < 0.001] and complete (median, 34.0 s [range, 26-66 s] vs. 47.5 s [range, 32-90]; P = 0.001] image fusion. Registration error of Positioning 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.

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

PubMed Central

Wang, Yangping; Wang, Song

2016-01-01

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

Research Issues in Image Registration for Remote Sensing

NASA Technical Reports Server (NTRS)

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

2007-01-01

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

Introduction to Remote Sensing Image Registration

NASA Technical Reports Server (NTRS)

Le Moigne, Jacqueline

2017-01-01

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

Objective and expert-independent validation of retinal image registration algorithms by a projective imaging distortion model.

PubMed

Lee, Sangyeol; Reinhardt, Joseph M; Cattin, Philippe C; Abràmoff, Michael D

2010-08-01

Fundus camera imaging of the retina is widely used to diagnose and manage ophthalmologic disorders including diabetic retinopathy, glaucoma, and age-related macular degeneration. Retinal images typically have a limited field of view, and multiple images can be joined together using an image registration technique to form a montage with a larger field of view. A variety of methods for retinal image registration have been proposed, but evaluating such methods objectively is difficult due to the lack of a reference standard for the true alignment of the individual images that make up the montage. A method of generating simulated retinal images by modeling the geometric distortions due to the eye geometry and the image acquisition process is described in this paper. We also present a validation process that can be used for any retinal image registration method by tracing through the distortion path and assessing the geometric misalignment in the coordinate system of the reference standard. The proposed method can be used to perform an accuracy evaluation over the whole image, so that distortion in the non-overlapping regions of the montage components can be easily assessed. We demonstrate the technique by generating test image sets with a variety of overlap conditions and compare the accuracy of several retinal image registration models. Copyright 2010 Elsevier B.V. All rights reserved.

A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs.

PubMed

Han, Lianghao; Dong, Hua; McClelland, Jamie R; Han, Liangxiu; Hawkes, David J; Barratt, Dean C

2017-07-01

This paper presents a new hybrid biomechanical model-based non-rigid image registration method for lung motion estimation. In the proposed method, a patient-specific biomechanical modelling process captures major physically realistic deformations with explicit physical modelling of sliding motion, whilst a subsequent non-rigid image registration process compensates for small residuals. The proposed algorithm was evaluated with 10 4D CT datasets of lung cancer patients. The target registration error (TRE), defined as the Euclidean distance of landmark pairs, was significantly lower with the proposed method (TRE = 1.37 mm) than with biomechanical modelling (TRE = 3.81 mm) and intensity-based image registration without specific considerations for sliding motion (TRE = 4.57 mm). The proposed method achieved a comparable accuracy as several recently developed intensity-based registration algorithms with sliding handling on the same datasets. A detailed comparison on the distributions of TREs with three non-rigid intensity-based algorithms showed that the proposed method performed especially well on estimating the displacement field of lung surface regions (mean TRE = 1.33 mm, maximum TRE = 5.3 mm). The effects of biomechanical model parameters (such as Poisson's ratio, friction and tissue heterogeneity) on displacement estimation were investigated. The potential of the algorithm in optimising biomechanical models of lungs through analysing the pattern of displacement compensation from the image registration process has also been demonstrated. Copyright © 2017 Elsevier B.V. All rights reserved.

Automatic Co-Registration of QuickBird Data for Change Detection Applications

NASA Technical Reports Server (NTRS)

Bryant, Nevin A.; Logan, Thomas L.; Zobrist, Albert L.

2006-01-01

This viewgraph presentation reviews the use Automatic Fusion of Image Data System (AFIDS) for Automatic Co-Registration of QuickBird Data to ascertain if changes have occurred in images. The process is outlined, and views from Iraq and Los Angelels are shown to illustrate the process.

WHOLE BODY NONRIGID CT-PET REGISTRATION USING WEIGHTED DEMONS.

PubMed

Suh, J W; Kwon, Oh-K; Scheinost, D; Sinusas, A J; Cline, Gary W; Papademetris, X

2011-03-30

We present a new registration method for whole-body rat computed tomography (CT) image and positron emission tomography (PET) images using a weighted demons algorithm. The CT and PET images are acquired in separate scanners at different times and the inherent differences in the imaging protocols produced significant nonrigid changes between the two acquisitions in addition to heterogeneous image characteristics. In this situation, we utilized both the transmission-PET and the emission-PET images in the deformable registration process emphasizing particular regions of the moving transmission-PET image using the emission-PET image. We validated our results with nine rat image sets using M-Hausdorff distance similarity measure. We demonstrate improved performance compared to standard methods such as Demons and normalized mutual information-based non-rigid FFD registration.

Deformable Medical Image Registration: A Survey

PubMed Central

Sotiras, Aristeidis; Davatzikos, Christos; Paragios, Nikos

2013-01-01

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

Learning intervention-induced deformations for non-rigid MR-CT registration and electrode localization in epilepsy patients

PubMed Central

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

2015-01-01

This paper describes a framework for learning a statistical model of non-rigid deformations induced by interventional procedures. We make use of this learned model to perform constrained non-rigid registration of pre-procedural and post-procedural imaging. We demonstrate results applying this framework to non-rigidly register post-surgical computed tomography (CT) brain images to pre-surgical magnetic resonance images (MRIs) of epilepsy patients who had intra-cranial electroencephalography electrodes surgically implanted. Deformations caused by this surgical procedure, imaging artifacts caused by the electrodes, and the use of multi-modal imaging data make non-rigid registration challenging. Our results show that the use of our proposed framework to constrain the non-rigid registration process results in significantly improved and more robust registration performance compared to using standard rigid and non-rigid registration methods. PMID:26900569

Local-search based prediction of medical image registration error

NASA Astrophysics Data System (ADS)

Saygili, Görkem

2018-03-01

Medical image registration is a crucial task in many different medical imaging applications. Hence, considerable amount of work has been published recently that aim to predict the error in a registration without any human effort. If provided, these error predictions can be used as a feedback to the registration algorithm to further improve its performance. Recent methods generally start with extracting image-based and deformation-based features, then apply feature pooling and finally train a Random Forest (RF) regressor to predict the real registration error. Image-based features can be calculated after applying a single registration but provide limited accuracy whereas deformation-based features such as variation of deformation vector field may require up to 20 registrations which is a considerably high time-consuming task. This paper proposes to use extracted features from a local search algorithm as image-based features to estimate the error of a registration. The proposed method comprises a local search algorithm to find corresponding voxels between registered image pairs and based on the amount of shifts and stereo confidence measures, it predicts the amount of registration error in millimetres densely using a RF regressor. Compared to other algorithms in the literature, the proposed algorithm does not require multiple registrations, can be efficiently implemented on a Graphical Processing Unit (GPU) and can still provide highly accurate error predictions in existence of large registration error. Experimental results with real registrations on a public dataset indicate a substantially high accuracy achieved by using features from the local search algorithm.

Research on registration algorithm for check seal verification

NASA Astrophysics Data System (ADS)

Wang, Shuang; Liu, Tiegen

2008-03-01

Nowadays seals play an important role in China. With the development of social economy, the traditional method of manual check seal identification can't meet the need s of banking transactions badly. This paper focus on pre-processing and registration algorithm for check seal verification using theory of image processing and pattern recognition. First of all, analyze the complex characteristics of check seals. To eliminate the difference of producing conditions and the disturbance caused by background and writing in check image, many methods are used in the pre-processing of check seal verification, such as color components transformation, linearity transform to gray-scale image, medium value filter, Otsu, close calculations and labeling algorithm of mathematical morphology. After the processes above, the good binary seal image can be obtained. On the basis of traditional registration algorithm, a double-level registration method including rough and precise registration method is proposed. The deflection angle of precise registration method can be precise to 0.1°. This paper introduces the concepts of difference inside and difference outside and use the percent of difference inside and difference outside to judge whether the seal is real or fake. The experimental results of a mass of check seals are satisfied. It shows that the methods and algorithmic presented have good robustness to noise sealing conditions and satisfactory tolerance of difference within class.

Intraoperative Image-based Multiview 2D/3D Registration for Image-Guided Orthopaedic Surgery: Incorporation of Fiducial-Based C-Arm Tracking and GPU-Acceleration

PubMed Central

Armand, Mehran; Armiger, Robert S.; Kutzer, Michael D.; Basafa, Ehsan; Kazanzides, Peter; Taylor, Russell H.

2012-01-01

Intraoperative patient registration may significantly affect the outcome of image-guided surgery (IGS). Image-based registration approaches have several advantages over the currently dominant point-based direct contact methods and are used in some industry solutions in image-guided radiation therapy with fixed X-ray gantries. However, technical challenges including geometric calibration and computational cost have precluded their use with mobile C-arms for IGS. We propose a 2D/3D registration framework for intraoperative patient registration using a conventional mobile X-ray imager combining fiducial-based C-arm tracking and graphics processing unit (GPU)-acceleration. The two-stage framework 1) acquires X-ray images and estimates relative pose between the images using a custom-made in-image fiducial, and 2) estimates the patient pose using intensity-based 2D/3D registration. Experimental validations using a publicly available gold standard dataset, a plastic bone phantom and cadaveric specimens have been conducted. The mean target registration error (mTRE) was 0.34 ± 0.04 mm (success rate: 100%, registration time: 14.2 s) for the phantom with two images 90° apart, and 0.99 ± 0.41 mm (81%, 16.3 s) for the cadaveric specimen with images 58.5° apart. The experimental results showed the feasibility of the proposed registration framework as a practical alternative for IGS routines. PMID:22113773

Accuracy of computer-assisted navigation: significant augmentation by facial recognition software.

PubMed

Glicksman, Jordan T; Reger, Christine; Parasher, Arjun K; Kennedy, David W

2017-09-01

Over the past 20 years, image guidance navigation has been used with increasing frequency as an adjunct during sinus and skull base surgery. These devices commonly utilize surface registration, where varying pressure of the registration probe and loss of contact with the face during the skin tracing process can lead to registration inaccuracies, and the number of registration points incorporated is necessarily limited. The aim of this study was to evaluate the use of novel facial recognition software for image guidance registration. Consecutive adults undergoing endoscopic sinus surgery (ESS) were prospectively studied. Patients underwent image guidance registration via both conventional surface registration and facial recognition software. The accuracy of both registration processes were measured at the head of the middle turbinate (MTH), middle turbinate axilla (MTA), anterior wall of sphenoid sinus (SS), and nasal tip (NT). Forty-five patients were included in this investigation. Facial recognition was accurate to within a mean of 0.47 mm at the MTH, 0.33 mm at the MTA, 0.39 mm at the SS, and 0.36 mm at the NT. Facial recognition was more accurate than surface registration at the MTH by an average of 0.43 mm (p = 0.002), at the MTA by an average of 0.44 mm (p < 0.001), and at the SS by an average of 0.40 mm (p < 0.001). The integration of facial recognition software did not adversely affect registration time. In this prospective study, automated facial recognition software significantly improved the accuracy of image guidance registration when compared to conventional surface registration. © 2017 ARS-AAOA, LLC.

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

PubMed

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.

Self-correcting multi-atlas segmentation

NASA Astrophysics Data System (ADS)

Gao, Yi; Wilford, Andrew; Guo, Liang

2016-03-01

In multi-atlas segmentation, one typically registers several atlases to the new image, and their respective segmented label images are transformed and fused to form the final segmentation. After each registration, the quality of the registration is reflected by the single global value: the final registration cost. Ideally, if the quality of the registration can be evaluated at each point, independent of the registration process, which also provides a direction in which the deformation can further be improved, the overall segmentation performance can be improved. We propose such a self-correcting multi-atlas segmentation method. The method is applied on hippocampus segmentation from brain images and statistically significantly improvement is observed.

Pre-processing, registration and selection of adaptive optics corrected retinal images.

PubMed

Ramaswamy, Gomathy; Devaney, Nicholas

2013-07-01

In this paper, the aim is to demonstrate enhanced processing of sequences of fundus images obtained using a commercial AO flood illumination system. The purpose of the work is to (1) correct for uneven illumination at the retina (2) automatically select the best quality images and (3) precisely register the best images. Adaptive optics corrected retinal images are pre-processed to correct uneven illumination using different methods; subtracting or dividing by the average filtered image, homomorphic filtering and a wavelet based approach. These images are evaluated to measure the image quality using various parameters, including sharpness, variance, power spectrum kurtosis and contrast. We have carried out the registration in two stages; a coarse stage using cross-correlation followed by fine registration using two approaches; parabolic interpolation on the peak of the cross-correlation and maximum-likelihood estimation. The angle of rotation of the images is measured using a combination of peak tracking and Procrustes transformation. We have found that a wavelet approach (Daubechies 4 wavelet at 6th level decomposition) provides good illumination correction with clear improvement in image sharpness and contrast. The assessment of image quality using a 'Designer metric' works well when compared to visual evaluation, although it is highly correlated with other metrics. In image registration, sub-pixel translation measured using parabolic interpolation on the peak of the cross-correlation function and maximum-likelihood estimation are found to give very similar results (RMS difference 0.047 pixels). We have confirmed that correcting rotation of the images provides a significant improvement, especially at the edges of the image. We observed that selecting the better quality frames (e.g. best 75% images) for image registration gives improved resolution, at the expense of poorer signal-to-noise. The sharpness map of the registered and de-rotated images shows increased sharpness over most of the field of view. Adaptive optics assisted images of the cone photoreceptors can be better pre-processed using a wavelet approach. These images can be assessed for image quality using a 'Designer Metric'. Two-stage image registration including correcting for rotation significantly improves the final image contrast and sharpness. © 2013 The Authors Ophthalmic & Physiological Optics © 2013 The College of Optometrists.

A Multistage Approach for Image Registration.

PubMed

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

2016-09-01

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

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

PubMed

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

2014-02-01

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

SAR image registration based on Susan algorithm

NASA Astrophysics Data System (ADS)

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

2011-10-01

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

CLASSIFYING MEDICAL IMAGES USING MORPHOLOGICAL APPEARANCE MANIFOLDS.

PubMed

Varol, Erdem; Gaonkar, Bilwaj; Davatzikos, Christos

2013-12-31

Input features for medical image classification algorithms are extracted from raw images using a series of pre processing steps. One common preprocessing step in computational neuroanatomy and functional brain mapping is the nonlinear registration of raw images to a common template space. Typically, the registration methods used are parametric and their output varies greatly with changes in parameters. Most results reported previously perform registration using a fixed parameter setting and use the results as input to the subsequent classification step. The variation in registration results due to choice of parameters thus translates to variation of performance of the classifiers that depend on the registration step for input. Analogous issues have been investigated in the computer vision literature, where image appearance varies with pose and illumination, thereby making classification vulnerable to these confounding parameters. The proposed methodology addresses this issue by sampling image appearances as registration parameters vary, and shows that better classification accuracies can be obtained this way, compared to the conventional approach.

Automatic Marker-free Longitudinal Infrared Image Registration by Shape Context Based Matching and Competitive Winner-guided Optimal Corresponding

PubMed Central

Lee, Chia-Yen; Wang, Hao-Jen; Lai, Jhih-Hao; Chang, Yeun-Chung; Huang, Chiun-Sheng

2017-01-01

Long-term comparisons of infrared image can facilitate the assessment of breast cancer tissue growth and early tumor detection, in which longitudinal infrared image registration is a necessary step. However, it is hard to keep markers attached on a body surface for weeks, and rather difficult to detect anatomic fiducial markers and match them in the infrared image during registration process. The proposed study, automatic longitudinal infrared registration algorithm, develops an automatic vascular intersection detection method and establishes feature descriptors by shape context to achieve robust matching, as well as to obtain control points for the deformation model. In addition, competitive winner-guided mechanism is developed for optimal corresponding. The proposed algorithm is evaluated in two ways. Results show that the algorithm can quickly lead to accurate image registration and that the effectiveness is superior to manual registration with a mean error being 0.91 pixels. These findings demonstrate that the proposed registration algorithm is reasonably accurate and provide a novel method of extracting a greater amount of useful data from infrared images. PMID:28145474

SU-F-I-51: CT/MR Image Deformation: The Clinical Assessment QA in Target Delineation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, C; Chen, Y

Purpose: To study the deformation effects in CT/MR image registration of head and neck (HN) cancers. We present a clinical indication in guiding and simplifying registration procedures of this process while CT images possessed artifacts. Methods: CT/MR image fusion provides better soft tissue contrast in intracranial GTV definition with artifacts. However, whether the fusion process should include the deformation process is questionable and not recommended. We performed CT/MR image registration of a HN patient with tonsil GTV and nodes delineation on Varian Velocity™ system. Both rigid transformation and deformable registration of the same CT/MR imaging data were processed separately. Physician’smore » selection of target delineation was implemented to identify the variations. Transformation matrix was shown with visual identification, as well as the deformation QA numbers and figures were assessed. Results: The deformable CT/MR images were traced with the calculated matrix, both translation and rotational parameters were summarized. In deformable quality QA, the calculated Jacobian matrix was analyzed, which the min/mean/max of 0.73/0/99/1.37, respectively. Jacobian matrix of right neck node was 0.84/1.13/1.41, which present dis-similarity of the nodal area. If Jacobian = 1, the deformation is at the optimum situation. In this case, the deformation results have shown better target delineation for CT/MR deformation than rigid transformation. Though the root-mean-square vector difference is 1.48 mm, with similar rotational components, the cord and vertebrae position were aligned much better in the deformable MR images than the rigid transformation. Conclusion: CT/MR with/without image deformation presents similar image registration matrix; there were significant differentiate the anatomical structures in the region of interest by deformable process. Though vendor suggested only rigid transformation between CT/MR assuming the geometry remain similar, our findings indicated with patient positional variations, deformation registration is needed to generate proper GTV coverage, which will be irradiated more accurately in the following boost phase.« less

Homographic Patch Feature Transform: A Robustness Registration for Gastroscopic Surgery.

PubMed

Hu, Weiling; Zhang, Xu; Wang, Bin; Liu, Jiquan; Duan, Huilong; Dai, Ning; Si, Jianmin

2016-01-01

Image registration is a key component of computer assistance in image guided surgery, and it is a challenging topic in endoscopic environments. In this study, we present a method for image registration named Homographic Patch Feature Transform (HPFT) to match gastroscopic images. HPFT can be used for tracking lesions and augmenting reality applications during gastroscopy. Furthermore, an overall evaluation scheme is proposed to validate the precision, robustness and uniformity of the registration results, which provides a standard for rejection of false matching pairs from corresponding results. Finally, HPFT is applied for processing in vivo gastroscopic data. The experimental results show that HPFT has stable performance in gastroscopic applications.

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

PubMed

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

2012-06-01

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

Agile Multi-Scale Decompositions for Automatic Image Registration

NASA Technical Reports Server (NTRS)

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

2016-01-01

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

Image Registration Algorithm Based on Parallax Constraint and Clustering Analysis

NASA Astrophysics Data System (ADS)

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.

[Registration and 3D rendering of serial tissue section images].

PubMed

Liu, Zhexing; Jiang, Guiping; Dong, Wu; Zhang, Yu; Xie, Xiaomian; Hao, Liwei; Wang, Zhiyuan; Li, Shuxiang

2002-12-01

It is an important morphological research method to reconstruct the 3D imaging from serial section tissue images. Registration of serial images is a key step to 3D reconstruction. Firstly, an introduction to the segmentation-counting registration algorithm is presented, which is based on the joint histogram. After thresholding of the two images to be registered, the criterion function is defined as counting in a specific region of the joint histogram, which greatly speeds up the alignment process. Then, the method is used to conduct the serial tissue image matching task, and lies a solid foundation for 3D rendering. Finally, preliminary surface rendering results are presented.

Image-guided radiotherapy quality control: Statistical process control using image similarity metrics.

PubMed

Shiraishi, Satomi; Grams, Michael P; Fong de Los Santos, Luis E

2018-05-01

The purpose of this study was to demonstrate an objective quality control framework for the image review process. A total of 927 cone-beam computed tomography (CBCT) registrations were retrospectively analyzed for 33 bilateral head and neck cancer patients who received definitive radiotherapy. Two registration tracking volumes (RTVs) - cervical spine (C-spine) and mandible - were defined, within which a similarity metric was calculated and used as a registration quality tracking metric over the course of treatment. First, sensitivity to large misregistrations was analyzed for normalized cross-correlation (NCC) and mutual information (MI) in the context of statistical analysis. The distribution of metrics was obtained for displacements that varied according to a normal distribution with standard deviation of σ = 2 mm, and the detectability of displacements greater than 5 mm was investigated. Then, similarity metric control charts were created using a statistical process control (SPC) framework to objectively monitor the image registration and review process. Patient-specific control charts were created using NCC values from the first five fractions to set a patient-specific process capability limit. Population control charts were created using the average of the first five NCC values for all patients in the study. For each patient, the similarity metrics were calculated as a function of unidirectional translation, referred to as the effective displacement. Patient-specific action limits corresponding to 5 mm effective displacements were defined. Furthermore, effective displacements of the ten registrations with the lowest similarity metrics were compared with a three dimensional (3DoF) couch displacement required to align the anatomical landmarks. Normalized cross-correlation identified suboptimal registrations more effectively than MI within the framework of SPC. Deviations greater than 5 mm were detected at 2.8σ and 2.1σ from the mean for NCC and MI, respectively. Patient-specific control charts using NCC evaluated daily variation and identified statistically significant deviations. This study also showed that subjective evaluations of the images were not always consistent. Population control charts identified a patient whose tracking metrics were significantly lower than those of other patients. The patient-specific action limits identified registrations that warranted immediate evaluation by an expert. When effective displacements in the anterior-posterior direction were compared to 3DoF couch displacements, the agreement was ±1 mm for seven of 10 patients for both C-spine and mandible RTVs. Qualitative review alone of IGRT images can result in inconsistent feedback to the IGRT process. Registration tracking using NCC objectively identifies statistically significant deviations. When used in conjunction with the current image review process, this tool can assist in improving the safety and consistency of the IGRT process. © 2018 American Association of Physicists in Medicine.

MO-FG-204-02: Reference Image Selection in the Presence of Multiple Scan Realizations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ruan, D; Dou, T; Thomas, D

Purpose: Fusing information from multiple correlated realizations (e.g., 4DCT) can improve image quality. This process often involves ill-conditioned and asymmetric nonlinear registration and the proper selection of a reference image is important. This work proposes to examine post-registration variation indirectly for such selection, and develops further insights to reduce the number of cross-registrations needed. Methods: We consider each individual scan as a noisy point in the vicinity of an image manifold, related by motion. Nonrigid registration “transports” a scan along the manifold to the reference neighborhood, and the residual is a surrogate for local variation. To test this conjecture, 10more » thoracic scans from the same session were reconstructed from a recently developed low-dose helical 4DCT protocol. Pairwise registration was repeated bi-directionally (81 times) and fusion was performed with each candidate reference. The fused image quality was assessed with SNR and CNR. Registration residuals in SSD, harmonic energy, and deformation Jacobian behavior were examined. The semi-symmetry is further utilized to reduce the number of registration needed. Results: The comparison of image quality between single image and fused ones identified reduction of local intensity variance as the major contributor of image quality, boosting SNR and CNR by 5 to 7 folds. This observation further suggests the criticality of good agreement across post-registration images. Triangle inequality on the SSD metric provides a proficient upper-bound and surrogate on such disagreement. Empirical observation also confirms that fused images with high residual SSD have lower SNR and CNR than the ones with low or intermediate SSDs. Registration SSD is structurally close enough to symmetry for reduced computation. Conclusion: Registration residual is shown to be a good predictor of post-fusion image quality and can be used to identify good reference centers. Semi-symmetry of the registration residual further reduces computation cost. Supported by in part by NIH R01 CA096679.« less

Digital data registration and differencing compression system

NASA Technical Reports Server (NTRS)

Ransford, Gary A. (Inventor); Cambridge, Vivien J. (Inventor)

1990-01-01

A process is disclosed for x ray registration and differencing which results in more efficient compression. Differencing of registered modeled subject image with a modeled reference image forms a differenced image for compression with conventional compression algorithms. Obtention of a modeled reference image includes modeling a relatively unrelated standard reference image upon a three-dimensional model, which three-dimensional model is also used to model the subject image for obtaining the modeled subject image. The registration process of the modeled subject image and modeled reference image translationally correlates such modeled images for resulting correlation thereof in spatial and spectral dimensions. Prior to compression, a portion of the image falling outside a designated area of interest may be eliminated, for subsequent replenishment with a standard reference image. The compressed differenced image may be subsequently transmitted and/or stored, for subsequent decompression and addition to a standard reference image so as to form a reconstituted or approximated subject image at either a remote location and/or at a later moment in time. Overall effective compression ratios of 100:1 are possible for thoracic x ray digital images.

Digital Data Registration and Differencing Compression System

NASA Technical Reports Server (NTRS)

Ransford, Gary A. (Inventor); Cambridge, Vivien J. (Inventor)

1996-01-01

A process for X-ray registration and differencing results in more efficient compression. Differencing of registered modeled subject image with a modeled reference image forms a differenced image for compression with conventional compression algorithms. Obtention of a modeled reference image includes modeling a relatively unrelated standard reference image upon a three-dimensional model, which three-dimensional model is also used to model the subject image for obtaining the modeled subject image. The registration process of the modeled subject image and modeled reference image translationally correlates such modeled images for resulting correlation thereof in spatial and spectral dimensions. Prior to compression, a portion of the image falling outside a designated area of interest may be eliminated, for subsequent replenishment with a standard reference image. The compressed differenced image may be subsequently transmitted and/or stored, for subsequent decompression and addition to a standard reference image so as to form a reconstituted or approximated subject image at either a remote location and/or at a later moment in time. Overall effective compression ratios of 100:1 are possible for thoracic X-ray digital images.

Digital data registration and differencing compression system

NASA Technical Reports Server (NTRS)

Ransford, Gary A. (Inventor); Cambridge, Vivien J. (Inventor)

1992-01-01

A process for x ray registration and differencing results in more efficient compression is discussed. Differencing of registered modeled subject image with a modeled reference image forms a differential image for compression with conventional compression algorithms. Obtention of a modeled reference image includes modeling a relatively unrelated standard reference image upon a three dimensional model, which three dimensional model is also used to model the subject image for obtaining the modeled subject image. The registration process of the modeled subject image and modeled reference image translationally correlates such modeled images for resulting correlation thereof in spatial and spectral dimensions. Prior to compression, a portion of the image falling outside a designated area of interest may be eliminated, for subsequent replenishment with a standard reference image. The compressed differenced image may be subsequently transmitted and/or stored, for subsequent decompression and addition to a standard reference image so as to form a reconstituted or approximated subject image at either remote location and/or at a later moment in time. Overall effective compression ratios of 100:1 are possible for thoracic x ray digital images.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mitrović, Uroš; Pernuš, Franjo; Likar, Boštjan

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 3Dmore » 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 template matching and final registration involving C-arm calibration were 36%, 73%, and 93%, respectively, while registration accuracy of 0.59 mm was the best after final registration. By compensating in-plane translation errors by initial template matching, the success rates achieved after the final stage improved consistently for all methods, especially if C-arm calibration was performed simultaneously with the 3D–2D image registration. Conclusions: Because the tested methods perform simultaneous C-arm calibration and 3D–2D registration based solely on anatomical information, they have a high potential for automation and thus for an immediate integration into current interventional workflow. One of the authors’ main contributions is also comprehensive and representative validation performed under realistic conditions as encountered during cerebral EIGI.« less

Device and methods for "gold standard" registration of clinical 3D and 2D cerebral angiograms

NASA Astrophysics Data System (ADS)

Madan, Hennadii; Likar, Boštjan; Pernuš, Franjo; Å piclin, Žiga

2015-03-01

Translation of any novel and existing 3D-2D image registration methods into clinical image-guidance systems is limited due to lack of their objective validation on clinical image datasets. The main reason is that, besides the calibration of the 2D imaging system, a reference or "gold standard" registration is very difficult to obtain on clinical image datasets. In the context of cerebral endovascular image-guided interventions (EIGIs), we present a calibration device in the form of a headband with integrated fiducial markers and, secondly, propose an automated pipeline comprising 3D and 2D image processing, analysis and annotation steps, the result of which is a retrospective calibration of the 2D imaging system and an optimal, i.e., "gold standard" registration of 3D and 2D images. The device and methods were used to create the "gold standard" on 15 datasets of 3D and 2D cerebral angiograms, whereas each dataset was acquired on a patient undergoing EIGI for either aneurysm coiling or embolization of arteriovenous malformation. The use of the device integrated seamlessly in the clinical workflow of EIGI. While the automated pipeline eliminated all manual input or interactive image processing, analysis or annotation. In this way, the time to obtain the "gold standard" was reduced from 30 to less than one minute and the "gold standard" of 3D-2D registration on all 15 datasets of cerebral angiograms was obtained with a sub-0.1 mm accuracy.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Automatic selection of landmarks in T1-weighted head MRI with regression forests for image registration initialization.

PubMed

Wang, Jianing; Liu, Yuan; Noble, Jack H; Dawant, Benoit M

2017-10-01

Medical image registration establishes a correspondence between images of biological structures, and it is at the core of many applications. Commonly used deformable image registration methods depend on a good preregistration initialization. We develop a learning-based method to automatically find a set of robust landmarks in three-dimensional MR image volumes of the head. These landmarks are then used to compute a thin plate spline-based initialization transformation. The process involves two steps: (1) identifying a set of landmarks that can be reliably localized in the images and (2) selecting among them the subset that leads to a good initial transformation. To validate our method, we use it to initialize five well-established deformable registration algorithms that are subsequently used to register an atlas to MR images of the head. We compare our proposed initialization method with a standard approach that involves estimating an affine transformation with an intensity-based approach. We show that for all five registration algorithms the final registration results are statistically better when they are initialized with the method that we propose than when a standard approach is used. The technique that we propose is generic and could be used to initialize nonrigid registration algorithms for other applications.

A MULTICORE BASED PARALLEL IMAGE REGISTRATION METHOD

PubMed Central

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

2012-01-01

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

Registration of interferometric SAR images

NASA Technical Reports Server (NTRS)

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

1992-01-01

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

SU-E-J-89: Comparative Analysis of MIM and Velocity’s Image Deformation Algorithm Using Simulated KV-CBCT Images for Quality Assurance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cline, K; Narayanasamy, G; Obediat, M

Purpose: Deformable image registration (DIR) is used routinely in the clinic without a formalized quality assurance (QA) process. Using simulated deformations to digitally deform images in a known way and comparing to DIR algorithm predictions is a powerful technique for DIR QA. This technique must also simulate realistic image noise and artifacts, especially between modalities. This study developed an algorithm to create simulated daily kV cone-beam computed-tomography (CBCT) images from CT images for DIR QA between these modalities. Methods: A Catphan and physical head-and-neck phantom, with known deformations, were used. CT and kV-CBCT images of the Catphan were utilized tomore » characterize the changes in Hounsfield units, noise, and image cupping that occur between these imaging modalities. The algorithm then imprinted these changes onto a CT image of the deformed head-and-neck phantom, thereby creating a simulated-CBCT image. CT and kV-CBCT images of the undeformed and deformed head-and-neck phantom were also acquired. The Velocity and MIM DIR algorithms were applied between the undeformed CT image and each of the deformed CT, CBCT, and simulated-CBCT images to obtain predicted deformations. The error between the known and predicted deformations was used as a metric to evaluate the quality of the simulated-CBCT image. Ideally, the simulated-CBCT image registration would produce the same accuracy as the deformed CBCT image registration. Results: For Velocity, the mean error was 1.4 mm for the CT-CT registration, 1.7 mm for the CT-CBCT registration, and 1.4 mm for the CT-simulated-CBCT registration. These same numbers were 1.5, 4.5, and 5.9 mm, respectively, for MIM. Conclusion: All cases produced similar accuracy for Velocity. MIM produced similar values of accuracy for CT-CT registration, but was not as accurate for CT-CBCT registrations. The MIM simulated-CBCT registration followed this same trend, but overestimated MIM DIR errors relative to the CT-CBCT registration.« less

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

NASA Astrophysics Data System (ADS)

Li, Senhu; Sarment, David

2015-12-01

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

Alternative radiation-free registration technique for image-guided pedicle screw placement in deformed cervico-thoracic segments.

PubMed

Kantelhardt, Sven R; Neulen, Axel; Keric, Naureen; Gutenberg, Angelika; Conrad, Jens; Giese, Alf

2017-10-01

Image-guided pedicle screw placement in the cervico-thoracic region is a commonly applied technique. In some patients with deformed cervico-thoracic segments, conventional or 3D fluoroscopy based registration of image-guidance might be difficult or impossible because of the anatomic/pathological conditions. Landmark based registration has been used as an alternative, mostly using separate registration of each vertebra. We here investigated a routine for landmark based registration of rigid spinal segments as single objects, using cranial image-guidance software. Landmark based registration of image-guidance was performed using cranial navigation software. After surgical exposure of the spinous processes, lamina and facet joints and fixation of a reference marker array, up to 26 predefined landmarks were acquired using a pointer. All pedicle screws were implanted using image guidance alone. Following image-guided screw placement all patients underwent postoperative CT scanning. Screw positions as well as intraoperative and clinical parameters were retrospectively analyzed. Thirteen patients received 73 pedicle screws at levels C6 to Th8. Registration of spinal segments, using the cranial image-guidance succeeded in all cases. Pedicle perforations were observed in 11.0%, severe perforations of >2 mm occurred in 5.4%. One patient developed a transient C8 syndrome and had to be revised for deviation of the C7 pedicle screw. No other pedicle screw-related complications were observed. In selected patients suffering from pathologies of the cervico-thoracic region, which impair intraoperative fluoroscopy or 3D C-arm imaging, landmark based registration of image-guidance using cranial software is a feasible, radiation-saving and a safe alternative.

Object-constrained meshless deformable algorithm for high speed 3D nonrigid registration between CT and CBCT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen Ting; Kim, Sung; Goyal, Sharad

2010-01-15

Purpose: High-speed nonrigid registration between the planning CT and the treatment CBCT data is critical for real time image guided radiotherapy (IGRT) to improve the dose distribution and to reduce the toxicity to adjacent organs. The authors propose a new fully automatic 3D registration framework that integrates object-based global and seed constraints with the grayscale-based ''demons'' algorithm. Methods: Clinical objects were segmented on the planning CT images and were utilized as meshless deformable models during the nonrigid registration process. The meshless models reinforced a global constraint in addition to the grayscale difference between CT and CBCT in order to maintainmore » the shape and the volume of geometrically complex 3D objects during the registration. To expedite the registration process, the framework was stratified into hierarchies, and the authors used a frequency domain formulation to diffuse the displacement between the reference and the target in each hierarchy. Also during the registration of pelvis images, they replaced the air region inside the rectum with estimated pixel values from the surrounding rectal wall and introduced an additional seed constraint to robustly track and match the seeds implanted into the prostate. The proposed registration framework and algorithm were evaluated on 15 real prostate cancer patients. For each patient, prostate gland, seminal vesicle, bladder, and rectum were first segmented by a radiation oncologist on planning CT images for radiotherapy planning purpose. The same radiation oncologist also manually delineated the tumor volumes and critical anatomical structures in the corresponding CBCT images acquired at treatment. These delineated structures on the CBCT were only used as the ground truth for the quantitative validation, while structures on the planning CT were used both as the input to the registration method and the ground truth in validation. By registering the planning CT to the CBCT, a displacement map was generated. Segmented volumes in the CT images deformed using the displacement field were compared against the manual segmentations in the CBCT images to quantitatively measure the convergence of the shape and the volume. Other image features were also used to evaluate the overall performance of the registration. Results: The algorithm was able to complete the segmentation and registration process within 1 min, and the superimposed clinical objects achieved a volumetric similarity measure of over 90% between the reference and the registered data. Validation results also showed that the proposed registration could accurately trace the deformation inside the target volume with average errors of less than 1 mm. The method had a solid performance in registering the simulated images with up to 20 Hounsfield unit white noise added. Also, the side by side comparison with the original demons algorithm demonstrated its improved registration performance over the local pixel-based registration approaches. Conclusions: Given the strength and efficiency of the algorithm, the proposed method has significant clinical potential to accelerate and to improve the CBCT delineation and targets tracking in online IGRT applications.« less

Non-invasive breast biopsy method using GD-DTPA contrast enhanced MRI series and F-18-FDG PET/CT dynamic image series

NASA Astrophysics Data System (ADS)

Magri, Alphonso William

This study was undertaken to develop a nonsurgical breast biopsy from Gd-DTPA Contrast Enhanced Magnetic Resonance (CE-MR) images and F-18-FDG PET/CT dynamic image series. A five-step process was developed to accomplish this. (1) Dynamic PET series were nonrigidly registered to the initial frame using a finite element method (FEM) based registration that requires fiducial skin markers to sample the displacement field between image frames. A commercial FEM package (ANSYS) was used for meshing and FEM calculations. Dynamic PET image series registrations were evaluated using similarity measurements SAVD and NCC. (2) Dynamic CE-MR series were nonrigidly registered to the initial frame using two registration methods: a multi-resolution free-form deformation (FFD) registration driven by normalized mutual information, and a FEM-based registration method. Dynamic CE-MR image series registrations were evaluated using similarity measurements, localization measurements, and qualitative comparison of motion artifacts. FFD registration was found to be superior to FEM-based registration. (3) Nonlinear curve fitting was performed for each voxel of the PET/CT volume of activity versus time, based on a realistic two-compartmental Patlak model. Three parameters for this model were fitted; two of them describe the activity levels in the blood and in the cellular compartment, while the third characterizes the washout rate of F-18-FDG from the cellular compartment. (4) Nonlinear curve fitting was performed for each voxel of the MR volume of signal intensity versus time, based on a realistic two-compartment Brix model. Three parameters for this model were fitted: rate of Gd exiting the compartment, representing the extracellular space of a lesion; rate of Gd exiting a blood compartment; and a parameter that characterizes the strength of signal intensities. Curve fitting used for PET/CT and MR series was accomplished by application of the Levenburg-Marquardt nonlinear regression algorithm. The best-fit parameters were used to create 3D parametric images. Compartmental modeling evaluation was based on the ability of parameter values to differentiate between tissue types. This evaluation was used on registered and unregistered image series and found that registration improved results. (5) PET and MR parametric images were registered through FEM- and FFD-based registration. Parametric image registration was evaluated using similarity measurements, target registration error, and qualitative comparison. Comparing FFD and FEM-based registration results showed that the FEM method is superior. This five-step process constitutes a novel multifaceted approach to a nonsurgical breast biopsy that successfully executes each step. Comparison of this method to biopsy still needs to be done with a larger set of subject data.

Phantom Study Investigating the Accuracy of Manual and Automatic Image Fusion with the GE Logiq E9: Implications for use in Percutaneous Liver Interventions.

PubMed

Burgmans, Mark Christiaan; den Harder, J Michiel; Meershoek, Philippa; van den Berg, Nynke S; Chan, Shaun Xavier Ju Min; van Leeuwen, Fijs W B; van Erkel, Arian R

2017-06-01

To determine the accuracy of automatic and manual co-registration methods for image fusion of three-dimensional computed tomography (CT) with real-time ultrasonography (US) for image-guided liver interventions. CT images of a skills phantom with liver lesions were acquired and co-registered to US using GE Logiq E9 navigation software. Manual co-registration was compared to automatic and semiautomatic co-registration using an active tracker. Also, manual point registration was compared to plane registration with and without an additional translation point. Finally, comparison was made between manual and automatic selection of reference points. In each experiment, accuracy of the co-registration method was determined by measurement of the residual displacement in phantom lesions by two independent observers. Mean displacements for a superficial and deep liver lesion were comparable after manual and semiautomatic co-registration: 2.4 and 2.0 mm versus 2.0 and 2.5 mm, respectively. Both methods were significantly better than automatic co-registration: 5.9 and 5.2 mm residual displacement (p < 0.001; p < 0.01). The accuracy of manual point registration was higher than that of plane registration, the latter being heavily dependent on accurate matching of axial CT and US images by the operator. Automatic reference point selection resulted in significantly lower registration accuracy compared to manual point selection despite lower root-mean-square deviation (RMSD) values. The accuracy of manual and semiautomatic co-registration is better than that of automatic co-registration. For manual co-registration using a plane, choosing the correct plane orientation is an essential first step in the registration process. Automatic reference point selection based on RMSD values is error-prone.

Automated Geo/Co-Registration of Multi-Temporal Very-High-Resolution Imagery.

PubMed

Han, Youkyung; Oh, Jaehong

2018-05-17

For time-series analysis using very-high-resolution (VHR) multi-temporal satellite images, both accurate georegistration to the map coordinates and subpixel-level co-registration among the images should be conducted. However, applying well-known matching methods, such as scale-invariant feature transform and speeded up robust features for VHR multi-temporal images, has limitations. First, they cannot be used for matching an optical image to heterogeneous non-optical data for georegistration. Second, they produce a local misalignment induced by differences in acquisition conditions, such as acquisition platform stability, the sensor's off-nadir angle, and relief displacement of the considered scene. Therefore, this study addresses the problem by proposing an automated geo/co-registration framework for full-scene multi-temporal images acquired from a VHR optical satellite sensor. The proposed method comprises two primary steps: (1) a global georegistration process, followed by (2) a fine co-registration process. During the first step, two-dimensional multi-temporal satellite images are matched to three-dimensional topographic maps to assign the map coordinates. During the second step, a local analysis of registration noise pixels extracted between the multi-temporal images that have been mapped to the map coordinates is conducted to extract a large number of well-distributed corresponding points (CPs). The CPs are finally used to construct a non-rigid transformation function that enables minimization of the local misalignment existing among the images. Experiments conducted on five Kompsat-3 full scenes confirmed the effectiveness of the proposed framework, showing that the georegistration performance resulted in an approximately pixel-level accuracy for most of the scenes, and the co-registration performance further improved the results among all combinations of the georegistered Kompsat-3 image pairs by increasing the calculated cross-correlation values.

Minimisation of Signal Intensity Differences in Distortion Correction Approaches of Brain Magnetic Resonance Diffusion Tensor Imaging.

PubMed

Lee, Dong-Hoon; Lee, Do-Wan; Henry, David; Park, Hae-Jin; Han, Bong-Soo; Woo, Dong-Cheol

2018-04-12

To evaluate the effects of signal intensity differences between the b0 image and diffusion tensor imaging (DTI) in the image registration process. To correct signal intensity differences between the b0 image and DTI data, a simple image intensity compensation (SIMIC) method, which is a b0 image re-calculation process from DTI data, was applied before the image registration. The re-calculated b0 image (b0 ext ) from each diffusion direction was registered to the b0 image acquired through the MR scanning (b0 nd ) with two types of cost functions and their transformation matrices were acquired. These transformation matrices were then used to register the DTI data. For quantifications, the dice similarity coefficient (DSC) values, diffusion scalar matrix, and quantified fibre numbers and lengths were calculated. The combined SIMIC method with two cost functions showed the highest DSC value (0.802 ± 0.007). Regarding diffusion scalar values and numbers and lengths of fibres from the corpus callosum, superior longitudinal fasciculus, and cortico-spinal tract, only using normalised cross correlation (NCC) showed a specific tendency toward lower values in the brain regions. Image-based distortion correction with SIMIC for DTI data would help in image analysis by accounting for signal intensity differences as one additional option for DTI analysis. • We evaluated the effects of signal intensity differences at DTI registration. • The non-diffusion-weighted image re-calculation process from DTI data was applied. • SIMIC can minimise the signal intensity differences at DTI registration.

Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

NASA Technical Reports Server (NTRS)

Solarna, David; Moser, Gabriele; Le Moigne-Stewart, Jacqueline; Serpico, Sebastiano B.

2017-01-01

Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.

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

PubMed

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.

Extra-dimensional Demons: a method for incorporating missing tissue in deformable image registration.

PubMed

Nithiananthan, Sajendra; Schafer, Sebastian; Mirota, Daniel J; Stayman, J Webster; Zbijewski, Wojciech; Reh, Douglas D; Gallia, Gary L; Siewerdsen, Jeffrey H

2012-09-01

A deformable registration method capable of accounting for missing tissue (e.g., excision) is reported for application in cone-beam CT (CBCT)-guided surgical procedures. Excisions are identified by a segmentation step performed simultaneous to the registration process. Tissue excision is explicitly modeled by increasing the dimensionality of the deformation field to allow motion beyond the dimensionality of the image. The accuracy of the model is tested in phantom, simulations, and cadaver models. A variant of the Demons deformable registration algorithm is modified to include excision segmentation and modeling. Segmentation is performed iteratively during the registration process, with initial implementation using a threshold-based approach to identify voxels corresponding to "tissue" in the moving image and "air" in the fixed image. With each iteration of the Demons process, every voxel is assigned a probability of excision. Excisions are modeled explicitly during registration by increasing the dimensionality of the deformation field so that both deformations and excisions can be accounted for by in- and out-of-volume deformations, respectively. The out-of-volume (i.e., fourth) component of the deformation field at each voxel carries a magnitude proportional to the excision probability computed in the excision segmentation step. The registration accuracy of the proposed "extra-dimensional" Demons (XDD) and conventional Demons methods was tested in the presence of missing tissue in phantom models, simulations investigating the effect of excision size on registration accuracy, and cadaver studies emulating realistic deformations and tissue excisions imparted in CBCT-guided endoscopic skull base surgery. Phantom experiments showed the normalized mutual information (NMI) in regions local to the excision to improve from 1.10 for the conventional Demons approach to 1.16 for XDD, and qualitative examination of the resulting images revealed major differences: the conventional Demons approach imparted unrealistic distortions in areas around tissue excision, whereas XDD provided accurate "ejection" of voxels within the excision site and maintained the registration accuracy throughout the rest of the image. Registration accuracy in areas far from the excision site (e.g., > ∼5 mm) was identical for the two approaches. Quantitation of the effect was consistent in analysis of NMI, normalized cross-correlation (NCC), target registration error (TRE), and accuracy of voxels ejected from the volume (true-positive and false-positive analysis). The registration accuracy for conventional Demons was found to degrade steeply as a function of excision size, whereas XDD was robust in this regard. Cadaver studies involving realistic excision of the clivus, vidian canal, and ethmoid sinuses demonstrated similar results, with unrealistic distortion of anatomy imparted by conventional Demons and accurate ejection and deformation for XDD. Adaptation of the Demons deformable registration process to include segmentation (i.e., identification of excised tissue) and an extra dimension in the deformation field provided a means to accurately accommodate missing tissue between image acquisitions. The extra-dimensional approach yielded accurate "ejection" of voxels local to the excision site while preserving the registration accuracy (typically subvoxel) of the conventional Demons approach throughout the rest of the image. The ability to accommodate missing tissue volumes is important to application of CBCT for surgical guidance (e.g., skull base drillout) and may have application in other areas of CBCT guidance.

Extra-dimensional Demons: A method for incorporating missing tissue in deformable image registration

PubMed Central

Nithiananthan, Sajendra; Schafer, Sebastian; Mirota, Daniel J.; Stayman, J. Webster; Zbijewski, Wojciech; Reh, Douglas D.; Gallia, Gary L.; Siewerdsen, Jeffrey H.

2012-01-01

Purpose: A deformable registration method capable of accounting for missing tissue (e.g., excision) is reported for application in cone-beam CT (CBCT)-guided surgical procedures. Excisions are identified by a segmentation step performed simultaneous to the registration process. Tissue excision is explicitly modeled by increasing the dimensionality of the deformation field to allow motion beyond the dimensionality of the image. The accuracy of the model is tested in phantom, simulations, and cadaver models. Methods: A variant of the Demons deformable registration algorithm is modified to include excision segmentation and modeling. Segmentation is performed iteratively during the registration process, with initial implementation using a threshold-based approach to identify voxels corresponding to “tissue” in the moving image and “air” in the fixed image. With each iteration of the Demons process, every voxel is assigned a probability of excision. Excisions are modeled explicitly during registration by increasing the dimensionality of the deformation field so that both deformations and excisions can be accounted for by in- and out-of-volume deformations, respectively. The out-of-volume (i.e., fourth) component of the deformation field at each voxel carries a magnitude proportional to the excision probability computed in the excision segmentation step. The registration accuracy of the proposed “extra-dimensional” Demons (XDD) and conventional Demons methods was tested in the presence of missing tissue in phantom models, simulations investigating the effect of excision size on registration accuracy, and cadaver studies emulating realistic deformations and tissue excisions imparted in CBCT-guided endoscopic skull base surgery. Results: Phantom experiments showed the normalized mutual information (NMI) in regions local to the excision to improve from 1.10 for the conventional Demons approach to 1.16 for XDD, and qualitative examination of the resulting images revealed major differences: the conventional Demons approach imparted unrealistic distortions in areas around tissue excision, whereas XDD provided accurate “ejection” of voxels within the excision site and maintained the registration accuracy throughout the rest of the image. Registration accuracy in areas far from the excision site (e.g., > ∼5 mm) was identical for the two approaches. Quantitation of the effect was consistent in analysis of NMI, normalized cross-correlation (NCC), target registration error (TRE), and accuracy of voxels ejected from the volume (true-positive and false-positive analysis). The registration accuracy for conventional Demons was found to degrade steeply as a function of excision size, whereas XDD was robust in this regard. Cadaver studies involving realistic excision of the clivus, vidian canal, and ethmoid sinuses demonstrated similar results, with unrealistic distortion of anatomy imparted by conventional Demons and accurate ejection and deformation for XDD. Conclusions: Adaptation of the Demons deformable registration process to include segmentation (i.e., identification of excised tissue) and an extra dimension in the deformation field provided a means to accurately accommodate missing tissue between image acquisitions. The extra-dimensional approach yielded accurate “ejection” of voxels local to the excision site while preserving the registration accuracy (typically subvoxel) of the conventional Demons approach throughout the rest of the image. The ability to accommodate missing tissue volumes is important to application of CBCT for surgical guidance (e.g., skull base drillout) and may have application in other areas of CBCT guidance. PMID:22957637

dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images

NASA Astrophysics Data System (ADS)

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

2014-09-01

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

Improvement of the Accuracy of InSAR Image Co-Registration Based On Tie Points - A Review.

PubMed

Zou, Weibao; Li, Yan; Li, Zhilin; Ding, Xiaoli

2009-01-01

Interferometric Synthetic Aperture Radar (InSAR) is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR) images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs) and the measurement of ground surface deformations. However, many critical factors affect the quality of InSAR data and limit its applications. One of the factors is InSAR data processing, which consists of image co-registration, interferogram generation, phase unwrapping and geocoding. The co-registration of InSAR images is the first step and dramatically influences the accuracy of InSAR products. In this paper, the principle and processing procedures of InSAR techniques are reviewed. One of important factors, tie points, to be considered in the improvement of the accuracy of InSAR image co-registration are emphatically reviewed, such as interval of tie points, extraction of feature points, window size for tie point matching and the measurement for the quality of an interferogram.

Improvement of the Accuracy of InSAR Image Co-Registration Based On Tie Points – A Review

PubMed Central

Zou, Weibao; Li, Yan; Li, Zhilin; Ding, Xiaoli

2009-01-01

Interferometric Synthetic Aperture Radar (InSAR) is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR) images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs) and the measurement of ground surface deformations. However, many critical factors affect the quality of InSAR data and limit its applications. One of the factors is InSAR data processing, which consists of image co-registration, interferogram generation, phase unwrapping and geocoding. The co-registration of InSAR images is the first step and dramatically influences the accuracy of InSAR products. In this paper, the principle and processing procedures of InSAR techniques are reviewed. One of important factors, tie points, to be considered in the improvement of the accuracy of InSAR image co-registration are emphatically reviewed, such as interval of tie points, extraction of feature points, window size for tie point matching and the measurement for the quality of an interferogram. PMID:22399966

Demons deformable registration of CT and cone-beam CT using an iterative intensity matching approach.

PubMed

Nithiananthan, Sajendra; Schafer, Sebastian; Uneri, Ali; Mirota, Daniel J; Stayman, J Webster; Zbijewski, Wojciech; Brock, Kristy K; Daly, Michael J; Chan, Harley; Irish, Jonathan C; Siewerdsen, Jeffrey H

2011-04-01

A method of intensity-based deformable registration of CT and cone-beam CT (CBCT) images is described, in which intensity correction occurs simultaneously within the iterative registration process. The method preserves the speed and simplicity of the popular Demons algorithm while providing robustness and accuracy in the presence of large mismatch between CT and CBCT voxel values ("intensity"). A variant of the Demons algorithm was developed in which an estimate of the relationship between CT and CBCT intensity values for specific materials in the image is computed at each iteration based on the set of currently overlapping voxels. This tissue-specific intensity correction is then used to estimate the registration output for that iteration and the process is repeated. The robustness of the method was tested in CBCT images of a cadaveric head exhibiting a broad range of simulated intensity variations associated with x-ray scatter, object truncation, and/or errors in the reconstruction algorithm. The accuracy of CT-CBCT registration was also measured in six real cases, exhibiting deformations ranging from simple to complex during surgery or radiotherapy guided by a CBCT-capable C-arm or linear accelerator, respectively. The iterative intensity matching approach was robust against all levels of intensity variation examined, including spatially varying errors in voxel value of a factor of 2 or more, as can be encountered in cases of high x-ray scatter. Registration accuracy without intensity matching degraded severely with increasing magnitude of intensity error and introduced image distortion. A single histogram match performed prior to registration alleviated some of these effects but was also prone to image distortion and was quantifiably less robust and accurate than the iterative approach. Within the six case registration accuracy study, iterative intensity matching Demons reduced mean TRE to (2.5 +/- 2.8) mm compared to (3.5 +/- 3.0) mm with rigid registration. A method was developed to iteratively correct CT-CBCT intensity disparity during Demons registration, enabling fast, intensity-based registration in CBCT-guided procedures such as surgery and radiotherapy, in which CBCT voxel values may be inaccurate. Accurate CT-CBCT registration in turn facilitates registration of multimodality preoperative image and planning data to intraoperative CBCT by way of the preoperative CT, thereby linking the intraoperative frame of reference to a wealth of preoperative information that could improve interventional guidance.

GPU accelerated optical coherence tomography angiography using strip-based registration (Conference Presentation)

NASA Astrophysics Data System (ADS)

Heisler, Morgan; Lee, Sieun; Mammo, Zaid; Jian, Yifan; Ju, Myeong Jin; Miao, Dongkai; Raposo, Eric; Wahl, Daniel J.; Merkur, Andrew; Navajas, Eduardo; Balaratnasingam, Chandrakumar; Beg, Mirza Faisal; Sarunic, Marinko V.

2017-02-01

High quality visualization of the retinal microvasculature can improve our understanding of the onset and development of retinal vascular diseases, which are a major cause of visual morbidity and are increasing in prevalence. Optical Coherence Tomography Angiography (OCT-A) images are acquired over multiple seconds and are particularly susceptible to motion artifacts, which are more prevalent when imaging patients with pathology whose ability to fixate is limited. The acquisition of multiple OCT-A images sequentially can be performed for the purpose of removing motion artifact and increasing the contrast of the vascular network through averaging. Due to the motion artifacts, a robust registration pipeline is needed before feature preserving image averaging can be performed. In this report, we present a novel method for a GPU-accelerated pipeline for acquisition, processing, segmentation, and registration of multiple, sequentially acquired OCT-A images to correct for the motion artifacts in individual images for the purpose of averaging. High performance computing, blending CPU and GPU, was introduced to accelerate processing in order to provide high quality visualization of the retinal microvasculature and to enable a more accurate quantitative analysis in a clinically useful time frame. Specifically, image discontinuities caused by rapid micro-saccadic movements and image warping due to smoother reflex movements were corrected by strip-wise affine registration estimated using Scale Invariant Feature Transform (SIFT) keypoints and subsequent local similarity-based non-rigid registration. These techniques improve the image quality, increasing the value for clinical diagnosis and increasing the range of patients for whom high quality OCT-A images can be acquired.

Real-time CT-video registration for continuous endoscopic guidance

NASA Astrophysics Data System (ADS)

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.

Panorama imaging for image-to-physical registration of narrow drill holes inside spongy bones

NASA Astrophysics Data System (ADS)

Bergmeier, Jan; Fast, Jacob Friedemann; Ortmaier, Tobias; Kahrs, Lüder Alexander

2017-03-01

Image-to-physical registration based on volumetric data like computed tomography on the one side and intraoperative endoscopic images on the other side is an important method for various surgical applications. In this contribution, we present methods to generate panoramic views from endoscopic recordings for image-to-physical registration of narrow drill holes inside spongy bone. One core application is the registration of drill poses inside the mastoid during minimally invasive cochlear implantations. Besides the development of image processing software for registration, investigations are performed on a miniaturized optical system, achieving 360° radial imaging with one shot by extending a conventional, small, rigid, rod lens endoscope. A reflective cone geometry is used to deflect radially incoming light rays into the endoscope optics. Therefore, a cone mirror is mounted in front of a conventional 0° endoscope. Furthermore, panoramic images of inner drill hole surfaces in artificial bone material are created. Prior to drilling, cone beam computed tomography data is acquired from this artificial bone and simulated endoscopic views are generated from this data. A qualitative and quantitative image comparison of resulting views in terms of image-to-image registration is performed. First results show that downsizing of panoramic optics to a diameter of 3mm is possible. Conventional rigid rod lens endoscopes can be extended to produce suitable panoramic one-shot image data. Using unrolling and stitching methods, images of the inner drill hole surface similar to computed tomography image data of the same surface were created. Registration is performed on ten perturbations of the search space and results in target registration errors of (0:487 +/- 0:438)mm at the entry point and (0:957 +/- 0:948)mm at the exit as well as an angular error of (1:763 +/- 1:536)°. The results show suitability of this image data for image-to-image registration. Analysis of the error components in different directions reveals a strong influence of the pattern structure, meaning higher diversity results into smaller errors.

Medical image registration: basic science and clinical implications.

PubMed

Imran, Muhammad Babar; Meo, Sultan Ayoub; Yousuf, Mohammad; Othman, Saleh; Shahid, Abubakar

2010-01-01

Image Registration is a process of aligning two or more images so that corresponding feature can be related objectively. Integration of corresponding and complementary information from various images has become an important area of computation in medical imaging. Merging different images of the same patient taken by different modalities or acquired at different times is quite useful in interpreting lower resolution functional images, such as those provided by nuclear medicine, in determining spatial relationships of structures seen in different modalities. This will help in planning surgery and longitudinal follow up. The aim of this article was to introduce image registration to all those who are working in field of medical sciences in general and medical doctors in particular; and indicate how and where this specialty is moving to provide better health care services.

Quality assurance of registration of CT and MRI data sets for treatment planning of radiotherapy for head and neck cancers

PubMed Central

Moore, Craig S.; Liney, Gary P.; Beavis, Andrew W.

2004-01-01

We are implementing the use of magnetic resonance (MR) images for head and neck radiotherapy planning, which involves their registration with computed tomography (CT). The quality assurance (QA) of the registration process was an initial step of this program. A phantom was built, and appropriate materials were identified to produce clinically relevant MR T1 and T2 contrast for its constituent “anatomy.” We performed a characterization of the distortion detectable within our phantom. Finally, we assessed the accuracy of image registration by contouring structures in the registered/fused data sets using the treatment planning system. Each structure was contoured using each modality, in turn, blind of the other. The position, area, and perimeter of each structure were assessed as a measure of accuracy of the entire image registration process. Distortion effects in the MR image were shown to be minimized by choosing a suitable (≥±30 kHz) receiver bandwidth. Remaining distortion was deemed clinically acceptable within ±15 cm of the magnetic field isocenter. A coefficient of agreement (A) analysis gave values to be within 9% of unity, where A=RaRp and Ra/p is the ratio of the area/perimeter of a particular structure on CT to that on MR. The center of each structure of interest agreed to within 1.8 mm. A QA process has been developed to assess the accuracy of using multimodality image registration in the planning of radiotherapy for the head and neck; we believe its introduction is feasible and safe. PACS numbers: 87.53.Xd, 87.57.Gg, 87.59.Fm; 87.61.‐c, 87.66.Xa PMID:15753931

A two-step framework for the registration of HE stained and FTIR images

NASA Astrophysics Data System (ADS)

Peñaranda, Francisco; Naranjo, Valery; Verdú, Rafaél.; Lloyd, Gavin R.; Nallala, Jayakrupakar; Stone, Nick

2016-03-01

FTIR spectroscopy is an emerging technology with high potential for cancer diagnosis but with particular physical phenomena that require special processing. Little work has been done in the field with the aim of registering hyperspectral Fourier-Transform Infrared (FTIR) spectroscopic images and Hematoxilin and Eosin (HE) stained histological images of contiguous slices of tissue. This registration is necessary to transfer the location of relevant structures that the pathologist may identify in the gold standard HE images. A two-step registration framework is presented where a representative gray image extracted from the FTIR hypercube is used as an input. This representative image, which must have a spatial contrast as similar as possible to a gray image obtained from the HE image, is calculated through the spectrum variation in the fingerprint region. In the first step of the registration algorithm a similarity transformation is estimated from interest points, which are automatically detected by the popular SURF algorithm. In the second stage, a variational registration framework defined in the frequency domain compensates for local anatomical variations between both images. After a proper tuning of some parameters the proposed registration framework works in an automated way. The method was tested on 7 samples of colon tissue in different stages of cancer. Very promising qualitative and quantitative results were obtained (a mean correlation ratio of 92.16% with a standard deviation of 3.10%).

Propagation of registration uncertainty during multi-fraction cervical cancer brachytherapy

NASA Astrophysics Data System (ADS)

Amir-Khalili, A.; Hamarneh, G.; Zakariaee, R.; Spadinger, I.; Abugharbieh, R.

2017-10-01

Multi-fraction cervical cancer brachytherapy is a form of image-guided radiotherapy that heavily relies on 3D imaging during treatment planning, delivery, and quality control. In this context, deformable image registration can increase the accuracy of dosimetric evaluations, provided that one can account for the uncertainties associated with the registration process. To enable such capability, we propose a mathematical framework that first estimates the registration uncertainty and subsequently propagates the effects of the computed uncertainties from the registration stage through to the visualizations, organ segmentations, and dosimetric evaluations. To ensure the practicality of our proposed framework in real world image-guided radiotherapy contexts, we implemented our technique via a computationally efficient and generalizable algorithm that is compatible with existing deformable image registration software. In our clinical context of fractionated cervical cancer brachytherapy, we perform a retrospective analysis on 37 patients and present evidence that our proposed methodology for computing and propagating registration uncertainties may be beneficial during therapy planning and quality control. Specifically, we quantify and visualize the influence of registration uncertainty on dosimetric analysis during the computation of the total accumulated radiation dose on the bladder wall. We further show how registration uncertainty may be leveraged into enhanced visualizations that depict the quality of the registration and highlight potential deviations from the treatment plan prior to the delivery of radiation treatment. Finally, we show that we can improve the transfer of delineated volumetric organ segmentation labels from one fraction to the next by encoding the computed registration uncertainties into the segmentation labels.

Demons registration for in vivo and deformable laser scanning confocal endomicroscopy.

PubMed

Chiew, Wei-Ming; Lin, Feng; Seah, Hock Soon

2017-09-01

A critical effect found in noninvasive in vivo endomicroscopic imaging modalities is image distortions due to sporadic movement exhibited by living organisms. In three-dimensional confocal imaging, this effect results in a dataset that is tilted across deeper slices. Apart from that, the sequential flow of the imaging-processing pipeline restricts real-time adjustments due to the unavailability of information obtainable only from subsequent stages. To solve these problems, we propose an approach to render Demons-registered datasets as they are being captured, focusing on the coupling between registration and visualization. To improve the acquisition process, we also propose a real-time visual analytics tool, which complements the imaging pipeline and the Demons registration pipeline with useful visual indicators to provide real-time feedback for immediate adjustments. We highlight the problem of deformation within the visualization pipeline for object-ordered and image-ordered rendering. Visualizations of critical information including registration forces and partial renderings of the captured data are also presented in the analytics system. We demonstrate the advantages of the algorithmic design through experimental results with both synthetically deformed datasets and actual in vivo, time-lapse tissue datasets expressing natural deformations. Remarkably, this algorithm design is for embedded implementation in intelligent biomedical imaging instrumentation with customizable circuitry. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Demons registration for in vivo and deformable laser scanning confocal endomicroscopy

NASA Astrophysics Data System (ADS)

Chiew, Wei Ming; Lin, Feng; Seah, Hock Soon

2017-09-01

A critical effect found in noninvasive in vivo endomicroscopic imaging modalities is image distortions due to sporadic movement exhibited by living organisms. In three-dimensional confocal imaging, this effect results in a dataset that is tilted across deeper slices. Apart from that, the sequential flow of the imaging-processing pipeline restricts real-time adjustments due to the unavailability of information obtainable only from subsequent stages. To solve these problems, we propose an approach to render Demons-registered datasets as they are being captured, focusing on the coupling between registration and visualization. To improve the acquisition process, we also propose a real-time visual analytics tool, which complements the imaging pipeline and the Demons registration pipeline with useful visual indicators to provide real-time feedback for immediate adjustments. We highlight the problem of deformation within the visualization pipeline for object-ordered and image-ordered rendering. Visualizations of critical information including registration forces and partial renderings of the captured data are also presented in the analytics system. We demonstrate the advantages of the algorithmic design through experimental results with both synthetically deformed datasets and actual in vivo, time-lapse tissue datasets expressing natural deformations. Remarkably, this algorithm design is for embedded implementation in intelligent biomedical imaging instrumentation with customizable circuitry.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Q; School of Nuclear Science and Technology, Hefei, Anhui; Anhui Medical University, Hefei, Anhui

Purpose: The purpose of this work was to develop a registration framework and method based on the software platform of ARTS-IGRT and implement in C++ based on ITK libraries to register CT images and CBCT images. ARTS-IGRT was a part of our self-developed accurate radiation planning system ARTS. Methods: Mutual information (MI) registration treated each voxel equally. Actually, different voxels even having same intensity should be treated differently in the registration procedure. According to their importance values calculated from self-information, a similarity measure was proposed which combined the spatial importance of a voxel with MI (S-MI). For lung registration, Firstly,more » a global alignment method was adopted to minimize the margin error and achieve the alignment of these two images on the whole. The result obtained at the low resolution level was then interpolated to become the initial conditions for the higher resolution computation. Secondly, a new similarity measurement S-MI was established to quantify how close the two input image volumes were to each other. Finally, Demons model was applied to compute the deformable map. Results: Registration tools were tested for head-neck and lung images and the average region was 128*128*49. The rigid registration took approximately 2 min and converged 10% faster than traditional MI algorithm, the accuracy reached 1mm for head-neck images. For lung images, the improved symmetric Demons registration process was completed in an average of 5 min using a 2.4GHz dual core CPU. Conclusion: A registration framework was developed to correct patient's setup according to register the planning CT volume data and the daily reconstructed 3D CBCT data. The experiments showed that the spatial MI algorithm can be adopted for head-neck images. The improved Demons deformable registration was more suitable to lung images, and rigid alignment should be applied before deformable registration to get more accurate result. Supported by National Natural Science Foundation of China (NO.81101132) and Natural Science Foundation of Anhui Province (NO.11040606Q55)« less

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

An accuracy assessment of different rigid body image registration methods and robotic couch positional corrections using a novel phantom

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arumugam, Sankar; Xing Aitang; Jameson, Michael G.

2013-03-15

Purpose: Image guided radiotherapy (IGRT) using cone beam computed tomography (CBCT) images greatly reduces interfractional patient positional uncertainties. An understanding of uncertainties in the IGRT process itself is essential to ensure appropriate use of this technology. The purpose of this study was to develop a phantom capable of assessing the accuracy of IGRT hardware and software including a 6 degrees of freedom patient positioning system and to investigate the accuracy of the Elekta XVI system in combination with the HexaPOD robotic treatment couch top. Methods: The constructed phantom enabled verification of the three automatic rigid body registrations (gray value, bone,more » seed) available in the Elekta XVI software and includes an adjustable mount that introduces known rotational offsets to the phantom from its reference position. Repeated positioning of the phantom was undertaken to assess phantom rotational accuracy. Using this phantom the accuracy of the XVI registration algorithms was assessed considering CBCT hardware factors and image resolution together with the residual error in the overall image guidance process when positional corrections were performed through the HexaPOD couch system. Results: The phantom positioning was found to be within 0.04 ({sigma}= 0.12) Degree-Sign , 0.02 ({sigma}= 0.13) Degree-Sign , and -0.03 ({sigma}= 0.06) Degree-Sign in X, Y, and Z directions, respectively, enabling assessment of IGRT with a 6 degrees of freedom patient positioning system. The gray value registration algorithm showed the least error in calculated offsets with maximum mean difference of -0.2({sigma}= 0.4) mm in translational and -0.1({sigma}= 0.1) Degree-Sign in rotational directions for all image resolutions. Bone and seed registration were found to be sensitive to CBCT image resolution. Seed registration was found to be most sensitive demonstrating a maximum mean error of -0.3({sigma}= 0.9) mm and -1.4({sigma}= 1.7) Degree-Sign in translational and rotational directions over low resolution images, and this is reduced to -0.1({sigma}= 0.2) mm and -0.1({sigma}= 0.79) Degree-Sign using high resolution images. Conclusions: The phantom, capable of rotating independently about three orthogonal axes was successfully used to assess the accuracy of an IGRT system considering 6 degrees of freedom. The overall residual error in the image guidance process of XVI in combination with the HexaPOD couch was demonstrated to be less than 0.3 mm and 0.3 Degree-Sign in translational and rotational directions when using the gray value registration with high resolution CBCT images. However, the residual error, especially in rotational directions, may increase when the seed registration is used with low resolution images.« less

MISTICA: Minimum Spanning Tree-based Coarse Image Alignment for Microscopy Image Sequences

PubMed Central

Ray, Nilanjan; McArdle, Sara; Ley, Klaus; Acton, Scott T.

2016-01-01

Registration of an in vivo microscopy image sequence is necessary in many significant studies, including studies of atherosclerosis in large arteries and the heart. Significant cardiac and respiratory motion of the living subject, occasional spells of focal plane changes, drift in the field of view, and long image sequences are the principal roadblocks. The first step in such a registration process is the removal of translational and rotational motion. Next, a deformable registration can be performed. The focus of our study here is to remove the translation and/or rigid body motion that we refer to here as coarse alignment. The existing techniques for coarse alignment are unable to accommodate long sequences often consisting of periods of poor quality images (as quantified by a suitable perceptual measure). Many existing methods require the user to select an anchor image to which other images are registered. We propose a novel method for coarse image sequence alignment based on minimum weighted spanning trees (MISTICA) that overcomes these difficulties. The principal idea behind MISTICA is to re-order the images in shorter sequences, to demote nonconforming or poor quality images in the registration process, and to mitigate the error propagation. The anchor image is selected automatically making MISTICA completely automated. MISTICA is computationally efficient. It has a single tuning parameter that determines graph width, which can also be eliminated by way of additional computation. MISTICA outperforms existing alignment methods when applied to microscopy image sequences of mouse arteries. PMID:26415193

MISTICA: Minimum Spanning Tree-Based Coarse Image Alignment for Microscopy Image Sequences.

PubMed

Ray, Nilanjan; McArdle, Sara; Ley, Klaus; Acton, Scott T

2016-11-01

Registration of an in vivo microscopy image sequence is necessary in many significant studies, including studies of atherosclerosis in large arteries and the heart. Significant cardiac and respiratory motion of the living subject, occasional spells of focal plane changes, drift in the field of view, and long image sequences are the principal roadblocks. The first step in such a registration process is the removal of translational and rotational motion. Next, a deformable registration can be performed. The focus of our study here is to remove the translation and/or rigid body motion that we refer to here as coarse alignment. The existing techniques for coarse alignment are unable to accommodate long sequences often consisting of periods of poor quality images (as quantified by a suitable perceptual measure). Many existing methods require the user to select an anchor image to which other images are registered. We propose a novel method for coarse image sequence alignment based on minimum weighted spanning trees (MISTICA) that overcomes these difficulties. The principal idea behind MISTICA is to reorder the images in shorter sequences, to demote nonconforming or poor quality images in the registration process, and to mitigate the error propagation. The anchor image is selected automatically making MISTICA completely automated. MISTICA is computationally efficient. It has a single tuning parameter that determines graph width, which can also be eliminated by the way of additional computation. MISTICA outperforms existing alignment methods when applied to microscopy image sequences of mouse arteries.

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

PubMed

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

2011-01-01

Recent years have witnessed tremendous progress in image guide radiotherapy technology and a growing interest in the possibilities for adapting treatment planning and delivery over the course of treatment. One obstacle faced by the research community has been the lack of a comprehensive open-source software toolkit dedicated for adaptive radiotherapy (ART). To address this need, the authors have developed a software suite called the Deformable Image Registration and Adaptive Radiotherapy Toolkit (DIRART). DIRART is an open-source toolkit developed in MATLAB. It is designed in an object-oriented style with focus on user-friendliness, features, and flexibility. It contains four classes of DIR algorithms, including the newer inverse consistency algorithms to provide consistent displacement vector field in both directions. It also contains common ART functions, an integrated graphical user interface, a variety of visualization and image-processing features, dose metric analysis functions, and interface routines. These interface routines make DIRART a powerful complement to the Computational Environment for Radiotherapy Research (CERR) and popular image-processing toolkits such as ITK. DIRART provides a set of image processing/registration algorithms and postprocessing functions to facilitate the development and testing of DIR algorithms. It also offers a good amount of options for DIR results visualization, evaluation, and validation. By exchanging data with treatment planning systems via DICOM-RT files and CERR, and by bringing image registration algorithms closer to radiotherapy applications, DIRART is potentially a convenient and flexible platform that may facilitate ART and DIR research. 0 2011 Ameri-

Automatic Image Registration of Multimodal Remotely Sensed Data with Global Shearlet Features

NASA Technical Reports Server (NTRS)

Murphy, James M.; Le Moigne, Jacqueline; Harding, David J.

2015-01-01

Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone.

Automatic Image Registration of Multi-Modal Remotely Sensed Data with Global Shearlet Features

PubMed Central

Murphy, James M.; Le Moigne, Jacqueline; Harding, David J.

2017-01-01

Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone. PMID:29123329

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

PubMed Central

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

2010-01-01

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

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

PubMed

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.

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

NASA Astrophysics Data System (ADS)

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

2007-03-01

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

Scope and applications of translation invariant wavelets to image registration

NASA Technical Reports Server (NTRS)

Chettri, Samir; LeMoigne, Jacqueline; Campbell, William

1997-01-01

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

Phantom Study Investigating the Accuracy of Manual and Automatic Image Fusion with the GE Logiq E9: Implications for use in Percutaneous Liver Interventions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burgmans, Mark Christiaan, E-mail: m.c.burgmans@lumc.nl; Harder, J. Michiel den, E-mail: chiel.den.harder@gmail.com; Meershoek, Philippa, E-mail: P.Meershoek@lumc.nl

PurposeTo determine the accuracy of automatic and manual co-registration methods for image fusion of three-dimensional computed tomography (CT) with real-time ultrasonography (US) for image-guided liver interventions.Materials and MethodsCT images of a skills phantom with liver lesions were acquired and co-registered to US using GE Logiq E9 navigation software. Manual co-registration was compared to automatic and semiautomatic co-registration using an active tracker. Also, manual point registration was compared to plane registration with and without an additional translation point. Finally, comparison was made between manual and automatic selection of reference points. In each experiment, accuracy of the co-registration method was determined bymore » measurement of the residual displacement in phantom lesions by two independent observers.ResultsMean displacements for a superficial and deep liver lesion were comparable after manual and semiautomatic co-registration: 2.4 and 2.0 mm versus 2.0 and 2.5 mm, respectively. Both methods were significantly better than automatic co-registration: 5.9 and 5.2 mm residual displacement (p < 0.001; p < 0.01). The accuracy of manual point registration was higher than that of plane registration, the latter being heavily dependent on accurate matching of axial CT and US images by the operator. Automatic reference point selection resulted in significantly lower registration accuracy compared to manual point selection despite lower root-mean-square deviation (RMSD) values.ConclusionThe accuracy of manual and semiautomatic co-registration is better than that of automatic co-registration. For manual co-registration using a plane, choosing the correct plane orientation is an essential first step in the registration process. Automatic reference point selection based on RMSD values is error-prone.« less

Automatic Registration of GF4 Pms: a High Resolution Multi-Spectral Sensor on Board a Satellite on Geostationary Orbit

NASA Astrophysics Data System (ADS)

Gao, M.; Li, J.

2018-04-01

Geometric correction is an important preprocessing process in the application of GF4 PMS image. The method of geometric correction that is based on the manual selection of geometric control points is time-consuming and laborious. The more common method, based on a reference image, is automatic image registration. This method involves several steps and parameters. For the multi-spectral sensor GF4 PMS, it is necessary for us to identify the best combination of parameters and steps. This study mainly focuses on the following issues: necessity of Rational Polynomial Coefficients (RPC) correction before automatic registration, base band in the automatic registration and configuration of GF4 PMS spatial resolution.

SU-F-J-57: Effectiveness of Daily CT-Based Three-Dimensional Image Guided and Adaptive Proton Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moriya, S; National Cancer Center, Kashiwa, Chiba; Tachibana, H

Purpose: Daily CT-based three-dimensional image-guided and adaptive (CTIGRT-ART) proton therapy system was designed and developed. We also evaluated the effectiveness of the CTIGRT-ART. Methods: Retrospective analysis was performed in three lung cancer patients: Proton treatment planning was performed using CT image datasets acquired by Toshiba Aquilion ONE. Planning target volume and surrounding organs were contoured by a well-trained radiation oncologist. Dose distribution was optimized using 180-deg. and 270-deg. two fields in passive scattering proton therapy. Well commissioned Simplified Monte Carlo algorithm was used as dose calculation engine. Daily consecutive CT image datasets was acquired by an in-room CT (Toshiba Aquilionmore » LB). In our in-house program, two image registrations for bone and tumor were performed to shift the isocenter using treatment CT image dataset. Subsequently, dose recalculation was performed after the shift of the isocenter. When the dose distribution after the tumor registration exhibits change of dosimetric parameter of CTV D90% compared to the initial plan, an additional process of was performed that the range shifter thickness was optimized. Dose distribution with CTV D90% for the bone registration, the tumor registration only and adaptive plan with the tumor registration was compared to the initial plan. Results: In the bone registration, tumor dose coverage was decreased by 16% on average (Maximum: 56%). The tumor registration shows better coverage than the bone registration, however the coverage was also decreased by 9% (Maximum: 22%) The adaptive plan shows similar dose coverage of the tumor (Average: 2%, Maximum: 7%). Conclusion: There is a high possibility that only image registration for bone and tumor may reduce tumor coverage. Thus, our proposed methodology of image guidance and adaptive planning using the range adaptation after tumor registration would be effective for proton therapy. This research is partially supported by Japan Agency for Medical Research and Development (AMED).« less

Demons deformable registration of CT and cone-beam CT using an iterative intensity matching approach

PubMed Central

Nithiananthan, Sajendra; Schafer, Sebastian; Uneri, Ali; Mirota, Daniel J.; Stayman, J. Webster; Zbijewski, Wojciech; Brock, Kristy K.; Daly, Michael J.; Chan, Harley; Irish, Jonathan C.; Siewerdsen, Jeffrey H.

2011-01-01

Purpose: A method of intensity-based deformable registration of CT and cone-beam CT (CBCT) images is described, in which intensity correction occurs simultaneously within the iterative registration process. The method preserves the speed and simplicity of the popular Demons algorithm while providing robustness and accuracy in the presence of large mismatch between CT and CBCT voxel values (“intensity”). Methods: A variant of the Demons algorithm was developed in which an estimate of the relationship between CT and CBCT intensity values for specific materials in the image is computed at each iteration based on the set of currently overlapping voxels. This tissue-specific intensity correction is then used to estimate the registration output for that iteration and the process is repeated. The robustness of the method was tested in CBCT images of a cadaveric head exhibiting a broad range of simulated intensity variations associated with x-ray scatter, object truncation, and∕or errors in the reconstruction algorithm. The accuracy of CT-CBCT registration was also measured in six real cases, exhibiting deformations ranging from simple to complex during surgery or radiotherapy guided by a CBCT-capable C-arm or linear accelerator, respectively. Results: The iterative intensity matching approach was robust against all levels of intensity variation examined, including spatially varying errors in voxel value of a factor of 2 or more, as can be encountered in cases of high x-ray scatter. Registration accuracy without intensity matching degraded severely with increasing magnitude of intensity error and introduced image distortion. A single histogram match performed prior to registration alleviated some of these effects but was also prone to image distortion and was quantifiably less robust and accurate than the iterative approach. Within the six case registration accuracy study, iterative intensity matching Demons reduced mean TRE to (2.5±2.8) mm compared to (3.5±3.0) mm with rigid registration. Conclusions: A method was developed to iteratively correct CT-CBCT intensity disparity during Demons registration, enabling fast, intensity-based registration in CBCT-guided procedures such as surgery and radiotherapy, in which CBCT voxel values may be inaccurate. Accurate CT-CBCT registration in turn facilitates registration of multimodality preoperative image and planning data to intraoperative CBCT by way of the preoperative CT, thereby linking the intraoperative frame of reference to a wealth of preoperative information that could improve interventional guidance. PMID:21626913

Demons deformable registration of CT and cone-beam CT using an iterative intensity matching approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nithiananthan, Sajendra; Schafer, Sebastian; Uneri, Ali

2011-04-15

Purpose: A method of intensity-based deformable registration of CT and cone-beam CT (CBCT) images is described, in which intensity correction occurs simultaneously within the iterative registration process. The method preserves the speed and simplicity of the popular Demons algorithm while providing robustness and accuracy in the presence of large mismatch between CT and CBCT voxel values (''intensity''). Methods: A variant of the Demons algorithm was developed in which an estimate of the relationship between CT and CBCT intensity values for specific materials in the image is computed at each iteration based on the set of currently overlapping voxels. This tissue-specificmore » intensity correction is then used to estimate the registration output for that iteration and the process is repeated. The robustness of the method was tested in CBCT images of a cadaveric head exhibiting a broad range of simulated intensity variations associated with x-ray scatter, object truncation, and/or errors in the reconstruction algorithm. The accuracy of CT-CBCT registration was also measured in six real cases, exhibiting deformations ranging from simple to complex during surgery or radiotherapy guided by a CBCT-capable C-arm or linear accelerator, respectively. Results: The iterative intensity matching approach was robust against all levels of intensity variation examined, including spatially varying errors in voxel value of a factor of 2 or more, as can be encountered in cases of high x-ray scatter. Registration accuracy without intensity matching degraded severely with increasing magnitude of intensity error and introduced image distortion. A single histogram match performed prior to registration alleviated some of these effects but was also prone to image distortion and was quantifiably less robust and accurate than the iterative approach. Within the six case registration accuracy study, iterative intensity matching Demons reduced mean TRE to (2.5{+-}2.8) mm compared to (3.5{+-}3.0) mm with rigid registration. Conclusions: A method was developed to iteratively correct CT-CBCT intensity disparity during Demons registration, enabling fast, intensity-based registration in CBCT-guided procedures such as surgery and radiotherapy, in which CBCT voxel values may be inaccurate. Accurate CT-CBCT registration in turn facilitates registration of multimodality preoperative image and planning data to intraoperative CBCT by way of the preoperative CT, thereby linking the intraoperative frame of reference to a wealth of preoperative information that could improve interventional guidance.« less

Co-registration of cone beam CT and preoperative MRI for improved accuracy of electrode localization following cochlear implantation.

PubMed

Dragovic, A S; Stringer, A K; Campbell, L; Shaul, C; O'Leary, S J; Briggs, R J

2018-05-01

To investigate the clinical usefulness and practicality of co-registration of Cone Beam CT (CBCT) with preoperative Magnetic Resonance Imaging (MRI) for intracochlear localization of electrodes after cochlear implantation. Images of 20 adult patients who underwent CBCT after implantation were co-registered with preoperative MRI scans. Time taken for co-registration was recorded. The images were analysed by clinicians of varying levels of expertise to determine electrode position and ease of interpretation. After a short learning curve, the average co-registration time was 10.78 minutes (StdDev 2.37). All clinicians found the co-registered images easier to interpret than CBCT alone. The mean concordance of CBCT vs. co-registered image analysis between consultant otologists was 60% (17-100%) and 86% (60-100%), respectively. The sensitivity and specificity for CBCT to identify Scala Vestibuli insertion or translocation was 100 and 75%, respectively. The negative predictive value was 100%. CBCT should be performed following adult cochlear implantation for audit and quality control of surgical technique. If SV insertion or translocation is suspected, co-registration with preoperative MRI should be performed to enable easier analysis. There will be a learning curve for this process in terms of both the co-registration and the interpretation of images by clinicians.

On-line range images registration with GPGPU

NASA Astrophysics Data System (ADS)

Będkowski, J.; Naruniec, J.

2013-03-01

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

A fast and mobile system for registration of low-altitude visual and thermal aerial images using multiple small-scale UAVs

NASA Astrophysics Data System (ADS)

Yahyanejad, Saeed; Rinner, Bernhard

2015-06-01

The use of multiple small-scale UAVs to support first responders in disaster management has become popular because of their speed and low deployment costs. We exploit such UAVs to perform real-time monitoring of target areas by fusing individual images captured from heterogeneous aerial sensors. Many approaches have already been presented to register images from homogeneous sensors. These methods have demonstrated robustness against scale, rotation and illumination variations and can also cope with limited overlap among individual images. In this paper we focus on thermal and visual image registration and propose different methods to improve the quality of interspectral registration for the purpose of real-time monitoring and mobile mapping. Images captured by low-altitude UAVs represent a very challenging scenario for interspectral registration due to the strong variations in overlap, scale, rotation, point of view and structure of such scenes. Furthermore, these small-scale UAVs have limited processing and communication power. The contributions of this paper include (i) the introduction of a feature descriptor for robustly identifying corresponding regions of images in different spectrums, (ii) the registration of image mosaics, and (iii) the registration of depth maps. We evaluated the first method using a test data set consisting of 84 image pairs. In all instances our approach combined with SIFT or SURF feature-based registration was superior to the standard versions. Although we focus mainly on aerial imagery, our evaluation shows that the presented approach would also be beneficial in other scenarios such as surveillance and human detection. Furthermore, we demonstrated the advantages of the other two methods in case of multiple image pairs.

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

NASA Astrophysics Data System (ADS)

Zhang, Wanjun; Yang, Xu

2017-12-01

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

SU-E-J-42: Motion Adaptive Image Filter for Low Dose X-Ray Fluoroscopy in the Real-Time Tumor-Tracking Radiotherapy System.

PubMed

Miyamoto, N; Ishikawa, M; Sutherland, K; Suzuki, R; Matsuura, T; Takao, S; Toramatsu, C; Nihongi, H; Shimizu, S; Onimaru, R; Umegaki, K; Shirato, H

2012-06-01

In the real-time tumor-tracking radiotherapy system, fiducial markers are detected by X-ray fluoroscopy. The fluoroscopic parameters should be optimized as low as possible in order to reduce unnecessary imaging dose. However, the fiducial markers could not be recognized due to effect of statistical noise in low dose imaging. Image processing is envisioned to be a solution to improve image quality and to maintain tracking accuracy. In this study, a recursive image filter adapted to target motion is proposed. A fluoroscopy system was used for the experiment. A spherical gold marker was used as a fiducial marker. About 450 fluoroscopic images of the marker were recorded. In order to mimic respiratory motion of the marker, the images were shifted sequentially. The tube voltage, current and exposure duration were fixed at 65 kV, 50 mA and 2.5 msec as low dose imaging condition, respectively. The tube current was 100 mA as high dose imaging. A pattern recognition score (PRS) ranging from 0 to 100 and image registration error were investigated by performing template pattern matching to each sequential image. The results with and without image processing were compared. In low dose imaging, theimage registration error and the PRS without the image processing were 2.15±1.21 pixel and 46.67±6.40, respectively. Those with the image processing were 1.48±0.82 pixel and 67.80±4.51, respectively. There was nosignificant difference in the image registration error and the PRS between the results of low dose imaging with the image processing and that of high dose imaging without the image processing. The results showed that the recursive filter was effective in order to maintain marker tracking stability and accuracy in low dose fluoroscopy. © 2012 American Association of Physicists in Medicine.

Experiences with digital processing of images at INPE

NASA Technical Reports Server (NTRS)

Mascarenhas, N. D. A. (Principal Investigator)

1984-01-01

Four different research experiments with digital image processing at INPE will be described: (1) edge detection by hypothesis testing; (2) image interpolation by finite impulse response filters; (3) spatial feature extraction methods in multispectral classification; and (4) translational image registration by sequential tests of hypotheses.

Automated retina identification based on multiscale elastic registration.

PubMed

Figueiredo, Isabel N; Moura, Susana; Neves, Júlio S; Pinto, Luís; Kumar, Sunil; Oliveira, Carlos M; Ramos, João D

2016-12-01

In this work we propose a novel method for identifying individuals based on retinal fundus image matching. The method is based on the image registration of retina blood vessels, since it is known that the retina vasculature of an individual is a signature, i.e., a distinctive pattern of the individual. The proposed image registration consists of a multiscale affine registration followed by a multiscale elastic registration. The major advantage of this particular two-step image registration procedure is that it is able to account for both rigid and non-rigid deformations either inherent to the retina tissues or as a result of the imaging process itself. Afterwards a decision identification measure, relying on a suitable normalized function, is defined to decide whether or not the pair of images belongs to the same individual. The method is tested on a data set of 21721 real pairs generated from a total of 946 retinal fundus images of 339 different individuals, consisting of patients followed in the context of different retinal diseases and also healthy patients. The evaluation of its performance reveals that it achieves a very low false rejection rate (FRR) at zero FAR (the false acceptance rate), equal to 0.084, as well as a low equal error rate (EER), equal to 0.053. Moreover, the tests performed by using only the multiscale affine registration, and discarding the multiscale elastic registration, clearly show the advantage of the proposed approach. The outcome of this study also indicates that the proposed method is reliable and competitive with other existing retinal identification methods, and forecasts its future appropriateness and applicability in real-life applications. Copyright © 2016 Elsevier Ltd. All rights reserved.

A gaussian mixture + demons deformable registration method for cone-beam CT-guided robotic transoral base-of-tongue surgery

NASA Astrophysics Data System (ADS)

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

2013-03-01

Purpose: An increasingly popular minimally invasive approach to resection of oropharyngeal / base-of-tongue cancer is made possible by a transoral technique conducted with the assistance of a surgical robot. However, the highly deformed surgical setup (neck flexed, mouth open, and tongue retracted) compared to the typical patient orientation in preoperative images poses a challenge to guidance and localization of the tumor target and adjacent critical anatomy. Intraoperative cone-beam CT (CBCT) can account for such deformation, but due to the low contrast of soft-tissue in CBCT images, direct localization of the target and critical tissues in CBCT images can be difficult. Such structures may be more readily delineated in preoperative CT or MR images, so a method to deformably register such information to intraoperative CBCT could offer significant value. This paper details the initial implementation of a deformable registration framework to align preoperative images with the deformed intraoperative scene and gives preliminary evaluation of the geometric accuracy of registration in CBCT-guided TORS. Method: The deformable registration aligns preoperative CT or MR to intraoperative CBCT by integrating two established approaches. The volume of interest is first segmented (specifically, the region of the tongue from the tip to the hyoid), and a Gaussian mixture (GM) mode1 of surface point clouds is used for rigid initialization (GMRigid) as well as an initial deformation (GMNonRigid). Next, refinement of the registration is performed using the Demons algorithm applied to distance transformations of the GM-registered and CBCT volumes. The registration accuracy of the framework was quantified in preliminary studies using a cadaver emulating preoperative and intraoperative setups. Geometric accuracy of registration was quantified in terms of target registration error (TRE) and surface distance error. Result: With each step of the registration process, the framework demonstrated improved registration, achieving mean TRE of 3.0 mm following the GM rigid, 1.9 mm following GM nonrigid, and 1.5 mm at the output of the registration process. Analysis of surface distance demonstrated a corresponding improvement of 2.2, 0.4, and 0.3 mm, respectively. The evaluation of registration error revealed the accurate alignment in the region of interest for base-of-tongue robotic surgery owing to point-set selection in the GM steps and refinement in the deep aspect of the tongue in the Demons step. Conclusions: A promising framework has been developed for CBCT-guided TORS in which intraoperative CBCT provides a basis for registration of preoperative images to the highly deformed intraoperative setup. The registration framework is invariant to imaging modality (accommodating preoperative CT or MR) and is robust against CBCT intensity variations and artifact, provided corresponding segmentation of the volume of interest. The approach could facilitate overlay of preoperative planning data directly in stereo-endoscopic video in support of CBCT-guided TORS.

3D/2D image registration using weighted histogram of gradient directions

NASA Astrophysics Data System (ADS)

Ghafurian, Soheil; Hacihaliloglu, Ilker; Metaxas, Dimitris N.; Tan, Virak; Li, Kang

2015-03-01

Three dimensional (3D) to two dimensional (2D) image registration is crucial in many medical applications such as image-guided evaluation of musculoskeletal disorders. One of the key problems is to estimate the 3D CT- reconstructed bone model positions (translation and rotation) which maximize the similarity between the digitally reconstructed radiographs (DRRs) and the 2D fluoroscopic images using a registration method. This problem is computational-intensive due to a large search space and the complicated DRR generation process. Also, finding a similarity measure which converges to the global optimum instead of local optima adds to the challenge. To circumvent these issues, most existing registration methods need a manual initialization, which requires user interaction and is prone to human error. In this paper, we introduce a novel feature-based registration method using the weighted histogram of gradient directions of images. This method simplifies the computation by searching the parameter space (rotation and translation) sequentially rather than simultaneously. In our numeric simulation experiments, the proposed registration algorithm was able to achieve sub-millimeter and sub-degree accuracies. Moreover, our method is robust to the initial guess. It can tolerate up to +/-90°rotation offset from the global optimal solution, which minimizes the need for human interaction to initialize the algorithm.

Preliminary results in large bone segmentation from 3D freehand ultrasound

NASA Astrophysics Data System (ADS)

Fanti, Zian; Torres, Fabian; Arámbula Cosío, Fernando

2013-11-01

Computer Assisted Orthopedic Surgery (CAOS) requires a correct registration between the patient in the operating room and the virtual models representing the patient in the computer. In order to increase the precision and accuracy of the registration a set of new techniques that eliminated the need to use fiducial markers have been developed. The majority of these newly developed registration systems are based on costly intraoperative imaging systems like Computed Tomography (CT scan) or Magnetic resonance imaging (MRI). An alternative to these methods is the use of an Ultrasound (US) imaging system for the implementation of a more cost efficient intraoperative registration solution. In order to develop the registration solution with the US imaging system, the bone surface is segmented in both preoperative and intraoperative images, and the registration is done using the acquire surface. In this paper, we present the a preliminary results of a new approach to segment bone surface from ultrasound volumes acquired by means 3D freehand ultrasound. The method is based on the enhancement of the voxels that belongs to surface and its posterior segmentation. The enhancement process is based on the information provided by eigenanalisis of the multiscale 3D Hessian matrix. The preliminary results shows that from the enhance volume the final bone surfaces can be extracted using a singular value thresholding.

Automatic registration of fused lidar/digital imagery (texel images) for three-dimensional image creation

NASA Astrophysics Data System (ADS)

Budge, Scott E.; Badamikar, Neeraj S.; Xie, Xuan

2015-03-01

Several photogrammetry-based methods have been proposed that the derive three-dimensional (3-D) information from digital images from different perspectives, and lidar-based methods have been proposed that merge lidar point clouds and texture the merged point clouds with digital imagery. Image registration alone has difficulty with smooth regions with low contrast, whereas point cloud merging alone has difficulty with outliers and a lack of proper convergence in the merging process. This paper presents a method to create 3-D images that uses the unique properties of texel images (pixel-fused lidar and digital imagery) to improve the quality and robustness of fused 3-D images. The proposed method uses both image processing and point-cloud merging to combine texel images in an iterative technique. Since the digital image pixels and the lidar 3-D points are fused at the sensor level, more accurate 3-D images are generated because registration of image data automatically improves the merging of the point clouds, and vice versa. Examples illustrate the value of this method over other methods. The proposed method also includes modifications for the situation where an estimate of position and attitude of the sensor is known, when obtained from low-cost global positioning systems and inertial measurement units sensors.

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Multimodality Non-Rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation

PubMed Central

Elhawary, Haytham; Oguro, Sota; Tuncali, Kemal; Morrison, Paul R.; Tatli, Servet; Shyn, Paul B.; Silverman, Stuart G.; Hata, Nobuhiko

2010-01-01

Rationale and Objectives To develop non-rigid image registration between pre-procedure contrast enhanced MR images and intra-procedure unenhanced CT images, to enhance tumor visualization and localization during CT-guided liver tumor cryoablation procedures. Materials and Methods After IRB approval, a non-rigid registration (NRR) technique was evaluated with different pre-processing steps and algorithm parameters and compared to a standard rigid registration (RR) approach. The Dice Similarity Coefficient (DSC), Target Registration Error (TRE), 95% Hausdorff distance (HD) and total registration time (minutes) were compared using a two-sided Student’s t-test. The entire registration method was then applied during five CT-guided liver cryoablation cases with the intra-procedural CT data transmitted directly from the CT scanner, with both accuracy and registration time evaluated. Results Selected optimal parameters for registration were section thickness of 5mm, cropping the field of view to 66% of its original size, manual segmentation of the liver, B-spline control grid of 5×5×5 and spatial sampling of 50,000 pixels. Mean 95% HD of 3.3mm (2.5x improvement compared to RR, p<0.05); mean DSC metric of 0.97 (13% increase); and mean TRE of 4.1mm (2.7x reduction) were measured. During the cryoablation procedure registration between the pre-procedure MR and the planning intra-procedure CT took a mean time of 10.6 minutes, the MR to targeting CT image took 4 minutes and MR to monitoring CT took 4.3 minutes. Mean registration accuracy was under 3.4mm. Conclusion Non-rigid registration allowed improved visualization of the tumor during interventional planning, targeting and evaluation of tumor coverage by the ice ball. Future work is focused on reducing segmentation time to make the method more clinically acceptable. PMID:20817574

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

NASA Astrophysics Data System (ADS)

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.

Image Segmentation, Registration, Compression, and Matching

NASA Technical Reports Server (NTRS)

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

2011-01-01

A novel computational framework was developed of a 2D affine invariant matching exploiting a parameter space. Named as affine invariant parameter space (AIPS), the technique can be applied to many image-processing and computer-vision problems, including image registration, template matching, and object tracking from image sequence. The AIPS is formed by the parameters in an affine combination of a set of feature points in the image plane. In cases where the entire image can be assumed to have undergone a single affine transformation, the new AIPS match metric and matching framework becomes very effective (compared with the state-of-the-art methods at the time of this reporting). No knowledge about scaling or any other transformation parameters need to be known a priori to apply the AIPS framework. An automated suite of software tools has been created to provide accurate image segmentation (for data cleaning) and high-quality 2D image and 3D surface registration (for fusing multi-resolution terrain, image, and map data). These tools are capable of supporting existing GIS toolkits already in the marketplace, and will also be usable in a stand-alone fashion. The toolkit applies novel algorithmic approaches for image segmentation, feature extraction, and registration of 2D imagery and 3D surface data, which supports first-pass, batched, fully automatic feature extraction (for segmentation), and registration. A hierarchical and adaptive approach is taken for achieving automatic feature extraction, segmentation, and registration. Surface registration is the process of aligning two (or more) data sets to a common coordinate system, during which the transformation between their different coordinate systems is determined. Also developed here are a novel, volumetric surface modeling and compression technique that provide both quality-guaranteed mesh surface approximations and compaction of the model sizes by efficiently coding the geometry and connectivity/topology components of the generated models. The highly efficient triangular mesh compression compacts the connectivity information at the rate of 1.5-4 bits per vertex (on average for triangle meshes), while reducing the 3D geometry by 40-50 percent. Finally, taking into consideration the characteristics of 3D terrain data, and using the innovative, regularized binary decomposition mesh modeling, a multistage, pattern-drive modeling, and compression technique has been developed to provide an effective framework for compressing digital elevation model (DEM) surfaces, high-resolution aerial imagery, and other types of NASA data.

A comparison of registration errors with imageless computer navigation during MIS total knee arthroplasty versus standard incision total knee arthroplasty: a cadaveric study.

PubMed

Davis, Edward T; Pagkalos, Joseph; Gallie, Price A M; Macgroarty, Kelly; Waddell, James P; Schemitsch, Emil H

2015-01-01

Optimal component alignment in total knee arthroplasty has been associated with better functional outcome as well as improved implant longevity. The ability to align components optimally during minimally invasive (MIS) total knee replacement (TKR) has been a cause of concern. Computer navigation is a useful aid in achieving the desired alignment although it is limited by the error during the manual registration of landmarks. Our study aims to compare the registration process error between a standard and a MIS surgical approach. We hypothesized that performing the registration error via an MIS approach would increase the registration process error. Five fresh frozen lower limbs were routinely prepared and draped. The registration process was performed through an MIS approach. This was then extended to the standard approach and the registration was performed again. Two surgeons performed the registration process five times with each approach. Performing the registration process through the MIS approach was not associated with higher error compared to the standard approach in the alignment parameters of interest. This rejects our hypothesis. Image-free navigated MIS TKR does not appear to carry higher risk of component malalignment due to the registration process error. Navigation can be used during MIS TKR to improve alignment without reduced accuracy due to the approach.

ACIR: automatic cochlea image registration

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Automatic patient alignment system using 3D ultrasound.

PubMed

Kaar, Marcus; Figl, Michael; Hoffmann, Rainer; Birkfellner, Wolfgang; Stock, Markus; Georg, Dietmar; Goldner, Gregor; Hummel, Johann

2013-04-01

Recent developments in radiation therapy such as intensity modulated radiotherapy (IMRT) or dose painting promise to provide better dose distribution on the tumor. For effective application of these methods the exact positioning of the patient and the localization of the irradiated organ and surrounding structures is crucial. Especially with respect to the treatment of the prostate, ultrasound (US) allows for differentiation between soft tissue and was therefore applied by various repositioning systems, such as BAT or Clarity. The authors built a new system which uses 3D US at both sites, the CT room and the intervention room and applied a 3D/3D US/US registration for automatic repositioning. In a first step the authors applied image preprocessing methods to prepare the US images for an optimal registration process. For the 3D/3D registration procedure five different metrics were evaluated. To find the image metric which fits best for a particular patient three 3D US images were taken at the CT site and registered to each other. From these results an US registration error was calculated. The most successful image metric was then applied for the US/US registration process. The success of the whole repositioning method was assessed by taking the results of an ExacTrac system as golden standard. The US/US registration error was found to be 2.99 ± 1.54 mm with respect to the mutual information metric by Mattes (eleven patients) which revealed to be the most suitable of the assessed metrics. For complete repositioning chain the error amounted to 4.15 ± 1.20 mm (ten patients). The authors developed a system for patient repositioning which works automatically without the necessity of user interaction with an accuracy which seems to be suitable for clinical application.

Experimental study of digital image processing techniques for LANDSAT data

NASA Technical Reports Server (NTRS)

Rifman, S. S. (Principal Investigator); Allendoerfer, W. B.; Caron, R. H.; Pemberton, L. J.; Mckinnon, D. M.; Polanski, G.; Simon, K. W.

1976-01-01

The author has identified the following significant results. Results are reported for: (1) subscene registration, (2) full scene rectification and registration, (3) resampling techniques, (4) and ground control point (GCP) extraction. Subscenes (354 pixels x 234 lines) were registered to approximately 1/4 pixel accuracy and evaluated by change detection imagery for three cases: (1) bulk data registration, (2) precision correction of a reference subscene using GCP data, and (3) independently precision processed subscenes. Full scene rectification and registration results were evaluated by using a correlation technique to measure registration errors of 0.3 pixel rms thoughout the full scene. Resampling evaluations of nearest neighbor and TRW cubic convolution processed data included change detection imagery and feature classification. Resampled data were also evaluated for an MSS scene containing specular solar reflections.

Implementation and evaluation of a new workflow for registration and segmentation of pulmonary MRI data for regional lung perfusion assessment.

PubMed

Böttger, T; Grunewald, K; Schöbinger, M; Fink, C; Risse, F; Kauczor, H U; Meinzer, H P; Wolf, Ivo

2007-03-07

Recently it has been shown that regional lung perfusion can be assessed using time-resolved contrast-enhanced magnetic resonance (MR) imaging. Quantification of the perfusion images has been attempted, based on definition of small regions of interest (ROIs). Use of complete lung segmentations instead of ROIs could possibly increase quantification accuracy. Due to the low signal-to-noise ratio, automatic segmentation algorithms cannot be applied. On the other hand, manual segmentation of the lung tissue is very time consuming and can become inaccurate, as the borders of the lung to adjacent tissues are not always clearly visible. We propose a new workflow for semi-automatic segmentation of the lung from additionally acquired morphological HASTE MR images. First the lung is delineated semi-automatically in the HASTE image. Next the HASTE image is automatically registered with the perfusion images. Finally, the transformation resulting from the registration is used to align the lung segmentation from the morphological dataset with the perfusion images. We evaluated rigid, affine and locally elastic transformations, suitable optimizers and different implementations of mutual information (MI) metrics to determine the best possible registration algorithm. We located the shortcomings of the registration procedure and under which conditions automatic registration will succeed or fail. Segmentation results were evaluated using overlap and distance measures. Integration of the new workflow reduces the time needed for post-processing of the data, simplifies the perfusion quantification and reduces interobserver variability in the segmentation process. In addition, the matched morphological data set can be used to identify morphologic changes as the source for the perfusion abnormalities.

Robust multi-site MR data processing: iterative optimization of bias correction, tissue classification, and registration.

PubMed

Young Kim, Eun; Johnson, Hans J

2013-01-01

A robust multi-modal tool, for automated registration, bias correction, and tissue classification, has been implemented for large-scale heterogeneous multi-site longitudinal MR data analysis. This work focused on improving the an iterative optimization framework between bias-correction, registration, and tissue classification inspired from previous work. The primary contributions are robustness improvements from incorporation of following four elements: (1) utilize multi-modal and repeated scans, (2) incorporate high-deformable registration, (3) use extended set of tissue definitions, and (4) use of multi-modal aware intensity-context priors. The benefits of these enhancements were investigated by a series of experiments with both simulated brain data set (BrainWeb) and by applying to highly-heterogeneous data from a 32 site imaging study with quality assessments through the expert visual inspection. The implementation of this tool is tailored for, but not limited to, large-scale data processing with great data variation with a flexible interface. In this paper, we describe enhancements to a joint registration, bias correction, and the tissue classification, that improve the generalizability and robustness for processing multi-modal longitudinal MR scans collected at multi-sites. The tool was evaluated by using both simulated and simulated and human subject MRI images. With these enhancements, the results showed improved robustness for large-scale heterogeneous MRI processing.

The One to Multiple Automatic High Accuracy Registration of Terrestrial LIDAR and Optical Images

NASA Astrophysics Data System (ADS)

Wang, Y.; Hu, C.; Xia, G.; Xue, H.

2018-04-01

The registration of ground laser point cloud and close-range image is the key content of high-precision 3D reconstruction of cultural relic object. In view of the requirement of high texture resolution in the field of cultural relic at present, The registration of point cloud and image data in object reconstruction will result in the problem of point cloud to multiple images. In the current commercial software, the two pairs of registration of the two kinds of data are realized by manually dividing point cloud data, manual matching point cloud and image data, manually selecting a two - dimensional point of the same name of the image and the point cloud, and the process not only greatly reduces the working efficiency, but also affects the precision of the registration of the two, and causes the problem of the color point cloud texture joint. In order to solve the above problems, this paper takes the whole object image as the intermediate data, and uses the matching technology to realize the automatic one-to-one correspondence between the point cloud and multiple images. The matching of point cloud center projection reflection intensity image and optical image is applied to realize the automatic matching of the same name feature points, and the Rodrigo matrix spatial similarity transformation model and weight selection iteration are used to realize the automatic registration of the two kinds of data with high accuracy. This method is expected to serve for the high precision and high efficiency automatic 3D reconstruction of cultural relic objects, which has certain scientific research value and practical significance.

Real-time Enhancement, Registration, and Fusion for a Multi-Sensor Enhanced Vision System

NASA Technical Reports Server (NTRS)

Hines, Glenn D.; Rahman, Zia-ur; Jobson, Daniel J.; Woodell, Glenn A.

2006-01-01

Over the last few years NASA Langley Research Center (LaRC) has been developing an Enhanced Vision System (EVS) to aid pilots while flying in poor visibility conditions. The EVS captures imagery using two infrared video cameras. The cameras are placed in an enclosure that is mounted and flown forward-looking underneath the NASA LaRC ARIES 757 aircraft. The data streams from the cameras are processed in real-time and displayed on monitors on-board the aircraft. With proper processing the camera system can provide better-than- human-observed imagery particularly during poor visibility conditions. However, to obtain this goal requires several different stages of processing including enhancement, registration, and fusion, and specialized processing hardware for real-time performance. We are using a real-time implementation of the Retinex algorithm for image enhancement, affine transformations for registration, and weighted sums to perform fusion. All of the algorithms are executed on a single TI DM642 digital signal processor (DSP) clocked at 720 MHz. The image processing components were added to the EVS system, tested, and demonstrated during flight tests in August and September of 2005. In this paper we briefly discuss the EVS image processing hardware and algorithms. We then discuss implementation issues and show examples of the results obtained during flight tests. Keywords: enhanced vision system, image enhancement, retinex, digital signal processing, sensor fusion

Methods And Systems For Using Reference Images In Acoustic Image Processing

DOEpatents

Moore, Thomas L.; Barter, Robert Henry

2005-01-04

A method and system of examining tissue are provided in which a field, including at least a portion of the tissue and one or more registration fiducials, is insonified. Scattered acoustic information, including both transmitted and reflected waves, is received from the field. A representation of the field, including both the tissue and the registration fiducials, is then derived from the received acoustic radiation.

A discriminative structural similarity measure and its application to video-volume registration for endoscope three-dimensional motion tracking.

PubMed

Luo, Xiongbiao; Mori, Kensaku

2014-06-01

Endoscope 3-D motion tracking, which seeks to synchronize pre- and intra-operative images in endoscopic interventions, is usually performed as video-volume registration that optimizes the similarity between endoscopic video and pre-operative images. The tracking performance, in turn, depends significantly on whether a similarity measure can successfully characterize the difference between video sequences and volume rendering images driven by pre-operative images. The paper proposes a discriminative structural similarity measure, which uses the degradation of structural information and takes image correlation or structure, luminance, and contrast into consideration, to boost video-volume registration. By applying the proposed similarity measure to endoscope tracking, it was demonstrated to be more accurate and robust than several available similarity measures, e.g., local normalized cross correlation, normalized mutual information, modified mean square error, or normalized sum squared difference. Based on clinical data evaluation, the tracking error was reduced significantly from at least 14.6 mm to 4.5 mm. The processing time was accelerated more than 30 frames per second using graphics processing unit.

Vision based tunnel inspection using non-rigid registration

NASA Astrophysics Data System (ADS)

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.

Image Quality Improvement in Adaptive Optics Scanning Laser Ophthalmoscopy Assisted Capillary Visualization Using B-spline-based Elastic Image Registration

PubMed Central

Uji, Akihito; Ooto, Sotaro; Hangai, Masanori; Arichika, Shigeta; Yoshimura, Nagahisa

2013-01-01

Purpose To investigate the effect of B-spline-based elastic image registration on adaptive optics scanning laser ophthalmoscopy (AO-SLO)-assisted capillary visualization. Methods AO-SLO videos were acquired from parafoveal areas in the eyes of healthy subjects and patients with various diseases. After nonlinear image registration, the image quality of capillary images constructed from AO-SLO videos using motion contrast enhancement was compared before and after B-spline-based elastic (nonlinear) image registration performed using ImageJ. For objective comparison of image quality, contrast-to-noise ratios (CNRS) for vessel images were calculated. For subjective comparison, experienced ophthalmologists ranked images on a 5-point scale. Results All AO-SLO videos were successfully stabilized by elastic image registration. CNR was significantly higher in capillary images stabilized by elastic image registration than in those stabilized without registration. The average ratio of CNR in images with elastic image registration to CNR in images without elastic image registration was 2.10 ± 1.73, with no significant difference in the ratio between patients and healthy subjects. Improvement of image quality was also supported by expert comparison. Conclusions Use of B-spline-based elastic image registration in AO-SLO-assisted capillary visualization was effective for enhancing image quality both objectively and subjectively. PMID:24265796

Automated Registration of Sequential Breath-Hold Dynamic Contrast-Enhanced MRI Images: a Comparison of 3 Techniques

PubMed Central

Rajaraman, Sivaramakrishnan; Rodriguez, Jeffery J.; Graff, Christian; Altbach, Maria I.; Dragovich, Tomislav; Sirlin, Claude B.; Korn, Ronald L.; Raghunand, Natarajan

2011-01-01

Dynamic Contrast-Enhanced MRI (DCE-MRI) is increasingly in use as an investigational biomarker of response in cancer clinical studies. Proper registration of images acquired at different time-points is essential for deriving diagnostic information from quantitative pharmacokinetic analysis of these data. Motion artifacts in the presence of time-varying intensity due to contrast-enhancement make this registration problem challenging. DCE-MRI of chest and abdominal lesions is typically performed during sequential breath-holds, which introduces misregistration due to inconsistent diaphragm positions, and also places constraints on temporal resolution vis-à-vis free-breathing. In this work, we have employed a computer-generated DCE-MRI phantom to compare the performance of two published methods, Progressive Principal Component Registration and Pharmacokinetic Model-Driven Registration, with Sequential Elastic Registration (SER) to register adjacent time-sample images using a published general-purpose elastic registration algorithm. In all 3 methods, a 3-D rigid-body registration scheme with a mutual information similarity measure was used as a pre-processing step. The DCE-MRI phantom images were mathematically deformed to simulate misregistration which was corrected using the 3 schemes. All 3 schemes were comparably successful in registering large regions of interest (ROIs) such as muscle, liver, and spleen. SER was superior in retaining tumor volume and shape, and in registering smaller but important ROIs such as tumor core and tumor rim. The performance of SER on clinical DCE-MRI datasets is also presented. PMID:21531108

Localization accuracy from automatic and semi-automatic rigid registration of locally-advanced lung cancer targets during image-guided radiation therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

2012-01-15

Purpose: To evaluate localization accuracy resulting from rigid registration of locally-advanced lung cancer targets using fully automatic and semi-automatic protocols for image-guided radiation therapy. Methods: Seventeen lung cancer patients, fourteen also presenting with involved lymph nodes, received computed tomography (CT) scans once per week throughout treatment under active breathing control. A physician contoured both lung and lymph node targets for all weekly scans. Various automatic and semi-automatic rigid registration techniques were then performed for both individual and simultaneous alignments of the primary gross tumor volume (GTV{sub P}) and involved lymph nodes (GTV{sub LN}) to simulate the localization process in image-guidedmore » radiation therapy. Techniques included ''standard'' (direct registration of weekly images to a planning CT), ''seeded'' (manual prealignment of targets to guide standard registration), ''transitive-based'' (alignment of pretreatment and planning CTs through one or more intermediate images), and ''rereferenced'' (designation of a new reference image for registration). Localization error (LE) was assessed as the residual centroid and border distances between targets from planning and weekly CTs after registration. Results: Initial bony alignment resulted in centroid LE of 7.3 {+-} 5.4 mm and 5.4 {+-} 3.4 mm for the GTV{sub P} and GTV{sub LN}, respectively. Compared to bony alignment, transitive-based and seeded registrations significantly reduced GTV{sub P} centroid LE to 4.7 {+-} 3.7 mm (p = 0.011) and 4.3 {+-} 2.5 mm (p < 1 x 10{sup -3}), respectively, but the smallest GTV{sub P} LE of 2.4 {+-} 2.1 mm was provided by rereferenced registration (p < 1 x 10{sup -6}). Standard registration significantly reduced GTV{sub LN} centroid LE to 3.2 {+-} 2.5 mm (p < 1 x 10{sup -3}) compared to bony alignment, with little additional gain offered by the other registration techniques. For simultaneous target alignment, centroid LE as low as 3.9 {+-} 2.7 mm and 3.8 {+-} 2.3 mm were achieved for the GTV{sub P} and GTV{sub LN}, respectively, using rereferenced registration. Conclusions: Target shape, volume, and configuration changes during radiation therapy limited the accuracy of standard rigid registration for image-guided localization in locally-advanced lung cancer. Significant error reductions were possible using other rigid registration techniques, with LE approaching the lower limit imposed by interfraction target variability throughout treatment.« less

Registration of opthalmic images using control points

NASA Astrophysics Data System (ADS)

Heneghan, Conor; Maguire, Paul

2003-03-01

A method for registering pairs of digital ophthalmic images of the retina is presented using anatomical features as control points present in both images. The anatomical features chosen are blood vessel crossings and bifurcations. These control points are identified by a combination of local contrast enhancement, and morphological processing. In general, the matching between control points is unknown, however, so an automated algorithm is used to determine the matching pairs of control points in the two images as follows. Using two control points from each image, rigid global transform (RGT) coefficients are calculated for all possible combinations of control point pairs, and the set of RGT coefficients is identified. Once control point pairs are established, registration of two images can be achieved by using linear regression to optimize an RGT, bilinear or second order polynomial global transform. An example of cross-modal image registration using an optical image and a fluorescein angiogram of an eye is presented to illustrate the technique.

Mutual information registration of multi-spectral and multi-resolution images of DigitalGlobe's WorldView-3 imaging satellite

NASA Astrophysics Data System (ADS)

Miecznik, Grzegorz; Shafer, Jeff; Baugh, William M.; Bader, Brett; Karspeck, Milan; Pacifici, Fabio

2017-05-01

WorldView-3 (WV-3) is a DigitalGlobe commercial, high resolution, push-broom imaging satellite with three instruments: visible and near-infrared VNIR consisting of panchromatic (0.3m nadir GSD) plus multi-spectral (1.2m), short-wave infrared SWIR (3.7m), and multi-spectral CAVIS (30m). Nine VNIR bands, which are on one instrument, are nearly perfectly registered to each other, whereas eight SWIR bands, belonging to the second instrument, are misaligned with respect to VNIR and to each other. Geometric calibration and ortho-rectification results in a VNIR/SWIR alignment which is accurate to approximately 0.75 SWIR pixel at 3.7m GSD, whereas inter-SWIR, band to band registration is 0.3 SWIR pixel. Numerous high resolution, spectral applications, such as object classification and material identification, require more accurate registration, which can be achieved by utilizing image processing algorithms, for example Mutual Information (MI). Although MI-based co-registration algorithms are highly accurate, implementation details for automated processing can be challenging. One particular challenge is how to compute bin widths of intensity histograms, which are fundamental building blocks of MI. We solve this problem by making the bin widths proportional to instrument shot noise. Next, we show how to take advantage of multiple VNIR bands, and improve registration sensitivity to image alignment. To meet this goal, we employ Canonical Correlation Analysis, which maximizes VNIR/SWIR correlation through an optimal linear combination of VNIR bands. Finally we explore how to register images corresponding to different spatial resolutions. We show that MI computed at a low-resolution grid is more sensitive to alignment parameters than MI computed at a high-resolution grid. The proposed modifications allow us to improve VNIR/SWIR registration to better than ¼ of a SWIR pixel, as long as terrain elevation is properly accounted for, and clouds and water are masked out.

Inverse-consistent rigid registration of CT and MR for MR-based planning and adaptive prostate radiation therapy

NASA Astrophysics Data System (ADS)

Rivest-Hénault, David; Dowson, Nicholas; Greer, Peter; Dowling, Jason

2014-03-01

MRI-alone treatment planning and adaptive MRI-based prostate radiation therapy are two promising techniques that could significantly increase the accuracy of the curative dose delivery processes while reducing the total radiation dose. State-of-the-art methods rely on the registration of a patient MRI with a MR-CT atlas for the estimation of pseudo-CT [5]. This atlas itself is generally created by registering many CT and MRI pairs. Most registration methods are not symmetric, but the order of the images influences the result [8]. The computed transformation is therefore biased, introducing unwanted variability. This work examines how much a symmetric algorithm improves the registration. Methods: A robust symmetric registration algorithm is proposed that simultaneously optimises a half space transform and its inverse. During the registration process, the two input volumetric images are transformed to a common position in space, therefore minimising any computational bias. An asymmetrical implementation of the same algorithm was used for comparison purposes. Results: Whole pelvis MRI and CT scans from 15 prostate patients were registered, as in the creation of MR-CT atlases. In each case, two registrations were performed, with different input image orders, and the transformation error quantified. Mean residuals of 0.63±0.26 mm (translation) and (8.7±7.3) × 10--3 rad (rotation) were found for the asymmetrical implementation with corresponding values of 0.038±0.039 mm and (1.6 ± 1.3) × 10--3 rad for the proposed symmetric algorithm, a substantial improvement. Conclusions: The increased registration precision will enhance the generation of pseudo-CT from MRI for atlas based MR planning methods.

A software tool of digital tomosynthesis application for patient positioning in radiotherapy.

PubMed

Yan, Hui; Dai, Jian-Rong

2016-03-08

Digital Tomosynthesis (DTS) is an image modality in reconstructing tomographic images from two-dimensional kV projections covering a narrow scan angles. Comparing with conventional cone-beam CT (CBCT), it requires less time and radiation dose in data acquisition. It is feasible to apply this technique in patient positioning in radiotherapy. To facilitate its clinical application, a software tool was developed and the reconstruction processes were accelerated by graphic process-ing unit (GPU). Two reconstruction and two registration processes are required for DTS application which is different from conventional CBCT application which requires one image reconstruction process and one image registration process. The reconstruction stage consists of productions of two types of DTS. One type of DTS is reconstructed from cone-beam (CB) projections covering a narrow scan angle and is named onboard DTS (ODTS), which represents the real patient position in treatment room. Another type of DTS is reconstructed from digitally reconstructed radiography (DRR) and is named reference DTS (RDTS), which represents the ideal patient position in treatment room. Prior to the reconstruction of RDTS, The DRRs are reconstructed from planning CT using the same acquisition setting of CB projections. The registration stage consists of two matching processes between ODTS and RDTS. The target shift in lateral and longitudinal axes are obtained from the matching between ODTS and RDTS in coronal view, while the target shift in longitudinal and vertical axes are obtained from the matching between ODTS and RDTS in sagittal view. In this software, both DRR and DTS reconstruction algorithms were implemented on GPU environments for acceleration purpose. The comprehensive evaluation of this software tool was performed including geometric accuracy, image quality, registration accuracy, and reconstruction efficiency. The average correlation coefficient between DRR/DTS generated by GPU-based algorithm and CPU-based algorithm is 0.99. Based on the measurements of cube phantom on DTS, the geometric errors are within 0.5 mm in three axes. For both cube phantom and pelvic phantom, the registration errors are within 0.5 mm in three axes. Compared with reconstruction performance of CPU-based algorithms, the performances of DRR and DTS reconstructions are improved by a factor of 15 to 20. A GPU-based software tool was developed for DTS application for patient positioning of radiotherapy. The geometric and registration accuracy met the clinical requirement in patient setup of radiotherapy. The high performance of DRR and DTS reconstruction algorithms was achieved by the GPU-based computation environments. It is a useful software tool for researcher and clinician in evaluating DTS application in patient positioning of radiotherapy.

Marker Registration Technique for Handwritten Text Marker in Augmented Reality Applications

NASA Astrophysics Data System (ADS)

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.

Joint detection of anatomical points on surface meshes and color images for visual registration of 3D dental models

NASA Astrophysics Data System (ADS)

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

2015-04-01

Computer aided planning for orthodontic treatment requires knowing occlusion of separately scanned dental casts. A visual guided registration is conducted starting by extracting corresponding features in both photographs and 3D scans. To achieve this, dental neck and occlusion surface are firstly extracted by image segmentation and 3D curvature analysis. Then, an iterative registration process is conducted during which feature positions are refined, guided by previously found anatomic edges. The occlusal edge image detection is improved by an original algorithm which follows Canny's poorly detected edges using a priori knowledge of tooth shapes. Finally, the influence of feature extraction and position optimization is evaluated in terms of the quality of the induced registration. Best combination of feature detection and optimization leads to a positioning average error of 1.10 mm and 2.03°.

Improving depth estimation from a plenoptic camera by patterned illumination

NASA Astrophysics Data System (ADS)

Marshall, Richard J.; Meah, Chris J.; Turola, Massimo; Claridge, Ela; Robinson, Alex; Bongs, Kai; Gruppetta, Steve; Styles, Iain B.

2015-05-01

Plenoptic (light-field) imaging is a technique that allows a simple CCD-based imaging device to acquire both spatially and angularly resolved information about the "light-field" from a scene. It requires a microlens array to be placed between the objective lens and the sensor of the imaging device1 and the images under each microlens (which typically span many pixels) can be computationally post-processed to shift perspective, digital refocus, extend the depth of field, manipulate the aperture synthetically and generate a depth map from a single image. Some of these capabilities are rigid functions that do not depend upon the scene and work by manipulating and combining a well-defined set of pixels in the raw image. However, depth mapping requires specific features in the scene to be identified and registered between consecutive microimages. This process requires that the image has sufficient features for the registration, and in the absence of such features the algorithms become less reliable and incorrect depths are generated. The aim of this study is to investigate the generation of depth-maps from light-field images of scenes with insufficient features for accurate registration, using projected patterns to impose a texture on the scene that provides sufficient landmarks for the registration methods.

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Estimation of the uncertainty of elastic image registration with the demons algorithm.

PubMed

Hub, M; Karger, C P

2013-05-07

The accuracy of elastic image registration is limited. We propose an approach to detect voxels where registration based on the demons algorithm is likely to perform inaccurately, compared to other locations of the same image. The approach is based on the assumption that the local reproducibility of the registration can be regarded as a measure of uncertainty of the image registration. The reproducibility is determined as the standard deviation of the displacement vector components obtained from multiple registrations. These registrations differ in predefined initial deformations. The proposed approach was tested with artificially deformed lung images, where the ground truth on the deformation is known. In voxels where the result of the registration was less reproducible, the registration turned out to have larger average registration errors as compared to locations of the same image, where the registration was more reproducible. The proposed method can show a clinician in which area of the image the elastic registration with the demons algorithm cannot be expected to be accurate.

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

PubMed Central

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

2015-01-01

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

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

PubMed

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

2016-07-01

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

Rendering-based video-CT registration with physical constraints for image-guided endoscopic sinus surgery

NASA Astrophysics Data System (ADS)

Otake, Y.; Leonard, S.; Reiter, A.; Rajan, P.; Siewerdsen, J. H.; Ishii, M.; Taylor, R. H.; Hager, G. D.

2015-03-01

We present a system for registering the coordinate frame of an endoscope to pre- or intra- operatively acquired CT data based on optimizing the similarity metric between an endoscopic image and an image predicted via rendering of CT. Our method is robust and semi-automatic because it takes account of physical constraints, specifically, collisions between the endoscope and the anatomy, to initialize and constrain the search. The proposed optimization method is based on a stochastic optimization algorithm that evaluates a large number of similarity metric functions in parallel on a graphics processing unit. Images from a cadaver and a patient were used for evaluation. The registration error was 0.83 mm and 1.97 mm for cadaver and patient images respectively. The average registration time for 60 trials was 4.4 seconds. The patient study demonstrated robustness of the proposed algorithm against a moderate anatomical deformation.

Mapping cardiac fiber orientations from high-resolution DTI to high-frequency 3D ultrasound

NASA Astrophysics Data System (ADS)

Qin, Xulei; Wang, Silun; Shen, Ming; Zhang, Xiaodong; Wagner, Mary B.; Fei, Baowei

2014-03-01

The orientation of cardiac fibers affects the anatomical, mechanical, and electrophysiological properties of the heart. Although echocardiography is the most common imaging modality in clinical cardiac examination, it can only provide the cardiac geometry or motion information without cardiac fiber orientations. If the patient's cardiac fiber orientations can be mapped to his/her echocardiography images in clinical examinations, it may provide quantitative measures for diagnosis, personalized modeling, and image-guided cardiac therapies. Therefore, this project addresses the feasibility of mapping personalized cardiac fiber orientations to three-dimensional (3D) ultrasound image volumes. First, the geometry of the heart extracted from the MRI is translated to 3D ultrasound by rigid and deformable registration. Deformation fields between both geometries from MRI and ultrasound are obtained after registration. Three different deformable registration methods were utilized for the MRI-ultrasound registration. Finally, the cardiac fiber orientations imaged by DTI are mapped to ultrasound volumes based on the extracted deformation fields. Moreover, this study also demonstrated the ability to simulate electricity activations during the cardiac resynchronization therapy (CRT) process. The proposed method has been validated in two rat hearts and three canine hearts. After MRI/ultrasound image registration, the Dice similarity scores were more than 90% and the corresponding target errors were less than 0.25 mm. This proposed approach can provide cardiac fiber orientations to ultrasound images and can have a variety of potential applications in cardiac imaging.

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

PubMed

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

2012-02-01

A common problem in image-guided radiation therapy (IGRT) of lung cancer as well as other malignant diseases is the compensation of periodic and aperiodic motion during dose delivery. Modern systems for image-guided radiation oncology allow for the acquisition of cone-beam computed tomography data in the treatment room as well as the acquisition of planar radiographs during the treatment. A mid-term research goal is the compensation of tumor target volume motion by 2D/3D Registration. In 2D/3D registration, spatial information on organ location is derived by an iterative comparison of perspective volume renderings, so-called digitally rendered radiographs (DRR) from computed tomography volume data, and planar reference x-rays. Currently, this rendering process is very time consuming, and real-time registration, which should at least provide data on organ position in less than a second, has not come into existence. We present two GPU-based rendering algorithms which generate a DRR of 512×512 pixels size from a CT dataset of 53 MB size at a pace of almost 100 Hz. This rendering rate is feasible by applying a number of algorithmic simplifications which range from alternative volume-driven rendering approaches - namely so-called wobbled splatting - to sub-sampling of the DRR-image by means of specialized raycasting techniques. Furthermore, general purpose graphics processing unit (GPGPU) programming paradigms were consequently utilized. Rendering quality and performance as well as the influence on the quality and performance of the overall registration process were measured and analyzed in detail. The results show that both methods are competitive and pave the way for fast motion compensation by rigid and possibly even non-rigid 2D/3D registration and, beyond that, adaptive filtering of motion models in IGRT. Copyright © 2011. Published by Elsevier GmbH.

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

PubMed Central

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

2012-01-01

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

Evaluation of similarity measures for use in the intensity-based rigid 2D-3D registration for patient positioning in radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu Jian; Kim, Minho; Peters, Jorg

2009-12-15

Purpose: Rigid 2D-3D registration is an alternative to 3D-3D registration for cases where largely bony anatomy can be used for patient positioning in external beam radiation therapy. In this article, the authors evaluated seven similarity measures for use in the intensity-based rigid 2D-3D registration using a variation in Skerl's similarity measure evaluation protocol. Methods: The seven similarity measures are partitioned intensity uniformity, normalized mutual information (NMI), normalized cross correlation (NCC), entropy of the difference image, pattern intensity (PI), gradient correlation (GC), and gradient difference (GD). In contrast to traditional evaluation methods that rely on visual inspection or registration outcomes, themore » similarity measure evaluation protocol probes the transform parameter space and computes a number of similarity measure properties, which is objective and optimization method independent. The variation in protocol offers an improved property in the quantification of the capture range. The authors used this protocol to investigate the effects of the downsampling ratio, the region of interest, and the method of the digitally reconstructed radiograph (DRR) calculation [i.e., the incremental ray-tracing method implemented on a central processing unit (CPU) or the 3D texture rendering method implemented on a graphics processing unit (GPU)] on the performance of the similarity measures. The studies were carried out using both the kilovoltage (kV) and the megavoltage (MV) images of an anthropomorphic cranial phantom and the MV images of a head-and-neck cancer patient. Results: Both the phantom and the patient studies showed the 2D-3D registration using the GPU-based DRR calculation yielded better robustness, while providing similar accuracy compared to the CPU-based calculation. The phantom study using kV imaging suggested that NCC has the best accuracy and robustness, but its slow function value change near the global maximum requires a stricter termination condition for an optimization method. The phantom study using MV imaging indicated that PI, GD, and GC have the best accuracy, while NCC and NMI have the best robustness. The clinical study using MV imaging showed that NCC and NMI have the best robustness. Conclusions: The authors evaluated the performance of seven similarity measures for use in 2D-3D image registration using the variation in Skerl's similarity measure evaluation protocol. The generalized methodology can be used to select the best similarity measures, determine the optimal or near optimal choice of parameter, and choose the appropriate registration strategy for the end user in his specific registration applications in medical imaging.« less

An atlas-based multimodal registration method for 2D images with discrepancy structures.

PubMed

Lv, Wenchao; Chen, Houjin; Peng, Yahui; Li, Yanfeng; Li, Jupeng

2018-06-04

An atlas-based multimodal registration method for 2-dimension images with discrepancy structures was proposed in this paper. Atlas was utilized for complementing the discrepancy structure information in multimodal medical images. The scheme includes three steps: floating image to atlas registration, atlas to reference image registration, and field-based deformation. To evaluate the performance, a frame model, a brain model, and clinical images were employed in registration experiments. We measured the registration performance by the squared sum of intensity differences. Results indicate that this method is robust and performs better than the direct registration for multimodal images with discrepancy structures. We conclude that the proposed method is suitable for multimodal images with discrepancy structures. Graphical Abstract An Atlas-based multimodal registration method schematic diagram.

Cellular neural network-based hybrid approach toward automatic image registration

NASA Astrophysics Data System (ADS)

Arun, Pattathal VijayaKumar; Katiyar, Sunil Kumar

2013-01-01

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

NOTE: Wobbled splatting—a fast perspective volume rendering method for simulation of x-ray images from CT

NASA Astrophysics Data System (ADS)

Birkfellner, Wolfgang; Seemann, Rudolf; Figl, Michael; Hummel, Johann; Ede, Christopher; Homolka, Peter; Yang, Xinhui; Niederer, Peter; Bergmann, Helmar

2005-05-01

3D/2D registration, the automatic assignment of a global rigid-body transformation matching the coordinate systems of patient and preoperative volume scan using projection images, is an important topic in image-guided therapy and radiation oncology. A crucial part of most 3D/2D registration algorithms is the fast computation of digitally rendered radiographs (DRRs) to be compared iteratively to radiographs or portal images. Since registration is an iterative process, fast generation of DRRs—which are perspective summed voxel renderings—is desired. In this note, we present a simple and rapid method for generation of DRRs based on splat rendering. As opposed to conventional splatting, antialiasing of the resulting images is not achieved by means of computing a discrete point spread function (a so-called footprint), but by stochastic distortion of either the voxel positions in the volume scan or by the simulation of a focal spot of the x-ray tube with non-zero diameter. Our method generates slightly blurred DRRs suitable for registration purposes at framerates of approximately 10 Hz when rendering volume images with a size of 30 MB.

Multimodal Image Registration through Simultaneous Segmentation.

PubMed

Aganj, Iman; Fischl, Bruce

2017-11-01

Multimodal image registration facilitates the combination of complementary information from images acquired with different modalities. Most existing methods require computation of the joint histogram of the images, while some perform joint segmentation and registration in alternate iterations. In this work, we introduce a new non-information-theoretical method for pairwise multimodal image registration, in which the error of segmentation - using both images - is considered as the registration cost function. We empirically evaluate our method via rigid registration of multi-contrast brain magnetic resonance images, and demonstrate an often higher registration accuracy in the results produced by the proposed technique, compared to those by several existing methods.

Image registration with uncertainty analysis

DOEpatents

Simonson, Katherine M [Cedar Crest, NM

2011-03-22

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

Spline function approximation techniques for image geometric distortion representation. [for registration of multitemporal remote sensor imagery

NASA Technical Reports Server (NTRS)

Anuta, P. E.

1975-01-01

Least squares approximation techniques were developed for use in computer aided correction of spatial image distortions for registration of multitemporal remote sensor imagery. Polynomials were first used to define image distortion over the entire two dimensional image space. Spline functions were then investigated to determine if the combination of lower order polynomials could approximate a higher order distortion with less computational difficulty. Algorithms for generating approximating functions were developed and applied to the description of image distortion in aircraft multispectral scanner imagery. Other applications of the techniques were suggested for earth resources data processing areas other than geometric distortion representation.

Real-time Enhancement, Registration, and Fusion for an Enhanced Vision System

NASA Technical Reports Server (NTRS)

Hines, Glenn D.; Rahman, Zia-ur; Jobson, Daniel J.; Woodell, Glenn A.

2006-01-01

Over the last few years NASA Langley Research Center (LaRC) has been developing an Enhanced Vision System (EVS) to aid pilots while flying in poor visibility conditions. The EVS captures imagery using two infrared video cameras. The cameras are placed in an enclosure that is mounted and flown forward-looking underneath the NASA LaRC ARIES 757 aircraft. The data streams from the cameras are processed in real-time and displayed on monitors on-board the aircraft. With proper processing the camera system can provide better-than-human-observed imagery particularly during poor visibility conditions. However, to obtain this goal requires several different stages of processing including enhancement, registration, and fusion, and specialized processing hardware for real-time performance. We are using a real-time implementation of the Retinex algorithm for image enhancement, affine transformations for registration, and weighted sums to perform fusion. All of the algorithms are executed on a single TI DM642 digital signal processor (DSP) clocked at 720 MHz. The image processing components were added to the EVS system, tested, and demonstrated during flight tests in August and September of 2005. In this paper we briefly discuss the EVS image processing hardware and algorithms. We then discuss implementation issues and show examples of the results obtained during flight tests.

Multi-system verification of registrations for image-guided radiotherapy in clinical trials.

PubMed

Cui, Yunfeng; Galvin, James M; Straube, William L; Bosch, Walter R; Purdy, James A; Li, X Allen; Xiao, Ying

2011-09-01

To provide quantitative information on the image registration differences from multiple systems for image-guided radiotherapy (IGRT) credentialing and margin reduction in clinical trials. Images and IGRT shift results from three different treatment systems (Tomotherapy Hi-Art, Elekta Synergy, Varian Trilogy) have been sent from various institutions to the Image-Guided Therapy QA Center (ITC) for evaluation for the Radiation Therapy Oncology Group (RTOG) trials. Nine patient datasets (five head-and-neck and four prostate) were included in the comparison, with each patient having 1-4 daily individual IGRT studies. In all cases, daily shifts were re-calculated by re-registration of the planning CT with the daily IGRT data using three independent software systems (MIMvista, FocalSim, VelocityAI). Automatic fusion was used in all calculations. The results were compared with those submitted from institutions. Similar regions of interest (ROIs) and same initial positions were used in registrations for inter-system comparison. Different slice spacings for CBCT sampling and different ROIs for registration were used in some cases to observe the variation of registration due to these factors. For the 54 comparisons with head-and-neck datasets, the absolute values of differences of the registration results between different systems were 2.6±2.1 mm (mean±SD; range 0.1-8.6 mm, left-right [LR]), 1.7±1.3 mm (0.0-4.9 mm, superior-inferior [SI]), and 1.8±1.1 mm (0.1-4.0 mm, anterior-posterior [AP]). For the 66 comparisons in prostate cases, the differences were 1.1±1.0 mm (0.0-4.6 mm, LR), 2.1±1.7 mm (0.0-6.6 mm, SI), and 2.0±1.8 mm (0.1-6.9 mm, AP). The differences caused by the slice spacing variation were relatively small, and the different ROI selections in FocalSim and MIMvista also had limited impact. The extent of differences was reported when different systems were used for image registration. Careful examination and quality assurance of the image registration process are crucial before considering margin reduction using IGRT in clinical trials. Copyright © 2011 Elsevier Inc. All rights reserved.

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

PubMed

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.

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

PubMed

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.

Technical Note: DIRART – A software suite for deformable image registration and adaptive radiotherapy research

PubMed Central

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

2011-01-01

Purpose: Recent years have witnessed tremendous progress in image guide radiotherapy technology and a growing interest in the possibilities for adapting treatment planning and delivery over the course of treatment. One obstacle faced by the research community has been the lack of a comprehensive open-source software toolkit dedicated for adaptive radiotherapy (ART). To address this need, the authors have developed a software suite called the Deformable Image Registration and Adaptive Radiotherapy Toolkit (DIRART). Methods:DIRART is an open-source toolkit developed in MATLAB. It is designed in an object-oriented style with focus on user-friendliness, features, and flexibility. It contains four classes of DIR algorithms, including the newer inverse consistency algorithms to provide consistent displacement vector field in both directions. It also contains common ART functions, an integrated graphical user interface, a variety of visualization and image-processing features, dose metric analysis functions, and interface routines. These interface routines make DIRART a powerful complement to the Computational Environment for Radiotherapy Research (CERR) and popular image-processing toolkits such as ITK. Results: DIRART provides a set of image processing∕registration algorithms and postprocessing functions to facilitate the development and testing of DIR algorithms. It also offers a good amount of options for DIR results visualization, evaluation, and validation. Conclusions: By exchanging data with treatment planning systems via DICOM-RT files and CERR, and by bringing image registration algorithms closer to radiotherapy applications, DIRART is potentially a convenient and flexible platform that may facilitate ART and DIR research. PMID:21361176

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

PubMed

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

2007-10-07

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.

Robust image registration for multiple exposure high dynamic range image synthesis

NASA Astrophysics Data System (ADS)

Yao, Susu

2011-03-01

Image registration is an important preprocessing technique in high dynamic range (HDR) image synthesis. This paper proposed a robust image registration method for aligning a group of low dynamic range images (LDR) that are captured with different exposure times. Illumination change and photometric distortion between two images would result in inaccurate registration. We propose to transform intensity image data into phase congruency to eliminate the effect of the changes in image brightness and use phase cross correlation in the Fourier transform domain to perform image registration. Considering the presence of non-overlapped regions due to photometric distortion, evolutionary programming is applied to search for the accurate translation parameters so that the accuracy of registration is able to be achieved at a hundredth of a pixel level. The proposed algorithm works well for under and over-exposed image registration. It has been applied to align LDR images for synthesizing high quality HDR images..

Tools and Methods for the Registration and Fusion of Remotely Sensed Data

NASA Technical Reports Server (NTRS)

Goshtasby, Arthur Ardeshir; LeMoigne, Jacqueline

2010-01-01

Tools and methods for image registration were reviewed. Methods for the registration of remotely sensed data at NASA were discussed. Image fusion techniques were reviewed. Challenges in registration of remotely sensed data were discussed. Examples of image registration and image fusion were given.

2D-3D rigid registration to compensate for prostate motion during 3D TRUS-guided biopsy.

PubMed

De Silva, Tharindu; Fenster, Aaron; Cool, Derek W; Gardi, Lori; Romagnoli, Cesare; Samarabandu, Jagath; Ward, Aaron D

2013-02-01

Three-dimensional (3D) transrectal ultrasound (TRUS)-guided systems have been developed to improve targeting accuracy during prostate biopsy. However, prostate motion during the procedure is a potential source of error that can cause target misalignments. The authors present an image-based registration technique to compensate for prostate motion by registering the live two-dimensional (2D) TRUS images acquired during the biopsy procedure to a preacquired 3D TRUS image. The registration must be performed both accurately and quickly in order to be useful during the clinical procedure. The authors implemented an intensity-based 2D-3D rigid registration algorithm optimizing the normalized cross-correlation (NCC) metric using Powell's method. The 2D TRUS images acquired during the procedure prior to biopsy gun firing were registered to the baseline 3D TRUS image acquired at the beginning of the procedure. The accuracy was measured by calculating the target registration error (TRE) using manually identified fiducials within the prostate; these fiducials were used for validation only and were not provided as inputs to the registration algorithm. They also evaluated the accuracy when the registrations were performed continuously throughout the biopsy by acquiring and registering live 2D TRUS images every second. This measured the improvement in accuracy resulting from performing the registration, continuously compensating for motion during the procedure. To further validate the method using a more challenging data set, registrations were performed using 3D TRUS images acquired by intentionally exerting different levels of ultrasound probe pressures in order to measure the performance of our algorithm when the prostate tissue was intentionally deformed. In this data set, biopsy scenarios were simulated by extracting 2D frames from the 3D TRUS images and registering them to the baseline 3D image. A graphics processing unit (GPU)-based implementation was used to improve the registration speed. They also studied the correlation between NCC and TREs. The root-mean-square (RMS) TRE of registrations performed prior to biopsy gun firing was found to be 1.87 ± 0.81 mm. This was an improvement over 4.75 ± 2.62 mm before registration. When the registrations were performed every second during the biopsy, the RMS TRE was reduced to 1.63 ± 0.51 mm. For 3D data sets acquired under different probe pressures, the RMS TRE was found to be 3.18 ± 1.6 mm. This was an improvement from 6.89 ± 4.1 mm before registration. With the GPU based implementation, the registrations were performed with a mean time of 1.1 s. The TRE showed a weak correlation with the similarity metric. However, the authors measured a generally convex shape of the metric around the ground truth, which may explain the rapid convergence of their algorithm to accurate results. Registration to compensate for prostate motion during 3D TRUS-guided biopsy can be performed with a measured accuracy of less than 2 mm and a speed of 1.1 s, which is an important step toward improving the targeting accuracy of a 3D TRUS-guided biopsy system.

A Parallel Point Matching Algorithm for Landmark Based Image Registration Using Multicore Platform

PubMed Central

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

2013-01-01

Point matching is crucial for many computer vision applications. Establishing the correspondence between a large number of data points is a computationally intensive process. Some point matching related applications, such as medical image registration, require real time or near real time performance if applied to critical clinical applications like image assisted surgery. In this paper, we report a new multicore platform based parallel algorithm for fast point matching in the context of landmark based medical image registration. We introduced a non-regular data partition algorithm which utilizes the K-means clustering algorithm to group the landmarks based on the number of available processing cores, which optimize the memory usage and data transfer. We have tested our method using the IBM Cell Broadband Engine (Cell/B.E.) platform. The results demonstrated a significant speed up over its sequential implementation. The proposed data partition and parallelization algorithm, though tested only on one multicore platform, is generic by its design. Therefore the parallel algorithm can be extended to other computing platforms, as well as other point matching related applications. PMID:24308014

SU-E-J-97: Evaluation of Multi-Modality (CT/MR/PET) Image Registration Accuracy in Radiotherapy Planning.

PubMed

Sethi, A; Rusu, I; Surucu, M; Halama, J

2012-06-01

Evaluate accuracy of multi-modality image registration in radiotherapy planning process. A water-filled anthropomorphic head phantom containing eight 'donut-shaped' fiducial markers (3 internal + 5 external) was selected for this study. Seven image sets (3CTs, 3MRs and PET) of phantom were acquired and fused in a commercial treatment planning system. First, a narrow slice (0.75mm) baseline CT scan was acquired (CT1). Subsequently, the phantom was re-scanned with a coarse slice width = 1.5mm (CT2) and after subjecting phantom to rotation/displacement (CT3). Next, the phantom was scanned in a 1.5 Tesla MR scanner and three MR image sets (axial T1, axial T2, coronal T1) were acquired at 2mm slice width. Finally, the phantom and center of fiducials were doped with 18F and a PET scan was performed with 2mm cubic voxels. All image scans (CT/MR/PET) were fused to the baseline (CT1) data using automated mutual-information based fusion algorithm. Difference between centroids of fiducial markers in various image modalities was used to assess image registration accuracy. CT/CT image registration was superior to CT/MR and CT/PET: average CT/CT fusion error was found to be 0.64 ± 0.14 mm. Corresponding values for CT/MR and CT/PET fusion were 1.33 ± 0.71mm and 1.11 ± 0.37mm. Internal markers near the center of phantom fused better than external markers placed on the phantom surface. This was particularly true for the CT/MR and CT/PET. The inferior quality of external marker fusion indicates possible distortion effects toward the edges of MR image. Peripheral targets in the PET scan may be subject to parallax error caused by depth of interaction of photons in detectors. Current widespread use of multimodality imaging in radiotherapy planning calls for periodic quality assurance of image registration process. Such studies may help improve safety and accuracy in treatment planning. © 2012 American Association of Physicists in Medicine.

Hardware implementation of hierarchical volume subdivision-based elastic registration.

PubMed

Dandekar, Omkar; Walimbe, Vivek; Shekhar, Raj

2006-01-01

Real-time, elastic and fully automated 3D image registration is critical to the efficiency and effectiveness of many image-guided diagnostic and treatment procedures relying on multimodality image fusion or serial image comparison. True, real-time performance will make many 3D image registration-based techniques clinically viable. Hierarchical volume subdivision-based image registration techniques are inherently faster than most elastic registration techniques, e.g. free-form deformation (FFD)-based techniques, and are more amenable for achieving real-time performance through hardware acceleration. Our group has previously reported an FPGA-based architecture for accelerating FFD-based image registration. In this article we show how our existing architecture can be adapted to support hierarchical volume subdivision-based image registration. A proof-of-concept implementation of the architecture achieved speedups of 100 for elastic registration against an optimized software implementation on a 3.2 GHz Pentium III Xeon workstation. Due to inherent parallel nature of the hierarchical volume subdivision-based image registration techniques further speedup can be achieved by using several computing modules in parallel.

An accelerated image matching technique for UAV orthoimage registration

NASA Astrophysics Data System (ADS)

Tsai, Chung-Hsien; Lin, Yu-Ching

2017-06-01

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

Textured digital elevation model formation from low-cost UAV LADAR/digital image data

NASA Astrophysics Data System (ADS)

Bybee, Taylor C.; Budge, Scott E.

2015-05-01

Textured digital elevation models (TDEMs) have valuable use in precision agriculture, situational awareness, and disaster response. However, scientific-quality models are expensive to obtain using conventional aircraft-based methods. The cost of creating an accurate textured terrain model can be reduced by using a low-cost (<$20k) UAV system fitted with ladar and electro-optical (EO) sensors. A texel camera fuses calibrated ladar and EO data upon simultaneous capture, creating a texel image. This eliminates the problem of fusing the data in a post-processing step and enables both 2D- and 3D-image registration techniques to be used. This paper describes formation of TDEMs using simulated data from a small UAV gathering swaths of texel images of the terrain below. Being a low-cost UAV, only a coarse knowledge of position and attitude is known, and thus both 2D- and 3D-image registration techniques must be used to register adjacent swaths of texel imagery to create a TDEM. The process of creating an aggregate texel image (a TDEM) from many smaller texel image swaths is described. The algorithm is seeded with the rough estimate of position and attitude of each capture. Details such as the required amount of texel image overlap, registration models, simulated flight patterns (level and turbulent), and texture image formation are presented. In addition, examples of such TDEMs are shown and analyzed for accuracy.

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

PubMed

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.

WE-AB-BRA-07: Quantitative Evaluation of 2D-2D and 2D-3D Image Guided Radiation Therapy for Clinical Trial Credentialing, NRG Oncology/RTOG

DOE Office of Scientific and Technical Information (OSTI.GOV)

Giaddui, T; Yu, J; Xiao, Y

Purpose: 2D-2D kV image guided radiation therapy (IGRT) credentialing evaluation for clinical trial qualification was historically qualitative through submitting screen captures of the fusion process. However, as quantitative DICOM 2D-2D and 2D-3D image registration tools are implemented in clinical practice for better precision, especially in centers that treat patients with protons, better IGRT credentialing techniques are needed. The aim of this work is to establish methodologies for quantitatively reviewing IGRT submissions based on DICOM 2D-2D and 2D-3D image registration and to test the methodologies in reviewing 2D-2D and 2D-3D IGRT submissions for RTOG/NRG Oncology clinical trials qualifications. Methods: DICOM 2D-2Dmore » and 2D-3D automated and manual image registration have been tested using the Harmony tool in MIM software. 2D kV orthogonal portal images are fused with the reference digital reconstructed radiographs (DRR) in the 2D-2D registration while the 2D portal images are fused with DICOM planning CT image in the 2D-3D registration. The Harmony tool allows alignment of the two images used in the registration process and also calculates the required shifts. Shifts calculated using MIM are compared with those submitted by institutions for IGRT credentialing. Reported shifts are considered to be acceptable if differences are less than 3mm. Results: Several tests have been performed on the 2D-2D and 2D-3D registration. The results indicated good agreement between submitted and calculated shifts. A workflow for reviewing these IGRT submissions has been developed and will eventually be used to review IGRT submissions. Conclusion: The IROC Philadelphia RTQA center has developed and tested a new workflow for reviewing DICOM 2D-2D and 2D-3D IGRT credentialing submissions made by different cancer clinical centers, especially proton centers. NRG Center for Innovation in Radiation Oncology (CIRO) and IROC RTQA center continue their collaborative efforts to enhance quality assurance services and to be consistently adaptive to the new advances in radiation therapy. This project was supported by NCI grants U10CA180868, U10CA180822, U24CA180803, U24CA12014 and PA CURE Grant.« less

Serial volumetric registration of pulmonary CT studies

NASA Astrophysics Data System (ADS)

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.

Combined registration of 3D tibia and femur implant models in 3D magnetic resonance images

NASA Astrophysics Data System (ADS)

Englmeier, Karl-Hans; Siebert, Markus; von Eisenhart-Rothe, Ruediger; Graichen, Heiko

2008-03-01

The most frequent reasons for revision of total knee arthroplasty are loosening and abnormal axial alignment leading to an unphysiological kinematic of the knee implant. To get an idea about the postoperative kinematic of the implant, it is essential to determine the position and orientation of the tibial and femoral prosthesis. Therefore we developed a registration method for fitting 3D CAD-models of knee joint prostheses into an 3D MR image. This rigid registration is the basis for a quantitative analysis of the kinematics of knee implants. Firstly the surface data of the prostheses models are converted into a voxel representation; a recursive algorithm determines all boundary voxels of the original triangular surface data. Secondly an initial preconfiguration of the implants by the user is still necessary for the following step: The user has to perform a rough preconfiguration of both remaining prostheses models, so that the fine matching process gets a reasonable starting point. After that an automated gradient-based fine matching process determines the best absolute position and orientation: This iterative process changes all 6 parameters (3 rotational- and 3 translational parameters) of a model by a minimal amount until a maximum value of the matching function is reached. To examine the spread of the final solutions of the registration, the interobserver variability was measured in a group of testers. This variability, calculated by the relative standard deviation, improved from about 50% (pure manual registration) to 0.5% (rough manual preconfiguration and subsequent fine registration with the automatic fine matching process).

SU-E-J-88: Deformable Registration Using Multi-Resolution Demons Algorithm for 4DCT.

PubMed

Li, Dengwang; Yin, Yong

2012-06-01

In order to register 4DCT efficiently, we propose an improved deformable registration algorithm based on improved multi-resolution demons strategy to improve the efficiency of the algorithm. 4DCT images of lung cancer patients are collected from a General Electric Discovery ST CT scanner from our cancer hospital. All of the images are sorted into groups and reconstructed according to their phases, and eachrespiratory cycle is divided into 10 phases with the time interval of 10%. Firstly, in our improved demons algorithm we use gradients of both reference and floating images as deformation forces and also redistribute the forces according to the proportion of the two forces. Furthermore, we introduce intermediate variable to cost function for decreasing the noise in registration process. At the same time, Gaussian multi-resolution strategy and BFGS method for optimization are used to improve speed and accuracy of the registration. To validate the performance of the algorithm, we register the previous 10 phase-images. We compared the difference of floating and reference images before and after registered where two landmarks are decided by experienced clinician. We registered 10 phase-images of 4D-CT which is lung cancer patient from cancer hospital and choose images in exhalationas the reference images, and all other images were registered into the reference images. This method has a good accuracy demonstrated by a higher similarity measure for registration of 4D-CT and it can register a large deformation precisely. Finally, we obtain the tumor target achieved by the deformation fields using proposed method, which is more accurately than the internal margin (IM) expanded by the Gross Tumor Volume (GTV). Furthermore, we achieve tumor and normal tissue tracking and dose accumulation using 4DCT data. An efficient deformable registration algorithm was proposed by using multi-resolution demons algorithm for 4DCT. © 2012 American Association of Physicists in Medicine.

A survey of medical image registration - under review.

PubMed

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

2016-10-01

A retrospective view on the past two decades of the field of medical image registration is presented, guided by the article "A survey of medical image registration" (Maintz and Viergever, 1998). It shows that the classification of the field introduced in that article is still usable, although some modifications to do justice to advances in the field would be due. The main changes over the last twenty years are the shift from extrinsic to intrinsic registration, the primacy of intensity-based registration, the breakthrough of nonlinear registration, the progress of inter-subject registration, and the availability of generic image registration software packages. Two problems that were called urgent already 20 years ago, are even more urgent nowadays: Validation of registration methods, and translation of results of image registration research to clinical practice. It may be concluded that the field of medical image registration has evolved, but still is in need of further development in various aspects. Copyright © 2016 Elsevier B.V. All rights reserved.

Concepts for on-board satellite image registration, volume 1

NASA Technical Reports Server (NTRS)

Ruedger, W. H.; Daluge, D. R.; Aanstoos, J. V.

1980-01-01

The NASA-NEEDS program goals present a requirement for on-board signal processing to achieve user-compatible, information-adaptive data acquisition. One very specific area of interest is the preprocessing required to register imaging sensor data which have been distorted by anomalies in subsatellite-point position and/or attitude control. The concepts and considerations involved in using state-of-the-art positioning systems such as the Global Positioning System (GPS) in concert with state-of-the-art attitude stabilization and/or determination systems to provide the required registration accuracy are discussed with emphasis on assessing the accuracy to which a given image picture element can be located and identified, determining those algorithms required to augment the registration procedure and evaluating the technology impact on performing these procedures on-board the satellite.

3D nonrigid registration via optimal mass transport on the GPU.

PubMed

Ur Rehman, Tauseef; Haber, Eldad; Pryor, Gallagher; Melonakos, John; Tannenbaum, Allen

2009-12-01

In this paper, we present a new computationally efficient numerical scheme for the minimizing flow approach for optimal mass transport (OMT) with applications to non-rigid 3D image registration. The approach utilizes all of the gray-scale data in both images, and the optimal mapping from image A to image B is the inverse of the optimal mapping from B to A. Further, no landmarks need to be specified, and the minimizer of the distance functional involved is unique. Our implementation also employs multigrid, and parallel methodologies on a consumer graphics processing unit (GPU) for fast computation. Although computing the optimal map has been shown to be computationally expensive in the past, we show that our approach is orders of magnitude faster then previous work and is capable of finding transport maps with optimality measures (mean curl) previously unattainable by other works (which directly influences the accuracy of registration). We give results where the algorithm was used to compute non-rigid registrations of 3D synthetic data as well as intra-patient pre-operative and post-operative 3D brain MRI datasets.

Facial recognition techniques applied to the automated registration of patients in the emergency treatment of head injuries.

PubMed

Gooroochurn, M; Kerr, D; Bouazza-Marouf, K; Ovinis, M

2011-02-01

This paper describes the development of a registration framework for image-guided solutions to the automation of certain routine neurosurgical procedures. The registration process aligns the pose of the patient in the preoperative space to that of the intraoperative space. Computerized tomography images are used in the preoperative (planning) stage, whilst white light (TV camera) images are used to capture the intraoperative pose. Craniofacial landmarks, rather than artificial markers, are used as the registration basis for the alignment. To create further synergy between the user and the image-guided system, automated methods for extraction of these landmarks have been developed. The results obtained from the application of a polynomial neural network classifier based on Gabor features for the detection and localization of the selected craniofacial landmarks, namely the ear tragus and eye corners in the white light modality are presented. The robustness of the classifier to variations in intensity and noise is analysed. The results show that such a classifier gives good performance for the extraction of craniofacial landmarks.

Registration of Panoramic/Fish-Eye Image Sequence and LiDAR Points Using Skyline Features

PubMed Central

Zhu, Ningning; Jia, Yonghong; Ji, Shunping

2018-01-01

We propose utilizing a rigorous registration model and a skyline-based method for automatic registration of LiDAR points and a sequence of panoramic/fish-eye images in a mobile mapping system (MMS). This method can automatically optimize original registration parameters and avoid the use of manual interventions in control point-based registration methods. First, the rigorous registration model between the LiDAR points and the panoramic/fish-eye image was built. Second, skyline pixels from panoramic/fish-eye images and skyline points from the MMS’s LiDAR points were extracted, relying on the difference in the pixel values and the registration model, respectively. Third, a brute force optimization method was used to search for optimal matching parameters between skyline pixels and skyline points. In the experiments, the original registration method and the control point registration method were used to compare the accuracy of our method with a sequence of panoramic/fish-eye images. The result showed: (1) the panoramic/fish-eye image registration model is effective and can achieve high-precision registration of the image and the MMS’s LiDAR points; (2) the skyline-based registration method can automatically optimize the initial attitude parameters, realizing a high-precision registration of a panoramic/fish-eye image and the MMS’s LiDAR points; and (3) the attitude correction values of the sequences of panoramic/fish-eye images are different, and the values must be solved one by one. PMID:29883431

Automatic parameter selection for feature-based multi-sensor image registration

NASA Astrophysics Data System (ADS)

DelMarco, Stephen; Tom, Victor; Webb, Helen; Chao, Alan

2006-05-01

Accurate image registration is critical for applications such as precision targeting, geo-location, change-detection, surveillance, and remote sensing. However, the increasing volume of image data is exceeding the current capacity of human analysts to perform manual registration. This image data glut necessitates the development of automated approaches to image registration, including algorithm parameter value selection. Proper parameter value selection is crucial to the success of registration techniques. The appropriate algorithm parameters can be highly scene and sensor dependent. Therefore, robust algorithm parameter value selection approaches are a critical component of an end-to-end image registration algorithm. In previous work, we developed a general framework for multisensor image registration which includes feature-based registration approaches. In this work we examine the problem of automated parameter selection. We apply the automated parameter selection approach of Yitzhaky and Peli to select parameters for feature-based registration of multisensor image data. The approach consists of generating multiple feature-detected images by sweeping over parameter combinations and using these images to generate estimated ground truth. The feature-detected images are compared to the estimated ground truth images to generate ROC points associated with each parameter combination. We develop a strategy for selecting the optimal parameter set by choosing the parameter combination corresponding to the optimal ROC point. We present numerical results showing the effectiveness of the approach using registration of collected SAR data to reference EO data.

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

PubMed

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-interventional 2D images.

Improving multispectral satellite image compression using onboard subpixel registration

NASA Astrophysics Data System (ADS)

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

2013-09-01

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

Hierarchical and symmetric infant image registration by robust longitudinal-example-guided correspondence detection

PubMed Central

Wu, Yao; Wu, Guorong; Wang, Li; Munsell, Brent C.; Wang, Qian; Lin, Weili; Feng, Qianjin; Chen, Wufan; Shen, Dinggang

2015-01-01

Purpose: To investigate anatomical differences across individual subjects, or longitudinal changes in early brain development, it is important to perform accurate image registration. However, due to fast brain development and dynamic tissue appearance changes, it is very difficult to align infant brain images acquired from birth to 1-yr-old. Methods: To solve this challenging problem, a novel image registration method is proposed to align two infant brain images, regardless of age at acquisition. The main idea is to utilize the growth trajectories, or spatial-temporal correspondences, learned from a set of longitudinal training images, for guiding the registration of two different time-point images with different image appearances. Specifically, in the training stage, an intrinsic growth trajectory is first estimated for each training subject using the longitudinal images. To register two new infant images with potentially a large age gap, the corresponding images patches between each new image and its respective training images with similar age are identified. Finally, the registration between the two new images can be assisted by the learned growth trajectories from one time point to another time point that have been established in the training stage. To further improve registration accuracy, the proposed method is combined with a hierarchical and symmetric registration framework that can iteratively add new key points in both images to steer the estimation of the deformation between the two infant brain images under registration. Results: To evaluate image registration accuracy, the proposed method is used to align 24 infant subjects at five different time points (2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old). Compared to the state-of-the-art methods, the proposed method demonstrated superior registration performance. Conclusions: The proposed method addresses the difficulties in the infant brain registration and produces better results compared to existing state-of-the-art registration methods. PMID:26133617

Automatic motion correction for in vivo human skin optical coherence tomography angiography through combined rigid and nonrigid registration

NASA Astrophysics Data System (ADS)

Wei, David Wei; Deegan, Anthony J.; Wang, Ruikang K.

2017-06-01

When using optical coherence tomography angiography (OCTA), the development of artifacts due to involuntary movements can severely compromise the visualization and subsequent quantitation of tissue microvasculatures. To correct such an occurrence, we propose a motion compensation method to eliminate artifacts from human skin OCTA by means of step-by-step rigid affine registration, rigid subpixel registration, and nonrigid B-spline registration. To accommodate this remedial process, OCTA is conducted using two matching all-depth volume scans. Affine transformation is first performed on the large vessels of the deep reticular dermis, and then the resulting affine parameters are applied to all-depth vasculatures with a further subpixel registration to refine the alignment between superficial smaller vessels. Finally, the coregistration of both volumes is carried out to result in the final artifact-free composite image via an algorithm based upon cubic B-spline free-form deformation. We demonstrate that the proposed method can provide a considerable improvement to the final en face OCTA images with substantial artifact removal. In addition, the correlation coefficients and peak signal-to-noise ratios of the corrected images are evaluated and compared with those of the original images, further validating the effectiveness of the proposed method. We expect that the proposed method can be useful in improving qualitative and quantitative assessment of the OCTA images of scanned tissue beds.

Automatic motion correction for in vivo human skin optical coherence tomography angiography through combined rigid and nonrigid registration.

PubMed

Wei, David Wei; Deegan, Anthony J; Wang, Ruikang K

2017-06-01

When using optical coherence tomography angiography (OCTA), the development of artifacts due to involuntary movements can severely compromise the visualization and subsequent quantitation of tissue microvasculatures. To correct such an occurrence, we propose a motion compensation method to eliminate artifacts from human skin OCTA by means of step-by-step rigid affine registration, rigid subpixel registration, and nonrigid B-spline registration. To accommodate this remedial process, OCTA is conducted using two matching all-depth volume scans. Affine transformation is first performed on the large vessels of the deep reticular dermis, and then the resulting affine parameters are applied to all-depth vasculatures with a further subpixel registration to refine the alignment between superficial smaller vessels. Finally, the coregistration of both volumes is carried out to result in the final artifact-free composite image via an algorithm based upon cubic B-spline free-form deformation. We demonstrate that the proposed method can provide a considerable improvement to the final en face OCTA images with substantial artifact removal. In addition, the correlation coefficients and peak signal-to-noise ratios of the corrected images are evaluated and compared with those of the original images, further validating the effectiveness of the proposed method. We expect that the proposed method can be useful in improving qualitative and quantitative assessment of the OCTA images of scanned tissue beds.

Pixel Perfect

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perrine, Kenneth A.; Hopkins, Derek F.; Lamarche, Brian L.

2005-09-01

Biologists and computer engineers at Pacific Northwest National Laboratory have specified, designed, and implemented a hardware/software system for performing real-time, multispectral image processing on a confocal microscope. This solution is intended to extend the capabilities of the microscope, enabling scientists to conduct advanced experiments on cell signaling and other kinds of protein interactions. FRET (fluorescence resonance energy transfer) techniques are used to locate and monitor protein activity. In FRET, it is critical that spectral images be precisely aligned with each other despite disturbances in the physical imaging path caused by imperfections in lenses and cameras, and expansion and contraction ofmore » materials due to temperature changes. The central importance of this work is therefore automatic image registration. This runs in a framework that guarantees real-time performance (processing pairs of 1024x1024, 8-bit images at 15 frames per second) and enables the addition of other types of advanced image processing algorithms such as image feature characterization. The supporting system architecture consists of a Visual Basic front-end containing a series of on-screen interfaces for controlling various aspects of the microscope and a script engine for automation. One of the controls is an ActiveX component written in C++ for handling the control and transfer of images. This component interfaces with a pair of LVDS image capture boards and a PCI board containing a 6-million gate Xilinx Virtex-II FPGA. Several types of image processing are performed on the FPGA in a pipelined fashion, including the image registration. The FPGA offloads work that would otherwise need to be performed by the main CPU and has a guaranteed real-time throughput. Image registration is performed in the FPGA by applying a cubic warp on one image to precisely align it with the other image. Before each experiment, an automated calibration procedure is run in order to set up the cubic warp. During image acquisitions, the cubic warp is evaluated by way of forward differencing. Unwanted pixelation artifacts are minimized by bilinear sampling. The resulting system is state-of-the-art for biological imaging. Precisely registered images enable the reliable use of FRET techniques. In addition, real-time image processing performance allows computed images to be fed back and displayed to scientists immediately, and the pipelined nature of the FPGA allows additional image processing algorithms to be incorporated into the system without slowing throughput.« less

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

PubMed

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.

Fundamental limits of image registration performance: Effects of image noise and resolution in CT-guided interventions.

PubMed

Ketcha, M D; de Silva, T; Han, R; Uneri, A; Goerres, J; Jacobson, M; Vogt, S; Kleinszig, G; Siewerdsen, J H

2017-02-11

In image-guided procedures, image acquisition is often performed primarily for the task of geometrically registering information from another image dataset, rather than detection / visualization of a particular feature. While the ability to detect a particular feature in an image has been studied extensively with respect to image quality characteristics (noise, resolution) and is an ongoing, active area of research, comparatively little has been accomplished to relate such image quality characteristics to registration performance. To establish such a framework, we derived Cramer-Rao lower bounds (CRLB) for registration accuracy, revealing the underlying dependencies on image variance and gradient strength. The CRLB was analyzed as a function of image quality factors (in particular, dose) for various similarity metrics and compared to registration accuracy using CT images of an anthropomorphic head phantom at various simulated dose levels. Performance was evaluated in terms of root mean square error (RMSE) of the registration parameters. Analysis of the CRLB shows two primary dependencies: 1) noise variance (related to dose); and 2) sum of squared image gradients (related to spatial resolution and image content). Comparison of the measured RMSE to the CRLB showed that the best registration method, RMSE achieved the CRLB to within an efficiency factor of 0.21, and optimal estimators followed the predicted inverse proportionality between registration performance and radiation dose. Analysis of the CRLB for image registration is an important step toward understanding and evaluating an intraoperative imaging system with respect to a registration task. While the CRLB is optimistic in absolute performance, it reveals a basis for relating the performance of registration estimators as a function of noise content and may be used to guide acquisition parameter selection (e.g., dose) for purposes of intraoperative registration.

Intrasubject multimodal groupwise registration with the conditional template entropy.

PubMed

Polfliet, Mathias; Klein, Stefan; Huizinga, Wyke; Paulides, Margarethus M; Niessen, Wiro J; Vandemeulebroucke, Jef

2018-05-01

Image registration is an important task in medical image analysis. Whereas most methods are designed for the registration of two images (pairwise registration), there is an increasing interest in simultaneously aligning more than two images using groupwise registration. Multimodal registration in a groupwise setting remains difficult, due to the lack of generally applicable similarity metrics. In this work, a novel similarity metric for such groupwise registration problems is proposed. The metric calculates the sum of the conditional entropy between each image in the group and a representative template image constructed iteratively using principal component analysis. The proposed metric is validated in extensive experiments on synthetic and intrasubject clinical image data. These experiments showed equivalent or improved registration accuracy compared to other state-of-the-art (dis)similarity metrics and improved transformation consistency compared to pairwise mutual information. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Multi-Mission Laser Altimeter Data Processing and Co-Registration of Image and Laser Data at DLR

NASA Astrophysics Data System (ADS)

Stark, A.; Matz, K.-D.; Roatsch, T.

2018-04-01

We designed a system for the processing and storage of large laser altimeter data sets for various past and operating laser altimeter instruments. Furthermore, we developed a technique to accurately co-register multi-mission laser and image data.

CT to Cone-beam CT Deformable Registration With Simultaneous Intensity Correction

PubMed Central

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

2012-01-01

Computed tomography (CT) to cone-beam computed tomography (CBCT) deformable image registration (DIR) is a crucial step in adaptive radiation therapy. Current intensity-based registration algorithms, such as demons, may fail in the context of CT-CBCT DIR because of inconsistent intensities between the two modalities. In this paper, we propose a variant of demons, called Deformation with Intensity Simultaneously Corrected (DISC), to deal with CT-CBCT DIR. DISC distinguishes itself from the original demons algorithm by performing an adaptive intensity correction step on the CBCT image at every iteration step of the demons registration. Specifically, the intensity correction of a voxel in CBCT is achieved by matching the first and the second moments of the voxel intensities inside a patch around the voxel with those on the CT image. It is expected that such a strategy can remove artifacts in the CBCT image, as well as ensuring the intensity consistency between the two modalities. DISC is implemented on computer graphics processing units (GPUs) in compute unified device architecture (CUDA) programming environment. The performance of DISC is evaluated on a simulated patient case and six clinical head-and-neck cancer patient data. It is found that DISC is robust against the CBCT artifacts and intensity inconsistency and significantly improves the registration accuracy when compared with the original demons. PMID:23032638

Temporal subtraction contrast-enhanced dedicated breast CT

NASA Astrophysics Data System (ADS)

Gazi, Peymon M.; Aminololama-Shakeri, Shadi; Yang, Kai; Boone, John M.

2016-09-01

The development of a framework of deformable image registration and segmentation for the purpose of temporal subtraction contrast-enhanced breast CT is described. An iterative histogram-based two-means clustering method was used for the segmentation. Dedicated breast CT images were segmented into background (air), adipose, fibroglandular and skin components. Fibroglandular tissue was classified as either normal or contrast-enhanced then divided into tiers for the purpose of categorizing degrees of contrast enhancement. A variant of the Demons deformable registration algorithm, intensity difference adaptive Demons (IDAD), was developed to correct for the large deformation forces that stemmed from contrast enhancement. In this application, the accuracy of the proposed method was evaluated in both mathematically-simulated and physically-acquired phantom images. Clinical usage and accuracy of the temporal subtraction framework was demonstrated using contrast-enhanced breast CT datasets from five patients. Registration performance was quantified using normalized cross correlation (NCC), symmetric uncertainty coefficient, normalized mutual information (NMI), mean square error (MSE) and target registration error (TRE). The proposed method outperformed conventional affine and other Demons variations in contrast enhanced breast CT image registration. In simulation studies, IDAD exhibited improvement in MSE (0-16%), NCC (0-6%), NMI (0-13%) and TRE (0-34%) compared to the conventional Demons approaches, depending on the size and intensity of the enhancing lesion. As lesion size and contrast enhancement levels increased, so did the improvement. The drop in the correlation between the pre- and post-contrast images for the largest enhancement levels in phantom studies is less than 1.2% (150 Hounsfield units). Registration error, measured by TRE, shows only submillimeter mismatches between the concordant anatomical target points in all patient studies. The algorithm was implemented using a parallel processing architecture resulting in rapid execution time for the iterative segmentation and intensity-adaptive registration techniques. Characterization of contrast-enhanced lesions is improved using temporal subtraction contrast-enhanced dedicated breast CT. Adaptation of Demons registration forces as a function of contrast-enhancement levels provided a means to accurately align breast tissue in pre- and post-contrast image acquisitions, improving subtraction results. Spatial subtraction of the aligned images yields useful diagnostic information with respect to enhanced lesion morphology and uptake.

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

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

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

PubMed Central

2010-01-01

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. PMID:20064262

Matching Aerial Images to 3D Building Models Using Context-Based Geometric Hashing

PubMed Central

Jung, Jaewook; Sohn, Gunho; Bang, Kiin; Wichmann, Andreas; Armenakis, Costas; Kada, Martin

2016-01-01

A city is a dynamic entity, which environment is continuously changing over time. Accordingly, its virtual city models also need to be regularly updated to support accurate model-based decisions for various applications, including urban planning, emergency response and autonomous navigation. A concept of continuous city modeling is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. A first critical step for continuous city modeling is to coherently register remotely sensed data taken at different epochs with existing building models. This paper presents a new model-to-image registration method using a context-based geometric hashing (CGH) method to align a single image with existing 3D building models. This model-to-image registration process consists of three steps: (1) feature extraction; (2) similarity measure; and matching, and (3) estimating exterior orientation parameters (EOPs) of a single image. For feature extraction, we propose two types of matching cues: edged corner features representing the saliency of building corner points with associated edges, and contextual relations among the edged corner features within an individual roof. A set of matched corners are found with given proximity measure through geometric hashing, and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on collinearity equations. The result shows that acceptable accuracy of EOPs of a single image can be achievable using the proposed registration approach as an alternative to a labor-intensive manual registration process. PMID:27338410

Enhanced echolocation via robust statistics and super-resolution of sonar images

NASA Astrophysics Data System (ADS)

Kim, Kio

Echolocation is a process in which an animal uses acoustic signals to exchange information with environments. In a recent study, Neretti et al. have shown that the use of robust statistics can significantly improve the resiliency of echolocation against noise and enhance its accuracy by suppressing the development of sidelobes in the processing of an echo signal. In this research, the use of robust statistics is extended to problems in underwater explorations. The dissertation consists of two parts. Part I describes how robust statistics can enhance the identification of target objects, which in this case are cylindrical containers filled with four different liquids. Particularly, this work employs a variation of an existing robust estimator called an L-estimator, which was first suggested by Koenker and Bassett. As pointed out by Au et al.; a 'highlight interval' is an important feature, and it is closely related with many other important features that are known to be crucial for dolphin echolocation. A varied L-estimator described in this text is used to enhance the detection of highlight intervals, which eventually leads to a successful classification of echo signals. Part II extends the problem into 2 dimensions. Thanks to the advances in material and computer technology, various sonar imaging modalities are available on the market. By registering acoustic images from such video sequences, one can extract more information on the region of interest. Computer vision and image processing allowed application of robust statistics to the acoustic images produced by forward looking sonar systems, such as Dual-frequency Identification Sonar and ProViewer. The first use of robust statistics for sonar image enhancement in this text is in image registration. Random Sampling Consensus (RANSAC) is widely used for image registration. The registration algorithm using RANSAC is optimized for sonar image registration, and the performance is studied. The second use of robust statistics is in fusing the images. It is shown that the maximum a posteriori fusion method can be formulated in a Kalman filter-like manner, and also that the resulting expression is identical to a W-estimator with a specific weight function.

MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection

PubMed Central

Ou, Yangming; Resnick, Susan M.; Gur, Ruben C.; Gur, Raquel E.; Satterthwaite, Theodore D.; Furth, Susan; Davatzikos, Christos

2016-01-01

Atlas-based automated anatomical labeling is a fundamental tool in medical image segmentation, as it defines regions of interest for subsequent analysis of structural and functional image data. The extensive investigation of multi-atlas warping and fusion techniques over the past 5 or more years has clearly demonstrated the advantages of consensus-based segmentation. However, the common approach is to use multiple atlases with a single registration method and parameter set, which is not necessarily optimal for every individual scan, anatomical region, and problem/data-type. Different registration criteria and parameter sets yield different solutions, each providing complementary information. Herein, we present a consensus labeling framework that generates a broad ensemble of labeled atlases in target image space via the use of several warping algorithms, regularization parameters, and atlases. The label fusion integrates two complementary sources of information: a local similarity ranking to select locally optimal atlases and a boundary modulation term to refine the segmentation consistently with the target image's intensity profile. The ensemble approach consistently outperforms segmentations using individual warping methods alone, achieving high accuracy on several benchmark datasets. The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results. MUSE is publicly available both as a downloadable software package, and as an application that can be run on the CBICA Image Processing Portal (https://ipp.cbica.upenn.edu), a web based platform for remote processing of medical images. PMID:26679328

Validation of a deformable MRI to CT registration algorithm employing same day planning MRI for surrogate analysis.

PubMed

Padgett, Kyle R; Stoyanova, Radka; Pirozzi, Sara; Johnson, Perry; Piper, Jon; Dogan, Nesrin; Pollack, Alan

2018-03-01

Validating deformable multimodality image registrations is challenging due to intrinsic differences in signal characteristics and their spatial intensity distributions. Evaluating multimodality registrations using these spatial intensity distributions is also complicated by the fact that these metrics are often employed in the registration optimization process. This work evaluates rigid and deformable image registrations of the prostate in between diagnostic-MRI and radiation treatment planning-CT by utilizing a planning-MRI after fiducial marker placement as a surrogate. The surrogate allows for the direct quantitative analysis that can be difficult in the multimodality domain. For thirteen prostate patients, T2 images were acquired at two different time points, the first several weeks prior to planning (diagnostic-MRI) and the second on the same day as the planning-CT (planning-MRI). The diagnostic-MRI was deformed to the planning-CT utilizing a commercially available algorithm which synthesizes a deformable image registration (DIR) algorithm from local rigid registrations. The planning-MRI provided an independent surrogate for the planning-CT for assessing registration accuracy using image similarity metrics, including Pearson correlation and normalized mutual information (NMI). A local analysis was performed by looking only within the prostate, proximal seminal vesicles, penile bulb, and combined areas. The planning-MRI provided an excellent surrogate for the planning-CT with residual error in fiducial alignment between the two datasets being submillimeter, 0.78 mm. DIR was superior to the rigid registration in 11 of 13 cases demonstrating a 27.37% improvement in NMI (P < 0.009) within a regional area surrounding the prostate and associated critical organs. Pearson correlations showed similar results, demonstrating a 13.02% improvement (P < 0.013). By utilizing the planning-MRI as a surrogate for the planning-CT, an independent evaluation of registration accuracy is possible. This population provides an ideal testing ground for MRI to CT DIR by obviating the need for multimodality comparisons which are inherently more challenging. © 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Survey of Non-Rigid Registration Tools in Medicine.

PubMed

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.

Multiscale multimodal fusion of histological and MRI volumes for characterization of lung inflammation

NASA Astrophysics Data System (ADS)

Rusu, Mirabela; Wang, Haibo; Golden, Thea; Gow, Andrew; Madabhushi, Anant

2013-03-01

Mouse lung models facilitate the investigation of conditions such as chronic inflammation which are associated with common lung diseases. The multi-scale manifestation of lung inflammation prompted us to use multi-scale imaging - both in vivo, ex vivo MRI along with ex vivo histology, for its study in a new quantitative way. Some imaging modalities, such as MRI, are non-invasive and capture macroscopic features of the pathology, while others, e.g. ex vivo histology, depict detailed structures. Registering such multi-modal data to the same spatial coordinates will allow the construction of a comprehensive 3D model to enable the multi-scale study of diseases. Moreover, it may facilitate the identification and definition of quantitative of in vivo imaging signatures for diseases and pathologic processes. We introduce a quantitative, image analytic framework to integrate in vivo MR images of the entire mouse with ex vivo histology of the lung alone, using lung ex vivo MRI as conduit to facilitate their co-registration. In our framework, we first align the MR images by registering the in vivo and ex vivo MRI of the lung using an interactive rigid registration approach. Then we reconstruct the 3D volume of the ex vivo histological specimen by efficient group wise registration of the 2D slices. The resulting 3D histologic volume is subsequently registered to the MRI volumes by interactive rigid registration, directly to the ex vivo MRI, and implicitly to in vivo MRI. Qualitative evaluation of the registration framework was performed by comparing airway tree structures in ex vivo MRI and ex vivo histology where airways are visible and may be annotated. We present a use case for evaluation of our co-registration framework in the context of studying chronic inammation in a diseased mouse.

Image registration with auto-mapped control volumes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schreibmann, Eduard; Xing Lei

2006-04-15

Many image registration algorithms rely on the use of homologous control points on the two input image sets to be registered. In reality, the interactive identification of the control points on both images is tedious, difficult, and often a source of error. We propose a two-step algorithm to automatically identify homologous regions that are used as a priori information during the image registration procedure. First, a number of small control volumes having distinct anatomical features are identified on the model image in a somewhat arbitrary fashion. Instead of attempting to find their correspondences in the reference image through user interaction,more » in the proposed method, each of the control regions is mapped to the corresponding part of the reference image by using an automated image registration algorithm. A normalized cross-correlation (NCC) function or mutual information was used as the auto-mapping metric and a limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) was employed to optimize the function to find the optimal mapping. For rigid registration, the transformation parameters of the system are obtained by averaging that derived from the individual control volumes. In our deformable calculation, the mapped control volumes are treated as the nodes or control points with known positions on the two images. If the number of control volumes is not enough to cover the whole image to be registered, additional nodes are placed on the model image and then located on the reference image in a manner similar to the conventional BSpline deformable calculation. For deformable registration, the established correspondence by the auto-mapped control volumes provides valuable guidance for the registration calculation and greatly reduces the dimensionality of the problem. The performance of the two-step registrations was applied to three rigid registration cases (two PET-CT registrations and a brain MRI-CT registration) and one deformable registration of inhale and exhale phases of a lung 4D CT. Algorithm convergence was confirmed by starting the registration calculations from a large number of initial transformation parameters. An accuracy of {approx}2 mm was achieved for both deformable and rigid registration. The proposed image registration method greatly reduces the complexity involved in the determination of homologous control points and allows us to minimize the subjectivity and uncertainty associated with the current manual interactive approach. Patient studies have indicated that the two-step registration technique is fast, reliable, and provides a valuable tool to facilitate both rigid and nonrigid image registrations.« less

Image Registration for Stability Testing of MEMS

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

Automatic intraoperative fiducial-less patient registration using cortical surface

NASA Astrophysics Data System (ADS)

Fan, Xiaoyao; Roberts, David W.; Olson, Jonathan D.; Ji, Songbai; Paulsen, Keith D.

2017-03-01

In image-guided neurosurgery, patient registration is typically performed in the operating room (OR) at the beginning of the procedure to establish the patient-to-image transformation. The accuracy and efficiency of patient registration are crucial as they are associated with surgical outcome, workflow, and healthcare costs. In this paper, we present an automatic fiducial-less patient registration (FLR) by directly registering cortical surface acquired from intraoperative stereovision (iSV) with preoperative MR (pMR) images without incorporating any prior information, and illustrate the method using one patient example. T1-weighted MR images were acquired prior to surgery and the brain was segmented. After dural opening, an image pair of the exposed cortical surface was acquired using an intraoperative stereovision (iSV) system, and a three-dimensional (3D) texture-encoded profile of the cortical surface was reconstructed. The 3D surface was registered with pMR using a multi-start binary registration method to determine the location and orientation of the iSV patch with respect to the segmented brain. A final transformation was calculated to establish the patient-to-MR relationship. The total computational time was 30 min, and can be significantly improved through code optimization, parallel computing, and/or graphical processing unit (GPU) acceleration. The results show that the iSV texture map aligned well with pMR using the FLR transformation, while misalignment was evident with fiducial-based registration (FBR). The difference between FLR and FBR was calculated at the center of craniotomy and the resulting distance was 4.34 mm. The results presented in this paper suggest potential for clinical application in the future.

Increasing feasibility of the field-programmable gate array implementation of an iterative image registration using a kernel-warping algorithm

NASA Astrophysics Data System (ADS)

Nguyen, An Hung; Guillemette, Thomas; Lambert, Andrew J.; Pickering, Mark R.; Garratt, Matthew A.

2017-09-01

Image registration is a fundamental image processing technique. It is used to spatially align two or more images that have been captured at different times, from different sensors, or from different viewpoints. There have been many algorithms proposed for this task. The most common of these being the well-known Lucas-Kanade (LK) and Horn-Schunck approaches. However, the main limitation of these approaches is the computational complexity required to implement the large number of iterations necessary for successful alignment of the images. Previously, a multi-pass image interpolation algorithm (MP-I2A) was developed to considerably reduce the number of iterations required for successful registration compared with the LK algorithm. This paper develops a kernel-warping algorithm (KWA), a modified version of the MP-I2A, which requires fewer iterations to successfully register two images and less memory space for the field-programmable gate array (FPGA) implementation than the MP-I2A. These reductions increase feasibility of the implementation of the proposed algorithm on FPGAs with very limited memory space and other hardware resources. A two-FPGA system rather than single FPGA system is successfully developed to implement the KWA in order to compensate insufficiency of hardware resources supported by one FPGA, and increase parallel processing ability and scalability of the system.

NOTE: Optimization of megavoltage CT scan registration settings for thoracic cases on helical tomotherapy

NASA Astrophysics Data System (ADS)

Woodford, Curtis; Yartsev, Slav; Van Dyk, Jake

2007-08-01

This study aims to investigate the settings that provide optimum registration accuracy when registering megavoltage CT (MVCT) studies acquired on tomotherapy with planning kilovoltage CT (kVCT) studies of patients with lung cancer. For each experiment, the systematic difference between the actual and planned positions of the thorax phantom was determined by setting the phantom up at the planning isocenter, generating and registering an MVCT study. The phantom was translated by 5 or 10 mm, MVCT scanned, and registration was performed again. A root-mean-square equation that calculated the residual error of the registration based on the known shift and systematic difference was used to assess the accuracy of the registration process. The phantom study results for 18 combinations of different MVCT/kVCT registration options are presented and compared to clinical registration data from 17 lung cancer patients. MVCT studies acquired with coarse (6 mm), normal (4 mm) and fine (2 mm) slice spacings could all be registered with similar residual errors. No specific combination of resolution and fusion selection technique resulted in a lower residual error. A scan length of 6 cm with any slice spacing registered with the full image fusion selection technique and fine resolution will result in a low residual error most of the time. On average, large corrections made manually by clinicians to the automatic registration values are infrequent. Small manual corrections within the residual error averages of the registration process occur, but their impact on the average patient position is small. Registrations using the full image fusion selection technique and fine resolution of 6 cm MVCT scans with coarse slices have a low residual error, and this strategy can be clinically used for lung cancer patients treated on tomotherapy. Automatic registration values are accurate on average, and a quick verification on a sagittal MVCT slice should be enough to detect registration outliers.

Deformable registration of CT and cone-beam CT with local intensity matching.

PubMed

Park, Seyoun; Plishker, William; Quon, Harry; Wong, John; Shekhar, Raj; Lee, Junghoon

2017-02-07

Cone-beam CT (CBCT) is a widely used intra-operative imaging modality in image-guided radiotherapy and surgery. A short scan followed by a filtered-backprojection is typically used for CBCT reconstruction. While data on the mid-plane (plane of source-detector rotation) is complete, off-mid-planes undergo different information deficiency and the computed reconstructions are approximate. This causes different reconstruction artifacts at off-mid-planes depending on slice locations, and therefore impedes accurate registration between CT and CBCT. In this paper, we propose a method to accurately register CT and CBCT by iteratively matching local CT and CBCT intensities. We correct CBCT intensities by matching local intensity histograms slice by slice in conjunction with intensity-based deformable registration. The correction-registration steps are repeated in an alternating way until the result image converges. We integrate the intensity matching into three different deformable registration methods, B-spline, demons, and optical flow that are widely used for CT-CBCT registration. All three registration methods were implemented on a graphics processing unit for efficient parallel computation. We tested the proposed methods on twenty five head and neck cancer cases and compared the performance with state-of-the-art registration methods. Normalized cross correlation (NCC), structural similarity index (SSIM), and target registration error (TRE) were computed to evaluate the registration performance. Our method produced overall NCC of 0.96, SSIM of 0.94, and TRE of 2.26 → 2.27 mm, outperforming existing methods by 9%, 12%, and 27%, respectively. Experimental results also show that our method performs consistently and is more accurate than existing algorithms, and also computationally efficient.

Deformable registration of CT and cone-beam CT with local intensity matching

NASA Astrophysics Data System (ADS)

Park, Seyoun; Plishker, William; Quon, Harry; Wong, John; Shekhar, Raj; Lee, Junghoon

2017-02-01

Cone-beam CT (CBCT) is a widely used intra-operative imaging modality in image-guided radiotherapy and surgery. A short scan followed by a filtered-backprojection is typically used for CBCT reconstruction. While data on the mid-plane (plane of source-detector rotation) is complete, off-mid-planes undergo different information deficiency and the computed reconstructions are approximate. This causes different reconstruction artifacts at off-mid-planes depending on slice locations, and therefore impedes accurate registration between CT and CBCT. In this paper, we propose a method to accurately register CT and CBCT by iteratively matching local CT and CBCT intensities. We correct CBCT intensities by matching local intensity histograms slice by slice in conjunction with intensity-based deformable registration. The correction-registration steps are repeated in an alternating way until the result image converges. We integrate the intensity matching into three different deformable registration methods, B-spline, demons, and optical flow that are widely used for CT-CBCT registration. All three registration methods were implemented on a graphics processing unit for efficient parallel computation. We tested the proposed methods on twenty five head and neck cancer cases and compared the performance with state-of-the-art registration methods. Normalized cross correlation (NCC), structural similarity index (SSIM), and target registration error (TRE) were computed to evaluate the registration performance. Our method produced overall NCC of 0.96, SSIM of 0.94, and TRE of 2.26 → 2.27 mm, outperforming existing methods by 9%, 12%, and 27%, respectively. Experimental results also show that our method performs consistently and is more accurate than existing algorithms, and also computationally efficient.

3D medical volume reconstruction using web services.

PubMed

Kooper, Rob; Shirk, Andrew; Lee, Sang-Chul; Lin, Amy; Folberg, Robert; Bajcsy, Peter

2008-04-01

We address the problem of 3D medical volume reconstruction using web services. The use of proposed web services is motivated by the fact that the problem of 3D medical volume reconstruction requires significant computer resources and human expertise in medical and computer science areas. Web services are implemented as an additional layer to a dataflow framework called data to knowledge. In the collaboration between UIC and NCSA, pre-processed input images at NCSA are made accessible to medical collaborators for registration. Every time UIC medical collaborators inspected images and selected corresponding features for registration, the web service at NCSA is contacted and the registration processing query is executed using the image to knowledge library of registration methods. Co-registered frames are returned for verification by medical collaborators in a new window. In this paper, we present 3D volume reconstruction problem requirements and the architecture of the developed prototype system at http://isda.ncsa.uiuc.edu/MedVolume. We also explain the tradeoffs of our system design and provide experimental data to support our system implementation. The prototype system has been used for multiple 3D volume reconstructions of blood vessels and vasculogenic mimicry patterns in histological sections of uveal melanoma studied by fluorescent confocal laser scanning microscope.

A combined registration and finite element analysis method for fast estimation of intraoperative brain shift; phantom and animal model study.

PubMed

Mohammadi, Amrollah; Ahmadian, Alireza; Rabbani, Shahram; Fattahi, Ehsan; Shirani, Shapour

2017-12-01

Finite element models for estimation of intraoperative brain shift suffer from huge computational cost. In these models, image registration and finite element analysis are two time-consuming processes. The proposed method is an improved version of our previously developed Finite Element Drift (FED) registration algorithm. In this work the registration process is combined with the finite element analysis. In the Combined FED (CFED), the deformation of whole brain mesh is iteratively calculated by geometrical extension of a local load vector which is computed by FED. While the processing time of the FED-based method including registration and finite element analysis was about 70 s, the computation time of the CFED was about 3.2 s. The computational cost of CFED is almost 50% less than similar state of the art brain shift estimators based on finite element models. The proposed combination of registration and structural analysis can make the calculation of brain deformation much faster. Copyright © 2016 John Wiley & Sons, Ltd.

SU-E-J-29: Automatic Image Registration Performance of Three IGRT Systems for Prostate Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barber, J; University of Sydney, Sydney, NSW; Sykes, J

Purpose: To compare the performance of an automatic image registration algorithm on image sets collected on three commercial image guidance systems, and explore its relationship with imaging parameters such as dose and sharpness. Methods: Images of a CIRS Virtually Human Male Pelvis phantom (VHMP) were collected on the CBCT systems of Varian TrueBeam/OBI and Elekta Synergy/XVI linear accelerators, across a range of mAs settings; and MVCT on a Tomotherapy Hi-ART accelerator with a range of pitch. Using the 6D correlation ratio algorithm of XVI, each image was registered to a mask of the prostate volume with a 5 mm expansion.more » Registrations were repeated 100 times, with random initial offsets introduced to simulate daily matching. Residual registration errors were calculated by correcting for the initial phantom set-up error. Automatic registration was also repeated after reconstructing images with different sharpness filters. Results: All three systems showed good registration performance, with residual translations <0.5mm (1σ) for typical clinical dose and reconstruction settings. Residual rotational error had larger range, with 0.8°, 1.2° and 1.9° for 1σ in XVI, OBI and Tomotherapy respectively. The registration accuracy of XVI images showed a strong dependence on imaging dose, particularly below 4mGy. No evidence of reduced performance was observed at the lowest dose settings for OBI and Tomotherapy, but these were above 4mGy. Registration failures (maximum target registration error > 3.6 mm on the surface of a 30mm sphere) occurred in 5% to 10% of registrations. Changing the sharpness of image reconstruction had no significant effect on registration performance. Conclusions: Using the present automatic image registration algorithm, all IGRT systems tested provided satisfactory registrations for clinical use, within a normal range of acquisition settings.« less

Unwarping confocal microscopy images of bee brains by nonrigid registration to a magnetic resonance microscopy image.

PubMed

Rohlfing, Torsten; Schaupp, Frank; Haddad, Daniel; Brandt, Robert; Haase, Axel; Menzel, Randolf; Maurer, Calvin R

2005-01-01

Confocal microscopy (CM) is a powerful image acquisition technique that is well established in many biological applications. It provides 3-D acquisition with high spatial resolution and can acquire several different channels of complementary image information. Due to the specimen extraction and preparation process, however, the shapes of imaged objects may differ considerably from their in vivo appearance. Magnetic resonance microscopy (MRM) is an evolving variant of magnetic resonance imaging, which achieves microscopic resolutions using a high magnetic field and strong magnetic gradients. Compared to CM imaging, MRM allows for in situ imaging and is virtually free of geometrical distortions. We propose to combine the advantages of both methods by unwarping CM images using a MRM reference image. Our method incorporates a sequence of image processing operators applied to the MRM image, followed by a two-stage intensity-based registration to compute a nonrigid coordinate transformation between the CM images and the MRM image. We present results obtained using CM images from the brains of 20 honey bees and a MRM image of an in situ bee brain. Copyright 2005 Society of Photo-Optical Instrumentation Engineers.

Comparison of subpixel image registration algorithms

NASA Astrophysics Data System (ADS)

Boye, R. R.; Nelson, C. L.

2009-02-01

Research into the use of multiframe superresolution has led to the development of algorithms for providing images with enhanced resolution using several lower resolution copies. An integral component of these algorithms is the determination of the registration of each of the low resolution images to a reference image. Without this information, no resolution enhancement can be attained. We have endeavored to find a suitable method for registering severely undersampled images by comparing several approaches. To test the algorithms, an ideal image is input to a simulated image formation program, creating several undersampled images with known geometric transformations. The registration algorithms are then applied to the set of low resolution images and the estimated registration parameters compared to the actual values. This investigation is limited to monochromatic images (extension to color images is not difficult) and only considers global geometric transformations. Each registration approach will be reviewed and evaluated with respect to the accuracy of the estimated registration parameters as well as the computational complexity required. In addition, the effects of image content, specifically spatial frequency content, as well as the immunity of the registration algorithms to noise will be discussed.

A software tool of digital tomosynthesis application for patient positioning in radiotherapy

PubMed Central

Dai, Jian‐Rong

2016-01-01

Digital Tomosynthesis (DTS) is an image modality in reconstructing tomographic images from two‐dimensional kV projections covering a narrow scan angles. Comparing with conventional cone‐beam CT (CBCT), it requires less time and radiation dose in data acquisition. It is feasible to apply this technique in patient positioning in radiotherapy. To facilitate its clinical application, a software tool was developed and the reconstruction processes were accelerated by graphic processing unit (GPU). Two reconstruction and two registration processes are required for DTS application which is different from conventional CBCT application which requires one image reconstruction process and one image registration process. The reconstruction stage consists of productions of two types of DTS. One type of DTS is reconstructed from cone‐beam (CB) projections covering a narrow scan angle and is named onboard DTS (ODTS), which represents the real patient position in treatment room. Another type of DTS is reconstructed from digitally reconstructed radiography (DRR) and is named reference DTS (RDTS), which represents the ideal patient position in treatment room. Prior to the reconstruction of RDTS, The DRRs are reconstructed from planning CT using the same acquisition setting of CB projections. The registration stage consists of two matching processes between ODTS and RDTS. The target shift in lateral and longitudinal axes are obtained from the matching between ODTS and RDTS in coronal view, while the target shift in longitudinal and vertical axes are obtained from the matching between ODTS and RDTS in sagittal view. In this software, both DRR and DTS reconstruction algorithms were implemented on GPU environments for acceleration purpose. The comprehensive evaluation of this software tool was performed including geometric accuracy, image quality, registration accuracy, and reconstruction efficiency. The average correlation coefficient between DRR/DTS generated by GPU‐based algorithm and CPU‐based algorithm is 0.99. Based on the measurements of cube phantom on DTS, the geometric errors are within 0.5 mm in three axes. For both cube phantom and pelvic phantom, the registration errors are within 0.5 mm in three axes. Compared with reconstruction performance of CPU‐based algorithms, the performances of DRR and DTS reconstructions are improved by a factor of 15 to 20. A GPU‐based software tool was developed for DTS application for patient positioning of radiotherapy. The geometric and registration accuracy met the clinical requirement in patient setup of radiotherapy. The high performance of DRR and DTS reconstruction algorithms was achieved by the GPU‐based computation environments. It is a useful software tool for researcher and clinician in evaluating DTS application in patient positioning of radiotherapy. PACS number(s): 87.57.nf PMID:27074482

Medical imaging and registration in computer assisted surgery.

PubMed

Simon, D A; Lavallée, S

1998-09-01

Imaging, sensing, and computing technologies that are being introduced to aid in the planning and execution of surgical procedures are providing orthopaedic surgeons with a powerful new set of tools for improving clinical accuracy, reliability, and patient outcomes while reducing costs and operating times. Current computer assisted surgery systems typically include a measurement process for collecting patient specific medical data, a decision making process for generating a surgical plan, a registration process for aligning the surgical plan to the patient, and an action process for accurately achieving the goals specified in the plan. Some of the key concepts in computer assisted surgery applied to orthopaedics with a focus on the basic framework and underlying technologies is outlined. In addition, technical challenges and future trends in the field are discussed.

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

PubMed

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

2016-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Scalable Joint Segmentation and Registration Framework for Infant Brain Images.

PubMed

Dong, Pei; Wang, Li; Lin, Weili; Shen, Dinggang; Wu, Guorong

2017-03-15

The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study.

Space Imagery Enhancement Investigations; Software for Processing Middle Atmosphere Data

DTIC Science & Technology

2011-12-19

SUPPLEMENTARY NOTES 14. ABSTRACT This report summarizes work related to optical superresolution for the ideal incoherent 1D spread function...optical superresolution , incoherent image eigensystem, image registration, multi-frame image reconstruction, deconvolution 16. SECURITY... Superresolution -Related Investigations ............................................................................. 1 2.2.1 Eigensystem Formulations

Image Registration: A Necessary Evil

NASA Technical Reports Server (NTRS)

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

1995-01-01

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

Enhancing hyperspectral spatial resolution using multispectral image fusion: A wavelet approach

NASA Astrophysics Data System (ADS)

Jazaeri, Amin

High spectral and spatial resolution images have a significant impact in remote sensing applications. Because both spatial and spectral resolutions of spaceborne sensors are fixed by design and it is not possible to further increase the spatial or spectral resolution, techniques such as image fusion must be applied to achieve such goals. This dissertation introduces the concept of wavelet fusion between hyperspectral and multispectral sensors in order to enhance the spectral and spatial resolution of a hyperspectral image. To test the robustness of this concept, images from Hyperion (hyperspectral sensor) and Advanced Land Imager (multispectral sensor) were first co-registered and then fused using different wavelet algorithms. A regression-based fusion algorithm was also implemented for comparison purposes. The results show that the fused images using a combined bi-linear wavelet-regression algorithm have less error than other methods when compared to the ground truth. In addition, a combined regression-wavelet algorithm shows more immunity to misalignment of the pixels due to the lack of proper registration. The quantitative measures of average mean square error show that the performance of wavelet-based methods degrades when the spatial resolution of hyperspectral images becomes eight times less than its corresponding multispectral image. Regardless of what method of fusion is utilized, the main challenge in image fusion is image registration, which is also a very time intensive process. Because the combined regression wavelet technique is computationally expensive, a hybrid technique based on regression and wavelet methods was also implemented to decrease computational overhead. However, the gain in faster computation was offset by the introduction of more error in the outcome. The secondary objective of this dissertation is to examine the feasibility and sensor requirements for image fusion for future NASA missions in order to be able to perform onboard image fusion. In this process, the main challenge of image registration was resolved by registering the input images using transformation matrices of previously acquired data. The composite image resulted from the fusion process remarkably matched the ground truth, indicating the possibility of real time onboard fusion processing.

Registration of T2-weighted and diffusion-weighted MR images of the prostate: comparison between manual and landmark-based methods

NASA Astrophysics Data System (ADS)

Peng, Yahui; Jiang, Yulei; Soylu, Fatma N.; Tomek, Mark; Sensakovic, William; Oto, Aytekin

2012-02-01

Quantitative analysis of multi-parametric magnetic resonance (MR) images of the prostate, including T2-weighted (T2w) and diffusion-weighted (DW) images, requires accurate image registration. We compared two registration methods between T2w and DW images. We collected pre-operative MR images of 124 prostate cancer patients (68 patients scanned with a GE scanner and 56 with Philips scanners). A landmark-based rigid registration was done based on six prostate landmarks in both T2w and DW images identified by a radiologist. Independently, a researcher manually registered the same images. A radiologist visually evaluated the registration results by using a 5-point ordinal scale of 1 (worst) to 5 (best). The Wilcoxon signed-rank test was used to determine whether the radiologist's ratings of the results of the two registration methods were significantly different. Results demonstrated that both methods were accurate: the average ratings were 4.2, 3.3, and 3.8 for GE, Philips, and all images, respectively, for the landmark-based method; and 4.6, 3.7, and 4.2, respectively, for the manual method. The manual registration results were more accurate than the landmark-based registration results (p < 0.0001 for GE, Philips, and all images). Therefore, the manual method produces more accurate registration between T2w and DW images than the landmark-based method.

Exploration of mineral resource deposits based on analysis of aerial and satellite image data employing artificial intelligence methods

NASA Astrophysics Data System (ADS)

Osipov, Gennady

2013-04-01

We propose a solution to the problem of exploration of various mineral resource deposits, determination of their forms / classification of types (oil, gas, minerals, gold, etc.) with the help of satellite photography of the region of interest. Images received from satellite are processed and analyzed to reveal the presence of specific signs of deposits of various minerals. Course of data processing and making forecast can be divided into some stages: Pre-processing of images. Normalization of color and luminosity characteristics, determination of the necessary contrast level and integration of a great number of separate photos into a single map of the region are performed. Construction of semantic map image. Recognition of bitmapped image and allocation of objects and primitives known to system are realized. Intelligent analysis. At this stage acquired information is analyzed with the help of a knowledge base, which contain so-called "attention landscapes" of experts. Used methods of recognition and identification of images: a) combined method of image recognition, b)semantic analysis of posterized images, c) reconstruction of three-dimensional objects from bitmapped images, d)cognitive technology of processing and interpretation of images. This stage is fundamentally new and it distinguishes suggested technology from all others. Automatic registration of allocation of experts` attention - registration of so-called "attention landscape" of experts - is the base of the technology. Landscapes of attention are, essentially, highly effective filters that cut off unnecessary information and emphasize exactly the factors used by an expert for making a decision. The technology based on denoted principles involves the next stages, which are implemented in corresponding program agents. Training mode -> Creation of base of ophthalmologic images (OI) -> Processing and making generalized OI (GOI) -> Mode of recognition and interpretation of unknown images. Training mode includes noncontact registration of eye motion, reconstruction of "attention landscape" fixed by the expert, recording the comments of the expert who is a specialist in the field of images` interpretation, and transfer this information into knowledge base.Creation of base of ophthalmologic images (OI) includes making semantic contacts from great number of OI based on analysis of OI and expert's comments.Processing of OI and making generalized OI (GOI) is realized by inductive logic algorithms and consists in synthesis of structural invariants of OI. The mode of recognition and interpretation of unknown images consists of several stages, which include: comparison of unknown image with the base of structural invariants of OI; revealing of structural invariants in unknown images; ynthesis of interpretive message of the structural invariants base and OI base (the experts` comments stored in it). We want to emphasize that the training mode does not assume special involvement of experts to teach the system - it is realized in the process of regular experts` work on image interpretation and it becomes possible after installation of a special apparatus for non contact registration of experts` attention. Consequently, the technology, which principles is described there, provides fundamentally new effective solution to the problem of exploration of mineral resource deposits based on computer analysis of aerial and satellite image data.

SU-F-J-180: A Reference Data Set for Testing Two Dimension Registration Algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dankwa, A; Castillo, E; Guerrero, T

Purpose: To create and characterize a reference data set for testing image registration algorithms that transform portal image (PI) to digitally reconstructed radiograph (DRR). Methods: Anterior-posterior (AP) and Lateral (LAT) projection and DRR image pairs from nine cases representing four different anatomical sites (head and neck, thoracic, abdominal, and pelvis) were selected for this study. Five experts will perform manual registration by placing landmarks points (LMPs) on the DRR and finding their corresponding points on the PI using computer assisted manual point selection tool (CAMPST), a custom-made MATLAB software tool developed in house. The landmark selection process will be repeatedmore » on both the PI and the DRR in order to characterize inter- and -intra observer variations associated with the point selection process. Inter and an intra observer variation in LMPs was done using Bland-Altman (B&A) analysis and one-way analysis of variance. We set our limit such that the absolute value of the mean difference between the readings should not exceed 3mm. Later on in this project we will test different two dimension (2D) image registration algorithms and quantify the uncertainty associated with their registration. Results: Using one-way analysis of variance (ANOVA) there was no variations within the readers. When Bland-Altman analysis was used the variation within the readers was acceptable. The variation was higher in the PI compared to the DRR.ConclusionThe variation seen for the PI is because although the PI has a much better spatial resolution the poor resolution on the DRR makes it difficult to locate the actual corresponding anatomical feature on the PI. We hope this becomes more evident when all the readers complete the point selection. The reason for quantifying inter- and -intra observer variation tells us to what degree of accuracy a manual registration can be done. Research supported by William Beaumont Hospital Research Start Up Fund.« less

Evaluation of mathematical algorithms for automatic patient alignment in radiosurgery.

PubMed

Williams, Kenneth M; Schulte, Reinhard W; Schubert, Keith E; Wroe, Andrew J

2015-06-01

Image registration techniques based on anatomical features can serve to automate patient alignment for intracranial radiosurgery procedures in an effort to improve the accuracy and efficiency of the alignment process as well as potentially eliminate the need for implanted fiducial markers. To explore this option, four two-dimensional (2D) image registration algorithms were analyzed: the phase correlation technique, mutual information (MI) maximization, enhanced correlation coefficient (ECC) maximization, and the iterative closest point (ICP) algorithm. Digitally reconstructed radiographs from the treatment planning computed tomography scan of a human skull were used as the reference images, while orthogonal digital x-ray images taken in the treatment room were used as the captured images to be aligned. The accuracy of aligning the skull with each algorithm was compared to the alignment of the currently practiced procedure, which is based on a manual process of selecting common landmarks, including implanted fiducials and anatomical skull features. Of the four algorithms, three (phase correlation, MI maximization, and ECC maximization) demonstrated clinically adequate (ie, comparable to the standard alignment technique) translational accuracy and improvements in speed compared to the interactive, user-guided technique; however, the ICP algorithm failed to give clinically acceptable results. The results of this work suggest that a combination of different algorithms may provide the best registration results. This research serves as the initial groundwork for the translation of automated, anatomy-based 2D algorithms into a real-world system for 2D-to-2D image registration and alignment for intracranial radiosurgery. This may obviate the need for invasive implantation of fiducial markers into the skull and may improve treatment room efficiency and accuracy. © The Author(s) 2014.

Sentinel 2 global reference image

NASA Astrophysics Data System (ADS)

Dechoz, C.; Poulain, V.; Massera, S.; Languille, F.; Greslou, D.; de Lussy, F.; Gaudel, A.; L'Helguen, C.; Picard, C.; Trémas, T.

2015-10-01

Sentinel-2 is a multispectral, high-resolution, optical imaging mission, developed by the European Space Agency (ESA) in the frame of the Copernicus program of the European Commission. In cooperation with ESA, the Centre National d'Etudes Spatiales (CNES) is responsible for the image quality of the project, and will ensure the CAL/VAL commissioning phase. Sentinel-2 mission is devoted the operational monitoring of land and coastal areas, and will provide a continuity of SPOT- and Landsat-type data. Sentinel-2 will also deliver information for emergency services. Launched in 2015 and 2016, there will be a constellation of 2 satellites on a polar sun-synchronous orbit, imaging systematically terrestrial surfaces with a revisit time of 5 days, in 13 spectral bands in visible and shortwave infra-red. Therefore, multi-temporal series of images, taken under the same viewing conditions, will be available. So as to ensure for the multi-temporal registration of the products, specified to be better than 0.3 pixels at 2σ, a Global Reference Image (GRI) will be produced during the CAL/VAL period. This GRI is composed of a set of Sentinel-2 acquisitions, which geometry has been corrected by bundle block adjustment. During L1B processing, Ground Control Points will be taken between this reference image and the sentinel-2 acquisition processed and the geometric model of the image corrected, so as to ensure the good multi-temporal registration. This paper first details the production of the reference during the CALVAL period, and then details the qualification and geolocation performance assessment of the GRI. It finally presents its use in the Level-1 processing chain and gives a first assessment of the multi-temporal registration.

Lesion registration for longitudinal disease tracking in an imaging informatics-based multiple sclerosis eFolder

NASA Astrophysics Data System (ADS)

Ma, Kevin; Liu, Joseph; Zhang, Xuejun; Lerner, Alex; Shiroishi, Mark; Amezcua, Lilyana; Liu, Brent

2016-03-01

We have designed and developed a multiple sclerosis eFolder system for patient data storage, image viewing, and automatic lesion quantification results stored in DICOM-SR format. The web-based system aims to be integrated in DICOM-compliant clinical and research environments to aid clinicians in patient treatments and data analysis. The system needs to quantify lesion volumes, identify and register lesion locations to track shifts in volume and quantity of lesions in a longitudinal study. In order to perform lesion registration, we have developed a brain warping and normalizing methodology using Statistical Parametric Mapping (SPM) MATLAB toolkit for brain MRI. Patients' brain MR images are processed via SPM's normalization processes, and the brain images are analyzed and warped according to the tissue probability map. Lesion identification and contouring are completed by neuroradiologists, and lesion volume quantification is completed by the eFolder's CAD program. Lesion comparison results in longitudinal studies show key growth and active regions. The results display successful lesion registration and tracking over a longitudinal study. Lesion change results are graphically represented in the web-based user interface, and users are able to correlate patient progress and changes in the MRI images. The completed lesion and disease tracking tool would enable the eFolder to provide complete patient profiles, improve the efficiency of patient care, and perform comprehensive data analysis through an integrated imaging informatics system.

Multimodal Registration of White Matter Brain Data via Optimal Mass Transport.

PubMed

Rehman, Tauseefur; Haber, Eldad; Pohl, Kilian M; Haker, Steven; Halle, Mike; Talos, Florin; Wald, Lawrence L; Kikinis, Ron; Tannenbaum, Allen

2008-09-01

The elastic registration of medical scans from different acquisition sequences is becoming an important topic for many research labs that would like to continue the post-processing of medical scans acquired via the new generation of high-field-strength scanners. In this note, we present a parameter-free registration algorithm that is well suited for this scenario as it requires no tuning to specific acquisition sequences. The algorithm encompasses a new numerical scheme for computing elastic registration maps based on the minimizing flow approach to optimal mass transport. The approach utilizes all of the gray-scale data in both images, and the optimal mapping from image A to image B is the inverse of the optimal mapping from B to A . Further, no landmarks need to be specified, and the minimizer of the distance functional involved is unique. We apply the algorithm to register the white matter folds of two different scans and use the results to parcellate the cortex of the target image. To the best of our knowledge, this is the first time that the optimal mass transport function has been applied to register large 3D multimodal data sets.

Multimodal Registration of White Matter Brain Data via Optimal Mass Transport

PubMed Central

Rehman, Tauseefur; Haber, Eldad; Pohl, Kilian M.; Haker, Steven; Halle, Mike; Talos, Florin; Wald, Lawrence L.; Kikinis, Ron; Tannenbaum, Allen

2017-01-01

The elastic registration of medical scans from different acquisition sequences is becoming an important topic for many research labs that would like to continue the post-processing of medical scans acquired via the new generation of high-field-strength scanners. In this note, we present a parameter-free registration algorithm that is well suited for this scenario as it requires no tuning to specific acquisition sequences. The algorithm encompasses a new numerical scheme for computing elastic registration maps based on the minimizing flow approach to optimal mass transport. The approach utilizes all of the gray-scale data in both images, and the optimal mapping from image A to image B is the inverse of the optimal mapping from B to A. Further, no landmarks need to be specified, and the minimizer of the distance functional involved is unique. We apply the algorithm to register the white matter folds of two different scans and use the results to parcellate the cortex of the target image. To the best of our knowledge, this is the first time that the optimal mass transport function has been applied to register large 3D multimodal data sets. PMID:28626844

Thermal imaging as a biometrics approach to facial signature authentication.

PubMed

Guzman, A M; Goryawala, M; Wang, Jin; Barreto, A; Andrian, J; Rishe, N; Adjouadi, M

2013-01-01

A new thermal imaging framework with unique feature extraction and similarity measurements for face recognition is presented. The research premise is to design specialized algorithms that would extract vasculature information, create a thermal facial signature and identify the individual. The proposed algorithm is fully integrated and consolidates the critical steps of feature extraction through the use of morphological operators, registration using the Linear Image Registration Tool and matching through unique similarity measures designed for this task. The novel approach at developing a thermal signature template using four images taken at various instants of time ensured that unforeseen changes in the vasculature over time did not affect the biometric matching process as the authentication process relied only on consistent thermal features. Thirteen subjects were used for testing the developed technique on an in-house thermal imaging system. The matching using the similarity measures showed an average accuracy of 88.46% for skeletonized signatures and 90.39% for anisotropically diffused signatures. The highly accurate results obtained in the matching process clearly demonstrate the ability of the thermal infrared system to extend in application to other thermal imaging based systems. Empirical results applying this approach to an existing database of thermal images proves this assertion.

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

PubMed

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.

Automatic selection of landmarks in T1-weighted head MRI with regression forests for image registration initialization

NASA Astrophysics Data System (ADS)

Wang, Jianing; Liu, Yuan; Noble, Jack H.; Dawant, Benoit M.

2017-02-01

Medical image registration establishes a correspondence between images of biological structures and it is at the core of many applications. Commonly used deformable image registration methods are dependent on a good preregistration initialization. The initialization can be performed by localizing homologous landmarks and calculating a point-based transformation between the images. The selection of landmarks is however important. In this work, we present a learning-based method to automatically find a set of robust landmarks in 3D MR image volumes of the head to initialize non-rigid transformations. To validate our method, these selected landmarks are localized in unknown image volumes and they are used to compute a smoothing thin-plate splines transformation that registers the atlas to the volumes. The transformed atlas image is then used as the preregistration initialization of an intensity-based non-rigid registration algorithm. We show that the registration accuracy of this algorithm is statistically significantly improved when using the presented registration initialization over a standard intensity-based affine registration.

Assessing the intrinsic precision of 3D/3D rigid image registration results for patient setup in the absence of a ground truth.

PubMed

Wu, Jian; Murphy, Martin J

2010-06-01

To assess the precision and robustness of patient setup corrections computed from 3D/3D rigid registration methods using image intensity, when no ground truth validation is possible. Fifteen pairs of male pelvic CTs were rigidly registered using four different in-house registration methods. Registration results were compared for different resolutions and image content by varying the image down-sampling ratio and by thresholding out soft tissue to isolate bony landmarks. Intrinsic registration precision was investigated by comparing the different methods and by reversing the source and the target roles of the two images being registered. The translational reversibility errors for successful registrations ranged from 0.0 to 1.69 mm. Rotations were less than 1 degrees. Mutual information failed in most registrations that used only bony landmarks. The magnitude of the reversibility error was strongly correlated with the success/ failure of each algorithm to find the global minimum. Rigid image registrations have an intrinsic uncertainty and robustness that depends on the imaging modality, the registration algorithm, the image resolution, and the image content. In the absence of an absolute ground truth, the variation in the shifts calculated by several different methods provides a useful estimate of that uncertainty. The difference observed by reversing the source and target images can be used as an indication of robust convergence.

Image Registration to Compensate for EPI Distortion in Patients with Brain Tumors: An Evaluation of Tract-Specific Effects.

PubMed

Albi, Angela; Meola, Antonio; Zhang, Fan; Kahali, Pegah; Rigolo, Laura; Tax, Chantal M W; Ciris, Pelin Aksit; Essayed, Walid I; Unadkat, Prashin; Norton, Isaiah; Rathi, Yogesh; Olubiyi, Olutayo; Golby, Alexandra J; O'Donnell, Lauren J

2018-03-01

Diffusion magnetic resonance imaging (dMRI) provides preoperative maps of neurosurgical patients' white matter tracts, but these maps suffer from echo-planar imaging (EPI) distortions caused by magnetic field inhomogeneities. In clinical neurosurgical planning, these distortions are generally not corrected and thus contribute to the uncertainty of fiber tracking. Multiple image processing pipelines have been proposed for image-registration-based EPI distortion correction in healthy subjects. In this article, we perform the first comparison of such pipelines in neurosurgical patient data. Five pipelines were tested in a retrospective clinical dMRI dataset of 9 patients with brain tumors. Pipelines differed in the choice of fixed and moving images and the similarity metric for image registration. Distortions were measured in two important tracts for neurosurgery, the arcuate fasciculus and corticospinal tracts. Significant differences in distortion estimates were found across processing pipelines. The most successful pipeline used dMRI baseline and T2-weighted images as inputs for distortion correction. This pipeline gave the most consistent distortion estimates across image resolutions and brain hemispheres. Quantitative results of mean tract distortions on the order of 1-2 mm are in line with other recent studies, supporting the potential need for distortion correction in neurosurgical planning. Novel results include significantly higher distortion estimates in the tumor hemisphere and greater effect of image resolution choice on results in the tumor hemisphere. Overall, this study demonstrates possible pitfalls and indicates that care should be taken when implementing EPI distortion correction in clinical settings. Copyright © 2018 by the American Society of Neuroimaging.

Atlas-based segmentation of brainstem regions in neuromelanin-sensitive magnetic resonance images

NASA Astrophysics Data System (ADS)

Puigvert, Marc; Castellanos, Gabriel; Uranga, Javier; Abad, Ricardo; Fernández-Seara, María. A.; Pastor, Pau; Pastor, María. A.; Muñoz-Barrutia, Arrate; Ortiz de Solórzano, Carlos

2015-03-01

We present a method for the automatic delineation of two neuromelanin rich brainstem structures -substantia nigra pars compacta (SN) and locus coeruleus (LC)- in neuromelanin sensitive magnetic resonance images of the brain. The segmentation method uses a dynamic multi-image reference atlas and a pre-registration atlas selection strategy. To create the atlas, a pool of 35 images of healthy subjects was pair-wise pre-registered and clustered in groups using an affinity propagation approach. Each group of the atlas is represented by a single exemplar image. Each new target image to be segmented is registered to the exemplars of each cluster. Then all the images of the highest performing clusters are enrolled into the final atlas, and the results of the registration with the target image are propagated using a majority voting approach. All registration processes used combined one two-stage affine and one elastic B-spline algorithm, to account for global positioning, region selection and local anatomic differences. In this paper, we present the algorithm, with emphasis in the atlas selection method and the registration scheme. We evaluate the performance of the atlas selection strategy using 35 healthy subjects and 5 Parkinson's disease patients. Then, we quantified the volume and contrast ratio of neuromelanin signal of these structures in 47 normal subjects and 40 Parkinson's disease patients to confirm that this method can detect neuromelanin-containing neurons loss in Parkinson's disease patients and could eventually be used for the early detection of SN and LC damage.

Effects of EPI distortion correction pipelines on the connectome in Parkinson's Disease

NASA Astrophysics Data System (ADS)

Galvis, Justin; Mezher, Adam F.; Ragothaman, Anjanibhargavi; Villalon-Reina, Julio E.; Fletcher, P. Thomas; Thompson, Paul M.; Prasad, Gautam

2016-03-01

Echo-planar imaging (EPI) is commonly used for diffusion-weighted imaging (DWI) but is susceptible to nonlinear geometric distortions arising from inhomogeneities in the static magnetic field. These inhomogeneities can be measured and corrected using a fieldmap image acquired during the scanning process. In studies where the fieldmap image is not collected, these distortions can be corrected, to some extent, by nonlinearly registering the diffusion image to a corresponding anatomical image, either a T1- or T2-weighted image. Here we compared two EPI distortion correction pipelines, both based on nonlinear registration, which were optimized for the particular weighting of the structural image registration target. The first pipeline used a 3D nonlinear registration to a T1-weighted target, while the second pipeline used a 1D nonlinear registration to a T2-weighted target. We assessed each pipeline in its ability to characterize high-level measures of brain connectivity in Parkinson's disease (PD) in 189 individuals (58 healthy controls, 131 people with PD) from the Parkinson's Progression Markers Initiative (PPMI) dataset. We computed a structural connectome (connectivity map) for each participant using regions of interest from a cortical parcellation combined with DWI-based whole-brain tractography. We evaluated test-retest reliability of the connectome for each EPI distortion correction pipeline using a second diffusion scan acquired directly after the participants' first. Finally, we used support vector machine (SVM) classification to assess how accurately each pipeline classified PD versus healthy controls using each participants' structural connectome.

Groupwise registration of MR brain images with tumors.

PubMed

Tang, Zhenyu; Wu, Yihong; Fan, Yong

2017-08-04

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

Towards operational multisensor registration

NASA Technical Reports Server (NTRS)

Rignot, Eric J. M.; Kwok, Ronald; Curlander, John C.

1991-01-01

To use data from a number of different remote sensors in a synergistic manner, a multidimensional analysis of the data is necessary. However, prior to this analysis, processing to correct for the systematic geometric distortion characteristic of each sensor is required. Furthermore, the registration process must be fully automated to handle a large volume of data and high data rates. A conceptual approach towards an operational multisensor registration algorithm is presented. The performance requirements of the algorithm are first formulated given the spatially, temporally, and spectrally varying factors that influence the image characteristics and the science requirements of various applications. Several registration techniques that fit within the structure of this algorithm are also presented. Their performance was evaluated using a multisensor test data set assembled from LANDSAT TM, SEASAT, SIR-B, Thermal Infrared Multispectral Scanner (TIMS), and SPOT sensors.

Deformable registration of x-ray to MRI for post-implant dosimetry in prostate brachytherapy

NASA Astrophysics Data System (ADS)

Park, Seyoun; Song, Danny Y.; Lee, Junghoon

2016-03-01

Post-implant dosimetric assessment in prostate brachytherapy is typically performed using CT as the standard imaging modality. However, poor soft tissue contrast in CT causes significant variability in target contouring, resulting in incorrect dose calculations for organs of interest. CT-MR fusion-based approach has been advocated taking advantage of the complementary capabilities of CT (seed identification) and MRI (soft tissue visibility), and has proved to provide more accurate dosimetry calculations. However, seed segmentation in CT requires manual review, and the accuracy is limited by the reconstructed voxel resolution. In addition, CT deposits considerable amount of radiation to the patient. In this paper, we propose an X-ray and MRI based post-implant dosimetry approach. Implanted seeds are localized using three X-ray images by solving a combinatorial optimization problem, and the identified seeds are registered to MR images by an intensity-based points-to-volume registration. We pre-process the MR images using geometric and Gaussian filtering. To accommodate potential soft tissue deformation, our registration is performed in two steps, an initial affine transformation and local deformable registration. An evolutionary optimizer in conjunction with a points-to-volume similarity metric is used for the affine registration. Local prostate deformation and seed migration are then adjusted by the deformable registration step with external and internal force constraints. We tested our algorithm on six patient data sets, achieving registration error of (1.2+/-0.8) mm in < 30 sec. Our proposed approach has the potential to be a fast and cost-effective solution for post-implant dosimetry with equivalent accuracy as the CT-MR fusion-based approach.

Automation of image data processing. (Polish Title: Automatyzacja proces u przetwarzania danych obrazowych)

NASA Astrophysics Data System (ADS)

Preuss, R.

2014-12-01

This article discusses the current capabilities of automate processing of the image data on the example of using PhotoScan software by Agisoft. At present, image data obtained by various registration systems (metric and non - metric cameras) placed on airplanes, satellites, or more often on UAVs is used to create photogrammetric products. Multiple registrations of object or land area (large groups of photos are captured) are usually performed in order to eliminate obscured area as well as to raise the final accuracy of the photogrammetric product. Because of such a situation t he geometry of the resulting image blocks is far from the typical configuration of images. For fast images georeferencing automatic image matching algorithms are currently applied. They can create a model of a block in the local coordinate system or using initial exterior orientation and measured control points can provide image georeference in an external reference frame. In the case of non - metric image application, it is also possible to carry out self - calibration process at this stage. Image matching algorithm is also used in generation of dense point clouds reconstructing spatial shape of the object (area). In subsequent processing steps it is possible to obtain typical photogrammetric products such as orthomosaic, DSM or DTM and a photorealistic solid model of an object . All aforementioned processing steps are implemented in a single program in contrary to standard commercial software dividing all steps into dedicated modules. Image processing leading to final geo referenced products can be fully automated including sequential implementation of the processing steps at predetermined control parameters. The paper presents the practical results of the application fully automatic generation of othomosaic for both images obtained by a metric Vexell camera and a block of images acquired by a non - metric UAV system

Intermediate Templates Guided Groupwise Registration of Diffusion Tensor Images

PubMed Central

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

2010-01-01

Registration of a population of diffusion tensor images (DTIs) is one of the key steps in medical image analysis, and it plays an important role in the statistical analysis of white matter related neurological diseases. However, pairwise registration with respect to a pre-selected template may not give precise results if the selected template deviates significantly from the distribution of images. To cater for more accurate and consistent registration, a novel framework is proposed for groupwise registration with the guidance from one or more intermediate templates determined from the population of images. Specifically, we first use a Euclidean distance, defined as a combinative measure based on the FA map and ADC map, for gauging the similarity of each pair of DTIs. A fully connected graph is then built with each node denoting an image and each edge denoting the distance between a pair of images. The root template image is determined automatically as the image with the overall shortest path length to all other images on the minimum spanning tree (MST) of the graph. Finally, a sequence of registration steps is applied to progressively warping each image towards the root template image with the help of intermediate templates distributed along its path to the root node on the MST. Extensive experimental results using diffusion tensor images of real subjects indicate that registration accuracy and fiber tract alignment are significantly improved, compared with the direct registration from each image to the root template image. PMID:20851197

Geometry Processing of Conventionally Produced Mouse Brain Slice Images.

PubMed

Agarwal, Nitin; Xu, Xiangmin; Gopi, M

2018-04-21

Brain mapping research in most neuroanatomical laboratories relies on conventional processing techniques, which often introduce histological artifacts such as tissue tears and tissue loss. In this paper we present techniques and algorithms for automatic registration and 3D reconstruction of conventionally produced mouse brain slices in a standardized atlas space. This is achieved first by constructing a virtual 3D mouse brain model from annotated slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed model generates ARA-based slice images corresponding to the microscopic images of histological brain sections. These image pairs are aligned using a geometric approach through contour images. Histological artifacts in the microscopic images are detected and removed using Constrained Delaunay Triangulation before performing global alignment. Finally, non-linear registration is performed by solving Laplace's equation with Dirichlet boundary conditions. Our methods provide significant improvements over previously reported registration techniques for the tested slices in 3D space, especially on slices with significant histological artifacts. Further, as one of the application we count the number of neurons in various anatomical regions using a dataset of 51 microscopic slices from a single mouse brain. To the best of our knowledge the presented work is the first that automatically registers both clean as well as highly damaged high-resolutions histological slices of mouse brain to a 3D annotated reference atlas space. This work represents a significant contribution to this subfield of neuroscience as it provides tools to neuroanatomist for analyzing and processing histological data. Copyright © 2018 Elsevier B.V. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2014-11-01

Continuous and automated co-registration and geo-tagging of images from multiple bands of Liss-4 camera is one of the interesting challenges of Resourcesat-2 data processing. Three arrays of the Liss-4 camera are physically separated in the focal plane in alongtrack direction. Thus, same line on the ground will be imaged by extreme bands with a time interval of as much as 2.1 seconds. During this time, the satellite would have covered a distance of about 14 km on the ground and the earth would have rotated through an angle of 30". A yaw steering is done to compensate the earth rotation effects, thus ensuring a first level registration between the bands. But this will not do a perfect co-registration because of the attitude fluctuations, satellite movement, terrain topography, PSM steering and small variations in the angular placement of the CCD lines (from the pre-launch values) in the focal plane. This paper describes an algorithm based on the viewing geometry of the satellite to do an automatic band to band registration of Liss-4 MX image of Resourcesat-2 in Level 1A. The algorithm is using the principles of photogrammetric collinearity equations. The model employs an orbit trajectory and attitude fitting with polynomials. Then, a direct geo-referencing with a global DEM with which every pixel in the middle band is mapped to a particular position on the surface of the earth with the given attitude. Attitude is estimated by interpolating measurement data obtained from star sensors and gyros, which are sampled at low frequency. When the sampling rate of attitude information is low compared to the frequency of jitter or micro-vibration, images processed by geometric correction suffer from distortion. Therefore, a set of conjugate points are identified between the bands to perform a relative attitude error estimation and correction which will ensure the internal accuracy and co-registration of bands. Accurate calculation of the exterior orientation parameters with GCPs is not required. Instead, the relative line of sight vector of each detector in different bands in relation to the payload is addressed. With this method a band to band registration accuracy of better than 0.3 pixels could be achieved even in high hill areas.

Distance-Dependent Multimodal Image Registration for Agriculture Tasks

PubMed Central

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

2015-01-01

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

Strain Rate Tensor Estimation in Cine Cardiac MRI Based on Elastic Image Registration

NASA Astrophysics Data System (ADS)

Sánchez-Ferrero, Gonzalo Vegas; Vega, Antonio Tristán; Grande, Lucilio Cordero; de La Higuera, Pablo Casaseca; Fernández, Santiago Aja; Fernández, Marcos Martín; López, Carlos Alberola

In this work we propose an alternative method to estimate and visualize the Strain Rate Tensor (SRT) in Magnetic Resonance Images (MRI) when Phase Contrast MRI (PCMRI) and Tagged MRI (TMRI) are not available. This alternative is based on image processing techniques. Concretely, image registration algorithms are used to estimate the movement of the myocardium at each point. Additionally, a consistency checking method is presented to validate the accuracy of the estimates when no golden standard is available. Results prove that the consistency checking method provides an upper bound of the mean squared error of the estimate. Our experiments with real data show that the registration algorithm provides a useful deformation field to estimate the SRT fields. A classification between regional normal and dysfunctional contraction patterns, as compared with experts diagnosis, points out that the parameters extracted from the estimated SRT can represent these patterns. Additionally, a scheme for visualizing and analyzing the local behavior of the SRT field is presented.

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

PubMed

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

2016-01-01

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

Hybrid registration of PET/CT in thoracic region with pre-filtering PET sinogram

NASA Astrophysics Data System (ADS)

Mokri, S. S.; Saripan, M. I.; Marhaban, M. H.; Nordin, A. J.; Hashim, S.

2015-11-01

The integration of physiological (PET) and anatomical (CT) images in cancer delineation requires an accurate spatial registration technique. Although hybrid PET/CT scanner is used to co-register these images, significant misregistrations exist due to patient and respiratory/cardiac motions. This paper proposes a hybrid feature-intensity based registration technique for hybrid PET/CT scanner. First, simulated PET sinogram was filtered with a 3D hybrid mean-median before reconstructing the image. The features were then derived from the segmented structures (lung, heart and tumor) from both images. The registration was performed based on modified multi-modality demon registration with multiresolution scheme. Apart from visual observations improvements, the proposed registration technique increased the normalized mutual information index (NMI) between the PET/CT images after registration. All nine tested datasets show marked improvements in mutual information (MI) index than free form deformation (FFD) registration technique with the highest MI increase is 25%.

SU-E-J-111: Finite Element-Based Deformable Image Registration of Pleural Cavity for Photodynamic Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Penjweini, R; Zhu, T

Purpose: The pleural volumes will deform during surgery portion of the pleural photodynamic therapy (PDT) of lung cancer when the pleural cavity is opened. This impact the delivered dose when using highly conformal treatment techniques. In this study, a finite element-based (FEM) deformable image registration is used to quantify the anatomical variation between the contours for the pleural cavities obtained in the operating room and those determined from pre-surgery computed tomography (CT) scans. Methods: An infrared camera-based navigation system (NDI) is used during PDT to track the anatomical changes and contour the lung and chest cavity. A series of CTsmore » of the lungs, in the same patient, are also acquired before the surgery. The structure contour of lung and the CTs are processed and contoured in Matlab and MeshLab. Then, the contours are imported into COMSOL Multiphysics 5.0, where the FEM-based deformable image registration is obtained using the deformed mesh - moving mesh (ALE) model. The NDI acquired lung contour is considered as the reference contour, and the CT contour is used as the target one, which will be deformed. Results: The reconstructed three-dimensional contours from both NDI and CT can be converted to COMSOL so that a three-dimensional ALE model can be developed. The contours can be registered using COMSOL ALE moving mesh model, which takes into account the deformation along x, y and z-axes. The deformed contour has good matches to the reference contour after the dynamic matching process. The resulting 3D deformation map can be used to obtain the locations of other critical anatomic structures, e.g., heart, during surgery. Conclusion: Deformable image registration can fuse images acquired by different modalities. It provides insights into the development of phenomenon and variation in normal anatomical structures over time. The initial assessments of three-dimensional registration show good agreement.« less

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

PubMed

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

2017-02-01

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

Image registration and analysis for quantitative myocardial perfusion: application to dynamic circular cardiac CT.

PubMed

Isola, A A; Schmitt, H; van Stevendaal, U; Begemann, P G; Coulon, P; Boussel, L; Grass, M

2011-09-21

Large area detector computed tomography systems with fast rotating gantries enable volumetric dynamic cardiac perfusion studies. Prospectively, ECG-triggered acquisitions limit the data acquisition to a predefined cardiac phase and thereby reduce x-ray dose and limit motion artefacts. Even in the case of highly accurate prospective triggering and stable heart rate, spatial misalignment of the cardiac volumes acquired and reconstructed per cardiac cycle may occur due to small motion pattern variations from cycle to cycle. These misalignments reduce the accuracy of the quantitative analysis of myocardial perfusion parameters on a per voxel basis. An image-based solution to this problem is elastic 3D image registration of dynamic volume sequences with variable contrast, as it is introduced in this contribution. After circular cone-beam CT reconstruction of cardiac volumes covering large areas of the myocardial tissue, the complete series is aligned with respect to a chosen reference volume. The results of the registration process and the perfusion analysis with and without registration are evaluated quantitatively in this paper. The spatial alignment leads to improved quantification of myocardial perfusion for three different pig data sets.

Deformable and rigid registration of MRI and microPET images for photodynamic therapy of cancer in mice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fei Baowei; Wang Hesheng; Muzic, Raymond F. Jr.

2006-03-15

We are investigating imaging techniques to study the tumor response to photodynamic therapy (PDT). Positron emission tomography (PET) can provide physiological and functional information. High-resolution magnetic resonance imaging (MRI) can provide anatomical and morphological changes. Image registration can combine MRI and PET images for improved tumor monitoring. In this study, we acquired high-resolution MRI and microPET {sup 18}F-fluorodeoxyglucose (FDG) images from C3H mice with RIF-1 tumors that were treated with Pc 4-based PDT. We developed two registration methods for this application. For registration of the whole mouse body, we used an automatic three-dimensional, normalized mutual information algorithm. For tumor registration,more » we developed a finite element model (FEM)-based deformable registration scheme. To assess the quality of whole body registration, we performed slice-by-slice review of both image volumes; manually segmented feature organs, such as the left and right kidneys and the bladder, in each slice; and computed the distance between corresponding centroids. Over 40 volume registration experiments were performed with MRI and microPET images. The distance between corresponding centroids of organs was 1.5{+-}0.4 mm which is about 2 pixels of microPET images. The mean volume overlap ratios for tumors were 94.7% and 86.3% for the deformable and rigid registration methods, respectively. Registration of high-resolution MRI and microPET images combines anatomical and functional information of the tumors and provides a useful tool for evaluating photodynamic therapy.« less

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

PubMed

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

2015-06-01

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

Microscopic neural image registration based on the structure of mitochondria

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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.

Temporal subtraction contrast-enhanced dedicated breast CT

PubMed Central

Gazi, Peymon M.; Aminololama-Shakeri, Shadi; Yang, Kai; Boone, John M.

2016-01-01

Purpose To develop a framework of deformable image registration and segmentation for the purpose of temporal subtraction contrast-enhanced breast CT is described. Methods An iterative histogram-based two-means clustering method was used for the segmentation. Dedicated breast CT images were segmented into background (air), adipose, fibroglandular and skin components. Fibroglandular tissue was classified as either normal or contrast-enhanced then divided into tiers for the purpose of categorizing degrees of contrast enhancement. A variant of the Demons deformable registration algorithm, Intensity Difference Adaptive Demons (IDAD), was developed to correct for the large deformation forces that stemmed from contrast enhancement. In this application, the accuracy of the proposed method was evaluated in both mathematically-simulated and physically-acquired phantom images. Clinical usage and accuracy of the temporal subtraction framework was demonstrated using contrast-enhanced breast CT datasets from five patients. Registration performance was quantified using Normalized Cross Correlation (NCC), Symmetric Uncertainty Coefficient (SUC), Normalized Mutual Information (NMI), Mean Square Error (MSE) and Target Registration Error (TRE). Results The proposed method outperformed conventional affine and other Demons variations in contrast enhanced breast CT image registration. In simulation studies, IDAD exhibited improvement in MSE(0–16%), NCC (0–6%), NMI (0–13%) and TRE (0–34%) compared to the conventional Demons approaches, depending on the size and intensity of the enhancing lesion. As lesion size and contrast enhancement levels increased, so did the improvement. The drop in the correlation between the pre- and post-contrast images for the largest enhancement levels in phantom studies is less than 1.2% (150 Hounsfield units). Registration error, measured by TRE, shows only submillimeter mismatches between the concordant anatomical target points in all patient studies. The algorithm was implemented using a parallel processing architecture resulting in rapid execution time for the iterative segmentation and intensity-adaptive registration techniques. Conclusion Characterization of contrast-enhanced lesions is improved using temporal subtraction contrast-enhanced dedicated breast CT. Adaptation of Demons registration forces as a function of contrast-enhancement levels provided a means to accurately align breast tissue in pre- and post-contrast image acquisitions, improving subtraction results. Spatial subtraction of the aligned images yields useful diagnostic information with respect to enhanced lesion morphology and uptake. PMID:27494376

Optimizing image registration and infarct definition in stroke research.

PubMed

Harston, George W J; Minks, David; Sheerin, Fintan; Payne, Stephen J; Chappell, Michael; Jezzard, Peter; Jenkinson, Mark; Kennedy, James

2017-03-01

Accurate representation of final infarct volume is essential for assessing the efficacy of stroke interventions in imaging-based studies. This study defines the impact of image registration methods used at different timepoints following stroke, and the implications for infarct definition in stroke research. Patients presenting with acute ischemic stroke were imaged serially using magnetic resonance imaging. Infarct volume was defined manually using four metrics: 24-h b1000 imaging; 1-week and 1-month T2-weighted FLAIR; and automatically using predefined thresholds of ADC at 24 h. Infarct overlap statistics and volumes were compared across timepoints following both rigid body and nonlinear image registration to the presenting MRI. The effect of nonlinear registration on a hypothetical trial sample size was calculated. Thirty-seven patients were included. Nonlinear registration improved infarct overlap statistics and consistency of total infarct volumes across timepoints, and reduced infarct volumes by 4.0 mL (13.1%) and 7.1 mL (18.2%) at 24 h and 1 week, respectively, compared to rigid body registration. Infarct volume at 24 h, defined using a predetermined ADC threshold, was less sensitive to infarction than b1000 imaging. 1-week T2-weighted FLAIR imaging was the most accurate representation of final infarct volume. Nonlinear registration reduced hypothetical trial sample size, independent of infarct volume, by an average of 13%. Nonlinear image registration may offer the opportunity of improving the accuracy of infarct definition in serial imaging studies compared to rigid body registration, helping to overcome the challenges of anatomical distortions at subacute timepoints, and reducing sample size for imaging-based clinical trials.

High-performance computing in image registration

NASA Astrophysics Data System (ADS)

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

2012-10-01

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

The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images.

PubMed

Shahidi, Shoaleh; Bahrampour, Ehsan; Soltanimehr, Elham; Zamani, Ali; Oshagh, Morteza; Moattari, Marzieh; Mehdizadeh, Alireza

2014-09-16

Two-dimensional projection radiographs have been traditionally considered the modality of choice for cephalometric analysis. To overcome the shortcomings of two-dimensional images, three-dimensional computed tomography (CT) has been used to evaluate craniofacial structures. However, manual landmark detection depends on medical expertise, and the process is time-consuming. The present study was designed to produce software capable of automated localization of craniofacial landmarks on cone beam (CB) CT images based on image registration and to evaluate its accuracy. The software was designed using MATLAB programming language. The technique was a combination of feature-based (principal axes registration) and voxel similarity-based methods for image registration. A total of 8 CBCT images were selected as our reference images for creating a head atlas. Then, 20 CBCT images were randomly selected as the test images for evaluating the method. Three experts twice located 14 landmarks in all 28 CBCT images during two examinations set 6 weeks apart. The differences in the distances of coordinates of each landmark on each image between manual and automated detection methods were calculated and reported as mean errors. The combined intraclass correlation coefficient for intraobserver reliability was 0.89 and for interobserver reliability 0.87 (95% confidence interval, 0.82 to 0.93). The mean errors of all 14 landmarks were <4 mm. Additionally, 63.57% of landmarks had a mean error of <3 mm compared with manual detection (gold standard method). The accuracy of our approach for automated localization of craniofacial landmarks, which was based on combining feature-based and voxel similarity-based methods for image registration, was acceptable. Nevertheless we recommend repetition of this study using other techniques, such as intensity-based methods.

Deformable registration of the inflated and deflated lung in cone-beam CT-guided thoracic surgery: Initial investigation of a combined model- and image-driven approach

PubMed Central

Uneri, Ali; Nithiananthan, Sajendra; Schafer, Sebastian; Otake, Yoshito; Stayman, J. Webster; Kleinszig, Gerhard; Sussman, Marc S.; Prince, Jerry L.; Siewerdsen, Jeffrey H.

2013-01-01

Purpose: Surgical resection is the preferred modality for curative treatment of early stage lung cancer, but localization of small tumors (<10 mm diameter) during surgery presents a major challenge that is likely to increase as more early-stage disease is detected incidentally and in low-dose CT screening. To overcome the difficulty of manual localization (fingers inserted through intercostal ports) and the cost, logistics, and morbidity of preoperative tagging (coil or dye placement under CT-fluoroscopy), the authors propose the use of intraoperative cone-beam CT (CBCT) and deformable image registration to guide targeting of small tumors in video-assisted thoracic surgery (VATS). A novel algorithm is reported for registration of the lung from its inflated state (prior to pleural breach) to the deflated state (during resection) to localize surgical targets and adjacent critical anatomy. Methods: The registration approach geometrically resolves images of the inflated and deflated lung using a coarse model-driven stage followed by a finer image-driven stage. The model-driven stage uses image features derived from the lung surfaces and airways: triangular surface meshes are morphed to capture bulk motion; concurrently, the airways generate graph structures from which corresponding nodes are identified. Interpolation of the sparse motion fields computed from the bounding surface and interior airways provides a 3D motion field that coarsely registers the lung and initializes the subsequent image-driven stage. The image-driven stage employs an intensity-corrected, symmetric form of the Demons method. The algorithm was validated over 12 datasets, obtained from porcine specimen experiments emulating CBCT-guided VATS. Geometric accuracy was quantified in terms of target registration error (TRE) in anatomical targets throughout the lung, and normalized cross-correlation. Variations of the algorithm were investigated to study the behavior of the model- and image-driven stages by modifying individual algorithmic steps and examining the effect in comparison to the nominal process. Results: The combined model- and image-driven registration process demonstrated accuracy consistent with the requirements of minimally invasive VATS in both target localization (∼3–5 mm within the target wedge) and critical structure avoidance (∼1–2 mm). The model-driven stage initialized the registration to within a median TRE of 1.9 mm (95% confidence interval (CI) maximum = 5.0 mm), while the subsequent image-driven stage yielded higher accuracy localization with 0.6 mm median TRE (95% CI maximum = 4.1 mm). The variations assessing the individual algorithmic steps elucidated the role of each step and in some cases identified opportunities for further simplification and improvement in computational speed. Conclusions: The initial studies show the proposed registration method to successfully register CBCT images of the inflated and deflated lung. Accuracy appears sufficient to localize the target and adjacent critical anatomy within ∼1–2 mm and guide localization under conditions in which the target cannot be discerned directly in CBCT (e.g., subtle, nonsolid tumors). The ability to directly localize tumors in the operating room could provide a valuable addition to the VATS arsenal, obviate the cost, logistics, and morbidity of preoperative tagging, and improve patient safety. Future work includes in vivo testing, optimization of workflow, and integration with a CBCT image guidance system. PMID:23298134

Deformable registration of the inflated and deflated lung in cone-beam CT-guided thoracic surgery: initial investigation of a combined model- and image-driven approach.

PubMed

Uneri, Ali; Nithiananthan, Sajendra; Schafer, Sebastian; Otake, Yoshito; Stayman, J Webster; Kleinszig, Gerhard; Sussman, Marc S; Prince, Jerry L; Siewerdsen, Jeffrey H

2013-01-01

Surgical resection is the preferred modality for curative treatment of early stage lung cancer, but localization of small tumors (<10 mm diameter) during surgery presents a major challenge that is likely to increase as more early-stage disease is detected incidentally and in low-dose CT screening. To overcome the difficulty of manual localization (fingers inserted through intercostal ports) and the cost, logistics, and morbidity of preoperative tagging (coil or dye placement under CT-fluoroscopy), the authors propose the use of intraoperative cone-beam CT (CBCT) and deformable image registration to guide targeting of small tumors in video-assisted thoracic surgery (VATS). A novel algorithm is reported for registration of the lung from its inflated state (prior to pleural breach) to the deflated state (during resection) to localize surgical targets and adjacent critical anatomy. The registration approach geometrically resolves images of the inflated and deflated lung using a coarse model-driven stage followed by a finer image-driven stage. The model-driven stage uses image features derived from the lung surfaces and airways: triangular surface meshes are morphed to capture bulk motion; concurrently, the airways generate graph structures from which corresponding nodes are identified. Interpolation of the sparse motion fields computed from the bounding surface and interior airways provides a 3D motion field that coarsely registers the lung and initializes the subsequent image-driven stage. The image-driven stage employs an intensity-corrected, symmetric form of the Demons method. The algorithm was validated over 12 datasets, obtained from porcine specimen experiments emulating CBCT-guided VATS. Geometric accuracy was quantified in terms of target registration error (TRE) in anatomical targets throughout the lung, and normalized cross-correlation. Variations of the algorithm were investigated to study the behavior of the model- and image-driven stages by modifying individual algorithmic steps and examining the effect in comparison to the nominal process. The combined model- and image-driven registration process demonstrated accuracy consistent with the requirements of minimally invasive VATS in both target localization (∼3-5 mm within the target wedge) and critical structure avoidance (∼1-2 mm). The model-driven stage initialized the registration to within a median TRE of 1.9 mm (95% confidence interval (CI) maximum = 5.0 mm), while the subsequent image-driven stage yielded higher accuracy localization with 0.6 mm median TRE (95% CI maximum = 4.1 mm). The variations assessing the individual algorithmic steps elucidated the role of each step and in some cases identified opportunities for further simplification and improvement in computational speed. The initial studies show the proposed registration method to successfully register CBCT images of the inflated and deflated lung. Accuracy appears sufficient to localize the target and adjacent critical anatomy within ∼1-2 mm and guide localization under conditions in which the target cannot be discerned directly in CBCT (e.g., subtle, nonsolid tumors). The ability to directly localize tumors in the operating room could provide a valuable addition to the VATS arsenal, obviate the cost, logistics, and morbidity of preoperative tagging, and improve patient safety. Future work includes in vivo testing, optimization of workflow, and integration with a CBCT image guidance system.

Groupwise Image Registration Guided by a Dynamic Digraph of Images.

PubMed

Tang, Zhenyu; Fan, Yong

2016-04-01

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

A method to map errors in the deformable registration of 4DCT images1

PubMed Central

Vaman, Constantin; Staub, David; Williamson, Jeffrey; Murphy, Martin J.

2010-01-01

Purpose: To present a new approach to the problem of estimating errors in deformable image registration (DIR) applied to sequential phases of a 4DCT data set. Methods: A set of displacement vector fields (DVFs) are made by registering a sequence of 4DCT phases. The DVFs are assumed to display anatomical movement, with the addition of errors due to the imaging and registration processes. The positions of physical landmarks in each CT phase are measured as ground truth for the physical movement in the DVF. Principal component analysis of the DVFs and the landmarks is used to identify and separate the eigenmodes of physical movement from the error eigenmodes. By subtracting the physical modes from the principal components of the DVFs, the registration errors are exposed and reconstructed as DIR error maps. The method is demonstrated via a simple numerical model of 4DCT DVFs that combines breathing movement with simulated maps of spatially correlated DIR errors. Results: The principal components of the simulated DVFs were observed to share the basic properties of principal components for actual 4DCT data. The simulated error maps were accurately recovered by the estimation method. Conclusions: Deformable image registration errors can have complex spatial distributions. Consequently, point-by-point landmark validation can give unrepresentative results that do not accurately reflect the registration uncertainties away from the landmarks. The authors are developing a method for mapping the complete spatial distribution of DIR errors using only a small number of ground truth validation landmarks. PMID:21158288

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

PubMed

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

2017-01-01

Many brain development studies have been devoted to investigate dynamic structural and functional changes in the first year of life. To quantitatively measure brain development in such a dynamic period, accurate image registration for different infant subjects with possible large age gap is of high demand. Although many state-of-the-art image registration methods have been proposed for young and elderly brain images, very few registration methods work for infant brain images acquired in the first year of life, because of (a) large anatomical changes due to fast brain development and (b) dynamic appearance changes due to white-matter myelination. To address these two difficulties, we propose a learning-based registration method to not only align the anatomical structures but also alleviate the appearance differences between two arbitrary infant MR images (with large age gap) by leveraging the regression forest to predict both the initial displacement vector and appearance changes. Specifically, in the training stage, two regression models are trained separately, with (a) one model learning the relationship between local image appearance (of one development phase) and its displacement toward the template (of another development phase) and (b) another model learning the local appearance changes between the two brain development phases. Then, in the testing stage, to register a new infant image to the template, we first predict both its voxel-wise displacement and appearance changes by the two learned regression models. Since such initializations can alleviate significant appearance and shape differences between new infant image and the template, it is easy to just use a conventional registration method to refine the remaining registration. We apply our proposed registration method to align 24 infant subjects at five different time points (i.e., 2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old), and achieve more accurate and robust registration results, compared to the state-of-the-art registration methods. The proposed learning-based registration method addresses the challenging task of registering infant brain images and achieves higher registration accuracy compared with other counterpart registration methods. © 2016 American Association of Physicists in Medicine.

Demons versus Level-Set motion registration for coronary 18F-sodium fluoride PET.

PubMed

Rubeaux, Mathieu; Joshi, Nikhil; Dweck, Marc R; Fletcher, Alison; Motwani, Manish; Thomson, Louise E; Germano, Guido; Dey, Damini; Berman, Daniel S; Newby, David E; Slomka, Piotr J

2016-02-27

Ruptured coronary atherosclerotic plaques commonly cause acute myocardial infarction. It has been recently shown that active microcalcification in the coronary arteries, one of the features that characterizes vulnerable plaques at risk of rupture, can be imaged using cardiac gated 18 F-sodium fluoride ( 18 F-NaF) PET. We have shown in previous work that a motion correction technique applied to cardiac-gated 18 F-NaF PET images can enhance image quality and improve uptake estimates. In this study, we further investigated the applicability of different algorithms for registration of the coronary artery PET images. In particular, we aimed to compare demons vs. level-set nonlinear registration techniques applied for the correction of cardiac motion in coronary 18 F-NaF PET. To this end, fifteen patients underwent 18 F-NaF PET and prospective coronary CT angiography (CCTA). PET data were reconstructed in 10 ECG gated bins; subsequently these gated bins were registered using demons and level-set methods guided by the extracted coronary arteries from CCTA, to eliminate the effect of cardiac motion on PET images. Noise levels, target-to-background ratios (TBR) and global motion were compared to assess image quality. Compared to the reference standard of using only diastolic PET image (25% of the counts from PET acquisition), cardiac motion registration using either level-set or demons techniques almost halved image noise due to the use of counts from the full PET acquisition and increased TBR difference between 18 F-NaF positive and negative lesions. The demons method produces smoother deformation fields, exhibiting no singularities (which reflects how physically plausible the registration deformation is), as compared to the level-set method, which presents between 4 and 8% of singularities, depending on the coronary artery considered. In conclusion, the demons method produces smoother motion fields as compared to the level-set method, with a motion that is physiologically plausible. Therefore, level-set technique will likely require additional post-processing steps. On the other hand, the observed TBR increases were the highest for the level-set technique. Further investigations of the optimal registration technique of this novel coronary PET imaging technique are warranted.

Demons versus level-set motion registration for coronary 18F-sodium fluoride PET

NASA Astrophysics Data System (ADS)

Rubeaux, Mathieu; Joshi, Nikhil; Dweck, Marc R.; Fletcher, Alison; Motwani, Manish; Thomson, Louise E.; Germano, Guido; Dey, Damini; Berman, Daniel S.; Newby, David E.; Slomka, Piotr J.

2016-03-01

Ruptured coronary atherosclerotic plaques commonly cause acute myocardial infarction. It has been recently shown that active microcalcification in the coronary arteries, one of the features that characterizes vulnerable plaques at risk of rupture, can be imaged using cardiac gated 18F-sodium fluoride (18F-NaF) PET. We have shown in previous work that a motion correction technique applied to cardiac-gated 18F-NaF PET images can enhance image quality and improve uptake estimates. In this study, we further investigated the applicability of different algorithms for registration of the coronary artery PET images. In particular, we aimed to compare demons vs. level-set nonlinear registration techniques applied for the correction of cardiac motion in coronary 18F-NaF PET. To this end, fifteen patients underwent 18F-NaF PET and prospective coronary CT angiography (CCTA). PET data were reconstructed in 10 ECG gated bins; subsequently these gated bins were registered using demons and level-set methods guided by the extracted coronary arteries from CCTA, to eliminate the effect of cardiac motion on PET images. Noise levels, target-to-background ratios (TBR) and global motion were compared to assess image quality. Compared to the reference standard of using only diastolic PET image (25% of the counts from PET acquisition), cardiac motion registration using either level-set or demons techniques almost halved image noise due to the use of counts from the full PET acquisition and increased TBR difference between 18F-NaF positive and negative lesions. The demons method produces smoother deformation fields, exhibiting no singularities (which reflects how physically plausible the registration deformation is), as compared to the level-set method, which presents between 4 and 8% of singularities, depending on the coronary artery considered. In conclusion, the demons method produces smoother motion fields as compared to the level-set method, with a motion that is physiologically plausible. Therefore, level-set technique will likely require additional post-processing steps. On the other hand, the observed TBR increases were the highest for the level-set technique. Further investigations of the optimal registration technique of this novel coronary PET imaging technique are warranted.

Edge-based correlation image registration for multispectral imaging

DOEpatents

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.

Parallel image registration with a thin client interface

NASA Astrophysics Data System (ADS)

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

2010-03-01

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

Reducing Interpolation Artifacts for Mutual Information Based Image Registration

PubMed Central

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

Three-dimensional nonrigid landmark-based magnetic resonance to transrectal ultrasound registration for image-guided prostate biopsy.

PubMed

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

2015-04-01

Registration of three-dimensional (3-D) magnetic resonance (MR) to 3-D transrectal ultrasound (TRUS) prostate images is an important step in the planning and guidance of 3-D TRUS guided prostate biopsy. In order to accurately and efficiently perform the registration, a nonrigid landmark-based registration method is required to account for the different deformations of the prostate when using these two modalities. We describe a nonrigid landmark-based method for registration of 3-D TRUS to MR prostate images. The landmark-based registration method first makes use of an initial rigid registration of 3-D MR to 3-D TRUS images using six manually placed approximately corresponding landmarks in each image. Following manual initialization, the two prostate surfaces are segmented from 3-D MR and TRUS images and then nonrigidly registered using the following steps: (1) rotationally reslicing corresponding segmented prostate surfaces from both 3-D MR and TRUS images around a specified axis, (2) an approach to find point correspondences on the surfaces of the segmented surfaces, and (3) deformation of the surface of the prostate in the MR image to match the surface of the prostate in the 3-D TRUS image and the interior using a thin-plate spline algorithm. The registration accuracy was evaluated using 17 patient prostate MR and 3-D TRUS images by measuring the target registration error (TRE). Experimental results showed that the proposed method yielded an overall mean TRE of [Formula: see text] for the rigid registration and [Formula: see text] for the nonrigid registration, which is favorably comparable to a clinical requirement for an error of less than 2.5 mm. A landmark-based nonrigid 3-D MR-TRUS registration approach is proposed, which takes into account the correspondences on the prostate surface, inside the prostate, as well as the centroid of the prostate. Experimental results indicate that the proposed method yields clinically sufficient accuracy.

The role of image registration in brain mapping

PubMed Central

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

2008-01-01

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

Multi-modal Registration for Correlative Microscopy using Image Analogies

PubMed Central

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

2014-01-01

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

Viewpoints on Medical Image Processing: From Science to Application

PubMed Central

Deserno (né Lehmann), Thomas M.; Handels, Heinz; Maier-Hein (né Fritzsche), Klaus H.; Mersmann, Sven; Palm, Christoph; Tolxdorff, Thomas; Wagenknecht, Gudrun; Wittenberg, Thomas

2013-01-01

Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment. PMID:24078804

Viewpoints on Medical Image Processing: From Science to Application.

PubMed

Deserno Né Lehmann, Thomas M; Handels, Heinz; Maier-Hein Né Fritzsche, Klaus H; Mersmann, Sven; Palm, Christoph; Tolxdorff, Thomas; Wagenknecht, Gudrun; Wittenberg, Thomas

2013-05-01

Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment.

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

PubMed

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

2014-04-01

We introduce a boosting algorithm to improve on existing methods for deformable image registration (DIR). The proposed DIRBoost algorithm is inspired by the theory on hypothesis boosting, well known in the field of machine learning. DIRBoost utilizes a method for automatic registration error detection to obtain estimates of local registration quality. All areas detected as erroneously registered are subjected to boosting, i.e. undergo iterative registrations by employing boosting masks on both the fixed and moving image. We validated the DIRBoost algorithm on three different DIR methods (ANTS gSyn, NiftyReg, and DROP) on three independent reference datasets of pulmonary image scan pairs. DIRBoost reduced registration errors significantly and consistently on all reference datasets for each DIR algorithm, yielding an improvement of the registration accuracy by 5-34% depending on the dataset and the registration algorithm employed. Copyright © 2014 Elsevier B.V. All rights reserved.

Error Estimation Techniques to Refine Overlapping Aerial Image Mosaic Processes via Detected Parameters

ERIC Educational Resources Information Center

Bond, William Glenn

2012-01-01

In this paper, I propose to demonstrate a means of error estimation preprocessing in the assembly of overlapping aerial image mosaics. The mosaic program automatically assembles several hundred aerial images from a data set by aligning them, via image registration using a pattern search method, onto a GIS grid. The method presented first locates…

Detecting fluorescence hot-spots using mosaic maps generated from multimodal endoscope imaging

NASA Astrophysics Data System (ADS)

Yang, Chenying; Soper, Timothy D.; Seibel, Eric J.

2013-03-01

Fluorescence labeled biomarkers can be detected during endoscopy to guide early cancer biopsies, such as high-grade dysplasia in Barrett's Esophagus. To enhance intraoperative visualization of the fluorescence hot-spots, a mosaicking technique was developed to create full anatomical maps of the lower esophagus and associated fluorescent hot-spots. The resultant mosaic map contains overlaid reflectance and fluorescence images. It can be used to assist biopsy and document findings. The mosaicking algorithm uses reflectance images to calculate image registration between successive frames, and apply this registration to simultaneously acquired fluorescence images. During this mosaicking process, the fluorescence signal is enhanced through multi-frame averaging. Preliminary results showed that the technique promises to enhance the detectability of the hot-spots due to enhanced fluorescence signal.

Real-time automatic registration in optical surgical navigation

NASA Astrophysics Data System (ADS)

Lin, Qinyong; Yang, Rongqian; Cai, Ken; Si, Xuan; Chen, Xiuwen; Wu, Xiaoming

2016-05-01

An image-guided surgical navigation system requires the improvement of the patient-to-image registration time to enhance the convenience of the registration procedure. A critical step in achieving this aim is performing a fully automatic patient-to-image registration. This study reports on a design of custom fiducial markers and the performance of a real-time automatic patient-to-image registration method using these markers on the basis of an optical tracking system for rigid anatomy. The custom fiducial markers are designed to be automatically localized in both patient and image spaces. An automatic localization method is performed by registering a point cloud sampled from the three dimensional (3D) pedestal model surface of a fiducial marker to each pedestal of fiducial markers searched in image space. A head phantom is constructed to estimate the performance of the real-time automatic registration method under four fiducial configurations. The head phantom experimental results demonstrate that the real-time automatic registration method is more convenient, rapid, and accurate than the manual method. The time required for each registration is approximately 0.1 s. The automatic localization method precisely localizes the fiducial markers in image space. The averaged target registration error for the four configurations is approximately 0.7 mm. The automatic registration performance is independent of the positions relative to the tracking system and the movement of the patient during the operation.

Demons deformable registration for CBCT-guided procedures in the head and neck: Convergence and accuracy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nithiananthan, S.; Brock, K. K.; Daly, M. J.

2009-10-15

Purpose: The accuracy and convergence behavior of a variant of the Demons deformable registration algorithm were investigated for use in cone-beam CT (CBCT)-guided procedures of the head and neck. Online use of deformable registration for guidance of therapeutic procedures such as image-guided surgery or radiation therapy places trade-offs on accuracy and computational expense. This work describes a convergence criterion for Demons registration developed to balance these demands; the accuracy of a multiscale Demons implementation using this convergence criterion is quantified in CBCT images of the head and neck. Methods: Using an open-source ''symmetric'' Demons registration algorithm, a convergence criterion basedmore » on the change in the deformation field between iterations was developed to advance among multiple levels of a multiscale image pyramid in a manner that optimized accuracy and computation time. The convergence criterion was optimized in cadaver studies involving CBCT images acquired using a surgical C-arm prototype modified for 3D intraoperative imaging. CBCT-to-CBCT registration was performed and accuracy was quantified in terms of the normalized cross-correlation (NCC) and target registration error (TRE). The accuracy and robustness of the algorithm were then tested in clinical CBCT images of ten patients undergoing radiation therapy of the head and neck. Results: The cadaver model allowed optimization of the convergence factor and initial measurements of registration accuracy: Demons registration exhibited TRE=(0.8{+-}0.3) mm and NCC=0.99 in the cadaveric head compared to TRE=(2.6{+-}1.0) mm and NCC=0.93 with rigid registration. Similarly for the patient data, Demons registration gave mean TRE=(1.6{+-}0.9) mm compared to rigid registration TRE=(3.6{+-}1.9) mm, suggesting registration accuracy at or near the voxel size of the patient images (1x1x2 mm{sup 3}). The multiscale implementation based on optimal convergence criteria completed registration in 52 s for the cadaveric head and in an average time of 270 s for the larger FOV patient images. Conclusions: Appropriate selection of convergence and multiscale parameters in Demons registration was shown to reduce computational expense without sacrificing registration performance. For intraoperative CBCT imaging with deformable registration, the ability to perform accurate registration within the stringent time requirements of the operating environment could offer a useful clinical tool allowing integration of preoperative information while accurately reflecting changes in the patient anatomy. Similarly for CBCT-guided radiation therapy, fast accurate deformable registration could further augment high-precision treatment strategies.« less

Demons deformable registration for CBCT-guided procedures in the head and neck: convergence and accuracy.

PubMed

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

2009-10-01

The accuracy and convergence behavior of a variant of the Demons deformable registration algorithm were investigated for use in cone-beam CT (CBCT)-guided procedures of the head and neck. Online use of deformable registration for guidance of therapeutic procedures such as image-guided surgery or radiation therapy places trade-offs on accuracy and computational expense. This work describes a convergence criterion for Demons registration developed to balance these demands; the accuracy of a multiscale Demons implementation using this convergence criterion is quantified in CBCT images of the head and neck. Using an open-source "symmetric" Demons registration algorithm, a convergence criterion based on the change in the deformation field between iterations was developed to advance among multiple levels of a multiscale image pyramid in a manner that optimized accuracy and computation time. The convergence criterion was optimized in cadaver studies involving CBCT images acquired using a surgical C-arm prototype modified for 3D intraoperative imaging. CBCT-to-CBCT registration was performed and accuracy was quantified in terms of the normalized cross-correlation (NCC) and target registration error (TRE). The accuracy and robustness of the algorithm were then tested in clinical CBCT images of ten patients undergoing radiation therapy of the head and neck. The cadaver model allowed optimization of the convergence factor and initial measurements of registration accuracy: Demons registration exhibited TRE=(0.8+/-0.3) mm and NCC =0.99 in the cadaveric head compared to TRE=(2.6+/-1.0) mm and NCC=0.93 with rigid registration. Similarly for the patient data, Demons registration gave mean TRE=(1.6+/-0.9) mm compared to rigid registration TRE=(3.6+/-1.9) mm, suggesting registration accuracy at or near the voxel size of the patient images (1 x 1 x 2 mm3). The multiscale implementation based on optimal convergence criteria completed registration in 52 s for the cadaveric head and in an average time of 270 s for the larger FOV patient images. Appropriate selection of convergence and multiscale parameters in Demons registration was shown to reduce computational expense without sacrificing registration performance. For intraoperative CBCT imaging with deformable registration, the ability to perform accurate registration within the stringent time requirements of the operating environment could offer a useful clinical tool allowing integration of preoperative information while accurately reflecting changes in the patient anatomy. Similarly for CBCT-guided radiation therapy, fast accurate deformable registration could further augment high-precision treatment strategies.

Demons deformable registration for CBCT-guided procedures in the head and neck: Convergence and accuracy

PubMed Central

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

2009-01-01

Purpose: The accuracy and convergence behavior of a variant of the Demons deformable registration algorithm were investigated for use in cone-beam CT (CBCT)-guided procedures of the head and neck. Online use of deformable registration for guidance of therapeutic procedures such as image-guided surgery or radiation therapy places trade-offs on accuracy and computational expense. This work describes a convergence criterion for Demons registration developed to balance these demands; the accuracy of a multiscale Demons implementation using this convergence criterion is quantified in CBCT images of the head and neck. Methods: Using an open-source “symmetric” Demons registration algorithm, a convergence criterion based on the change in the deformation field between iterations was developed to advance among multiple levels of a multiscale image pyramid in a manner that optimized accuracy and computation time. The convergence criterion was optimized in cadaver studies involving CBCT images acquired using a surgical C-arm prototype modified for 3D intraoperative imaging. CBCT-to-CBCT registration was performed and accuracy was quantified in terms of the normalized cross-correlation (NCC) and target registration error (TRE). The accuracy and robustness of the algorithm were then tested in clinical CBCT images of ten patients undergoing radiation therapy of the head and neck. Results: The cadaver model allowed optimization of the convergence factor and initial measurements of registration accuracy: Demons registration exhibited TRE=(0.8±0.3) mm and NCC=0.99 in the cadaveric head compared to TRE=(2.6±1.0) mm and NCC=0.93 with rigid registration. Similarly for the patient data, Demons registration gave mean TRE=(1.6±0.9) mm compared to rigid registration TRE=(3.6±1.9) mm, suggesting registration accuracy at or near the voxel size of the patient images (1×1×2 mm3). The multiscale implementation based on optimal convergence criteria completed registration in 52 s for the cadaveric head and in an average time of 270 s for the larger FOV patient images. Conclusions: Appropriate selection of convergence and multiscale parameters in Demons registration was shown to reduce computational expense without sacrificing registration performance. For intraoperative CBCT imaging with deformable registration, the ability to perform accurate registration within the stringent time requirements of the operating environment could offer a useful clinical tool allowing integration of preoperative information while accurately reflecting changes in the patient anatomy. Similarly for CBCT-guided radiation therapy, fast accurate deformable registration could further augment high-precision treatment strategies. PMID:19928106

Automatic elastic image registration by interpolation of 3D rotations and translations from discrete rigid-body transformations.

PubMed

Walimbe, Vivek; Shekhar, Raj

2006-12-01

We present an algorithm for automatic elastic registration of three-dimensional (3D) medical images. Our algorithm initially recovers the global spatial mismatch between the reference and floating images, followed by hierarchical octree-based subdivision of the reference image and independent registration of the floating image with the individual subvolumes of the reference image at each hierarchical level. Global as well as local registrations use the six-parameter full rigid-body transformation model and are based on maximization of normalized mutual information (NMI). To ensure robustness of the subvolume registration with low voxel counts, we calculate NMI using a combination of current and prior mutual histograms. To generate a smooth deformation field, we perform direct interpolation of six-parameter rigid-body subvolume transformations obtained at the last subdivision level. Our interpolation scheme involves scalar interpolation of the 3D translations and quaternion interpolation of the 3D rotational pose. We analyzed the performance of our algorithm through experiments involving registration of synthetically deformed computed tomography (CT) images. Our algorithm is general and can be applied to image pairs of any two modalities of most organs. We have demonstrated successful registration of clinical whole-body CT and positron emission tomography (PET) images using this algorithm. The registration accuracy for this application was evaluated, based on validation using expert-identified anatomical landmarks in 15 CT-PET image pairs. The algorithm's performance was comparable to the average accuracy observed for three expert-determined registrations in the same 15 image pairs.

Nonrigid registration of carotid ultrasound and MR images using a "twisting and bending" model

NASA Astrophysics Data System (ADS)

Nanayakkara, Nuwan D.; Chiu, Bernard; Samani, Abbas; Spence, J. David; Parraga, Grace; Samarabandu, Jagath; Fenster, Aaron

2008-03-01

Atherosclerosis at the carotid bifurcation resulting in cerebral emboli is a major cause of ischemic stroke. Most strokes associated with carotid atherosclerosis can be prevented by lifestyle/dietary changes and pharmacological treatments if identified early by monitoring carotid plaque changes. Plaque composition information from magnetic resonance (MR) carotid images and dynamic characteristics information from 3D ultrasound (US) are necessary for developing and validating US imaging tools to identify vulnerable carotid plaques. Combining these images requires nonrigid registration to correct the non-linear miss-alignments caused by relative twisting and bending in the neck due to different head positions during the two image acquisitions sessions. The high degree of freedom and large number of parameters associated with existing nonrigid image registration methods causes several problems including unnatural plaque morphology alteration, computational complexity, and low reliability. Our approach was to model the normal movement of the neck using a "twisting and bending model" with only six parameters for nonrigid registration. We evaluated our registration technique using intra-subject in-vivo 3D US and 3D MR carotid images acquired on the same day. We calculated the Mean Registration Error (MRE) between the segmented vessel surfaces in the target image and the registered image using a distance-based error metric after applying our "twisting bending model" based nonrigid registration algorithm. We achieved an average registration error of 1.33+/-0.41mm using our nonrigid registration technique. Visual inspection of segmented vessel surfaces also showed a substantial improvement of alignment with our non-rigid registration technique.

Developing image processing meta-algorithms with data mining of multiple metrics.

PubMed

Leung, Kelvin; Cunha, Alexandre; Toga, A W; Parker, D Stott

2014-01-01

People often use multiple metrics in image processing, but here we take a novel approach of mining the values of batteries of metrics on image processing results. We present a case for extending image processing methods to incorporate automated mining of multiple image metric values. Here by a metric we mean any image similarity or distance measure, and in this paper we consider intensity-based and statistical image measures and focus on registration as an image processing problem. We show how it is possible to develop meta-algorithms that evaluate different image processing results with a number of different metrics and mine the results in an automated fashion so as to select the best results. We show that the mining of multiple metrics offers a variety of potential benefits for many image processing problems, including improved robustness and validation.

The development of machine technology processing for earth resource survey

NASA Technical Reports Server (NTRS)

Landgrebe, D. A.

1970-01-01

The following technologies are considered for automatic processing of earth resources data: (1) registration of multispectral and multitemporal images, (2) digital image display systems, (3) data system parameter effects on satellite remote sensing systems, and (4) data compression techniques based on spectral redundancy. The importance of proper spectral band and compression algorithm selections is pointed out.

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

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

Adaptive Diffeomorphic Multiresolution Demons and Their Application to Same Modality Medical Image Registration with Large Deformation

PubMed Central

Wang, Chang; Ren, Qiongqiong; Qin, Xin

2018-01-01

Diffeomorphic demons can guarantee smooth and reversible deformation and avoid unreasonable deformation. However, the number of iterations needs to be set manually, and this greatly influences the registration result. In order to solve this problem, we proposed adaptive diffeomorphic multiresolution demons in this paper. We used an optimized framework with nonrigid registration and diffeomorphism strategy, designed a similarity energy function based on grey value, and stopped iterations adaptively. This method was tested by synthetic image and same modality medical image. Large deformation was simulated by rotational distortion and extrusion transform, medical image registration with large deformation was performed, and quantitative analyses were conducted using the registration evaluation indexes, and the influence of different driving forces and parameters on the registration result was analyzed. The registration results of same modality medical images were compared with those obtained using active demons, additive demons, and diffeomorphic demons. Quantitative analyses showed that the proposed method's normalized cross-correlation coefficient and structural similarity were the highest and mean square error was the lowest. Medical image registration with large deformation could be performed successfully; evaluation indexes remained stable with an increase in deformation strength. The proposed method is effective and robust, and it can be applied to nonrigid registration of same modality medical images with large deformation.

Adaptive Diffeomorphic Multiresolution Demons and Their Application to Same Modality Medical Image Registration with Large Deformation.

PubMed

Wang, Chang; Ren, Qiongqiong; Qin, Xin; Yu, Yi

2018-01-01

Diffeomorphic demons can guarantee smooth and reversible deformation and avoid unreasonable deformation. However, the number of iterations needs to be set manually, and this greatly influences the registration result. In order to solve this problem, we proposed adaptive diffeomorphic multiresolution demons in this paper. We used an optimized framework with nonrigid registration and diffeomorphism strategy, designed a similarity energy function based on grey value, and stopped iterations adaptively. This method was tested by synthetic image and same modality medical image. Large deformation was simulated by rotational distortion and extrusion transform, medical image registration with large deformation was performed, and quantitative analyses were conducted using the registration evaluation indexes, and the influence of different driving forces and parameters on the registration result was analyzed. The registration results of same modality medical images were compared with those obtained using active demons, additive demons, and diffeomorphic demons. Quantitative analyses showed that the proposed method's normalized cross-correlation coefficient and structural similarity were the highest and mean square error was the lowest. Medical image registration with large deformation could be performed successfully; evaluation indexes remained stable with an increase in deformation strength. The proposed method is effective and robust, and it can be applied to nonrigid registration of same modality medical images with large deformation.

Registering 2D and 3D imaging data of bone during healing.

PubMed

Hoerth, Rebecca M; Baum, Daniel; Knötel, David; Prohaska, Steffen; Willie, Bettina M; Duda, Georg N; Hege, Hans-Christian; Fratzl, Peter; Wagermaier, Wolfgang

2015-04-01

PURPOSE/AIMS OF THE STUDY: Bone's hierarchical structure can be visualized using a variety of methods. Many techniques, such as light and electron microscopy generate two-dimensional (2D) images, while micro-computed tomography (µCT) allows a direct representation of the three-dimensional (3D) structure. In addition, different methods provide complementary structural information, such as the arrangement of organic or inorganic compounds. The overall aim of the present study is to answer bone research questions by linking information of different 2D and 3D imaging techniques. A great challenge in combining different methods arises from the fact that they usually reflect different characteristics of the real structure. We investigated bone during healing by means of µCT and a couple of 2D methods. Backscattered electron images were used to qualitatively evaluate the tissue's calcium content and served as a position map for other experimental data. Nanoindentation and X-ray scattering experiments were performed to visualize mechanical and structural properties. We present an approach for the registration of 2D data in a 3D µCT reference frame, where scanning electron microscopies serve as a methodic link. Backscattered electron images are perfectly suited for registration into µCT reference frames, since both show structures based on the same physical principles. We introduce specific registration tools that have been developed to perform the registration process in a semi-automatic way. By applying this routine, we were able to exactly locate structural information (e.g. mineral particle properties) in the 3D bone volume. In bone healing studies this will help to better understand basic formation, remodeling and mineralization processes.

NASA IMAGESEER: NASA IMAGEs for Science, Education, Experimentation and Research

NASA Technical Reports Server (NTRS)

Le Moigne, Jacqueline; Grubb, Thomas G.; Milner, Barbara C.

2012-01-01

A number of web-accessible databases, including medical, military or other image data, offer universities and other users the ability to teach or research new Image Processing techniques on relevant and well-documented data. However, NASA images have traditionally been difficult for researchers to find, are often only available in hard-to-use formats, and do not always provide sufficient context and background for a non-NASA Scientist user to understand their content. The new IMAGESEER (IMAGEs for Science, Education, Experimentation and Research) database seeks to address these issues. Through a graphically-rich web site for browsing and downloading all of the selected datasets, benchmarks, and tutorials, IMAGESEER provides a widely accessible database of NASA-centric, easy to read, image data for teaching or validating new Image Processing algorithms. As such, IMAGESEER fosters collaboration between NASA and research organizations while simultaneously encouraging development of new and enhanced Image Processing algorithms. The first prototype includes a representative sampling of NASA multispectral and hyperspectral images from several Earth Science instruments, along with a few small tutorials. Image processing techniques are currently represented with cloud detection, image registration, and map cover/classification. For each technique, corresponding data are selected from four different geographic regions, i.e., mountains, urban, water coastal, and agriculture areas. Satellite images have been collected from several instruments - Landsat-5 and -7 Thematic Mappers, Earth Observing-1 (EO-1) Advanced Land Imager (ALI) and Hyperion, and the Moderate Resolution Imaging Spectroradiometer (MODIS). After geo-registration, these images are available in simple common formats such as GeoTIFF and raw formats, along with associated benchmark data.

a Band Selection Method for High Precision Registration of Hyperspectral Image

NASA Astrophysics Data System (ADS)

Yang, H.; Li, X.

2018-04-01

During the registration of hyperspectral images and high spatial resolution images, too much bands in a hyperspectral image make it difficult to select bands with good registration performance. Terrible bands are possible to reduce matching speed and accuracy. To solve this problem, an algorithm based on Cram'er-Rao lower bound theory is proposed to select good matching bands in this paper. The algorithm applies the Cram'er-Rao lower bound theory to the study of registration accuracy, and selects good matching bands by CRLB parameters. Experiments show that the algorithm in this paper can choose good matching bands and provide better data for the registration of hyperspectral image and high spatial resolution image.

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

PubMed

Ahmad, Sahar; Khan, Muhammad Faisal

2015-12-01

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

Registration of 4D time-series of cardiac images with multichannel Diffeomorphic Demons.

PubMed

Peyrat, Jean-Marc; Delingette, Hervé; Sermesant, Maxime; Pennec, Xavier; Xu, Chenyang; Ayache, Nicholas

2008-01-01

In this paper, we propose a generic framework for intersubject non-linear registration of 4D time-series images. In this framework, spatio-temporal registration is defined by mapping trajectories of physical points as opposed to spatial registration that solely aims at mapping homologous points. First, we determine the trajectories we want to register in each sequence using a motion tracking algorithm based on the Diffeomorphic Demons algorithm. Then, we perform simultaneously pairwise registrations of corresponding time-points with the constraint to map the same physical points over time. We show this trajectory registration can be formulated as a multichannel registration of 3D images. We solve it using the Diffeomorphic Demons algorithm extended to vector-valued 3D images. This framework is applied to the inter-subject non-linear registration of 4D cardiac CT sequences.

Optimized SIFTFlow for registration of whole-mount histology to reference optical images

PubMed Central

Shojaii, Rushin; Martel, Anne L.

2016-01-01

Abstract. The registration of two-dimensional histology images to reference images from other modalities is an important preprocessing step in the reconstruction of three-dimensional histology volumes. This is a challenging problem because of the differences in the appearances of histology images and other modalities, and the presence of large nonrigid deformations which occur during slide preparation. This paper shows the feasibility of using densely sampled scale-invariant feature transform (SIFT) features and a SIFTFlow deformable registration algorithm for coregistering whole-mount histology images with blockface optical images. We present a method for jointly optimizing the regularization parameters used by the SIFTFlow objective function and use it to determine the most appropriate values for the registration of breast lumpectomy specimens. We demonstrate that tuning the regularization parameters results in significant improvements in accuracy and we also show that SIFTFlow outperforms a previously described edge-based registration method. The accuracy of the histology images to blockface images registration using the optimized SIFTFlow method was assessed using an independent test set of images from five different lumpectomy specimens and the mean registration error was 0.32±0.22  mm. PMID:27774494

ImagingSIMS

DOE Office of Scientific and Technical Information (OSTI.GOV)

2017-11-06

ImagingSIMS is an open source application for loading, processing, manipulating and visualizing secondary ion mass spectrometry (SIMS) data. At PNNL, a separate branch has been further developed to incorporate application specific features for dynamic SIMS data sets. These include loading CAMECA IMS-1280, NanoSIMS and modified IMS-4f raw data, creating isotopic ratio images and stitching together images from adjacent interrogation regions. In addition to other modifications of the parent open source version, this version is equipped with a point-by-point image registration tool to assist with streamlining the image fusion process.

MRI and PET Compatible Bed for Direct Co-Registration in Small Animals

NASA Astrophysics Data System (ADS)

Bartoli, Antonietta; Esposito, Giovanna; D'Angeli, Luca; Chaabane, Linda; Terreno, Enzo

2013-06-01

To obtain an accurate co-registration with stand-alone PET and MRI scanners, we developed a compatible bed system for mice and rats that enables both images to be acquired without repositioning the animals. MRI acquisitions were performed on a preclinical 7T scanner (Pharmascan, Bruker), whereas PET scans were acquired on a YAP-(S)PET (ISE s.r.l.). The bed performance was tested both on a phantom (NEMA Image Quality phantom) and in vivo (healthy rats and mice brain). Fiducial markers filled up with a drop of 18 F were visible in both modalities. Co-registration process was performed using the point-based registration technique. The reproducibility and accuracy of the co-registration were assessed using the phantom. The reproducibility of the translation distances was 0.2 mm along the z axis. On the other hand, the accuracy depended on the physical size of the phantom structures under investigation but was always lower than 4%. Regions of Interest (ROIs) drawn on the fused images were used for quantification purposes. PET and MRI intensity profiles on small structures of the phantom showed that the underestimation in activity concentration reached 90% in regions that were smaller than the PET spatial resolution, while the MRI allowed a good visualization of the 1 mm 0 rod. PET/MRI images of healthy mice and rats highlighted the expected superior capability of MRI to define brain structures. The simplicity of our developed MRI/PET compatible bed and the quality of the fused images obtained offers a promising opportunity for a future preclinical translation, particularly for neuroimaging studies.

SU-E-J-237: Real-Time 3D Anatomy Estimation From Undersampled MR Acquisitions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Glitzner, M; Lagendijk, J; Raaymakers, B

Recent developments made MRI guided radiotherapy feasible. Performing simultaneous imaging during fractions can provide information about changing anatomy by means of deformable image registration for either immediate plan adaptations or accurate dose accumulation on the changing anatomy. In 3D MRI, however, acquisition time is considerable and scales with resolution. Furthermore, intra-scan motion degrades image quality.In this work, we investigate the sensitivity of registration quality on imageresolution: potentially, by employing spatial undersampling, the acquisition timeof MR images for the purpose of deformable image registration can be reducedsignificantly.On a volunteer, 3D-MR imaging data was sampled in a navigator-gated manner, acquiring one axialmore » volume (360×260×100mm{sup 3}) per 3s during exhale phase. A T1-weighted FFE sequence was used with an acquired voxel size of (2.5mm{sup 3}) for a duration of 17min. Deformation vector fields were evaluated for 100 imaging cycles with respect to the initial anatomy using deformable image registration based on optical flow. Subsequently, the imaging data was downsampled by a factor of 2, simulating a fourfold acquisition speed. Displacements of the downsampled volumes were then calculated by the same process.In kidneyliver boundaries and the region around stomach/duodenum, prominent organ drifts could be observed in both the original and the downsampled imaging data. An increasing displacement of approximately 2mm was observed for the kidney, while an area around the stomach showed sudden displacements of 4mm. Comparison of the motile points over time showed high reproducibility between the displacements of high-resolution and downsampled volumes: over a 17min acquisition, the componentwise RMS error was not more than 0.38mm.Based on the synthetic experiments, 3D nonrigid image registration shows little sensitivity to image resolution and the displacement information is preserved even when halving the resolution. This can be employed to greatly reduce image acquisition times for interventional applications in real-time. This work was funded by the SoRTS consortium, which includes the industry partners Elekta, Philips and Technolution.« less

Curve and Polygon Evolution Techniques for Image Processing

DTIC Science & Technology

2002-01-01

iterative image registration technique with an application to stereo vision. IJCAI, pages 674–679, 1981. 127 [93] R . Malladi , J.A. Sethian, and B.C...Notation A digital image to be processed is a 2-Dimensional (2-D) function denoted by I , I : ! R , where R2 is the domain of the function. Processing a...function Io(x; y), which depends on two spatial variables, x 2 R , and y 2 R , via a partial differential equation (PDE) takes the form; It = A(I; Ix

Computer-aided endovascular aortic repair using fully automated two- and three-dimensional fusion imaging.

PubMed

Panuccio, Giuseppe; Torsello, Giovanni Federico; Pfister, Markus; Bisdas, Theodosios; Bosiers, Michel J; Torsello, Giovanni; Austermann, Martin

2016-12-01

To assess the usability of a fully automated fusion imaging engine prototype, matching preinterventional computed tomography with intraoperative fluoroscopic angiography during endovascular aortic repair. From June 2014 to February 2015, all patients treated electively for abdominal and thoracoabdominal aneurysms were enrolled prospectively. Before each procedure, preoperative planning was performed with a fully automated fusion engine prototype based on computed tomography angiography, creating a mesh model of the aorta. In a second step, this three-dimensional dataset was registered with the two-dimensional intraoperative fluoroscopy. The main outcome measure was the applicability of the fully automated fusion engine. Secondary outcomes were freedom from failure of automatic segmentation or of the automatic registration as well as accuracy of the mesh model, measuring deviations from intraoperative angiography in millimeters, if applicable. Twenty-five patients were enrolled in this study. The fusion imaging engine could be used in successfully 92% of the cases (n = 23). Freedom from failure of automatic segmentation was 44% (n = 11). The freedom from failure of the automatic registration was 76% (n = 19), the median error of the automatic registration process was 0 mm (interquartile range, 0-5 mm). The fully automated fusion imaging engine was found to be applicable in most cases, albeit in several cases a fully automated data processing was not possible, requiring manual intervention. The accuracy of the automatic registration yielded excellent results and promises a useful and simple to use technology. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Influence of magnetic field strength and image registration strategy on voxel-based morphometry in a study of Alzheimer's disease.

PubMed

Marchewka, Artur; Kherif, Ferath; Krueger, Gunnar; Grabowska, Anna; Frackowiak, Richard; Draganski, Bogdan

2014-05-01

Multi-centre data repositories like the Alzheimer's Disease Neuroimaging Initiative (ADNI) offer a unique research platform, but pose questions concerning comparability of results when using a range of imaging protocols and data processing algorithms. The variability is mainly due to the non-quantitative character of the widely used structural T1-weighted magnetic resonance (MR) images. Although the stability of the main effect of Alzheimer's disease (AD) on brain structure across platforms and field strength has been addressed in previous studies using multi-site MR images, there are only sparse empirically-based recommendations for processing and analysis of pooled multi-centre structural MR data acquired at different magnetic field strengths (MFS). Aiming to minimise potential systematic bias when using ADNI data we investigate the specific contributions of spatial registration strategies and the impact of MFS on voxel-based morphometry in AD. We perform a whole-brain analysis within the framework of Statistical Parametric Mapping, testing for main effects of various diffeomorphic spatial registration strategies, of MFS and their interaction with disease status. Beyond the confirmation of medial temporal lobe volume loss in AD, we detect a significant impact of spatial registration strategy on estimation of AD related atrophy. Additionally, we report a significant effect of MFS on the assessment of brain anatomy (i) in the cerebellum, (ii) the precentral gyrus and (iii) the thalamus bilaterally, showing no interaction with the disease status. We provide empirical evidence in support of pooling data in multi-centre VBM studies irrespective of disease status or MFS. Copyright © 2013 Wiley Periodicals, Inc.

Non-rigid registration between 3D ultrasound and CT images of the liver based on intensity and gradient information

NASA Astrophysics Data System (ADS)

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

2011-01-01

In order to utilize both ultrasound (US) and computed tomography (CT) images of the liver concurrently for medical applications such as diagnosis and image-guided intervention, non-rigid registration between these two types of images is an essential step, as local deformation between US and CT images exists due to the different respiratory phases involved and due to the probe pressure that occurs in US imaging. This paper introduces a voxel-based non-rigid registration algorithm between the 3D B-mode US and CT images of the liver. In the proposed algorithm, to improve the registration accuracy, we utilize the surface information of the liver and gallbladder in addition to the information of the vessels inside the liver. For an effective correlation between US and CT images, we treat those anatomical regions separately according to their characteristics in US and CT images. Based on a novel objective function using a 3D joint histogram of the intensity and gradient information, vessel-based non-rigid registration is followed by surface-based non-rigid registration in sequence, which improves the registration accuracy. The proposed algorithm is tested for ten clinical datasets and quantitative evaluations are conducted. Experimental results show that the registration error between anatomical features of US and CT images is less than 2 mm on average, even with local deformation due to different respiratory phases and probe pressure. In addition, the lesion registration error is less than 3 mm on average with a maximum of 4.5 mm that is considered acceptable for clinical applications.

Evaluation of 4-dimensional Computed Tomography to 4-dimensional Cone-Beam Computed Tomography Deformable Image Registration for Lung Cancer Adaptive Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Balik, Salim; Weiss, Elisabeth; Jan, Nuzhat

2013-06-01

Purpose: To evaluate 2 deformable image registration (DIR) algorithms for the purpose of contour mapping to support image-guided adaptive radiation therapy with 4-dimensional cone-beam CT (4DCBCT). Methods and Materials: One planning 4D fan-beam CT (4DFBCT) and 7 weekly 4DCBCT scans were acquired for 10 locally advanced non-small cell lung cancer patients. The gross tumor volume was delineated by a physician in all 4D images. End-of-inspiration phase planning 4DFBCT was registered to the corresponding phase in weekly 4DCBCT images for day-to-day registrations. For phase-to-phase registration, the end-of-inspiration phase from each 4D image was registered to the end-of-expiration phase. Two DIR algorithms—smallmore » deformation inverse consistent linear elastic (SICLE) and Insight Toolkit diffeomorphic demons (DEMONS)—were evaluated. Physician-delineated contours were compared with the warped contours by using the Dice similarity coefficient (DSC), average symmetric distance, and false-positive and false-negative indices. The DIR results are compared with rigid registration of tumor. Results: For day-to-day registrations, the mean DSC was 0.75 ± 0.09 with SICLE, 0.70 ± 0.12 with DEMONS, 0.66 ± 0.12 with rigid-tumor registration, and 0.60 ± 0.14 with rigid-bone registration. Results were comparable to intraobserver variability calculated from phase-to-phase registrations as well as measured interobserver variation for 1 patient. SICLE and DEMONS, when compared with rigid-bone (4.1 mm) and rigid-tumor (3.6 mm) registration, respectively reduced the average symmetric distance to 2.6 and 3.3 mm. On average, SICLE and DEMONS increased the DSC to 0.80 and 0.79, respectively, compared with rigid-tumor (0.78) registrations for 4DCBCT phase-to-phase registrations. Conclusions: Deformable image registration achieved comparable accuracy to reported interobserver delineation variability and higher accuracy than rigid-tumor registration. Deformable image registration performance varied with the algorithm and the patient.« less

Accurate registration of temporal CT images for pulmonary nodules detection

NASA Astrophysics Data System (ADS)

Yan, Jichao; Jiang, Luan; Li, Qiang

2017-02-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Vision Algorithm for the Solar Aspect System of the HEROES Mission

NASA Technical Reports Server (NTRS)

Cramer, Alexander

2014-01-01

This work covers the design and test of a machine vision algorithm for generating high-accuracy pitch and yaw pointing solutions relative to the sun for the High Energy Replicated Optics to Explore the Sun (HEROES) mission. It describes how images were constructed by focusing an image of the sun onto a plate printed with a pattern of small fiducial markers. Images of this plate were processed in real time to determine relative position of the balloon payload to the sun. The algorithm is broken into four problems: circle detection, fiducial detection, fiducial identification, and image registration. Circle detection is handled by an "Average Intersection" method, fiducial detection by a matched filter approach, identification with an ad-hoc method based on the spacing between fiducials, and image registration with a simple least squares fit. Performance is verified on a combination of artificially generated images, test data recorded on the ground, and images from the 2013 flight

Vision Algorithm for the Solar Aspect System of the HEROES Mission

NASA Technical Reports Server (NTRS)

Cramer, Alexander; Christe, Steven; Shih, Albert

2014-01-01

This work covers the design and test of a machine vision algorithm for generating high-accuracy pitch and yaw pointing solutions relative to the sun for the High Energy Replicated Optics to Explore the Sun (HEROES) mission. It describes how images were constructed by focusing an image of the sun onto a plate printed with a pattern of small fiducial markers. Images of this plate were processed in real time to determine relative position of the balloon payload to the sun. The algorithm is broken into four problems: circle detection, fiducial detection, fiducial identification, and image registration. Circle detection is handled by an Average Intersection method, fiducial detection by a matched filter approach, identification with an ad-hoc method based on the spacing between fiducials, and image registration with a simple least squares fit. Performance is verified on a combination of artificially generated images, test data recorded on the ground, and images from the 2013 flight.

SU-E-J-92: CERR: New Tools to Analyze Image Registration Precision.

PubMed

Apte, A; Wang, Y; Oh, J; Saleh, Z; Deasy, J

2012-06-01

To present new tools in CERR (The Computational Environment for Radiotherapy Research) to analyze image registration and other software updates/additions. CERR continues to be a key environment (cited more than 129 times to date) for numerous RT-research studies involving outcomes modeling, prototyping algorithms for segmentation, and registration, experiments with phantom dosimetry, IMRT research, etc. Image registration is one of the key technologies required in many research studies. CERR has been interfaced with popular image registration frameworks like Plastimatch and ITK. Once the images have been autoregistered, CERR provides tools to analyze the accuracy of registration using the following innovative approaches (1)Distance Discordance Histograms (DDH), described in detail in a separate paper and (2)'MirrorScope', explained as follows: for any view plane the 2-d image is broken up into a 2d grid of medium-sized squares. Each square contains a right-half, which is the reference image, and a left-half, which is the mirror flipped version of the overlay image. The user can increase or decrease the size of this grid to control the resolution of the analysis. Other updates to CERR include tools to extract image and dosimetric features programmatically and storage in a central database and tools to interface with Statistical analysis software like SPSS and Matlab Statistics toolbox. MirrorScope was compared on various examples, including 'perfect' registration examples and 'artificially translated' registrations. for 'perfect' registration, the patterns obtained within each circles are symmetric, and are easily, visually recognized as aligned. For registrations that are off, the patterns obtained in the circles located in the regions of imperfections show unsymmetrical patterns that are easily recognized. The new updates to CERR further increase its utility for RT-research. Mirrorscope is a visually intuitive method of monitoring the accuracy of image registration that improves on the visual confusion of standard methods. © 2012 American Association of Physicists in Medicine.

A Kalman Filtering Perspective for Multiatlas Segmentation*

PubMed Central

Gao, Yi; Zhu, Liangjia; Cates, Joshua; MacLeod, Rob S.; Bouix, Sylvain; Tannenbaum, Allen

2016-01-01

In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only computed by direct registration but also inferred from the transformation that aligns the previous atlas to the image together with the transformation between the two atlases. This process is similar to the global positioning system on a vehicle, which gets position by inquiring from the satellite and by employing the previous location and velocity—neither answer in isolation being perfect. To solve this problem, a dynamical system scheme is crucial to combine the two pieces of information; for example, a Kalman filtering scheme is used. Accordingly, in this work, a Kalman multiatlas segmentation is proposed to stabilize the global/affine registration step. The contributions of this work are twofold. First, it provides a new dynamical systematic perspective for standard independent multiatlas registrations, and it is solved by Kalman filtering. Second, with very little extra computation, it can be combined with most existing multiatlas segmentation schemes for better registration/segmentation accuracy. PMID:26807162

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shang, K; Wang, J; Liu, D

2014-06-01

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

Automatic C-arm pose estimation via 2D/3D hybrid registration of a radiographic fiducial

NASA Astrophysics Data System (ADS)

Moult, E.; Burdette, E. C.; Song, D. Y.; Abolmaesumi, P.; Fichtinger, G.; Fallavollita, P.

2011-03-01

Motivation: In prostate brachytherapy, real-time dosimetry would be ideal to allow for rapid evaluation of the implant quality intra-operatively. However, such a mechanism requires an imaging system that is both real-time and which provides, via multiple C-arm fluoroscopy images, clear information describing the three-dimensional position of the seeds deposited within the prostate. Thus, accurate tracking of the C-arm poses proves to be of critical importance to the process. Methodology: We compute the pose of the C-arm relative to a stationary radiographic fiducial of known geometry by employing a hybrid registration framework. Firstly, by means of an ellipse segmentation algorithm and a 2D/3D feature based registration, we exploit known FTRAC geometry to recover an initial estimate of the C-arm pose. Using this estimate, we then initialize the intensity-based registration which serves to recover a refined and accurate estimation of the C-arm pose. Results: Ground-truth pose was established for each C-arm image through a published and clinically tested segmentation-based method. Using 169 clinical C-arm images and a +/-10° and +/-10 mm random perturbation of the ground-truth pose, the average rotation and translation errors were 0.68° (std = 0.06°) and 0.64 mm (std = 0.24 mm). Conclusion: Fully automated C-arm pose estimation using a 2D/3D hybrid registration scheme was found to be clinically robust based on human patient data.

Intensity-Based Registration for Lung Motion Estimation

NASA Astrophysics Data System (ADS)

Cao, Kunlin; Ding, Kai; Amelon, Ryan E.; Du, Kaifang; Reinhardt, Joseph M.; Raghavan, Madhavan L.; Christensen, Gary E.

Image registration plays an important role within pulmonary image analysis. The task of registration is to find the spatial mapping that brings two images into alignment. Registration algorithms designed for matching 4D lung scans or two 3D scans acquired at different inflation levels can catch the temporal changes in position and shape of the region of interest. Accurate registration is critical to post-analysis of lung mechanics and motion estimation. In this chapter, we discuss lung-specific adaptations of intensity-based registration methods for 3D/4D lung images and review approaches for assessing registration accuracy. Then we introduce methods for estimating tissue motion and studying lung mechanics. Finally, we discuss methods for assessing and quantifying specific volume change, specific ventilation, strain/ stretch information and lobar sliding.

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

PubMed

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

2010-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Heterogeneous Optimization Framework: Reproducible Preprocessing of Multi-Spectral Clinical MRI for Neuro-Oncology Imaging Research.

PubMed

Milchenko, Mikhail; Snyder, Abraham Z; LaMontagne, Pamela; Shimony, Joshua S; Benzinger, Tammie L; Fouke, Sarah Jost; Marcus, Daniel S

2016-07-01

Neuroimaging research often relies on clinically acquired magnetic resonance imaging (MRI) datasets that can originate from multiple institutions. Such datasets are characterized by high heterogeneity of modalities and variability of sequence parameters. This heterogeneity complicates the automation of image processing tasks such as spatial co-registration and physiological or functional image analysis. Given this heterogeneity, conventional processing workflows developed for research purposes are not optimal for clinical data. In this work, we describe an approach called Heterogeneous Optimization Framework (HOF) for developing image analysis pipelines that can handle the high degree of clinical data non-uniformity. HOF provides a set of guidelines for configuration, algorithm development, deployment, interpretation of results and quality control for such pipelines. At each step, we illustrate the HOF approach using the implementation of an automated pipeline for Multimodal Glioma Analysis (MGA) as an example. The MGA pipeline computes tissue diffusion characteristics of diffusion tensor imaging (DTI) acquisitions, hemodynamic characteristics using a perfusion model of susceptibility contrast (DSC) MRI, and spatial cross-modal co-registration of available anatomical, physiological and derived patient images. Developing MGA within HOF enabled the processing of neuro-oncology MR imaging studies to be fully automated. MGA has been successfully used to analyze over 160 clinical tumor studies to date within several research projects. Introduction of the MGA pipeline improved image processing throughput and, most importantly, effectively produced co-registered datasets that were suitable for advanced analysis despite high heterogeneity in acquisition protocols.

Developing Image Processing Meta-Algorithms with Data Mining of Multiple Metrics

PubMed Central

Cunha, Alexandre; Toga, A. W.; Parker, D. Stott

2014-01-01

People often use multiple metrics in image processing, but here we take a novel approach of mining the values of batteries of metrics on image processing results. We present a case for extending image processing methods to incorporate automated mining of multiple image metric values. Here by a metric we mean any image similarity or distance measure, and in this paper we consider intensity-based and statistical image measures and focus on registration as an image processing problem. We show how it is possible to develop meta-algorithms that evaluate different image processing results with a number of different metrics and mine the results in an automated fashion so as to select the best results. We show that the mining of multiple metrics offers a variety of potential benefits for many image processing problems, including improved robustness and validation. PMID:24653748

Joint estimation of subject motion and tracer kinetic parameters of dynamic PET data in an EM framework

NASA Astrophysics Data System (ADS)

Jiao, Jieqing; Salinas, Cristian A.; Searle, Graham E.; Gunn, Roger N.; Schnabel, Julia A.

2012-02-01

Dynamic Positron Emission Tomography is a powerful tool for quantitative imaging of in vivo biological processes. The long scan durations necessitate motion correction, to maintain the validity of the dynamic measurements, which can be particularly challenging due to the low signal-to-noise ratio (SNR) and spatial resolution, as well as the complex tracer behaviour in the dynamic PET data. In this paper we develop a novel automated expectation-maximisation image registration framework that incorporates temporal tracer kinetic information to correct for inter-frame subject motion during dynamic PET scans. We employ the Zubal human brain phantom to simulate dynamic PET data using SORTEO (a Monte Carlo-based simulator), in order to validate the proposed method for its ability to recover imposed rigid motion. We have conducted a range of simulations using different noise levels, and corrupted the data with a range of rigid motion artefacts. The performance of our motion correction method is compared with pairwise registration using normalised mutual information as a voxel similarity measure (an approach conventionally used to correct for dynamic PET inter-frame motion based solely on intensity information). To quantify registration accuracy, we calculate the target registration error across the images. The results show that our new dynamic image registration method based on tracer kinetics yields better realignment of the simulated datasets, halving the target registration error when compared to the conventional method at small motion levels, as well as yielding smaller residuals in translation and rotation parameters. We also show that our new method is less affected by the low signal in the first few frames, which the conventional method based on normalised mutual information fails to realign.

Feature-Based Retinal Image Registration Using D-Saddle Feature

PubMed Central

Hasikin, Khairunnisa; A. Karim, Noor Khairiah; Ahmedy, Fatimah

2017-01-01

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. PMID:29204257

Three-dimensional registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation.

PubMed

Prabhu, David; Mehanna, Emile; Gargesha, Madhusudhana; Brandt, Eric; Wen, Di; van Ditzhuijzen, Nienke S; Chamie, Daniel; Yamamoto, Hirosada; Fujino, Yusuke; Alian, Ali; Patel, Jaymin; Costa, Marco; Bezerra, Hiram G; Wilson, David L

2016-04-01

Evidence suggests high-resolution, high-contrast, [Formula: see text] intravascular optical coherence tomography (IVOCT) can distinguish plaque types, but further validation is needed, especially for automated plaque characterization. We developed experimental and three-dimensional (3-D) registration methods to provide validation of IVOCT pullback volumes using microscopic, color, and fluorescent cryo-image volumes with optional registered cryo-histology. A specialized registration method matched IVOCT pullback images acquired in the catheter reference frame to a true 3-D cryo-image volume. Briefly, an 11-parameter registration model including a polynomial virtual catheter was initialized within the cryo-image volume, and perpendicular images were extracted, mimicking IVOCT image acquisition. Virtual catheter parameters were optimized to maximize cryo and IVOCT lumen overlap. Multiple assessments suggested that the registration error was better than the [Formula: see text] spacing between IVOCT image frames. Tests on a digital synthetic phantom gave a registration error of only [Formula: see text] (signed distance). Visual assessment of randomly presented nearby frames suggested registration accuracy within 1 IVOCT frame interval ([Formula: see text]). This would eliminate potential misinterpretations confronted by the typical histological approaches to validation, with estimated 1-mm errors. The method can be used to create annotated datasets and automated plaque classification methods and can be extended to other intravascular imaging modalities.

Software for Automated Image-to-Image Co-registration

NASA Technical Reports Server (NTRS)

Benkelman, Cody A.; Hughes, Heidi

2007-01-01

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

SU-E-J-220: Evaluation of Atlas-Based Auto-Segmentation (ABAS) in Head-And-Neck Adaptive Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Q; Yan, D

2014-06-01

Purpose: Evaluate the accuracy of atlas-based auto segmentation of organs at risk (OARs) on both helical CT (HCT) and cone beam CT (CBCT) images in head and neck (HN) cancer adaptive radiotherapy (ART). Methods: Six HN patients treated in the ART process were included in this study. For each patient, three images were selected: pretreatment planning CT (PreTx-HCT), in treatment CT for replanning (InTx-HCT) and a CBCT acquired in the same day of the InTx-HCT. Three clinical procedures of auto segmentation and deformable registration performed in the ART process were evaluated: a) auto segmentation on PreTx-HCT using multi-subject atlases, b)more » intra-patient propagation of OARs from PreTx-HCT to InTx-HCT using deformable HCT-to-HCT image registration, and c) intra-patient propagation of OARs from PreTx-HCT to CBCT using deformable CBCT-to-HCT image registration. Seven OARs (brainstem, cord, L/R parotid, L/R submandibular gland and mandible) were manually contoured on PreTx-HCT and InTx-HCT for comparison. In addition, manual contours on InTx-CT were copied on the same day CBCT, and a local region rigid body registration was performed accordingly for each individual OAR. For procedures a) and b), auto contours were compared to manual contours, and for c) auto contours were compared to those rigidly transferred contours on CBCT. Dice similarity coefficients (DSC) and mean surface distances of agreement (MSDA) were calculated for evaluation. Results: For procedure a), the mean DSC/MSDA of most OARs are >80%/±2mm. For intra-patient HCT-to-HCT propagation, the Resultimproved to >85%/±1.5mm. Compared to HCT-to-HCT, the mean DSC for HCT-to-CBCT propagation drops ∼2–3% and MSDA increases ∼0.2mm. This Resultindicates that the inferior imaging quality of CBCT seems only degrade auto propagation performance slightly. Conclusion: Auto segmentation and deformable propagation can generate OAR structures on HCT and CBCT images with clinically acceptable accuracy. Therefore, they can be reliably implemented in the clinical HN ART process.« less

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

NASA Astrophysics Data System (ADS)

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.

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

NASA Astrophysics Data System (ADS)

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

2013-03-01

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

A system for the registration of arthroscopic images to magnetic resonance images of the knee: for improved virtual knee arthroscopy

NASA Astrophysics Data System (ADS)

Hu, Chengliang; Amati, Giancarlo; Gullick, Nicola; Oakley, Stephen; Hurmusiadis, Vassilios; Schaeffter, Tobias; Penney, Graeme; Rhode, Kawal

2009-02-01

Knee arthroscopy is a minimally invasive procedure that is routinely carried out for the diagnosis and treatment of pathologies of the knee joint. A high level of expertise is required to carry out this procedure and therefore the clinical training is extensive. There are several reasons for this that include the small field of view seen by the arthroscope and the high degree of distortion in the video images. Several virtual arthroscopy simulators have been proposed to augment the learning process. One of the limitations of these simulators is the generic models that are used. We propose to develop a new virtual arthroscopy simulator that will allow the use of pathology-specific models with an increased level of photo-realism. In order to generate these models we propose to use registered magnetic resonance images (MRI) and arthroscopic video images collected from patients with a variety of knee pathologies. We present a method to perform this registration based on the use of a combined X-ray and MR imaging system (XMR). In order to validate our technique we carried out MR imaging and arthroscopy of a custom-made acrylic phantom in the XMR environment. The registration between the two modalities was computed using a combination of XMR and camera calibration, and optical tracking. Both two-dimensional (2D) and three-dimensional (3D) registration errors were computed and shown to be approximately 0.8 and 3 mm, respectively. Further to this, we qualitatively tested our approach using a more realistic plastic knee model that is used for the arthroscopy training.

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

PubMed Central

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.

PubMed

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.

A novel augmented reality system for displaying inferior alveolar nerve bundles in maxillofacial surgery.

PubMed

Zhu, Ming; Liu, Fei; Chai, Gang; Pan, Jun J; Jiang, Taoran; Lin, Li; Xin, Yu; Zhang, Yan; Li, Qingfeng

2017-02-15

Augmented reality systems can combine virtual images with a real environment to ensure accurate surgery with lower risk. This study aimed to develop a novel registration and tracking technique to establish a navigation system based on augmented reality for maxillofacial surgery. Specifically, a virtual image is reconstructed from CT data using 3D software. The real environment is tracked by the augmented reality (AR) software. The novel registration strategy that we created uses an occlusal splint compounded with a fiducial marker (OSM) to establish a relationship between the virtual image and the real object. After the fiducial marker is recognized, the virtual image is superimposed onto the real environment, forming the "integrated image" on semi-transparent glass. Via the registration process, the integral image, which combines the virtual image with the real scene, is successfully presented on the semi-transparent helmet. The position error of this navigation system is 0.96 ± 0.51 mm. This augmented reality system was applied in the clinic and good surgical outcomes were obtained. The augmented reality system that we established for maxillofacial surgery has the advantages of easy manipulation and high accuracy, which can improve surgical outcomes. Thus, this system exhibits significant potential in clinical applications.

Model-based registration for assessment of spinal deformities in idiopathic scoliosis

NASA Astrophysics Data System (ADS)

Forsberg, Daniel; Lundström, Claes; Andersson, Mats; Knutsson, Hans

2014-01-01

Detailed analysis of spinal deformity is important within orthopaedic healthcare, in particular for assessment of idiopathic scoliosis. This paper addresses this challenge by proposing an image analysis method, capable of providing a full three-dimensional spine characterization. The proposed method is based on the registration of a highly detailed spine model to image data from computed tomography. The registration process provides an accurate segmentation of each individual vertebra and the ability to derive various measures describing the spinal deformity. The derived measures are estimated from landmarks attached to the spine model and transferred to the patient data according to the registration result. Evaluation of the method provides an average point-to-surface error of 0.9 mm ± 0.9 (comparing segmentations), and an average target registration error of 2.3 mm ± 1.7 (comparing landmarks). Comparing automatic and manual measurements of axial vertebral rotation provides a mean absolute difference of 2.5° ± 1.8, which is on a par with other computerized methods for assessing axial vertebral rotation. A significant advantage of our method, compared to other computerized methods for rotational measurements, is that it does not rely on vertebral symmetry for computing the rotational measures. The proposed method is fully automatic and computationally efficient, only requiring three to four minutes to process an entire image volume covering vertebrae L5 to T1. Given the use of landmarks, the method can be readily adapted to estimate other measures describing a spinal deformity by changing the set of employed landmarks. In addition, the method has the potential to be utilized for accurate segmentations of the vertebrae in routine computed tomography examinations, given the relatively low point-to-surface error.

Insight into efficient image registration techniques and the demons algorithm.

PubMed

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

2007-01-01

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

Automated geographic registration and radiometric correction for UAV-based mosaics

NASA Astrophysics Data System (ADS)

Thomasson, J. Alex; Shi, Yeyin; Sima, Chao; Yang, Chenghai; Cope, Dale A.

2017-05-01

Texas A and M University has been operating a large-scale, UAV-based, agricultural remote-sensing research project since 2015. To use UAV-based images in agricultural production, many high-resolution images must be mosaicked together to create an image of an agricultural field. Two key difficulties to science-based utilization of such mosaics are geographic registration and radiometric calibration. In our current research project, image files are taken to the computer laboratory after the flight, and semi-manual pre-processing is implemented on the raw image data, including ortho-mosaicking and radiometric calibration. Ground control points (GCPs) are critical for high-quality geographic registration of images during mosaicking. Applications requiring accurate reflectance data also require radiometric-calibration references so that reflectance values of image objects can be calculated. We have developed a method for automated geographic registration and radiometric correction with targets that are installed semi-permanently at distributed locations around fields. The targets are a combination of black (≍5% reflectance), dark gray (≍20% reflectance), and light gray (≍40% reflectance) sections that provide for a transformation of pixel-value to reflectance in the dynamic range of crop fields. The exact spectral reflectance of each target is known, having been measured with a spectrophotometer. At the time of installation, each target is measured for position with a real-time kinematic GPS receiver to give its precise latitude and longitude. Automated location of the reference targets in the images is required for precise, automated, geographic registration; and automated calculation of the digital-number to reflectance transformation is required for automated radiometric calibration. To validate the system for radiometric calibration, a calibrated UAV-based image mosaic of a field was compared to a calibrated single image from a manned aircraft. Reflectance values in selected zones of each image were strongly linearly related, and the average error of UAV-mosaic reflectances was 3.4% in the red band, 1.9% in the green band, and 1.5% in the blue band. Based on these results, the proposed physical system and automated software for calibrating UAV mosaics show excellent promise.

eHUGS: Enhanced Hierarchical Unbiased Graph Shrinkage for Efficient Groupwise Registration

PubMed Central

Wu, Guorong; Peng, Xuewei; Ying, Shihui; Wang, Qian; Yap, Pew-Thian; Shen, Dan; Shen, Dinggang

2016-01-01

Effective and efficient spatial normalization of a large population of brain images is critical for many clinical and research studies, but it is technically very challenging. A commonly used approach is to choose a certain image as the template and then align all other images in the population to this template by applying pairwise registration. To avoid the potential bias induced by the inappropriate template selection, groupwise registration methods have been proposed to simultaneously register all images to a latent common space. However, current groupwise registration methods do not make full use of image distribution information for more accurate registration. In this paper, we present a novel groupwise registration method that harnesses the image distribution information by capturing the image distribution manifold using a hierarchical graph with its nodes representing the individual images. More specifically, a low-level graph describes the image distribution in each subgroup, and a high-level graph encodes the relationship between representative images of subgroups. Given the graph representation, we can register all images to the common space by dynamically shrinking the graph on the image manifold. The topology of the entire image distribution is always maintained during graph shrinkage. Evaluations on two datasets, one for 80 elderly individuals and one for 285 infants, indicate that our method can yield promising results. PMID:26800361

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

PubMed

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

2013-06-01

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

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

PubMed

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.

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

PubMed

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

2008-09-01

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

SU-D-BRA-03: Analysis of Systematic Errors with 2D/3D Image Registration for Target Localization and Treatment Delivery in Stereotactic Radiosurgery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, H; Chetty, I; Wen, N

Purpose: Determine systematic deviations between 2D/3D and 3D/3D image registrations with six degrees of freedom (6DOF) for various imaging modalities and registration algorithms on the Varian Edge Linac. Methods: The 6DOF systematic errors were assessed by comparing automated 2D/3D (kV/MV vs. CT) with 3D/3D (CBCT vs. CT) image registrations from different imaging pairs, CT slice thicknesses, couch angles, similarity measures, etc., using a Rando head and a pelvic phantom. The 2D/3D image registration accuracy was evaluated at different treatment sites (intra-cranial and extra-cranial) by statistically analyzing 2D/3D pre-treatment verification against 3D/3D localization of 192 Stereotactic Radiosurgery/Stereotactic Body Radiation Therapy treatmentmore » fractions for 88 patients. Results: The systematic errors of 2D/3D image registration using kV-kV, MV-kV and MV-MV image pairs using 0.8 mm slice thickness CT images were within 0.3 mm and 0.3° for translations and rotations with a 95% confidence interval (CI). No significant difference between 2D/3D and 3D/3D image registrations (P>0.05) was observed for target localization at various CT slice thicknesses ranging from 0.8 to 3 mm. Couch angles (30, 45, 60 degree) did not impact the accuracy of 2D/3D image registration. Using pattern intensity with content image filtering was recommended for 2D/3D image registration to achieve the best accuracy. For the patient study, translational error was within 2 mm and rotational error was within 0.6 degrees in terms of 95% CI for 2D/3D image registration. For intra-cranial sites, means and std. deviations of translational errors were −0.2±0.7, 0.04±0.5, 0.1±0.4 mm for LNG, LAT, VRT directions, respectively. For extra-cranial sites, means and std. deviations of translational errors were - 0.04±1, 0.2±1, 0.1±1 mm for LNG, LAT, VRT directions, respectively. 2D/3D image registration uncertainties for intra-cranial and extra-cranial sites were comparable. Conclusion: The Varian Edge radiosurgery 6DOF-based system, can perform 2D/3D image registration with high accuracy for target localization in image-guided stereotactic radiosurgery. The work was supported by a Research Scholar Grant, RSG-15-137-01-CCE from the American Cancer Society.« less

Registration of angiographic image on real-time fluoroscopic image for image-guided percutaneous coronary intervention.

PubMed

Kim, Dongkue; Park, Sangsoo; Jeong, Myung Ho; Ryu, Jeha

2018-02-01

In percutaneous coronary intervention (PCI), cardiologists must study two different X-ray image sources: a fluoroscopic image and an angiogram. Manipulating a guidewire while alternately monitoring the two separate images on separate screens requires a deep understanding of the anatomy of coronary vessels and substantial training. We propose 2D/2D spatiotemporal image registration of the two images in a single image in order to provide cardiologists with enhanced visual guidance in PCI. The proposed 2D/2D spatiotemporal registration method uses a cross-correlation of two ECG series in each image to temporally synchronize two separate images and register an angiographic image onto the fluoroscopic image. A guidewire centerline is then extracted from the fluoroscopic image in real time, and the alignment of the centerline with vessel outlines of the chosen angiographic image is optimized using the iterative closest point algorithm for spatial registration. A proof-of-concept evaluation with a phantom coronary vessel model with engineering students showed an error reduction rate greater than 74% on wrong insertion to nontarget branches compared to the non-registration method and more than 47% reduction in the task completion time in performing guidewire manipulation for very difficult tasks. Evaluation with a small number of experienced doctors shows a potentially significant reduction in both task completion time and error rate for difficult tasks. The total registration time with real procedure X-ray (angiographic and fluoroscopic) images takes [Formula: see text] 60 ms, which is within the fluoroscopic image acquisition rate of 15 Hz. By providing cardiologists with better visual guidance in PCI, the proposed spatiotemporal image registration method is shown to be useful in advancing the guidewire to the coronary vessel branches, especially those difficult to insert into.

Non-rigid image registration using graph-cuts.

PubMed

Tang, Tommy W H; Chung, Albert C S

2007-01-01

Non-rigid image registration is an ill-posed yet challenging problem due to its supernormal high degree of freedoms and inherent requirement of smoothness. Graph-cuts method is a powerful combinatorial optimization tool which has been successfully applied into image segmentation and stereo matching. Under some specific constraints, graph-cuts method yields either a global minimum or a local minimum in a strong sense. Thus, it is interesting to see the effects of using graph-cuts in non-rigid image registration. In this paper, we formulate non-rigid image registration as a discrete labeling problem. Each pixel in the source image is assigned a displacement label (which is a vector) indicating which position in the floating image it is spatially corresponding to. A smoothness constraint based on first derivative is used to penalize sharp changes in displacement labels across pixels. The whole system can be optimized by using the graph-cuts method via alpha-expansions. We compare 2D and 3D registration results of our method with two state-of-the-art approaches. It is found that our method is more robust to different challenging non-rigid registration cases with higher registration accuracy.

MARS: a mouse atlas registration system based on a planar x-ray projector and an optical camera

NASA Astrophysics Data System (ADS)

Wang, Hongkai; Stout, David B.; Taschereau, Richard; Gu, Zheng; Vu, Nam T.; Prout, David L.; Chatziioannou, Arion F.

2012-10-01

This paper introduces a mouse atlas registration system (MARS), composed of a stationary top-view x-ray projector and a side-view optical camera, coupled to a mouse atlas registration algorithm. This system uses the x-ray and optical images to guide a fully automatic co-registration of a mouse atlas with each subject, in order to provide anatomical reference for small animal molecular imaging systems such as positron emission tomography (PET). To facilitate the registration, a statistical atlas that accounts for inter-subject anatomical variations was constructed based on 83 organ-labeled mouse micro-computed tomography (CT) images. The statistical shape model and conditional Gaussian model techniques were used to register the atlas with the x-ray image and optical photo. The accuracy of the atlas registration was evaluated by comparing the registered atlas with the organ-labeled micro-CT images of the test subjects. The results showed excellent registration accuracy of the whole-body region, and good accuracy for the brain, liver, heart, lungs and kidneys. In its implementation, the MARS was integrated with a preclinical PET scanner to deliver combined PET/MARS imaging, and to facilitate atlas-assisted analysis of the preclinical PET images.

MARS: a mouse atlas registration system based on a planar x-ray projector and an optical camera.

PubMed

Wang, Hongkai; Stout, David B; Taschereau, Richard; Gu, Zheng; Vu, Nam T; Prout, David L; Chatziioannou, Arion F

2012-10-07

This paper introduces a mouse atlas registration system (MARS), composed of a stationary top-view x-ray projector and a side-view optical camera, coupled to a mouse atlas registration algorithm. This system uses the x-ray and optical images to guide a fully automatic co-registration of a mouse atlas with each subject, in order to provide anatomical reference for small animal molecular imaging systems such as positron emission tomography (PET). To facilitate the registration, a statistical atlas that accounts for inter-subject anatomical variations was constructed based on 83 organ-labeled mouse micro-computed tomography (CT) images. The statistical shape model and conditional Gaussian model techniques were used to register the atlas with the x-ray image and optical photo. The accuracy of the atlas registration was evaluated by comparing the registered atlas with the organ-labeled micro-CT images of the test subjects. The results showed excellent registration accuracy of the whole-body region, and good accuracy for the brain, liver, heart, lungs and kidneys. In its implementation, the MARS was integrated with a preclinical PET scanner to deliver combined PET/MARS imaging, and to facilitate atlas-assisted analysis of the preclinical PET images.

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

PubMed Central

2014-01-01

Background Robust point matching (RPM) has been extensively used in non-rigid registration of images to robustly register two sets of image points. However, except for the location at control points, RPM cannot estimate the consistent correspondence between two images because RPM is a unidirectional image matching approach. Therefore, it is an important issue to make an improvement in image registration based on RPM. Methods In our work, a consistent image registration approach based on the point sets matching is proposed to incorporate the property of inverse consistency and improve registration accuracy. Instead of only estimating the forward transformation between the source point sets and the target point sets in state-of-the-art RPM algorithms, the forward and backward transformations between two point sets are estimated concurrently in our algorithm. The inverse consistency constraints are introduced to the cost function of RPM and the fuzzy correspondences between two point sets are estimated based on both the forward and backward transformations simultaneously. A modified consistent landmark thin-plate spline registration is discussed in detail to find the forward and backward transformations during the optimization of RPM. The similarity of image content is also incorporated into point matching in order to improve image matching. Results Synthetic data sets, medical images are employed to demonstrate and validate the performance of our approach. The inverse consistent errors of our algorithm are smaller than RPM. Especially, the topology of transformations is preserved well for our algorithm for the large deformation between point sets. Moreover, the distance errors of our algorithm are similar to that of RPM, and they maintain a downward trend as whole, which demonstrates the convergence of our algorithm. The registration errors for image registrations are evaluated also. Again, our algorithm achieves the lower registration errors in same iteration number. The determinant of the Jacobian matrix of the deformation field is used to analyse the smoothness of the forward and backward transformations. The forward and backward transformations estimated by our algorithm are smooth for small deformation. For registration of lung slices and individual brain slices, large or small determinant of the Jacobian matrix of the deformation fields are observed. Conclusions Results indicate the improvement of the proposed algorithm in bi-directional image registration and the decrease of the inverse consistent errors of the forward and the reverse transformations between two images. PMID:25559889

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

PubMed Central

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

2010-01-01

Images from different individuals typically cannot be registered precisely because anatomical features within the images differ across the people imaged and because the current methods for image registration have inherent technological limitations that interfere with perfect registration. Quantifying the inevitable error in image registration is therefore of crucial importance in assessing the effects that image misregistration may have on subsequent analyses in an imaging study. We have developed a mathematical framework for quantifying errors in registration by computing the confidence intervals of the estimated parameters (3 translations, 3 rotations, and 1 global scale) for the similarity transformation. The presence of noise in images and the variability in anatomy across individuals ensures that estimated registration parameters are always random variables. We assume a functional relation among intensities across voxels in the images, and we use the theory of nonlinear, least-squares estimation to show that the parameters are multivariate Gaussian distributed. We then use the covariance matrix of this distribution to compute the confidence intervals of the transformation parameters. These confidence intervals provide a quantitative assessment of the registration error across the images. Because transformation parameters are nonlinearly related to the coordinates of landmark points in the brain, we subsequently show that the coordinates of those landmark points are also multivariate Gaussian distributed. Using these distributions, we then compute the confidence intervals of the coordinates for landmark points in the image. Each of these confidence intervals in turn provides a quantitative assessment of the registration error at a particular landmark point. Because our method is computationally intensive, however, its current implementation is limited to assessing the error of the parameters in the similarity transformation across images. We assessed the performance of our method in computing the error in estimated similarity parameters by applying that method to real world dataset. Our results showed that the size of the confidence intervals computed using our method decreased – i.e. our confidence in the registration of images from different individuals increased – for increasing amounts of blur in the images. Moreover, the size of the confidence intervals increased for increasing amounts of noise, misregistration, and differing anatomy. Thus, our method precisely quantified confidence in the registration of images that contain varying amounts of misregistration and varying anatomy across individuals. PMID:19138877

SU-E-J-90: 2D/3D Registration Using KV-MV Image Pairs for Higher Accuracy Image Guided Radiotherapy.

PubMed

Furtado, H; Figl, M; Stock, M; Georg, D; Birkfellner, W

2012-06-01

In this work, we investigate the impact of using paired portal mega-voltage (MV) and kilo-voltage (kV) images, on 2D/3D registration accuracy with the purpose of improving tumor motion tracking during radiotherapy. Tumor motion tracking is important as motion remains one of the biggest sources of uncertainty in dose application. 2D/3D registration is successfully used in online tumor motion tracking, nevertheless, one limitation of this technique is the inability to resolve movement along the imaging beam axis using only one projection image. Our evaluation consisted in comparing the accuracy of registration using different 2D image combinations: only one 2D image (1-kV), one kV and one MV image (1kV-1MV) and two kV images (2-kV). For each of the image combinations we evaluated the registration results using 250 starting points as initial displacements from the gold standard. We measured the final mean target registration error (mTRE) and the success rate for each registration. Each of the combinations was evaluated using four different merit functions. When using the MI merit function (a popular choice for this application) the RMS mTRE drops from 6.4 mm when using only one image to 2.1 mm when using image pairs. The success rate increases from 62% to 99.6%. A similar trend was observed for all four merit functions. Typically, the results are slightly better with 2-kV images than with 1kV-1MV. We evaluated the impact of using different image combinations on accuracy of 2D/3D registration for tumor motion monitoring. Our results show that using a kV-MV image pair, leads to improved results as motion can be accurately resolved in six degrees of freedom. Given the possibility to acquire these two images simultaneously, this is not only very workflow efficient but is also shown to be a good approach to improve registration accuracy. © 2012 American Association of Physicists in Medicine.

Real-time registration of 3D to 2D ultrasound images for image-guided prostate biopsy.

PubMed

Gillies, Derek J; Gardi, Lori; De Silva, Tharindu; Zhao, Shuang-Ren; Fenster, Aaron

2017-09-01

During image-guided prostate biopsy, needles are targeted at tissues that are suspicious of cancer to obtain specimen for histological examination. Unfortunately, patient motion causes targeting errors when using an MR-transrectal ultrasound (TRUS) fusion approach to augment the conventional biopsy procedure. This study aims to develop an automatic motion correction algorithm approaching the frame rate of an ultrasound system to be used in fusion-based prostate biopsy systems. Two modes of operation have been investigated for the clinical implementation of the algorithm: motion compensation using a single user initiated correction performed prior to biopsy, and real-time continuous motion compensation performed automatically as a background process. Retrospective 2D and 3D TRUS patient images acquired prior to biopsy gun firing were registered using an intensity-based algorithm utilizing normalized cross-correlation and Powell's method for optimization. 2D and 3D images were downsampled and cropped to estimate the optimal amount of image information that would perform registrations quickly and accurately. The optimal search order during optimization was also analyzed to avoid local optima in the search space. Error in the algorithm was computed using target registration errors (TREs) from manually identified homologous fiducials in a clinical patient dataset. The algorithm was evaluated for real-time performance using the two different modes of clinical implementations by way of user initiated and continuous motion compensation methods on a tissue mimicking prostate phantom. After implementation in a TRUS-guided system with an image downsampling factor of 4, the proposed approach resulted in a mean ± std TRE and computation time of 1.6 ± 0.6 mm and 57 ± 20 ms respectively. The user initiated mode performed registrations with in-plane, out-of-plane, and roll motions computation times of 108 ± 38 ms, 60 ± 23 ms, and 89 ± 27 ms, respectively, and corresponding registration errors of 0.4 ± 0.3 mm, 0.2 ± 0.4 mm, and 0.8 ± 0.5°. The continuous method performed registration significantly faster (P < 0.05) than the user initiated method, with observed computation times of 35 ± 8 ms, 43 ± 16 ms, and 27 ± 5 ms for in-plane, out-of-plane, and roll motions, respectively, and corresponding registration errors of 0.2 ± 0.3 mm, 0.7 ± 0.4 mm, and 0.8 ± 1.0°. The presented method encourages real-time implementation of motion compensation algorithms in prostate biopsy with clinically acceptable registration errors. Continuous motion compensation demonstrated registration accuracy with submillimeter and subdegree error, while performing < 50 ms computation times. Image registration technique approaching the frame rate of an ultrasound system offers a key advantage to be smoothly integrated to the clinical workflow. In addition, this technique could be used further for a variety of image-guided interventional procedures to treat and diagnose patients by improving targeting accuracy. © 2017 American Association of Physicists in Medicine.

Deformable Image Registration based on Similarity-Steered CNN Regression.

PubMed

Cao, Xiaohuan; Yang, Jianhua; Zhang, Jun; Nie, Dong; Kim, Min-Jeong; Wang, Qian; Shen, Dinggang

2017-09-01

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

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

PubMed

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

2010-09-01

To develop a new metric for image registration that incorporates the (sub)pixelwise differential importance along spatial location and to demonstrate its application for image guided radiation therapy (IGRT). It is well known that rigid-body image registration with mutual information is dependent on the size and location of the image subset on which the alignment analysis is based [the designated region of interest (ROI)]. Therefore, careful review and manual adjustments of the resulting registration are frequently necessary. Although there were some investigations of weighted mutual information (WMI), these efforts could not apply the differential importance to a particular spatial location since WMI only applies the weight to the joint histogram space. The authors developed the spatially weighted mutual information (SWMI) metric by incorporating an adaptable weight function with spatial localization into mutual information. SWMI enables the user to apply the selected transform to medically "important" areas such as tumors and critical structures, so SWMI is neither dominated by, nor neglects the neighboring structures. Since SWMI can be utilized with any weight function form, the authors presented two examples of weight functions for IGRT application: A Gaussian-shaped weight function (GW) applied to a user-defined location and a structures-of-interest (SOI) based weight function. An image registration example using a synthesized 2D image is presented to illustrate the efficacy of SWMI. The convergence and feasibility of the registration method as applied to clinical imaging is illustrated by fusing a prostate treatment planning CT with a clinical cone beam CT (CBCT) image set acquired for patient alignment. Forty-one trials are run to test the speed of convergence. The authors also applied SWMI registration using two types of weight functions to two head and neck cases and a prostate case with clinically acquired CBCT/ MVCT image sets. The SWMI registration with a Gaussian weight function (SWMI-GW) was tested between two different imaging modalities: CT and MRI image sets. SWMI-GW converges 10% faster than registration using mutual information with an ROI. SWMI-GW as well as SWMI with SOI-based weight function (SWMI-SOI) shows better compensation of the target organ's deformation and neighboring critical organs' deformation. SWMI-GW was also used to successfully fuse MRI and CT images. Rigid-body image registration using our SWMI-GW and SWMI-SOI as cost functions can achieve better registration results in (a) designated image region(s) as well as faster convergence. With the theoretical foundation established, we believe SWMI could be extended to larger clinical testing.

Liver CT image processing: a short introduction of the technical elements.

PubMed

Masutani, Y; Uozumi, K; Akahane, Masaaki; Ohtomo, Kuni

2006-05-01

In this paper, we describe the technical aspects of image analysis for liver diagnosis and treatment, including the state-of-the-art of liver image analysis and its applications. After discussion on modalities for liver image analysis, various technical elements for liver image analysis such as registration, segmentation, modeling, and computer-assisted detection are covered with examples performed with clinical data sets. Perspective in the imaging technologies is also reviewed and discussed.

Ultrasound guidance system for prostate biopsy

NASA Astrophysics Data System (ADS)

Hummel, Johann; Kerschner, Reinhard; Kaar, Marcus; Birkfellner, Wolfgang; Figl, Michael

2017-03-01

We designed a guidance system for prostate biopsy based on PET/MR images and 3D ultrasound (US). With our proposed method common inter-modal MR-US (or CT-US in case of PET/CTs) registration can be replaced by an intra-modal 3D/3D-US/US registration and an optical tracking system (OTS). On the pre-operative site, a PET/MR calibration allows to link both hybrid modalities with an abdominal 3D-US. On the interventional site, another abdominal 3D US is taken to merge the pre-operative images with the real-time 3D-US via 3D/3D-US/US registration. Finally, the images of a tracked trans-rectal US probe can be displayed with the pre-operative images by overlay. For PET/MR image fusion we applied a point-to-point registration between PET and OTS and MR and OTS, respectively. 3D/3D-US/US registration was evaluated for images taken in supine and lateral patient position. To enable table shifts between PET/MR and US image acquisition a table calibration procedure is presented. We found fiducial registration errors of 0.9 mm and 2.8 mm, respectively, with respect to the MR and PET calibration. A target registration error between MR and 3D US amounted to 1.4 mm. The registration error for the 3D/3D-US/US registration was found to be 3.7 mm. Furthermore, we have shown that ultrasound is applicable in an MR environment.

A multi-resolution strategy for a multi-objective deformable image registration framework that accommodates large anatomical differences

NASA Astrophysics Data System (ADS)

Alderliesten, Tanja; Bosman, Peter A. N.; Sonke, Jan-Jakob; Bel, Arjan

2014-03-01

Currently, two major challenges dominate the field of deformable image registration. The first challenge is related to the tuning of the developed methods to specific problems (i.e. how to best combine different objectives such as similarity measure and transformation effort). This is one of the reasons why, despite significant progress, clinical implementation of such techniques has proven to be difficult. The second challenge is to account for large anatomical differences (e.g. large deformations, (dis)appearing structures) that occurred between image acquisitions. In this paper, we study a framework based on multi-objective optimization to improve registration robustness and to simplify tuning for specific applications. Within this framework we specifically consider the use of an advanced model-based evolutionary algorithm for optimization and a dual-dynamic transformation model (i.e. two "non-fixed" grids: one for the source- and one for the target image) to accommodate for large anatomical differences. The framework computes and presents multiple outcomes that represent efficient trade-offs between the different objectives (a so-called Pareto front). In image processing it is common practice, for reasons of robustness and accuracy, to use a multi-resolution strategy. This is, however, only well-established for single-objective registration methods. Here we describe how such a strategy can be realized for our multi-objective approach and compare its results with a single-resolution strategy. For this study we selected the case of prone-supine breast MRI registration. Results show that the well-known advantages of a multi-resolution strategy are successfully transferred to our multi-objective approach, resulting in superior (i.e. Pareto-dominating) outcomes.

RAMTaB: Robust Alignment of Multi-Tag Bioimages

PubMed Central

Raza, Shan-e-Ahmed; Humayun, Ahmad; Abouna, Sylvie; Nattkemper, Tim W.; Epstein, David B. A.; Khan, Michael; Rajpoot, Nasir M.

2012-01-01

Background In recent years, new microscopic imaging techniques have evolved to allow us to visualize several different proteins (or other biomolecules) in a visual field. Analysis of protein co-localization becomes viable because molecules can interact only when they are located close to each other. We present a novel approach to align images in a multi-tag fluorescence image stack. The proposed approach is applicable to multi-tag bioimaging systems which (a) acquire fluorescence images by sequential staining and (b) simultaneously capture a phase contrast image corresponding to each of the fluorescence images. To the best of our knowledge, there is no existing method in the literature, which addresses simultaneous registration of multi-tag bioimages and selection of the reference image in order to maximize the overall overlap between the images. Methodology/Principal Findings We employ a block-based method for registration, which yields a confidence measure to indicate the accuracy of our registration results. We derive a shift metric in order to select the Reference Image with Maximal Overlap (RIMO), in turn minimizing the total amount of non-overlapping signal for a given number of tags. Experimental results show that the Robust Alignment of Multi-Tag Bioimages (RAMTaB) framework is robust to variations in contrast and illumination, yields sub-pixel accuracy, and successfully selects the reference image resulting in maximum overlap. The registration results are also shown to significantly improve any follow-up protein co-localization studies. Conclusions For the discovery of protein complexes and of functional protein networks within a cell, alignment of the tag images in a multi-tag fluorescence image stack is a key pre-processing step. The proposed framework is shown to produce accurate alignment results on both real and synthetic data. Our future work will use the aligned multi-channel fluorescence image data for normal and diseased tissue specimens to analyze molecular co-expression patterns and functional protein networks. PMID:22363510

Concepts for on-board satellite image registration. Volume 2: IAS prototype performance evaluation standard definition

NASA Astrophysics Data System (ADS)

Daluge, D. R.; Ruedger, W. H.

1981-06-01

Problems encountered in testing onboard signal processing hardware designed to achieve radiometric and geometric correction of satellite imaging data are considered. These include obtaining representative image and ancillary data for simulation and the transfer and storage of a large quantity of image data at very high speed. The high resolution, high speed preprocessing of LANDSAT-D imagery is considered.

In-die mask registration measurement on 28nm-node and beyond

NASA Astrophysics Data System (ADS)

Chen, Shen Hung; Cheng, Yung Feng; Chen, Ming Jui

2013-09-01

As semiconductor go to smaller node, the critical dimension (CD) of process become more and more small. For lithography, RET (Resolution Enhancement Technology) applications can be used for wafer printing of smaller CD/pitch on 28nm node and beyond. SMO (Source Mask Optimization), DPT (Double Patterning Technology) and SADP (Self-Align Double Patterning) can provide lower k1 value for lithography. In another way, image placement error and overlay control also become more and more important for smaller chip size (advanced node). Mask registration (image placement error) and mask overlay are important factors to affect wafer overlay control/performance especially for DPT or SADP. In traditional method, the designed registration marks (cross type, square type) with larger CD were put into scribe-line of mask frame for registration and overlay measurement. However, these patterns are far way from real patterns. It does not show the registration of real pattern directly and is not a convincing method. In this study, the in-die (in-chip) registration measurement is introduced. We extract the dummy patterns that are close to main pattern from post-OPC (Optical Proximity Correction) gds by our desired rule and choose the patterns that distribute over whole mask uniformly. The convergence test shows 100 points measurement has a reliable result.

A fast alignment method for breast MRI follow-up studies using automated breast segmentation and current-prior registration

NASA Astrophysics Data System (ADS)

Wang, Lei; Strehlow, Jan; Rühaak, Jan; Weiler, Florian; Diez, Yago; Gubern-Merida, Albert; Diekmann, Susanne; Laue, Hendrik; Hahn, Horst K.

2015-03-01

In breast cancer screening for high-risk women, follow-up magnetic resonance images (MRI) are acquired with a time interval ranging from several months up to a few years. Prior MRI studies may provide additional clinical value when examining the current one and thus have the potential to increase sensitivity and specificity of screening. To build a spatial correlation between suspicious findings in both current and prior studies, a reliable alignment method between follow-up studies is desirable. However, long time interval, different scanners and imaging protocols, and varying breast compression can result in a large deformation, which challenges the registration process. In this work, we present a fast and robust spatial alignment framework, which combines automated breast segmentation and current-prior registration techniques in a multi-level fashion. First, fully automatic breast segmentation is applied to extract the breast masks that are used to obtain an initial affine transform. Then, a non-rigid registration algorithm using normalized gradient fields as similarity measure together with curvature regularization is applied. A total of 29 subjects and 58 breast MR images were collected for performance assessment. To evaluate the global registration accuracy, the volume overlap and boundary surface distance metrics are calculated, resulting in an average Dice Similarity Coefficient (DSC) of 0.96 and root mean square distance (RMSD) of 1.64 mm. In addition, to measure local registration accuracy, for each subject a radiologist annotated 10 pairs of markers in the current and prior studies representing corresponding anatomical locations. The average distance error of marker pairs dropped from 67.37 mm to 10.86 mm after applying registration.

Enhanced coronary calcium visualization and detection from dual energy chest x-rays with sliding organ registration.

PubMed

Wen, Di; Nye, Katelyn; Zhou, Bo; Gilkeson, Robert C; Gupta, Amit; Ranim, Shiraz; Couturier, Spencer; Wilson, David L

2018-03-01

We have developed a technique to image coronary calcium, an excellent biomarker for atherosclerotic disease, using low cost, low radiation dual energy (DE) chest radiography, with potential for widespread screening from an already ordered exam. Our dual energy coronary calcium (DECC) processing method included automatic heart silhouette segmentation, sliding organ registration and scatter removal to create a bone-image-like, coronary calcium image with significant reduction in motion artifacts and improved calcium conspicuity compared to standard, clinically available DE processing. Experiments with a physical dynamic cardiac phantom showed that DECC processing reduced 73% of misregistration error caused by cardiac motion over a wide range of heart rates and x-ray radiation exposures. Using the functional measurement test (FMT), we determined significant image quality improvement in clinical images with DECC processing (p < 0.0001), where DECC images were chosen best in 94% of human readings. Comparing DECC images to registered and projected CT calcium images, we found good correspondence between the size and location of calcification signals. In a very preliminary coronary calcium ROC study, we used CT Agatston calcium score >50 as the gold standard for an actual positive test result. AUC performance was significantly improved from 0.73 ± 0.14 with standard DE to 0.87 ± 0.10 with DECC (p = 0.0095) for this limited set of surgical patient data biased towards heavy calcifications. The proposed DECC processing shows good potential for coronary calcium detection in DE chest radiography, giving impetus for a larger clinical evaluation. Copyright © 2018. Published by Elsevier Ltd.

Three modality image registration of brain SPECT/CT and MR images for quantitative analysis of dopamine transporter imaging

NASA Astrophysics Data System (ADS)

Yamaguchi, Yuzuho; Takeda, Yuta; Hara, Takeshi; Zhou, Xiangrong; Matsusako, Masaki; Tanaka, Yuki; Hosoya, Kazuhiko; Nihei, Tsutomu; Katafuchi, Tetsuro; Fujita, Hiroshi

2016-03-01

Important features in Parkinson's disease (PD) are degenerations and losses of dopamine neurons in corpus striatum. 123I-FP-CIT can visualize activities of the dopamine neurons. The activity radio of background to corpus striatum is used for diagnosis of PD and Dementia with Lewy Bodies (DLB). The specific activity can be observed in the corpus striatum on SPECT images, but the location and the shape of the corpus striatum on SPECT images only are often lost because of the low uptake. In contrast, MR images can visualize the locations of the corpus striatum. The purpose of this study was to realize a quantitative image analysis for the SPECT images by using image registration technique with brain MR images that can determine the region of corpus striatum. In this study, the image fusion technique was used to fuse SPECT and MR images by intervening CT image taken by SPECT/CT. The mutual information (MI) for image registration between CT and MR images was used for the registration. Six SPECT/CT and four MR scans of phantom materials are taken by changing the direction. As the results of the image registrations, 16 of 24 combinations were registered within 1.3mm. By applying the approach to 32 clinical SPECT/CT and MR cases, all of the cases were registered within 0.86mm. In conclusions, our registration method has a potential in superimposing MR images on SPECT images.

SU-E-J-47: Comparison of Online Image Registrations of Varian TrueBeam Cone-Beam CT and BrainLab ExacTrac Imaging Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, J; Shi, W; Andrews, D

2015-06-15

Purpose To compare online image registrations of TrueBeam cone-beam CT (CBCT) and BrainLab ExacTrac imaging systems. Methods Tests were performed on a Varian TrueBeam STx linear accelerator (Version 2.0), which is integrated with a BrainLab ExacTrac imaging system (Version 6.0.5). The study was focused on comparing the online image registrations for translational shifts. A Rando head phantom was placed on treatment couch and immobilized with a BrainLab mask. The phantom was shifted by moving the couch translationally for 8 mm with a step size of 1 mm, in vertical, longitudinal, and lateral directions, respectively. At each location, the phantom wasmore » imaged with CBCT and ExacTrac x-ray. CBCT images were registered with TrueBeam and ExacTrac online registration algorithms, respectively. And ExacTrac x-ray image registrations were performed. Shifts calculated from different registrations were compared with nominal couch shifts. Results The averages and ranges of absolute differences between couch shifts and calculated phantom shifts obtained from ExacTrac x-ray registration, ExacTrac CBCT registration with default window, ExaxTrac CBCT registration with adjusted window (bone), Truebeam CBCT registration with bone window, and Truebeam CBCT registration with soft tissue window, were: 0.07 (0.02–0.14), 0.14 (0.01–0.35), 0.12 (0.02–0.28), 0.09 (0–0.20), and 0.06 (0–0.10) mm, in vertical direction; 0.06 (0.01–0.12), 0.27 (0.07–0.57), 0.23 (0.02–0.48), 0.04 (0–0.10), and 0.08 (0– 0.20) mm, in longitudinal direction; 0.05 (0.01–0.21), 0.35 (0.14–0.80), 0.25 (0.01–0.56), 0.19 (0–0.40), and 0.20 (0–0.40) mm, in lateral direction. Conclusion The shifts calculated from ExacTrac x-ray and TrueBeam CBCT registrations were close to each other (the differences between were less than 0.40 mm in any direction), and had better agreements with couch shifts than those from ExacTrac CBCT registrations. There were no significant differences between TrueBeam CBCT registrations using different windows. In ExacTrac CBCT registrations, using bone window led to better agreements than using default window.« less

Supervoxels for graph cuts-based deformable image registration using guided image filtering

NASA Astrophysics Data System (ADS)

Szmul, Adam; Papież, Bartłomiej W.; Hallack, Andre; Grau, Vicente; Schnabel, Julia A.

2017-11-01

We propose combining a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for three-dimensional (3-D) deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to two-dimensional (2-D) applications. However, our work overcomes some of the previous limitations by posing the problem on a graph created by adjacent supervoxels, where the number of nodes in the graph is reduced from the number of voxels to the number of supervoxels. We demonstrate how a supervoxel image representation combined with graph cuts-based optimization can be applied to 3-D data. We further show that the application of a relaxed graph representation of the image, followed by guided image filtering over the estimated deformation field, allows us to model "sliding motion." Applying this method to lung image registration results in highly accurate image registration and anatomically plausible estimations of the deformations. Evaluation of our method on a publicly available computed tomography lung image dataset leads to the observation that our approach compares very favorably with state of the art methods in continuous and discrete image registration, achieving target registration error of 1.16 mm on average per landmark.

Supervoxels for Graph Cuts-Based Deformable Image Registration Using Guided Image Filtering.

PubMed

Szmul, Adam; Papież, Bartłomiej W; Hallack, Andre; Grau, Vicente; Schnabel, Julia A

2017-10-04

In this work we propose to combine a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for 3D deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to 2D applications. However, our work overcomes some of the previous limitations by posing the problem on a graph created by adjacent supervoxels, where the number of nodes in the graph is reduced from the number of voxels to the number of supervoxels. We demonstrate how a supervoxel image representation, combined with graph cuts-based optimization can be applied to 3D data. We further show that the application of a relaxed graph representation of the image, followed by guided image filtering over the estimated deformation field, allows us to model 'sliding motion'. Applying this method to lung image registration, results in highly accurate image registration and anatomically plausible estimations of the deformations. Evaluation of our method on a publicly available Computed Tomography lung image dataset (www.dir-lab.com) leads to the observation that our new approach compares very favorably with state-of-the-art in continuous and discrete image registration methods achieving Target Registration Error of 1.16mm on average per landmark.

Supervoxels for Graph Cuts-Based Deformable Image Registration Using Guided Image Filtering

PubMed Central

Szmul, Adam; Papież, Bartłomiej W.; Hallack, Andre; Grau, Vicente; Schnabel, Julia A.

2017-01-01

In this work we propose to combine a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for 3D deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to 2D applications. However, our work overcomes some of the previous limitations by posing the problem on a graph created by adjacent supervoxels, where the number of nodes in the graph is reduced from the number of voxels to the number of supervoxels. We demonstrate how a supervoxel image representation, combined with graph cuts-based optimization can be applied to 3D data. We further show that the application of a relaxed graph representation of the image, followed by guided image filtering over the estimated deformation field, allows us to model ‘sliding motion’. Applying this method to lung image registration, results in highly accurate image registration and anatomically plausible estimations of the deformations. Evaluation of our method on a publicly available Computed Tomography lung image dataset (www.dir-lab.com) leads to the observation that our new approach compares very favorably with state-of-the-art in continuous and discrete image registration methods achieving Target Registration Error of 1.16mm on average per landmark. PMID:29225433

Fast algorithm for probabilistic bone edge detection (FAPBED)

NASA Astrophysics Data System (ADS)

Scepanovic, Danilo; Kirshtein, Joshua; Jain, Ameet K.; Taylor, Russell H.

2005-04-01

The registration of preoperative CT to intra-operative reality systems is a crucial step in Computer Assisted Orthopedic Surgery (CAOS). The intra-operative sensors include 3D digitizers, fiducials, X-rays and Ultrasound (US). FAPBED is designed to process CT volumes for registration to tracked US data. Tracked US is advantageous because it is real time, noninvasive, and non-ionizing, but it is also known to have inherent inaccuracies which create the need to develop a framework that is robust to various uncertainties, and can be useful in US-CT registration. Furthermore, conventional registration methods depend on accurate and absolute segmentation. Our proposed probabilistic framework addresses the segmentation-registration duality, wherein exact segmentation is not a prerequisite to achieve accurate registration. In this paper, we develop a method for fast and automatic probabilistic bone surface (edge) detection in CT images. Various features that influence the likelihood of the surface at each spatial coordinate are combined using a simple probabilistic framework, which strikes a fair balance between a high-level understanding of features in an image and the low-level number crunching of standard image processing techniques. The algorithm evaluates different features for detecting the probability of a bone surface at each voxel, and compounds the results of these methods to yield a final, low-noise, probability map of bone surfaces in the volume. Such a probability map can then be used in conjunction with a similar map from tracked intra-operative US to achieve accurate registration. Eight sample pelvic CT scans were used to extract feature parameters and validate the final probability maps. An un-optimized fully automatic Matlab code runs in five minutes per CT volume on average, and was validated by comparison against hand-segmented gold standards. The mean probability assigned to nonzero surface points was 0.8, while nonzero non-surface points had a mean value of 0.38 indicating clear identification of surface points on average. The segmentation was also sufficiently crisp, with a full width at half maximum (FWHM) value of 1.51 voxels.

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

PubMed

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

2016-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2015-03-01

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

Status report: Data management program algorithm evaluation activity at Marshall Space Flight Center

NASA Technical Reports Server (NTRS)

Jayroe, R. R., Jr.

1977-01-01

An algorithm evaluation activity was initiated to study the problems associated with image processing by assessing the independent and interdependent effects of registration, compression, and classification techniques on LANDSAT data for several discipline applications. The objective of the activity was to make recommendations on selected applicable image processing algorithms in terms of accuracy, cost, and timeliness or to propose alternative ways of processing the data. As a means of accomplishing this objective, an Image Coding Panel was established. The conduct of the algorithm evaluation is described.

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

NASA Astrophysics Data System (ADS)

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

2006-03-01

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

Fully automated rodent brain MR image processing pipeline on a Midas server: from acquired images to region-based statistics.

PubMed

Budin, Francois; Hoogstoel, Marion; Reynolds, Patrick; Grauer, Michael; O'Leary-Moore, Shonagh K; Oguz, Ipek

2013-01-01

Magnetic resonance imaging (MRI) of rodent brains enables study of the development and the integrity of the brain under certain conditions (alcohol, drugs etc.). However, these images are difficult to analyze for biomedical researchers with limited image processing experience. In this paper we present an image processing pipeline running on a Midas server, a web-based data storage system. It is composed of the following steps: rigid registration, skull-stripping, average computation, average parcellation, parcellation propagation to individual subjects, and computation of region-based statistics on each image. The pipeline is easy to configure and requires very little image processing knowledge. We present results obtained by processing a data set using this pipeline and demonstrate how this pipeline can be used to find differences between populations.

Concepts and Preliminary Data Toward the Realization of Image-guided Liver Surgery

PubMed Central

Cash, David M.; Miga, Michael I.; Glasgow, Sean C.; Dawant, Benoit M.; Clements, Logan W.; Cao, Zhujiang; Galloway, Robert L.; Chapman, William C.

2013-01-01

Image-guided surgery provides navigational assistance to the surgeon by displaying the surgical probe position on a set of preoperative tomograms in real time. In this study, the feasibility of implementing image-guided surgery concepts into liver surgery was examined during eight hepatic resection procedures. Preoperative tomographic image data were acquired and processed. Accompanying intraoperative data on liver shape and position were obtained through optically tracked probes and laser range scanning technology. The preoperative and intraoperative representations of the liver surface were aligned using the iterative closest point surface matching algorithm. Surface registrations resulted in mean residual errors from 2 to 6 mm, with errors of target surface regions being below a stated goal of 1 cm. Issues affecting registration accuracy include liver motion due to respiration, the quality of the intraoperative surface data, and intraoperative organ deformation. Respiratory motion was quantified during the procedures as cyclical, primarily along the cranial–caudal direction. The resulting registrations were more robust and accurate when using laser range scanning to rapidly acquire thousands of points on the liver surface and when capturing unique geometric regions on the liver surface, such as the inferior edge. Finally, finite element models recovered much of the observed intraoperative deformation, further decreasing errors in the registration. Image-guided liver surgery has shown the potential to provide surgeons with important navigation aids that could increase the accuracy of targeting lesions and the number of patients eligible for surgical resection. PMID:17458587

Integration of patient specific modeling and advanced image processing techniques for image-guided neurosurgery

NASA Astrophysics Data System (ADS)

Archip, Neculai; Fedorov, Andriy; Lloyd, Bryn; Chrisochoides, Nikos; Golby, Alexandra; Black, Peter M.; Warfield, Simon K.

2006-03-01

A major challenge in neurosurgery oncology is to achieve maximal tumor removal while avoiding postoperative neurological deficits. Therefore, estimation of the brain deformation during the image guided tumor resection process is necessary. While anatomic MRI is highly sensitive for intracranial pathology, its specificity is limited. Different pathologies may have a very similar appearance on anatomic MRI. Moreover, since fMRI and diffusion tensor imaging are not currently available during the surgery, non-rigid registration of preoperative MR with intra-operative MR is necessary. This article presents a translational research effort that aims to integrate a number of state-of-the-art technologies for MRI-guided neurosurgery at the Brigham and Women's Hospital (BWH). Our ultimate goal is to routinely provide the neurosurgeons with accurate information about brain deformation during the surgery. The current system is tested during the weekly neurosurgeries in the open magnet at the BWH. The preoperative data is processed, prior to the surgery, while both rigid and non-rigid registration algorithms are run in the vicinity of the operating room. The system is tested on 9 image datasets from 3 neurosurgery cases. A method based on edge detection is used to quantitatively validate the results. 95% Hausdorff distance between points of the edges is used to estimate the accuracy of the registration. Overall, the minimum error is 1.4 mm, the mean error 2.23 mm, and the maximum error 3.1 mm. The mean ratio between brain deformation estimation and rigid alignment is 2.07. It demonstrates that our results can be 2.07 times more precise then the current technology. The major contribution of the presented work is the rigid and non-rigid alignment of the pre-operative fMRI with intra-operative 0.5T MRI achieved during the neurosurgery.

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

PubMed

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.

MRI and CBCT image registration of temporomandibular joint: a systematic review.

PubMed

Al-Saleh, Mohammed A Q; Alsufyani, Noura A; Saltaji, Humam; Jaremko, Jacob L; Major, Paul W

2016-05-10

The purpose of the present review is to systematically and critically analyze the available literature regarding the importance, applicability, and practicality of (MRI), computerized tomography (CT) or cone-beam CT (CBCT) image registration for TMJ anatomy and assessment. A systematic search of 4 databases; MEDLINE, EMBASE, EBM reviews and Scopus, was conducted by 2 reviewers. An additional manual search of the bibliography was performed. All articles discussing the magnetic resonance imaging MRI and CT or CBCT image registration for temporomandibular joint (TMJ) visualization or assessment were included. Only 3 articles satisfied the inclusion criteria. All included articles were published within the last 7 years. Two articles described MRI to CT multimodality image registration as a complementary tool to visualize TMJ. Both articles used images of one patient only to introduce the complementary concept of MRI-CT fused image. One article assessed the reliability of using MRI-CBCT registration to evaluate the TMJ disc position and osseous pathology for 10 temporomandibular disorder (TMD) patients. There are very limited studies of MRI-CT/CBCT registration to reach a conclusion regarding its accuracy or clinical use in the temporomandibular joints.

Correction of patient motion in cone-beam CT using 3D-2D registration

NASA Astrophysics Data System (ADS)

Ouadah, S.; Jacobson, M.; Stayman, J. W.; Ehtiati, T.; Weiss, C.; Siewerdsen, J. H.

2017-12-01

Cone-beam CT (CBCT) is increasingly common in guidance of interventional procedures, but can be subject to artifacts arising from patient motion during fairly long (~5-60 s) scan times. We present a fiducial-free method to mitigate motion artifacts using 3D-2D image registration that simultaneously corrects residual errors in the intrinsic and extrinsic parameters of geometric calibration. The 3D-2D registration process registers each projection to a prior 3D image by maximizing gradient orientation using the covariance matrix adaptation-evolution strategy optimizer. The resulting rigid transforms are applied to the system projection matrices, and a 3D image is reconstructed via model-based iterative reconstruction. Phantom experiments were conducted using a Zeego robotic C-arm to image a head phantom undergoing 5-15 cm translations and 5-15° rotations. To further test the algorithm, clinical images were acquired with a CBCT head scanner in which long scan times were susceptible to significant patient motion. CBCT images were reconstructed using a penalized likelihood objective function. For phantom studies the structural similarity (SSIM) between motion-free and motion-corrected images was  >0.995, with significant improvement (p  <  0.001) compared to the SSIM values of uncorrected images. Additionally, motion-corrected images exhibited a point-spread function with full-width at half maximum comparable to that of the motion-free reference image. Qualitative comparison of the motion-corrupted and motion-corrected clinical images demonstrated a significant improvement in image quality after motion correction. This indicates that the 3D-2D registration method could provide a useful approach to motion artifact correction under assumptions of local rigidity, as in the head, pelvis, and extremities. The method is highly parallelizable, and the automatic correction of residual geometric calibration errors provides added benefit that could be valuable in routine use.

Decomposed multidimensional control grid interpolation for common consumer electronic image processing applications

NASA Astrophysics Data System (ADS)

Zwart, Christine M.; Venkatesan, Ragav; Frakes, David H.

2012-10-01

Interpolation is an essential and broadly employed function of signal processing. Accordingly, considerable development has focused on advancing interpolation algorithms toward optimal accuracy. Such development has motivated a clear shift in the state-of-the art from classical interpolation to more intelligent and resourceful approaches, registration-based interpolation for example. As a natural result, many of the most accurate current algorithms are highly complex, specific, and computationally demanding. However, the diverse hardware destinations for interpolation algorithms present unique constraints that often preclude use of the most accurate available options. For example, while computationally demanding interpolators may be suitable for highly equipped image processing platforms (e.g., computer workstations and clusters), only more efficient interpolators may be practical for less well equipped platforms (e.g., smartphones and tablet computers). The latter examples of consumer electronics present a design tradeoff in this regard: high accuracy interpolation benefits the consumer experience but computing capabilities are limited. It follows that interpolators with favorable combinations of accuracy and efficiency are of great practical value to the consumer electronics industry. We address multidimensional interpolation-based image processing problems that are common to consumer electronic devices through a decomposition approach. The multidimensional problems are first broken down into multiple, independent, one-dimensional (1-D) interpolation steps that are then executed with a newly modified registration-based one-dimensional control grid interpolator. The proposed approach, decomposed multidimensional control grid interpolation (DMCGI), combines the accuracy of registration-based interpolation with the simplicity, flexibility, and computational efficiency of a 1-D interpolation framework. Results demonstrate that DMCGI provides improved interpolation accuracy (and other benefits) in image resizing, color sample demosaicing, and video deinterlacing applications, at a computational cost that is manageable or reduced in comparison to popular alternatives.

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

PubMed Central

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. PMID:24000283

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

PubMed

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.

A novel augmented reality system for displaying inferior alveolar nerve bundles in maxillofacial surgery

PubMed Central

Zhu, Ming; Liu, Fei; Chai, Gang; Pan, Jun J.; Jiang, Taoran; Lin, Li; Xin, Yu; Zhang, Yan; Li, Qingfeng

2017-01-01

Augmented reality systems can combine virtual images with a real environment to ensure accurate surgery with lower risk. This study aimed to develop a novel registration and tracking technique to establish a navigation system based on augmented reality for maxillofacial surgery. Specifically, a virtual image is reconstructed from CT data using 3D software. The real environment is tracked by the augmented reality (AR) software. The novel registration strategy that we created uses an occlusal splint compounded with a fiducial marker (OSM) to establish a relationship between the virtual image and the real object. After the fiducial marker is recognized, the virtual image is superimposed onto the real environment, forming the “integrated image” on semi-transparent glass. Via the registration process, the integral image, which combines the virtual image with the real scene, is successfully presented on the semi-transparent helmet. The position error of this navigation system is 0.96 ± 0.51 mm. This augmented reality system was applied in the clinic and good surgical outcomes were obtained. The augmented reality system that we established for maxillofacial surgery has the advantages of easy manipulation and high accuracy, which can improve surgical outcomes. Thus, this system exhibits significant potential in clinical applications. PMID:28198442

Stopping Criteria for Log-Domain Diffeomorphic Demons Registration: An Experimental Survey for Radiotherapy Application.

PubMed

Peroni, M; Golland, P; Sharp, G C; Baroni, G

2016-02-01

A crucial issue in deformable image registration is achieving a robust registration algorithm at a reasonable computational cost. Given the iterative nature of the optimization procedure an algorithm must automatically detect convergence, and stop the iterative process when most appropriate. This paper ranks the performances of three stopping criteria and six stopping value computation strategies for a Log-Domain Demons Deformable registration method simulating both a coarse and a fine registration. The analyzed stopping criteria are: (a) velocity field update magnitude, (b) mean squared error, and (c) harmonic energy. Each stoping condition is formulated so that the user defines a threshold ∊, which quantifies the residual error that is acceptable for the particular problem and calculation strategy. In this work, we did not aim at assigning a value to e, but to give insights in how to evaluate and to set the threshold on a given exit strategy in a very popular registration scheme. Experiments on phantom and patient data demonstrate that comparing the optimization metric minimum over the most recent three iterations with the minimum over the fourth to sixth most recent iterations can be an appropriate algorithm stopping strategy. The harmonic energy was found to provide best trade-off between robustness and speed of convergence for the analyzed registration method at coarse registration, but was outperformed by mean squared error when all the original pixel information is used. This suggests the need of developing mathematically sound new convergence criteria in which both image and vector field information could be used to detect the actual convergence, which could be especially useful when considering multi-resolution registrations. Further work should be also dedicated to study same strategies performances in other deformable registration methods and body districts. © The Author(s) 2014.

2D to 3D fusion of echocardiography and cardiac CT for TAVR and TAVI image guidance.

PubMed

Khalil, Azira; Faisal, Amir; Lai, Khin Wee; Ng, Siew Cheok; Liew, Yih Miin

2017-08-01

This study proposed a registration framework to fuse 2D echocardiography images of the aortic valve with preoperative cardiac CT volume. The registration facilitates the fusion of CT and echocardiography to aid the diagnosis of aortic valve diseases and provide surgical guidance during transcatheter aortic valve replacement and implantation. The image registration framework consists of two major steps: temporal synchronization and spatial registration. Temporal synchronization allows time stamping of echocardiography time series data to identify frames that are at similar cardiac phase as the CT volume. Spatial registration is an intensity-based normalized mutual information method applied with pattern search optimization algorithm to produce an interpolated cardiac CT image that matches the echocardiography image. Our proposed registration method has been applied on the short-axis "Mercedes Benz" sign view of the aortic valve and long-axis parasternal view of echocardiography images from ten patients. The accuracy of our fully automated registration method was 0.81 ± 0.08 and 1.30 ± 0.13 mm in terms of Dice coefficient and Hausdorff distance for short-axis aortic valve view registration, whereas for long-axis parasternal view registration it was 0.79 ± 0.02 and 1.19 ± 0.11 mm, respectively. This accuracy is comparable to gold standard manual registration by expert. There was no significant difference in aortic annulus diameter measurement between the automatically and manually registered CT images. Without the use of optical tracking, we have shown the applicability of this technique for effective fusion of echocardiography with preoperative CT volume to potentially facilitate catheter-based surgery.

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

PubMed

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

2011-05-01

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

Monitoring tumor motion by real time 2D/3D registration during radiotherapy.

PubMed

Gendrin, Christelle; Furtado, Hugo; Weber, Christoph; Bloch, Christoph; Figl, Michael; Pawiro, Supriyanto Ardjo; Bergmann, Helmar; Stock, Markus; Fichtinger, Gabor; Georg, Dietmar; Birkfellner, Wolfgang

2012-02-01

In this paper, we investigate the possibility to use X-ray based real time 2D/3D registration for non-invasive tumor motion monitoring during radiotherapy. The 2D/3D registration scheme is implemented using general purpose computation on graphics hardware (GPGPU) programming techniques and several algorithmic refinements in the registration process. Validation is conducted off-line using a phantom and five clinical patient data sets. The registration is performed on a region of interest (ROI) centered around the planned target volume (PTV). The phantom motion is measured with an rms error of 2.56 mm. For the patient data sets, a sinusoidal movement that clearly correlates to the breathing cycle is shown. Videos show a good match between X-ray and digitally reconstructed radiographs (DRR) displacement. Mean registration time is 0.5 s. We have demonstrated that real-time organ motion monitoring using image based markerless registration is feasible. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

In vivo spatial correlation between (18)F-BPA and (18)F-FDG uptakes in head and neck cancer.

PubMed

Kobayashi, Kazuma; Kurihara, Hiroaki; Watanabe, Yoshiaki; Murakami, Naoya; Inaba, Koji; Nakamura, Satoshi; Wakita, Akihisa; Okamoto, Hiroyuki; Umezawa, Rei; Takahashi, Kana; Igaki, Hiroshi; Ito, Yoshinori; Yoshimoto, Seiichi; Shigematsu, Naoyuki; Itami, Jun

2016-09-01

Borono-2-(18)F-fluoro-phenylalanine ((18)F-BPA) has been used to estimate the therapeutic effects of boron neutron capture therapy (BNCT), while (18)F-fluorodeoxyglucose ((18)F-FDG) is the most commonly used positron emission tomography (PET) radiopharmaceutical in a routine clinical use. The aim of the present study was to evaluate spatial correlation between (18)F-BPA and (18)F-FDG uptakes using a deformable image registration-based technique. Ten patients with head and neck cancer were recruited from January 2014 to December 2014. All patients underwent whole-body (18)F-BPA PET/computed tomography (CT) and (18)F-FDG PET/CT within a 2-week period. For each patient, (18)F-BPA PET/CT and (18)F-FDG PET/CT images were aligned based on a deformable image registration framework. The voxel-by-voxel spatial correlation of standardized uptake value (SUV) within the tumor was analyzed. Our image processing framework achieved accurate and validated registration results for each PET/CT image. In 9/10 patients, the spatial distribution of SUVs between (18)F-BPA and (18)F-FDG showed a significant, positive correlation in the tumor volume. Deformable image registration-based voxel-wise analysis demonstrated a spatial correlation between (18)F-BPA and (18)F-FDG uptakes in the head and neck cancer. A tumor sub-volume with a high (18)F-FDG uptake may predict high accumulation of (18)F-BPA. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Super resolution for astronomical observations

NASA Astrophysics Data System (ADS)

Li, Zhan; Peng, Qingyu; Bhanu, Bir; Zhang, Qingfeng; He, Haifeng

2018-05-01

In order to obtain detailed information from multiple telescope observations a general blind super-resolution (SR) reconstruction approach for astronomical images is proposed in this paper. A pixel-reliability-based SR reconstruction algorithm is described and implemented, where the developed process incorporates flat field correction, automatic star searching and centering, iterative star matching, and sub-pixel image registration. Images captured by the 1-m telescope at Yunnan Observatory are used to test the proposed technique. The results of these experiments indicate that, following SR reconstruction, faint stars are more distinct, bright stars have sharper profiles, and the backgrounds have higher details; thus these results benefit from the high-precision star centering and image registration provided by the developed method. Application of the proposed approach not only provides more opportunities for new discoveries from astronomical image sequences, but will also contribute to enhancing the capabilities of most spatial or ground-based telescopes.

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

PubMed

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.

A Local Fast Marching-Based Diffusion Tensor Image Registration Algorithm by Simultaneously Considering Spatial Deformation and Tensor Orientation

PubMed Central

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

2010-01-01

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

Shearlet Features for Registration of Remotely Sensed Multitemporal Images

NASA Technical Reports Server (NTRS)

Murphy, James M.; Le Moigne, Jacqueline

2015-01-01

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

A fast rigid-registration method of inferior limb X-ray image and 3D CT images for TKA surgery

NASA Astrophysics Data System (ADS)

Ito, Fumihito; O. D. A, Prima; Uwano, Ikuko; Ito, Kenzo

2010-03-01

In this paper, we propose a fast rigid-registration method of inferior limb X-ray films (two-dimensional Computed Radiography (CR) images) and three-dimensional Computed Tomography (CT) images for Total Knee Arthroplasty (TKA) surgery planning. The position of the each bone, such as femur and tibia (shin bone), in X-ray film and 3D CT images is slightly different, and we must pay attention how to use the two different images, since X-ray film image is captured in the standing position, and 3D CT is captured in decubitus (face up) position, respectively. Though the conventional registration mainly uses cross-correlation function between two images,and utilizes optimization techniques, it takes enormous calculation time and it is difficult to use it in interactive operations. In order to solve these problems, we calculate the center line (bone axis) of femur and tibia (shin bone) automatically, and we use them as initial positions for the registration. We evaluate our registration method by using three patient's image data, and we compare our proposed method and a conventional registration, which uses down-hill simplex algorithm. The down-hill simplex method is an optimization algorithm that requires only function evaluations, and doesn't need the calculation of derivatives. Our registration method is more effective than the downhill simplex method in computational time and the stable convergence. We have developed the implant simulation system on a personal computer, in order to support the surgeon in a preoperative planning of TKA. Our registration method is implemented in the simulation system, and user can manipulate 2D/3D translucent templates of implant components on X-ray film and 3D CT images.

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

NASA Astrophysics Data System (ADS)

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 to allow the algorithm to more effectively register multimodal images. SERg is also tested within the free-form deformation framework driven by mutual information. Nine pairs of synthetic T1-weighted to T2-weighted brain MRI were registered under the following conditions: five levels of noise (0%, 1%, 3%, 5%, and 7%) and two levels of bias field (20% and 40%) each with and without noise. We demonstrate that across all of these conditions, SERg yields a mean squared error that is 81.51% lower than that of Demons driven by MRI intensity alone. We also spatially align twenty-six ex vivo histology sections and in vivo prostate MRI in order to map the spatial extent of prostate cancer onto corresponding radiologic imaging. SERg performs better than intensity registration by decreasing the root mean squared distance of annotated landmarks in the prostate gland via both Demons algorithm and mutual information-driven free-form deformation. In both synthetic and clinical experiments, the observed improvement in alignment of the template and target images suggest the utility of parametric eigenvector representations and hence SERg for multimodal image registration.

A finite element method to correct deformable image registration errors in low-contrast regions

NASA Astrophysics Data System (ADS)

Zhong, Hualiang; Kim, Jinkoo; Li, Haisen; Nurushev, Teamour; Movsas, Benjamin; Chetty, Indrin J.

2012-06-01

Image-guided adaptive radiotherapy requires deformable image registration to map radiation dose back and forth between images. The purpose of this study is to develop a novel method to improve the accuracy of an intensity-based image registration algorithm in low-contrast regions. A computational framework has been developed in this study to improve the quality of the ‘demons’ registration. For each voxel in the registration's target image, the standard deviation of image intensity in a neighborhood of this voxel was calculated. A mask for high-contrast regions was generated based on their standard deviations. In the masked regions, a tetrahedral mesh was refined recursively so that a sufficient number of tetrahedral nodes in these regions can be selected as driving nodes. An elastic system driven by the displacements of the selected nodes was formulated using a finite element method (FEM) and implemented on the refined mesh. The displacements of these driving nodes were generated with the ‘demons’ algorithm. The solution of the system was derived using a conjugated gradient method, and interpolated to generate a displacement vector field for the registered images. The FEM correction method was compared with the ‘demons’ algorithm on the computed tomography (CT) images of lung and prostate patients. The performance of the FEM correction relating to the ‘demons’ registration was analyzed based on the physical property of their deformation maps, and quantitatively evaluated through a benchmark model developed specifically for this study. Compared to the benchmark model, the ‘demons’ registration has the maximum error of 1.2 cm, which can be corrected by the FEM to 0.4 cm, and the average error of the ‘demons’ registration is reduced from 0.17 to 0.11 cm. For the CT images of lung and prostate patients, the deformation maps generated by the ‘demons’ algorithm were found unrealistic at several places. In these places, the displacement differences between the ‘demons’ registrations and their FEM corrections were found in the range of 0.4 and 1.1 cm. The mesh refinement and FEM simulation were implemented in a single thread application which requires about 45 min of computation time on a 2.6 GHz computer. This study has demonstrated that the FEM can be integrated with intensity-based image registration algorithms to improve their registration accuracy, especially in low-contrast regions.

Optical registration of spaceborne low light remote sensing camera

NASA Astrophysics Data System (ADS)

Li, Chong-yang; Hao, Yan-hui; Xu, Peng-mei; Wang, Dong-jie; Ma, Li-na; Zhao, Ying-long

2018-02-01

For the high precision requirement of spaceborne low light remote sensing camera optical registration, optical registration of dual channel for CCD and EMCCD is achieved by the high magnification optical registration system. System integration optical registration and accuracy of optical registration scheme for spaceborne low light remote sensing camera with short focal depth and wide field of view is proposed in this paper. It also includes analysis of parallel misalignment of CCD and accuracy of optical registration. Actual registration results show that imaging clearly, MTF and accuracy of optical registration meet requirements, it provide important guarantee to get high quality image data in orbit.

Improved image alignment method in application to X-ray images and biological images.

PubMed

Wang, Ching-Wei; Chen, Hsiang-Chou

2013-08-01

Alignment of medical images is a vital component of a large number of applications throughout the clinical track of events; not only within clinical diagnostic settings, but prominently so in the area of planning, consummation and evaluation of surgical and radiotherapeutical procedures. However, image registration of medical images is challenging because of variations on data appearance, imaging artifacts and complex data deformation problems. Hence, the aim of this study is to develop a robust image alignment method for medical images. An improved image registration method is proposed, and the method is evaluated with two types of medical data, including biological microscopic tissue images and dental X-ray images and compared with five state-of-the-art image registration techniques. The experimental results show that the presented method consistently performs well on both types of medical images, achieving 88.44 and 88.93% averaged registration accuracies for biological tissue images and X-ray images, respectively, and outperforms the benchmark methods. Based on the Tukey's honestly significant difference test and Fisher's least square difference test tests, the presented method performs significantly better than all existing methods (P ≤ 0.001) for tissue image alignment, and for the X-ray image registration, the proposed method performs significantly better than the two benchmark b-spline approaches (P < 0.001). The software implementation of the presented method and the data used in this study are made publicly available for scientific communities to use (http://www-o.ntust.edu.tw/∼cweiwang/ImprovedImageRegistration/). cweiwang@mail.ntust.edu.tw.

Grid Computing Application for Brain Magnetic Resonance Image Processing

NASA Astrophysics Data System (ADS)

Valdivia, F.; Crépeault, B.; Duchesne, S.

2012-02-01

This work emphasizes the use of grid computing and web technology for automatic post-processing of brain magnetic resonance images (MRI) in the context of neuropsychiatric (Alzheimer's disease) research. Post-acquisition image processing is achieved through the interconnection of several individual processes into pipelines. Each process has input and output data ports, options and execution parameters, and performs single tasks such as: a) extracting individual image attributes (e.g. dimensions, orientation, center of mass), b) performing image transformations (e.g. scaling, rotation, skewing, intensity standardization, linear and non-linear registration), c) performing image statistical analyses, and d) producing the necessary quality control images and/or files for user review. The pipelines are built to perform specific sequences of tasks on the alphanumeric data and MRIs contained in our database. The web application is coded in PHP and allows the creation of scripts to create, store and execute pipelines and their instances either on our local cluster or on high-performance computing platforms. To run an instance on an external cluster, the web application opens a communication tunnel through which it copies the necessary files, submits the execution commands and collects the results. We present result on system tests for the processing of a set of 821 brain MRIs from the Alzheimer's Disease Neuroimaging Initiative study via a nonlinear registration pipeline composed of 10 processes. Our results show successful execution on both local and external clusters, and a 4-fold increase in performance if using the external cluster. However, the latter's performance does not scale linearly as queue waiting times and execution overhead increase with the number of tasks to be executed.

Building generic anatomical models using virtual model cutting and iterative registration.

PubMed

Xiao, Mei; Soh, Jung; Meruvia-Pastor, Oscar; Schmidt, Eric; Hallgrímsson, Benedikt; Sensen, Christoph W

2010-02-08

Using 3D generic models to statistically analyze trends in biological structure changes is an important tool in morphometrics research. Therefore, 3D generic models built for a range of populations are in high demand. However, due to the complexity of biological structures and the limited views of them that medical images can offer, it is still an exceptionally difficult task to quickly and accurately create 3D generic models (a model is a 3D graphical representation of a biological structure) based on medical image stacks (a stack is an ordered collection of 2D images). We show that the creation of a generic model that captures spatial information exploitable in statistical analyses is facilitated by coupling our generalized segmentation method to existing automatic image registration algorithms. The method of creating generic 3D models consists of the following processing steps: (i) scanning subjects to obtain image stacks; (ii) creating individual 3D models from the stacks; (iii) interactively extracting sub-volume by cutting each model to generate the sub-model of interest; (iv) creating image stacks that contain only the information pertaining to the sub-models; (v) iteratively registering the corresponding new 2D image stacks; (vi) averaging the newly created sub-models based on intensity to produce the generic model from all the individual sub-models. After several registration procedures are applied to the image stacks, we can create averaged image stacks with sharp boundaries. The averaged 3D model created from those image stacks is very close to the average representation of the population. The image registration time varies depending on the image size and the desired accuracy of the registration. Both volumetric data and surface model for the generic 3D model are created at the final step. Our method is very flexible and easy to use such that anyone can use image stacks to create models and retrieve a sub-region from it at their ease. Java-based implementation allows our method to be used on various visualization systems including personal computers, workstations, computers equipped with stereo displays, and even virtual reality rooms such as the CAVE Automated Virtual Environment. The technique allows biologists to build generic 3D models of their interest quickly and accurately.

Active edge maps for medical image registration

NASA Astrophysics Data System (ADS)

Kerwin, William; Yuan, Chun

2001-07-01

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

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

PubMed Central

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

2013-01-01

Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4–5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15–60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license. PMID:24474917

Image-Guided Abdominal Surgery and Therapy Delivery

PubMed Central

Galloway, Robert L.; Herrell, S. Duke; Miga, Michael I.

2013-01-01

Image-Guided Surgery has become the standard of care in intracranial neurosurgery providing more exact resections while minimizing damage to healthy tissue. Moving that process to abdominal organs presents additional challenges in the form of image segmentation, image to physical space registration, organ motion and deformation. In this paper, we present methodologies and results for addressing these challenges in two specific organs: the liver and the kidney. PMID:25077012

Concepts for on-board satellite image registration. Volume 2: IAS prototype performance evaluation standard definition. [NEEDS Information Adaptive System

NASA Technical Reports Server (NTRS)

Daluge, D. R.; Ruedger, W. H.

1981-01-01

Problems encountered in testing onboard signal processing hardware designed to achieve radiometric and geometric correction of satellite imaging data are considered. These include obtaining representative image and ancillary data for simulation and the transfer and storage of a large quantity of image data at very high speed. The high resolution, high speed preprocessing of LANDSAT-D imagery is considered.

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

NASA Astrophysics Data System (ADS)

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

2016-05-01

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

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

PubMed

Ferrante, Enzo; Paragios, Nikos

2017-07-01

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

Registration uncertainties between 3D cone beam computed tomography and different reference CT datasets in lung stereotactic body radiation therapy.

PubMed

Oechsner, Markus; Chizzali, Barbara; Devecka, Michal; Combs, Stephanie Elisabeth; Wilkens, Jan Jakob; Duma, Marciana Nona

2016-10-26

The aim of this study was to analyze differences in couch shifts (setup errors) resulting from image registration of different CT datasets with free breathing cone beam CTs (FB-CBCT). As well automatic as manual image registrations were performed and registration results were correlated to tumor characteristics. FB-CBCT image registration was performed for 49 patients with lung lesions using slow planning CT (PCT), average intensity projection (AIP), maximum intensity projection (MIP) and mid-ventilation CTs (MidV) as reference images. Both, automatic and manual image registrations were applied. Shift differences were evaluated between the registered CT datasets for automatic and manual registration, respectively. Furthermore, differences between automatic and manual registration were analyzed for the same CT datasets. The registration results were statistically analyzed and correlated to tumor characteristics (3D tumor motion, tumor volume, superior-inferior (SI) distance, tumor environment). Median 3D shift differences over all patients were between 0.5 mm (AIPvsMIP) and 1.9 mm (MIPvsPCT and MidVvsPCT) for the automatic registration and between 1.8 mm (AIPvsPCT) and 2.8 mm (MIPvsPCT and MidVvsPCT) for the manual registration. For some patients, large shift differences (>5.0 mm) were found (maximum 10.5 mm, automatic registration). Comparing automatic vs manual registrations for the same reference CTs, ∆AIP achieved the smallest (1.1 mm) and ∆MIP the largest (1.9 mm) median 3D shift differences. The standard deviation (variability) for the 3D shift differences was also the smallest for ∆AIP (1.1 mm). Significant correlations (p < 0.01) between 3D shift difference and 3D tumor motion (AIPvsMIP, MIPvsMidV) and SI distance (AIPvsMIP) (automatic) and also for 3D tumor motion (∆PCT, ∆MidV; automatic vs manual) were found. Using different CT datasets for image registration with FB-CBCTs can result in different 3D couch shifts. Manual registrations achieved partly different 3D shifts than automatic registrations. AIP CTs yielded the smallest shift differences and might be the most appropriate CT dataset for registration with 3D FB-CBCTs.

Deformation field correction for spatial normalization of PET images

PubMed Central

Bilgel, Murat; Carass, Aaron; Resnick, Susan M.; Wong, Dean F.; Prince, Jerry L.

2015-01-01

Spatial normalization of positron emission tomography (PET) images is essential for population studies, yet the current state of the art in PET-to-PET registration is limited to the application of conventional deformable registration methods that were developed for structural images. A method is presented for the spatial normalization of PET images that improves their anatomical alignment over the state of the art. The approach works by correcting the deformable registration result using a model that is learned from training data having both PET and structural images. In particular, viewing the structural registration of training data as ground truth, correction factors are learned by using a generalized ridge regression at each voxel given the PET intensities and voxel locations in a population-based PET template. The trained model can then be used to obtain more accurate registration of PET images to the PET template without the use of a structural image. A cross validation evaluation on 79 subjects shows that the proposed method yields more accurate alignment of the PET images compared to deformable PET-to-PET registration as revealed by 1) a visual examination of the deformed images, 2) a smaller error in the deformation fields, and 3) a greater overlap of the deformed anatomical labels with ground truth segmentations. PMID:26142272

Automatic registration of Iphone images to LASER point clouds of the urban structures using shape features

NASA Astrophysics Data System (ADS)

Sirmacek, B.; Lindenbergh, R. C.; Menenti, M.

2013-10-01

Fusion of 3D airborne laser (LIDAR) data and terrestrial optical imagery can be applied in 3D urban modeling and model up-dating. The most challenging aspect of the fusion procedure is registering the terrestrial optical images on the LIDAR point clouds. In this article, we propose an approach for registering these two different data from different sensor sources. As we use iPhone camera images which are taken in front of the interested urban structure by the application user and the high resolution LIDAR point clouds of the acquired by an airborne laser sensor. After finding the photo capturing position and orientation from the iPhone photograph metafile, we automatically select the area of interest in the point cloud and transform it into a range image which has only grayscale intensity levels according to the distance from the image acquisition position. We benefit from local features for registering the iPhone image to the generated range image. In this article, we have applied the registration process based on local feature extraction and graph matching. Finally, the registration result is used for facade texture mapping on the 3D building surface mesh which is generated from the LIDAR point cloud. Our experimental results indicate possible usage of the proposed algorithm framework for 3D urban map updating and enhancing purposes.

Ultrasound fusion image error correction using subject-specific liver motion model and automatic image registration.

PubMed

Yang, Minglei; Ding, Hui; Zhu, Lei; Wang, Guangzhi

2016-12-01

Ultrasound fusion imaging is an emerging tool and benefits a variety of clinical applications, such as image-guided diagnosis and treatment of hepatocellular carcinoma and unresectable liver metastases. However, respiratory liver motion-induced misalignment of multimodal images (i.e., fusion error) compromises the effectiveness and practicability of this method. The purpose of this paper is to develop a subject-specific liver motion model and automatic registration-based method to correct the fusion error. An online-built subject-specific motion model and automatic image registration method for 2D ultrasound-3D magnetic resonance (MR) images were combined to compensate for the respiratory liver motion. The key steps included: 1) Build a subject-specific liver motion model for current subject online and perform the initial registration of pre-acquired 3D MR and intra-operative ultrasound images; 2) During fusion imaging, compensate for liver motion first using the motion model, and then using an automatic registration method to further correct the respiratory fusion error. Evaluation experiments were conducted on liver phantom and five subjects. In the phantom study, the fusion error (superior-inferior axis) was reduced from 13.90±2.38mm to 4.26±0.78mm by using the motion model only. The fusion error further decreased to 0.63±0.53mm by using the registration method. The registration method also decreased the rotation error from 7.06±0.21° to 1.18±0.66°. In the clinical study, the fusion error was reduced from 12.90±9.58mm to 6.12±2.90mm by using the motion model alone. Moreover, the fusion error decreased to 1.96±0.33mm by using the registration method. The proposed method can effectively correct the respiration-induced fusion error to improve the fusion image quality. This method can also reduce the error correction dependency on the initial registration of ultrasound and MR images. Overall, the proposed method can improve the clinical practicability of ultrasound fusion imaging. Copyright © 2016 Elsevier Ltd. All rights reserved.

An ITK framework for deterministic global optimization for medical image registration

NASA Astrophysics Data System (ADS)

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

2006-03-01

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

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

PubMed Central

Akbari, Hamed; Bilello, Michel; Da, Xiao; Davatzikos, Christos

2015-01-01

Evaluating various algorithms for the inter-subject registration of brain magnetic resonance images (MRI) is a necessary topic receiving growing attention. Existing studies evaluated image registration algorithms in specific tasks or using specific databases (e.g., only for skull-stripped images, only for single-site images, etc.). Consequently, the choice of registration algorithms seems task- and usage/parameter-dependent. Nevertheless, recent large-scale, often multi-institutional imaging-related studies create the need and raise the question whether some registration algorithms can 1) generally apply to various tasks/databases posing various challenges; 2) perform consistently well, and while doing so, 3) require minimal or ideally no parameter tuning. In seeking answers to this question, we evaluated 12 general-purpose registration algorithms, for their generality, accuracy and robustness. We fixed their parameters at values suggested by algorithm developers as reported in the literature. We tested them in 7 databases/tasks, which present one or more of 4 commonly-encountered challenges: 1) inter-subject anatomical variability in skull-stripped images; 2) intensity homogeneity, noise and large structural differences in raw images; 3) imaging protocol and field-of-view (FOV) differences in multi-site data; and 4) missing correspondences in pathology-bearing images. Totally 7,562 registrations were performed. Registration accuracies were measured by (multi-)expert-annotated landmarks or regions of interest (ROIs). To ensure reproducibility, we used public software tools, public databases (whenever possible), and we fully disclose the parameter settings. We show evaluation results, and discuss the performances in light of algorithms’ similarity metrics, transformation models and optimization strategies. We also discuss future directions for the algorithm development and evaluations. PMID:24951685

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

PubMed

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.

Correlation and registration of ERTS multispectral imagery. [by a digital processing technique

NASA Technical Reports Server (NTRS)

Bonrud, L. O.; Henrikson, P. J.

1974-01-01

Examples of automatic digital processing demonstrate the feasibility of registering one ERTS multispectral scanner (MSS) image with another obtained on a subsequent orbit, and automatic matching, correlation, and registration of MSS imagery with aerial photography (multisensor correlation) is demonstrated. Excellent correlation was obtained with patch sizes exceeding 16 pixels square. Qualities which lead to effective control point selection are distinctive features, good contrast, and constant feature characteristics. Results of the study indicate that more than 300 degrees of freedom are required to register two standard ERTS-1 MSS frames covering 100 by 100 nautical miles to an accuracy of 0.6 pixel mean radial displacement error. An automatic strip processing technique demonstrates 600 to 1200 degrees of freedom over a quater frame of ERTS imagery. Registration accuracies in the range of 0.3 pixel to 0.5 pixel mean radial error were confirmed by independent error analysis. Accuracies in the range of 0.5 pixel to 1.4 pixel mean radial error were demonstrated by semi-automatic registration over small geographic areas.

Improvement of registration accuracy in accelerated partial breast irradiation using the point-based rigid-body registration algorithm for patients with implanted fiducial markers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Inoue, Minoru; Yoshimura, Michio, E-mail: myossy@kuhp.kyoto-u.ac.jp; Sato, Sayaka

2015-04-15

Purpose: To investigate image-registration errors when using fiducial markers with a manual method and the point-based rigid-body registration (PRBR) algorithm in accelerated partial breast irradiation (APBI) patients, with accompanying fiducial deviations. Methods: Twenty-two consecutive patients were enrolled in a prospective trial examining 10-fraction APBI. Titanium clips were implanted intraoperatively around the seroma in all patients. For image-registration, the positions of the clips in daily kV x-ray images were matched to those in the planning digitally reconstructed radiographs. Fiducial and gravity registration errors (FREs and GREs, respectively), representing resulting misalignments of the edge and center of the target, respectively, were comparedmore » between the manual and algorithm-based methods. Results: In total, 218 fractions were evaluated. Although the mean FRE/GRE values for the manual and algorithm-based methods were within 3 mm (2.3/1.7 and 1.3/0.4 mm, respectively), the percentages of fractions where FRE/GRE exceeded 3 mm using the manual and algorithm-based methods were 18.8%/7.3% and 0%/0%, respectively. Manual registration resulted in 18.6% of patients with fractions of FRE/GRE exceeding 5 mm. The patients with larger clip deviation had significantly more fractions showing large FRE/GRE using manual registration. Conclusions: For image-registration using fiducial markers in APBI, the manual registration results in more fractions with considerable registration error due to loss of fiducial objectivity resulting from their deviation. The authors recommend the PRBR algorithm as a safe and effective strategy for accurate, image-guided registration and PTV margin reduction.« less

Proton radiography for inline treatment planning and positioning verification of small animals.

PubMed

Müller, Johannes; Neubert, Christian; von Neubeck, Cläre; Baumann, Michael; Krause, Mechthild; Enghardt, Wolfgang; Bütof, Rebecca; Dietrich, Antje; Lühr, Armin

2017-11-01

As proton therapy becomes increasingly well established, there is a need for high-quality clinically relevant in vivo data to gain better insight into the radiobiological effects of proton irradiation on both healthy and tumor tissue. This requires the development of easily applicable setups that allow for efficient, fractionated, image-guided proton irradiation of small animals, the most widely used pre-clinical model. Here, a method is proposed to perform dual-energy proton radiography for inline positioning verification and treatment planning. Dual-energy proton radiography exploits the differential enhancement of object features in two successively measured two-dimensional (2D) dose distributions at two different proton energies. The two raw images show structures that are dominated by energy absorption (absorption mode) or scattering (scattering mode) of protons in the object, respectively. Data post-processing allowed for the separation of both signal contributions in the respective images. The images were evaluated regarding recognizable object details and feasibility of rigid registration to acquired planar X-ray scans. Robust, automated rigid registration of proton radiography and planar X-ray images in scattering mode could be reliably achieved with the animal bedding unit used as registration landmark. Distinguishable external and internal features of the imaged mouse included the outer body contour, the skull with substructures, the lung, abdominal structures and the hind legs. Image analysis based on the combined information of both imaging modes allowed image enhancement and calculation of 2D water-equivalent path length (WEPL) maps of the object along the beam direction. Fractionated irradiation of exposed target volumes (e.g., subcutaneous tumor model or brain) can be realized with the suggested method being used for daily positioning and range determination. Robust registration of X-ray and proton radiography images allows for the irradiation of tumor entities that require conventional computed tomography (CT)-based planning, such as orthotopic lung or brain tumors, similar to conventional patient treatment.

Deformable image registration using convolutional neural networks

NASA Astrophysics Data System (ADS)

Eppenhof, Koen A. J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P. W.

2018-03-01

Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between pairs of three-dimensional images. The outputs of the network are three maps for the x, y, and z components of a thin plate spline transformation grid. The network is trained on synthetic random transformations, which are applied to a small set of representative images for the desired application. Training therefore does not require manually annotated ground truth deformation information. The methodology is demonstrated on public data sets of inspiration-expiration lung CT image pairs, which come with annotated corresponding landmarks for evaluation of the registration accuracy. Advantages of this methodology are its fast registration times and its minimal parameterization.

Smart align -- A new tool for robust non-rigid registration of scanning microscope data

DOE PAGES

Jones, Lewys; Yang, Hao; Pennycook, Timothy J.; ...

2015-07-10

Many microscopic investigations of materials may benefit from the recording of multiple successive images. This can include techniques common to several types of microscopy such as frame averaging to improve signal-to-noise ratios (SNR) or time series to study dynamic processes or more specific applications. In the scanning transmission electron microscope, this might include focal series for optical sectioning or aberration measurement, beam damage studies or camera-length series to study the effects of strain; whilst in the scanning tunnelling microscope, this might include bias voltage series to probe local electronic structure. Whatever the application, such investigations must begin with the carefulmore » alignment of these data stacks, an operation that is not always trivial. In addition, the presence of low-frequency scanning distortions can introduce intra-image shifts to the data. Here, we describe an improved automated method of performing non-rigid registration customised for the challenges unique to scanned microscope data specifically addressing the issues of low-SNR data, images containing a large proportion of crystalline material and/or local features of interest such as dislocations or edges. Careful attention has been paid to artefact testing of the non-rigid registration method used, and the importance of this registration for the quantitative interpretation of feature intensities and positions is evaluated.« less

Smart align -- A new tool for robust non-rigid registration of scanning microscope data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, Lewys; Yang, Hao; Pennycook, Timothy J.

Many microscopic investigations of materials may benefit from the recording of multiple successive images. This can include techniques common to several types of microscopy such as frame averaging to improve signal-to-noise ratios (SNR) or time series to study dynamic processes or more specific applications. In the scanning transmission electron microscope, this might include focal series for optical sectioning or aberration measurement, beam damage studies or camera-length series to study the effects of strain; whilst in the scanning tunnelling microscope, this might include bias voltage series to probe local electronic structure. Whatever the application, such investigations must begin with the carefulmore » alignment of these data stacks, an operation that is not always trivial. In addition, the presence of low-frequency scanning distortions can introduce intra-image shifts to the data. Here, we describe an improved automated method of performing non-rigid registration customised for the challenges unique to scanned microscope data specifically addressing the issues of low-SNR data, images containing a large proportion of crystalline material and/or local features of interest such as dislocations or edges. Careful attention has been paid to artefact testing of the non-rigid registration method used, and the importance of this registration for the quantitative interpretation of feature intensities and positions is evaluated.« less

Automatic Tracking Of Remote Sensing Precipitation Data Using Genetic Algorithm Image Registration Based Automatic Morphing: September 1999 Storm Floyd Case Study

NASA Astrophysics Data System (ADS)

Chiu, L.; Vongsaard, J.; El-Ghazawi, T.; Weinman, J.; Yang, R.; Kafatos, M.

U Due to the poor temporal sampling by satellites, data gaps exist in satellite derived time series of precipitation. This poses a challenge for assimilating rain- fall data into forecast models. To yield a continuous time series, the classic image processing technique of digital image morphing has been used. However, the digital morphing technique was applied manually and that is time consuming. In order to avoid human intervention in the process, an automatic procedure for image morphing is needed for real-time operations. For this purpose, Genetic Algorithm Based Image Registration Automatic Morphing (GRAM) model was developed and tested in this paper. Specifically, automatic morphing technique was integrated with Genetic Algo- rithm and Feature Based Image Metamorphosis technique to fill in data gaps between satellite coverage. The technique was tested using NOWRAD data which are gener- ated from the network of NEXRAD radars. Time series of NOWRAD data from storm Floyd that occurred at the US eastern region on September 16, 1999 for 00:00, 01:00, 02:00,03:00, and 04:00am were used. The GRAM technique was applied to data col- lected at 00:00 and 04:00am. These images were also manually morphed. Images at 01:00, 02:00 and 03:00am were interpolated from the GRAM and manual morphing and compared with the original NOWRAD rainrates. The results show that the GRAM technique outperforms manual morphing. The correlation coefficients between the im- ages generated using manual morphing are 0.905, 0.900, and 0.905 for the images at 01:00, 02:00,and 03:00 am, while the corresponding correlation coefficients are 0.946, 0.911, and 0.913, respectively, based on the GRAM technique. Index terms ­ Remote Sensing, Image Registration, Hydrology, Genetic Algorithm, Morphing, NEXRAD

Multimodal image registration based on binary gradient angle descriptor.

PubMed

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 application in time-sensitive clinical environments, such as for preoperative MRI and intraoperative US image registration for image-guided intervention.

New Protocol for Skin Landmark Registration in Image-Guided Neurosurgery: Technical Note.

PubMed

Gerard, Ian J; Hall, Jeffery A; Mok, Kelvin; Collins, D Louis

2015-09-01

Newer versions of the commercial Medtronic StealthStation allow the use of only 8 landmark pairs for patient-to-image registration as opposed to 9 landmarks in older systems. The choice of which landmark pair to drop in these newer systems can have an effect on the quality of the patient-to-image registration. To investigate 4 landmark registration protocols based on 8 landmark pairs and compare the resulting registration accuracy with a 9-landmark protocol. Four different protocols were tested on both phantoms and patients. Two of the protocols involved using 4 ear landmarks and 4 facial landmarks and the other 2 involved using 3 ear landmarks and 5 facial landmarks. Both the fiducial registration error and target registration error were evaluated for each of the different protocols to determine any difference between them and the 9-landmark protocol. No difference in fiducial registration error was found between any of the 8-landmark protocols and the 9-landmark protocol. A significant decrease (P < .05) in target registration error was found when using a protocol based on 4 ear landmarks and 4 facial landmarks compared with the other protocols based on 3 ear landmarks. When using 8 landmarks to perform the patient-to-image registration, the protocol using 4 ear landmarks and 4 facial landmarks greatly outperformed the other 8-landmark protocols and 9-landmark protocol, resulting in the lowest target registration error.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Presles, Benoît, E-mail: benoit.presles@creatis.insa-lyon.fr; Rit, Simon; Sarrut, David

2014-12-15

Purpose: The aim of the present work is to propose and evaluate registration algorithms of three-dimensional (3D) transabdominal (TA) ultrasound (US) images to setup postprostatectomy patients during radiation therapy. Methods: Three registration methods have been developed and evaluated to register a reference 3D-TA-US image acquired during the planning CT session and a 3D-TA-US image acquired before each treatment session. The first method (method A) uses only gray value information, whereas the second one (method B) uses only gradient information. The third one (method C) combines both sets of information. All methods restrict the comparison to a region of interest computedmore » from the dilated reference positioning volume drawn on the reference image and use mutual information as a similarity measure. The considered geometric transformations are translations and have been optimized by using the adaptive stochastic gradient descent algorithm. Validation has been carried out using manual registration by three operators of the same set of image pairs as the algorithms. Sixty-two treatment US images of seven patients irradiated after a prostatectomy have been registered to their corresponding reference US image. The reference registration has been defined as the average of the manual registration values. Registration error has been calculated by subtracting the reference registration from the algorithm result. For each session, the method has been considered a failure if the registration error was above both the interoperator variability of the session and a global threshold of 3.0 mm. Results: All proposed registration algorithms have no systematic bias. Method B leads to the best results with mean errors of −0.6, 0.7, and −0.2 mm in left–right (LR), superior–inferior (SI), and anterior–posterior (AP) directions, respectively. With this method, the standard deviations of the mean error are of 1.7, 2.4, and 2.6 mm in LR, SI, and AP directions, respectively. The latter are inferior to the interoperator registration variabilities which are of 2.5, 2.5, and 3.5 mm in LR, SI, and AP directions, respectively. Failures occur in 5%, 18%, and 10% of cases in LR, SI, and AP directions, respectively. 69% of the sessions have no failure. Conclusions: Results of the best proposed registration algorithm of 3D-TA-US images for postprostatectomy treatment have no bias and are in the same variability range as manual registration. As the algorithm requires a short computation time, it could be used in clinical practice provided that a visual review is performed.« less

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weon, Chijun; Hyun Nam, Woo; Lee, Duhgoon

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) imagesmore » 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 gradient-based similarity measure. Finally, if needed, they obtain the position information of the liver lesion using the 3D preoperative image to which the registered 2D preoperative slice belongs. Results: The proposed method was applied to 23 clinical datasets and quantitative evaluations were conducted. With the exception of one clinical dataset that included US images of extremely low quality, 22 datasets of various liver status were successfully applied in the evaluation. Experimental results showed that the registration error between the anatomical features of US and preoperative MR images is less than 3 mm on average. The lesion tracking error was also found to be less than 5 mm at maximum. Conclusions: A new system has been proposed for real-time registration between 2D US and successive multiple 3D preoperative MR/CT images of the liver and was applied for indirect lesion tracking for image-guided intervention. The system is fully automatic and robust even with images that had low quality due to patient status. Through visual examinations and quantitative evaluations, it was verified that the proposed system can provide high lesion tracking accuracy as well as high registration accuracy, at performance levels which were acceptable for various clinical applications.« less

Simultaneous automatic scoring and co-registration of hormone receptors in tumor areas in whole slide images of breast cancer tissue slides.

PubMed

Trahearn, Nicholas; Tsang, Yee Wah; Cree, Ian A; Snead, David; Epstein, David; Rajpoot, Nasir

2017-06-01

Automation of downstream analysis may offer many potential benefits to routine histopathology. One area of interest for automation is in the scoring of multiple immunohistochemical markers to predict the patient's response to targeted therapies. Automated serial slide analysis of this kind requires robust registration to identify common tissue regions across sections. We present an automated method for co-localized scoring of Estrogen Receptor and Progesterone Receptor (ER/PR) in breast cancer core biopsies using whole slide images. Regions of tumor in a series of fifty consecutive breast core biopsies were identified by annotation on H&E whole slide images. Sequentially cut immunohistochemical stained sections were scored manually, before being digitally scanned and then exported into JPEG 2000 format. A two-stage registration process was performed to identify the annotated regions of interest in the immunohistochemistry sections, which were then scored using the Allred system. Overall correlation between manual and automated scoring for ER and PR was 0.944 and 0.883, respectively, with 90% of ER and 80% of PR scores within in one point or less of agreement. This proof of principle study indicates slide registration can be used as a basis for automation of the downstream analysis for clinically relevant biomarkers in the majority of cases. The approach is likely to be improved by implantation of safeguarding analysis steps post registration. © 2016 International Society for Advancement of Cytometry. © 2016 International Society for Advancement of Cytometry.

STAMPS: Software Tool for Automated MRI Post-processing on a supercomputer.

PubMed

Bigler, Don C; Aksu, Yaman; Miller, David J; Yang, Qing X

2009-08-01

This paper describes a Software Tool for Automated MRI Post-processing (STAMP) of multiple types of brain MRIs on a workstation and for parallel processing on a supercomputer (STAMPS). This software tool enables the automation of nonlinear registration for a large image set and for multiple MR image types. The tool uses standard brain MRI post-processing tools (such as SPM, FSL, and HAMMER) for multiple MR image types in a pipeline fashion. It also contains novel MRI post-processing features. The STAMP image outputs can be used to perform brain analysis using Statistical Parametric Mapping (SPM) or single-/multi-image modality brain analysis using Support Vector Machines (SVMs). Since STAMPS is PBS-based, the supercomputer may be a multi-node computer cluster or one of the latest multi-core computers.

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

NASA Astrophysics Data System (ADS)

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.

Effective 2D-3D medical image registration using Support Vector Machine.

PubMed

Qi, Wenyuan; Gu, Lixu; Zhao, Qiang

2008-01-01

Registration of pre-operative 3D volume dataset and intra-operative 2D images gradually becomes an important technique to assist radiologists in diagnosing complicated diseases easily and quickly. In this paper, we proposed a novel 2D/3D registration framework based on Support Vector Machine (SVM) to compensate the disadvantages of generating large number of DRR images in the stage of intra-operation. Estimated similarity metric distribution could be built up from the relationship between parameters of transform and prior sparse target metric values by means of SVR method. Based on which, global optimal parameters of transform are finally searched out by an optimizer in order to guide 3D volume dataset to match intra-operative 2D image. Experiments reveal that our proposed registration method improved performance compared to conventional registration method and also provided a precise registration result efficiently.

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

PubMed

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

2013-01-01

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

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

NASA Astrophysics Data System (ADS)

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 fast, robust pattern recognition asystem for low light level image registration and its application to retinal imaging

NASA Astrophysics Data System (ADS)

Wade, Alex Robert; Fitzke, Frederick W.

1998-08-01

We describe an image processing system which we have developed to align autofluorescence and high-magnification images taken with a laser scanning ophthalmoscope. The low signal to noise ratio of these images makes pattern recognition a non-trivial task. However, once n images are aligned and averaged, the noise levels drop by a factor of n and the image quality is improved. We include examples of autofluorescence images and images of the cone photoreceptor mosaic obtained using this system.

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

PubMed

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.

Research on interpolation methods in medical image processing.

PubMed

Pan, Mei-Sen; Yang, Xiao-Li; Tang, Jing-Tian

2012-04-01

Image interpolation is widely used for the field of medical image processing. In this paper, interpolation methods are divided into three groups: filter interpolation, ordinary interpolation and general partial volume interpolation. Some commonly-used filter methods for image interpolation are pioneered, but the interpolation effects need to be further improved. When analyzing and discussing ordinary interpolation, many asymmetrical kernel interpolation methods are proposed. Compared with symmetrical kernel ones, the former are have some advantages. After analyzing the partial volume and generalized partial volume estimation interpolations, the new concept and constraint conditions of the general partial volume interpolation are defined, and several new partial volume interpolation functions are derived. By performing the experiments of image scaling, rotation and self-registration, the interpolation methods mentioned in this paper are compared in the entropy, peak signal-to-noise ratio, cross entropy, normalized cross-correlation coefficient and running time. Among the filter interpolation methods, the median and B-spline filter interpolations have a relatively better interpolating performance. Among the ordinary interpolation methods, on the whole, the symmetrical cubic kernel interpolations demonstrate a strong advantage, especially the symmetrical cubic B-spline interpolation. However, we have to mention that they are very time-consuming and have lower time efficiency. As for the general partial volume interpolation methods, from the total error of image self-registration, the symmetrical interpolations provide certain superiority; but considering the processing efficiency, the asymmetrical interpolations are better.

Multi-object model-based multi-atlas segmentation for rodent brains using dense discrete correspondences

NASA Astrophysics Data System (ADS)

Lee, Joohwi; Kim, Sun Hyung; Styner, Martin

2016-03-01

The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra- subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool.

Towards Ultra-High Resolution Fibre Tract Mapping of the Human Brain – Registration of Polarised Light Images and Reorientation of Fibre Vectors

PubMed Central

Palm, Christoph; Axer, Markus; Gräßel, David; Dammers, Jürgen; Lindemeyer, Johannes; Zilles, Karl; Pietrzyk, Uwe; Amunts, Katrin

2009-01-01

Polarised light imaging (PLI) utilises the birefringence of the myelin sheaths in order to visualise the orientation of nerve fibres in microtome sections of adult human post-mortem brains at ultra-high spatial resolution. The preparation of post-mortem brains for PLI involves fixation, freezing and cutting into 100-μm-thick sections. Hence, geometrical distortions of histological sections are inevitable and have to be removed for 3D reconstruction and subsequent fibre tracking. We here present a processing pipeline for 3D reconstruction of these sections using PLI derived multimodal images of post-mortem brains. Blockface images of the brains were obtained during cutting; they serve as reference data for alignment and elimination of distortion artefacts. In addition to the spatial image transformation, fibre orientation vectors were reoriented using the transformation fields, which consider both affine and subsequent non-linear registration. The application of this registration and reorientation approach results in a smooth fibre vector field, which reflects brain morphology. PLI combined with 3D reconstruction and fibre tracking is a powerful tool for human brain mapping. It can also serve as an independent method for evaluating in vivo fibre tractography. PMID:20461231

Nonrigid Image Registration in Digital Subtraction Angiography Using Multilevel B-Spline

PubMed Central

2013-01-01

We address the problem of motion artifact reduction in digital subtraction angiography (DSA) using image registration techniques. Most of registration algorithms proposed for application in DSA, have been designed for peripheral and cerebral angiography images in which we mainly deal with global rigid motions. These algorithms did not yield good results when applied to coronary angiography images because of complex nonrigid motions that exist in this type of angiography images. Multiresolution and iterative algorithms are proposed to cope with this problem, but these algorithms are associated with high computational cost which makes them not acceptable for real-time clinical applications. In this paper we propose a nonrigid image registration algorithm for coronary angiography images that is significantly faster than multiresolution and iterative blocking methods and outperforms competing algorithms evaluated on the same data sets. This algorithm is based on a sparse set of matched feature point pairs and the elastic registration is performed by means of multilevel B-spline image warping. Experimental results with several clinical data sets demonstrate the effectiveness of our approach. PMID:23971026

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

PubMed

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

2017-02-01

Deformable multimodal image registration, which can benefit radiotherapy and image guided surgery by providing complementary information, remains a challenging task in the medical image analysis field due to the difficulty of defining a proper similarity measure. This article presents a novel, robust and fast binary descriptor, the discriminative local derivative pattern (dLDP), which is able to encode images of different modalities into similar image representations. dLDP calculates a binary string for each voxel according to the pattern of intensity derivatives in its neighborhood. The descriptor similarity is evaluated using the Hamming distance, which can be efficiently computed, instead of conventional L1 or L2 norms. For the first time, we validated the effectiveness and feasibility of the local derivative pattern for multimodal deformable image registration with several multi-modal registration applications. dLDP was compared with three state-of-the-art methods in artificial image and clinical settings. In the experiments of deformable registration between different magnetic resonance imaging (MRI) modalities from BrainWeb, between computed tomography and MRI images from patient data, and between MRI and ultrasound images from BITE database, we show our method outperforms localized mutual information and entropy images in terms of both accuracy and time efficiency. We have further validated dLDP for the deformable registration of preoperative MRI and three-dimensional intraoperative ultrasound images. Our results indicate that dLDP reduces the average mean target registration error from 4.12 mm to 2.30 mm. This accuracy is statistically equivalent to the accuracy of the state-of-the-art methods in the study; however, in terms of computational complexity, our method significantly outperforms other methods and is even comparable to the sum of the absolute difference. The results reveal that dLDP can achieve superior performance regarding both accuracy and time efficiency in general multimodal image registration. In addition, dLDP also indicates the potential for clinical ultrasound guided intervention. © 2016 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Modified dixon‐based renal dynamic contrast‐enhanced MRI facilitates automated registration and perfusion analysis

PubMed Central

Leiner, Tim; Vink, Eva E.; Blankestijn, Peter J.; van den Berg, Cornelis A.T.

2017-01-01

Purpose Renal dynamic contrast‐enhanced (DCE) MRI provides information on renal perfusion and filtration. However, clinical implementation is hampered by challenges in postprocessing as a result of misalignment of the kidneys due to respiration. We propose to perform automated image registration using the fat‐only images derived from a modified Dixon reconstruction of a dual‐echo acquisition because these provide consistent contrast over the dynamic series. Methods DCE data of 10 hypertensive patients was used. Dual‐echo images were acquired at 1.5 T with temporal resolution of 3.9 s during contrast agent injection. Dixon fat, water, and in‐phase and opposed‐phase (OP) images were reconstructed. Postprocessing was automated. Registration was performed both to fat images and OP images for comparison. Perfusion and filtration values were extracted from a two‐compartment model fit. Results Automatic registration to fat images performed better than automatic registration to OP images with visible contrast enhancement. Median vertical misalignment of the kidneys was 14 mm prior to registration, compared to 3 mm and 5 mm with registration to fat images and OP images, respectively (P = 0.03). Mean perfusion values and MR‐based glomerular filtration rates (GFR) were 233 ± 64 mL/100 mL/min and 60 ± 36 mL/minute, respectively, based on fat‐registered images. MR‐based GFR correlated with creatinine‐based GFR (P = 0.04) for fat‐registered images. For unregistered and OP‐registered images, this correlation was not significant. Conclusion Absence of contrast changes on Dixon fat images improves registration in renal DCE MRI and enables automated postprocessing, resulting in a more accurate estimation of GFR. Magn Reson Med 80:66–76, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. PMID:29134673

Low-rank Atlas Image Analyses in the Presence of Pathologies

PubMed Central

Liu, Xiaoxiao; Niethammer, Marc; Kwitt, Roland; Singh, Nikhil; McCormick, Matt; Aylward, Stephen

2015-01-01

We present a common framework, for registering images to an atlas and for forming an unbiased atlas, that tolerates the presence of pathologies such as tumors and traumatic brain injury lesions. This common framework is particularly useful when a sufficient number of protocol-matched scans from healthy subjects cannot be easily acquired for atlas formation and when the pathologies in a patient cause large appearance changes. Our framework combines a low-rank-plus-sparse image decomposition technique with an iterative, diffeomorphic, group-wise image registration method. At each iteration of image registration, the decomposition technique estimates a “healthy” version of each image as its low-rank component and estimates the pathologies in each image as its sparse component. The healthy version of each image is used for the next iteration of image registration. The low-rank and sparse estimates are refined as the image registrations iteratively improve. When that framework is applied to image-to-atlas registration, the low-rank image is registered to a pre-defined atlas, to establish correspondence that is independent of the pathologies in the sparse component of each image. Ultimately, image-to-atlas registrations can be used to define spatial priors for tissue segmentation and to map information across subjects. When that framework is applied to unbiased atlas formation, at each iteration, the average of the low-rank images from the patients is used as the atlas image for the next iteration, until convergence. Since each iteration’s atlas is comprised of low-rank components, it provides a population-consistent, pathology-free appearance. Evaluations of the proposed methodology are presented using synthetic data as well as simulated and clinical tumor MRI images from the brain tumor segmentation (BRATS) challenge from MICCAI 2012. PMID:26111390

On removing interpolation and resampling artifacts in rigid image registration.

PubMed

Aganj, Iman; Yeo, Boon Thye Thomas; Sabuncu, Mert R; Fischl, Bruce

2013-02-01

We show that image registration using conventional interpolation and summation approximations of continuous integrals can generally fail because of resampling artifacts. These artifacts negatively affect the accuracy of registration by producing local optima, altering the gradient, shifting the global optimum, and making rigid registration asymmetric. In this paper, after an extensive literature review, we demonstrate the causes of the artifacts by comparing inclusion and avoidance of resampling analytically. We show the sum-of-squared-differences cost function formulated as an integral to be more accurate compared with its traditional sum form in a simple case of image registration. We then discuss aliasing that occurs in rotation, which is due to the fact that an image represented in the Cartesian grid is sampled with different rates in different directions, and propose the use of oscillatory isotropic interpolation kernels, which allow better recovery of true global optima by overcoming this type of aliasing. Through our experiments on brain, fingerprint, and white noise images, we illustrate the superior performance of the integral registration cost function in both the Cartesian and spherical coordinates, and also validate the introduced radial interpolation kernel by demonstrating the improvement in registration.

On Removing Interpolation and Resampling Artifacts in Rigid Image Registration

PubMed Central

Aganj, Iman; Yeo, Boon Thye Thomas; Sabuncu, Mert R.; Fischl, Bruce

2013-01-01

We show that image registration using conventional interpolation and summation approximations of continuous integrals can generally fail because of resampling artifacts. These artifacts negatively affect the accuracy of registration by producing local optima, altering the gradient, shifting the global optimum, and making rigid registration asymmetric. In this paper, after an extensive literature review, we demonstrate the causes of the artifacts by comparing inclusion and avoidance of resampling analytically. We show the sum-of-squared-differences cost function formulated as an integral to be more accurate compared with its traditional sum form in a simple case of image registration. We then discuss aliasing that occurs in rotation, which is due to the fact that an image represented in the Cartesian grid is sampled with different rates in different directions, and propose the use of oscillatory isotropic interpolation kernels, which allow better recovery of true global optima by overcoming this type of aliasing. Through our experiments on brain, fingerprint, and white noise images, we illustrate the superior performance of the integral registration cost function in both the Cartesian and spherical coordinates, and also validate the introduced radial interpolation kernel by demonstrating the improvement in registration. PMID:23076044

Medical image processing on the GPU - past, present and future.

PubMed

Eklund, Anders; Dufort, Paul; Forsberg, Daniel; LaConte, Stephen M

2013-12-01

Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges. Copyright © 2013 Elsevier B.V. All rights reserved.

An optical systems analysis approach to image resampling

NASA Technical Reports Server (NTRS)

Lyon, Richard G.

1997-01-01

All types of image registration require some type of resampling, either during the registration or as a final step in the registration process. Thus the image(s) must be regridded into a spatially uniform, or angularly uniform, coordinate system with some pre-defined resolution. Frequently the ending resolution is not the resolution at which the data was observed with. The registration algorithm designer and end product user are presented with a multitude of possible resampling methods each of which modify the spatial frequency content of the data in some way. The purpose of this paper is threefold: (1) to show how an imaging system modifies the scene from an end to end optical systems analysis approach, (2) to develop a generalized resampling model, and (3) empirically apply the model to simulated radiometric scene data and tabulate the results. A Hanning windowed sinc interpolator method will be developed based upon the optical characterization of the system. It will be discussed in terms of the effects and limitations of sampling, aliasing, spectral leakage, and computational complexity. Simulated radiometric scene data will be used to demonstrate each of the algorithms. A high resolution scene will be "grown" using a fractal growth algorithm based on mid-point recursion techniques. The result scene data will be convolved with a point spread function representing the optical response. The resultant scene will be convolved with the detection systems response and subsampled to the desired resolution. The resultant data product will be subsequently resampled to the correct grid using the Hanning windowed sinc interpolator and the results and errors tabulated and discussed.

WE-H-202-04: Advanced Medical Image Registration Techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Christensen, G.

Deformable image registration has now been commercially available for several years, with solid performance in a number of sites and for several applications including contour and dose mapping. However, more complex applications have arisen, such as assessing response to radiation therapy over time, registering images pre- and post-surgery, and auto-segmentation from atlases. These applications require innovative registration algorithms to achieve accurate alignment. The goal of this session is to highlight emerging registration technology and these new applications. The state of the art in image registration will be presented from an engineering perspective. Translational clinical applications will also be discussed tomore » tie these new registration approaches together with imaging and radiation therapy applications in specific diseases such as cervical and lung cancers. Learning Objectives: To understand developing techniques and algorithms in deformable image registration that are likely to translate into clinical tools in the near future. To understand emerging imaging and radiation therapy clinical applications that require such new registration algorithms. Research supported in part by the National Institutes of Health under award numbers P01CA059827, R01CA166119, and R01CA166703. Disclosures: Phillips Medical systems (Hugo), Roger Koch (Christensen) support, Varian Medical Systems (Brock), licensing agreements from Raysearch (Brock) and Varian (Hugo).; K. Brock, Licensing Agreement - RaySearch Laboratories. Research Funding - Varian Medical Systems; G. Hugo, Research grant from National Institutes of Health, award number R01CA166119.; G. Christensen, Research support from NIH grants CA166119 and CA166703 and a gift from Roger Koch. There are no conflicts of interest.« less

Potential accuracy of translation estimation between radar and optical images

NASA Astrophysics Data System (ADS)

Uss, M.; Vozel, B.; Lukin, V.; Chehdi, K.

2015-10-01

This paper investigates the potential accuracy achievable for optical to radar image registration by area-based approach. The analysis is carried out mainly based on the Cramér-Rao Lower Bound (CRLB) on translation estimation accuracy previously proposed by the authors and called CRLBfBm. This bound is now modified to take into account radar image speckle noise properties: spatial correlation and signal-dependency. The newly derived theoretical bound is fed with noise and texture parameters estimated for the co-registered pair of optical Landsat 8 and radar SIR-C images. It is found that difficulty of optical to radar image registration stems more from speckle noise influence than from dissimilarity of the considered kinds of images. At finer scales (and higher speckle noise level), probability of finding control fragments (CF) suitable for registration is low (1% or less) but overall number of such fragments is high thanks to image size. Conversely, at the coarse scale, where speckle noise level is reduced, probability of finding CFs suitable for registration can be as high as 40%, but overall number of such CFs is lower. Thus, the study confirms and supports area-based multiresolution approach for optical to radar registration where coarse scales are used for fast registration "lock" and finer scales for reaching higher registration accuracy. The CRLBfBm is found inaccurate for the main scale due to intensive speckle noise influence. For other scales, the validity of the CRLBfBm bound is confirmed by calculating statistical efficiency of area-based registration method based on normalized correlation coefficient (NCC) measure that takes high values of about 25%.

An approach to defect inspection for packing presswork with virtual orientation points and threshold template image

NASA Astrophysics Data System (ADS)

Hao, Xiangyang; Liu, Songlin; Zhao, Fulai; Jiang, Lixing

2015-05-01

The packing presswork is an important factor of industrial product, especially for the luxury commodities such as cigarettes. In order to ensure the packing presswork to be qualified, the products should be inspected and unqualified one be picked out piece by piece with the vision-based inspection method, which has such advantages as no-touch inspection, high efficiency and automation. Vision-based inspection of packing presswork mainly consists of steps as image acquisition, image registration and defect inspection. The registration between inspected image and reference image is the foundation and premise of visual inspection. In order to realize rapid, reliable and accurate image registration, a registration method based on virtual orientation points is put forward. The precision of registration between inspected image and reference image can reach to sub pixels. Since defect is without fixed position, shape, size and color, three measures are taken to improve the inspection effect. Firstly, the concept of threshold template image is put forward to resolve the problem of variable threshold of intensity difference. Secondly, the color difference is calculated by comparing each pixel with the adjacent pixels of its correspondence on reference image to avoid false defect resulted from color registration error. Thirdly, the strategy of image pyramid is applied in the inspection algorithm to enhance the inspection efficiency. Experiments show that the related algorithm is effective to defect inspection and it takes 27.4 ms on average to inspect a piece of cigarette packing presswork.

MIND Demons for MR-to-CT Deformable Image Registration In Image-Guided Spine Surgery

PubMed Central

Reaungamornrat, S.; De Silva, T.; Uneri, A.; Wolinsky, J.-P.; Khanna, A. J.; Kleinszig, G.; Vogt, S.; Prince, J. L.; Siewerdsen, J. H.

2016-01-01

Purpose Localization of target anatomy and critical structures defined in preoperative MR images can be achieved by means of multi-modality deformable registration to intraoperative CT. We propose a symmetric diffeomorphic deformable registration algorithm incorporating a modality independent neighborhood descriptor (MIND) and a robust Huber metric for MR-to-CT registration. Method The method, called MIND Demons, solves for the deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the velocity fields and the diffeomorphisms, a modality-insensitive similarity function suitable to multi-modality images, and constraints on geodesics in Lagrangian coordinates. Direct optimization (without relying on an exponential map of stationary velocity fields used in conventional diffeomorphic Demons) is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, in phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to conventional mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, and normalized MI (NMI) Demons. Result The method yielded sub-voxel invertibility (0.006 mm) and nonsingular spatial Jacobians with capability to preserve local orientation and topology. It demonstrated improved registration accuracy in comparison to the reference methods, with mean target registration error (TRE) of 1.5 mm compared to 10.9, 2.3, and 4.6 mm for MI FFD, LMI FFD, and NMI Demons methods, respectively. Validation in clinical studies demonstrated realistic deformation with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine. Conclusions A modality-independent deformable registration method has been developed to estimate a viscoelastic diffeomorphic map between preoperative MR and intraoperative CT. The method yields registration accuracy suitable to application in image-guided spine surgery across a broad range of anatomical sites and modes of deformation. PMID:27330239

MIND Demons for MR-to-CT deformable image registration in image-guided spine surgery

NASA Astrophysics Data System (ADS)

Reaungamornrat, S.; De Silva, T.; Uneri, A.; Wolinsky, J.-P.; Khanna, A. J.; Kleinszig, G.; Vogt, S.; Prince, J. L.; Siewerdsen, J. H.

2016-03-01

Purpose: Localization of target anatomy and critical structures defined in preoperative MR images can be achieved by means of multi-modality deformable registration to intraoperative CT. We propose a symmetric diffeomorphic deformable registration algorithm incorporating a modality independent neighborhood descriptor (MIND) and a robust Huber metric for MR-to-CT registration. Method: The method, called MIND Demons, solves for the deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the velocity fields and the diffeomorphisms, a modality-insensitive similarity function suitable to multi-modality images, and constraints on geodesics in Lagrangian coordinates. Direct optimization (without relying on an exponential map of stationary velocity fields used in conventional diffeomorphic Demons) is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, in phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to conventional mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, and normalized MI (NMI) Demons. Result: The method yielded sub-voxel invertibility (0.006 mm) and nonsingular spatial Jacobians with capability to preserve local orientation and topology. It demonstrated improved registration accuracy in comparison to the reference methods, with mean target registration error (TRE) of 1.5 mm compared to 10.9, 2.3, and 4.6 mm for MI FFD, LMI FFD, and NMI Demons methods, respectively. Validation in clinical studies demonstrated realistic deformation with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine. Conclusions: A modality-independent deformable registration method has been developed to estimate a viscoelastic diffeomorphic map between preoperative MR and intraoperative CT. The method yields registration accuracy suitable to application in image-guided spine surgery across a broad range of anatomical sites and modes of deformation.

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

PubMed Central

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

2013-01-01

Transoral robotic surgery (TORS) offers a minimally invasive approach to resection of base of tongue tumors. However, precise localization of the surgical target and adjacent critical structures can be challenged by the highly deformed intraoperative setup. We propose a deformable registration method using intraoperative cone-beam CT (CBCT) to accurately align preoperative CT or MR images with the intraoperative scene. The registration method combines a Gaussian mixture (GM) model followed by a variation of the Demons algorithm. First, following segmentation of the volume of interest (i.e., volume of the tongue extending to the hyoid), a GM model is applied to surface point clouds for rigid initialization (GM rigid) followed by nonrigid deformation (GM nonrigid). Second, the registration is refined using the Demons algorithm applied to distance map transforms of the (GM-registered) preoperative image and intraoperative CBCT. Performance was evaluated in repeat cadaver studies (25 image pairs) in terms of target registration error (TRE), entropy correlation coefficient (ECC), and normalized pointwise mutual information (NPMI). Retraction of the tongue in the TORS operative setup induced gross deformation >30 mm. The mean TRE following the GM rigid, GM nonrigid, and Demons steps was 4.6, 2.1, and 1.7 mm, respectively. The respective ECC was 0.57, 0.70, and 0.73 and NPMI was 0.46, 0.57, and 0.60. Registration accuracy was best across the superior aspect of the tongue and in proximity to the hyoid (by virtue of GM registration of surface points on these structures). The Demons step refined registration primarily in deeper portions of the tongue further from the surface and hyoid bone. Since the method does not use image intensities directly, it is suitable to multi-modality registration of preoperative CT or MR with intraoperative CBCT. Extending the 3D image registration to the fusion of image and planning data in stereo-endoscopic video is anticipated to support safer, high-precision base of tongue robotic surgery. PMID:23807549

MIND Demons for MR-to-CT Deformable Image Registration In Image-Guided Spine Surgery.

PubMed

Reaungamornrat, S; De Silva, T; Uneri, A; Wolinsky, J-P; Khanna, A J; Kleinszig, G; Vogt, S; Prince, J L; Siewerdsen, J H

2016-02-27

Localization of target anatomy and critical structures defined in preoperative MR images can be achieved by means of multi-modality deformable registration to intraoperative CT. We propose a symmetric diffeomorphic deformable registration algorithm incorporating a modality independent neighborhood descriptor (MIND) and a robust Huber metric for MR-to-CT registration. The method, called MIND Demons, solves for the deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the velocity fields and the diffeomorphisms, a modality-insensitive similarity function suitable to multi-modality images, and constraints on geodesics in Lagrangian coordinates. Direct optimization (without relying on an exponential map of stationary velocity fields used in conventional diffeomorphic Demons) is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, in phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to conventional mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, and normalized MI (NMI) Demons. The method yielded sub-voxel invertibility (0.006 mm) and nonsingular spatial Jacobians with capability to preserve local orientation and topology. It demonstrated improved registration accuracy in comparison to the reference methods, with mean target registration error (TRE) of 1.5 mm compared to 10.9, 2.3, and 4.6 mm for MI FFD, LMI FFD, and NMI Demons methods, respectively. Validation in clinical studies demonstrated realistic deformation with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine. A modality-independent deformable registration method has been developed to estimate a viscoelastic diffeomorphic map between preoperative MR and intraoperative CT. The method yields registration accuracy suitable to application in image-guided spine surgery across a broad range of anatomical sites and modes of deformation.

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

NASA Astrophysics Data System (ADS)

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

2013-07-01

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

Robust inverse-consistent affine CT-MR registration in MRI-assisted and MRI-alone prostate radiation therapy.

PubMed

Rivest-Hénault, David; Dowson, Nicholas; Greer, Peter B; Fripp, Jurgen; Dowling, Jason A

2015-07-01

CT-MR registration is a critical component of many radiation oncology protocols. In prostate external beam radiation therapy, it allows the propagation of MR-derived contours to reference CT images at the planning stage, and it enables dose mapping during dosimetry studies. The use of carefully registered CT-MR atlases allows the estimation of patient specific electron density maps from MRI scans, enabling MRI-alone radiation therapy planning and treatment adaptation. In all cases, the precision and accuracy achieved by registration influences the quality of the entire process. Most current registration algorithms do not robustly generalize and lack inverse-consistency, increasing the risk of human error and acting as a source of bias in studies where information is propagated in a particular direction, e.g. CT to MR or vice versa. In MRI-based treatment planning where both CT and MR scans serve as spatial references, inverse-consistency is critical, if under-acknowledged. A robust, inverse-consistent, rigid/affine registration algorithm that is well suited to CT-MR alignment in prostate radiation therapy is presented. The presented method is based on a robust block-matching optimization process that utilises a half-way space definition to maintain inverse-consistency. Inverse-consistency substantially reduces the influence of the order of input images, simplifying analysis, and increasing robustness. An open source implementation is available online at http://aehrc.github.io/Mirorr/. Experimental results on a challenging 35 CT-MR pelvis dataset demonstrate that the proposed method is more accurate than other popular registration packages and is at least as accurate as the state of the art, while being more robust and having an order of magnitude higher inverse-consistency than competing approaches. The presented results demonstrate that the proposed registration algorithm is readily applicable to prostate radiation therapy planning. Copyright © 2015. Published by Elsevier B.V.

Use of Multi-Resolution Wavelet Feature Pyramids for Automatic Registration of Multi-Sensor Imagery

NASA Technical Reports Server (NTRS)

Zavorin, Ilya; LeMoigne, Jacqueline

2003-01-01

The problem of image registration, or alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times, and that would provide sub-pixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the band-pass wavelets obtained from the Steerable Pyramid due to Simoncelli perform better than two types of low-pass pyramids when the images being registered have relatively small amount of nonlinear radiometric variations between them. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.

Image interpolation allows accurate quantitative bone morphometry in registered micro-computed tomography scans.

PubMed

Schulte, Friederike A; Lambers, Floor M; Mueller, Thomas L; Stauber, Martin; Müller, Ralph

2014-04-01

Time-lapsed in vivo micro-computed tomography is a powerful tool to analyse longitudinal changes in the bone micro-architecture. Registration can overcome problems associated with spatial misalignment between scans; however, it requires image interpolation which might affect the outcome of a subsequent bone morphometric analysis. The impact of the interpolation error itself, though, has not been quantified to date. Therefore, the purpose of this ex vivo study was to elaborate the effect of different interpolator schemes [nearest neighbour, tri-linear and B-spline (BSP)] on bone morphometric indices. None of the interpolator schemes led to significant differences between interpolated and non-interpolated images, with the lowest interpolation error found for BSPs (1.4%). Furthermore, depending on the interpolator, the processing order of registration, Gaussian filtration and binarisation played a role. Independent from the interpolator, the present findings suggest that the evaluation of bone morphometry should be done with images registered using greyscale information.

Registration of retinal sequences from new video-ophthalmoscopic camera.

PubMed

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.

A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation

PubMed Central

Duarte-Carvajalino, Julio M.; Sapiro, Guillermo; Harel, Noam; Lenglet, Christophe

2013-01-01

Registration of diffusion-weighted magnetic resonance images (DW-MRIs) is a key step for population studies, or construction of brain atlases, among other important tasks. Given the high dimensionality of the data, registration is usually performed by relying on scalar representative images, such as the fractional anisotropy (FA) and non-diffusion-weighted (b0) images, thereby ignoring much of the directional information conveyed by DW-MR datasets itself. Alternatively, model-based registration algorithms have been proposed to exploit information on the preferred fiber orientation(s) at each voxel. Models such as the diffusion tensor or orientation distribution function (ODF) have been used for this purpose. Tensor-based registration methods rely on a model that does not completely capture the information contained in DW-MRIs, and largely depends on the accurate estimation of tensors. ODF-based approaches are more recent and computationally challenging, but also better describe complex fiber configurations thereby potentially improving the accuracy of DW-MRI registration. A new algorithm based on angular interpolation of the diffusion-weighted volumes was proposed for affine registration, and does not rely on any specific local diffusion model. In this work, we first extensively compare the performance of registration algorithms based on (i) angular interpolation, (ii) non-diffusion-weighted scalar volume (b0), and (iii) diffusion tensor image (DTI). Moreover, we generalize the concept of angular interpolation (AI) to non-linear image registration, and implement it in the FMRIB Software Library (FSL). We demonstrate that AI registration of DW-MRIs is a powerful alternative to volume and tensor-based approaches. In particular, we show that AI improves the registration accuracy in many cases over existing state-of-the-art algorithms, while providing registered raw DW-MRI data, which can be used for any subsequent analysis. PMID:23596381

A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation.

PubMed

Duarte-Carvajalino, Julio M; Sapiro, Guillermo; Harel, Noam; Lenglet, Christophe

2013-01-01

Registration of diffusion-weighted magnetic resonance images (DW-MRIs) is a key step for population studies, or construction of brain atlases, among other important tasks. Given the high dimensionality of the data, registration is usually performed by relying on scalar representative images, such as the fractional anisotropy (FA) and non-diffusion-weighted (b0) images, thereby ignoring much of the directional information conveyed by DW-MR datasets itself. Alternatively, model-based registration algorithms have been proposed to exploit information on the preferred fiber orientation(s) at each voxel. Models such as the diffusion tensor or orientation distribution function (ODF) have been used for this purpose. Tensor-based registration methods rely on a model that does not completely capture the information contained in DW-MRIs, and largely depends on the accurate estimation of tensors. ODF-based approaches are more recent and computationally challenging, but also better describe complex fiber configurations thereby potentially improving the accuracy of DW-MRI registration. A new algorithm based on angular interpolation of the diffusion-weighted volumes was proposed for affine registration, and does not rely on any specific local diffusion model. In this work, we first extensively compare the performance of registration algorithms based on (i) angular interpolation, (ii) non-diffusion-weighted scalar volume (b0), and (iii) diffusion tensor image (DTI). Moreover, we generalize the concept of angular interpolation (AI) to non-linear image registration, and implement it in the FMRIB Software Library (FSL). We demonstrate that AI registration of DW-MRIs is a powerful alternative to volume and tensor-based approaches. In particular, we show that AI improves the registration accuracy in many cases over existing state-of-the-art algorithms, while providing registered raw DW-MRI data, which can be used for any subsequent analysis.

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

NASA Astrophysics Data System (ADS)

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

2010-02-01

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

Onboard Image Registration from Invariant Features

NASA Technical Reports Server (NTRS)

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

2008-01-01

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

A three-dimensional histological atlas of the human basal ganglia. II. Atlas deformation strategy and evaluation in deep brain stimulation for Parkinson disease.

PubMed

Bardinet, Eric; Bhattacharjee, Manik; Dormont, Didier; Pidoux, Bernard; Malandain, Grégoire; Schüpbach, Michael; Ayache, Nicholas; Cornu, Philippe; Agid, Yves; Yelnik, Jérôme

2009-02-01

The localization of any given target in the brain has become a challenging issue because of the increased use of deep brain stimulation to treat Parkinson disease, dystonia, and nonmotor diseases (for example, Tourette syndrome, obsessive compulsive disorders, and depression). The aim of this study was to develop an automated method of adapting an atlas of the human basal ganglia to the brains of individual patients. Magnetic resonance images of the brain specimen were obtained before extraction from the skull and histological processing. Adaptation of the atlas to individual patient anatomy was performed by reshaping the atlas MR images to the images obtained in the individual patient using a hierarchical registration applied to a region of interest centered on the basal ganglia, and then applying the reshaping matrix to the atlas surfaces. Results were evaluated by direct visual inspection of the structures visible on MR images and atlas anatomy, by comparison with electrophysiological intraoperative data, and with previous atlas studies in patients with Parkinson disease. The method was both robust and accurate, never failing to provide an anatomically reliable atlas to patient registration. The registration obtained did not exceed a 1-mm mismatch with the electrophysiological signatures in the region of the subthalamic nucleus. This registration method applied to the basal ganglia atlas forms a powerful and reliable method for determining deep brain stimulation targets within the basal ganglia of individual patients.

Blockface histology with optical coherence tomography: a comparison with Nissl staining.

PubMed

Magnain, Caroline; Augustinack, Jean C; Reuter, Martin; Wachinger, Christian; Frosch, Matthew P; Ragan, Timothy; Akkin, Taner; Wedeen, Van J; Boas, David A; Fischl, Bruce

2014-01-01

Spectral domain optical coherence tomography (SD-OCT) is a high resolution imaging technique that generates excellent contrast based on intrinsic optical properties of the tissue, such as neurons and fibers. The SD-OCT data acquisition is performed directly on the tissue block, diminishing the need for cutting, mounting and staining. We utilized SD-OCT to visualize the laminar structure of the isocortex and compared cortical cytoarchitecture with the gold standard Nissl staining, both qualitatively and quantitatively. In histological processing, distortions routinely affect registration to the blockface image and prevent accurate 3D reconstruction of regions of tissue. We compared blockface registration to SD-OCT and Nissl, respectively, and found that SD-OCT-blockface registration was significantly more accurate than Nissl-blockface registration. Two independent observers manually labeled cortical laminae (e.g. III, IV and V) in SD-OCT images and Nissl stained sections. Our results show that OCT images exhibit sufficient contrast in the cortex to reliably differentiate the cortical layers. Furthermore, the modalities were compared with regard to cortical laminar organization and showed good agreement. Taken together, these SD-OCT results suggest that SD-OCT contains information comparable to standard histological stains such as Nissl in terms of distinguishing cortical layers and architectonic areas. Given these data, we propose that SD-OCT can be used to reliably generate 3D reconstructions of multiple cubic centimeters of cortex that can be used to accurately and semi-automatically perform standard histological analyses. © 2013.

Blockface Histology with Optical Coherence Tomography: A Comparison with Nissl Staining

PubMed Central

Magnain, Caroline; Augustinack, Jean C.; Reuter, Martin; Wachinger, Christian; Frosch, Matthew P.; Ragan, Timothy; Akkin, Taner; Wedeen, Van J.; Boas, David A.; Fischl, Bruce

2015-01-01

Spectral domain optical coherence tomography (SD-OCT) is a high resolution imaging technique that generates excellent contrast based on intrinsic optical properties of the tissue, such as neurons and fibers. The SD-OCT data acquisition is performed directly on the tissue block, diminishing the need for cutting, mounting and staining. We utilized SD-OCT to visualize the laminar structure of the isocortex and compared cortical cytoarchitecture with the gold standard Nissl staining, both qualitatively and quantitatively. In histological processing, distortions routinely affect registration to the blockface image and prevent accurate 3D reconstruction of regions of tissue. We compared blockface registration to SD-OCT and Nissl, respectively, and found that SD-OCT-blockface registration was significantly more accurate than Nissl-blockface registration. Two independent observers manually labeled cortical laminae (e.g. III, IV and V) in SD-OCT images and Nissl stained sections. Our results show that OCT images exhibit sufficient contrast in the cortex to reliably differentiate the cortical layers. Furthermore, the modalities were compared with regard to cortical laminar organization and showed good agreement. Taken together, these SD-OCT results suggest that SD-OCT contains information comparable to standard histological stains such as Nissl in terms of distinguishing cortical layers and architectonic areas. Given these data, we propose that SD-OCT can be used to reliably generate 3D reconstructions of multiple cubic centimeters of cortex that can be used to accurately and semi-automatically perform standard histological analyses. PMID:24041872

Surface-based prostate registration with biomechanical regularization

NASA Astrophysics Data System (ADS)

van de Ven, Wendy J. M.; Hu, Yipeng; Barentsz, Jelle O.; Karssemeijer, Nico; Barratt, Dean; Huisman, Henkjan J.

2013-03-01

Adding MR-derived information to standard transrectal ultrasound (TRUS) images for guiding prostate biopsy is of substantial clinical interest. A tumor visible on MR images can be projected on ultrasound by using MRUS registration. A common approach is to use surface-based registration. We hypothesize that biomechanical modeling will better control deformation inside the prostate than a regular surface-based registration method. We developed a novel method by extending a surface-based registration with finite element (FE) simulation to better predict internal deformation of the prostate. For each of six patients, a tetrahedral mesh was constructed from the manual prostate segmentation. Next, the internal prostate deformation was simulated using the derived radial surface displacement as boundary condition. The deformation field within the gland was calculated using the predicted FE node displacements and thin-plate spline interpolation. We tested our method on MR guided MR biopsy imaging data, as landmarks can easily be identified on MR images. For evaluation of the registration accuracy we used 45 anatomical landmarks located in all regions of the prostate. Our results show that the median target registration error of a surface-based registration with biomechanical regularization is 1.88 mm, which is significantly different from 2.61 mm without biomechanical regularization. We can conclude that biomechanical FE modeling has the potential to improve the accuracy of multimodal prostate registration when comparing it to regular surface-based registration.

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

PubMed Central

Jia, Kebin

2015-01-01

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

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

PubMed

Zhao, Liya; Jia, Kebin

2015-01-01

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

Groupwise Registration and Atlas Construction of 4th-Order Tensor Fields Using the ℝ+ Riemannian Metric*

PubMed Central

Barmpoutis, Angelos

2010-01-01

Registration of Diffusion-Weighted MR Images (DW-MRI) can be achieved by registering the corresponding 2nd-order Diffusion Tensor Images (DTI). However, it has been shown that higher-order diffusion tensors (e.g. order-4) outperform the traditional DTI in approximating complex fiber structures such as fiber crossings. In this paper we present a novel method for unbiased group-wise non-rigid registration and atlas construction of 4th-order diffusion tensor fields. To the best of our knowledge there is no other existing method to achieve this task. First we define a metric on the space of positive-valued functions based on the Riemannian metric of real positive numbers (denoted by ℝ+). Then, we use this metric in a novel functional minimization method for non-rigid 4th-order tensor field registration. We define a cost function that accounts for the 4th-order tensor re-orientation during the registration process and has analytic derivatives with respect to the transformation parameters. Finally, the tensor field atlas is computed as the minimizer of the variance defined using the Riemannian metric. We quantitatively compare the proposed method with other techniques that register scalar-valued or diffusion tensor (rank-2) representations of the DWMRI. PMID:20436782

Feature-based US to CT registration of the aortic root

NASA Astrophysics Data System (ADS)

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.

Deformable 3D-2D registration for CT and its application to low dose tomographic fluoroscopy

NASA Astrophysics Data System (ADS)

Flach, Barbara; Brehm, Marcus; Sawall, Stefan; Kachelrieß, Marc

2014-12-01

Many applications in medical imaging include image registration for matching of images from the same or different modalities. In the case of full data sampling, the respective reconstructed images are usually of such a good image quality that standard deformable volume-to-volume (3D-3D) registration approaches can be applied. But research in temporal-correlated image reconstruction and dose reductions increases the number of cases where rawdata are available from only few projection angles. Here, deteriorated image quality leads to non-acceptable deformable volume-to-volume registration results. Therefore a registration approach is required that is robust against a decreasing number of projections defining the target position. We propose a deformable volume-to-rawdata (3D-2D) registration method that aims at finding a displacement vector field maximizing the alignment of a CT volume and the acquired rawdata based on the sum of squared differences in rawdata domain. The registration is constrained by a regularization term in accordance with a fluid-based diffusion. Both cost function components, the rawdata fidelity and the regularization term, are optimized in an alternating manner. The matching criterion is optimized by a conjugate gradient descent for nonlinear functions, while the regularization is realized by convolution of the vector fields with Gaussian kernels. We validate the proposed method and compare it to the demons algorithm, a well-known 3D-3D registration method. The comparison is done for a range of 4-60 target projections using datasets from low dose tomographic fluoroscopy as an application example. The results show a high correlation to the ground truth target position without introducing artifacts even in the case of very few projections. In particular the matching in the rawdata domain is improved compared to the 3D-3D registration for the investigated range. The proposed volume-to-rawdata registration increases the robustness regarding sparse rawdata and provides more stable results than volume-to-volume approaches. By applying the proposed registration approach to low dose tomographic fluoroscopy it is possible to improve the temporal resolution and thus to increase the robustness of low dose tomographic fluoroscopy.

SU-F-I-09: Improvement of Image Registration Using Total-Variation Based Noise Reduction Algorithms for Low-Dose CBCT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mukherjee, S; Farr, J; Merchant, T

Purpose: To study the effect of total-variation based noise reduction algorithms to improve the image registration of low-dose CBCT for patient positioning in radiation therapy. Methods: In low-dose CBCT, the reconstructed image is degraded by excessive quantum noise. In this study, we developed a total-variation based noise reduction algorithm and studied the effect of the algorithm on noise reduction and image registration accuracy. To study the effect of noise reduction, we have calculated the peak signal-to-noise ratio (PSNR). To study the improvement of image registration, we performed image registration between volumetric CT and MV- CBCT images of different head-and-neck patientsmore » and calculated the mutual information (MI) and Pearson correlation coefficient (PCC) as a similarity metric. The PSNR, MI and PCC were calculated for both the noisy and noise-reduced CBCT images. Results: The algorithms were shown to be effective in reducing the noise level and improving the MI and PCC for the low-dose CBCT images tested. For the different head-and-neck patients, a maximum improvement of PSNR of 10 dB with respect to the noisy image was calculated. The improvement of MI and PCC was 9% and 2% respectively. Conclusion: Total-variation based noise reduction algorithm was studied to improve the image registration between CT and low-dose CBCT. The algorithm had shown promising results in reducing the noise from low-dose CBCT images and improving the similarity metric in terms of MI and PCC.« less

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

NASA Astrophysics Data System (ADS)

Kacenjar, Steve; Zook, Matthew; Balint, Michael

2011-03-01

There are various scientific and technological areas in which it is imperative to rapidly detect and quantify changes in imagery over time. In fields such as earth remote sensing, aerospace systems, and medical imaging, searching for timedependent, regional changes across deformable topographies is complicated by varying camera acquisition geometries, lighting environments, background clutter conditions, and occlusion. Under these constantly-fluctuating conditions, the use of standard, rigid-body registration approaches often fail to provide sufficient fidelity to overlay image scenes together. This is problematic because incorrect assessments of the underlying changes of high-level topography can result in systematic errors in the quantification and classification of interested areas. For example, in the current naked-eye detection strategies of melanoma, a dermatologist often uses static morphological attributes to identify suspicious skin lesions for biopsy. This approach does not incorporate temporal changes which suggest malignant degeneration. By performing the co-registration of time-separated skin imagery, a dermatologist may more effectively detect and identify early morphological changes in pigmented lesions; enabling the physician to detect cancers at an earlier stage resulting in decreased morbidity and mortality. This paper describes an image processing system which will be used to detect changes in the characteristics of skin lesions over time. The proposed system consists of three main functional elements: 1.) coarse alignment of timesequenced imagery, 2.) refined alignment of local skin topographies, and 3.) assessment of local changes in lesion size. During the coarse alignment process, various approaches can be used to obtain a rough alignment, including: 1.) a manual landmark/intensity-based registration method1, and 2.) several flavors of autonomous optical matched filter methods2. These procedures result in the rough alignment of a patient's back topography. Since the skin is a deformable membrane, this process only provides an initial condition for subsequent refinements in aligning the localized topography of the skin. To achieve a refined enhancement, a Particle Swarm Optimizer (PSO) is used to optimally determine the local camera models associated with a generalized geometric transform. Here the optimization process is driven using the minimization of entropy between the multiple time-separated images. Once the camera models are corrected for local skin deformations, the images are compared using both pixel-based and regional-based methods. Limits on the detectability of change are established by the fidelity to which the algorithm corrects for local skin deformation and background alterations. These limits provide essential information in establishing early-warning thresholds for Melanoma detection. Key to this work is the development of a PSO alignment algorithm to perform the refined alignment in local skin topography between the time sequenced imagery (TSI). Test and validation of this alignment process is achieved using a forward model producing known geometric artifacts in the images and afterwards using a PSO algorithm to demonstrate the ability to identify and correct for these artifacts. Specifically, the forward model introduces local translational, rotational, and magnification changes within the image. These geometric modifiers are expected during TSI acquisition because of logistical issues to precisely align the patient to the image recording geometry and is therefore of paramount importance to any viable image registration system. This paper shows that the PSO alignment algorithm is effective in autonomously determining and mitigating these geometric modifiers. The degree of efficacy is measured by several statistically and morphologically based pre-image filtering operations applied to the TSI imagery before applying the PSO alignment algorithm. These trade studies show that global image threshold binarization provides rapid and superior convergence characteristics relative to that of morphologically based methods.

Non-rigid registration of serial dedicated breast CT, longitudinal dedicated breast CT and PET/CT images using the diffeomorphic demons method.

PubMed

Santos, Jonathan; Chaudhari, Abhijit J; Joshi, Anand A; Ferrero, Andrea; Yang, Kai; Boone, John M; Badawi, Ramsey D

2014-09-01

Dedicated breast CT and PET/CT scanners provide detailed 3D anatomical and functional imaging data sets and are currently being investigated for applications in breast cancer management such as diagnosis, monitoring response to therapy and radiation therapy planning. Our objective was to evaluate the performance of the diffeomorphic demons (DD) non-rigid image registration method to spatially align 3D serial (pre- and post-contrast) dedicated breast computed tomography (CT), and longitudinally-acquired dedicated 3D breast CT and positron emission tomography (PET)/CT images. The algorithmic parameters of the DD method were optimized for the alignment of dedicated breast CT images using training data and fixed. The performance of the method for image alignment was quantitatively evaluated using three separate data sets; (1) serial breast CT pre- and post-contrast images of 20 women, (2) breast CT images of 20 women acquired before and after repositioning the subject on the scanner, and (3) dedicated breast PET/CT images of 7 women undergoing neo-adjuvant chemotherapy acquired pre-treatment and after 1 cycle of therapy. The DD registration method outperformed no registration (p < 0.001) and conventional affine registration (p ≤ 0.002) for serial and longitudinal breast CT and PET/CT image alignment. In spite of the large size of the imaging data, the computational cost of the DD method was found to be reasonable (3-5 min). Co-registration of dedicated breast CT and PET/CT images can be performed rapidly and reliably using the DD method. This is the first study evaluating the DD registration method for the alignment of dedicated breast CT and PET/CT images. Copyright © 2014 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

PCA-based groupwise image registration for quantitative MRI.

PubMed

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

2016-04-01

Quantitative magnetic resonance imaging (qMRI) is a technique for estimating quantitative tissue properties, such as the T1 and T2 relaxation times, apparent diffusion coefficient (ADC), and various perfusion measures. This estimation is achieved by acquiring multiple images with different acquisition parameters (or at multiple time points after injection of a contrast agent) and by fitting a qMRI signal model to the image intensities. Image registration is often necessary to compensate for misalignments due to subject motion and/or geometric distortions caused by the acquisition. However, large differences in image appearance make accurate image registration challenging. In this work, we propose a groupwise image registration method for compensating misalignment in qMRI. The groupwise formulation of the method eliminates the requirement of choosing a reference image, thus avoiding a registration bias. The method minimizes a cost function that is based on principal component analysis (PCA), exploiting the fact that intensity changes in qMRI can be described by a low-dimensional signal model, but not requiring knowledge on the specific acquisition model. The method was evaluated on 4D CT data of the lungs, and both real and synthetic images of five different qMRI applications: T1 mapping in a porcine heart, combined T1 and T2 mapping in carotid arteries, ADC mapping in the abdomen, diffusion tensor mapping in the brain, and dynamic contrast-enhanced mapping in the abdomen. Each application is based on a different acquisition model. The method is compared to a mutual information-based pairwise registration method and four other state-of-the-art groupwise registration methods. Registration accuracy is evaluated in terms of the precision of the estimated qMRI parameters, overlap of segmented structures, distance between corresponding landmarks, and smoothness of the deformation. In all qMRI applications the proposed method performed better than or equally well as competing methods, while avoiding the need to choose a reference image. It is also shown that the results of the conventional pairwise approach do depend on the choice of this reference image. We therefore conclude that our groupwise registration method with a similarity measure based on PCA is the preferred technique for compensating misalignments in qMRI. Copyright © 2015 Elsevier B.V. All rights reserved.

3D ultrasound volume stitching using phase symmetry and harris corner detection for orthopaedic applications

NASA Astrophysics Data System (ADS)

Dalvi, Rupin; Hacihaliloglu, Ilker; Abugharbieh, Rafeef

2010-03-01

Stitching of volumes obtained from three dimensional (3D) ultrasound (US) scanners improves visualization of anatomy in many clinical applications. Fast but accurate volume registration remains the key challenge in this area.We propose a volume stitching method based on efficient registration of 3D US volumes obtained from a tracked US probe. Since the volumes, after adjusting for probe motion, are coarsely registered, we obtain salient correspondence points in the central slices of these volumes. This is done by first removing artifacts in the US slices using intensity invariant local phase image processing and then applying the Harris Corner detection algorithm. Fast sub-volume registration on a small neighborhood around the points then gives fast, accurate 3D registration parameters. The method has been tested on 3D US scans of phantom and real human radius and pelvis bones and a phantom human fetus. The method has also been compared to volumetric registration, as well as feature based registration using 3D-SIFT. Quantitative results show average post-registration error of 0.33mm which is comparable to volumetric registration accuracy (0.31mm) and much better than 3D-SIFT based registration which failed to register the volumes. The proposed method was also much faster than volumetric registration (~4.5 seconds versus 83 seconds).

Detection and Evaluation of Skin Disorders by One of Photogrammetric Image Analysis Methods

NASA Astrophysics Data System (ADS)

Güçin, M.; Patias, P.; Altan, M. O.

2012-08-01

Abnormalities on skin may vary from simple acne to painful wounds which affect a person's life quality. Detection of these kinds of disorders in early stages, followed by the evaluation of abnormalities is of high importance. At this stage, photogrammetry offers a non-contact solution to this concern by providing geometric highly accurate data. Photogrammetry, which has been used for firstly topographic purposes, in virtue of terrestrial photogrammetry became useful technique in non-topographic applications also (Wolf et al., 2000). Moreover the extension of usage of photogrammetry, in parallel with the development in technology, analogue photographs are replaced with digital images and besides digital image processing techniques, it provides modification of digital images by using filters, registration processes etc. Besides, photogrammetry (using same coordinate system by registration of images) can serve as a tool for the comparison of temporal imaging data. The aim of this study is to examine several digital image processing techniques, in particular the digital filters, which might be useful to determine skin disorders. In our study we examine affordable to purchase, user friendly software which needs neither expertise nor pre-training. Since it is a pre-work for subsequent and deeper studies, Adobe Photoshop 7.0 is used as a present software. In addition to that Adobe Photoshop released a DesAcc plug-ins with CS3 version and provides full compatibility with DICOM (Digital Imaging and Communications in Medicine) and PACS (Picture Archiving and Communications System) that enables doctors to store all medical data together with relevant images and share if necessary.

Registration of organs with sliding interfaces and changing topologies

NASA Astrophysics Data System (ADS)

Berendsen, Floris F.; Kotte, Alexis N. T. J.; Viergever, Max A.; Pluim, Josien P. W.

2014-03-01

Smoothness and continuity assumptions on the deformation field in deformable image registration do not hold for applications where the imaged objects have sliding interfaces. Recent extensions to deformable image registration that accommodate for sliding motion of organs are limited to sliding motion along approximately planar surfaces or cannot model sliding that changes the topological configuration in case of multiple organs. We propose a new extension to free-form image registration that is not limited in this way. Our method uses a transformation model that consists of uniform B-spline transformations for each organ region separately, which is based on segmentation of one image. Since this model can create overlapping regions or gaps between regions, we introduce a penalty term that minimizes this undesired effect. The penalty term acts on the surfaces of the organ regions and is optimized simultaneously with the image similarity. To evaluate our method registrations were performed on publicly available inhale-exhale CT scans for which performances of other methods are known. Target registration errors are computed on dense landmark sets that are available with these datasets. On these data our method outperforms the other methods in terms of target registration error and, where applicable, also in terms of overlap and gap volumes. The approximation of the other methods of sliding motion along planar surfaces is reasonably well suited for the motion present in the lung data. The ability of our method to handle sliding along curved boundaries and for changing region topology configurations was demonstrated on synthetic images.

3D/2D image registration method for joint motion analysis using low-quality images from mini C-arm machines

NASA Astrophysics Data System (ADS)

Ghafurian, Soheil; Hacihaliloglu, Ilker; Metaxas, Dimitris N.; Tan, Virak; Li, Kang

2017-03-01

A 3D kinematic measurement of joint movement is crucial for orthopedic surgery assessment and diagnosis. This is usually obtained through a frame-by-frame registration of the 3D bone volume to a fluoroscopy video of the joint movement. The high cost of a high-quality fluoroscopy imaging system has hindered the access of many labs to this application. This is while the more affordable and low-dosage version, the mini C-arm, is not commonly used for this application due to low image quality. In this paper, we introduce a novel method for kinematic analysis of joint movement using the mini C-arm. In this method the bone of interest is recovered and isolated from the rest of the image using a non-rigid registration of an atlas to each frame. The 3D/2D registration is then performed using the weighted histogram of image gradients as an image feature. In our experiments, the registration error was 0.89 mm and 2.36° for human C2 vertebra. While the precision is still lacking behind a high quality fluoroscopy machine, it is a good starting point facilitating the use of mini C-arms for motion analysis making this application available to lower-budget environments. Moreover, the registration was highly resistant to the initial distance from the true registration, converging to the answer from anywhere within +/-90° of it.

MR to CT registration of brains using image synthesis

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

Analysis and correction of Landsat 4 and 5 Thematic Mapper Sensor Data

NASA Technical Reports Server (NTRS)

Bernstein, R.; Hanson, W. A.

1985-01-01

Procedures for the correction and registration and registration of Landsat TM image data are examined. The registration of Landsat-4 TM images of San Francisco to Landsat-5 TM images of the San Francisco using the interactive geometric correction program and the cross-correlation technique is described. The geometric correction program and cross-correlation results are presented. The corrections of the TM data to a map reference and to a cartographic database are discussed; geometric and cartographic analyses are applied to the registration results.

Oceanographic satellite remote sensing: Registration, rectification, and data integration requirements

NASA Technical Reports Server (NTRS)

Nichols, D. A.

1982-01-01

The problem of data integration in oceanography is discussed. Recommendations are made for technique development and evaluation, understanding requirements, and packaging techniques for speed, efficiency and ease of use. The primary satellite sensors of interest to oceanography are summarized. It is concluded that imaging type sensors make image processing an important tool for oceanographic studies.

Accuracy assessment of fluoroscopy-transesophageal echocardiography registration

NASA Astrophysics Data System (ADS)

Lang, Pencilla; Seslija, Petar; Bainbridge, Daniel; Guiraudon, Gerard M.; Jones, Doug L.; Chu, Michael W.; Holdsworth, David W.; Peters, Terry M.

2011-03-01

This study assesses the accuracy of a new transesophageal (TEE) ultrasound (US) fluoroscopy registration technique designed to guide percutaneous aortic valve replacement. In this minimally invasive procedure, a valve is inserted into the aortic annulus via a catheter. Navigation and positioning of the valve is guided primarily by intra-operative fluoroscopy. Poor anatomical visualization of the aortic root region can result in incorrect positioning, leading to heart valve embolization, obstruction of the coronary ostia and acute kidney injury. The use of TEE US images to augment intra-operative fluoroscopy provides significant improvements to image-guidance. Registration is achieved using an image-based TEE probe tracking technique and US calibration. TEE probe tracking is accomplished using a single-perspective pose estimation algorithm. Pose estimation from a single image allows registration to be achieved using only images collected in standard OR workflow. Accuracy of this registration technique is assessed using three models: a point target phantom, a cadaveric porcine heart with implanted fiducials, and in-vivo porcine images. Results demonstrate that registration can be achieved with an RMS error of less than 1.5mm, which is within the clinical accuracy requirements of 5mm. US-fluoroscopy registration based on single-perspective pose estimation demonstrates promise as a method for providing guidance to percutaneous aortic valve replacement procedures. Future work will focus on real-time implementation and a visualization system that can be used in the operating room.

Image processing and recognition for biological images

PubMed Central

Uchida, Seiichi

2013-01-01

This paper reviews image processing and pattern recognition techniques, which will be useful to analyze bioimages. Although this paper does not provide their technical details, it will be possible to grasp their main tasks and typical tools to handle the tasks. Image processing is a large research area to improve the visibility of an input image and acquire some valuable information from it. As the main tasks of image processing, this paper introduces gray-level transformation, binarization, image filtering, image segmentation, visual object tracking, optical flow and image registration. Image pattern recognition is the technique to classify an input image into one of the predefined classes and also has a large research area. This paper overviews its two main modules, that is, feature extraction module and classification module. Throughout the paper, it will be emphasized that bioimage is a very difficult target for even state-of-the-art image processing and pattern recognition techniques due to noises, deformations, etc. This paper is expected to be one tutorial guide to bridge biology and image processing researchers for their further collaboration to tackle such a difficult target. PMID:23560739

Efficient methods for implementation of multi-level nonrigid mass-preserving image registration on GPUs and multi-threaded CPUs.

PubMed

Ellingwood, Nathan D; Yin, Youbing; Smith, Matthew; Lin, Ching-Long

2016-04-01

Faster and more accurate methods for registration of images are important for research involved in conducting population-based studies that utilize medical imaging, as well as improvements for use in clinical applications. We present a novel computation- and memory-efficient multi-level method on graphics processing units (GPU) for performing registration of two computed tomography (CT) volumetric lung images. We developed a computation- and memory-efficient Diffeomorphic Multi-level B-Spline Transform Composite (DMTC) method to implement nonrigid mass-preserving registration of two CT lung images on GPU. The framework consists of a hierarchy of B-Spline control grids of increasing resolution. A similarity criterion known as the sum of squared tissue volume difference (SSTVD) was adopted to preserve lung tissue mass. The use of SSTVD consists of the calculation of the tissue volume, the Jacobian, and their derivatives, which makes its implementation on GPU challenging due to memory constraints. The use of the DMTC method enabled reduced computation and memory storage of variables with minimal communication between GPU and Central Processing Unit (CPU) due to ability to pre-compute values. The method was assessed on six healthy human subjects. Resultant GPU-generated displacement fields were compared against the previously validated CPU counterpart fields, showing good agreement with an average normalized root mean square error (nRMS) of 0.044±0.015. Runtime and performance speedup are compared between single-threaded CPU, multi-threaded CPU, and GPU algorithms. Best performance speedup occurs at the highest resolution in the GPU implementation for the SSTVD cost and cost gradient computations, with a speedup of 112 times that of the single-threaded CPU version and 11 times over the twelve-threaded version when considering average time per iteration using a Nvidia Tesla K20X GPU. The proposed GPU-based DMTC method outperforms its multi-threaded CPU version in terms of runtime. Total registration time reduced runtime to 2.9min on the GPU version, compared to 12.8min on twelve-threaded CPU version and 112.5min on a single-threaded CPU. Furthermore, the GPU implementation discussed in this work can be adapted for use of other cost functions that require calculation of the first derivatives. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Robust Global Image Registration Based on a Hybrid Algorithm Combining Fourier and Spatial Domain Techniques

DTIC Science & Technology

2012-09-01

Robust global image registration based on a hybrid algorithm combining Fourier and spatial domain techniques Peter N. Crabtree, Collin Seanor...00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE Robust global image registration based on a hybrid algorithm combining Fourier and spatial domain...demonstrate performance of a hybrid algorithm . These results are from analysis of a set of images of an ISO 12233 [12] resolution chart captured in the

Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery

NASA Technical Reports Server (NTRS)

Zavorin, Ilya; Le Moigne, Jacqueline

2005-01-01

The problem of image registration, or the alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast, and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times and that would provide subpixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the bandpass wavelets obtained from the steerable pyramid due to Simoncelli performs best in terms of accuracy and consistency, while the low-pass wavelets obtained from the same pyramid give the best results in terms of the radius of convergence. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

The analysis of selected orientation methods of architectural objects' scans

NASA Astrophysics Data System (ADS)

Markiewicz, Jakub S.; Kajdewicz, Irmina; Zawieska, Dorota

2015-05-01

The terrestrial laser scanning is commonly used in different areas, inter alia in modelling architectural objects. One of the most important part of TLS data processing is scans registration. It significantly affects the accuracy of generation of high resolution photogrammetric documentation. This process is time consuming, especially in case of a large number of scans. It is mostly based on an automatic detection and a semi-automatic measurement of control points placed on the object. In case of the complicated historical buildings, sometimes it is forbidden to place survey targets on an object or it may be difficult to distribute survey targets in the optimal way. Such problems encourage the search for the new methods of scan registration which enable to eliminate the step of placing survey targets on the object. In this paper the results of target-based registration method are presented The survey targets placed on the walls of historical chambers of the Museum of King Jan III's Palace at Wilanów and on the walls of ruins of the Bishops Castle in Iłża were used for scan orientation. Several variants of orientation were performed, taking into account different placement and different number of survey marks. Afterwards, during next research works, raster images were generated from scans and the SIFT and SURF algorithms for image processing were used to automatically search for corresponding natural points. The case of utilisation of automatically identified points for TLS data orientation was analysed. The results of both methods for TLS data registration were summarized and presented in numerical and graphical forms.

A Demons algorithm for image registration with locally adaptive regularization.

PubMed

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

2009-01-01

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

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pursley, Jennifer; Risholm, Petter; Fedorov, Andriy

2012-11-15

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 measuremore » 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 variation in the shape and volume of the segmented prostate in diagnostic and intraprocedural images. The probabilistic method allowed us to convey registration results in terms of posterior distributions, with the dispersion providing a patient-specific estimate of the registration uncertainty. The median of the predictive distance distribution between the deformed prostate boundary and the segmented boundary was Less-Than-Or-Slanted-Equal-To 3 mm (95th percentiles within {+-}4 mm) for all ten patients. The accuracy and precision of the internal deformation was evaluated by comparing the posterior predictive distance distribution for the CZ-PZ interface for each patient, with the median distance ranging from -0.6 to 2.4 mm. Posterior predictive distances between naturally occurring landmarks showed registration errors of Less-Than-Or-Slanted-Equal-To 5 mm in any direction. The uncertainty was not a global measure, but instead was local and varied throughout the registration region. Registration uncertainties were largest in the apical region of the prostate. Conclusions: Using a Bayesian nonrigid registration method, the authors determined the posterior distribution on deformations between diagnostic and intraprocedural MR images and quantified the uncertainty in the registration results. The feasibility of this approach was tested and results were positive. The probabilistic framework allows us to evaluate both patient-specific and location-specific estimates of the uncertainty in the registration result. Although the framework was tested on MR-guided procedures, the preliminary results suggest that it may be applied to TRUS-guided procedures as well, where the addition of diagnostic MR information may have a larger impact on target definition and clinical guidance.« less

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

PubMed Central

Pursley, Jennifer; Risholm, Petter; Fedorov, Andriy; Tuncali, Kemal; Fennessy, Fiona M.; Wells, William M.; 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 variation in the shape and volume of the segmented prostate in diagnostic and intraprocedural images. The probabilistic method allowed us to convey registration results in terms of posterior distributions, with the dispersion providing a patient-specific estimate of the registration uncertainty. The median of the predictive distance distribution between the deformed prostate boundary and the segmented boundary was ⩽3 mm (95th percentiles within ±4 mm) for all ten patients. The accuracy and precision of the internal deformation was evaluated by comparing the posterior predictive distance distribution for the CZ-PZ interface for each patient, with the median distance ranging from −0.6 to 2.4 mm. Posterior predictive distances between naturally occurring landmarks showed registration errors of ⩽5 mm in any direction. The uncertainty was not a global measure, but instead was local and varied throughout the registration region. Registration uncertainties were largest in the apical region of the prostate. Conclusions: Using a Bayesian nonrigid registration method, the authors determined the posterior distribution on deformations between diagnostic and intraprocedural MR images and quantified the uncertainty in the registration results. The feasibility of this approach was tested and results were positive. The probabilistic framework allows us to evaluate both patient-specific and location-specific estimates of the uncertainty in the registration result. Although the framework was tested on MR-guided procedures, the preliminary results suggest that it may be applied to TRUS-guided procedures as well, where the addition of diagnostic MR information may have a larger impact on target definition and clinical guidance. PMID:23127078

Image Registration of Cone-Beam Computer Tomography and Preprocedural Computer Tomography Aids in Localization of Adrenal Veins and Decreasing Radiation Dose in Adrenal Vein Sampling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Busser, Wendy M. H., E-mail: wendy.busser@radboudumc.nl; Arntz, Mark J.; Jenniskens, Sjoerd F. M.

2015-08-15

PurposeWe assessed whether image registration of cone-beam computed tomography (CT) (CBCT) and contrast-enhanced CT (CE-CT) images indicating the locations of the adrenal veins can aid in increasing the success rate of first-attempts adrenal vein sampling (AVS) and therefore decreasing patient radiation dose.Materials and Methods CBCT scans were acquired in the interventional suite (Philips Allura Xper FD20) and rigidly registered to the vertebra in previously acquired CE-CT. Adrenal vein locations were marked on the CT image and superimposed with live fluoroscopy and digital-subtraction angiography (DSA) to guide the AVS. Seventeen first attempts at AVS were performed with image registration and retrospectivelymore » compared with 15 first attempts without image registration performed earlier by the same 2 interventional radiologists. First-attempt AVS was considered successful when both adrenal vein samples showed representative cortisol levels. Sampling time, dose-area product (DAP), number of DSA runs, fluoroscopy time, and skin dose were recorded.ResultsWithout image registration, the first attempt at sampling was successful in 8 of 15 procedures indicating a success rate of 53.3 %. This increased to 76.5 % (13 of 17) by adding CBCT and CE-CT image registration to AVS procedures (p = 0.266). DAP values (p = 0.001) and DSA runs (p = 0.026) decreased significantly by adding image registration guidance. Sampling and fluoroscopy times and skin dose showed no significant changes.ConclusionGuidance based on registration of CBCT and previously acquired diagnostic CE-CT can aid in enhancing localization of the adrenal veins thereby increasing the success rate of first-attempt AVS with a significant decrease in the number of used DSA runs and, consequently, radiation dose required.« less

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

PubMed

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

2018-01-01

To compare registration strategies to align arterial spin labeling (ASL) with 3D T1-weighted (T1w) images, with the goal of reducing the between-subject variability of cerebral blood flow (CBF) images. Multi-center 3T ASL data were collected at eight sites with four different sequences in the multi-center GENetic Frontotemporal dementia Initiative (GENFI) study. In a total of 48 healthy controls, we compared the following image registration options: (I) which images to use for registration (perfusion-weighted images [PWI] to the segmented gray matter (GM) probability map (pGM) (CBF-pGM) or M0 to T1w (M0-T1w); (II) which transformation to use (rigid-body or non-rigid); and (III) whether to mask or not (no masking, M0-based FMRIB software library Brain Extraction Tool [BET] masking). In addition to visual comparison, we quantified image similarity using the Pearson correlation coefficient (CC), and used the Mann-Whitney U rank sum test. CBF-pGM outperformed M0-T1w (CC improvement 47.2% ± 22.0%; P < 0.001), and the non-rigid transformation outperformed rigid-body (20.6% ± 5.3%; P < 0.001). Masking only improved the M0-T1w rigid-body registration (14.5% ± 15.5%; P = 0.007). The choice of image registration strategy impacts ASL group analyses. The non-rigid transformation is promising but requires validation. CBF-pGM rigid-body registration without masking can be used as a default strategy. In patients with expansive perfusion deficits, M0-T1w may outperform CBF-pGM in sequences with high effective spatial resolution. BET-masking only improves M0-T1w registration when the M0 image has sufficient contrast. 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:131-140. © 2017 International Society for Magnetic Resonance in Medicine.

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

NASA Astrophysics Data System (ADS)

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.

Processing Satellite Images on Tertiary Storage: A Study of the Impact of Tile Size on Performance

NASA Technical Reports Server (NTRS)

Yu, JieBing; DeWitt, David J.

1996-01-01

Before raw data from a satellite can be used by an Earth scientist, it must first undergo a number of processing steps including basic processing, cleansing, and geo-registration. Processing actually expands the volume of data collected by a factor of 2 or 3 and the original data is never deleted. Thus processing and storage requirements can exceed 2 terrabytes/day. Once processed data is ready for analysis, a series of algorithms (typically developed by the Earth scientists) is applied to a large number of images in a data set. The focus of this paper is how best to handle such images stored on tape using the following assumptions: (1) all images of interest to a scientist are stored on a single tape, (2) images are accessed and processed in the order that they are stored on tape, and (3) the analysis requires access to only a portion of each image and not the entire image.