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Sample records for sar image registration

  1. Investigation on Fine Registration for SAR and Optical Image

    Directory of Open Access Journals (Sweden)

    You Hong-jian

    2014-02-01

    Full Text Available The registration of SAR and optical remote sensing image is the basise for fusing of multi-source image and comprehensive analysis. In this paper a new fine registration method for SAR and optical image is proposed. Firstly, three to four corresponding points are selected manually to realize a coarse registration that eliminates the differences in scale and rotation. Many characteristic points in the optical image are detected and the corresponding points in SAR image are extracted using normalized gradient correlations based on the different gradients by operators. An irregular triangle network is constructed using these corresponding points and each triangle region is finely registered. Finally SAR image and optical image are finely registered. Experiment and processed results demonstrate the feasibility of this method.

  2. An Advanced Rotation Invariant Descriptor for SAR Image Registration

    Directory of Open Access Journals (Sweden)

    Yuming Xiang

    2017-07-01

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

  3. Automatic Registration of SAR Images with the Integrated Complementary Invariant Feature

    Directory of Open Access Journals (Sweden)

    Xiao-hua Wang

    2014-01-01

    Full Text Available The accurate Synthetic Aperture Radar (SAR image registration is important for exact analyses of mine deformation and ecological environment change. Currently, many image registration algorithms have been proposed, but these registration algorithms cannot be directly applied to SAR image, so an integrated registration approach is presented in this paper. Firstly, it is the coarse matching with Canny edge dividing regions; secondly, it is the fine matching by SIFT algorithm with improved Canny edge features; finally, obtain accurate registration SAR image. This approach has fewer computations than that simply using SIFT feature matching. Experimental analyses with SAR images of Yanzhou Mine demonstrate the efficiency and the accuracy of this approach for mine SAR image registration, which provides a simple and effective tool in SAR monitoring of mining deformation and ecological changes

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

    Science.gov (United States)

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

    2013-07-01

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

  5. A SAR IMAGE REGISTRATION METHOD BASED ON SIFT ALGORITHM

    Directory of Open Access Journals (Sweden)

    W. Lu

    2017-09-01

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

  6. a SAR Image Registration Method Based on Sift Algorithm

    Science.gov (United States)

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

    2017-09-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Eichel, Paul H.

    2013-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Xiaoli Ding

    2009-02-01

    Full Text Available 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.

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

    Directory of Open Access Journals (Sweden)

    Zhenwei Chen

    2016-09-01

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

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

    Science.gov (United States)

    Chen, Zhenwei; Zhang, Lei; Zhang, Guo

    2016-09-17

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

  11. An Optical Flow Method Applied to Co-Registration of Remote Sensing Images: Example for SAR/SAR, SAR/LIDAR, SAR/Optical Images of BIOSAR 2010 Campaign

    Science.gov (United States)

    Colin-Koeniguer, Elise

    2016-08-01

    This article proposes an optical flow type method for coregistration of forest remote sensing images. The principle of the algorithm called GeFolki is first explained. Results are shown on the images of the BioSAR 3 campaign, for the production of SAR interferograms, the coregistration a SAR and LIDAR image, and the coregistration an optical image and SAR image.The advantages of such an algorithm over conventional algorithms are explained. Finally, we propose various applications within the operating data for future BIOMASS mission: massive interferometry, ground truth production, upscaling by fusion of LIDAR and SAR data, and image mining.

  12. Image registration

    CERN Document Server

    Goshtasby, A Ardeshir

    2012-01-01

    This book presents a thorough and detailed guide to image registration, outlining the principles and reviewing state-of-the-art tools and methods. The book begins by identifying the components of a general image registration system, and then describes the design of each component using various image analysis tools. The text reviews a vast array of tools and methods, not only describing the principles behind each tool and method, but also measuring and comparing their performances using synthetic and real data. Features: discusses similarity/dissimilarity measures, point detectors, feature extr

  13. Mass preserving image registration

    DEFF Research Database (Denmark)

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

    2010-01-01

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

  14. Bistatic SAR: Imagery & Image Products.

    Energy Technology Data Exchange (ETDEWEB)

    Yocky, David A.; Wahl, Daniel E.; Jakowatz, Charles V,

    2014-10-01

    While typical SAR imaging employs a co-located (monostatic) RADAR transmitter and receiver, bistatic SAR imaging separates the transmitter and receiver locations. The transmitter and receiver geometry determines if the scattered signal is back scatter, forward scatter, or side scatter. The monostatic SAR image is backscatter. Therefore, depending on the transmitter/receiver collection geometry, the captured imagery may be quite different that that sensed at the monostatic SAR. This document presents imagery and image products formed from captured signals during the validation stage of the bistatic SAR research. Image quality and image characteristics are discussed first. Then image products such as two-color multi-view (2CMV) and coherent change detection (CCD) are presented.

  15. Geometric registration and rectification of spaceborne SAR imagery

    Science.gov (United States)

    Curlander, J. C.; Pang, S. N.

    1982-01-01

    This paper describes the development of automated location and geometric rectification techniques for digitally processed synthetic aperture radar (SAR) imagery. A software package has been developed that is capable of determining the absolute location of an image pixel to within 60 m using only the spacecraft ephemeris data and the characteristics of the SAR data collection and processing system. Based on this location capability algorithms have been developed that geometrically rectify the imagery, register it to a common coordinate system and mosaic multiple frames to form extended digital SAR maps. These algorithms have been optimized using parallel processing techniques to minimize the operating time. Test results are given using Seasat SAR data.

  16. Image registration with uncertainty analysis

    Science.gov (United States)

    Simonson, Katherine M.

    2011-03-22

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

  17. SAR Image Enhancement using Particle Filters

    Data.gov (United States)

    National Aeronautics and Space Administration — In this paper, we propose a novel approach to reduce the noise in Synthetic Aperture Radar (SAR) images using particle filters. Interpretation of SAR images is a...

  18. SAR image multi-scale registration based on FKICA-SIFT features%基于FKICA-SIFT特征的合成孔径图像多尺度配准

    Institute of Scientific and Technical Information of China (English)

    刘向增; 田铮; 史振广; 陈占寿

    2011-01-01

    针对合成孔径(SAR)图像的配准,提出一种基于仿射不变快速核独立成分分析-尺度不变特征变换(FKICA-SIFT)的多尺度配准方法.首先,根据特征点的Hessian矩阵构建仿射不变SIFT描述子.接着,利用FKICA提取该描述子的独立成分得到新的描述子FKICA-SIFT.然后,利用该描述子对Steerable滤波后的各层带通合成子图像提取的特征点进行匹配.最后,采用由粗到细的匹配策略逐步优化变换参数,实现图像的多尺度精确配准.实验结果表明,对有较大仿射变化的SAR图像,当阈值小于0.7时,该方法的匹配正确率大于85%,阈值小于0.5时,匹配正确率可达90%以上,配准精度达到亚像素水平,优于SIFT,PCA-SIFT,ICA-SIFT及SURF等相关方法.使用该方法准确地检测出了地震前后唐家山堰塞湖水域的变化情况,基本满足了SAR图像变换检测前精确配准的要求.%In order to realize automatic registration of a Synthetic Aperture Radar(SAR) image, an approach of image multi-scale registration based on affine invariant Fast Kernel Independent Component Analysis-Scale Invariant Feature Transform(FKICA-SIFT) features is presented. First, the affine invariant SIFT descriptors are constructed according to the Hessian matrix of feature points. The FKI-CA is used to extract the independent components of the affine invariant SIFT descriptors to obtain new descriptors (FKICA-SIFT). After filtering the input images by using Steerable pyramid, the new descriptors are used to match the feature points detected from the synthetic images of the band-pass sub-images in each layer. Finally, a coarse-to-fine procedure is adopted for gradual optimizing transformation parameters to achieve the multi-scale registration results. Experimental results show that the correct matching rate of proposed algorithm is more than 85 % when the threshold is less than 0. 7and that is more than 90% when the threshold is less than 0. 5. The registration

  19. Convolutional Neural Networks for SAR Image Segmentation

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David; Nobel-Jørgensen, Morten

    2015-01-01

    Segmentation of Synthetic Aperture Radar (SAR) images has several uses, but it is a difficult task due to a number of properties related to SAR images. In this article we show how Convolutional Neural Networks (CNNs) can easily be trained for SAR image segmentation with good results. Besides...

  20. SAR Image Complex Pixel Representations

    Energy Technology Data Exchange (ETDEWEB)

    Doerry, Armin W. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-03-01

    Complex pixel values for Synthetic Aperture Radar (SAR) images of uniform distributed clutter can be represented as either real/imaginary (also known as I/Q) values, or as Magnitude/Phase values. Generally, these component values are integers with limited number of bits. For clutter energy well below full-scale, Magnitude/Phase offers lower quantization noise than I/Q representation. Further improvement can be had with companding of the Magnitude value.

  1. Spaceborne SAR Imaging Algorithm for Coherence Optimized.

    Directory of Open Access Journals (Sweden)

    Zhiwei Qiu

    Full Text Available This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR research and application.

  2. Precision Rectification of Airborne SAR Image

    DEFF Research Database (Denmark)

    Dall, Jørgen; Liao, M.; Zhang, Zhe

    1997-01-01

    A simple and direct procedure for the rectification of a certain class of airborne SAR data is presented. The relief displacements of SAR data are effectively removed by means of a digital elevation model and the image is transformed to the ground coordinate system. SAR data from the Danish EMISAR...... for the application of SAR data in the difficult process of map revision and updating....

  3. Numerical methods for image registration

    CERN Document Server

    Modersitzki, Jan

    2003-01-01

    Based on the author's lecture notes and research, this well-illustrated and comprehensive text is one of the first to provide an introduction to image registration with particular emphasis on numerical methods in medical imaging. Ideal for researchers in industry and academia, it is also a suitable study guide for graduate mathematicians, computer scientists, engineers, medical physicists, and radiologists.Image registration is utilised whenever information obtained from different viewpoints needs to be combined or compared and unwanted distortion needs to be eliminated. For example, CCTV imag

  4. Radar image registration and rectification

    Science.gov (United States)

    Naraghi, M.; Stromberg, W. D.

    1983-01-01

    Two techniques for radar image registration and rectification are presented. In the registration method, a general 2-D polynomial transform is defined to accomplish the geometric mapping from one image into the other. The degree and coefficients of the polynomial are obtained using an a priori found tiepoint data set. In the second part of the paper, a rectification procedure is developed that models the distortion present in the radar image in terms of the radar sensor's platform parameters and the topographic variations of the imaged scene. This model, the ephemeris data and the digital topographic data are then used in rectifying the radar image. The two techniques are then used in registering and rectifying two examples of radar imagery. Each method is discussed as to its benefits, shortcomings and registration accuracy.

  5. ACIR: automatic cochlea image registration

    Science.gov (United States)

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

    2017-02-01

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

  6. Refocusing vibrating targets in SAR images

    Science.gov (United States)

    Wang, Qi; Santhanam, Balu; Pepin, Matthew; Atwood, Tom; Hayat, Majeed M.

    2012-06-01

    In synthetic-aperture radar (SAR) returned signals, ground-target vibrations introduce a phase modulation that is linearly proportional to the vibration displacement. Such modulation, termed the micro-Doppler effect, introduces ghost targets along the azimuth direction in reconstructed SAR images that prevents SAR from forming focused images of the vibrating targets. Recently, a discrete fractional Fourier transform (DFrFT) based method was developed to estimate the vibration frequencies and instantaneous vibration accelerations of the vibrating targets from SAR returned signals. In this paper, a demodulation-based algorithm is proposed to reconstruct focused SAR images of vibrating targets by exploiting the estimation results of the DFrFT-based vibration estimation method. For a single-component harmonic vibration, the history of the vibration displacement is first estimated from the estimated vibration frequency and the instantaneous vibration accelerations. Then a reference signal whose phase is modulated by the estimated vibration displacement with a delay of 180 degree is constructed. After that, the SAR phase history from the vibration target is multiplied by the reference signal and the vibration-induced phase modulation is canceled. Finally, the SAR image containing the re-focused vibration target is obtained by applying the 2-D Fourier transform to the demodulated SAR phase history. This algorithm is applied to simulated SAR data and successfully reconstructs the SAR image containing the re-focused vibrating target.

  7. Composite SAR imaging using sequential joint sparsity

    Science.gov (United States)

    Sanders, Toby; Gelb, Anne; Platte, Rodrigo B.

    2017-06-01

    This paper investigates accurate and efficient ℓ1 regularization methods for generating synthetic aperture radar (SAR) images. Although ℓ1 regularization algorithms are already employed in SAR imaging, practical and efficient implementation in terms of real time imaging remain a challenge. Here we demonstrate that fast numerical operators can be used to robustly implement ℓ1 regularization methods that are as or more efficient than traditional approaches such as back projection, while providing superior image quality. In particular, we develop a sequential joint sparsity model for composite SAR imaging which naturally combines the joint sparsity methodology with composite SAR. Our technique, which can be implemented using standard, fractional, or higher order total variation regularization, is able to reduce the effects of speckle and other noisy artifacts with little additional computational cost. Finally we show that generalizing total variation regularization to non-integer and higher orders provides improved flexibility and robustness for SAR imaging.

  8. Bistatic SAR: Signal Processing and Image Formation.

    Energy Technology Data Exchange (ETDEWEB)

    Wahl, Daniel E.; Yocky, David A.

    2014-10-01

    This report describes the significant processing steps that were used to take the raw recorded digitized signals from the bistatic synthetic aperture RADAR (SAR) hardware built for the NCNS Bistatic SAR project to a final bistatic SAR image. In general, the process steps herein are applicable to bistatic SAR signals that include the direct-path signal and the reflected signal. The steps include preprocessing steps, data extraction to for a phase history, and finally, image format. Various plots and values will be shown at most steps to illustrate the processing for a bistatic COSMO SkyMed collection gathered on June 10, 2013 on Kirtland Air Force Base, New Mexico.

  9. Groupwise registration of aerial images

    OpenAIRE

    Arandjelovic, Ognjen; Pham, Duc-Son; Venkatesh, Svetha

    2015-01-01

    This paper addresses the task of time separated aerial image registration. The ability to solve this problem accurately and reliably is important for a variety of subsequent image understanding applications. The principal challenge lies in the extent and nature of transient appearance variation that a land area can undergo, such as that caused by the change in illumination conditions, seasonal variations, or the occlusion by non-persistent objects (people, cars). Our work introduces several n...

  10. Controlling Data Collection to Support SAR Image Rotation

    Science.gov (United States)

    Doerry, Armin W.; Cordaro, J. Thomas; Burns, Bryan L.

    2008-10-14

    A desired rotation of a synthetic aperture radar (SAR) image can be facilitated by adjusting a SAR data collection operation based on the desired rotation. The SAR data collected by the adjusted SAR data collection operation can be efficiently exploited to form therefrom a SAR image having the desired rotational orientation.

  11. Deformable Registration of Digital Images

    Institute of Scientific and Technical Information of China (English)

    管伟光; 解林; 等

    1998-01-01

    is paper proposes a novel elastic model and presents a deformable registration method based on the model.The method registers images without the need to extract reatures from the images,and therefore works directly on grey-level images.A new similarity metric is given on which the formation of external forces is based.The registration method,taking the coarse-to-fine strategy,constructs external forces in larger scales for the first few iterations to rely more on global evidence,and ther in smaller scales for later iterations to allow local refinements.The stiffness of the elastic body decreases as the process proceeds.To make it widely applicable,the method is not restricted to any type of transformation.The variations between images are thought as general free-form deformations.Because the elastic model designed is linearized,it can be solved very efficiently with high accuracy.The method has been successfully tested on MRI images.It will certainly find other uses such as matching time-varying sequences of pictures for motion analysis,fitting templates into images for non-rigid object recognition,matching stereo images for shape recovery,etc.

  12. Ionosphere correction algorithm for spaceborne SAR imaging

    Institute of Scientific and Technical Information of China (English)

    Lin Yang; Mengdao Xing; Guangcai Sun

    2016-01-01

    For spaceborne synthetic aperture radar (SAR) ima-ging, the dispersive ionosphere has significant effects on the pro-pagation of the low frequency (especial y P-band) radar signal. The ionospheric effects can be a significant source of the phase error in the radar signal, which causes a degeneration of the image quality in spaceborne SAR imaging system. The background ionospheric effects on spaceborne SAR through modeling and simulation are analyzed, and the qualitative and quantitative analysis based on the spatio-temporal variability of the ionosphere is given. A novel ionosphere correction algorithm (ICA) is proposed to deal with the ionospheric effects on the low frequency spaceborne SAR radar signal. With the proposed algorithm, the degradation of the image quality caused by the ionosphere is corrected. The simulation re-sults show the effectiveness of the proposed algorithm.

  13. Progress in Circular SAR Imaging Technique

    Directory of Open Access Journals (Sweden)

    Hong Wen

    2012-06-01

    Full Text Available Circular SAR (CSAR is a newly developed all-directional high resolution 3D imaging mode in recent years, to satisfy the demand of finer observation. The National Key Laboratory of Science and Technology on Microwave Imaging, Institute of Electronics, Chinese Academy of Sciences (MITL, IECAS, had the first test flight experiment in Aug. 2011 with a P-band full polarization SAR system, and successfully obtained the all-directional high resolution circular SAR image. The initial results show that CSAR technique has the encouraging potential capability in the fields of high precision mapping, disaster evaluation, resource management and the other related applications. This paper firstly makes a detailed discussion on the progress of circular SAR imaging technique, which emphases on the several airborne experiments performed these years to show CSAR’s attractive features, then studies and illustrates the key techniques, and finally discusses the development trends.

  14. Image registration method for medical image sequences

    Science.gov (United States)

    Gee, Timothy F.; Goddard, James S.

    2013-03-26

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

  15. Three-dimensional surface reconstruction from multistatic SAR images.

    Science.gov (United States)

    Rigling, Brian D; Moses, Randolph L

    2005-08-01

    This paper discusses reconstruction of three-dimensional surfaces from multiple bistatic synthetic aperture radar (SAR) images. Techniques for surface reconstruction from multiple monostatic SAR images already exist, including interferometric processing and stereo SAR. We generalize these methods to obtain algorithms for bistatic interferometric SAR and bistatic stereo SAR. We also propose a framework for predicting the performance of our multistatic stereo SAR algorithm, and, from this framework, we suggest a metric for use in planning strategic deployment of multistatic assets.

  16. Optimal Approach to SAR Image Despeckling

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Speckle filtering of synthetic aperture radar (SAR) images while preserving the spatial signal variability (texture and fine structures) still remains a challenge. Many algorithms have been proposed for the SAR imagery despeckling. However,simulated annealing (SA) method is one of excellent choices currently. A critical problem in the study on SA is to provide appropriate cooling schedules that ensure fast convergence to near-optimal solutions. This paper gives a new necessary and sufficient condition for the cooling schedule so that the algorithm state converges in all probability to the set of globally minimum cost states.Moreover, it constructs an appropriate objective function for SAR image despeckling. An experimental result of the actual SAR image processing is obtained.

  17. Semiautomated Multimodal Breast Image Registration

    Directory of Open Access Journals (Sweden)

    Charlotte Curtis

    2012-01-01

    However, due to the highly deformable nature of breast tissues, comparison of 3D and 2D modalities is a challenge. To enable this comparison, a registration technique was developed to map features from 2D mammograms to locations in the 3D image space. This technique was developed and tested using magnetic resonance (MR images as a reference 3D modality, as MR breast imaging is an established technique in clinical practice. The algorithm was validated using a numerical phantom then successfully tested on twenty-four image pairs. Dice's coefficient was used to measure the external goodness of fit, resulting in an excellent overall average of 0.94. Internal agreement was evaluated by examining internal features in consultation with a radiologist, and subjective assessment concludes that reasonable alignment was achieved.

  18. [Progress of research in retinal image registration].

    Science.gov (United States)

    Yu, Lun; Wei, Lifang; Pan, Lin

    2011-10-01

    The retinal image registration has important applications in the processes of auxiliary diagnosis and treatment for a variety of diseases. The retinal image registration can be used to measure the disease process and the therapeutic effect. A variety of retinal image registration techniques have been studied extensively in recent years. However, there are still many problems existing and there are numerous research possibilities. Based on extensive investigation of existing literatures, the present paper analyzes the feature of retinal image and current challenges of retinal image registration, and reviews the transformation models of the retinal image registration technology and the main research algorithms in current retinal image registration, and analyzes the advantages and disadvantages of various types of algorithms. Some research challenges and future developing trends are also discussed.

  19. Geometric calibration of ERS satellite SAR images

    DEFF Research Database (Denmark)

    Mohr, Johan Jacob; Madsen, Søren Nørvang

    2001-01-01

    Geometric calibration of the European Remote Sensing (ERS) Satellite synthetic aperture radar (SAR) slant range images is important in relation to mapping areas without ground reference points and also in relation to automated processing. The relevant SAR system parameters are discussed...... on a seven-year ERS-1 and a four-year ERS-2 time series, the long term stability is found to be sufficient to allow a single calibration covering the entire mission period. A descending and an ascending orbit tandem pair of the ESA calibration site on Flevoland, suitable for calibration of ERS SAR processors...

  20. CFAR Edge Detector for Polarimetric SAR Images

    DEFF Research Database (Denmark)

    Schou, Jesper; Skriver, Henning; Nielsen, Allan Aasbjerg;

    2003-01-01

    Finding the edges between different regions in an image is one of the fundamental steps of image analysis, and several edge detectors suitable for the special statistics of synthetic aperture radar (SAR) intensity images have previously been developed. In this paper, a new edge detector...

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

    Science.gov (United States)

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

    2016-12-01

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

  2. On Bistatic Forward-looking SAR Imaging

    OpenAIRE

    Vu, Viet Thuy; Pettersson, Mats

    2014-01-01

    Left/right ambiguity and low angular (azimuth) resolution are severe problems for monostatic forward-looking SAR imaging. It is strongly believed that these technical issues can definitely be solved with bistatic forward-looking SAR. The analysis presented in this paper points out that the left/right ambiguity problem still exits. However, an appropriate selection of the position of bistatic base line and antenna beamwidth allows us to conceal it. The paper also gives some recommendations whi...

  3. Mass preserving image registration for lung CT

    DEFF Research Database (Denmark)

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

    2012-01-01

    This paper presents a mass preserving image registration algorithm for lung CT images. To account for the local change in lung tissue intensity during the breathing cycle, a tissue appearance model based on the principle of preservation of total lung mass is proposed. This model is incorporated...... into a standard image registration framework with a composition of a global affine and several free-form B-Spline transformations with increasing grid resolution. The proposed mass preserving registration method is compared to registration using the sum of squared intensity differences as a similarity function...... inhale phases of 4D-CT images. Registration errors, measured as the average distance between vessel tree centerlines in the matched images, are significantly lower for the proposed mass preserving image registration method in the second, third and fourth group, while there is no statistically significant...

  4. A modified algorithm for SAR parallel imaging

    Institute of Scientific and Technical Information of China (English)

    HU Ju-rong; WANG Fei; CAO Ning; LU Hao

    2009-01-01

    Synthetic aperture radar can provide two dimension images by converting the acquired echoed SAR signal to targets coordinate and reflectivity. With the advancement of sophisticated SAR signal processing, more and more SAR imaging methods have been proposed for synthetic aperture radar which works at near field and the Fresnel approximation is not appropriate. Time domain correlation is a kind of digital reconstruction method based on processing the synthetic aperture radar data in the two-dimensional frequency domain via Fourier transform. It reconstructs SAR image via simply correlation without any need for approximation or interpolation. But its high computational cost for correlation makes it unsuitable for real time imaging. In order to reduce the computational burden a modified algorithm about time domain correlation was given in this paper. It also can take full advantage of parallel computations of the imaging processor. Its practical implementation was proposed and the preliminary simulation results were presented. Simulation results show that the proposed algorithm is a computationally efficient way of implementing the reconstruction in real time SAR image processing.

  5. Estimating IMU heading error from SAR images.

    Energy Technology Data Exchange (ETDEWEB)

    Doerry, Armin Walter

    2009-03-01

    Angular orientation errors of the real antenna for Synthetic Aperture Radar (SAR) will manifest as undesired illumination gradients in SAR images. These gradients can be measured, and the pointing error can be calculated. This can be done for single images, but done more robustly using multi-image methods. Several methods are provided in this report. The pointing error can then be fed back to the navigation Kalman filter to correct for problematic heading (yaw) error drift. This can mitigate the need for uncomfortable and undesired IMU alignment maneuvers such as S-turns.

  6. Heuristic approach to image registration

    Science.gov (United States)

    Gertner, Izidor; Maslov, Igor V.

    2000-08-01

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

  7. Synthetic SAR Image Generation using Sensor, Terrain and Target Models

    DEFF Research Database (Denmark)

    Kusk, Anders; Abulaitijiang, Adili; Dall, Jørgen

    2016-01-01

    A tool to generate synthetic SAR images of objects set on a clutter background is described. The purpose is to generate images for training Automatic Target Recognition and Identification algorithms. The tool employs a commercial electromagnetic simulation program to calculate radar cross sections...... of the object using a CAD-model. The raw measurements are input to a SAR system and terrain model, which models thermal noise, terrain clutter, and SAR focusing to produce synthetic SAR images. Examples of SAR images at 0.3m and 0.1m resolution, and a comparison with real SAR imagery from the MSTAR dataset...

  8. Fast SAR Imaging Algorithm for FLGPR

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A fast SAR imaging algorithm for near- field subsurface forward-looking ground penetrating radar (FLGPR) is presented. By using nonstationary convolution filter, the refocused image spectrum can be reconstructed directly from the backscattered signal spectrum of target area. The experimental results show the proposed method can fast achieve image refocusing. Also it has higher computational efficiency than the phase-shift migration approach and the delay-and-sum (DAS) approach.

  9. Tensor scale-based image registration

    Science.gov (United States)

    Saha, Punam K.; Zhang, Hui; Udupa, Jayaram K.; Gee, James C.

    2003-05-01

    Tangible solutions to image registration are paramount in longitudinal as well as multi-modal medical imaging studies. In this paper, we introduce tensor scale - a recently developed local morphometric parameter - in rigid image registration. A tensor scale-based registration method incorporates local structure size, orientation and anisotropy into the matching criterion, and therefore, allows efficient multi-modal image registration and holds potential to overcome the effects of intensity inhomogeneity in MRI. Two classes of two-dimensional image registration methods are proposed - (1) that computes angular shift between two images by correlating their tensor scale orientation histogram, and (2) that registers two images by maximizing the similarity of tensor scale features. Results of applications of the proposed methods on proton density and T2-weighted MR brain images of (1) the same slice of the same subject, and (2) different slices of the same subject are presented. The basic superiority of tensor scale-based registration over intensity-based registration is that it may allow the use of local Gestalts formed by the intensity patterns over the image instead of simply considering intensities as isolated events at the pixel level. This would be helpful in dealing with the effects of intensity inhomogeneity and noise in MRI.

  10. Multichannel imaging with the AMBER FMCW SAR

    NARCIS (Netherlands)

    Otten, M.P.G.; Rossum, W.L. van; Graaf, M.W. van der; Vlothuizen, W.J.; Tan, R.G.

    2014-01-01

    An X-band Digital Array Synthetic Aperture Radar for a Short Range Tactical UAV is presented. The Frequency Modulated Continuous Wave radar principle in combination with digital beam forming over 24 receive channels is used to achieve low power and advanced imaging SAR capabilities on small platform

  11. Multichannel imaging with the AMBER FMCW SAR

    NARCIS (Netherlands)

    Otten, M.P.G.; Rossum, W.L. van; Graaf, M.W. van der; Vlothuizen, W.J.; Tan, R.G.

    2014-01-01

    An X-band Digital Array Synthetic Aperture Radar for a Short Range Tactical UAV is presented. The Frequency Modulated Continuous Wave radar principle in combination with digital beam forming over 24 receive channels is used to achieve low power and advanced imaging SAR capabilities on small platform

  12. Multichannel imaging with the AMBER FMCW SAR

    NARCIS (Netherlands)

    Otten, M.P.G.; Rossum, W.L. van; Graaf, M.W. van der; Vlothuizen, W.J.; Tan, R.G.

    2014-01-01

    An X-band Digital Array Synthetic Aperture Radar for a Short Range Tactical UAV is presented. The Frequency Modulated Continuous Wave radar principle in combination with digital beam forming over 24 receive channels is used to achieve low power and advanced imaging SAR capabilities on small

  13. An Image Registration Method for Colposcopic Images

    Directory of Open Access Journals (Sweden)

    Efrén Mezura-Montes

    2013-01-01

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

  14. ANALYSIS OF MULTIPATH PIXELS IN SAR IMAGES

    Directory of Open Access Journals (Sweden)

    J. W. Zhao

    2016-06-01

    Full Text Available As the received radar signal is the sum of signal contributions overlaid in one single pixel regardless of the travel path, the multipath effect should be seriously tackled as the multiple bounce returns are added to direct scatter echoes which leads to ghost scatters. Most of the existing solution towards the multipath is to recover the signal propagation path. To facilitate the signal propagation simulation process, plenty of aspects such as sensor parameters, the geometry of the objects (shape, location, orientation, mutual position between adjacent buildings and the physical parameters of the surface (roughness, correlation length, permittivitywhich determine the strength of radar signal backscattered to the SAR sensor should be given in previous. However, it's not practical to obtain the highly detailed object model in unfamiliar area by field survey as it's a laborious work and time-consuming. In this paper, SAR imaging simulation based on RaySAR is conducted at first aiming at basic understanding of multipath effects and for further comparison. Besides of the pre-imaging simulation, the product of the after-imaging, which refers to radar images is also taken into consideration. Both Cosmo-SkyMed ascending and descending SAR images of Lupu Bridge in Shanghai are used for the experiment. As a result, the reflectivity map and signal distribution map of different bounce level are simulated and validated by 3D real model. The statistic indexes such as the phase stability, mean amplitude, amplitude dispersion, coherence and mean-sigma ratio in case of layover are analyzed with combination of the RaySAR output.

  15. Edge-based correlation image registration for multispectral imaging

    Science.gov (United States)

    Nandy, Prabal [Albuquerque, NM

    2009-11-17

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

  16. A Multistage Approach for Image Registration.

    Science.gov (United States)

    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.

  17. InSAR Forensics: Tracing InSAR Scatterers in High Resolution Optical Image

    Science.gov (United States)

    Wang, Yuanyuan; Zhu, XiaoXiang

    2015-05-01

    This paper presents a step towards a better interpretation of the scattering mechanism of different objects and their deformation histories in SAR interferometry (InSAR). The proposed technique traces individual SAR scatterer in high resolution optical images where their geometries, materials, and other properties can be better analyzed and classified. And hence scatterers of a same object can be analyzed in group, which brings us to a new level of InSAR deformation monitoring.

  18. Introduction to Remote Sensing Image Registration

    Science.gov (United States)

    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

  19. Fast fluid registration of medical images

    DEFF Research Database (Denmark)

    Bro-Nielsen, Morten; Gramkow, Claus

    1996-01-01

    This paper offers a new fast algorithm for non-rigid viscous fluid registration of medical images that is at least an order of magnitude faster than the previous method by (Christensen et al., 1994). The core algorithm in the fluid registration method is based on a linear elastic deformation...... of the velocity field of the fluid. Using the linearity of this deformation we derive a convolution filter which we use in a scale-space framework. We also demonstrate that the `demon'-based registration method of (Thirion, 1996) can be seen as an approximation to the fluid registration method and point...

  20. SAR-SIFT: A SIFT-LIKE ALGORITHM FOR SAR IMAGES

    OpenAIRE

    Dellinger, Flora; Delon, Julie; Gousseau, Yann; Michel, Julien; Tupin, Florence

    2015-01-01

    International audience; The Scale Invariant Feature Transform (SIFT) algorithm is widely used in computer vision to match features between images or to localize and recognize objets. However, mostly because of speckle noise, it does not perform well on synthetic aperture radar (SAR) images. We present here an improvement of this algorithm for SAR images, named SAR-SIFT. A new gradient computation, yielding an orientation and a magnitude robust to speckle noise, is first introduced. It is then...

  1. Non-parametric partitioning of SAR images

    Science.gov (United States)

    Delyon, G.; Galland, F.; Réfrégier, Ph.

    2006-09-01

    We describe and analyse a generalization of a parametric segmentation technique adapted to Gamma distributed SAR images to a simple non parametric noise model. The partition is obtained by minimizing the stochastic complexity of a quantized version on Q levels of the SAR image and lead to a criterion without parameters to be tuned by the user. We analyse the reliability of the proposed approach on synthetic images. The quality of the obtained partition will be studied for different possible strategies. In particular, one will discuss the reliability of the proposed optimization procedure. Finally, we will precisely study the performance of the proposed approach in comparison with the statistical parametric technique adapted to Gamma noise. These studies will be led by analyzing the number of misclassified pixels, the standard Hausdorff distance and the number of estimated regions.

  2. Bayesian technique for image classifying registration.

    Science.gov (United States)

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

    2012-09-01

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

  3. Cellular recurrent deep network for image registration

    Science.gov (United States)

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

    2015-09-01

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

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

    Science.gov (United States)

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

    2017-08-01

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

  5. Progressive refinement for robust image registration

    Institute of Scientific and Technical Information of China (English)

    Li Song; Yuanhua Zhou; Jun Zhou

    2005-01-01

    @@ A new image registration algorithm with robust cost function and progressive refinement estimation is developed on the basis of direct method (DM). The robustness lies in M-estimation to avert larger local noise and outliers.

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

    OpenAIRE

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

    2013-01-01

    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 georeferenc...

  7. SAR Image Texture Analysis of Oil Spill

    Science.gov (United States)

    Ma, Long; Li, Ying; Liu, Yu

    Oil spills are seriously affecting the marine ecosystem and cause political and scientific concern since they have serious affect on fragile marine and coastal ecosystem. In order to implement an emergency in case of oil spills, it is necessary to monitor oil spill using remote sensing. Spaceborne SAR is considered a promising method to monitor oil spill, which causes attention from many researchers. However, research in SAR image texture analysis of oil spill is rarely reported. On 7 December 2007, a crane-carrying barge hit the Hong Kong-registered tanker "Hebei Spirit", which released an estimated 10,500 metric tons of crude oil into the sea. The texture features on this oil spill were acquired based on extracted GLCM (Grey Level Co-occurrence Matrix) by using SAR as data source. The affected area was extracted successfully after evaluating capabilities of different texture features to monitor the oil spill. The results revealed that the texture is an important feature for oil spill monitoring. Key words: oil spill, texture analysis, SAR

  8. Unparallel trajectory bistatic spotlight SAR imaging

    Institute of Scientific and Technical Information of China (English)

    ZHANG Lei; JING Wei; XING MengDao; BAO Zheng

    2009-01-01

    A new approach for unparallel trajectory bistatic spotlight SAR imaging is proposed. The approach utilizes the concept of instantaneous Doppler wavenumber and introduces two variants, the sum-range and subtraction-range, to develop the 2D frequency analytical formula. Based on the assumption of plane wavefront, the transmitting and receiving Doppler are separated and formulated via series reversion. And frequency scaling is applied to focus image. The algorithm is with high computational efficiency, and provides well focus for limited scene imaging. Simulation result confirms the validity of the approach.

  9. Mass preserving image registration for lung CT

    DEFF Research Database (Denmark)

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

    2012-01-01

    on four groups of data: 44 pairs of longitudinal inspiratory chest CT scans with small difference in lung volume; 44 pairs of longitudinal inspiratory chest CT scans with large difference in lung volume; 16 pairs of expiratory and inspiratory CT scans; and 5 pairs of images extracted at end exhale and end...... inhale phases of 4D-CT images. Registration errors, measured as the average distance between vessel tree centerlines in the matched images, are significantly lower for the proposed mass preserving image registration method in the second, third and fourth group, while there is no statistically significant......This paper presents a mass preserving image registration algorithm for lung CT images. To account for the local change in lung tissue intensity during the breathing cycle, a tissue appearance model based on the principle of preservation of total lung mass is proposed. This model is incorporated...

  10. Onboard Image Registration from Invariant Features

    Science.gov (United States)

    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.

  11. Coastline detection in SAR images using discriminant cuts segmentation

    Science.gov (United States)

    Ding, Xianwen; Zou, Xiaolin; Yu, Tan

    2016-11-01

    The discriminant cut algorithm is used to detect coastlines in synthetic aperture radar (SAR) images. The proposed approach is a region-based one, which is able to capture and utilize spatial information in the image. The real SAR images, e.g. ALOS-1 PALSAR and COSMO-SkyMed SAR images, together with in-situ GPS data were collected and used to validate the performance of the proposed approach for coastline detection in SAR images. The accuracy is better than 4 times the image resolution. The efficiency is also tested.

  12. Phase Correlation Based Iris Image Registration Model

    Institute of Scientific and Technical Information of China (English)

    Jun-Zhou Huang; Tie-Niu Tan; Li Ma; Yun-Hong Wang

    2005-01-01

    Iris recognition is one of the most reliable personal identification methods. In iris recognition systems, image registration is an important component. Accurately registering iris images leads to higher recognition rate for an iris recognition system. This paper proposes a phase correlation based method for iris image registration with sub-pixel accuracy.Compared with existing methods, it is insensitive to image intensity and can compensate to a certain extent the non-linear iris deformation caused by pupil movement. Experimental results show that the proposed algorithm has an encouraging performance.

  13. Image registration using adaptive polar transform.

    Science.gov (United States)

    Matungka, Rittavee; Zheng, Yuan F; Ewing, Robert L

    2009-10-01

    Image registration is an essential step in many image processing applications that need visual information from multiple images for comparison, integration, or analysis. Recently, researchers have introduced image registration techniques using the log-polar transform (LPT) for its rotation and scale invariant properties. However, it suffers from nonuniform sampling which makes it not suitable for applications in which the registered images are altered or occluded. Inspired by LPT, this paper presents a new registration algorithm that addresses the problems of the conventional LPT while maintaining the robustness to scale and rotation. We introduce a novel adaptive polar transform (APT) technique that evenly and effectively samples the image in the Cartesian coordinates. Combining APT with an innovative projection transform along with a matching mechanism, the proposed method yields less computational load and more accurate registration than that of the conventional LPT. Translation between the registered images is recovered with the new search scheme using Gabor feature extraction to accelerate the localization procedure. Moreover an image comparison scheme is proposed for locating the area where the image pairs differ. Experiments on real images demonstrate the effectiveness and robustness of the proposed approach for registering images that are subjected to occlusion and alteration in addition to scale, rotation, and translation.

  14. Mid-space-independent deformable image registration.

    Science.gov (United States)

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

    2017-02-24

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

  15. The first Sentinel-1 SAR image of a typhoon

    Institute of Scientific and Technical Information of China (English)

    LI Xiaofeng

    2015-01-01

    In this note, we present the first Sentinel-1 synthetic aperture radar (SAR) typhoon image acquired in the northwest Pacific on October 4, 2014. The eye shape and sea surface wind patterns associated with Typhoon Phanfone are clearly shown in the high-quality SAR image. SAR winds retrieval procedure was given but the actual wind estimates will only be available after the European Space Agency (ESA) releases the official calibration coefficients in order to accurately derive the SAR-measured normalized radar cross section. This study demonstrates the advantage of Sentinel-1 SAR with regards to imaging fine scale typhoon patterns on the sea surface beneath storm clouds. This paper also advocates the use of Sentinel-1 SAR data that is made freely and openly available worldwide for the first time in civilian SAR history.

  16. Two dimensional estimates from ocean SAR images

    Directory of Open Access Journals (Sweden)

    J. M. Le Caillec

    1996-01-01

    Full Text Available Synthetic Aperture Radar (SAR images of the ocean yield a lot of information on the sea-state surface providing that the mapping process between the surface and the image is clearly defined. However it is well known that SAR images exhibit non-gaussian statistics and that the motion of the scatterers on the surface, while the image is being formed, may yield to nonlinearities. The detection and quantification of these nonlinearities are made possible by using Higher Order Spectra (HOS methods and more specifically, bispectrum estimation. The development of the latter method allowed us to find phase relations between different parts of the image and to recognise their level of coupling, i.e. if and how waves of different wavelengths interacted nonlinearly. This information is quite important as the usual models assume strong nonlinearities when the waves are propagating in the azimuthal direction (i.e. along the satellite track and almost no nonlinearities when propagating in the range direction. In this paper, the mapping of the ocean surface to the SAR image is reinterpreted and a specific model (i.e. a Second Order Volterra Model is introduced. The nonlinearities are thus explained as either produced by a nonlinear system or due to waves propagating into selected directions (azimuth or range and interacting during image formation. It is shown that quadratic nonlinearities occur for waves propagating near the range direction while for those travelling in the azimuthal direction the nonlinearities, when present, are mostly due to wave interactions but are almost completely removed by the filtering effect coming from the surface motion itself (azimuth cut-off. An inherent quadratic interaction filtering (azimuth high pass filter is also present. But some other effects, apparently nonlinear, are not detected with the methods described here, meaning that either the usual relation developed for the Ocean-to-SAR transform is somewhat incomplete

  17. SAR-PC: Edge Detection in SAR Images via an Advanced Phase Congruency Model

    Directory of Open Access Journals (Sweden)

    Yuming Xiang

    2017-02-01

    Full Text Available Edge detection in Synthetic Aperture Radar (SAR images has been a challenging task due to the speckle noise. Ratio-based edge detectors are robust operators for SAR images that provide constant false alarm rates, but they are only optimal for step edges. Edge detectors developed by the phase congruency model provide the identification of different types of edge features, but they suffer from speckle noise. By combining the advantages of the two edge detectors, we propose a SAR phase congruency detector (SAR-PC. Firstly, an improved local energy model for SAR images is obtained by replacing the convolution of raw image and the quadrature filters by the ratio responses. Secondly, a new noise level is estimated for the multiplicative noise. Substituting the SAR local energy and the new noise level into the phase congruency model, SAR-PC is derived. Edge response corresponds to the max moment of SAR-PC. We compare the proposed detector with the ratio-based edge detectors and the phase congruency edge detectors. Receiver Operating Characteristic (ROC curves and visual effects are used to evaluate the performance. Experimental results of simulated images and real-world images show that the proposed edge detector is robust to speckle noise and it provides a consecutive edge response.

  18. Fractional Regularization Term for Variational Image Registration

    Directory of Open Access Journals (Sweden)

    Rafael Verdú-Monedero

    2009-01-01

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

  19. Automated image registration for FDOPA PET studies

    Science.gov (United States)

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

    1996-12-01

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

  20. Speckle-reducing scale-invariant feature transform match for synthetic aperture radar image registration

    Science.gov (United States)

    Wang, Xianmin; Li, Bo; Xu, Qizhi

    2016-07-01

    The anisotropic scale space (ASS) is often used to enhance the performance of a scale-invariant feature transform (SIFT) algorithm in the registration of synthetic aperture radar (SAR) images. The existing ASS-based methods usually suffer from unstable keypoints and false matches, since the anisotropic diffusion filtering has limitations in reducing the speckle noise from SAR images while building the ASS image representation. We proposed a speckle reducing SIFT match method to obtain stable keypoints and acquire precise matches for the SAR image registration. First, the keypoints are detected in a speckle reducing anisotropic scale space constructed by the speckle reducing anisotropic diffusion, so that speckle noise is greatly reduced and prominent structures of the images are preserved, consequently the stable keypoints can be derived. Next, the probabilistic relaxation labeling approach is employed to establish the matches of the keypoints then the correct match rate of the keypoints is significantly increased. Experiments conducted on simulated speckled images and real SAR images demonstrate the effectiveness of the proposed method.

  1. Medical Image Registration and Surgery Simulation

    DEFF Research Database (Denmark)

    Bro-Nielsen, Morten

    1996-01-01

    This thesis explores the application of physical models in medical image registration and surgery simulation. The continuum models of elasticity and viscous fluids are described in detail, and this knowledge is used as a basis for most of the methods described here. Real-time deformable models......, and the use of selective matrix vector multiplication. Fluid medical image registration A new and faster algorithm for non-rigid registration using viscous fluid models is presented. This algorithm replaces the core part of the original algorithm with multi-resolution convolution using a new filter, which...... for surgery simulation Real-time deformable models, using finite element models of linear elasticity, have been developed for surgery simulation. The time consumption of the finite element method is reduced dramaticly, by the use of condensation techniques, explicit inversion of the stiffness matrix...

  2. Mitigating illumination gradients in a SAR image based on the image data and antenna beam pattern

    Science.gov (United States)

    Doerry, Armin W.

    2013-04-30

    Illumination gradients in a synthetic aperture radar (SAR) image of a target can be mitigated by determining a correction for pixel values associated with the SAR image. This correction is determined based on information indicative of a beam pattern used by a SAR antenna apparatus to illuminate the target, and also based on the pixel values associated with the SAR image. The correction is applied to the pixel values associated with the SAR image to produce corrected pixel values that define a corrected SAR image.

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

    Science.gov (United States)

    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.

  4. Deformable image registration with geometric changes

    Institute of Scientific and Technical Information of China (English)

    Yu LIU; Bo ZHU

    2015-01-01

    Geometric changes present a number of difficulties in deformable image registration. In this paper, we propose a global deformation framework to model geometric changes whilst promoting a smooth transformation between source and target images. To achieve this, we have developed an innovative model which significantly reduces the side effects of geometric changes in image registration, and thus improves the registration accuracy. Our key contribution is the introduction of a sparsity-inducing norm, which is typically L1 norm regularization targeting regions where geometric changes occur. This preserves the smoothness of global transformation by eliminating local transformation under different conditions. Numerical solutions are discussed and analyzed to guarantee the stability and fast convergence of our algorithm. To demonstrate the effectiveness and utility of this method, we evaluate it on both synthetic data and real data from traumatic brain injury (TBI). We show that the transformation estimated from our model is able to reconstruct the target image with lower instances of error than a standard elastic registration model.

  5. Algorithm for Fast Registration of Radar Images

    Directory of Open Access Journals (Sweden)

    Subrata Rakshit

    2002-07-01

    Full Text Available Radar imagery provides an all-weather and 24 h coverage, making it ideal for critical defence applications. In some applications, multiple images acquired of an area need to be registered for further processing. Such situations arise for battlefield surveillance based on satellite imagery. The registration has to be done between an earlier (reference image and a new (live image. For automated surveillance, registration is a prerequisite for change detection. Speed is essential due to large volumes of data involved and the need for quick responses. The registration transformation is quite simple, being mainly a global translation. (Scale and rotation corrections can be applied based on known camera parameters. The challenge lies in the fact that the radar images are not as feature-rich as optical images and the image content variation can be as high as 90 per cent. Even though the change on the ground may not be drastic, seasonal variations can significantly alter the radar signatures of ground, vegetation, and water bodies. This necessitates a novel approach different from the techniques developed for optical images. An algorithm has been developed that leads to fast registration of radar images, even in the presence of specular noise and significant scene content variation. The key features of this approach are adaptability to sensor/terrain types, ability to handle large content variations and false positive rejection. The present work shows that this algorithm allows for various cost-performance trade-offs, making it suitable for a wide variety of applications. The algorithm, in various cost-performance configurations, is tested on a set of ERS images. Results of such tests have been reported, indicating the performance of the algorithm for various cost-performance trade-offs.

  6. Speckle Suppression Method for SAR Image

    Directory of Open Access Journals (Sweden)

    Jiming Guo

    2013-04-01

    Full Text Available In this study, a new speckle reduction method was proposed in terms of by Bidimensional Empirical Mode Decomposition (BEMD. In this method, the SAR image containing speckle noise was decomposed into a number of elementary components by using BEMD and then the extremal points are done the boundary equivalent extension after screening and the residual continue to be done the boundary equivalent extension until screening is completed, finally, the image was reconstructed, which reduced the speckle noise. Experimental results show that this method has good effect on suppressing speckle noise, compared to the average filter, median filter and gaussian filter and has advantages of sufficiently retaining edge and detail information while suppressing speckle noise.

  7. SAR Image Desp eckling by Sparse Reconstruction Based on Shearlets

    Institute of Scientific and Technical Information of China (English)

    JI Jian; LI Xiao; XU Shuang-Xing; LIU Huan; HUANG Jing-Jing

    2015-01-01

    Synthetic aperture radar (SAR) image is usually polluted by multiplicative speckle noise, which can affect further processing of SAR image. This paper presents a new approach for multiplicative noise removal in SAR images based on sparse coding by shearlets filtering. First, a SAR despeckling model is built by the theory of compressed sensing (CS). Secondly, obtain shearlets coefficient through shearlet transform, each scale coefficient is represented as a unit. For each unit, sparse coefficient is iteratively estimated by using Bayesian estimation based on shearlets domain. The represented units are finally collaboratively aggregated to construct the despeckling image. Our results in SAR image despeckling show the good performance of this algorithm, and prove that the algorithm proposed is robustness to noise, which is not only good for reducing speckle, but also has an advantage in holding information of the edge.

  8. Image Classifying Registration and Dynamic Region Merging

    Directory of Open Access Journals (Sweden)

    Himadri Nath Moulick

    2013-07-01

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

  9. Image Segmentation, Registration, Compression, and Matching

    Science.gov (United States)

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

    2011-01-01

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

  10. A beamforming algorithm for bistatic SAR image formation.

    Energy Technology Data Exchange (ETDEWEB)

    Yocky, David Alan; Wahl, Daniel Eugene; Jakowatz, Charles V., Jr.

    2010-03-01

    Beamforming is a methodology for collection-mode-independent SAR image formation. It is essentially equivalent to backprojection. The authors have in previous papers developed this idea and discussed the advantages and disadvantages of the approach to monostatic SAR image formation vis--vis the more standard and time-tested polar formatting algorithm (PFA). In this paper we show that beamforming for bistatic SAR imaging leads again to a very simple image formation algorithm that requires a minimal number of lines of code and that allows the image to be directly formed onto a three-dimensional surface model, thus automatically creating an orthorectified image. The same disadvantage of beamforming applied to monostatic SAR imaging applies to the bistatic case, however, in that the execution time for the beamforming algorithm is quite long compared to that of PFA. Fast versions of beamforming do exist to help alleviate this issue. Results of image reconstructions from phase history data are presented.

  11. A beamforming algorithm for bistatic SAR image formation

    Science.gov (United States)

    Jakowatz, Charles V., Jr.; Wahl, Daniel E.; Yocky, David A.

    2010-04-01

    Beamforming is a methodology for collection-mode-independent SAR image formation. It is essentially equivalent to backprojection. The authors have in previous papers developed this idea and discussed the advantages and disadvantages of the approach to monostatic SAR image formation vis-à-vis the more standard and time-tested polar formatting algorithm (PFA). In this paper we show that beamforming for bistatic SAR imaging leads again to a very simple image formation algorithm that requires a minimal number of lines of code and that allows the image to be directly formed onto a three-dimensional surface model, thus automatically creating an orthorectified image. The same disadvantage of beamforming applied to monostatic SAR imaging applies to the bistatic case, however, in that the execution time for the beamforming algorithm is quite long compared to that of PFA. Fast versions of beamforming do exist to help alleviate this issue. Results of image reconstructions from phase history data are presented.

  12. The Accuratre Signal Model and Imaging Processing in Geosynchronous SAR

    Science.gov (United States)

    Hu, Cheng

    With the development of synthetic aperture radar (SAR) application, the disadvantage of low earth orbit (LEO) SAR becomes more and more apparent. The increase of orbit altitude can shorten the revisit time and enlarge the coverage area in single look, and then satisfy the application requirement. The concept of geosynchronous earth orbit (GEO) SAR system is firstly presented and deeply discussed by K.Tomiyasi and other researchers. A GEO SAR, with its fine temporal resolution, would overcome the limitations of current imaging systems, allowing dense interpretation of transient phenomena as GPS time-series analysis with a spatial density several orders of magnitude finer. Until now, the related literatures about GEO SAR are mainly focused in the system parameter design and application requirement. As for the signal characteristic, resolution calculation and imaging algorithms, it is nearly blank in the related literatures of GEO SAR. In the LEO SAR, the signal model analysis adopts the `Stop-and-Go' assumption in general, and this assumption can satisfy the imaging requirement in present advanced SAR system, such as TerraSAR, Radarsat2 and so on. However because of long propagation distance and non-negligible earth rotation, the `Stop-and-Go' assumption does not exist and will cause large propagation distance error, and then affect the image formation. Furthermore the long propagation distance will result in the long synthetic aperture time such as hundreds of seconds, therefore the linear trajectory model in LEO SAR imaging will fail in GEO imaging, and the new imaging model needs to be proposed for the GEO SAR imaging processing. In this paper, considering the relative motion between satellite and earth during signal propagation time, the accurate analysis method for propagation slant range is firstly presented. Furthermore, the difference between accurate analysis method and `Stop-and-Go' assumption is analytically obtained. Meanwhile based on the derived

  13. TIMER: tensor image morphing for elastic registration.

    Science.gov (United States)

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

    2009-08-15

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

  14. SAR Image Segmentation using Vector Quantization Technique on Entropy Images

    CERN Document Server

    Kekre, H B; Sarode, Tanuja K

    2010-01-01

    The development and application of various remote sensing platforms result in the production of huge amounts of satellite image data. Therefore, there is an increasing need for effective querying and browsing in these image databases. In order to take advantage and make good use of satellite images data, we must be able to extract meaningful information from the imagery. Hence we proposed a new algorithm for SAR image segmentation. In this paper we propose segmentation using vector quantization technique on entropy image. Initially, we obtain entropy image and in second step we use Kekre's Fast Codebook Generation (KFCG) algorithm for segmentation of the entropy image. Thereafter, a codebook of size 128 was generated for the Entropy image. These code vectors were further clustered in 8 clusters using same KFCG algorithm and converted into 8 images. These 8 images were displayed as a result. This approach does not lead to over segmentation or under segmentation. We compared these results with well known Gray L...

  15. Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images

    Directory of Open Access Journals (Sweden)

    Nina Merkle

    2017-06-01

    Full Text Available Improving the geo-localization of optical satellite images is an important pre-processing step for many remote sensing tasks like monitoring by image time series or scene analysis after sudden events. These tasks require geo-referenced and precisely co-registered multi-sensor data. Images captured by the high resolution synthetic aperture radar (SAR satellite TerraSAR-X exhibit an absolute geo-location accuracy within a few decimeters. These images represent therefore a reliable source to improve the geo-location accuracy of optical images, which is in the order of tens of meters. In this paper, a deep learning-based approach for the geo-localization accuracy improvement of optical satellite images through SAR reference data is investigated. Image registration between SAR and optical images requires few, but accurate and reliable matching points. These are derived from a Siamese neural network. The network is trained using TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe, in order to learn the two-dimensional spatial shifts between optical and SAR image patches. Results confirm that accurate and reliable matching points can be generated with higher matching accuracy and precision with respect to state-of-the-art approaches.

  16. Deformable image registration in radiation therapy

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-06-15

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

  17. Canny edge-based deformable image registration

    Science.gov (United States)

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

    2017-02-01

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

  18. Optimized imaging using non-rigid registration

    Energy Technology Data Exchange (ETDEWEB)

    Berkels, Benjamin, E-mail: berkels@aices.rwth-aachen.de [Interdisciplinary Mathematics Institute, 1523 Greene Street, University of South Carolina, Columbia, SC 29208 (United States); Binev, Peter, E-mail: binev@math.sc.edu [Interdisciplinary Mathematics Institute, 1523 Greene Street, University of South Carolina, Columbia, SC 29208 (United States); Department of Mathematics, 1523 Greene Street, University of South Carolina, Columbia, SC 29208 (United States); Blom, Douglas A., E-mail: doug.blom@sc.edu [NanoCenter, 1212 Greene Street, University of South Carolina, Columbia, SC 29208 (United States); Dahmen, Wolfgang, E-mail: dahmen@igpm.rwth-aachen.de [Interdisciplinary Mathematics Institute, 1523 Greene Street, University of South Carolina, Columbia, SC 29208 (United States); Institut für Geometrie und Praktische Mathematik, RWTH Aachen, Templergraben 55, 52056 Aachen (Germany); Sharpley, Robert C., E-mail: rcsharpley@gmail.com [Interdisciplinary Mathematics Institute, 1523 Greene Street, University of South Carolina, Columbia, SC 29208 (United States); Department of Mathematics, 1523 Greene Street, University of South Carolina, Columbia, SC 29208 (United States); Vogt, Thomas, E-mail: tvogt@mailbox.sc.edu [Interdisciplinary Mathematics Institute, 1523 Greene Street, University of South Carolina, Columbia, SC 29208 (United States); NanoCenter, 1212 Greene Street, University of South Carolina, Columbia, SC 29208 (United States); Department of Chemistry and Biochemistry, 631 Sumter Street, University of South Carolina, Columbia, SC 29208 (United States)

    2014-03-01

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

  19. The ONERA Airborne Multi Frequency SAR Imaging Systems (PREPRINT)

    Science.gov (United States)

    2014-10-09

    The ONERA Airborne Multi-Frequency SAR Imaging Systems Olivier Ruault du Plessis Electromagnetism and Radar Department ONERA Salon de...Provence FRANCE Olivier.Ruault_du_Plessis@onera.fr Philippe Dreuillet Electromagnetism and Radar Department ONERA Palaiseau FRANCE...Philippe.Dreuillet@onera.fr Abstract—RAMSES-NG and SETHI, the airborne SAR systems developed by ONERA , integrate new generation of radar and optronics

  20. Classification of Targets in SAR Images Using ISAR Data

    NARCIS (Netherlands)

    Wit, J.J.M. de; Dekker, R.J.; Broek, A.C. van den

    2005-01-01

    Feature-based classification of targets in SAR images by using ISAR measurements was studied, based on polarimetric SAR and ISAR data acquired with the MEMPHIS radar system of FGAN-FHR. The data contained one T-72 battle tank, one BMP combat vehicle, and several confusers. The resolution was 75 cm.

  1. Symmetry in Image Registration and Deformation Modeling

    DEFF Research Database (Denmark)

    Sommer, Stefan; Jacobs, Henry O.

    We survey the role of symmetry in diffeomorphic registration of landmarks, curves, surfaces, images and higher-order data. The infinite dimensional problem of finding correspondences between objects can for a range of concrete data types be reduced resulting in compact representations of shape...... and spatial structure. This reduction is possible because the available data is incomplete in encoding the full deformation model. Using reduction by symmetry, we describe the reduced models in a common theoretical framework that draws on links between the registration problem and geometric mechanics....... Symmetry also arises in reduction to the Lie algebra using particle relabeling symmetry allowing the equations of motion to be written purely in terms of Eulerian velocity field. Reduction by symmetry has recently been applied for jet-matching and higher-order discrete approximations of the image matching...

  2. A New Extended Projection-Based Image Registration Algorithm

    Institute of Scientific and Technical Information of China (English)

    CHENHuafu; YAODezhong

    2005-01-01

    In the presence of fixed -pattern noise, the projection-based image registration technique is effective but its implementation is only confined to translation registration. Presented in this paper is an extended projectionbased image registration technique in which, by rearranging the projections of images, the image registration is implemented in two steps: rotation and translation, to accomplish two-dimensional (2-D) image registration. Thisapproach transforms the general 2-D optimization procedure into an 1-D projection optimization, thus considerably reducing the amount of computation. The validity ofthe new method is testified by simulation experiment.

  3. Research on Airborne SAR Imaging Based on Esc Algorithm

    Science.gov (United States)

    Dong, X. T.; Yue, X. J.; Zhao, Y. H.; Han, C. M.

    2017-09-01

    Due to the ability of flexible, accurate, and fast obtaining abundant information, airborne SAR is significant in the field of Earth Observation and many other applications. Optimally the flight paths are straight lines, but in reality it is not the case since some portion of deviation from the ideal path is impossible to avoid. A small disturbance from the ideal line will have a major effect on the signal phase, dramatically deteriorating the quality of SAR images and data. Therefore, to get accurate echo information and radar images, it is essential to measure and compensate for nonlinear motion of antenna trajectories. By means of compensating each flying trajectory to its reference track, MOCO method corrects linear phase error and quadratic phase error caused by nonlinear antenna trajectories. Position and Orientation System (POS) data is applied to acquiring accuracy motion attitudes and spatial positions of antenna phase centre (APC). In this paper, extend chirp scaling algorithm (ECS) is used to deal with echo data of airborne SAR. An experiment is done using VV-Polarization raw data of C-band airborne SAR. The quality evaluations of compensated SAR images and uncompensated SAR images are done in the experiment. The former always performs better than the latter. After MOCO processing, azimuth ambiguity is declined, peak side lobe ratio (PSLR) effectively improves and the resolution of images is improved obviously. The result shows the validity and operability of the imaging process for airborne SAR.

  4. Multi-look polarimetric SAR image filtering using simulated annealing

    DEFF Research Database (Denmark)

    Schou, Jesper

    2000-01-01

    Based on a previously published algorithm capable of estimating the radar cross-section in synthetic aperture radar (SAR) intensity images, a new filter is presented utilizing multi-look polarimetric SAR images. The underlying mean covariance matrix is estimated from the observed sample covariance...... matrices, and by applying a set of small orientation-dependent filters in an iterative scheme, the input image becomes highly filtered while maintaining most of the structures in the scene. Results using multi-look polarimetric C-band data from the Danish airborne polarimetric SAR, EMISAR, are presented....

  5. SAR image effects on coherence and coherence estimation.

    Energy Technology Data Exchange (ETDEWEB)

    Bickel, Douglas Lloyd

    2014-01-01

    Radar coherence is an important concept for imaging radar systems such as synthetic aperture radar (SAR). This document quantifies some of the effects in SAR which modify the coherence. Although these effects can disrupt the coherence within a single SAR image, this report will focus on the coherence between separate images, such as for coherent change detection (CCD) processing. There have been other presentations on aspects of this material in the past. The intent of this report is to bring various issues that affect the coherence together in a single report to support radar engineers in making decisions about these matters.

  6. Automated landmark-guided deformable image registration

    Science.gov (United States)

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

    2015-01-01

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

  7. Surface driven biomechanical breast image registration

    Science.gov (United States)

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

    2016-03-01

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

  8. Multi-modal image registration using structural features.

    Science.gov (United States)

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

    2014-01-01

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

  9. Road network extraction in classified SAR images using genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    肖志强; 鲍光淑; 蒋晓确

    2004-01-01

    Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road network from high-resolution SAR image. Firstly, fuzzy C means is used to classify the filtered SAR image unsupervisedly, and the road pixels are isolated from the image to simplify the extraction of road network. Secondly, according to the features of roads and the membership of pixels to roads, a road model is constructed, which can reduce the extraction of road network to searching globally optimization continuous curves which pass some seed points. Finally, regarding the curves as individuals and coding a chromosome using integer code of variance relative to coordinates, the genetic operations are used to search global optimization roads. The experimental results show that the algorithm can effectively extract road network from high-resolution SAR images.

  10. Enhancing retinal images by nonlinear registration

    CERN Document Server

    Molodij, Guillaume; Glanc, Marie; Chenegros, Guillaume

    2014-01-01

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

  11. Iterative Refinement of Transformation Parameters for Image Registration

    OpenAIRE

    Jharna Majumdar; B. Vanathy; S. Lekshmi

    2010-01-01

    Image registration is an important process in high-level image interpretation systems developed for civilian and defense applications. Registration is carried out in two phases: namely feature extraction and feature correspondence. The basic building block of feature based image registration scheme involves matching feature points that are extracted from a sensed image to their counter parts in the reference image. Features may be control points, corners, junctions or interest points. The obj...

  12. The Registration of Knee Joint Images with Preprocessing

    Directory of Open Access Journals (Sweden)

    Zhenyan Ji

    2011-06-01

    Full Text Available the registration of CT and MR images is important to analyze the effect of PCL and ACL deficiency on knee joint. Because CT and MR images have different limitations, we need register CT and MR images of knee joint and then build a model to do an analysis of the stress distribution on knee joint. In our project, we adopt image registration based on mutual information. In the knee joint images, the information about adipose, muscle and other soft tissue affects the registration accuracy. To eliminate the interference, we propose a combined preprocessing solution BEBDO, which consists of five steps, image blurring, image enhancement, image blurring, image edge detection and image outline preprocessing. We also designed the algorithm of image outline preprocessing. At the end of the paper, an experiment is done to compare the image registration results without the preprocessing and with the preprocessing. The results prove that the preprocessing can improve the image registration accuracy.

  13. High performance deformable image registration algorithms for manycore processors

    CERN Document Server

    Shackleford, James; Sharp, Gregory

    2013-01-01

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

  14. Deformable image registration for image guided prostate radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-07-01

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

  15. A Review of Spaceborne SAR Algorithm for Image Formation

    Directory of Open Access Journals (Sweden)

    Li Chun-sheng

    2013-03-01

    Full Text Available This paper firstly reviews the history and trends in development of spaceborne Synthetic Aperture Radar (SAR satellite technology in American and European countries. Besides, the basic information of the launched satellites and the future satellite plans are introduced. Then this paper summaries and assorts the imaging algorithm of spaceborn SAR satellite and analyzes the advantages and disadvantages of each algorithm. Moreover, the scope and the application status of each algorithm are presented. And then the paper elaborates trends of SAR imaging algorithm, which mainly introduces the algorithms based on compressive sensing theory and new image modes, and the results of simulation are also illustrated. At last, the paper summaries the development direction of spaceborne SAR imaging algorithm.

  16. SAR Imaging of Moving Targets via Compressive Sensing

    CERN Document Server

    Wang, Jun; Zhang, Hao; Wang, Xiqin

    2011-01-01

    An algorithm based on compressive sensing (CS) is proposed for synthetic aperture radar (SAR) imaging of moving targets. The received SAR echo is decomposed into the sum of basis sub-signals, which are generated by discretizing the target spatial domain and velocity domain and synthesizing the SAR received data for every discretized spatial position and velocity candidate. In this way, the SAR imaging problem is converted into sub-signal selection problem. In the case that moving targets are sparsely distributed in the observed scene, their reflectivities, positions and velocities can be obtained by using the CS technique. It is shown that, compared with traditional algorithms, the target image obtained by the proposed algorithm has higher resolution and lower side-lobe while the required number of measurements can be an order of magnitude less than that by sampling at Nyquist sampling rate. Moreover, multiple targets with different speeds can be imaged simultaneously, so the proposed algorithm has higher eff...

  17. Improved SAR Image Coregistration Using Pixel-Offset Series

    KAUST Repository

    Wang, Teng

    2014-03-14

    Synthetic aperture radar (SAR) image coregistration is a key procedure before interferometric SAR (InSAR) time-series analysis can be started. However, many geophysical data sets suffer from severe decorrelation problems due to a variety of reasons, making precise coregistration a nontrivial task. Here, we present a new strategy that uses a pixel-offset series of detected subimage patches dominated by point-like targets (PTs) to improve SAR image coregistrations. First, all potentially coherent image pairs are coregistered in a conventional way. In this step, we propose a coregistration quality index for each image to rank its relative “significance” within the data set and to select a reference image for the SAR data set. Then, a pixel-offset series of detected PTs is made from amplitude maps to improve the geometrical mapping functions. Finally, all images are resampled depending on the pixel offsets calculated from the updated geometrical mapping functions. We used images from a rural region near the North Anatolian Fault in eastern Turkey to test the proposed method, and clear coregistration improvements were found based on amplitude stability. This enhanced the fact that the coregistration strategy should therefore lead to improved InSAR time-series analysis results.

  18. Accuracy Validation for Medical Image Registration Algorithms: a Review

    Institute of Scientific and Technical Information of China (English)

    Zhe Liu; Xiang Deng; Guang-zhi Wang

    2012-01-01

    Accuracy validation is essential to clinical application of medical image registration techniques.Registration validation remains a challenging problem in practice mainly due to lack of 'ground truth'.In this paper,an overview of current validation methods for medical image registration is presented with detailed discussion of their benefits and drawbacks.Special focus is on non-rigid registration validation.Promising solution is also discussed.

  19. Hierarchical Non-linear Image Registration Integrating Deformable Segmentation

    Institute of Scientific and Technical Information of China (English)

    RAN Xin; QI Fei-hu

    2005-01-01

    A hierarchical non-linear method for image registration was presented, which integrates image segmentation and registration under a variational framework. An improved deformable model is used to simultaneously segment and register feature from multiple images. The objects in the image pair are segmented by evolving a single contour and meanwhile the parameters of affine registration transformation are found out. After that, a contour-constrained elastic registration is applied to register the images correctly. The experimental results indicate that the proposed approach is effective to segment and register medical images.

  20. Noise Removal in SAR Images using Orthonormal Ridgelet Transform

    Directory of Open Access Journals (Sweden)

    A. Ravi,

    2015-05-01

    Full Text Available Development in the field of image processing for reducing speckle noise from digital images/satellite images is a challenging task for image processing applications. Previously many algorithms were proposed to de-speckle the noise in digital images. Here in this article we are presenting experimental results on de-speckling of Synthetic Aperture RADAR (SAR images. SAR images have wide applications in remote sensing and mapping the surfaces of all planets. SAR can also be implemented as "inverse SAR" by observing a moving target over a substantial time with a stationary antenna. Hence denoising of SAR images is an essential task for viewing the information. Here we introduce a transformation technique called ―Ridgelet‖, which is an extension level of wavelet. Ridgelet analysis can be done in the similar way how wavelet analysis was done in the Radon domain as it translates singularities along lines into point singularities under different frequencies. Simulation results were show cased for proving that proposed work is more reliable than compared to other despeckling processes, and the quality of de-speckled image is measured in terms of Peak Signal to Noise Ratio and Mean Square Error.

  1. Sparse SAR imaging based on L1/2 regularization

    Institute of Scientific and Technical Information of China (English)

    ZENG JinShan; FANG Jian; XU ZongBen

    2012-01-01

    In this paper,a novel method for synthetic aperture radar (SAR) imaging is proposed.The approach is based on L1/2 regularization to reconstruct the scattering field,which optimizes a quadratic error term of the SAR observation process subject to the interested scene sparsity. Compared to the conventional SAR imaging technique,the new method implements SAR imaging effectively at much lower sampling rate than the Nyquist rate,and produces high-quality images with reduced sidelobes and increased resolution. Also,over the prevalent greedy pursuit and L1 regularization based SAR imaging methods,there are remarkable performance improvements of the new method.On one hand,the new method significantly reduces the number of measurements needed for reconstruction,as supported by a phase transition diagram study.On the other hand,the new method is more robust to the observation noise.These fundamental properties of the new method are supported and demonstrated both by simulations and real SAR data experiments.

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

    Science.gov (United States)

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

    2016-06-01

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

  3. 3D/2D Registration of medical images

    OpenAIRE

    Tomaževič, D.

    2008-01-01

    The topic of this doctoral dissertation is registration of 3D medical images to corresponding projective 2D images, referred to as 3D/2D registration. There are numerous possible applications of 3D/2D registration in image-aided diagnosis and treatment. In most of the applications, 3D/2D registration provides the location and orientation of the structures in a preoperative 3D CT or MR image with respect to intraoperative 2D X-ray images. The proposed doctoral dissertation tries to find origin...

  4. Nonrigid registration of volumetric images using ranked order statistics

    DEFF Research Database (Denmark)

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

    2014-01-01

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

  5. 3-D Target Location from Stereoscopic SAR Images

    Energy Technology Data Exchange (ETDEWEB)

    DOERRY,ARMIN W.

    1999-10-01

    SAR range-Doppler images are inherently 2-dimensional. Targets with a height offset lay over onto offset range and azimuth locations. Just which image locations are laid upon depends on the imaging geometry, including depression angle, squint angle, and target bearing. This is the well known layover phenomenon. Images formed with different aperture geometries will exhibit different layover characteristics. These differences can be exploited to ascertain target height information, in a stereoscopic manner. Depending on the imaging geometries, height accuracy can be on the order of horizontal position accuracies, thereby rivaling the best IFSAR capabilities in fine resolution SAR images. All that is required for this to work are two distinct passes with suitably different geometries from any plain old SAR.

  6. Regional landslide forecasting model using interferometric SAR images

    Institute of Scientific and Technical Information of China (English)

    董育烦; 张发明; 高正夏; 蒯志要

    2008-01-01

    Method of obtaining landslide evaluating information by using Interferometric Synthetic Aperture Radar (InSAR) technique was discussed. More precision landslide surface deformation data extracted from InSAR image need take suitable SAR interferometric data selecting, path tracking, phase unwrapping processes. Then, the DEM model of scope and surface shape of the landslide was built. Combining with geological property of landslide and sliding displacements obtained from InSAR/D-InSAR images, a new landslide forecasting model called equal central angle slice method for those not obviously deformed landslides was put forward. This model breaks the limits of traditional research methods of geology. In this model, the landslide safety factor was calculated by equal central angle slice method, then considering the persistence ratio of the sliding surface based on plastic theory, the minimum safety factor was the phase when plastic area were complete persistence. This new model makes the application of InSAR/D-InSAR technology become more practical in geology hazard research.

  7. Automatic registration of terrestrial point cloud using panoramic reflectance images

    NARCIS (Netherlands)

    Kang, Z.

    2008-01-01

    Much attention is paid to registration of terrestrial point clouds nowadays. Research is carried out towards improved efficiency and automation of the registration process. This paper reports a new approach for point clouds registration utilizing reflectance panoramic images. The approach follows a

  8. Automatic Image Registration Technique of Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    M. Wahed

    2013-03-01

    Full Text Available Image registration is a crucial step in most image processing tasks for which the final result is achieved from a combination of various resources. Automatic registration of remote-sensing images is a difficult task as it must deal with the intensity changes and variation of scale, rotation and illumination of the images. This paper proposes image registration technique of multi-view, multi- temporal and multi-spectral remote sensing images. Firstly, a preprocessing step is performed by applying median filtering to enhance the images. Secondly, the Steerable Pyramid Transform is adopted to produce multi-resolution levels of reference and sensed images; then, the Scale Invariant Feature Transform (SIFT is utilized for extracting feature points that can deal with the large variations of scale, rotation and illumination between images .Thirdly, matching the features points by using the Euclidian distance ratio; then removing the false matching pairs using the RANdom SAmple Consensus (RANSAC algorithm. Finally, the mapping function is obtained by the affine transformation. Quantitative comparisons of our technique with the related techniques show a significant improvement in the presence of large scale, rotation changes, and the intensity changes. The effectiveness of the proposed technique is demonstrated by the experimental results.

  9. Separated Component-Based Restoration of Speckled SAR Images

    Science.gov (United States)

    2014-01-01

    other documentation. 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS (ES) U.S. Army Research Office P.O. Box 12211 Research Triangle Park, NC...and Image Processing IX, 2001. [29] J.-F. Aujol, G. Aubert, L. Blanc- Fraud , and A. Chambolle, “Image decomposition application to SAR images,” in

  10. SAR image target segmentation based on entropy maximization and morphology

    Institute of Scientific and Technical Information of China (English)

    柏正尧; 刘洲峰; 何佩琨

    2004-01-01

    Entropy maximization thresholding is a simple, effective image segmentation method. The relation between the histogram entropy and the gray level of an image is analyzed. An approach, which speeds the computation of optimal threshold based on entropy maximization, is proposed. The suggested method has been applied to the synthetic aperture radar (SAR) image targets segmentation. Mathematical morphology works well in reducing the residual noise.

  11. Multi-modality image registration using the decomposition model

    Science.gov (United States)

    Ibrahim, Mazlinda; Chen, Ke

    2017-04-01

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

  12. A NEW IMAGE REGISTRATION METHOD FOR GREY IMAGES

    Institute of Scientific and Technical Information of China (English)

    Nie Xuan; Zhao Rongchun; Jiang Zetao

    2004-01-01

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

  13. A statistical framework for inter-group image registration.

    Science.gov (United States)

    Liao, Shu; Wu, Guorong; Shen, Dinggang

    2012-10-01

    Groupwise image registration plays an important role in medical image analysis. The principle of groupwise image registration is to align a given set of images to a hidden template space in an iteratively manner without explicitly selecting any individual image as the template. Although many approaches have been proposed to address the groupwise image registration problem for registering a single group of images, few attentions and efforts have been paid to the registration problem between two or more different groups of images. In this paper, we propose a statistical framework to address the registration problems between two different image groups. The main contributions of this paper lie in the following aspects: (1) In this paper, we demonstrate that directly registering the group mean images estimated from two different image groups is not sufficient to establish the reliable transformation from one image group to the other image group. (2) A novel statistical framework is proposed to extract anatomical features from the white matter, gray matter and cerebrospinal fluid tissue maps of all aligned images as morphological signatures for each voxel. The extracted features provide much richer anatomical information than the voxel intensity of the group mean image, and can be integrated with the multi-channel Demons registration algorithm to perform the registration process. (3) The proposed method has been extensively evaluated on two publicly available brain MRI databases: the LONI LPBA40 and the IXI databases, and it is also compared with a conventional inter-group image registration approach which directly performs deformable registration between the group mean images of two image groups. Experimental results show that the proposed method consistently achieves higher registration accuracy than the method under comparison.

  14. SAR image formation with azimuth interpolation after azimuth transform

    Science.gov (United States)

    Doerry; Armin W. , Martin; Grant D. , Holzrichter; Michael W.

    2008-07-08

    Two-dimensional SAR data can be processed into a rectangular grid format by subjecting the SAR data to a Fourier transform operation, and thereafter to a corresponding interpolation operation. Because the interpolation operation follows the Fourier transform operation, the interpolation operation can be simplified, and the effect of interpolation errors can be diminished. This provides for the possibility of both reducing the re-grid processing time, and improving the image quality.

  15. Efficient Variational Approaches for Deformable Registration of Images

    Directory of Open Access Journals (Sweden)

    Mehmet Ali Akinlar

    2012-01-01

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

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

    Science.gov (United States)

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

    2012-01-01

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

  17. DOCUMENT IMAGE REGISTRATION FOR IMPOSED LAYER EXTRACTION

    Directory of Open Access Journals (Sweden)

    Surabhi Narayan

    2017-02-01

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

  18. Cauchy pdf modelling and its application to SAR image despeckling

    Institute of Scientific and Technical Information of China (English)

    Chen Guozhong; Liu Xingzhao

    2008-01-01

    Synthetic aperture radar(SAR)imagery is a kind of coherent system that produces a random pattern,named speckle,which degrades the merit of SAR images and affects their further application seriously.Therefore,how to restore SAR image from the speckle has become a necessary step in post-processing of image.A new despeckling method is putforth on the basis of wavelet.First.a new approach on the basis of"second kind statistics"is used to estimate the dispersion parameter of the Cauchy distribution.Then,this Cauchy prior is applied to model the distribution of the wavelet coefficients for the log-transformed reflectance of SAR image.Based on the above ideas,a new homomorphic wavelet-based maximum a posterior(MAP)despeckling method is proposed.Finally,the simulated speckled image and the real SAR image are used to verify our proposed method and the results show that it outperforms the other methods in terms of the speckle reduction and the feature retention.

  19. Groupwise registration of MR brain images with tumors

    Science.gov (United States)

    Tang, Zhenyu; Wu, Yihong; Fan, Yong

    2017-09-01

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

  20. Enhancing retinal images by nonlinear registration

    Science.gov (United States)

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

    2015-05-01

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

  1. Multi-image Matching of Airborne SAR Imagery by SANCC

    Directory of Open Access Journals (Sweden)

    DING Hao

    2015-03-01

    Full Text Available In order to improve accuracy of SAR matching, a multi-image matching method based on sum of adaptive normalized cross-correlation (SANCC is proposed. It utilizes geometrical and radiometric information of multi-baselinesynthetic aperture radar (SARimages effectively. Firstly, imaging parameters, platform parameters and approximate digital surface model (DSM are used to predict matching line. Secondly, similarity and proximity in Gestalt theory are introduced to SANCC, and SANCC measures of potential matching points along the matching line are calculated. Thirdly, multi-image matching results and object coordinates of matching points are obtained by winner-take-all (WTA optimization strategy. The approach has been demonstrated with airborne SAR images acquired by a Chinese airborne SAR system (CASMSAR system. The experimental results indicate that the proposed algorithm is effective for providing dense and accuracy matching points, reducing the number of mismatches caused by repeated textures, and offering a better solution to match in poor textured areas.

  2. DTI Image Registration under Probabilistic Fiber Bundles Tractography Learning

    Science.gov (United States)

    Lei, Tao; Fan, Yangyu; Zhang, Xiuwei

    2016-01-01

    Diffusion Tensor Imaging (DTI) image registration is an essential step for diffusion tensor image analysis. Most of the fiber bundle based registration algorithms use deterministic fiber tracking technique to get the white matter fiber bundles, which will be affected by the noise and volume. In order to overcome the above problem, we proposed a Diffusion Tensor Imaging image registration method under probabilistic fiber bundles tractography learning. Probabilistic tractography technique can more reasonably trace to the structure of the nerve fibers. The residual error estimation step in active sample selection learning is improved by modifying the residual error model using finite sample set. The calculated deformation field is then registered on the DTI images. The results of our proposed registration method are compared with 6 state-of-the-art DTI image registration methods under visualization and 3 quantitative evaluation standards. The experimental results show that our proposed method has a good comprehensive performance.

  3. Separated Component-Based Restoration of Speckled SAR Images

    Science.gov (United States)

    2013-01-01

    This new process is also valuable for many SAR image understanding tasks such as road detection, railway detection, ship wake detection, texture...Starck, and L. Boubchir, “Morphological diversity and sparse image denoising,” in Proc. IEEE Int. Conf. Acoust . Speech Signal Process., vol. 1. Apr

  4. Synthetic SAR Image Generation using Sensor, Terrain and Target Models

    DEFF Research Database (Denmark)

    Kusk, Anders; Abulaitijiang, Adili; Dall, Jørgen

    2016-01-01

    A tool to generate synthetic SAR images of objects set on a clutter background is described. The purpose is to generate images for training Automatic Target Recognition and Identification algorithms. The tool employs a commercial electromagnetic simulation program to calculate radar cross section...

  5. GPUs benchmarking in subpixel image registration algorithm

    Science.gov (United States)

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

    2015-05-01

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

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

    Science.gov (United States)

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

    2007-03-01

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

  7. Classification of Polarimetric SAR Image Based on the Subspace Method

    Science.gov (United States)

    Xu, J.; Li, Z.; Tian, B.; Chen, Q.; Zhang, P.

    2013-07-01

    Land cover classification is one of the most significant applications in remote sensing. Compared to optical sensing technologies, synthetic aperture radar (SAR) can penetrate through clouds and have all-weather capabilities. Therefore, land cover classification for SAR image is important in remote sensing. The subspace method is a novel method for the SAR data, which reduces data dimensionality by incorporating feature extraction into the classification process. This paper uses the averaged learning subspace method (ALSM) method that can be applied to the fully polarimetric SAR image for classification. The ALSM algorithm integrates three-component decomposition, eigenvalue/eigenvector decomposition and textural features derived from the gray-level cooccurrence matrix (GLCM). The study site, locates in the Dingxing county, in Hebei Province, China. We compare the subspace method with the traditional supervised Wishart classification. By conducting experiments on the fully polarimetric Radarsat-2 image, we conclude the proposed method yield higher classification accuracy. Therefore, the ALSM classification method is a feasible and alternative method for SAR image.

  8. G0-WISHART DISTRIBUTION BASED CLASSIFICATION FROM POLARIMETRIC SAR IMAGES

    Directory of Open Access Journals (Sweden)

    G. C. Hu

    2017-09-01

    Full Text Available Enormous scientific and technical developments have been carried out to further improve the remote sensing for decades, particularly Polarimetric Synthetic Aperture Radar(PolSAR technique, so classification method based on PolSAR images has getted much more attention from scholars and related department around the world. The multilook polarmetric G0-Wishart model is a more flexible model which describe homogeneous, heterogeneous and extremely heterogeneous regions in the image. Moreover, the polarmetric G0-Wishart distribution dose not include the modified Bessel function of the second kind. It is a kind of simple statistical distribution model with less parameter. To prove its feasibility, a process of classification has been tested with the full-polarized Synthetic Aperture Radar (SAR image by the method. First, apply multilook polarimetric SAR data process and speckle filter to reduce speckle influence for classification result. Initially classify the image into sixteen classes by H/A/α decomposition. Using the ICM algorithm to classify feature based on the G0-Wshart distance. Qualitative and quantitative results show that the proposed method can classify polaimetric SAR data effectively and efficiently.

  9. G0-WISHART Distribution Based Classification from Polarimetric SAR Images

    Science.gov (United States)

    Hu, G. C.; Zhao, Q. H.

    2017-09-01

    Enormous scientific and technical developments have been carried out to further improve the remote sensing for decades, particularly Polarimetric Synthetic Aperture Radar(PolSAR) technique, so classification method based on PolSAR images has getted much more attention from scholars and related department around the world. The multilook polarmetric G0-Wishart model is a more flexible model which describe homogeneous, heterogeneous and extremely heterogeneous regions in the image. Moreover, the polarmetric G0-Wishart distribution dose not include the modified Bessel function of the second kind. It is a kind of simple statistical distribution model with less parameter. To prove its feasibility, a process of classification has been tested with the full-polarized Synthetic Aperture Radar (SAR) image by the method. First, apply multilook polarimetric SAR data process and speckle filter to reduce speckle influence for classification result. Initially classify the image into sixteen classes by H/A/α decomposition. Using the ICM algorithm to classify feature based on the G0-Wshart distance. Qualitative and quantitative results show that the proposed method can classify polaimetric SAR data effectively and efficiently.

  10. Ensemble polarimetric SAR image classification based on contextual sparse representation

    Science.gov (United States)

    Zhang, Lamei; Wang, Xiao; Zou, Bin; Qiao, Zhijun

    2016-05-01

    Polarimetric SAR image interpretation has become one of the most interesting topics, in which the construction of the reasonable and effective technique of image classification is of key importance. Sparse representation represents the data using the most succinct sparse atoms of the over-complete dictionary and the advantages of sparse representation also have been confirmed in the field of PolSAR classification. However, it is not perfect, like the ordinary classifier, at different aspects. So ensemble learning is introduced to improve the issue, which makes a plurality of different learners training and obtained the integrated results by combining the individual learner to get more accurate and ideal learning results. Therefore, this paper presents a polarimetric SAR image classification method based on the ensemble learning of sparse representation to achieve the optimal classification.

  11. Modelling Iteration Convergence Condition for Single SAR Image Geocoding

    Science.gov (United States)

    Dong, Yuting; Liao, Minghsheng; Zhang, Lu; Shi, Xuguo

    2014-11-01

    Single SAR image geocoding is to determine the ground coordinate for each pixel in the SAR image assisted with an external DEM. Due to the uncertainty of the elevation of each pixel in SAR image, an iterative procedure is needed, which suffers from the problem of divergence in some difficult areas such as shaded and serious layover areas. This paper aims at theoretically analysing the convergence conditions that has not been intensively studied till now. To make the discussion easier, the Range-Doppler (RD) model is simplified and then the general surface is simplified into a planar surface. Mathematical deduction is carried out to derive the convergence conditions and the impact factors for the convergence speed are analysed. The theoretical findings are validated by experiments for both simulated and real surfaces.

  12. Learning-Based Approaches to Deformable Image Registration

    NARCIS (Netherlands)

    Munzing, S.E.A.

    2014-01-01

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

  13. Compressive SAR imaging with joint sparsity and local similarity exploitation.

    Science.gov (United States)

    Shen, Fangfang; Zhao, Guanghui; Shi, Guangming; Dong, Weisheng; Wang, Chenglong; Niu, Yi

    2015-02-12

    Compressive sensing-based synthetic aperture radar (SAR) imaging has shown its superior capability in high-resolution image formation. However, most of those works focus on the scenes that can be sparsely represented in fixed spaces. When dealing with complicated scenes, these fixed spaces lack adaptivity in characterizing varied image contents. To solve this problem, a new compressive sensing-based radar imaging approach with adaptive sparse representation is proposed. Specifically, an autoregressive model is introduced to adaptively exploit the structural sparsity of an image. In addition, similarity among pixels is integrated into the autoregressive model to further promote the capability and thus an adaptive sparse representation facilitated by a weighted autoregressive model is derived. Since the weighted autoregressive model is inherently determined by the unknown image, we propose a joint optimization scheme by iterative SAR imaging and updating of the weighted autoregressive model to solve this problem. Eventually, experimental results demonstrated the validity and generality of the proposed approach.

  14. Effect of beam-pointing errors on bistatic SAR imaging

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The purpose is to conduct a research in the energy variation of echo wave and the imaging effect caused by the aero bistatic SAR pointing errors.Based on the moving geometry configuration of aero bistatic SAR,a model of beam pointing errors is built.Based on this,the azimuth Doppler frequency center estimation caused by these errors and the limitation to the beam pointing synchronization error are studied,and then the imaging result of different errors are analyzed.The computer's simulations are provided to prove the validity of the above analysis.

  15. Imaging Algorithm for Bistatic SAR Based on GNSS Signal

    Directory of Open Access Journals (Sweden)

    Tian Wei-ming

    2013-03-01

    Full Text Available In this paper imaging processing method for Bistatic Synthetic Aperture Radar (BiSAR utilizing navigation satellite is investigated. Considering the special problems of using Global Navigation Satellite System (GNSS signal to form SAR image, direct signal is used to estimate range migration parameters and range migration is corrected in azimuth time domain. Doppler sensitivity of phase-coded signal was solved by Doppler compensation. Through fitting the Doppler phase history with high-order polynomial, Doppler phase history is accurately approximated and azimuth compression is implemented by de-chirp processing. Through simulation and experimental data processing, the proposed method is verified.

  16. An automatic coastline detector for use with SAR images

    Energy Technology Data Exchange (ETDEWEB)

    Erteza, Ireena A.

    1998-09-01

    SAR imagery for coastline detection has many potential advantages over conventional optical stereoscopic techniques. For example, SAR does not have restrictions on being collected during daylight or when there is no cloud cover. In addition, the techniques for coastline detection witth SAR images can be automated. In this paper, we present the algorithmic development of an automatic coastline detector for use with SAR imagery. Three main algorithms comprise the automatic coastline detection algorithm, The first algorithm considers the image pre-processing steps that must occur on the original image in order to accentuate the land/water boundary. The second algorithm automatically follows along the accentuated land/water boundary and produces a single-pixel-wide coastline. The third algorithm identifies islands and marks them. This report describes in detail the development of these three algorithms. Examples of imagery are used throughout the paper to illustrate the various steps in algorithms. Actual code is included in appendices. The algorithms presented are preliminary versions that can be applied to automatic coastline detection in SAR imagery. There are many variations and additions to the algorithms that can be made to improve robustness and automation, as required by a particular application.

  17. Spacial Variation in SAR Images of Different Resolution for Agricultural Fields

    DEFF Research Database (Denmark)

    Sandholt, Inge; Skriver, Henning

    1999-01-01

    The spatial variation in two types of Synthetic Aperture Radar (SAR) images covering agricultural fields is analysed. C-band polarimetric SAR data from the Danish airborne SAR, EMISAR, have been compared to space based ERS-1 C-band SAR with respect to scale and effect of polarization. The general...

  18. A framework for shape matching in deformable image registration

    DEFF Research Database (Denmark)

    Noe, Karsten Østergaard; Mosegaard, Jesper; Tanderup, Kari

    2008-01-01

    Many existing image registration methods have difficulties in accurately describing significant rotation and bending of entities (e.g. organs) between two datasets. A common problem in this case is to ensure that the resulting registration is physically plausible, i.e. that the registration...... describes the actual bending/rotation occurring rather than just introducing expansion in some areas and shrinkage in others. In this work we developed a general framework for deformable image registration of two 3D datasets that alleviates this problem. To ensure that only physically feasible and plausible...

  19. Polar format algorithm for SAR imaging with Matlab

    Science.gov (United States)

    Deming, Ross; Best, Matthew; Farrell, Sean

    2014-06-01

    Due to its computational efficiency, the polar format algorithm (PFA) is considered by many to be the workhorse for airborne synthetic aperture radar (SAR) imaging. PFA is implemented in spatial Fourier space, also known as "K-space", which is a convenient domain for understanding SAR performance metrics, sampling requirements, etc. In this paper the mathematics behind PFA are explained and computed examples are presented, both using simulated data, and experimental airborne radar data from the Air Force Research Laboratory (AFRL) Gotcha Challenge collect. In addition, a simple graphical method is described that can be used to model and predict wavefront curvature artifacts in PFA imagery, which are due to the limited validity of the underlying far-field approximation. The appendix includes Matlab code for computing SAR images using PFA.

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

    Science.gov (United States)

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

    2017-02-01

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

  1. SAR Data Fusion Imaging Method Oriented to Target Feature Extraction

    Directory of Open Access Journals (Sweden)

    Yang Wei

    2015-02-01

    Full Text Available To deal with the difficulty for target outlines extracting precisely due to neglect of target scattering characteristic variation during the processing of high-resolution space-borne SAR data, a novel fusion imaging method is proposed oriented to target feature extraction. Firstly, several important aspects that affect target feature extraction and SAR image quality are analyzed, including curved orbit, stop-and-go approximation, atmospheric delay, and high-order residual phase error. Furthermore, the corresponding compensation methods are addressed as well. Based on the analysis, the mathematical model of SAR echo combined with target space-time spectrum is established for explaining the space-time-frequency change rule of target scattering characteristic. Moreover, a fusion imaging strategy and method under high-resolution and ultra-large observation angle range conditions are put forward to improve SAR quality by fusion processing in range-doppler and image domain. Finally, simulations based on typical military targets are used to verify the effectiveness of the fusion imaging method.

  2. Multi-band Image Registration Method Based on Fourier Transform

    Institute of Scientific and Technical Information of China (English)

    庹红娅; 刘允才

    2004-01-01

    This paper presented a registration method based on Fourier transform for multi-band images which is involved in translation and small rotation. Although different band images differ a lot in the intensity and features,they contain certain common information which we can exploit. A model was given that the multi-band images have linear correlations under the least-square sense. It is proved that the coefficients have no effect on the registration progress if two images have linear correlations. Finally, the steps of the registration method were proposed. The experiments show that the model is reasonable and the results are satisfying.

  3. THE SIMULATION OF THE SAR IMAGE OF INTERNAL SOLITARY WAVES IN ALBORAN SEA

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    SAR imaging mechanism of internal wave is studied. The numerical modelling of internal waves is obtained by the two-level scheme. The simulaed SAR images that have better expressed the features of internal waves are given by the internal waves SAR imaging theory and numerical modelling result.

  4. Registration of Standardized Histological Images in Feature Space

    CERN Document Server

    Bagci, Ulas; 10.1117/12.770219

    2009-01-01

    In this paper, we propose three novel and important methods for the registration of histological images for 3D reconstruction. First, possible intensity variations and nonstandardness in images are corrected by an intensity standardization process which maps the image scale into a standard scale where the similar intensities correspond to similar tissues meaning. Second, 2D histological images are mapped into a feature space where continuous variables are used as high confidence image features for accurate registration. Third, we propose an automatic best reference slice selection algorithm that improves reconstruction quality based on both image entropy and mean square error of the registration process. We demonstrate that the choice of reference slice has a significant impact on registration error, standardization, feature space and entropy information. After 2D histological slices are registered through an affine transformation with respect to an automatically chosen reference, the 3D volume is reconstruct...

  5. Texture analysis and classification of SAR images of urban areas

    NARCIS (Netherlands)

    Dekker, R.J.

    2003-01-01

    In SAR image classification texture holds useful information. In a study after the ability of texture to discriminate urban land-cover, a set of measures was investigated. Among them were histogram measures, wavelet energy, fractal dimension, lacunarity and semivariograms. The latter were chosen as

  6. Unsupervised Classification of SAR Images using Hierarchical Agglomeration and EM

    NARCIS (Netherlands)

    Kayabol, K.; Krylov, V.; Zerubia, J.; Salerno, E.; Cetin, A.E.; Salvetti, O.

    2012-01-01

    We implement an unsupervised classification algorithm for high resolution Synthetic Aperture Radar (SAR) images. The foundation of algorithm is based on Classification Expectation-Maximization (CEM). To get rid of two drawbacks of EM type algorithms, namely the initialization and the model order sel

  7. SAR image autofocus by sharpness optimization: a theoretical study.

    Science.gov (United States)

    Morrison, Robert L; Do, Minh N; Munson, David C

    2007-09-01

    Synthetic aperture radar (SAR) autofocus techniques that optimize sharpness metrics can produce excellent restorations in comparison with conventional autofocus approaches. To help formalize the understanding of metric-based SAR autofocus methods, and to gain more insight into their performance, we present a theoretical analysis of these techniques using simple image models. Specifically, we consider the intensity-squared metric, and a dominant point-targets image model, and derive expressions for the resulting objective function. We examine the conditions under which the perfectly focused image models correspond to stationary points of the objective function. A key contribution is that we demonstrate formally, for the specific case of intensity-squared minimization autofocus, the mechanism by which metric-based methods utilize the multichannel defocusing model of SAR autofocus to enforce the stationary point property for multiple image columns. Furthermore, our analysis shows that the objective function has a special separble property through which it can be well approximated locally by a sum of 1-D functions of each phase error component. This allows fast performance through solving a sequence of 1-D optimization problems for each phase component simultaneously. Simulation results using the proposed models and actual SAR imagery confirm that the analysis extends well to realistic situations.

  8. UWB front-end for SAR-based imaging system

    NARCIS (Netherlands)

    Monni, S.; Grooters, R.; Neto, A.; Nennie, F.A.

    2010-01-01

    A planarly fed UWB leaky lens antenna is presented integrated with wide band transmit and receive front-end electronics, to be used in a SAR-based imaging system. The unique non-dispersive characteristics of this antenna over a very wide bandwidth, together with the dual band front-end electronics b

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

    Directory of Open Access Journals (Sweden)

    Wei-Yen Hsu

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

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

    Science.gov (United States)

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

    2008-04-01

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

  11. Non-rigid image registration using bone growth model

    DEFF Research Database (Denmark)

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

    1997-01-01

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

  12. 基于SIFT特征的SAR图像配准方法在玉树地震中的应用%SAR Image Registration Based on SIFT Algorithm and its Application to the 2010 Yushu Earthquake

    Institute of Scientific and Technical Information of China (English)

    贺素歌; 董彦芳; 袁小祥

    2013-01-01

    An improved matching method based on Scale Invariant Features Transform (SIFT) algorithm is proposed in this paper. The Infinite Symmetric Exponential Filter (ISEF) algorithm is adopted to reduce speckle noise before computation of the scale space pyramid. SIFT algorithm is utilized to detect the feature points and skip the first scale-space octave to reduce processing time. And then false matches are deleted in the Euclidean space. Experiments show that the proposed method increases the number of the features and improves the robustness. The match accuracy could meet the requirement of sub-pixel matching and the processing time has been cut by 60%. Finally, earthquake change detection is implemented from ALOS PALSAR images, and the building damage information detected is consistent with the results from high spatial resolution aerial image.%本文针对SAR图像特点,提出了基于改进SIFT(尺度不变特征变换)算法的SAR图像配准方案:①对待配准图像进行ISEF(无限对称指数滤波器)滤波处理,降低图像的斑点噪声;②采用SIFT算法提取特征点,略过差分金字塔第一层的特征点检测,提高时间效率;③在欧氏空间内剔除误匹配点,提高配准精度.实验表明,本文提出的SAR图像配准方案检测到的匹配点对的数量和稳健性都有提高,精度能够满足亚像元级SAR图像的应用需求,且用时比传统SIFT方法减少60%以上.最后对精配准的SAR图像进行震害变化检测,得到的震害分布与高分辨率光学图像上判读的建筑物毁坏情况基本一致.

  13. The role of image registration in brain mapping.

    Science.gov (United States)

    Toga, A W; Thompson, P M

    2001-01-01

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

  14. THz-SAR Vibrating Target Imaging via the Bayesian Method

    Directory of Open Access Journals (Sweden)

    Bin Deng

    2017-01-01

    Full Text Available Target vibration bears important information for target recognition, and terahertz, due to significant micro-Doppler effects, has strong advantages for remotely sensing vibrations. In this paper, the imaging characteristics of vibrating targets with THz-SAR are at first analyzed. An improved algorithm based on an excellent Bayesian approach, that is, the expansion-compression variance-component (ExCoV method, has been proposed for reconstructing scattering coefficients of vibrating targets, which provides more robust and efficient initialization and overcomes the deficiencies of sidelobes as well as artifacts arising from the traditional correlation method. A real vibration measurement experiment of idle cars was performed to validate the range model. Simulated SAR data of vibrating targets and a tank model in a real background in 220 GHz show good performance at low SNR. Rapidly evolving high-power terahertz devices will offer viable THz-SAR application at a distance of several kilometers.

  15. An Improved Shape Contexts Based Ship Classification in SAR Images

    Directory of Open Access Journals (Sweden)

    Ji-Wei Zhu

    2017-02-01

    Full Text Available In synthetic aperture radar (SAR imagery, relating to maritime surveillance studies, the ship has always been the main focus of study. In this letter, a method of ship classification in SAR images is proposed to enhance classification accuracy. In the proposed method, to fully exploit the distinguishing characters of the ship targets, both topology and intensity of the scattering points of the ship are considered. The results of testing the proposed method on a data set of three types of ships, collected via a space-borne SAR sensor designed by the Institute of Electronics, Chinese Academy of Sciences (IECAS, establish that the proposed method is superior to several existing methods, including the original shape contexts method, traditional invariant moments and the recent approach.

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

    Directory of Open Access Journals (Sweden)

    Wu Zhou

    2014-01-01

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

  17. Chest X-ray imaging of patients with SARS

    Institute of Scientific and Technical Information of China (English)

    陆普选; 周伯平; 陈心春; 袁明远; 龚小龙; 杨根东; 刘锦清; 袁本通; 郑广平; 杨桂林; 王火生

    2003-01-01

    Objective To investigate the chest X-ray manifestations of SARS cases.Methods A retrospective study was conducted among 52 clinically confirmed SARS patients from February 9 to May 10, 2003. Chest X-ray scanning was performed at a interval of 1-3 days according to the requirements. The manifestations and special features of SARS in X-ray were analyzed. Results Small or large patchy shadows with intensive density in both lungs were observed in 31 cases, ground-glass like opacification in 16, small patchy shadows in one lung lobe or one lung segment in 18, nodular shadows in one lung segment in 1, and increased lung marking in lung interstitial tissues in 2. Rapidly changing consolidations revealed in chest X-ray images were found to be associated with SARS infections, and they were not affected by treatment with antibiotics.Conclusion Chest X-ray provides a sensitive and specific method for the diagnosis and treatment of SARS, and those present with symptoms and signs should undergo chest X-ray scanning every 1-3 days.

  18. Feature preserving compression of high resolution SAR images

    Science.gov (United States)

    Yang, Zhigao; Hu, Fuxiang; Sun, Tao; Qin, Qianqing

    2006-10-01

    Compression techniques are required to transmit the large amounts of high-resolution synthetic aperture radar (SAR) image data over the available channels. Common Image compression methods may lose detail and weak information in original images, especially at smoothness areas and edges with low contrast. This is known as "smoothing effect". It becomes difficult to extract and recognize some useful image features such as points and lines. We propose a new SAR image compression algorithm that can reduce the "smoothing effect" based on adaptive wavelet packet transform and feature-preserving rate allocation. For the reason that images should be modeled as non-stationary information resources, a SAR image is partitioned to overlapped blocks. Each overlapped block is then transformed by adaptive wavelet packet according to statistical features of different blocks. In quantifying and entropy coding of wavelet coefficients, we integrate feature-preserving technique. Experiments show that quality of our algorithm up to 16:1 compression ratio is improved significantly, and more weak information is reserved.

  19. A General Epipolar-Line Model between Optical and SAR Images and Used in Image Matching

    Directory of Open Access Journals (Sweden)

    Shuai Xing

    2014-02-01

    Full Text Available The search space and strategy are important for optical and SAR image matching. In this paper a general epipolar-line model has been proposed between linear array push-broom optical and SAR images. Then a dynamic approximate epipolar-line constraint model (DAELCM has been constructed and used to construct a new image matching algorithm with Harris operator and CRA. Experimental results have shown that the general epipolar-line model is valid and successfully used in optical and SAR image matching, and effectively limits the search space and decreased computation.

  20. Current trends in medical image registration and fusion

    Directory of Open Access Journals (Sweden)

    Fatma El-Zahraa Ahmed El-Gamal

    2016-03-01

    Full Text Available Recently, medical image registration and fusion processes are considered as a valuable assistant for the medical experts. The role of these processes arises from their ability to help the experts in the diagnosis, following up the diseases’ evolution, and deciding the necessary therapies regarding the patient’s condition. Therefore, the aim of this paper is to focus on medical image registration as well as medical image fusion. In addition, the paper presents a description of the common diagnostic images along with the main characteristics of each of them. The paper also illustrates most well-known toolkits that have been developed to help the working with the registration and fusion processes. Finally, the paper presents the current challenges associated with working with medical image registration and fusion through illustrating the recent diseases/disorders that were addressed through such an analyzing process.

  1. Restoration of polarimetric SAR images using simulated annealing

    DEFF Research Database (Denmark)

    Schou, Jesper; Skriver, Henning

    2001-01-01

    approach favoring one of the objectives. An algorithm for estimating the radar cross-section (RCS) for intensity SAR images has previously been proposed in the literature based on Markov random fields and the stochastic optimization method simulated annealing. A new version of the algorithm is presented...... are obtained while at the same time preserving most of the structures in the image. The algorithm is evaluated using multilook polarimetric L-band data from the Danish airborne EMISAR system, and the impact of the algorithm on the unsupervised H-α classification is demonstrated......Filtering synthetic aperture radar (SAR) images ideally results in better estimates of the parameters characterizing the distributed targets in the images while preserving the structures of the nondistributed targets. However, these objectives are normally conflicting, often leading to a filtering...

  2. SAR IMAGE ENHANCEMENT BASED ON BEAM SHARPENING TECHNIQUE

    Institute of Scientific and Technical Information of China (English)

    LIYong; ZI-IANGKun-hui; ZHUDai-yin; ZHUZhao-da

    2004-01-01

    A major problem encountered in enhancing SAR image is the total loss of phase information and the unknown parameters of imaging system. The beam sharpening technique, combined with synthetic aperture radiation pattern estimation provides an approach to process this kind of data to achieve higher apparent resolution. Based on the criterion of minimizing the expected quadratic estimation error, an optimum FIR filter with a symmetrical structure is designed whose coefficients depend on the azimuth response of local isolated prominent points because this response can be approximately regarded as the synthetic aperture radiation pattern of the imaging system. The point target simulation shows that the angular resolution is improved by a ratio of almost two to one. The processing results of a live SAR image demonstrate the validity of the method.

  3. Image Combination Analysis in SPECAN Algorithm of Spaceborne SAR

    Institute of Scientific and Technical Information of China (English)

    臧铁飞; 李方慧; 龙腾

    2003-01-01

    An analysis of image combination in SPECAN algorithm is delivered in time-frequency domain in detail and a new image combination method is proposed. For four multi-looks processing one sub-aperture data in every three sub-apertures is processed in this combination method. The continual sub-aperture processing in SPECAN algorithm is realized and the processing efficiency can be dramatically increased. A new parameter is also put forward to measure the processing efficient of SAR image processing. Finally, the raw data of RADARSAT are used to test the method and the result proves that this method is feasible to be used in SPECAN algorithm of spaceborne SAR and can improve processing efficiently. SPECAN algorithm with this method can be used in quick-look imaging.

  4. Multiscale Segmentation of Polarimetric SAR Image Based on Srm Superpixels

    Science.gov (United States)

    Lang, F.; Yang, J.; Wu, L.; Li, D.

    2016-06-01

    Multi-scale segmentation of remote sensing image is more systematic and more convenient for the object-oriented image analysis compared to single-scale segmentation. However, the existing pixel-based polarimetric SAR (PolSAR) image multi-scale segmentation algorithms are usually inefficient and impractical. In this paper, we proposed a superpixel-based binary partition tree (BPT) segmentation algorithm by combining the generalized statistical region merging (GSRM) algorithm and the BPT algorithm. First, superpixels are obtained by setting a maximum region number threshold to GSRM. Then, the region merging process of the BPT algorithm is implemented based on superpixels but not pixels. The proposed algorithm inherits the advantages of both GSRM and BPT. The operation efficiency is obviously improved compared to the pixel-based BPT segmentation. Experiments using the Lband ESAR image over the Oberpfaffenhofen test site proved the effectiveness of the proposed method.

  5. Optimal atlas construction through hierarchical image registration

    Science.gov (United States)

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

    2016-03-01

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

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

    Science.gov (United States)

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

    2016-06-14

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

  7. Piecewise nonlinear image registration using DCT basis functions

    Science.gov (United States)

    Gan, Lin; Agam, Gady

    2015-03-01

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

  8. A Level Set Filter for Speckle Reduction in SAR Images

    OpenAIRE

    Huang Bo; Li Hongga; Huang Xiaoxia

    2010-01-01

    Despite much effort and significant progress in recent years, speckle removal for Synthetic Aperture Radar (SAR) image still is a challenging problem in image processing. Unlike the traditional noise filters, which are mainly based on local neighborhood statistical average or frequencies transform, in this paper, we propose a speckle reduction method based on the theory of level set, one form of curvature flow propagation. Firstly, based on partial differential equation, the Lee filter can b...

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

    Science.gov (United States)

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

    2013-12-31

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

  10. Image Classifying Registration for Gaussian & Bayesian Techniques: A Review

    Directory of Open Access Journals (Sweden)

    Rahul Godghate,

    2014-04-01

    Full Text Available A Bayesian Technique for Image Classifying Registration to perform simultaneously image registration and pixel classification. Medical image registration is critical for the fusion of complementary information about patient anatomy and physiology, for the longitudinal study of a human organ over time and the monitoring of disease development or treatment effect, for the statistical analysis of a population variation in comparison to a so-called digital atlas, for image-guided therapy, etc. A Bayesian Technique for Image Classifying Registration is well-suited to deal with image pairs that contain two classes of pixels with different inter-image intensity relationships. We will show through different experiments that the model can be applied in many different ways. For instance if the class map is known, then it can be used for template-based segmentation. If the full model is used, then it can be applied to lesion detection by image comparison. Experiments have been conducted on both real and simulated data. It show that in the presence of an extra-class, the classifying registration improves both the registration and the detection, especially when the deformations are small. The proposed model is defined using only two classes but it is straightforward to extend it to an arbitrary number of classes.

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

    Science.gov (United States)

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

    2010-01-01

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

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

    Science.gov (United States)

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

    2014-08-01

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

  13. A New SAR Image Segmentation Algorithm for the Detection of Target and Shadow Regions

    Science.gov (United States)

    Huang, Shiqi; Huang, Wenzhun; Zhang, Ting

    2016-12-01

    The most distinctive characteristic of synthetic aperture radar (SAR) is that it can acquire data under all weather conditions and at all times. However, its coherent imaging mechanism introduces a great deal of speckle noise into SAR images, which makes the segmentation of target and shadow regions in SAR images very difficult. This paper proposes a new SAR image segmentation method based on wavelet decomposition and a constant false alarm rate (WD-CFAR). The WD-CFAR algorithm not only is insensitive to the speckle noise in SAR images but also can segment target and shadow regions simultaneously, and it is also able to effectively segment SAR images with a low signal-to-clutter ratio (SCR). Experiments were performed to assess the performance of the new algorithm on various SAR images. The experimental results show that the proposed method is effective and feasible and possesses good characteristics for general application.

  14. Spatial Information Based Medical Image Registration using Mutual Information

    Directory of Open Access Journals (Sweden)

    Benzheng Wei

    2011-06-01

    Full Text Available Image registration is a valuable technique for medical diagnosis and treatment. Due to the inferiority of image registration using maximum mutual information, a new hybrid method of multimodality medical image registration based on mutual information of spatial information is proposed. The new measure that combines mutual information, spatial information and feature characteristics, is proposed. Edge points are used as features, obtained from a morphology gradient detector. Feature characteristics like location, edge strength and orientation are taken into account to compute a joint probability distribution of corresponding edge points in two images. Mutual information based on this function is minimized to find the best alignment parameters. Finally, the translation parameters are calculated by using a modified Particle Swarm Optimization (MPSO algorithm. The experimental results demonstrate the effectiveness of the proposed registration scheme.

  15. A Compressive Sensing SAR Imaging Approach Based on Wavelet Package Algorithm

    Directory of Open Access Journals (Sweden)

    Shi Yan

    2013-06-01

    Full Text Available Compressive sensing SAR imaging can significantly reduce the sampling rate and the amount of data,but it is essential only in the case where the reflection coefficients of SAR scene are sparse. This paper proposed a compressive sensing SAR imaging method based on wavelet packet sparse representation. The wavelet packet algorithm is used to choose the most sparse representation of the SAR scene by training the same type of SAR images. By solving for the minimum 1 l norm optimization, the SAR scene reflection coefficients can be reconstructed. Unambiguous SAR image can be produced with the proposed method even with fewer samples. SAR data simulation experiments demonstrate the efficiency of the proposed method.

  16. SAR Image Segmentation Based On Hybrid PSOGSA Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Amandeep Kaur

    2014-09-01

    Full Text Available Image segmentation is useful in many applications. It can identify the regions of interest in a scene or annotate the data. It categorizes the existing segmentation algorithm into region-based segmentation, data clustering, and edge-base segmentation. Region-based segmentation includes the seeded and unseeded region growing algorithms, the JSEG, and the fast scanning algorithm. Due to the presence of speckle noise, segmentation of Synthetic Aperture Radar (SAR images is still a challenging problem. We proposed a fast SAR image segmentation method based on Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA. In this method, threshold estimation is regarded as a search procedure that examinations for an appropriate value in a continuous grayscale interval. Hence, PSO-GSA algorithm is familiarized to search for the optimal threshold. Experimental results indicate that our method is superior to GA based, AFS based and ABC based methods in terms of segmentation accuracy, segmentation time, and Thresholding.

  17. Dynamic and data-driven classification for polarimetric SAR images

    Science.gov (United States)

    Uhlmann, S.; Kiranyaz, S.; Ince, T.; Gabbouj, M.

    2011-11-01

    In this paper, we introduce dynamic and scalable Synthetic Aperture Radar (SAR) terrain classification based on the Collective Network of Binary Classifiers (CNBC). The CNBC framework is primarily adapted to maximize the SAR classification accuracy on dynamically varying databases where variations do occur in any time in terms of (new) images, classes, features and users' relevance feedback. Whenever a "change" occurs, the CNBC dynamically and "optimally" adapts itself to the change by means of its topology and the underlying evolutionary method MD PSO. Thanks to its "Divide and Conquer" type approach, the CNBC can also support varying and large set of (PolSAR) features among which it optimally selects, weighs and fuses the most discriminative ones for a particular class. Each SAR terrain class is discriminated by a dedicated Network of Binary Classifiers (NBC), which encapsulates a set of evolutionary Binary Classifiers (BCs) discriminating the class with a distinctive feature set. Moreover, with each incremental evolution session, new classes/features can be introduced which signals the CNBC to create new corresponding NBCs and BCs within to adapt and scale dynamically to the change. This can in turn be a significant advantage when the current CNBC is used to classify multiple SAR images with similar terrain classes since no or only minimal (incremental) evolution sessions are needed to adapt it to a new classification problem while using the previously acquired knowledge. We demonstrate our proposed classification approach over several medium and highresolution NASA/JPL AIRSAR images applying various polarimetric decompositions. We evaluate and compare the computational complexity and classification accuracy against static Neural Network classifiers. As CNBC classification accuracy can compete and even surpass them, the computational complexity of CNBC is significantly lower as the CNBC body supports high parallelization making it applicable to grid

  18. A novel SAR fusion image segmentation method based on triplet Markov field

    Science.gov (United States)

    Wang, Jiajing; Jiao, Shuhong; Sun, Zhenyu

    2015-03-01

    Markov random field (MRF) has been widely used in SAR image segmentation because of the advantage of directly modeling the posterior distribution and suppresses the speckle on the influence of the segmentation result. However, when the real SAR images are nonstationary images, the unsupervised segmentation results by MRF can be poor. The recent proposed triplet Markov field (TMF) model is well appropriate for nonstationary SAR image processing due to the introduction of an auxiliary field which reflects the nonstationarity. In addition, on account of the texture features of SAR image, a fusion image segmentation method is proposed by fusing the gray level image and texture feature image. The effectiveness of the proposed method in this paper is demonstrated by a synthesis SAR image and the real SAR images segmentation experiments, and it is better than the state-of-art methods.

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

    Science.gov (United States)

    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.

  20. Adaptive Local Image Registration: Analysis on Filter Size

    OpenAIRE

    Vishnukumar S; M.Wilscy

    2012-01-01

    Adaptive Local Image Registration is a Local Image Registration based on an Adaptive Filtering frame work. A filter of appropriate size convolves with reference image and gives the pixel values corresponding to the distorted image and the filter is updated in each stage of the convolution. When the filter converges to the system model, it provides the registered image. The filter size plays an important role in this method. The analysis on the filter size is done using Peak Signal-to-Noise Ra...

  1. Nonrigid Medical Image Registration Based on Mesh Deformation Constraints

    Directory of Open Access Journals (Sweden)

    XiangBo Lin

    2013-01-01

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

  2. KW-SIFT descriptor for remote-sensing image registration

    Institute of Scientific and Technical Information of China (English)

    Xiangzeng Liu; Zheng Tian; Weidong Yan; Xifa Duan

    2011-01-01

    A technique to construct an affine invariant descriptor for remote-sensing image registration based on the scale invariant features transform (SIFT) in a kernel space is proposed.Affine invariant SIFT descriptor is first developed in an elliptical region determined by the Hessian matrix of the feature points.Thereafter,the descriptor is mapped to a feature space induced by a kernel, and a new descriptor is constructed by whitening the mapped descriptor in the feature space, with the transform called KW-SIFT.In a final step, the new descriptor is used to register remote-sensing images.Experimental results for remote-sensing image registration indicate that the proposed method improves the registration performance as compared with other related methods.%@@ A technique to construct an affine invariant descriptor for remote-sensing image registration based on the scale invariant features transform (SIFT) in a kernel space is proposed.Affine invariant SIFT descriptor is first developed in an elliptical region determined by the Hessian matrix of the feature points.Thereafter,the descriptor is mapped to a feature space induced by a kernel, and a new descriptor is constructed by whitening the mapped descriptor in the feature space, with the transform called KW-SIFT.In a final step, the new descriptor is used to register remote-sensing images.Experimental results for remote-sensing image registration indicate that the proposed method improves the registration performance as compared with other related methods.

  3. Image enhancements of Landsat 8 (OLI) and SAR data for preliminary landslide identification and mapping applied to the central region of Kenya

    Science.gov (United States)

    Mwaniki, M. W.; Kuria, D. N.; Boitt, M. K.; Ngigi, T. G.

    2017-04-01

    Image enhancements lead to improved performance and increased accuracy of feature extraction, recognition, identification, classification and hence change detection. This increases the utility of remote sensing to suit environmental applications and aid disaster monitoring of geohazards involving large areas. The main aim of this study was to compare the effect of image enhancement applied to synthetic aperture radar (SAR) data and Landsat 8 imagery in landslide identification and mapping. The methodology involved pre-processing Landsat 8 imagery, image co-registration, despeckling of the SAR data, after which Landsat 8 imagery was enhanced by Principal and Independent Component Analysis (PCA and ICA), a spectral index involving bands 7 and 4, and using a False Colour Composite (FCC) with the components bearing the most geologic information. The SAR data were processed using textural and edge filters, and computation of SAR incoherence. The enhanced spatial, textural and edge information from the SAR data was incorporated to the spectral information from Landsat 8 imagery during the knowledge based classification. The methodology was tested in the central highlands of Kenya, characterized by rugged terrain and frequent rainfall induced landslides. The results showed that the SAR data complemented Landsat 8 data which had enriched spectral information afforded by the FCC with enhanced geologic information. The SAR classification depicted landslides along the ridges and lineaments, important information lacking in the Landsat 8 image classification. The success of landslide identification and classification was attributed to the enhanced geologic features by spectral, textural and roughness properties.

  4. Interferometric SAR imaging by transmitting stepped frequency chaotic noise signals

    Science.gov (United States)

    Zhang, Yunhua; Gu, Xiang; Zhai, Wenshuai; Dong, Xiao; Shi, Xiaojin; Kang, Xueyan

    2015-10-01

    Noise radar has been applied in many fields since it was proposed more than 50 years ago. However, it has not been applied to interferometric SAR imaging yet as far as we know. This paper introduces our recent work on interferometric noise radar. An interferometric SAR system was developed which can transmit both chirp signal and chaotic noise signal (CNS) at multiple carrier frequencies. An airborne experiment with this system by transmitting both signals was carried out, and the data were processed to show the capability of interferometric SAR imaging with CNS. The results shows that although the interferometric phase quality of CNS is degraded due to the signal to noise ratio (SNR) is lower compared with that of chirp signal, we still can get satisfied DEM after multi-looking processing. Another work of this paper is to apply compressed sensing (CS) theory to the interferometric SAR imaging with CNS. The CS theory states that if a signal is sparse, then it can be accurately reconstructed with much less sampled data than that regularly required according to Nyquist Sampling Theory. To form a structured random matrix, if the transmitted signal is of fixed waveform, then random subsampling is needed. However, if the transmitted signal is of random waveform, then only uniform subsampling is needed. This is another advantage of noise signal. Both the interferometric phase images and the DEMs by regular method and by CS method are processed with results compared. It is shown that the degradation of interferometric phases due to subsampling is larger than that of amplitude image.

  5. Adaptive mesh generation for image registration and segmentation

    DEFF Research Database (Denmark)

    Fogtmann, Mads; Larsen, Rasmus

    2013-01-01

    This paper deals with the problem of generating quality tetrahedral meshes for image registration. From an initial coarse mesh the approach matches the mesh to the image volume by combining red-green subdivision and mesh evolution through mesh-to-image matching regularized with a mesh quality...

  6. A MNCIE method for registration of ultrasound images

    Institute of Scientific and Technical Information of China (English)

    JIN Jing; WANG Qiang; SHEN Yi

    2007-01-01

    A new approach to the problem of registration of ultrasound images is presented, using a concept of Nonlinear Correlation Information Entropy (NCIE) as the matching criterion. The proposed method applies NCIE to measure the correlation degree between the image intensities of corresponding voxel in the floating and reference images. Registration is achieved by adjustment of the relative position until NCIE between the images is maximized. However, unlike mutual information (MI), NCIE varies in the closed interval [0, 1 ], and around the extremum it varies sharply, which makes it possible that thresholds of NCIE can be used to boost the search for the registration transformation. Using this feature of NCIE, we combine the downhill simplex searching algorithm to register the ultrasound images. The simulations are conducted to testify the effectiveness and rapidity of the proposed registration method, in which the ultrasound floating images are aligned to the reference images with required registration accuracy. Moreover, the NCIE based method can overcome local minima problem by setting thresholds and can take care of the differences in contrast between the floating and reference images.

  7. SAR image regularization with fast approximate discrete minimization.

    Science.gov (United States)

    Denis, Loïc; Tupin, Florence; Darbon, Jérôme; Sigelle, Marc

    2009-07-01

    Synthetic aperture radar (SAR) images, like other coherent imaging modalities, suffer from speckle noise. The presence of this noise makes the automatic interpretation of images a challenging task and noise reduction is often a prerequisite for successful use of classical image processing algorithms. Numerous approaches have been proposed to filter speckle noise. Markov random field (MRF) modelization provides a convenient way to express both data fidelity constraints and desirable properties of the filtered image. In this context, total variation minimization has been extensively used to constrain the oscillations in the regularized image while preserving its edges. Speckle noise follows heavy-tailed distributions, and the MRF formulation leads to a minimization problem involving nonconvex log-likelihood terms. Such a minimization can be performed efficiently by computing minimum cuts on weighted graphs. Due to memory constraints, exact minimization, although theoretically possible, is not achievable on large images required by remote sensing applications. The computational burden of the state-of-the-art algorithm for approximate minimization (namely the alpha -expansion) is too heavy specially when considering joint regularization of several images. We show that a satisfying solution can be reached, in few iterations, by performing a graph-cut-based combinatorial exploration of large trial moves. This algorithm is applied to joint regularization of the amplitude and interferometric phase in urban area SAR images.

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

    Science.gov (United States)

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

    2016-10-01

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

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

    Science.gov (United States)

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

    2011-03-01

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

  10. Multiscale MRF-based Texture Segmentation of SAR Image

    Institute of Scientific and Technical Information of China (English)

    XUXin; LIDeren; SUNHong

    2004-01-01

    We propose a multiscale Bayesian segmentation algorithm for SAR image in this paper. A hierarchical two-level Markov random field (MRF) is applied to represent both texture and region label over the wavelet lattice. The high level uses an isotropic Multi-level logistic (MLL) random field to characterize the blob-like region formation process at each scale and the interscale dependencies over the corresponding multiresolution region. At lower level a novel Causal Gaussian autoregressive (CGAR) process is proposed to describe the fill-in of multiresolution region. Once the multiscale double MRFs model is established, in term of Sequential maximum a posteriori (SMAP), model parameter estimate and region segmentation are performed alternately from coarse to fine scale. Our segmentation method is tested on both synthetic and ERS-1 SAR images.

  11. Sparse representation-based spectral clustering for SAR image segmentation

    Science.gov (United States)

    Zhang, Xiangrong; Wei, Zhengli; Feng, Jie; Jiao, Licheng

    2011-12-01

    A new method, sparse representation based spectral clustering (SC) with Nyström method, is proposed for synthetic aperture radar (SAR) image segmentation. Different from the conventional SC, this proposed technique is developed by using the sparse coefficients which obtained by solving l1 minimization problem to construct the affinity matrix and the Nyström method is applied to alleviate the segmentation process. The advantage of our proposed method is that we do not need to select the scaling parameter in the Gaussian kernel function artificially. We apply the proposed method, k-means and the classic spectral clustering algorithm with Nyström method to SAR image segmentation. The results show that compared with the other two methods, the proposed method can obtain much better segmentation results.

  12. Temporal mammogram image registration using optimized curvilinear coordinates.

    Science.gov (United States)

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

    2016-04-01

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

  13. Underwater topography detection of Shuangzi Reefs with SAR images acquired in different time

    Institute of Scientific and Technical Information of China (English)

    YANG Jungang; ZHANG Jie; MENG Junmin

    2007-01-01

    Imaging mechanism of underwater topography by SAR and a underwater topography SAR detection model built on the theory of underwater topography detection with SAR image presented by Yuan Yeli are used to detect the underwater topography of Shuangzi Reefs in the Nansha Islands with three scenes of SAR images acquired in different time. Detection results of three SAR images are compared with the chart topography and the detection errors are analyzed. Underwater topography detection experiments of Shuangzi Reefs show that the detection model is practicable. The detection results indicate that SAR images acquired in different time also can be used to detect the underwater topography, and the detection results are affected by the ocean conditions in the SAR acquiring time.

  14. Image registration based on matrix perturbation analysis using spectral graph

    Institute of Scientific and Technical Information of China (English)

    Chengcai Leng; Zheng Tian; Jing Li; Mingtao Ding

    2009-01-01

    @@ We present a novel perspective on characterizing the spectral correspondence between nodes of the weighted graph with application to image registration.It is based on matrix perturbation analysis on the spectral graph.The contribution may be divided into three parts.Firstly, the perturbation matrix is obtained by perturbing the matrix of graph model.Secondly, an orthogonal matrix is obtained based on an optimal parameter, which can better capture correspondence features.Thirdly, the optimal matching matrix is proposed by adjusting signs of orthogonal matrix for image registration.Experiments on both synthetic images and real-world images demonstrate the effectiveness and accuracy of the proposed method.

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

    Science.gov (United States)

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

    2017-07-11

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

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

    Science.gov (United States)

    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.

  17. Circular SAR Optimization Imaging Method of Buildings

    Directory of Open Access Journals (Sweden)

    Wang Jian-feng

    2015-12-01

    Full Text Available The Circular Synthetic Aperture Radar (CSAR can obtain the entire scattering properties of targets because of its great ability of 360° observation. In this study, an optimal orientation of the CSAR imaging algorithm of buildings is proposed by applying a combination of coherent and incoherent processing techniques. FEKO software is used to construct the electromagnetic scattering modes and simulate the radar echo. The FEKO imaging results are compared with the isotropic scattering results. On comparison, the optimal azimuth coherent accumulation angle of CSAR imaging of buildings is obtained. Practically, the scattering directions of buildings are unknown; therefore, we divide the 360° echo of CSAR into many overlapped and few angle echoes corresponding to the sub-aperture and then perform an imaging procedure on each sub-aperture. Sub-aperture imaging results are applied to obtain the all-around image using incoherent fusion techniques. The polarimetry decomposition method is used to decompose the all-around image and further retrieve the edge information of buildings successfully. The proposed method is validated with P-band airborne CSAR data from Sichuan, China.

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

    Science.gov (United States)

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

    2007-12-01

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

  19. Registration and 3D visualization of large microscopy images

    Science.gov (United States)

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

    2006-03-01

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

  20. Compressive SAR Imaging with Joint Sparsity and Local Similarity Exploitation

    Directory of Open Access Journals (Sweden)

    Fangfang Shen

    2015-02-01

    Full Text Available Compressive sensing-based synthetic aperture radar (SAR imaging has shown its superior capability in high-resolution image formation. However, most of those works focus on the scenes that can be sparsely represented in fixed spaces. When dealing with complicated scenes, these fixed spaces lack adaptivity in characterizing varied image contents. To solve this problem, a new compressive sensing-based radar imaging approach with adaptive sparse representation is proposed. Specifically, an autoregressive model is introduced to adaptively exploit the structural sparsity of an image. In addition, similarity among pixels is integrated into the autoregressive model to further promote the capability and thus an adaptive sparse representation facilitated by a weighted autoregressive model is derived. Since the weighted autoregressive model is inherently determined by the unknown image, we propose a joint optimization scheme by iterative SAR imaging and updating of the weighted autoregressive model to solve this problem. Eventually, experimental results demonstrated the validity and generality of the proposed approach.

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

    Science.gov (United States)

    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

  2. Decreasing range resolution of a SAR image to permit correction of motion measurement errors beyond the SAR range resolution

    Science.gov (United States)

    Doerry, Armin W.; Heard, Freddie E.; Cordaro, J. Thomas

    2010-07-20

    Motion measurement errors that extend beyond the range resolution of a synthetic aperture radar (SAR) can be corrected by effectively decreasing the range resolution of the SAR in order to permit measurement of the error. Range profiles can be compared across the slow-time dimension of the input data in order to estimate the error. Once the error has been determined, appropriate frequency and phase correction can be applied to the uncompressed input data, after which range and azimuth compression can be performed to produce a desired SAR image.

  3. Radar image preprocessing. [of SEASAT-A SAR data

    Science.gov (United States)

    Frost, V. S.; Stiles, J. A.; Holtzman, J. C.; Held, D. N.

    1980-01-01

    Standard image processing techniques are not applicable to radar images because of the coherent nature of the sensor. Therefore there is a need to develop preprocessing techniques for radar images which will then allow these standard methods to be applied. A random field model for radar image data is developed. This model describes the image data as the result of a multiplicative-convolved process. Standard techniques, those based on additive noise and homomorphic processing are not directly applicable to this class of sensor data. Therefore, a minimum mean square error (MMSE) filter was designed to treat this class of sensor data. The resulting filter was implemented in an adaptive format to account for changes in local statistics and edges. A radar image processing technique which provides the MMSE estimate inside homogeneous areas and tends to preserve edge structure was the result of this study. Digitally correlated Seasat-A synthetic aperture radar (SAR) imagery was used to test the technique.

  4. A detection model of underwater topography with a series of SAR images acquired at different time

    Institute of Scientific and Technical Information of China (English)

    YANG Jungang; ZHANG Jie; MENG Junmin

    2010-01-01

    underwater topography is one of oceanic features detected by Synthetic Aperture Radar. Under-water topography SAR imaging mechanism shows that tidal current is the important factor for underwater topography SAR imaging. Thus under the same wind field condition, SAR images for the same area acquired at different time include different information of the underwater topogra-phy. To utilize synchronously SAR images acquired at different time for the underwater topography SAR detection and improve the precision of detection, based on the detection model of underwater topography with single SAR image and the periodicity of tidal current, a detection model of under- water topography with a series of SAR images acquired at different time is developed by combing with tide and tidal current numerical simulation. To testify the feasibility of the presented model, Taiwan Shoal located at the south outlet of Taiwan Strait is selected as study area and three SAR images are used in the underwater topography detection. The detection results are compared with the field observation data of water depth carried out by R/V Dongfanghong 2, and the errors of the detection are compared with those of the single SAR image. All comparisons show that the detec- tion model presented in the paper improves the precision of underwater topography SAR detection, and the presented model is feasible.

  5. Registration of multitemporal aerial optical images using line features

    Science.gov (United States)

    Zhao, Chenyang; Goshtasby, A. Ardeshir

    2016-07-01

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

  6. SAR image segmentation using MSER and improved spectral clustering

    Science.gov (United States)

    Gui, Yang; Zhang, Xiaohu; Shang, Yang

    2012-12-01

    A novel approach is presented for synthetic aperture radar (SAR) image segmentation. By incorporating the advantages of maximally stable extremal regions (MSER) algorithm and spectral clustering (SC) method, the proposed approach provides effective and robust segmentation. First, the input image is transformed from a pixel-based to a region-based model by using the MSER algorithm. The input image after MSER procedure is composed of some disjoint regions. Then the regions are treated as nodes in the image plane, and a graph structure is applied to represent them. Finally, the improved SC is used to perform globally optimal clustering, by which the result of image segmentation can be generated. To avoid some incorrect partitioning when considering each region as one graph node, we assign different numbers of nodes to represent the regions according to area ratios among the regions. In addition, K-harmonic means instead of K-means is applied in the improved SC procedure in order to raise its stability and performance. Experimental results show that the proposed approach is effective on SAR image segmentation and has the advantage of calculating quickly.

  7. A Level Set Filter for Speckle Reduction in SAR Images

    Directory of Open Access Journals (Sweden)

    Xiaoxia Huang

    2010-01-01

    Full Text Available Despite much effort and significant progress in recent years, speckle removal for Synthetic Aperture Radar (SAR image still is a challenging problem in image processing. Unlike the traditional noise filters, which are mainly based on local neighborhood statistical average or frequencies transform, in this paper, we propose a speckle reduction method based on the theory of level set, one form of curvature flow propagation. Firstly, based on partial differential equation, the Lee filter can be cast as a formulation of anisotropic diffusion function; furthermore, we continued to deduce it into a level set formulation. Level set flow into the method allows the front interface to propagate naturally with topological changes, where the speed is proportional to the curvature of the intensity contours in an image. Hence, small speckle will disappear quickly, while large scale interfaces will be slow to evolve. Secondly, for preserving finer detailed structures in images when smoothing the speckle, the evolution is switched between minimum or maximum curvature speed depending on the scale of speckle. The proposed method has been illustrated by experiments on simulation image and ERS-2 SAR images under different circumstances. Its advantages over the traditional speckle reduction filter approaches have also been demonstrated.

  8. A Level Set Filter for Speckle Reduction in SAR Images

    Science.gov (United States)

    Li, Hongga; Huang, Bo; Huang, Xiaoxia

    2010-12-01

    Despite much effort and significant progress in recent years, speckle removal for Synthetic Aperture Radar (SAR) image still is a challenging problem in image processing. Unlike the traditional noise filters, which are mainly based on local neighborhood statistical average or frequencies transform, in this paper, we propose a speckle reduction method based on the theory of level set, one form of curvature flow propagation. Firstly, based on partial differential equation, the Lee filter can be cast as a formulation of anisotropic diffusion function; furthermore, we continued to deduce it into a level set formulation. Level set flow into the method allows the front interface to propagate naturally with topological changes, where the speed is proportional to the curvature of the intensity contours in an image. Hence, small speckle will disappear quickly, while large scale interfaces will be slow to evolve. Secondly, for preserving finer detailed structures in images when smoothing the speckle, the evolution is switched between minimum or maximum curvature speed depending on the scale of speckle. The proposed method has been illustrated by experiments on simulation image and ERS-2 SAR images under different circumstances. Its advantages over the traditional speckle reduction filter approaches have also been demonstrated.

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

    Directory of Open Access Journals (Sweden)

    Chengjin Lyu

    2017-05-01

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

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

    Science.gov (United States)

    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

  11. Scalable Track Detection in SAR CCD Images

    Energy Technology Data Exchange (ETDEWEB)

    Chow, James G [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Quach, Tu-Thach [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-03-01

    Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images ta ken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are often too simple to capture natural track features such as continuity and parallelism. We present a simple convolutional network architecture consisting of a series of 3-by-3 convolutions to detect tracks. The network is trained end-to-end to learn natural track features entirely from data. The network is computationally efficient and improves the F-score on a standard dataset to 0.988, up fr om 0.907 obtained by the current state-of-the-art method.

  12. Method of airborne SAR image match integrating multi-information for block adjustment

    Science.gov (United States)

    Yang, S. C.; Huang, G. M.; Zhao, Z.; Lu, L. J.

    2015-06-01

    For the automation of SAR image Block Adjustment, this paper proposed a method of SAR image matching integrating multiinformation. It takes full advantage of SAR image geometric information, feature information, gray-related information and external auxiliary terrain information for SAR image matching. And then Image Tie Points (ITPs) of Block Adjustment can be achieved automatically. The main parts of extracting ITPs automatically include: First, SAR images were rectified geometrically based on the geometric information and external auxiliary terrain information (existed DEM) before match. Second, ground grid points with a certain interval can be get in the block area and approximate ITPs were acquired based on external auxiliary terrain information. Then match reference point was extracted for homologous image blocks with Harris feature detection operator and ITPs were obtained with pyramid matching based on gray-related information. At last, ITPs were transferred from rectified images to original SAR images and used in block adjustment. In the experiment, X band airborne SAR images acquired by Chinese airborne SAR system - CASMSAR system were used to make up the block. The result had showed that the method is effective for block adjustment of SAR data.

  13. A novel method for multi-angle SAR image matching

    Institute of Scientific and Technical Information of China (English)

    Li Dapeng

    2015-01-01

    Multi-angle synthetic aperture radar (SAR) image matching is very challenging, because the same object may cause different backscattering patterns, heavily depending on the radar incident angle. A technique based on the relations between the invariant positions of ground targets among the reference and sensed images is proposed to accommodate the nonmatching patterns. It involves a target extraction using wavelet coefficient fusion, as well as a geometric voting matching routine for searching the matched control points (CPs) in the reference and sensed images, respec-tively. To accelerate the speed of the search, a robust, rapidly corresponding CPs determination strategy is exploited by utilizing the global spatial transformation model, as well as a procedure of outlier removal to ensure the desired accuracy. Meanwhile, the positions of the matched point pairs are relocated using mutual information. The final warping of the images according to the CPs is performed by using a polynomial function. The results show the possibility of matching multi-angle SAR images in general cases.

  14. A high-resolution, four-band SAR testbed with real-time image formation

    Energy Technology Data Exchange (ETDEWEB)

    Walker, B.; Sander, G.; Thompson, M.; Burns, B.; Fellerhoff, R.; Dubbert, D.

    1996-03-01

    This paper describes the Twin-Otter SAR Testbed developed at Sandia National Laboratories. This SAR is a flexible, adaptable testbed capable of operation on four frequency bands: Ka, Ku, X, and VHF/UHF bands. The SAR features real-time image formation at fine resolution in spotlight and stripmap modes. High-quality images are formed in real time using the overlapped subaperture (OSA) image-formation and phase gradient autofocus (PGA) algorithms.

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

    Science.gov (United States)

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

    2014-01-01

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

  16. 3D image registration using a fast noniterative algorithm.

    Science.gov (United States)

    Zhilkin, P; Alexander, M E

    2000-11-01

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

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

    Science.gov (United States)

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

    2012-01-01

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

  18. Automatic Image Registration Algorithm Based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    LIU Qiong; NI Guo-qiang

    2006-01-01

    An automatic image registration approach based on wavelet transform is proposed. This proposed method utilizes multiscale wavelet transform to extract feature points. A coarse-to-fine feature matching method is utilized in the feature matching phase. A two-way matching method based on cross-correlation to get candidate point pairs and a fine matching based on support strength combine to form the matching algorithm. At last, based on an affine transformation model, the parameters are iteratively refined by using the least-squares estimation approach. Experimental results have verified that the proposed algorithm can realize automatic registration of various kinds of images rapidly and effectively.

  19. Coercive Region-level Registration for Multi-modal Images

    CERN Document Server

    Chen, Yu-Hui; Newstadt, Gregory; Simmons, Jeffrey; hero, Alfred

    2015-01-01

    We propose a coercive approach to simultaneously register and segment multi-modal images which share similar spatial structure. Registration is done at the region level to facilitate data fusion while avoiding the need for interpolation. The algorithm performs alternating minimization of an objective function informed by statistical models for pixel values in different modalities. Hypothesis tests are developed to determine whether to refine segmentations by splitting regions. We demonstrate that our approach has significantly better performance than the state-of-the-art registration and segmentation methods on microscopy images.

  20. Entropy-Based Block Processing for Satellite Image Registration

    Directory of Open Access Journals (Sweden)

    Ikhyun Lee

    2012-11-01

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

  1. Color fusion of SAR and FLIR images using a natural color transfer technique

    Institute of Scientific and Technical Information of China (English)

    Shaoyuan Sun; Zhongliang Jing; Zhenhua Li; Gang Liu

    2005-01-01

    Fusion of synthetic aperture radar (SAR) and forward looking infrared (FLIR) images is an important subject for aerospace and sensor surveillance. This paper presents a scheme to achieve a natural color image based on the contours feature of SAR and the target region feature of FLIR so that the overall scene recognition and situational awareness can be improved. The SAR and FLIR images are first decomposed into steerable pyramids, and the contour maps in the SAR image and the region maps in the FLIR image are calculated. The contour and region features are fused at each level of the steerable pyramids. A color image is then formed by transferring daytime color to the monochromic image by using the natural color transfer technique. Experimental results show that the proposed method is effective in providing a color fusion of SAR and FLIR images.

  2. Agile multi-scale decompositions for automatic image registration

    Science.gov (United States)

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

    2016-05-01

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

  3. Azimuth resolution improvement for spaceborne SAR images with quasi-non-overlapped Doppler bandwidth

    Institute of Scientific and Technical Information of China (English)

    Zheng Bao

    2014-01-01

    The azimuth resolution improvement problem is solved via a coherent combination of synthetic aperture radar (SAR) ima-ges with the quasi-non-overlapped Doppler bandwidth. Prior to the spectra combination, SAR images should be co-registered, while phase biases induced by topography, atmospheric propagation de-lays and baseline measurement errors should be calibrated. How-ever, the coregistration accuracy suffers from large Doppler decorrelation caused by the quasi-non-overlapped Doppler band-width. Furthermore, the method used to estimate phase biases from interferogram of azimuth pre-filtered SAR image pairs wil fail when there is no overlapped spectrum. The fringe simulation and maximum sharpness optimization are adopted to deal with the problems. Accordingly, a novel algorithm to coherently synthesize SAR images is presented. The experiment with the Terra SAR X-band (TerraSAR-X) satel ite data validates the performance of the presented method.

  4. SAR and Oblique Aerial Optical Image Fusion for Urban Area Image Segmentation

    Science.gov (United States)

    Fagir, J.; Schubert, A.; Frioud, M.; Henke, D.

    2017-05-01

    The fusion of synthetic aperture radar (SAR) and optical data is a dynamic research area, but image segmentation is rarely treated. While a few studies use low-resolution nadir-view optical images, we approached the segmentation of SAR and optical images acquired from the same airborne platform - leading to an oblique view with high resolution and thus increased complexity. To overcome the geometric differences, we generated a digital surface model (DSM) from adjacent optical images and used it to project both the DSM and SAR data into the optical camera frame, followed by segmentation with each channel. The fused segmentation algorithm was found to out-perform the single-channel version.

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

    Science.gov (United States)

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

    2011-12-01

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

  6. 2D/3D Image Registration using Regression Learning.

    Science.gov (United States)

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

    2013-09-01

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

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

    NARCIS (Netherlands)

    D.P. Shamonin (Denis); E.E. Bron (Esther); B.P.F. Lelieveldt (Boudewijn); M. Smits (Marion); S.K. Klein (Stefan); M. Staring (Marius)

    2014-01-01

    textabstractNonrigid 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

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

    NARCIS (Netherlands)

    Shamonin, D.P.; Bron, E.E.; Lelieveldt, B.P.F.; Smits, M.; Klein, S.; Staring, M.

    2014-01-01

    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.

  9. Collocation for Diffeomorphic Deformations in Medical Image Registration

    DEFF Research Database (Denmark)

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

    2017-01-01

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

  10. Registration of multimodal brain images: some experimental results

    Science.gov (United States)

    Chen, Hua-mei; Varshney, Pramod K.

    2002-03-01

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

  11. Diffeomorphic image registration with automatic time-step adjustment

    DEFF Research Database (Denmark)

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

    2015-01-01

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

  12. GRAY TONE FILTERING ON ERS-SAR IMAGES APPLIED TO CHANGE DETECTION AND MAPPING

    Directory of Open Access Journals (Sweden)

    Gilles André

    2011-05-01

    Full Text Available In SAR images, the pixel values are tightly related to physical parameters of the soil such as topography, roughness and humidity, regardless to atmospheric conditions. Therefore, SAR images may be used to detect, and quantify changes in land cover, by comparison of time series SAR data. Classical change detection techniques from SAR images are based on additive synthesis of RGB colors and arithmetic operations between images. The noisy aspect of ERS image due to the original speckle is an obstacle for available mapping and quantification of the changes. Here, statistical and morphological filters are used to reduce the speckle noise. Combined techniques of change detection and noise filtering are applied here to assess and map from ERS-SAR images the impact of regular or catastrophic flood and deforestation in the East Coast of Madagascar.

  13. Estimating the Video Registration Using Image Motions

    Directory of Open Access Journals (Sweden)

    N.Kannaiya Raja

    2012-07-01

    Full Text Available In this research, we consider the problems of registering multiple video sequences dynamic scenes which are not limited non rigid objects such as fireworks, blasting, high speed car moving taken from different vantage points. In this paper we propose a simple algorithm we can create different frames on particular videos moving for matching such complex scenes. Our algorithm does not require the cameras to be synchronized, and is not based on frame-by-frame or volume-by-volume registration. Instead, we model each video as the output of a linear dynamical system and transform the task of registering the video sequences to that of registering the parameters of the corresponding dynamical models. In this paper we use of a joint frame together to form distinct frame concurrently. The joint identification and the Jordan canonical form are not only applicable to the case of registering video sequences, but also to the entire genre of algorithms based on the dynamic texture model. We have also shown that out of all the possible choices for the method of identification and canonical form, the JID using JCF performs the best.

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

    Directory of Open Access Journals (Sweden)

    WANG Ruiyan

    2016-01-01

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

  15. Illicit vessel identification in inland waters using SAR image

    Science.gov (United States)

    Zhang, Fengli; Wu, Bingfang; Zhang, Lei; Huang, Huiping; Tian, Yichen

    2006-10-01

    Synthetic Aperture Radar remote sensing has been effectively used in water compliance and enforcement, especially in ship detection, but it is still very difficult to classify or identify vessels in inland water only using existing SAR image. Nevertheless some experience knowledge can help, for example waterway channel is of great significance for water traffic management and illegal activity monitoring. It can be used for judging a vessel complying with traffic rules or not, and also can be used to indicate illicit fishing vessels which are usually far away from navigable waterway channel. For illicit vessel identification speed and efficiency are very important, so it will be significant if we can extract waterway channel directly from SAR images and use it to identify illicit vessels. The paper first introduces the modified two-parameter CFAR algorithm used to detect ship targets in inland waters, and then uses principal curves and neural networks to extract waterway channel. Through comparing the detection results and the extracted waterway channel those vessels not complying with water traffic rules or potential illicit fishing vessels can be easily identified.

  16. Recognizing articulated objects and object articulation in SAR images

    Science.gov (United States)

    Bhanu, Bir; Jones, Grinnell, III; Ahn, Joon S.

    1998-09-01

    The focus of this paper is recognizing articulated objects and the pose of the articulated parts in SAR images. Using SAR scattering center locations as features, the invariance with articulation (i.e. turret rotation for the T72, T80 and M1a tanks, missile erect vs. down for the SCUD launcher) is shown as a function of object azimuth. Similar data is shown for configuration differences in the MSTAR (Public) Targets. The UCR model-based recognition engine (which uses non- articulated models to recognize articulated, occluded and non-standard configuration objects) is described and target identification performance results are given as confusion matrices and ROC curves for six inch and one foot resolution XPATCH images and the one foot resolution MSTAR data. Separate body and turret models are developed that are independent of the relative positions between the body and the turret. These models are used with a subsequent matching technique to refine the pose of the body and determine the pose of the turret. An expression of the probability that a random match will occur is derived and this function is used to set thresholds to minimize the probability of a random match for the recognition system. Results for identification, body pose and turret pose are presented as a function of percent occlusion for articulated XPATCH data and results are given for identification and body pose for articulated MSTAR data.

  17. Multimodality image registration and fusion using neural network

    Institute of Scientific and Technical Information of China (English)

    Mostafa G Mostafa; Aly A Farag; Edward Essock

    2003-01-01

    Multimodality image registration and fusion are essential steps in building 3-D models from remotesensing data. We present in this paper a neural network technique for the registration and fusion of multimodali-ty remote sensing data for the reconstruction of 3-D models of terrain regions. A FeedForward neural network isused to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data arepresented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusionresults.

  18. Flight path-driven mitigation of wavefront curvature effects in SAR images

    Science.gov (United States)

    Doerry, Armin W.

    2009-06-23

    A wavefront curvature effect associated with a complex image produced by a synthetic aperture radar (SAR) can be mitigated based on which of a plurality of possible flight paths is taken by the SAR when capturing the image. The mitigation can be performed differently for different ones of the flight paths.

  19. An accelerated image matching technique for UAV orthoimage registration

    Science.gov (United States)

    Tsai, Chung-Hsien; Lin, Yu-Ching

    2017-06-01

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

  20. Automatic Image Registration Using Free and Open Source Software

    OpenAIRE

    Giri Babu, D.; Raja Shekhar, S. S.; Chandrasekar, K.; M. V. R. Sesha Sai; P.G. Diwakar; Dadhwal, V. K.

    2014-01-01

    Image registration is the most critical operation in remote sensing applications to enable location based referencing and analysis of earth features. This is the first step for any process involving identification, time series analysis or change detection using a large set of imagery over a region. Most of the reliable procedures involve time consuming and laborious manual methods of finding the corresponding matching features of the input image with respect to reference. Also the ...

  1. Advanced methods for image registration applied to JET videos

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-10-15

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

  2. Video image stabilization and registration--plus

    Science.gov (United States)

    Hathaway, David H. (Inventor)

    2009-01-01

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

  3. Efficient DPCA SAR imaging with fast iterative spectrum reconstruction method

    Institute of Scientific and Technical Information of China (English)

    FANG Jian; ZENG JinShan; XU ZongBen; ZHAO Yao

    2012-01-01

    The displaced phase center antenna (DPCA) technique is an effective strategy to achieve wide-swath synthetic aperture radar (SAR) imaging with high azimuth resolution.However,traditionally,it requires strict limitation of the pulse repetition frequency (PRF) to avoid non-uniform sampling.Otherwise,any deviation could bring serious ambiguity if the data are directly processed using a matched filter.To break this limitation,a recently proposed spectrum reconstruction method is capable of recovering the true spectrum from the nonuniform samples. However,the performance is sensitive to the selection of the PRF.Sparse regularization based imaging may provide a way to overcome this sensitivity. The existing time-domain method,however,requires a large-scale observation matrix to be built,which brings a high computational cost.In this paper,we propose a frequency domain method,called the iterative spectrum reconstruction method,through integration of the sparse regularization technique with spectrum analysis of the DPCA signal.By approximately expressing the observation in the frequency domain,which is realized via a series of decoupled linear operations,the method performs SAR imaging which is then not directly based on the observation matrix,which reduces the computational cost from O(N2) to O(NlogN) (where N is the number of range cells),and is therefore more efficient than the time domain method. The sparse regularization scheme,realized via a fast thresholding iteration,has been adopted in this method,which brings the robustness of the imaging process to the PRF selection.We provide a series of simulations and ground based experiments to demonstrate the high efficiency and robustness of the method.The simulations show that the new method is almost as fast as the traditional mono-channel algorithm,and works well almost independently of the PRF selection.Consequently,the suggested method can be accepted as a practical and efficient wide-swath SAR imaging technique.

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

    Science.gov (United States)

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

    2010-05-01

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

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

    Science.gov (United States)

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

    2008-12-01

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

  6. Improving JWST Coronagraphic Performance with Accurate Image Registration

    Science.gov (United States)

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

    2016-06-01

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

  7. Verification and Validation of a Fingerprint Image Registration Software

    Directory of Open Access Journals (Sweden)

    Liu Yan

    2006-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  9. Uncertainty driven probabilistic voxel selection for image registration.

    Science.gov (United States)

    Oreshkin, Boris N; Arbel, Tal

    2013-10-01

    This paper presents a novel probabilistic voxel selection strategy for medical image registration in time-sensitive contexts, where the goal is aggressive voxel sampling (e.g., using less than 1% of the total number) while maintaining registration accuracy and low failure rate. We develop a Bayesian framework whereby, first, a voxel sampling probability field (VSPF) is built based on the uncertainty on the transformation parameters. We then describe a practical, multi-scale registration algorithm, where, at each optimization iteration, different voxel subsets are sampled based on the VSPF. The approach maximizes accuracy without committing to a particular fixed subset of voxels. The probabilistic sampling scheme developed is shown to manage the tradeoff between the robustness of traditional random voxel selection (by permitting more exploration) and the accuracy of fixed voxel selection (by permitting a greater proportion of informative voxels).

  10. A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution

    Directory of Open Access Journals (Sweden)

    Huanxin Zou

    2016-07-01

    Full Text Available The simple linear iterative clustering (SLIC method is a recently proposed popular superpixel algorithm. However, this method may generate bad superpixels for synthetic aperture radar (SAR images due to effects of speckle and the large dynamic range of pixel intensity. In this paper, an improved SLIC algorithm for SAR images is proposed. This algorithm exploits the likelihood information of SAR image pixel clusters. Specifically, a local clustering scheme combining intensity similarity with spatial proximity is proposed. Additionally, for post-processing, a local edge-evolving scheme that combines spatial context and likelihood information is introduced as an alternative to the connected components algorithm. To estimate the likelihood information of SAR image clusters, we incorporated a generalized gamma distribution (GГD. Finally, the superiority of the proposed algorithm was validated using both simulated and real-world SAR images.

  11. SIMULATION OF SHIP GENERATED TURBULENT AND VORTICAL WAKE IMAGING BY SAR

    Institute of Scientific and Technical Information of China (English)

    Wang Aiming; Zhu Minhui

    2004-01-01

    Synthetic Aperture Radar (SAR) imaging of ocean surface features is studied. The simulation of the turbulent and vortical features generated by a moving ship and SAR imaging of these wakes is carried out. The turbulent wake damping the ocean surface capillary waves may be partially responsible for the suppression of surface waves near the ship track. The vortex pair generating a change in the lateral flow field behind the ship may be partially responsible for an enhancement of the waves near the edges of the smooth area. These hydrodynamic phenomena as well as the changes of radar backscatter generated by turbulence and vortex are simulated.An SAR imaging model is then used on such ocean surface features to provide SAR images.Comparison of two ships' simulated SAR images shows that the wake features are different for various ship parameters.

  12. SEGMENTATION OF POLARIMETRIC SAR IMAGES USIG WAVELET TRANSFORMATION AND TEXTURE FEATURES

    Directory of Open Access Journals (Sweden)

    A. Rezaeian

    2015-12-01

    Full Text Available Polarimetric Synthetic Aperture Radar (PolSAR sensors can collect useful observations from earth’s surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed method is based on the multi-resolution analysis of texture features is based on wavelet transformation. Here, we use the information of gray level value and the information of texture. First, we produce coherency or covariance matrices and then generate span image from them. In the next step of proposed method is texture feature extraction from sub-bands is generated from discrete wavelet transform (DWT. Finally, PolSAR image are segmented using clustering methods as fuzzy c-means (FCM and k-means clustering. We have applied the proposed methodology to full polarimetric SAR images acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR L-band system, during July, in 2012 over an agricultural area in Winnipeg, Canada.

  13. FULLY AUTOMATIC IMAGE-BASED REGISTRATION OF UNORGANIZED TLS DATA

    Directory of Open Access Journals (Sweden)

    M. Weinmann

    2012-09-01

    Full Text Available The estimation of the transformation parameters between different point clouds is still a crucial task as it is usually followed by scene reconstruction, object detection or object recognition. Therefore, the estimates should be as accurate as possible. Recent developments show that it is feasible to utilize both the measured range information and the reflectance information sampled as image, as 2D imagery provides additional information. In this paper, an image-based registration approach for TLS data is presented which consists of two major steps. In the first step, the order of the scans is calculated by checking the similarity of the respective reflectance images via the total number of SIFT correspondences between them. Subsequently, in the second step, for each SIFT correspondence the respective SIFT features are filtered with respect to their reliability concerning the range information and projected to 3D space. Combining the 3D points with 2D observations on a virtual plane yields 3D-to-2D correspondences from which the coarse transformation parameters can be estimated via a RANSAC-based registration scheme including the EPnP algorithm. After this coarse registration, the 3D points are again checked for consistency by using constraints based on the 3D distance, and, finally, the remaining 3D points are used for an ICP-based fine registration. Thus, the proposed methodology provides a fast, reliable, accurate and fully automatic image-based approach for the registration of unorganized point clouds without the need of a priori information about the order of the scans, the presence of regular surfaces or human interaction.

  14. Image quality specification and maintenance for airborne SAR

    Science.gov (United States)

    Clinard, Mark S.

    2004-08-01

    Specification, verification, and maintenance of image quality over the lifecycle of an operational airborne SAR begin with the specification for the system itself. Verification of image quality-oriented specification compliance can be enhanced by including a specification requirement that a vendor provide appropriate imagery at the various phases of the system life cycle. The nature and content of the imagery appropriate for each stage of the process depends on the nature of the test, the economics of collection, and the availability of techniques to extract the desired information from the data. At the earliest lifecycle stages, Concept and Technology Development (CTD) and System Development and Demonstration (SDD), the test set could include simulated imagery to demonstrate the mathematical and engineering concepts being implemented thus allowing demonstration of compliance, in part, through simulation. For Initial Operational Test and Evaluation (IOT&E), imagery collected from precisely instrumented test ranges and targets of opportunity consisting of a priori or a posteriori ground-truthed cultural and natural features are of value to the analysis of product quality compliance. Regular monitoring of image quality is possible using operational imagery and automated metrics; more precise measurements can be performed with imagery of instrumented scenes, when available. A survey of image quality measurement techniques is presented along with a discussion of the challenges of managing an airborne SAR program with the scarce resources of time, money, and ground-truthed data. Recommendations are provided that should allow an improvement in the product quality specification and maintenance process with a minimal increase in resource demands on the customer, the vendor, the operational personnel, and the asset itself.

  15. Improving image registration by correspondence interpolation

    DEFF Research Database (Denmark)

    Ólafsdóttir, Hildur; Pedersen, Henrik; Hansen, Michael Sass;

    2011-01-01

    ) quantitatively by registering downsampled brain data using two different interpolators and subsequently applying the deformation fields to the original data. The results show that the interpolator provides better gradient images and a more sharp cardiac atlas. Moreover, it provides better deformation fields...

  16. Registration of Large Motion Blurred Images

    Science.gov (United States)

    2016-05-09

    in which the horizontal rows of the sensor array are scanned at different times. This behaviour results in additional deformations when capturing...public release: distribution unlimited. [11] W.-H. Cho, D.-W. Kim, and K.-S. Hong, “Cmos digital image stabilization,” Consumer Electronics, IEEE

  17. Elastic registration of multiphase CT images of liver

    Science.gov (United States)

    Heldmann, Stefan; Zidowitz, Stephan

    2009-02-01

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

  18. Polarimetric SAR Image Supervised Classification Method Integrating Eigenvalues

    Directory of Open Access Journals (Sweden)

    Xing Yanxiao

    2016-04-01

    Full Text Available Since classification methods based on H/α space have the drawback of yielding poor classification results for terrains with similar scattering features, in this study, we propose a polarimetric Synthetic Aperture Radar (SAR image classification method based on eigenvalues. First, we extract eigenvalues and fit their distribution with an adaptive Gaussian mixture model. Then, using the naive Bayesian classifier, we obtain preliminary classification results. The distribution of eigenvalues in two kinds of terrains may be similar, leading to incorrect classification in the preliminary step. So, we calculate the similarity of every terrain pair, and add them to the similarity table if their similarity is greater than a given threshold. We then apply the Wishart distance-based KNN classifier to these similar pairs to obtain further classification results. We used the proposed method on both airborne and spaceborne SAR datasets, and the results show that our method can overcome the shortcoming of the H/α-based unsupervised classification method for eigenvalues usage, and produces comparable results with the Support Vector Machine (SVM-based classification method.

  19. MIMO-OFDM signal optimization for SAR imaging radar

    Science.gov (United States)

    Baudais, J.-Y.; Méric, S.; Riché, V.; Pottier, É.

    2016-12-01

    This paper investigates the optimization of the coded orthogonal frequency division multiplexing (OFDM) transmitted signal in a synthetic aperture radar (SAR) context. We propose to design OFDM signals to achieve range ambiguity mitigation. Indeed, range ambiguities are well known to be a limitation for SAR systems which operates with pulsed transmitted signal. The ambiguous reflected signal corresponding to one pulse is then detected when the radar has already transmitted the next pulse. In this paper, we demonstrate that the range ambiguity mitigation is possible by using orthogonal transmitted wave as OFDM pulses. The coded OFDM signal is optimized through genetic optimization procedures based on radar image quality parameters. Moreover, we propose to design a multiple-input multiple-output (MIMO) configuration to enhance the noise robustness of a radar system and this configuration is mainly efficient in the case of using orthogonal waves as OFDM pulses. The results we obtain show that OFDM signals outperform conventional radar chirps for range ambiguity suppression and for robustness enhancement in 2 ×2 MIMO configuration.

  20. Information compression and speckle reduction for multifrequency polarimetric SAR images based on kernel PCA

    Institute of Scientific and Technical Information of China (English)

    Li Ying; Lei Xiaogang; Bai Bendu; Zhang Yanning

    2008-01-01

    Multifrequency polarimetric SAR imagery provides a very convenient approach for signal processing and acquisition of radar image. However, the amount of information is scattered in several images, and redundancies exist between different bands and polarizations. Similar to signal-polarimetric SAR image, multifrequency polarimetric SAR image is corrupted with speckle noise at the same time. A method of information compression and speckle reduction for multifrequency polarimetric SAR imagery is presented based on kernel principal component analysis (KPCA). KPCA is a nonlinear generalization of the linear principal component analysis using the kernel trick. The NASA/JPL polarimetric SAR imagery of P, L, and C bands quadpolarizations is used for illustration. The experimental results show that KPCA has better capability in information compression and speckle reduction as compared with linear PCA.

  1. Comparison of Simultaneous and Sequential Two-View Registration for 3D/2D Registration of Vascular Images

    OpenAIRE

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

    2005-01-01

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

  2. THE FAST FIXED-POINT ALGORITHM FOR SPECKLE REDUCTION OF POLARIMETRIC SAR IMAGE

    Institute of Scientific and Technical Information of China (English)

    Fu Yusheng; Chen Xiaoning; Pi Yiming; Hou Yinming

    2005-01-01

    In this letter, a simple and efficient method of image speckle reduction for polarimetric SAR is put forward. It is based on the fast fixed-point ICA (Independent Component Analysis) algorithm of orthogonal and symmetric matrix. Simulation experiment is carried out to separate speckle noise from the polarimetric SAR images, and it indicates that this algorithm has high convergency speed and stability, the image speckle noise is reduced effectively and the speckle index is low, and the image quality is improved obviously.

  3. COMPARISON OF FILTERS DEDICATED TO SPECKLE SUPPRESSION IN SAR IMAGES

    Directory of Open Access Journals (Sweden)

    P. Kupidura

    2016-06-01

    Full Text Available This paper presents the results of research on the effectiveness of different filtering methods dedicated to speckle suppression in SAR images. The tests were performed on RadarSat-2 images and on an artificial image treated with simulated speckle noise. The research analysed the performance of particular filters related to the effectiveness of speckle suppression and to the ability to preserve image details and edges. Speckle is a phenomenon inherent to radar images – a deterministic noise connected with land cover type, but also causing significant changes in digital numbers of pixels. As a result, it may affect interpretation, classification and other processes concerning radar images. Speckle, resembling “salt and pepper” noise, has the form of a set of relatively small groups of pixels of values markedly different from values of other pixels representing the same type of land cover. Suppression of this noise may also cause suppression of small image details, therefore the ability to preserve the important parts of an image, was analysed as well. In the present study, selected filters were tested, and methods dedicated particularly to speckle noise suppression: Frost, Gamma-MAP, Lee, Lee-Sigma, Local Region, general filtering methods which might be effective in this respect: Mean, Median, in addition to morphological filters (alternate sequential filters with multiple structuring element and by reconstruction. The analysis presented in this paper compared the effectiveness of different filtering methods. It proved that some of the dedicated radar filters are efficient tools for speckle suppression, but also demonstrated a significant efficiency of the morphological approach, especially its ability to preserve image details.

  4. Image registration reveals central lens thickness minimally increases during accommodation

    Directory of Open Access Journals (Sweden)

    Schachar RA

    2017-09-01

    Full Text Available Ronald A Schachar,1 Majid Mani,2 Ira H Schachar31Department of Physics, University of Texas at Arlington, Arlington, TX, 2California Retina Associates, El Centro, 3Byers Eye Institute of Stanford University, Palo Alto, CA, USAPurpose: To evaluate anterior chamber depth, central crystalline lens thickness and lens curvature during accommodation.Setting: California Retina Associates, El Centro, CA, USA.Design: Healthy volunteer, prospective, clinical research swept-source optical coherence biometric image registration study of accommodation.Methods: Ten subjects (4 females and 6 males with an average age of 22.5 years (range: 20–26 years participated in the study. A 45° beam splitter attached to a Zeiss IOLMaster 700 (Carl Zeiss Meditec Inc., Jena, Germany biometer enabled simultaneous imaging of the cornea, anterior chamber, entire central crystalline lens and fovea in the dilated right eyes of subjects before, and during focus on a target 11 cm from the cornea. Images with superimposable foveal images, obtained before and during accommodation, that met all of the predetermined alignment criteria were selected for comparison. This registration requirement assured that changes in anterior chamber depth and central lens thickness could be accurately and reliably measured. The lens radii of curvatures were measured with a pixel stick circle.Results: Images from only 3 of 10 subjects met the predetermined criteria for registration. Mean anterior chamber depth decreased, −67 µm (range: −0.40 to −110 µm, and mean central lens thickness increased, 117 µm (range: 100–130 µm. The lens surfaces steepened, anterior greater than posterior, while the lens, itself, did not move or shift its position as appeared from the lack of movement of the lens nucleus, during 7.8 diopters of accommodation, (range: 6.6–9.7 diopters.Conclusion: Image registration, with stable invariant references for image correspondence, reveals that during accommodation a

  5. Feature Fusion Based Road Extraction for HJ-1-C SAR Image

    OpenAIRE

    Lu Ping-ping; Du Kang-ning; Yu Wei-dong; Wang Yu; Deng Yun-kai

    2014-01-01

    Road network extraction in SAR images is one of the key tasks of military and civilian technologies. To solve the issues of road extraction of HJ-1-C SAR images, a road extraction algorithm is proposed based on the integration of ratio and directional information. Due to the characteristic narrow dynamic range and low signal to noise ratio of HJ-1-C SAR images, a nonlinear quantization and an image filtering method based on a multi-scale autoregressive model are proposed here. A road extracti...

  6. PERFORMANCE EVALUATION OF SEVERAL FUSION APPROACHES FOR CCD/SAR IMAGES

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    Several image fusion approaches for CCD/SAR images are studied and the performance evaluation of these fusion approaches is completed in this paper. Firstly, the preprocessing of CCD/SAR images before fusion is fulfilled. Then, the image fusion methods including linear superposition, nonlinear operator method and multiresolution methods, of which the multiresolution methods include Laplacian pyramid, ratio pyramid, contrast pyramid, gradient pyramid, morphological pyramid and discrete wavelet transform, are adopted to fuse two types of images. Lastly, the four performance measures, standard deviation, entropy, cross entropy and spatial frequency, are calculated to compare the fusion results by different fusion approaches in this paper. Experimental results show that contrast pyramid, morphology pyramid and discrete wavelet transformation in multiresolution approaches are more suitable for CCD/SAR image fusion than other ones proposed in this paper and the objective performance evaluation of CCD/SAR image fusion approaches are effective.

  7. Decorrelating Clutter Statistics for Long Integration Time SAR Imaging

    Science.gov (United States)

    Leanza, Antonio; Monti Guarnieri, Andrea; Recchia, Andrea; Broquetas Ibars, Antoni; Ruiz Rodon, Josep

    2015-05-01

    It is presented an experiment aimed to assess and eventually complement the Billingsley Internal Clutter Motion (ICM) model for long integration time SAR imaging. Exploiting a real-aperture rotating antenna Ground-Based RADAR, observations of rural areas in different periods of the year have been performed. The collected data, obtained from two different acquisition modes, have been processed to obtain short-term and long-term clutter de-correlation analysis. The results obtained revealed interesting aspects of the phenomenon. In particular, it can be observed that the process is non-stationary with time, say minutes to hours, and that DC/AC ratio follows a day/night characteristic. Moreover, the results showed values of the AC component decay rate β higher than the foreseen ones in the considered spectral interval, which is quite below the one analyzed in the Billingsley experiment.

  8. Fast Detection of Oil Spills and Ships Using SAR Images

    Directory of Open Access Journals (Sweden)

    Alberto Lupidi

    2017-03-01

    Full Text Available In this paper, we show the capabilities of a new maritime control system based on the processing of COSMO-SkyMed Synthetic Aperture Radar (SAR images. This system aims at fast detection of ships that may be responsible for illegal oil dumping. In particular, a novel detection algorithm based on the joint use of the significance parameter, wavelet correlator and a two-dimensional Constant False Alarm Rate (2D-CFAR is designed. Results show the effectiveness of such algorithms, which can be used by the maritime authorities to have a faster although still reliable response. The proposed algorithm, together with the short revisit time of the COSMO-SkyMed constellation, can help with tracking the scenario evolution from one acquisition to the next.

  9. Batch settling curve registration via image data modeling.

    Science.gov (United States)

    Derlon, Nicolas; Thürlimann, Christian; Dürrenmatt, David; Villez, Kris

    2017-05-01

    To this day, obtaining reliable characterization of sludge settling properties remains a challenging and time-consuming task. Without such assessments however, optimal design and operation of secondary settling tanks is challenging and conservative approaches will remain necessary. With this study, we show that automated sludge blanket height registration and zone settling velocity estimation is possible thanks to analysis of images taken during batch settling experiments. The experimental setup is particularly interesting for practical applications as it consists of off-the-shelf components only, no moving parts are required, and the software is released publicly. Furthermore, the proposed multivariate shape constrained spline model for image analysis appears to be a promising method for reliable sludge blanket height profile registration. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Improving multispectral satellite image compression using onboard subpixel registration

    Science.gov (United States)

    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.

  11. Land cover detection with SAR images of Delta del Llobregat

    Science.gov (United States)

    Godinho, R.; Borges, P. A. V.; Calado, H.; Broquetas, A.

    2016-08-01

    This work presents a study of a multitemporal set of C-band images collected by ERS-2, aiming to understand the differentiations of the backscatter intensity and the phase coherence of different land covers to find possible synergies that could improve land cover detection. The land cover analysis allowed to observe the perfect differentiation of urban areas from intensity images. The observation of multitemporal RGB compositions combining key dates of the different points of crops growth make possible to differentiate this land cover and also to observe fluctuations inside the class itself. This fluctuations present a pattern that correspond to the crop field structure, which suggests that more information can be obtained. The shrubs are difficult to detect from the intensity images, but once the observation is combined with coherence images the detection is possible. However, the coherence image must be generated from pairs of images with a temporal interval lower than three months, independently from the year of registration of each image due to the general decrease of coherence when larger intervals are used. The analysis allowed to observe the potential of this data to perfect distinguish urban, crops and shrubs. The study of the seasonal fluctuations of intensity for the crops land cover with precise ground truth for crops type and points of growth is proposed as a future line of research.

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

    Science.gov (United States)

    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.

  13. A hybrid genetic algorithm for multi-modal image registration

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    This paper describes a new method for three-dimensional medical image registration. In the interactive image-guided HIFU (High Intensity Focused Ultrasound) therapy system, a fast and precise localization of the tumor is very important. An automatic system is developed for registering pre-operative MR images with intra-operative ultrasound images based on the vessels visible in both of the modalities. When the MR and the ultrasound images are aligned, the centerline points of the vessels in the MR image will align with bright intensities in the ultrasound image. The method applies an optimization strategy combining the genetic algorithm with the conjugated gradients algorithm to minimize the objective function. It provides a feasible way of determining the global solution and makes the method robust to local maximum and insensitive to initial position. Two experiments were designed to evaluate the method, and the results show that our method has better registration accuracy and convergence rate than the other two classic algorithms.

  14. Monitoring river morphological changes using high resolution multitemporal sar images: a case study on orco river, italy

    Science.gov (United States)

    Mitidieri, Francesco; Nicolina Papa, Maria; Ruello, Giuseppe; Amitrano, Donato; Bizzi, Simone; Demarchi, Luca

    2016-04-01

    Improving the knowledge about river processes by applying innovative monitoring techniques is extremely needed to face the challenge of a better river management. In this paper we test the capability of satellite synthetic aperture radar (SAR) images to enrich the monitoring of river geomorphological processes. Multitemporal SAR images provide observations and measurements at high spatial (3 m), and in particular temporal resolution (15 days). This information if properly processed and classified may significantly enrich our ability to monitor the evolution of river morphological phenomena (erosion/deposition, narrowing/widening, riparian vegetation's evolution and interferences with river flow). This is expected to lead to an enhancements in the river management capabilities, in particular as regards the assessment of hydro-morphological river quality, as strongly suggested by European Commission's Water Framework Directive (2000/60/EC). A case study on the Italian River Orco is here presented. The case study has used a set of 100 COSMO-SkyMed stripmap images (from October 2008 to November 2014) from Italian Space Agency. All the data were acquired with medium look angle (almost 30°) and HH polarization, also for increasing the land-water contrast. Calibration, registration and despeckling procedures were applied on the acquired dataset. In particular, the optimal weighting multitemporal De Grandi filter was adopted in order to allow an effective extraction of the water surfaces contour. This method was applied to extract water contours over the entire historical series of SAR datasets available. Thanks to the generated information we were able to monitor the lateral dynamic of the water channels and infer on the evolutions of erosion/deposition phenomena. To this aim, an RGB representation of multitemporal SAR data was implemented. The series of detected river channel morphological changes was then analyzed in the light of the series of discharge measurements in

  15. Registration and recognition in images and videos

    CERN Document Server

    Battiato, Sebastiano; Farinella, Giovanni

    2014-01-01

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

  16. Hierarchical segmentation-assisted multimodal registration for MR brain images.

    Science.gov (United States)

    Lu, Huanxiang; Beisteiner, Roland; Nolte, Lutz-Peter; Reyes, Mauricio

    2013-04-01

    Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.

  17. SAR Imaging Technology Makes Major Advances in China over the Past 25 Years

    Institute of Scientific and Technical Information of China (English)

    2004-01-01

    @@ On September 17, 1979, scientists from the CAS Institute of Electronics succeeded in obtaining their first microwave remote sensing images from a prototype airborne synthetic aperture radar (SAR) system. Over the past 25 years, Chinese scientists have won many R&D results and made remarkable progress in developing SAR and its ground receiving systems.

  18. An imaging algorithm based on keystone transform for one-stationary bistatic SAR of spotlight mode

    Science.gov (United States)

    Qiu, Xiaolan; Behner, Florian; Reuter, Simon; Nies, Holger; Loffeld, Otmar; Huang, Lijia; Hu, Donghui; Ding, Chibiao

    2012-12-01

    This article proposes an imaging algorithm based on Keystone Transform for bistatic SAR with a stationary receiver. It can efficiently be applied to high-resolution spotlight mode, and can directly be process the bistatic SAR data which have been ranged compressed by the synchronization reference pulses. Both simulation and experimental results validate the good performance of this algorithm.

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

    Directory of Open Access Journals (Sweden)

    Jingya Zhang

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

  20. Detectability Analysis of Road Vehicles in Radarsat-2 Fully Polarimetric SAR Images for Traffic Monitoring

    Directory of Open Access Journals (Sweden)

    Bo Zhang

    2017-02-01

    Full Text Available By acquiring information over a wide area regardless of weather conditions and solar illumination, space-borne Synthetic Aperture Radar (SAR has the potential to be a promising application for traffic monitoring. However, the backscatter character of a vehicle in a SAR image is unstable and varies with image parameters, such as aspect and incidence angle. To investigate vehicle detectability in SAR images for traffic monitoring applications, images of four common types of vehicles in China were acquired using the fully polarimetric (FP SAR of Radarsat-2 in our experiments. Methods for measuring a vehicle’s aspect angle and backscatter intensity are introduced. The experimental FP SAR images are used to analyze the detectability, which is affected by factors such as vehicle size, vehicle shape, and aspect angle. Moreover, a new metric to improve vehicle detectability in FP SAR images is proposed and compared with the well-known intensity metric. The experimental results show that shape is a crucial factor in affecting the backscatter intensity of vehicles, which also oscillates with varying aspect angle. If the size of a vehicle is smaller than the SAR image resolution, using the intensity metric would result in low detectability. However, it could be improved in an FP SAR image by using the proposed metric. Compared with the intensity metric, the overall detectability is improved from 72% to 90% in our experiments. Therefore, this study indicates that FP SAR images have the ability to detect stationary vehicles on the road and are meaningful for traffic monitoring.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2017-01-01

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

  3. Evaluation of whole-body MR to CT deformable image registration

    NARCIS (Netherlands)

    Akbarzadeh, A.; Gutierrez, D.; Baskin, A.; Ay, M. R.; Ahmadian, A.; Alam, N. Riahi; Loevblad, K. O.; Zaidi, H.

    2013-01-01

    Multimodality image registration plays a crucial role in various clinical and research applications. The aim of this study is to present an optimized MR to CT whole-body deformable image registration algorithm and its validation using clinical studies. A 3D intermodality registration technique based

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

    Science.gov (United States)

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

    2016-10-01

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

  5. A one-bit approach for image registration

    Science.gov (United States)

    Nguyen, An Hung; Pickering, Mark; Lambert, Andrew

    2015-02-01

    Motion estimation or optic flow computation for automatic navigation and obstacle avoidance programs running on Unmanned Aerial Vehicles (UAVs) is a challenging task. These challenges come from the requirements of real-time processing speed and small light-weight image processing hardware with very limited resources (especially memory space) embedded on the UAVs. Solutions towards both simplifying computation and saving hardware resources have recently received much interest. This paper presents an approach for image registration using binary images which addresses these two requirements. This approach uses translational information between two corresponding patches of binary images to estimate global motion. These low bit-resolution images require a very small amount of memory space to store them and allow simple logic operations such as XOR and AND to be used instead of more complex computations such as subtractions and multiplications.

  6. An improved SIFT algorithm based on KFDA in image registration

    Science.gov (United States)

    Chen, Peng; Yang, Lijuan; Huo, Jinfeng

    2016-03-01

    As a kind of stable feature matching algorithm, SIFT has been widely used in many fields. In order to further improve the robustness of the SIFT algorithm, an improved SIFT algorithm with Kernel Discriminant Analysis (KFDA-SIFT) is presented for image registration. The algorithm uses KFDA to SIFT descriptors for feature extraction matrix, and uses the new descriptors to conduct the feature matching, finally chooses RANSAC to deal with the matches for further purification. The experiments show that the presented algorithm is robust to image changes in scale, illumination, perspective, expression and tiny pose with higher matching accuracy.

  7. SAR Images Unsupervised Change Detection Based on Combination of Texture Feature Vector with Maximum Entropy Principle

    Directory of Open Access Journals (Sweden)

    ZHUANG Huifu

    2016-03-01

    Full Text Available Generally, spatial-contextual information would be used in change detection because there is significant speckle noise in synthetic aperture radar(SAR images. In this paper, using the rich texture information of SAR images, an unsupervised change detection approach to high-resolution SAR images based on texture feature vector and maximum entropy principle is proposed. The difference image is generated by using the 32-dimensional texture feature vector of gray-level co-occurrence matrix(GLCM. And the automatic threshold is obtained by maximum entropy principle. In this method, the appropriate window size to change detection is 11×11 according to the regression analysis of window size and precision index. The experimental results show that the proposed approach is better could both reduce the influence of speckle noise and improve the detection accuracy of high-resolution SAR image effectively; and it is better than Markov random field.

  8. SAR imaging simulation for an inhomogeneous undulated lunar surface based on triangulated irregular network

    Institute of Scientific and Technical Information of China (English)

    FA WenZhe; XU Feng; JIN YaQiu

    2009-01-01

    Based on the statistics of the lunar cratered terrain, e.g., population, dimension and shape of craters, the terrain feature of cratered lunar surface is numerically generated. According to the Inhomogeneous distribution of the lunar surface slope, the triangulated irregular network (TIN) is employed to make the digital elevation of lunar surface model. The Kirchhoff approximation of surface scattering is then applied to simulation of lunar surface scattering. The synthetic aperture radar (SAR) image for compre-hensive cratered lunar surface is numerically generated using back projection (BP) algorithm of SAR Imaging. Making use of the digital elevation and Clementlne UVVIS data at Apollo 15 landing site as the ground truth, an SAR Image at Apollo 15 landing site Is simulated. The image simulation is verified using real SAR image and echoes statistics.

  9. Multimodality imaging combination in small animal via point-based registration

    Science.gov (United States)

    Yang, C. C.; Wu, T. H.; Lin, M. H.; Huang, Y. H.; Guo, W. Y.; Chen, C. L.; Wang, T. C.; Yin, W. H.; Lee, J. S.

    2006-12-01

    We present a system of image co-registration in small animal study. Marker-based registration is chosen because of its considerable advantage that the fiducial feature is independent of imaging modality. We also experimented with different scanning protocols and different fiducial marker sizes to improve registration accuracy. Co-registration was conducted using rat phantom fixed by stereotactic frame. Overall, the co-registration accuracy was in sub-millimeter level and close to intrinsic system error. Therefore, we conclude that the system is an accurate co-registration method to be used in small animal studies.

  10. On-line range images registration with GPGPU

    Science.gov (United States)

    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.

  11. Robust image registration using adaptive coherent point drift method

    Science.gov (United States)

    Yang, Lijuan; Tian, Zheng; Zhao, Wei; Wen, Jinhuan; Yan, Weidong

    2016-04-01

    Coherent point drift (CPD) method is a powerful registration tool under the framework of the Gaussian mixture model (GMM). However, the global spatial structure of point sets is considered only without other forms of additional attribute information. The equivalent simplification of mixing parameters and the manual setting of the weight parameter in GMM make the CPD method less robust to outlier and have less flexibility. An adaptive CPD method is proposed to automatically determine the mixing parameters by embedding the local attribute information of features into the construction of GMM. In addition, the weight parameter is treated as an unknown parameter and automatically determined in the expectation-maximization algorithm. In image registration applications, the block-divided salient image disk extraction method is designed to detect sparse salient image features and local self-similarity is used as attribute information to describe the local neighborhood structure of each feature. The experimental results on optical images and remote sensing images show that the proposed method can significantly improve the matching performance.

  12. Fluid Registration of Diffusion Tensor Images Using Information Theory

    Science.gov (United States)

    Chiang, Ming-Chang; Leow, Alex D.; Klunder, Andrea D.; Dutton, Rebecca A.; Barysheva, Marina; Rose, Stephen E.; McMahon, Katie L.; de Zubicaray, Greig I.; Toga, Arthur W.; Thompson, Paul M.

    2008-01-01

    We apply an information-theoretic cost metric, the symmetrized Kullback-Leibler (sKL) divergence, or J-divergence, to fluid registration of diffusion tensor images. The difference between diffusion tensors is quantified based on the sKL-divergence of their associated probability density functions (PDFs). Three-dimensional DTI data from 34 subjects were fluidly registered to an optimized target image. To allow large image deformations but preserve image topology, we regularized the flow with a large-deformation diffeomorphic mapping based on the kinematics of a Navier-Stokes fluid. A driving force was developed to minimize the J-divergence between the deforming source and target diffusion functions, while reorienting the flowing tensors to preserve fiber topography. In initial experiments, we showed that the sKL-divergence based on full diffusion PDFs is adaptable to higher-order diffusion models, such as high angular resolution diffusion imaging (HARDI). The sKL-divergence was sensitive to subtle differences between two diffusivity profiles, showing promise for nonlinear registration applications and multisubject statistical analysis of HARDI data. PMID:18390342

  13. Incorporating global information in feature-based multimodal image registration

    Science.gov (United States)

    Li, Yong; Stevenson, Robert

    2014-03-01

    A multimodal image registration framework based on searching the best matched keypoints and the incorporation of global information is proposed. It comprises two key elements: keypoint detection and an iterative process. Keypoints are detected from both the reference and test images. For each test keypoint, a number of reference keypoints are chosen as mapping candidates. A triplet of keypoint mappings determine an affine transformation that is evaluated using a similarity metric between the reference image and the transformed test image by the determined transformation. An iterative process is conducted on triplets of keypoint mappings, keeping track of the best matched reference keypoint. Random sample consensus and mutual information are applied to eliminate outlier keypoint mappings. The similarity metric is defined to be the number of overlapped edge pixels over the entire images, allowing for global information to be incorporated in the evaluation of triplets of mappings. The performance of the framework is investigated with keypoints extracted by scale invariant feature transform and partial intensity invariant feature descriptor. Experimental results show that the proposed framework can provide more accurate registration than existing methods.

  14. Registration Method for CT-MR Image Based on Mutual Information

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Medical image registration is important in many medical applications. Registration method based on maximization of mutual information of voxel intensities is one of the most popular methods for 3-D multi-modality medical image registration. Generally, the optimization process is easily trapped in local maximum, resulting in wrong registration results. In order to find the correct optimum, a new multi-resolution approach for brain image registration based on normalized mutual information is proposed. In this method, to eliminate the effect of local optima, multi-scale wavelet transformation is adopted to extract the image edge features. Then the feature images are registered,and the result at this level is taken as the initial estimate for the registration of the original images.Three-dimensional volumes are used to test the algorithm. Experimental results show that the registration strategy proposed is a robust and efficient method which can reach sub-voxel accuracy and improve the optimization speed.

  15. Using image synthesis for multi-channel registration of different image modalities

    Science.gov (United States)

    Chen, Min; Jog, Amod; Carass, Aaron; Prince, Jerry L.

    2015-01-01

    This paper presents a multi-channel approach for performing registration between magnetic resonance (MR) images with different modalities. In general, a multi-channel registration cannot be used when the moving and target images do not have analogous modalities. In this work, we address this limitation by using a random forest regression technique to synthesize the missing modalities from the available ones. This allows a single channel registration between two different modalities to be converted into a multi-channel registration with two mono-modal channels. To validate our approach, two openly available registration algorithms and five cost functions were used to compare the label transfer accuracy of the registration with (and without) our multi-channel synthesis approach. Our results show that the proposed method produced statistically significant improvements in registration accuracy (at an α level of 0.001) for both algorithms and all cost functions when compared to a standard multi-modal registration using the same algorithms with mutual information. PMID:26246653

  16. Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing.

    Science.gov (United States)

    Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin

    2016-04-07

    With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

  17. Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

    Directory of Open Access Journals (Sweden)

    Fan Zhang

    2016-04-01

    Full Text Available With the development of synthetic aperture radar (SAR technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO. However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

  18. MIND Demons for MR-to-CT deformable image registration in image-guided spine surgery

    Science.gov (United States)

    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

  19. Multislice CT brain image registration for perfusion studies

    Science.gov (United States)

    Lin, Zhong Min; Pohlman, Scott; Chandra, Shalabh

    2002-04-01

    During the last several years perfusion CT techniques have been developed as an effective technique for clinically evaluating cerebral hemodynamics. Perfusion CT techniques are capable of measurings functional parameters such as tissue perfusion, blood flow, blood volume, and mean transit time and are commonly used to evaluate stroke patients. However, the quality of functional images of the brain frequently suffers from patient head motion. Because the time window for an effective treatment of stroke patient is narrow, a fast motion correction is required. The purpose of the paper is to present a fast and accurate registration technique for motion correction of multi-slice CT and to demonstrate the effects of the registration on perfusion calculation.

  20. Gradient-Based Approach for Fine Registration of Panorama Images

    Institute of Scientific and Technical Information of China (English)

    Hui Chen

    2004-01-01

    This paper studies the application of gradient-based motion detection techniques (i.e., optical flow methods) for registration of adjacent images taken using a hand-held camera for the purposes of building a panorama. A general 8-parameter model or a more compact 3-parameter model is commonly used for transformation estimation. However, both models are approximations to the real situation when viewpoint position is not absolutely fixed but includes a small translation, and thus distortion and blurring are sometimes present in the final registration results. This paper proposes a new 5-parameter model that shows better result and has less strict requirement on good choice of unknown initial parameters. An analysis of disparity recovery range and its enlargement using Gaussian filter is also given.

  1. Registration of image feature points using differential evolution

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hao; HUANG Zhan-hua; YU Dao-ying

    2005-01-01

    This paper introduces a robust global nonlinear optimizer-differential evolution(DE),which is a simple evolution algorithm to search for an optimal transformation that makes the best alignment of two sets of feature points.To map the problem of matching into the framework of DE,the objective function is proportional to the registration error which is measured by Hausdorff distance,while the parameters of transformation are encoded in floating-point as the functional variables.Three termination criteria are proposed for DE.A simulation of 2-dimensional point sets and a similarity transformation are presented to compare the robustness and convergence properties of DE with genetic algorithm's (GA).And the registration of an object and its contour model have been demonstrated by using of DE to natural images.

  2. Fast SAR Image Change Detection Using Bayesian Approach Based Difference Image and Modified Statistical Region Merging

    Directory of Open Access Journals (Sweden)

    Han Zhang

    2014-01-01

    Full Text Available A novel fast SAR image change detection method is presented in this paper. Based on a Bayesian approach, the prior information that speckles follow the Nakagami distribution is incorporated into the difference image (DI generation process. The new DI performs much better than the familiar log ratio (LR DI as well as the cumulant based Kullback-Leibler divergence (CKLD DI. The statistical region merging (SRM approach is first introduced to change detection context. A new clustering procedure with the region variance as the statistical inference variable is exhibited to tailor SAR image change detection purposes, with only two classes in the final map, the unchanged and changed classes. The most prominent advantages of the proposed modified SRM (MSRM method are the ability to cope with noise corruption and the quick implementation. Experimental results show that the proposed method is superior in both the change detection accuracy and the operation efficiency.

  3. Direct Image-To Registration Using Mobile Sensor Data

    Science.gov (United States)

    Kehl, C.; Buckley, S. J.; Gawthorpe, R. L.; Viola, I.; Howell, J. A.

    2016-06-01

    Adding supplementary texture and 2D image-based annotations to 3D surface models is a useful next step for domain specialists to make use of photorealistic products of laser scanning and photogrammetry. This requires a registration between the new camera imagery and the model geometry to be solved, which can be a time-consuming task without appropriate automation. The increasing availability of photorealistic models, coupled with the proliferation of mobile devices, gives users the possibility to complement their models in real time. Modern mobile devices deliver digital photographs of increasing quality, as well as on-board sensor data, which can be used as input for practical and automatic camera registration procedures. Their familiar user interface also improves manual registration procedures. This paper introduces a fully automatic pose estimation method using the on-board sensor data for initial exterior orientation, and feature matching between an acquired photograph and a synthesised rendering of the orientated 3D scene as input for fine alignment. The paper also introduces a user-friendly manual camera registration- and pose estimation interface for mobile devices, based on existing surface geometry and numerical optimisation methods. The article further assesses the automatic algorithm's accuracy compared to traditional methods, and the impact of computational- and environmental parameters. Experiments using urban and geological case studies show a significant sensitivity of the automatic procedure to the quality of the initial mobile sensor values. Changing natural lighting conditions remain a challenge for automatic pose estimation techniques, although progress is presented here. Finally, the automatically-registered mobile images are used as the basis for adding user annotations to the input textured model.

  4. Rapid registration of multimodal images using a reduced number of voxels

    Science.gov (United States)

    Huang, Xishi; Hill, Nicholas A.; Ren, Jing; Peters, Terry M.

    2006-03-01

    Rapid registration of multimodal cardiac images can improve image-guided cardiac surgeries and cardiac disease diagnosis. While mutual information (MI) is arguably the most suitable registration technique, this method is too slow to converge for real time cardiac image registration; moreover, correct registration may not coincide with a global or even local maximum of MI. These limitations become quite evident when registering three-dimensional (3D) ultrasound (US) images and dynamic 3D magnetic resonance (MR) images of the beating heart. To overcome these issues, we present a registration method that uses a reduced number of voxels, while retaining adequate registration accuracy. Prior to registration we preprocess the images such that only the most representative anatomical features are depicted. By selecting samples from preprocessed images, our method dramatically speeds up the registration process, as well as ensuring correct registration. We validated this registration method for registering dynamic US and MR images of the beating heart of a volunteer. Experimental results on in vivo cardiac images demonstrate significant improvements in registration speed without compromising registration accuracy. A second validation study was performed registering US and computed tomography (CT) images of a rib cage phantom. Two similarity metrics, MI and normalized crosscorrelation (NCC) were used to register the image sets. Experimental results on the rib cage phantom indicate that our method can achieve adequate registration accuracy within 10% of the computation time of conventional registration methods. We believe this method has the potential to facilitate intra-operative image fusion for minimally invasive cardio-thoracic surgical navigation.

  5. Airborne SAR Real-time Imaging Algorithm Design and Implementation with CUDA on NVIDIA GPU

    Directory of Open Access Journals (Sweden)

    Meng Da-di

    2013-12-01

    Full Text Available Synthetic Aperture Radar (SAR image processing requires huge computation amount. Traditionally, this task runs on the workstation or server based on Central Processing Unit (CPU and is rather time-consuming, hence real-time processing of SAR data is impossible. Based on Compute Unified Device Architecture (CUDA technology, a new plan of SAR imaging algorithm operated on NVIDIA Graphic Processing Unit (GPU is proposed. The new proposal makes it possible that the data processing procedure and CPU/GPU data exchanging execute concurrently, especially when SAR data size exceeds total GPU global memory size. Multi-GPU is suitably supported by the new proposal and all of computational resources are fully exploited. It is shown by experiment on NVIDIA K20C and INTEL E5645 that the proposed solution accelerates SAR data processing by tens of times. Consequently, the GPU based SAR processing system with the proposed solution embedded is much more power saving and portable, which makes it qualified to be a real-time SAR data processing system. Experiment shows that SAR data of 36 Mega points can be processed in real-time per second by K20C with the new solution equipped.

  6. Segmentation-Based PolSAR Image Classification Using Visual Features: RHLBP and Color Features

    Directory of Open Access Journals (Sweden)

    Jian Cheng

    2015-05-01

    Full Text Available A segmentation-based fully-polarimetric synthetic aperture radar (PolSAR image classification method that incorporates texture features and color features is designed and implemented. This method is based on the framework that conjunctively uses statistical region merging (SRM for segmentation and support vector machine (SVM for classification. In the segmentation step, we propose an improved local binary pattern (LBP operator named the regional homogeneity local binary pattern (RHLBP to guarantee the regional homogeneity in PolSAR images. In the classification step, the color features extracted from false color images are applied to improve the classification accuracy. The RHLBP operator and color features can provide discriminative information to separate those pixels and regions with similar polarimetric features, which are from different classes. Extensive experimental comparison results with conventional methods on L-band PolSAR data demonstrate the effectiveness of our proposed method for PolSAR image classification.

  7. Medical image retrieval based on plaque appearance and image registration.

    Science.gov (United States)

    Amores, Jaume; Radeva, Petia

    2005-01-01

    The increasing amount of medical images produced and stored daily in hospitals needs a datrabase management system that organizes them in a meaningful way, without the necessity of time-consuming textual annotations for each image. One of the basic ways to organize medical images in taxonomies consists of clustering them depending of plaque appearance (for example, intravascular ultrasound images). Although lately, there has been a lot of research in the field of Content-Based Image Retrieval systems, mostly these systems are designed for dealing a wide range of images but not medical images. Medical image retrieval by content is still an emerging field, and few works are presented in spite of the obvious applications and the complexity of the images demanding research studies. In this chapter, we overview the work on medical image retrieval and present a general framework of medical image retrieval based on plaque appearance. We stress on two basic features of medical image retrieval based on plaque appearance: plaque medical images contain complex information requiring not only local and global descriptors but also context determined by image features and their spatial relations. Additionally, given that most objects in medical images usually have high intra- and inter-patient shape variance, retrieval based on plaque should be invariant to a family of transformations predetermined by the application domain. To illustrate the medical image retrieval based on plaque appearance, we consider a specific image modality: intravascular ultrasound images and present extensive results on the retrieval performance.

  8. Ship Detection in SAR Image Based on the Alpha-stable Distribution

    Directory of Open Access Journals (Sweden)

    Xiaofeng Li

    2008-08-01

    Full Text Available This paper describes an improved Constant False Alarm Rate (CFAR ship detection algorithm in spaceborne synthetic aperture radar (SAR image based on Alphastable distribution model. Typically, the CFAR algorithm uses the Gaussian distribution model to describe statistical characteristics of a SAR image background clutter. However, the Gaussian distribution is only valid for multilook SAR images when several radar looks are averaged. As sea clutter in SAR images shows spiky or heavy-tailed characteristics, the Gaussian distribution often fails to describe background sea clutter. In this study, we replace the Gaussian distribution with the Alpha-stable distribution, which is widely used in impulsive or spiky signal processing, to describe the background sea clutter in SAR images. In our proposed algorithm, an initial step for detecting possible ship targets is employed. Then, similar to the typical two-parameter CFAR algorithm, a local process is applied to the pixel identified as possible target. A RADARSAT-1 image is used to validate this Alpha-stable distribution based algorithm. Meanwhile, known ship location data during the time of RADARSAT-1 SAR image acquisition is used to validate ship detection results. Validation results show improvements of the new CFAR algorithm based on the Alpha-stable distribution over the CFAR algorithm based on the Gaussian distribution.

  9. A Novel Ship Wake Detection Method of SAR Images Based on Frequency Domain

    Institute of Scientific and Technical Information of China (English)

    Liu Hao; Zhu Minhui

    2003-01-01

    Moving ships produce a set of waves of "V' pattern on the ocean. These waves can often be seen by Synthetic Aperture Radar (SAR). The detection of these wakes can provide important information for surveillance of shipping, such as ship traveling direction and speed. A novel approach to the detection of ship wakes in SAR images based on frequency domain is provided in this letter. Compared with traditional Radon-based approaches, computation is reduced by 20%-40% without losing nearly any of detection performance. The testing results using real data and simulation of synthetic SAR images test the algorithm's feasibility and robustness.

  10. REALIZATION OF QUICK-LOOK IMAGING FOR SPACEBORNE SAR BASED ON PARALLEL PROCESSING

    Institute of Scientific and Technical Information of China (English)

    Tang Zhi; Zhou Yinqing; Li Jingwen

    2004-01-01

    Large range cell migration is a severe challenge to imaging algorithm for spaceborne SAR. Based on design of Finite Impulse Response (FIR) filter and Range Doppler (RD) algorithm,a realization of quick-look imaging for large range cell migration is proposed. It realized quicklook imaging of 8 times reduced resolution with parallel processing on memory shared 8 CPU SGI server. According to simulation experiment, this quick-look imaging algorithm with parallel processing can image 16384× 16384 SAR raw data within 6 seconds. It reaches the requirement of real-time imaging.

  11. Automatic compensation of antenna beam roll-off in SAR images.

    Energy Technology Data Exchange (ETDEWEB)

    Doerry, Armin Walter

    2006-04-01

    The effects of a non-uniform antenna beam are sometimes visible in Synthetic Aperture Radar (SAR) images. This might be due to near-range operation, wide scenes, or inadequate antenna pointing accuracy. The effects can be mitigated in the SAR image by fitting very a simple model to the illumination profile and compensating the pixel brightness accordingly, in an automated fashion. This is accomplished without a detailed antenna pattern calibration, and allows for drift in the antenna beam alignments.

  12. STUDY ON THE TECHNIQUE TO DETECT TEXTURE FEATURES IN SAR IMAGES

    Institute of Scientific and Technical Information of China (English)

    Fu Yusheng; Ding Dongtao; Hou Yinming

    2004-01-01

    This letter studies on the detection of texture features in Synthetic Aperture Radar (SAR) images. Through analyzing the feature detection method proposed by Lopes, an improved texture detection method is proposed, which can not only detect the edge and lines but also avoid stretching edge and suppressing lines of the former algorithm. Experimental results with both simulated and real SAR images verify the advantage and practicability of the improved method.

  13. Directional Filter for SAR Images Based on Nonsubsampled Contourlet Transform and Immune Clonal Selection

    Institute of Scientific and Technical Information of China (English)

    Xiao-Hui Yang; Li-Cheng Jiao; Deng-Feng Li

    2009-01-01

    A directional filter algorithm for intensity synthetic aperture radar (SAR) image based on nonsubsampled contourlet transform (NSCT) and immune clonal selection (ICS) is presented. The proposed filter mainly focuses on exploiting different features of edges and noises by NSCT. Furthermore, ICS strategy is introduced to optimize threshold parameter and amplify parameter adaptively. Numerical experiments on real SAR images show that there are improvements in both visual effects and objective indexes.

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

    Science.gov (United States)

    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

  15. Image navigation and registration for the geostationary lightning mapper (GLM)

    Science.gov (United States)

    van Bezooijen, Roel W. H.; Demroff, Howard; Burton, Gregory; Chu, Donald; Yang, Shu S.

    2016-10-01

    The Geostationary Lightning Mappers (GLM) for the Geostationary Operational Environmental Satellite (GOES) GOES-R series will, for the first time, provide hemispherical lightning information 24 hours a day from longitudes of 75 and 137 degrees west. The first GLM of a series of four is planned for launch in November, 2016. Observation of lightning patterns by GLM holds promise to improve tornado warning lead times to greater than 20 minutes while halving the present false alarm rates. In addition, GLM will improve airline traffic flow management, and provide climatology data allowing us to understand the Earth's evolving climate. The paper describes the method used for translating the pixel position of a lightning event to its corresponding geodetic longitude and latitude, using the J2000 attitude of the GLM mount frame reported by the spacecraft, the position of the spacecraft, and the alignment of the GLM coordinate frame relative to its mount frame. Because the latter alignment will experience seasonal variation, this alignment is determined daily using GLM background images collected over the previous 7 days. The process involves identification of coastlines in the background images and determination of the alignment change necessary to match the detected coastline with the coastline predicted using the GSHHS database. Registration is achieved using a variation of the Lucas-Kanade algorithm where we added a dither and average technique to improve performance significantly. An innovative water mask technique was conceived to enable self-contained detection of clear coastline sections usable for registration. Extensive simulations using accurate visible images from GOES13 and GOES15 have been used to demonstrate the performance of the coastline registration method, the results of which are presented in the paper.

  16. Enhancement of SAR images using fuzzy shrinkage technique in curvelet domain

    Indian Academy of Sciences (India)

    SHIVAKUMARA SWAMY PURANIK MATH; VANI KALIYAPERUMAL

    2017-09-01

    The synthetic aperture radar (SAR) images are mainly affected by speckle noise. Speckle degrades the features in the image and reduces the ability of a human observer to resolve fine detail, hence despeckling is very much required for SAR images. This paper presents speckle noise reduction in SAR images using a combination of curvelet and fuzzy logic technique to restore speckle-affected images. This method overcomes the limitation of discontinuity in hard threshold and permanent deviation in soft threshold. First, it decomposes noise image into different frequency scales using curvelet transform, and then applies the fuzzy shrinking technique to high-frequency coefficients to restore noise-contaminated coefficients. The proposed method does not use threshold approach only by proper selection of shrinking parameter the speckle in SAR image is suppressed. The experiment is carried out on different resolutions of RISAT-1 SAR images, and results are compared with the existing filtering algorithms in terms of noise mean variance (NMV), mean square difference (MSD), equal number of looks (ENL), noise standard deviation (NSD) and speckle suppression index (SSI). A comparison of the results shows that the proposed technique suppresses noise significantly, preserves the details of the image and improves the visual quality of the image

  17. A Method for Sea Surface Wind Field Retrieval from SAR Image Mode Data

    Institute of Scientific and Technical Information of China (English)

    SHAO Weizeng; SUN Jian; GUAN Changlong; SUN Zhanfeng

    2014-01-01

    To retrieve wind field from SAR images, the development for surface wind field retrieval from SAR images based on the improvement of new inversion model is present. Geophysical Model Functions (GMFs) have been widely applied for wind field retrieval from SAR images. Among them CMOD4 has a good performance under low and moderate wind conditions. Although CMOD5 is developed recently with a more fundamental basis, it has ambiguity of wind speed and a shape gradient of normalized radar cross section under low wind speed condition. This study proposes a method of wind field retrieval from SAR image by com-bining CMOD5 and CMOD4 Five VV-polarisation RADARSAT2 SAR images are implemented for validation and the retrieval re-sults by a combination method (CMOD5 and CMOD4) together with CMOD4 GMF are compared with QuikSCAT wind data. The root-mean-square error (RMSE) of wind speed is 0.75 m s-1 with correlation coefficient 0.84 using the combination method and the RMSE of wind speed is 1.01 m s-1 with correlation coefficient 0.72 using CMOD4 GMF alone for those cases. The proposed method can be applied to SAR image for avoiding the internal defect in CMOD5 under low wind speed condition.

  18. Pixel Classification of SAR ice images using ANFIS-PSO Classifier

    Directory of Open Access Journals (Sweden)

    G. Vasumathi

    2016-12-01

    Full Text Available Synthetic Aperture Radar (SAR is playing a vital role in taking extremely high resolution radar images. It is greatly used to monitor the ice covered ocean regions. Sea monitoring is important for various purposes which includes global climate systems and ship navigation. Classification on the ice infested area gives important features which will be further useful for various monitoring process around the ice regions. Main objective of this paper is to classify the SAR ice image that helps in identifying the regions around the ice infested areas. In this paper three stages are considered in classification of SAR ice images. It starts with preprocessing in which the speckled SAR ice images are denoised using various speckle removal filters; comparison is made on all these filters to find the best filter in speckle removal. Second stage includes segmentation in which different regions are segmented using K-means and watershed segmentation algorithms; comparison is made between these two algorithms to find the best in segmenting SAR ice images. The last stage includes pixel based classification which identifies and classifies the segmented regions using various supervised learning classifiers. The algorithms includes Back propagation neural networks (BPN, Fuzzy Classifier, Adaptive Neuro Fuzzy Inference Classifier (ANFIS classifier and proposed ANFIS with Particle Swarm Optimization (PSO classifier; comparison is made on all these classifiers to propose which classifier is best suitable for classifying the SAR ice image. Various evaluation metrics are performed separately at all these three stages.

  19. Distance-Dependent Multimodal Image Registration for Agriculture Tasks

    Directory of Open Access Journals (Sweden)

    Ron Berenstein

    2015-08-01

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

  20. Higher-order Spatial Accuracy in Diffeomorphic Image Registration

    DEFF Research Database (Denmark)

    Jacobs, Henry O.; Sommer, Stefan

    -jets. We show that the solutions convergence to optimal solutions of the original cost functional as the number of particles increases with a convergence rate of O(hd+k) where h is a resolution parameter. The effect of this approach over traditional particle methods is illustrated on synthetic examples......We discretize a cost functional for image registration problems by deriving Taylor expansions for the matching term. Minima of the discretized cost functionals can be computed with no spatial discretization error, and the optimal solutions are equivalent to minimal energy curves in the space of kk...

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

    Science.gov (United States)

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

    2016-07-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  3. Three modality image registration of brain SPECT/CT and MR images for quantitative analysis of dopamine transporter imaging

    Science.gov (United States)

    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.

  4. Wind mapping offshore in coastal Mediterranean area using SAR images

    DEFF Research Database (Denmark)

    Calaudi, Rosamaria; Arena, Felice; Badger, Merete

    Satellite observations of the ocean surface from Synthetic Aperture Radars (SAR) provide information about the spatial wind variability over large areas. This is of special interest in the Mediterranean, where spatial wind information is only provided by sparse buoys, often with long periods...... of missing data. Here, we focus on evaluating the use of SAR for offshore wind mapping. Preliminary results from the analysis of SAR-based ocean winds in Mediterranean areas show interesting large scale wind flow features consistent with results from previous studies using numerical models and space borne...

  5. Offshore Wind Resource Estimation in Mediterranean Area Using SAR Images

    DEFF Research Database (Denmark)

    Calaudi, Rosamaria; Arena, Felice; Badger, Merete

    Satellite observations of the ocean surface from Synthetic Aperture Radars (SAR) provide information about the spatial wind variability over large areas. This is of special interest in the Mediterranean, where spatial wind information is only provided by sparse buoys, often with long periods...... of missing data. Here, we focus on evaluating the use of SAR for offshore wind mapping. Preliminary results from the analysis of SAR-based ocean winds in Mediterranean areas show interesting large scale wind flow features consistent with results from previous studies using numerical models and space borne...

  6. An integrative synchronization and imaging approach for bistatic spaceborne/stratospheric SAR with a fixed receiver

    Science.gov (United States)

    Zhang, Qilei; Chang, Wenge; Li, Xiangyang

    2013-12-01

    Bistatic spaceborne/stratospheric synthetic aperture radar (SAR) with a fixed receiver is a novel hybrid bistatic SAR system, in which a spaceborne SAR serves as the transmitter of opportunity, while a fixed receiver is mounted on a stratospheric platform. This paper presents an integrative synchronization and imaging approach for this particular system. Firstly, a novel synchronization method using the direct-path signal, which can be collected by a dedicated antenna, is proposed and applied. The synchronization error can be completely removed using the proposed method. However, as the cost of synchronization, the characteristic of synchronized echo's range history becomes quite different from that of general bistatic SAR data. To focus this particular synchronized data, its 2-D spectrum is derived under linear approximations and then a frequency-domain imaging algorithm using two-dimensional inverse scaled Fourier transform (2-DISFT) is proposed. At last, the proposed integrative synchronization and imaging algorithm is verified by simulations.

  7. Image registration and averaging of low laser power two-photon fluorescence images of mouse retina.

    Science.gov (United States)

    Alexander, Nathan S; Palczewska, Grazyna; Stremplewski, Patrycjusz; Wojtkowski, Maciej; Kern, Timothy S; Palczewski, Krzysztof

    2016-07-01

    Two-photon fluorescence microscopy (TPM) is now being used routinely to image live cells for extended periods deep within tissues, including the retina and other structures within the eye . However, very low laser power is a requirement to obtain TPM images of the retina safely. Unfortunately, a reduction in laser power also reduces the signal-to-noise ratio of collected images, making it difficult to visualize structural details. Here, image registration and averaging methods applied to TPM images of the eye in living animals (without the need for auxiliary hardware) demonstrate the structural information obtained with laser power down to 1 mW. Image registration provided between 1.4% and 13.0% improvement in image quality compared to averaging images without registrations when using a high-fluorescence template, and between 0.2% and 12.0% when employing the average of collected images as the template. Also, a diminishing return on image quality when more images were used to obtain the averaged image is shown. This work provides a foundation for obtaining informative TPM images with laser powers of 1 mW, compared to previous levels for imaging mice ranging between 6.3 mW [Palczewska G., Nat Med.20, 785 (2014) Sharma R., Biomed. Opt. Express4, 1285 (2013)].

  8. [Research on non-rigid medical image registration algorithm based on SIFT feature extraction].

    Science.gov (United States)

    Wang, Anna; Lu, Dan; Wang, Zhe; Fang, Zhizhen

    2010-08-01

    In allusion to non-rigid registration of medical images, the paper gives a practical feature points matching algorithm--the image registration algorithm based on the scale-invariant features transform (Scale Invariant Feature Transform, SIFT). The algorithm makes use of the image features of translation, rotation and affine transformation invariance in scale space to extract the image feature points. Bidirectional matching algorithm is chosen to establish the matching relations between the images, so the accuracy of image registrations is improved. On this basis, affine transform is chosen to complement the non-rigid registration, and normalized mutual information measure and PSO optimization algorithm are also chosen to optimize the registration process. The experimental results show that the method can achieve better registration results than the method based on mutual information.

  9. Algorithm for image registration and clutter and jitter noise reduction

    Energy Technology Data Exchange (ETDEWEB)

    Brower, K.L.

    1997-02-01

    This paper presents an analytical, computational method whereby two-dimensional images of an optical source represented in terms of a set of detector array signals can be registered with respect to a reference set of detector array signals. The detector image is recovered from the detector array signals and represented over a local region by a fourth order, two-dimensional taylor series. This local detector image can then be registered by a general linear transformation with respect to a reference detector image. The detector signal in the reference frame is reconstructed by integrating this detector image over the respective reference pixel. For cases in which the general linear transformation is uncertain by up to plus-or-minus two pixels, the general linear transformation can be determined by least squares fitting the detector image to the reference detector image. This registration process reduces clutter and jitter noise to a level comparable to the electronic noise level of the detector system. Test results with and without electronic noise using an analytical test function are presented.

  10. An extension of digital volume correlation for multimodality image registration

    Science.gov (United States)

    Tudisco, E.; Jailin, C.; Mendoza, A.; Tengattini, A.; Andò, E.; Hall, Stephen A.; Viggiani, Gioacchino; Hild, F.; Roux, S.

    2017-09-01

    The question of registering two images (or image volumes) acquired with different modalities, and thus exhibiting different contrast, at different positions is addressed based on an extension of global digital image (or volume) correlation. A specific comparison metric is introduced allowing the signature of the different phases to be related. A first solution consists of a Gaussian mixture to describe the joint distribution of gray levels, which not only provides a matching of both images, but also offers a natural segmentation indicator. A second ‘self-adapting’ solution does not include any postulated a priori model for the joint histogram and leads to a registration of the images based on their initial histograms. The algorithm is implemented with a pyramidal multiscale framework for the sake of robustness. The proposed multiscale technique is tested on two 3D images obtained from x-ray and neutron tomography respectively. The proposed approach brings the two images to coincidence with a sub-pixel accuracy and allows for a ‘natural’ segmentation of the different phases.

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

    Science.gov (United States)

    Lee, Joohwi; Lyu, Ilwoo; Styner, Martin

    2014-03-21

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

  12. Change detection in high resolution SAR images based on multiscale texture features

    Science.gov (United States)

    Wen, Caihuan; Gao, Ziqiang

    2011-12-01

    This paper studied on change detection algorithm of high resolution (HR) Synthetic Aperture Radar (SAR) images based on multi-scale texture features. Firstly, preprocessed multi-temporal Terra-SAR images were decomposed by 2-D dual tree complex wavelet transform (DT-CWT), and multi-scale texture features were extracted from those images. Then, log-ratio operation was utilized to get difference images, and the Bayes minimum error theory was used to extract change information from difference images. Lastly, precision assessment was done. Meanwhile, we compared with the result of method based on texture features extracted from gray-level cooccurrence matrix (GLCM). We had a conclusion that, change detection algorithm based on multi-scale texture features has a great more improvement, which proves an effective method to change detect of high spatial resolution SAR images.

  13. A novel point cloud registration using 2D image features

    Science.gov (United States)

    Lin, Chien-Chou; Tai, Yen-Chou; Lee, Jhong-Jin; Chen, Yong-Sheng

    2017-01-01

    Since a 3D scanner only captures a scene of a 3D object at a time, a 3D registration for multi-scene is the key issue of 3D modeling. This paper presents a novel and an efficient 3D registration method based on 2D local feature matching. The proposed method transforms the point clouds into 2D bearing angle images and then uses the 2D feature based matching method, SURF, to find matching pixel pairs between two images. The corresponding points of 3D point clouds can be obtained by those pixel pairs. Since the corresponding pairs are sorted by their distance between matching features, only the top half of the corresponding pairs are used to find the optimal rotation matrix by the least squares approximation. In this paper, the optimal rotation matrix is derived by orthogonal Procrustes method (SVD-based approach). Therefore, the 3D model of an object can be reconstructed by aligning those point clouds with the optimal transformation matrix. Experimental results show that the accuracy of the proposed method is close to the ICP, but the computation cost is reduced significantly. The performance is six times faster than the generalized-ICP algorithm. Furthermore, while the ICP requires high alignment similarity of two scenes, the proposed method is robust to a larger difference of viewing angle.

  14. The chest X-ray image features of patients with severe SARS: a preliminary study

    Institute of Scientific and Technical Information of China (English)

    刘晋新; 唐小平; 江松峰; 陈碧华; 张烈光; 黄德扬; 黄务枝; 史红玲; 尹炽标; 陈金城

    2003-01-01

    Objective To study the chest X-ray image features of patients with severe SARS.Methods Chest X-ray image features in 36 patients with severe SARS were retrospectively analyzed. The image characteristics were compared with those of 224 patients with common SARS. Results The important chest X-ray imaging features of 36 patients with severe SARS included small patch of infiltration (n=27, 75.0%), large patch of infiltration (n=22, 61.1%), large area of lung consolidation (n=10, 27.3%), interstitial lung lesion (n=26, 72.2%), ground-glass shadow (n=28, 77.8%), irregular linear opacity (n=15, 41.7%), diffuse lung lesion (n=12, 33.3%), with single lung involved (n=9, 25.0%), and both lungs involved (n=32, 88.9%). The rates of large patch of infiltration, large area of lung consolidation, ground-glass shadow, diffuse lung lesion and involvement of both lungs in patients with severe SARS were significantly higher than those in patients with common type of SARS (all P<0.01). Out of the 11 severe SARS patients who died, nine had large area of ground-glass shadow with air bronchogram in both lungs before death.Conclusions Large patch of infiltration, large area of consolidation, ground-glass shadow, diffuse lung lesion and involvement of both lungs were the main X-ray image characteristics of patients with severe SARS. Large area of ground-glass shadow with air bronchogram in both lungs indicated a bad prognosis.

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

    Science.gov (United States)

    Li, Qi; Liu, Jin; Zang, Bo

    2015-12-01

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

  16. A three-component method for timely detection of land cover changes using polarimetric SAR images

    Science.gov (United States)

    Qi, Zhixin; Yeh, Anthony Gar-On; Li, Xia; Zhang, Xiaohu

    2015-09-01

    This study proposes a new three-component method for timely detection of land cover changes using polarimetric synthetic aperture radar (PolSAR) images. The three components are object-oriented image analysis (OOIA), change vector analysis (CVA), and post-classification comparison (PCC). First, two PolSAR images acquired over the same area at different dates are segmented hierarchically to delineate land parcels (image objects). Then, parcel-based CVA is performed with the coherency matrices of the PolSAR data to detect changed parcels. Finally, PCC based on a parcel-based classification algorithm integrating polarimetric decomposition, decision tree algorithms, and support vector machines is used to determine the type of change for the changed parcels. Compared with conventional PCC based on the widely used Wishart supervised classification, the three-component method achieves much higher accuracy for land cover change detection with PolSAR images. The contribution of each component is evaluated by excluding it from the method. The integration of OOIA in the method greatly reduces the false alarms caused by speckle noise in PolSAR images as well as improves the accuracy of PolSAR image classification. CVA contributes to the method by significantly reducing the effect of the classification errors on the change detection. The use of PCC in the method not only identifies different types of land cover change but also reduces the false alarms introduced by the change in the environment. The three-component method is validated in land development detection, which is important to many developing countries that are confronting a growing problem of unauthorized construction land expansion. The results show that the three-component method is effective in detecting land developments with PolSAR images.

  17. Constrained branch-and-bound algorithm for image registration

    Institute of Scientific and Technical Information of China (English)

    JIN Jian-qiu; WANG Zhang-ye; PENG Qun-sheng

    2005-01-01

    In this paper, the authors propose a refined Branch-and-Bound algorithm for affine-transformation based image registration. Given two feature point-sets in two images respectively, the authors first extract a sequence of high-probability matched point-pairs by considering well-defined features. Each resultant point-pair can be regarded as a constraint in the search space of Branch-and-Bound algorithm guiding the search process. The authors carry out Branch-and-Bound search with the constraint of a pair-point selected by using Monte Carlo sampling according to the match measures of point-pairs. If such one cannot lead to correct result, additional candidate is chosen to start another search. High-probability matched point-pairs usually results in fewer loops and the search process is accelerated greatly. Experimental results verify the high efficiency and robustness of the author's approach.

  18. Image Registration and Optimization in the Virtual Slaughterhouse

    DEFF Research Database (Denmark)

    Vester-Christensen, Martin

    This thesis presents the development and application of algorithms for the analysis of pig carcasses. Focus is on the simulation and quality estimation of meat products produced in a Danish slaughterhouse. Computed Tomography scans of pig carcasses provide the data used in the application. Image...... analysis is applied in order to imitate some of the cutting processes found in a slaughterhouse but also to give a quantitative measure of the composition of each carcass. The basis of the algorithms is non-linear image registration. This method finds the anatomical correspondence between a reference...... carcass and a template carcass. By iteratively comparing the transformed template with the reference a resulting dense deformation field is found. Propagating a set of landmarks from the reference coordinate system onto the template enables the simulation of slaughtering processes. Non...

  19. Deformable image registration for multimodal lung-cancer staging

    Science.gov (United States)

    Cheirsilp, Ronnarit; Zang, Xiaonan; Bascom, Rebecca; Allen, Thomas W.; Mahraj, Rickhesvar P. M.; Higgins, William E.

    2016-03-01

    Positron emission tomography (PET) and X-ray computed tomography (CT) serve as major diagnostic imaging modalities in the lung-cancer staging process. Modern scanners provide co-registered whole-body PET/CT studies, collected while the patient breathes freely, and high-resolution chest CT scans, collected under a brief patient breath hold. Unfortunately, no method exists for registering a PET/CT study into the space of a high-resolution chest CT scan. If this could be done, vital diagnostic information offered by the PET/CT study could be brought seamlessly into the procedure plan used during live cancer-staging bronchoscopy. We propose a method for the deformable registration of whole-body PET/CT data into the space of a high-resolution chest CT study. We then demonstrate its potential for procedure planning and subsequent use in multimodal image-guided bronchoscopy.

  20. Polyaffine parametrization of image registration based on geodesic flows

    DEFF Research Database (Denmark)

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

    2012-01-01

    Image registration based on geodesic flows has gained much popularity in recent years. We describe a novel parametrization of the velocity field in a stationary flow equation. We show that the method offers both precision, flexibility, and simplicity of evaluation. With our representation, which...... of geodesic shooting for computational anatomy. We avoid to do warp field convolution by interpolation in a dense field, we can easily calculate warp derivatives in a reference frame of choice, and we can consequently avoid interpolation in the image space altogether....... is very similar to existing methods, we show that we can find an analytical solution. This solution converges exponentially to the true solution, and the gradients may be determined similarly. We compare to existing prominent methods; the log-euclidean polyaffine framework, and the DARTEL implementation...

  1. SAR imaging method based on coprime sampling and nested sparse sampling

    Institute of Scientific and Technical Information of China (English)

    Hongyin Shi; Baojing Jia

    2015-01-01

    As the signal bandwidth and the number of channels increase, the synthetic aperture radar (SAR) imaging system pro-duces huge amount of data according to the Shannon-Nyquist theorem, causing a huge burden for data transmission. This pa-per concerns the coprime sampling and nested sparse sampling, which are proposed recently but have never been applied to real world for target detection, and proposes a novel way which uti-lizes these new sub-Nyquist sampling structures for SAR sam-pling in azimuth and reconstructs the data of SAR sampling by compressive sensing (CS). Both the simulated and real data are processed to test the algorithm, and the results indicate the way which combines these new undersampling structures and CS is able to achieve the SAR imaging effectively with much less data than regularly ways required. Final y, the influence of a little sam-pling jitter to SAR imaging is analyzed by theoretical analysis and experimental analysis, and then it concludes a little sampling jitter have no effect on image quality of SAR.

  2. 3D Elastic Registration of Ultrasound Images Based on Skeleton Feature

    Institute of Scientific and Technical Information of China (English)

    LI Dan-dan; LIU Zhi-Yan; SHEN Yi

    2005-01-01

    In order to eliminate displacement and elastic deformation between images of adjacent frames in course of 3D ultrasonic image reconstruction, elastic registration based on skeleton feature was adopt in this paper. A new automatically skeleton tracking extract algorithm is presented, which can extract connected skeleton to express figure feature. Feature points of connected skeleton are extracted automatically by accounting topical curvature extreme points several times. Initial registration is processed according to barycenter of skeleton. Whereafter, elastic registration based on radial basis function are processed according to feature points of skeleton. Result of example demonstrate that according to traditional rigid registration, elastic registration based on skeleton feature retain natural difference in shape for organ's different part, and eliminate slight elastic deformation between frames caused by image obtained process simultaneously. This algorithm has a high practical value for image registration in course of 3D ultrasound image reconstruction.

  3. Automatic Image Registration Using Free and Open Source Software

    Science.gov (United States)

    Giri Babu, D.; Raja Shekhar, S. S.; Chandrasekar, K.; Sesha Sai, M. V. R.; Diwakar, P. G.; Dadhwal, V. K.

    2014-11-01

    Image registration is the most critical operation in remote sensing applications to enable location based referencing and analysis of earth features. This is the first step for any process involving identification, time series analysis or change detection using a large set of imagery over a region. Most of the reliable procedures involve time consuming and laborious manual methods of finding the corresponding matching features of the input image with respect to reference. Also the process, as it involves human interaction, does not converge with multiple operations at different times. Automated procedures rely on accurately determining the matching locations or points from both the images under comparison and the procedures are robust and consistent over time. Different algorithms are available to achieve this, based on pattern recognition, feature based detection, similarity techniques etc. In the present study and implementation, Correlation based methods have been used with a improvement over newly developed technique of identifying and pruning the false points of match. Free and Open Source Software (FOSS) have been used to develop the methodology to reach a wider audience, without any dependency on COTS (Commercially off the shelf) software. Standard deviation from foci of the ellipse of correlated points, is a statistical means of ensuring the best match of the points of interest based on both intensity values and location correspondence. The methodology is developed and standardised by enhancements to meet the registration requirements of remote sensing imagery. Results have shown a performance improvement, nearly matching the visual techniques and have been implemented in remote sensing operational projects. The main advantage of the proposed methodology is its viability in production mode environment. This paper also shows that the visualization capabilities of MapWinGIS, GDAL's image handling abilities and OSSIM's correlation facility can be efficiently

  4. Registration of Airborne Infrared Images using Platform Attitude Information

    Directory of Open Access Journals (Sweden)

    Ravi Shankar Chekuri

    2014-03-01

    Full Text Available In current warfare scenario stealth and passive threat detection capabilities are considered as prime requirements to accomplish desired mission by the fighter aircrafts. To improve the stealth of an aircraft, the trend is towards detecting threats with the help of passive sensors (Electro Optic or Infrared. Current situation caters for systems like Infra-red Search and Track (IRST and Passive Missile Warning Systems (PMWS. IRST system is a passive target detection system, used for detecting aerial & ground targets. PMWS is a threat detection system used for detecting missiles approaching towards aircraft. Both of these systems detect targets of interest by processing IR images acquired in mid-IR region. The prime challenge in IRST system or PMWS is detecting a moving target of size typically 1~2 pixels in acquired image sequences. The temporal change caused by moving target in consecutive frames can be considered as one important factor to detect them. The temporal change caused by moving target is identified through absolute frame differencing of successive frames. This principle has limitation in application to IRST & PMWS as the imaging sensor with the aircraft is moving. This motion also imparts temporal change in the acquired images. In this paper authors are proposing a method for removing the temporal change caused by the platform motion in two consequently acquired frames using registration process.  The proposed method uses the platform attitude information at frame sampling times. Authors have analyzed the sensitivity of registration process to noisy platform attitude information.Defence Science Journal, 2014, 64(2, pp. 130-135. DOI: http://dx.doi.org/10.14429/dsj.64.5460

  5. Error Estimation and Unambiguous Reconstruction for Chinese First Dual-Channel Spaceborne SAR Imaging

    Science.gov (United States)

    Jin, T.; Qiu, X.; Hu, D.; Ding, C.

    2017-09-01

    Multichannel synthetic aperture radar (SAR) is a significant breakthrough to the inherent limitation between high-resolution and wide-swath (HRWS) faced with conventional SAR. Error estimation and unambiguous reconstruction are two crucial techniques for obtaining high-quality imagery. This paper demonstrates the experimental results of the two techniques for Chinese first dualchannel spaceborne SAR imaging. The model of Chinese Gaofen-3 dual-channel mode is established and the mechanism of channel mismatches is first discussed. Particularly, we propose a digital beamforming (DBF) process composed of the subspace-based error estimation algorithm and the reconstruction algorithm before imaging. The results exhibit the effective suppression of azimuth ambiguities with the proposed DBF process, and indicate the feasibility of this technique for future HRWS SAR systems.

  6. A SIFT Algorithm for Bistatic SAR Imaging in Spaceborne Constant-offset Configuration (in English

    Directory of Open Access Journals (Sweden)

    Chen Shi-chao

    2013-03-01

    Full Text Available Focusing on the problem of the space-variance of the range cell migration term for bistatic Synthetic Aperture Radar (SAR, a Scaled Inverse Fourier Transform (SIFT based imaging algorithm for constant-offset configuration bistatic SAR data processing is proposed in this article. Range cell migration correction is realized through two times phase multiplies and one time convolution operation. Since the imaging algorithm is based on an exact precise spectrum which is deduced from the Geometry-Based Formula (GBF algorithm, the proposed algorithm can handle the bistatic SAR data which are obtained with a large baseline to ratio. The advantages and effectiveness of the proposed imaging method have been verified by simulated and comparable experiments. Moreover, unlike the other scaling imaging algorithms which are dependent on the frequency modulated characteristics of the signal, the SIFT imaging algorithm is also suitable for phase-coded signal, which has a wider application areas.

  7. AN EVIDENT SIDELOBE CONTROL METHOD BASED ON NSCT FOR SHIP TARGET IN SAR IMAGES

    Institute of Scientific and Technical Information of China (English)

    Li Xueying; Yin Dong; Zhang Rong; Wang Kui

    2011-01-01

    Evident sidelobe on faint ship target seriously affects the accuracy of the target segmentation in Synthetic Aperture Radar (SAR) images.To avoid this problem,a novel sidelobe control method based on NonSubsampled Contourlet Transform (NSCT) for ship targets in SAR images is presented in this paper.This method enhances the SAR images in NSCT domain based on target azimuth estimation and then inhibits the sidelobe directionally in NSCT high-pass frequency subbands.Experimental results on RADARSAT-2 images demonstrate that the proposed method can not only reduce the strong sidelobes effectively,but also enhance the intensity of the objects successfully.Therefore,it gives a good segmentation result on the dark ship images with strong sidelobe,and enhances the detection rate on these targets.

  8. Detection and Imaging of Moving Targets with LiMIT SAR Data

    Science.gov (United States)

    2017-03-03

    1 Detection and Imaging of Moving Targets with LiMIT SAR Data Michael Newey, Gerald Benitz, David Barrett MIT Lincoln Laboratory Lexington...sandeep.mishra@baesystems.com Abstract Detecting moving targets in SAR imagery has recently gained a lot of interest as a way to replace optical...moving target detection and classification in adverse (e.g. cloudy) weather conditions. This can be particularly important for small radar antennas

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

    Directory of Open Access Journals (Sweden)

    Barış Gökçe

    2016-10-01

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

  10. Use of Wavelet-Fuzzy Features with PCA for Image Registration

    Directory of Open Access Journals (Sweden)

    Safia Sadruddin

    2014-01-01

    Full Text Available In this paper, we discuss an Image Registration system based on neural network, which uses Wavelet-fuzzy features of an image. In this system, Wavelet-fuzzy features are extracted from an image and then reduced using Principal Component Analysis (PCA. The reduced feature set is then used for training the neural network for image registration. The geometric transformation between the reference and sensed image sets are evaluated using affine transformation parameters. The trained neural network produces registration parameters (translation, rotation and scaling with respect to reference and sensed image. Two parameters namely Mean Absolute Registration Error and Mutual Information are used as evaluation parameters. Experimentally, we show that the proposed technique for image registration is accurate and robust for distorted and noisy inputs.

  11. On the Implementation of a Land Cover Classification System for SAR Images Using Khoros

    Science.gov (United States)

    Medina Revera, Edwin J.; Espinosa, Ramon Vasquez

    1997-01-01

    The Synthetic Aperture Radar (SAR) sensor is widely used to record data about the ground under all atmospheric conditions. The SAR acquired images have very good resolution which necessitates the development of a classification system that process the SAR images to extract useful information for different applications. In this work, a complete system for the land cover classification was designed and programmed using the Khoros, a data flow visual language environment, taking full advantages of the polymorphic data services that it provides. Image analysis was applied to SAR images to improve and automate the processes of recognition and classification of the different regions like mountains and lakes. Both unsupervised and supervised classification utilities were used. The unsupervised classification routines included the use of several Classification/Clustering algorithms like the K-means, ISO2, Weighted Minimum Distance, and the Localized Receptive Field (LRF) training/classifier. Different texture analysis approaches such as Invariant Moments, Fractal Dimension and Second Order statistics were implemented for supervised classification of the images. The results and conclusions for SAR image classification using the various unsupervised and supervised procedures are presented based on their accuracy and performance.

  12. MULTI-REGION SEGMENTATION OF SAR IMAGE BY A MULTIPHASE LEVEL SET APPROACH

    Institute of Scientific and Technical Information of China (English)

    Fu Yusheng; Cao Zongjie; Pi Yiming

    2008-01-01

    In this letter, a multiphase level set approach unifying region and boundary-based infor- mation for multi-region segmentation of Synthetic Aperture Radar (SAR) image is presented. An energy functional that is applicable for SAR image segmentation is defined. It consists of two terms describing the local statistic characteristics and the gradient characteristics of SAR image respectively. A multiphase level set model that explicitly describes the different regions in one image is proposed. The purpose of such a multiphase model is not only to simplify the way of denoting multi-region by level set but also to guarantee the accuracy of segmentation. According to the presented multiphase model, the curve evolution equations with respect to edge curves are deduced. The multi-region segmentation is implemented by the numeric solution of the partial differential equations. The performance of the approach is verified by both simulation and real SAR images. The experiments show that the proposed algorithm reduces the speckle effect on segmentation and increases the boundary alignment accuracy, thus correctly divides the multi-region SAR image into different homogenous regions.

  13. SAR image change detection algorithm based on stationary wavelet and bi-dimensional intrinsic mode function

    Science.gov (United States)

    Huang, S. Q.; Wang, Z. L.; Xie, T. G.; Li, Z. C.

    2017-09-01

    Speckle noise in synthetic aperture radar (SAR) image is produced by the coherent imaging mechanism, which brings a great impact on the change information acquisition of multi-temporal SAR images. Two-dimensional stationary wavelet transform (SWT) and bi-dimensional empirical mode decomposition (BEMD) are the non-stationary signal processing theory of multi-scale transform. According to their implementation process and SAR image characteristic, this paper proposed a new multi-temporal SAR image change detection method based on the combination of the stationary wavelet transform and the bi-dimensional intrinsic mode function (BIMF) features, called SWT-BIMF algorithm. The contribution of the new algorithm includes two aspects. One is the design of the two selections of decomposition features, that is, the speckle noise filtering; another is the selected features to perform the enhance processing, so more effective change information will obtain. The feasibility of the SWT-BIMF algorithm is verified by the measured SAR image data, and good experimental results are obtained.

  14. Feature Fusion Based Road Extraction for HJ-1-C SAR Image

    Directory of Open Access Journals (Sweden)

    Lu Ping-ping

    2014-06-01

    Full Text Available Road network extraction in SAR images is one of the key tasks of military and civilian technologies. To solve the issues of road extraction of HJ-1-C SAR images, a road extraction algorithm is proposed based on the integration of ratio and directional information. Due to the characteristic narrow dynamic range and low signal to noise ratio of HJ-1-C SAR images, a nonlinear quantization and an image filtering method based on a multi-scale autoregressive model are proposed here. A road extraction algorithm based on information fusion, which considers ratio and direction information, is also proposed. By processing Radon transformation, main road directions can be extracted. Cross interferences can be suppressed, and the road continuity can then be improved by the main direction alignment and secondary road extraction. The HJ-1-C SAR image acquired in Wuhan, China was used to evaluate the proposed method. The experimental results show good performance with correctness (80.5% and quality (70.1% when applied to a SAR image with complex content.

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

    Science.gov (United States)

    Hu, Liang; Wang, Manning; Song, Zhijian

    2013-03-01

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

  16. 利用高分辨率聚束模式TerraSAR-X影像的PSInSAR监测地表变形%Monitoring Land Deformation Using PSInSAR with TerraSAR-X High Resolution Spotlight SAR Images

    Institute of Scientific and Technical Information of China (English)

    李永生; 张景发; 罗毅; 姜文亮

    2012-01-01

    利用20景于2010-03~2010-11期间采集的高分辨率聚束(1m分辨率)模式的TerraSAR-X SAR数据,采用永久散射体干涉测量技术(PSInSAR)获取了西藏羊八井地区由地热电站开采地下水引起的地面沉降。结果显示,羊八井地热电站周边及地热开采井地区在2010年期间的地面沉降速率最大达到25mm·a-1,而盆地其他地区的地面平均沉降速率为1mm·a-1。将其与ASAR获取的平均沉降速率结果对比,两者的相关性达到了0.76,这说明TerraSAR-X高分辨率SAR数据不仅可以提供高密度PS点,而且更好地体现了散射体的细节变化和微量位移情况。%Persistent scatterer InSAR was used to detect surface subsidence in the the Yangbajing geothermal power plant due to extraction of ground water with area aroun twent resolution TerraSAR-X spotlight SAR images collected between March and November y hig 2010 d h The results suggest that land subsidence in the areas of geothermal wells is up to 25 mm · a^-1 whilst the subsidence in the basin is less than 1 mm · a^- 1. And the correlation between TerraSAR-X and ASAR derived mean velocities is 0.76. TerraSAR-X high resolution spot- light SAR images can provide higher density of PS points than ASAR data, and also can re- veal the detail change and micro-displacement in a single ground object.

  17. An automated deformable image registration evaluation of confidence tool

    Science.gov (United States)

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

    2016-04-01

    Deformable image registration (DIR) is a powerful tool for radiation oncology, but it can produce errors. Beyond this, DIR accuracy is not a fixed quantity and varies on a case-by-case basis. The purpose of this study is to explore the possibility of an automated program to create a patient- and voxel-specific evaluation of DIR accuracy. AUTODIRECT is a software tool that was developed to perform this evaluation for the application of a clinical DIR algorithm to a set of patient images. In brief, AUTODIRECT uses algorithms to generate deformations and applies them to these images (along with processing) to generate sets of test images, with known deformations that are similar to the actual ones and with realistic noise properties. The clinical DIR algorithm is applied to these test image sets (currently 4). From these tests, AUTODIRECT generates spatial and dose uncertainty estimates for each image voxel based on a Student’s t distribution. In this study, four commercially available DIR algorithms were used to deform a dose distribution associated with a virtual pelvic phantom image set, and AUTODIRECT was used to generate dose uncertainty estimates for each deformation. The virtual phantom image set has a known ground-truth deformation, so the true dose-warping errors of the DIR algorithms were also known. AUTODIRECT predicted error patterns that closely matched the actual error spatial distribution. On average AUTODIRECT overestimated the magnitude of the dose errors, but tuning the AUTODIRECT algorithms should improve agreement. This proof-of-principle test demonstrates the potential for the AUTODIRECT algorithm as an empirical method to predict DIR errors.

  18. First Image Products from EcoSAR - Osa Peninsula, Costa Rica

    Science.gov (United States)

    Osmanoglu, Batuhan; Lee, SeungKuk; Rincon, Rafael; Fatuyinbo, Lola; Bollian, Tobias; Ranson, Jon

    2016-01-01

    Designed especially for forest ecosystem studies, EcoSAR employs state-of-the-art digital beamforming technology to generate wide-swath, high-resolution imagery. EcoSARs dual antenna single-pass imaging capability eliminates temporal decorrelation from polarimetric and interferometric analysis, increasing the signal strength and simplifying models used to invert forest structure parameters. Antennae are physically separated by 25 meters providing single pass interferometry. In this mode the radar is most sensitive to topography. With 32 active transmit and receive channels, EcoSARs digital beamforming is an order of magnitude more versatile than the digital beamforming employed on the upcoming NISAR mission. EcoSARs long wavelength (P-band, 435 MHz, 69 cm) measurements can be used to simulate data products for ESAs future BIOMASS mission, allowing scientists to develop algorithms before the launch of the satellite. EcoSAR can also be deployed to collect much needed data where BIOMASS satellite wont be allowed to collect data (North America, Europe and Arctic), filling in the gaps to keep a watchful eye on the global carbon cycle. EcoSAR can play a vital role in monitoring, reporting and verification schemes of internationals programs such as UN-REDD (United Nations Reducing Emissions from Deforestation and Degradation) benefiting global society. EcoSAR was developed and flown with support from NASA Earth Sciences Technology Offices Instrument Incubator Program.

  19. Geodetic imaging of tectonic deformation with InSAR

    Science.gov (United States)

    Fattahi, Heresh

    Precise measurements of ground deformation across the plate boundaries are crucial observations to evaluate the location of strain localization and to understand the pattern of strain accumulation at depth. Such information can be used to evaluate the possible location and magnitude of future earthquakes. Interferometric Synthetic Aperture Radar (InSAR) potentially can deliver small-scale (few mm/yr) ground displacement over long distances (hundreds of kilometers) across the plate boundaries and over continents. However, Given the ground displacement as our signal of interest, the InSAR observations of ground deformation are usually affected by several sources of systematic and random noises. In this dissertation I identify several sources of systematic and random noise, develop new methods to model and mitigate the systematic noise and to evaluate the uncertainty of the ground displacement measured with InSAR. I use the developed approach to characterize the tectonic deformation and evaluate the rate of strain accumulation along the Chaman fault system, the western boundary of the India with Eurasia tectonic plates. I evaluate the bias due to the topographic residuals in the InSAR range-change time-series and develope a new method to estimate the topographic residuals and mitigate the effect from the InSAR range-change time-series (Chapter 2). I develop a new method to evaluate the uncertainty of the InSAR velocity field due to the uncertainty of the satellite orbits (Chapter 3) and a new algorithm to automatically detect and correct the phase unwrapping errors in a dense network of interferograms (Chapter 4). I develop a new approach to evaluate the impact of systematic and stochastic components of the tropospheric delay on the InSAR displacement time-series and its uncertainty (Chapter 5). Using the new InSAR time-series approach developed in the previous chapters, I study the tectonic deformation across the western boundary of the India plate with Eurasia and

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

    Science.gov (United States)

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

    2016-07-01

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

  1. Configurable automatic detection and registration of fiducial frames for device-to-image registration in MRI-guided prostate interventions.

    Science.gov (United States)

    Tokuda, Junichi; Song, Sang-Eun; Tuncali, Kemal; Tempany, Clare; Hata, Nobuhiko

    2013-01-01

    We propose a novel automatic fiducial frame detection and registration method for device-to-image registration in MRI-guided prostate interventions. The proposed method does not require any manual selection of markers, and can be applied to a variety of fiducial frames, which consist of multiple cylindrical MR-visible markers placed in different orientations. The key idea is that automatic extraction of linear features using a line filter is more robust than that of bright spots by thresholding; by applying a line set registration algorithm to the detected markers, the frame can be registered to the MRI. The method was capable of registering the fiducial frame to the MRI with an accuracy of 1.00 +/- 0.73 mm and 1.41 +/- 1.06 degrees in a phantom study, and was sufficiently robust to detect the fiducial frame in 98% of images acquired in clinical cases despite the existence of anatomical structures in the field of view.

  2. Extraction of Coastlines with Fuzzy Approach Using SENTINEL-1 SAR Image

    Science.gov (United States)

    Demir, N.; Kaynarca, M.; Oy, S.

    2016-06-01

    Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Then the image is terrain-corrected using SRTM digital surface model. Classification of SAR image is a challenging task since SAR and optical sensors have very different properties. Even between different bands of the SAR sensors, the images look very different. So, the classification of SAR image is difficult with the traditional unsupervised methods. In this study, a fuzzy approach has been applied to distinguish the coastal pixels than the land surface pixels. The standard deviation and the mean, median values are calculated to use as parameters in fuzzy approach. The Mean-standard-deviation (MS) Large membership function is used because the large amounts of land and ocean pixels dominate the SAR image with large mean and standard deviation values. The pixel values are multiplied with 1000 to easify the calculations. The mean is calculated as 23 and the standard deviation is calculated as 12 for the whole image. The multiplier parameters are selected as a: 0.58, b: 0.05 to maximize the land surface membership. The result is evaluated using airborne LIDAR data, only for the areas where LIDAR dataset is available and secondly manually digitized coastline. The laser points which are below 0,5 m are classified as the ocean points. The 3D alpha-shapes algorithm is used to detect the coastline points from LIDAR data. Minimum distances are calculated between the LIDAR points of

  3. THE REALIZATION OF THE PPP AUTOFOCUS METHOD IN PFA FOR SPOTLIGHT MODE SAR IMAGING

    Institute of Scientific and Technical Information of China (English)

    Fu Wenxian; Hong Wen; Li Shaohong

    2002-01-01

    This paper first studies the phase errors for fine-resolution spotlight mode SAR imaging and decomposes the phase errors into two kinds, one is caused by translation and the other by rotation. Mathematical analysis and computer simulations show the above mentioned motion kinds and their corresponding damages on spotlight mode SAR imaging. Based on this analysis, a single PPP is introduced for spotlight mode SAR imaging with the PFA on the assumption that relative rotation between APC and imaged scene is uniform. The selected single point is used first to correct the quadratic and higher order phase errors and then to adjust the linear errors. After this compensation, the space-invariant phase errors caused by translation are almost corrected. Finally results are presented with the simulated data.

  4. THE REALIZATION OF THE PPP AUTOFOCUS METHOD IN PFA FOR SPOTLIGHT MODE SAR IMAGING

    Institute of Scientific and Technical Information of China (English)

    FuWenxian; HongWen; 等

    2002-01-01

    This paper first studies the phase errors for fine-resolution spotlight mode SAR imaging and decomposes the phase errors into two kinds, one is caused by translation and the other by rotation.Mathematical analysis and computer simulations show the sbove mentioned motion kinds and their corresponding damages on spotlight mode SAR imaging.Based on this analysis, a single PPP is introduced for spotlight mode SAR imaging with the PFA on the assumption that relative rotation between APC and imaged sceme is uniform.The selected single point is used first to correct the quadratic and higher order phase errors and then to adjust the linear errors.After this compensation, the space-invariant phase errors caused by translation are almost corrected.Finally results are presented with the simulated data.

  5. Compressed Sensing Imaging Algorithm for High-squint SAR Based on NCS Operator

    Directory of Open Access Journals (Sweden)

    Gu Fufei

    2016-02-01

    Full Text Available A novel compressed sensing imaging algorithm for high-squint Synthetic Aperture Radar (SAR based on a Nonlinear Chirp-Scaling (NCS operator is proposed. First, the echo signal of high-squint SAR is analyzed, and a novel imaging method based on the Nyquist-sampled echo signal is proposed. With the proposed method, the range migration is corrected and the coupling problem in the range and azimuth directions is solved. Then, to solve the problem of high-squint SAR imaging using undersampled echo signals, the NCS operator and compressed sensing algorithm based on this operator are constructed. Imaging results are obtained by solving an optimization problem. The proposed method can recover a sparse scene using undersampled echo data. Furthermore, it can recover a nonsparse scene using fully sampled data. Finally, simulations show the effectiveness of the proposed method.

  6. An Optimal Method For Wake Detection In SAR Images Using Radon Transformation Combined With Wavelet Filters

    CERN Document Server

    Krishnaveni, M; Subashini, P

    2009-01-01

    A new fangled method for ship wake detection in synthetic aperture radar (SAR) images is explored here. Most of the detection procedure applies the Radon transform as its properties outfit more than any other transformation for the detection purpose. But still it holds problems when the transform is applied to an image with a high level of noise. Here this paper articulates the combination between the radon transformation and the shrinkage methods which increase the mode of wake detection process. The latter shrinkage method with RT maximize the signal to noise ratio hence it leads to most optimal detection of lines in the SAR images. The originality mainly works on the denoising segment of the proposed algorithm. Experimental work outs are carried over both in simulated and real SAR images. The detection process is more adequate with the proposed method and improves better than the conventional methods.

  7. Refocusing of Moving Targets in SAR Images via Parametric Sparse Representation

    Directory of Open Access Journals (Sweden)

    Yichang Chen

    2017-08-01

    Full Text Available In this paper, a parametric sparse representation (PSR method is proposed for refocusing of moving targets in synthetic aperture radar (SAR images. In regular SAR images, moving targets are defocused due to unknown motion parameters. Refocusing of moving targets requires accurate phase compensation of echo data. In the proposed method, the region of interest (ROI data containing the moving targets are extracted from the complex SAR image and represented in a sparse fashion through a parametric transform, which is related to the phase compensation parameter. By updating the reflectivities of moving target scatterers and the parametric transform in an iterative fashion, the phase compensation parameter can be accurately estimated and the SAR images of moving targets can be refocused well. The proposed method directly operates on small-size defocused ROI data, which helps to reduce the computational burden and suppress the clutter. Compared to other existing ROI-based methods, the proposed method can suppress asymmetric side-lobes and improve the image quality. Both simulated data and real SAR data collected by GF-3 satellite are used to validate the effectiveness of the proposed method.

  8. Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis

    Directory of Open Access Journals (Sweden)

    Rong Gui

    2016-08-01

    Full Text Available Accurate building information plays a crucial role for urban planning, human settlements and environmental management. Synthetic aperture radar (SAR images, which deliver images with metric resolution, allow for analyzing and extracting detailed information on urban areas. In this paper, we consider the problem of extracting individual buildings from SAR images based on domain ontology. By analyzing a building scattering model with different orientations and structures, the building ontology model is set up to express multiple characteristics of individual buildings. Under this semantic expression framework, an object-based SAR image segmentation method is adopted to provide homogeneous image objects, and three categories of image object features are extracted. Semantic rules are implemented by organizing image object features, and the individual building objects expression based on an ontological semantic description is formed. Finally, the building primitives are used to detect buildings among the available image objects. Experiments on TerraSAR-X images of Foshan city, China, with a spatial resolution of 1.25 m × 1.25 m, have shown the total extraction rates are above 84%. The results indicate the ontological semantic method can exactly extract flat-roof and gable-roof buildings larger than 250 pixels with different orientations.

  9. Fast correlation technique for glacier flow monitoring by digital camera and space-borne SAR images

    Directory of Open Access Journals (Sweden)

    Moreau Luc

    2011-01-01

    Full Text Available Abstract Most of the image processing techniques have been first proposed and developed on small size images and progressively applied to larger and larger data sets resulting from new sensors and application requirements. In geosciences, digital cameras and remote sensing images can be used to monitor glaciers and to measure their surface velocity by different techniques. However, the image size and the number of acquisitions to be processed to analyze time series become a critical issue to derive displacement fields by the conventional correlation technique. In this paper, a mathematical optimization of the classical normalized cross-correlation and its implementation are described to overcome the computation time and window size limitations. The proposed implementation is performed with a specific memory management to avoid most of the temporary result re-computations. The performances of the software resulting from this optimization are assessed by computing the correlation between optical images of a serac fall, and between Synthetic Aperture Radar (SAR images of Alpine glaciers. The optical images are acquired by a digital camera installed near the Argentière glacier (Chamonix, France and the SAR images are acquired by the high resolution TerraSAR-X satellite over the Mont-Blanc area. The results illustrate the potential of this implementation to derive dense displacement fields with a computational time compatible with the camera images acquired every 2 h and with the size of the TerraSAR-X scenes covering 30 × 50 km2.

  10. Target Detection in SAR Images Based on a Level Set Approach

    Energy Technology Data Exchange (ETDEWEB)

    Marques, Regis C.P.; Medeiros, Fatima N.S.; Ushizima, Daniela M.

    2008-09-01

    This paper introduces a new framework for point target detection in synthetic aperture radar (SAR) images. We focus on the task of locating reflective small regions using alevel set based algorithm. Unlike most of the approaches in image segmentation, we address an algorithm which incorporates speckle statistics instead of empirical parameters and also discards speckle filtering. The curve evolves according to speckle statistics, initially propagating with a maximum upward velocity in homogeneous areas. Our approach is validated by a series of tests on synthetic and real SAR images and compared with three other segmentation algorithms, demonstrating that it configures a novel and efficient method for target detection purpose.

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

    Science.gov (United States)

    Xu, Peng; Yao, Dezhong; Luo, Fen

    2005-08-01

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

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

    Science.gov (United States)

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

    2015-11-01

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

  13. Two new approaches for image registration based on spatial-temporal relationship

    Institute of Scientific and Technical Information of China (English)

    Deng Zhipeng; Yang Jie; Liu Xiaojun

    2005-01-01

    How to improve the probability of registration and precision of localization is a hard problem, which is desiderated to solve. The two basic approaches (normalized cross-correlation and phase correlation) for image registration are analysed, two improved approaches based on spatial-temporal relationship are presented. This method adds the correlation matrix according to the displacements in x- cirection and y- directions, and the registration pose is searched in the added matrix. The method overcomes the shortcoming that the probability of registration decreasing with area increasing owing to geometric distortion, improves the probability and the robustness of registration.

  14. Rotation and translation registration of bandlimited interferometric images using a chirp z-transform

    Science.gov (United States)

    Iacchetta, Alexander S.; Fienup, James R.; Leisawitz, David T.

    2016-07-01

    Image reconstruction algorithms for wide-field spatio-spectral interferometry require knowledge of registration parameters associated with low-resolution image measurements at various baseline orientations, such that the images can be registered to within the fine resolution of the final desired image. We have developed an image registration procedure that combines a nonlinear optimization algorithm with the sub-pixel precision of chirp z-transform resampling, particularly for rotation and translation, of bandlimited images with non-radially symmetric aberrations. We show the accuracy of this image registration technique on simulated images that have a complexity comparable to scenes observed experimentally with NASA's wide-field imaging interferometry testbed. Registration to within a tenth of a pixel for translation and within three arcminutes for rotation is demonstrated at the largest simulated noise levels.

  15. Joint Change Detection and Image Registration for Optical Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Wang Luo

    2012-03-01

    Full Text Available In this letter, a novel method is proposed for jointly unsupervised change detection and image registration over multi-temporal optical remote sensing images. An iterative energy minimization scheme is employed to extract the pixel opacity. Specifically, we extract the consistent points which provide the initial seed nodes and the feature nodes for random walker image segmentation and image registration, respectively. And the seed nodes will be updated according to the analysis of the changed and unchanged regions. Experimental results demonstrate that the proposed method can perform change detection as well as the state of the art methods. In particular, it can perform change detection rapidly and automatically over unregistered optical remote sensing images.

  16. SAR Computation inside Fetus by RF Coil during MR Imaging Employing Realistic Numerical Pregnant Woman Model

    Science.gov (United States)

    Kikuchi, Satoru; Saito, Kazuyuki; Takahashi, Masaharu; Ito, Koichi; Ikehira, Hiroo

    This paper presents the computational electromagnetic dosimetry inside an anatomically based pregnant woman models exposed to electromagnetic wave during magnetic resonance imaging. The two types of pregnant woman models corresponding to early gestation and 26 weeks gestation were used for this study. The specific absorption rate (SAR) in and around a fetus were calculated by radiated electromagnetic wave from highpass and lowpass birdcage coil. Numerical calculation results showed that high SAR region is observed at the body in the vicinity of gaps of the coil, and is related to concentrated electric field in the gaps of human body such as armpit and thigh. Moreover, it has confirmed that the SAR in the fetus is less than International Electrotechnical Commission limit of 10W/kg, when whole-body average SARs are 2W/kg and 4W/kg, which are the normal operating mode and first level controlled operating mode, respectively.

  17. A fully automatic image-to-world registration method for image-guided procedure with intraoperative imaging updates

    Science.gov (United States)

    Li, Senhu; Sarment, David

    2016-03-01

    Image-guided procedure with intraoperative imaging updates has made a big impact on minimally invasive surgery. Compact and mobile CT imaging device combining with current commercial available image guided navigation system is a legitimate and cost-efficient solution for a typical operating room setup. However, the process of manual fiducial-based registration between image and physical spaces (image-to-world) is troublesome for surgeons during the procedure, which results in much procedure interruptions and is the main source of registration errors. In this study, we developed a novel method to eliminate the manual registration process. Instead of using probe to manually localize the fiducials during the surgery, a tracking plate with known fiducial positions relative to the reference coordinates is designed and fabricated through 3D printing technique. The workflow and feasibility of this method has been studied through a phantom experiment.

  18. 3D-2D registration of cerebral angiograms: a method and evaluation on clinical images.

    Science.gov (United States)

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

    2013-08-01

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

  19. An Algorithm for Ship Wake Detection from the SAR Images Using the Radon Transform and Morphological Image Processing

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Using the Rador transform and morphological image processing, an algorithm for ship's wake detection in the SAR (synthetic aperture radar) image is developed. Being manipulated in the Radon space to invert the gray-level and binary images, the linesr texture of ship wake in oceanic clutter can be well detected. It has been applied to the automatic detection of a moving ship from the SEASAT SAR image. The results show that this algorithm is well robust in a strong noisy background and is not very sensitive to the threshold parameter and the working window size.

  20. A sparsity-driven approach for joint SAR imaging and phase error correction.

    Science.gov (United States)

    Önhon, N Özben; Cetin, Müjdat

    2012-04-01

    Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data, which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm, where each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements that it provides over existing techniques for model error compensation in SAR.

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

    Science.gov (United States)

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

    2014-11-01

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

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

    Science.gov (United States)

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

    2015-03-01

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

  3. Building detection and building parameter retrieval in InSAR phase images

    Science.gov (United States)

    Dubois, Clémence; Thiele, Antje; Hinz, Stefan

    2016-04-01

    The high resolution provided by the current satellite SAR missions makes them an attractive solution for the detailed analysis of urban areas. Especially due to their weather and daylight independency, they can be employed when optical sensors come to their limits. Due to the specific oblique side-looking configuration of such SAR sensors, phenomena such as layover, double bounce and shadow appear at building location, which can be better understood with very high resolution (VHR) SAR data. The detection of those areas, as well as the retrieval of building parameters through a detailed analysis of the extracted structures, is a challenging task. Indeed, depending on the acquisition configuration, on building material and surroundings, those patterns are not always consistent in amplitude SAR images. They can be difficult to recognize and distinguish automatically. Considering InSAR phase images instead of amplitude images is very helpful for this task, as InSAR is more depending on the geometry. Therefore, in this paper, we focus on the detection and extraction of building layover in InSAR phase images. Two complementing detectors are proposed, and their results are combined, in order to provide reliable building hypotheses. Based on the extracted segments, further analysis is conducted. Especially, the number of connected facades is analyzed. Characteristically geometrical shapes are finally fitted for each facade to permit the determination of the final building parameters as length, width, and height. Results of this approach are shown for three different datasets, first in terms of correctness and completeness of the extraction, and second in terms of accuracy of the extracted building parameters. For the considered datasets, the completeness and correctness are of about 70% and 90%, respectively. Eliminating clear outliers, the determined parameters present an accuracy up to 4 m (length), 2 m (height) and 3 ° (orientation). In this article isolated, middle to

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

    Science.gov (United States)

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

    2015-10-05

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

  5. Polarimetric Contextual Classification of PolSAR Images Using Sparse Representation and Superpixels

    Directory of Open Access Journals (Sweden)

    Jilan Feng

    2014-07-01

    Full Text Available In recent years, sparse representation-based techniques have shown great potential for pattern recognition problems. In this paper, the problem of polarimetric synthetic aperture radar (PolSAR image classification is investigated using sparse representation-based classifiers (SRCs. We propose to take advantage of both polarimetric information and contextual information by combining sparsity-based classification methods with the concept of superpixels. Based on polarimetric feature vectors constructed by stacking a variety of polarimetric signatures and a superpixel map, two strategies are considered to perform polarimetric-contextual classification of PolSAR images. The first strategy starts by classifying the PolSAR image with pixel-wise SRC. Then, spatial regularization is imposed on the pixel-wise classification map by using majority voting within superpixels. In the second strategy, the PolSAR image is classified by taking superpixels as processing elements. The joint sparse representation-based classifier (JSRC is employed to combine the polarimetric information contained in feature vectors and the contextual information provided by superpixels. Experimental results on real PolSAR datasets demonstrate the feasibility of the proposed approaches. It is proven that the classification performance is improved by using contextual information. A comparison with several other approaches also verifies the effectiveness of the proposed approach.

  6. Evaluation of 3 different registration techniques in image-guided bimaxillary surgery.

    Science.gov (United States)

    Sun, Yi; Luebbers, Heinz-Theo; Agbaje, Jimoh Olubanwo; Schepers, Serge; Vrielinck, Luc; Lambrichts, Ivo; Politis, Constantinus

    2013-07-01

    Perioperative navigation is an upcoming tool in orthognathic surgery. This study aimed to access the feasibility of the technique and to evaluate the success rate of 3 different registration methods--facial surface registration, anatomic landmark-based registration, and template-based registration. The BrainLab navigation system (BrainLab AG, Feldkirchen, Germany) was used as an additional precision tool for 85 patients who underwent bimaxillary orthognathic surgery from February 2010 to June 2012. Eighteen cases of facial surface-based registration, 63 cases of anatomic landmark-based registration, and 8 cases of template-based registration were analyzed. The overall success rate of facial surface-based registration was 39%, which was significant lower than template-based (100%, P = 0.013) and anatomic landmark-based registration (95%, P bimaxillary orthognathic surgery has been proved to be feasible. The registration process is the critical point regarding success of intraoperative navigation. Anatomic landmark-based registration is a reliable technique for image-guided bimaxillary surgery. In contrast, facial surface-based registration is highly unreliable.

  7. Echocardiography to magnetic resonance image registration for use in image-guided cardiac catheterization procedures

    Energy Technology Data Exchange (ETDEWEB)

    Ma Yingliang; Penney, Graeme P; Razavi, Reza; Rhode, Kawal S [Division of Imaging Sciences, King' s College, London SE1 7EH (United Kingdom); Rinaldi, C Aldo; Cooklin, Mike [Department of Cardiology, Guy' s and St Thomas' NHS Foundation Trust, London, SE1 7EH (United Kingdom)], E-mail: y.ma@kcl.ac.uk

    2009-08-21

    We present a robust method to register three-dimensional echocardiography (echo) images to magnetic resonance images (MRI) based on anatomical features, which is designed to be used in the registration pipeline for overlaying MRI-derived roadmaps onto two-dimensional live x-ray images during cardiac catheterization procedures. The features used in image registration are the endocardial surface of the left ventricle and the centre line of the descending aorta. The MR-derived left ventricle surface is generated using a fully automated algorithm, and the echo-derived left ventricle surface is produced using a semi-automatic segmentation method provided by the QLab software (Philips Healthcare) that it is routinely used in clinical practice. We test our method on data from six volunteers and four patients. We validated registration accuracy using two methods: the first calculated a root mean square distance error using expert identified anatomical landmarks, and the second method used catheters as landmarks in two clinical electrophysiology procedures. Results show a mean error of 4.1 mm, which is acceptable for our clinical application, and no failed registrations were observed. In addition, our algorithm works on clinical data, is fast and only requires a small amount of manual input, and so it is applicable for use during cardiac catheterization procedures.

  8. Multi-Core DSP Based Parallel Architecture for FMCW SAR Real-Time Imaging

    Directory of Open Access Journals (Sweden)

    C. F. Gu

    2015-12-01

    Full Text Available This paper presents an efficient parallel processing architecture using multi-core Digital Signal Processor (DSP to improve the capability of real-time imaging for Frequency Modulated Continuous Wave Synthetic Aperture Radar (FMCW SAR. With the application of the proposed processing architecture, the imaging algorithm is modularized, and each module is efficiently realized by the proposed processing architecture. In each module, the data processing of different cores is executed in parallel, also the data transmission and data processing of each core are synchronously carried out, so that the processing time for SAR imaging is reduced significantly. Specifically, the time of corner turning operation, which is very time-consuming, is ignored under computationally intensive case. The proposed parallel architecture is applied to a compact Ku-band FMCW SAR prototype to achieve real-time imageries with 34 cm x 51 cm (range x azimuth resolution.

  9. SAR images classification method based on Dempster-Shafer theory and kernel estimate

    Institute of Scientific and Technical Information of China (English)

    He Chu; Xia Guisong; Sun Hong

    2007-01-01

    To study the scene classification in the Synthetic Aperture Radar (SAR) image, a novel method based on kernel estimate, with the Markov context and Dempster-Shafer evidence theory is proposed.Initially, a nonparametric Probability Density Function (PDF) estimate method is introduced, to describe the scene of SAR images.And then under the Markov context, both the determinate PDF and the kernel estimate method are adopted respectively, to form a primary classification.Next, the primary classification results are fused using the evidence theory in an unsupervised way to get the scene classification.Finally, a regularization step is used, in which an iterated maximum selecting approach is introduced to control the fragments and modify the errors of the classification.Use of the kernel estimate and evidence theory can describe the complicated scenes with little prior knowledge and eliminate the ambiguities of the primary classification results.Experimental results on real SAR images illustrate a rather impressive performance.

  10. Edge Detection of PolSAR Image Based on Stochastic Distance

    Directory of Open Access Journals (Sweden)

    WANG Qing

    2015-07-01

    Full Text Available A new edge detection methodology in PolSAR images is proposed, which is based on stochastic distance in the statistical theory and combined with complex Wishart distribution. Its main principle is inspired from the phenomenon that stochastic distance of two classes separated by an edge is closely related to the edge direction and the contrast of two classes. Simulation experiments are carried out to analyze the performance of the proposed methods. Results prove that methods have better capabilities in edge orientation and edge positioning than common used methods. Then, the proposed detection procedure is tested on a simulated PolSAR image with the complex Wishart distribution, as well as an airborne fully polarimetric SAR image.

  11. Polarimetric SAR Image Object Segmentation via Level Set with Stationary Global Minimum

    Directory of Open Access Journals (Sweden)

    Wen Yang

    2010-01-01

    Full Text Available We present a level set-based method for object segmentation in polarimetric synthetic aperture radar (PolSAR images. In our method, a modified energy functional via active contour model is proposed based on complex Gaussian/Wishart distribution model for both single-look and multilook PolSAR images. The modified functional has two interesting properties: (1 the curve evolution does not enter into local minimum; (2 the level set function has a unique stationary convergence state. With these properties, the desired object can be segmented more accurately. Besides, the modified functional allows us to set an effective automatic termination criterion and makes the algorithm more practical. The experimental results on synthetic and real PolSAR images demonstrate the effectiveness of our method.

  12. Classification of agricultural fields using time series of dual polarimetry TerraSAR-X images

    Directory of Open Access Journals (Sweden)

    S. Mirzaee

    2014-10-01

    Full Text Available Due to its special imaging characteristics, Synthetic Aperture Radar (SAR has become an important source of information for a variety of remote sensing applications dealing with environmental changes. SAR images contain information about both phase and intensity in different polarization modes, making them sensitive to geometrical structure and physical properties of the targets such as dielectric and plant water content. In this study we investigate multi temporal changes occurring to different crop types due to phenological changes using high-resolution TerraSAR-X imagers. The dataset includes 17 dual-polarimetry TSX data acquired from June 2012 to August 2013 in Lorestan province, Iran. Several features are extracted from polarized data and classified using support vector machine (SVM classifier. Training samples and different features employed in classification are also assessed in the study. Results show a satisfactory accuracy for classification which is about 0.91 in kappa coefficient.

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

    NARCIS (Netherlands)

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

    2016-01-01

    Image registration is part of a large variety of medical applications including diagnosis, monitoring disease progression and/or treatment effectiveness and, more recently, therapy guidance. Such applications usually involve several imaging modalities such as ultrasound, computed tomography,

  14. Evaluation of accuracy of B-spline transformation-based deformable image registration with different parameter settings for thoracic images

    OpenAIRE

    Kanai, Takayuki; Kadoya, Noriyuki; Ito, Kengo; Onozato, Yusuke; Cho, Sang Yong; Kishi, Kazuma; Dobashi, Suguru; Umezawa, Rei; Matsushita, Haruo; Takeda, Ken; Jingu, Keiichi

    2014-01-01

    Deformable image registration (DIR) is fundamental technique for adaptive radiotherapy and image-guided radiotherapy. However, further improvement of DIR is still needed. We evaluated the accuracy of B-spline transformation-based DIR implemented in elastix. This registration package is largely based on the Insight Segmentation and Registration Toolkit (ITK), and several new functions were implemented to achieve high DIR accuracy. The purpose of this study was to clarify whether new functions ...

  15. Automatic masking for robust 3D-2D image registration in image-guided spine surgery

    Science.gov (United States)

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

    2016-03-01

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

  16. Comparing C- and L-band SAR images for sea ice motion estimation

    Science.gov (United States)

    Lehtiranta, J.; Siiriä, S.; Karvonen, J.

    2015-02-01

    Pairs of consecutive C-band synthetic-aperture radar (SAR) images are routinely used for sea ice motion estimation. The L-band radar has a fundamentally different character, as its longer wavelength penetrates deeper into sea ice. L-band SAR provides information on the seasonal sea ice inner structure in addition to the surface roughness that dominates C-band images. This is especially useful in the Baltic Sea, which lacks multiyear ice and icebergs, known to be confusing targets for L-band sea ice classification. In this work, L-band SAR images are investigated for sea ice motion estimation using the well-established maximal cross-correlation (MCC) approach. This work provides the first comparison of L-band and C-band SAR images for the purpose of motion estimation. The cross-correlation calculations are hardware accelerated using new OpenCL-based source code, which is made available through the author's web site. It is found that L-band images are preferable for motion estimation over C-band images. It is also shown that motion estimation is possible between a C-band and an L-band image using the maximal cross-correlation technique.

  17. a New 2d Otsu for Water Extraction from SAR Image

    Science.gov (United States)

    Guo, Y.; Zhang, J.

    2017-09-01

    SAR image segmentation is a crucial step that heavily influences the performance of image interpretation. The texture factor to replace the neighborhood mean dimension in the traditional Otsu method is proposed in this work, aiming at the problem that the SAR image has unique characteristics and the original 2D Otsu method only considers the pixel neighborhood mean information. In this paper, TerraSAR image with the single band and single polarization is used to water extraction. Firstly, the semantic function is used to analyze the structural characteristics of the sample image to determine the optimal parameters of the texture information extraction. Then, calculate the textural measures such as contrast, entropy, homogeneity, mean and second moment based on gray level co-occurrence matrix(GLCM) method. The results are compared with the artificially marked images and the results of the original 2D Otsu.The experimental results achieve higher objective values, which shows the proposed algorithm using texture factor has a high practical value for SAR Image water segmentation.

  18. Speckle Filtering in PolSAR Images by Enhanced Wavelet Thresholding

    Science.gov (United States)

    Boutarfa, Souhila; Bouchemakh, Lynda; Smara, Youcef

    2016-08-01

    The PolSAR images are affected by a noise called speckle, which deteriorates image quality and complicates image interpretation. The polarimetric filtering is a necessary treatment prior to analysis that allows to reduce speckle and to obtain an improved image quality.In this paper, we present a polarimetric speckle filtering method based on enhancement of wavelet thresholding, hard and soft thresholding using directional coefficients improvement to reduce speckle without destroying the information. This algorithm is based on the classification of significant coefficients and applying the thresholding to obtain a better image quality.The methods are applied to the three polarimetric E-SAR images acquired on Oberpfaffenhofen area located in Munich, Germany, in P-band and the fully polarimetric RADARSAT-2 images acquired on Algiers, Algeria, in C-band.To evaluate the performance of each filter, we based it on the following criteria: smoothing homogeneous areas, preserving structural characteristics of objects and maintaining the information.

  19. SAR image segmentation with entropy ranking based adaptive semi-supervised spectral clustering

    Science.gov (United States)

    Zhang, Xiangrong; Yang, Jie; Hou, Biao; Jiao, Licheng

    2010-10-01

    Spectral clustering has become one of the most popular modern clustering algorithms in recent years. In this paper, a new algorithm named entropy ranking based adaptive semi-supervised spectral clustering for SAR image segmentation is proposed. We focus not only on finding a suitable scaling parameter but also determining automatically the cluster number with the entropy ranking theory. Also, two kinds of constrains must-link and cannot-link based semi-supervised spectral clustering is applied to gain better segmentation results. Experimental results on SAR images show that the proposed method outperforms other spectral clustering algorithms.

  20. Modeling lung motion using consistent image registration in four-dimensional computed tomography for radiation therapy

    Science.gov (United States)

    Lu, Wei; Song, Joo Hyun; Christensen, Gary E.; Parikh, Parag J.; Bradley, Jeffrey D.; Low, Daniel A.

    2006-03-01

    Respiratory motion is a significant source of error in conformal radiation therapy for the thorax and upper abdomen. Four-dimensional computed tomography (4D CT) has been proposed to reduce the uncertainty caused by internal respiratory organ motion. A 4D CT dataset is retrospectively reconstructed at various stages of a respiratory cycle. An important tool for 4D treatment planning is deformable image registration. An inverse consistent image registration is used to model lung motion from one respiratory stage to another during a breathing cycle. This diffeomorphic registration jointly estimates the forward and reverse transformations providing more accurate correspondence between two images. Registration results and modeled motions in the lung are shown for three example respiratory stages. The results demonstrate that the consistent image registration satisfactorily models the large motions in the lung, providing a useful tool for 4D planning and delivering.

  1. 3-D Registration on Carotid Artery imaging data: MRI for different timesteps

    CERN Document Server

    Bizopoulos, Paschalis A; Michalis, Lampros K; Koutsouris, Dimitrios D; Fotiadis, Dimitrios I

    2016-01-01

    A common problem which is faced by the researchers when dealing with arterial carotid imaging data is the registration of the geometrical structures between different imaging modalities or different timesteps. The use of the "Patient Position" DICOM field is not adequate to achieve accurate results due to the fact that the carotid artery is a relatively small structure and even imperceptible changes in patient position and/or direction make it difficult. While there is a wide range of simple/advanced registration techniques in the literature, there is a considerable number of studies which address the geometrical structure of the carotid artery without using any registration technique. On the other hand the existence of various registration techniques prohibits an objective comparison of the results using different registration techniques. In this paper we present a method for estimating the statistical significance that the choice of the registration technique has on the carotid geometry. One-Way Analysis of...

  2. Change detection of polarimetric SAR images based on the KummerU Distribution

    Science.gov (United States)

    Chen, Quan; Zou, Pengfei; Li, Zhen; Zhang, Ping

    2014-11-01

    In the society of PolSAR image segmentation, change detection and classification, the classical Wishart distribution has been used for a long time, but it especially suit to low-resolution SAR image, because in traditional sensors, only a small number of scatterers are present in each resolution cell. With the improving of SAR systems these years, the classical statistical models can therefore be reconsidered for high resolution and polarimetric information contained in the images acquired by these advanced systems. In this study, SAR image segmentation algorithm based on level-set method, added with distance regularized level-set evolution (DRLSE) is performed using Envisat/ASAR single-polarization data and Radarsat-2 polarimetric images, respectively. KummerU heterogeneous clutter model is used in the later to overcome the homogeneous hypothesis at high resolution cell. An enhanced distance regularized level-set evolution (DRLSE-E) is also applied in the later, to ensure accurate computation and stable level-set evolution. Finally, change detection based on four polarimetric Radarsat-2 time series images is carried out at Genhe area of Inner Mongolia Autonomous Region, NorthEastern of China, where a heavy flood disaster occurred during the summer of 2013, result shows the recommend segmentation method can detect the change of watershed effectively.

  3. Fusion of Spaceborne Optical and SAR Images for Building Height Quick Extraction in Big Urban Areas

    Directory of Open Access Journals (Sweden)

    TIAN Feng

    2017-07-01

    Full Text Available The spaceborne high-resolution optical images and synthetic aperture radar (SAR images are applied to extract building height in urban areas widely. But the lack of optical satellite parameters along with the SAR images' incomplete scattering characteristics and inefficient extraction make the application flawed. To cure the above problems, we investigated the joint use of the spaceborne high-resolution optical images and SAR images to extract building height information quickly in big urban areas. The chain is decomposed into the main following steps: First, the building shadows are extracted by integrating support vector machines (SVM with morphological shadow index (MSI and their lengths are measured automatically. Then, the height extraction from SAR images based on a model matching technique for some appropriate samples. Finally, obtain the other heights based on the simple linear regression analysis. This approach which combines the data and feature from different satellite systems to make up the flaws for each other is not only efficient and low-cost, but also satisfy the basic accuracy requirement.

  4. Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images

    Directory of Open Access Journals (Sweden)

    Yuanzhi Zhang

    2017-02-01

    Full Text Available In this paper, we present a comparison between several algorithms for oil spill classifications using fully and compact polarimetric SAR images. Oil spill is considered as one of the most significant sources of marine pollution. As a major difficulty of SAR-based oil spill detection algorithms is the classification between mineral and biogenic oil, we focus on quantitatively analyzing and comparing fully and compact polarimetric satellite synthetic aperture radar (SAR modes to detect hydrocarbon slicks over the sea surface, discriminating them from weak-damping surfactants, such as biogenic slicks. The experiment was conducted on quad-pol SAR data acquired during the Norwegian oil-on-water experiment in 2011. A universal procedure was used to extract the features from quad-, dual- and compact polarimetric SAR modes to rank different polarimetric SAR modes and common supervised classifiers. Among all the dual- and compact polarimetric SAR modes, the π/2 mode has the best performance. The best supervised classifiers vary and depended on whether sufficient polarimetric information can be obtained in each polarimetric mode. We also analyzed the influence of the number of polarimetric parameters considered as inputs for the supervised classifiers, onto the detection/discrimination performance. We discovered that a feature set with four features is sufficient for most polarimetric feature-based oil spill classifications. Moreover, dimension reduction algorithms, including principle component analysis (PCA and the local linear embedding (LLE algorithm, were employed to learn low dimensional and distinctive information from quad-polarimetric SAR features. The performance of the new feature sets has comparable performance in oil spill classification.

  5. A registration strategy for long spatio-temporal aerial remote sensing image sequence

    Science.gov (United States)

    Cao, Yutian; Yan, Dongmei; Li, Jianming; Wang, Gang

    2015-12-01

    A novel registration strategy for aerial image sequence is put forward to adapt to the long spatio-temporal span of the aerial remote sensing imaging. By setting keyframe, this strategy aligns all images in sequence to a unified datum with high registration sustainability and precision. The contrast experiment on different registration strategies is carried out based on SIFT feature matching of mid-infrared aerial sequences. The experiment results show that the proposed strategy performs well on long spatio-temporal sequences with different imaging resolutions and scenes.

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

    Science.gov (United States)

    Green, W. B.

    1978-01-01

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

  7. A despeckle filter for the Cassini SAR images of Titan's surface

    Science.gov (United States)

    Bratsolis, Emmanuel; Solomonidou, Anezina; Bampasidis, Georgios; Le Mouelic, Stephane; Sotin, Christophe; Coustenis, Athena; Moussas, Xenophon; Kyriakopoulos, Konstantinos

    2010-05-01

    Cassini carries a multimode Ku-band (13.78 GHz) radar instrument designed to probe the surface of Titan and that of other targets in the Saturn system in four operating modes: imaging, altimetry, scatterometry, and radiometry. The Synthetic Aperture Radar (SAR) mode is used at altitudes under ~4000 km, resulting in spatial resolution ranging from ~350 m to >1 km. Images are acquired either left or right of nadir using 2-7 looks. A swath 120-450 km wide is created from 5 antenna beams. SAR coverage is dependent on spacecraft range and orbital geometry. Radar backscatter variations in SAR images can be interpreted in terms of variations of surface slope, near-surface roughness, or near-surface dielectric properties. The images obtained using SAR revealed that Titan has very complex surface (Elachi et al. 2005). A filtering technique is applied to obtain the restored image. One of the major problems hampering the derivation of meaningful texture information from SAR imagery is the speckle noise. It overlays "real" structures and causes gray value variations even in homogeneous parts of the image. Our method is based on probabilistic methods and regards an image as a random element drawn from a prespecified set of possible images. The TSPR (Total Sum Preserving Regularization) filter used here is based on a membrane model Markov random field approximation with a Gaussian conditional probability density function optimized by a synchronous local iterative method. The final form of despeckling gives a sum-preserving regularization for the pixel values of the image. The TSPR method preserves the mean values of local homogeneous regions and decreases the standard deviation up to six times (Bratsolis and Sigelle, 2003). The despeckle filter can be used as intermediate stage for the extraction of meaningful regions that correspond to structural units in the scene or distinguish objects of interest (Bratsolis, 2009). References E. Bratsolis, and M. Sigelle, "Fast SAR Image

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

    Science.gov (United States)

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

    2014-03-12

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

  9. Technical Note: Deformable image registration on partially matched images for radiotherapy applications

    Science.gov (United States)

    Yang, Deshan; Goddu, S. Murty; Lu, Wei; Pechenaya, Olga L.; Wu, Yu; Deasy, Joseph O.; El Naqa, Issam; Low, Daniel A.

    2010-01-01

    In radiation therapy applications, deformable image registrations (DIRs) are often carried out between two images that only partially match. Image mismatching could present as superior-inferior coverage differences, field-of-view (FOV) cutoffs, or motion crossing the image boundaries. In this study, the authors propose a method to improve the existing DIR algorithms so that DIR can be carried out in such situations. The basic idea is to extend the image volumes and define the extension voxels (outside the FOV or outside the original image volume) as NaN (not-a-number) values that are transparent to all floating-point computations in the DIR algorithms. Registrations are then carried out with one additional rule that NaN voxels can match any voxels. In this way, the matched sections of the images are registered properly, and the mismatched sections of the images are registered to NaN voxels. This method makes it possible to perform DIR on partially matched images that otherwise are difficult to register. It may also improve DIR accuracy, especially near or in the mismatched image regions. PMID:20175475

  10. Technical Note: Deformable image registration on partially matched images for radiotherapy applications

    Energy Technology Data Exchange (ETDEWEB)

    Yang Deshan; Goddu, S. Murty; Lu Wei; Pechenaya, Olga L.; Wu Yu; Deasy, Joseph O.; El Naqa, Issam; Low, Daniel A. [Department of Radiation Oncology, Washington University in Saint Louis, Missouri 63110 (United States)

    2010-01-15

    In radiation therapy applications, deformable image registrations (DIRs) are often carried out between two images that only partially match. Image mismatching could present as superior-inferior coverage differences, field-of-view (FOV) cutoffs, or motion crossing the image boundaries. In this study, the authors propose a method to improve the existing DIR algorithms so that DIR can be carried out in such situations. The basic idea is to extend the image volumes and define the extension voxels (outside the FOV or outside the original image volume) as NaN (not-a-number) values that are transparent to all floating-point computations in the DIR algorithms. Registrations are then carried out with one additional rule that NaN voxels can match any voxels. In this way, the matched sections of the images are registered properly, and the mismatched sections of the images are registered to NaN voxels. This method makes it possible to perform DIR on partially matched images that otherwise are difficult to register. It may also improve DIR accuracy, especially near or in the mismatched image regions.

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

    Science.gov (United States)

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

    2017-06-01

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

  12. Heat equation inversion framework for average SAR calculation from magnetic resonance thermal imaging.

    Science.gov (United States)

    Alon, Leeor; Sodickson, Daniel K; Deniz, Cem M

    2016-10-01

    Deposition of radiofrequency (RF) energy can be quantified via electric field or temperature change measurements. Magnetic resonance imaging has been used as a tool to measure three dimensional small temperature changes associated with RF radiation exposure. When duration of RF exposure is long, conversion from temperature change to specific absorption rate (SAR) is nontrivial due to prominent heat-diffusion and conduction effects. In this work, we demonstrated a method for calculation of SAR via an inversion of the heat equation including heat-diffusion and conduction effects. This method utilizes high-resolution three dimensional magnetic resonance temperature images and measured thermal properties of the phantom to achieve accurate calculation of SAR. Accuracy of the proposed method was analyzed with respect to operating frequency of a dipole antenna and parameters used in heat equation inversion. Bioelectromagnetics. 37:493-503, 2016. © 2016 Wiley Periodicals, Inc.

  13. Ka-band InSAR Imaging and Analysis Based on IMU Data

    Directory of Open Access Journals (Sweden)

    Shi Jun

    2014-02-01

    Full Text Available Compared with other bands, the millimeter wave Interferometric Synthetic Aperture Radar (InSAR has high accuracy and small size, which is a hot topic in InSAR research. On the other hand, shorter wavelength causes difficulties in 2D imaging and interferometric phase extraction. In this study, the imaging and phase performance of the streaming Back Projection (BP method combined with IMU data are analyzed and discussed on the basis of actual Ka-band InSAR data. It is found that because the wavelength of the Ka-band is short, it is more sensitive to the antenna phase-center history. To ensure the phase-preserving capacity, the IMU data must be used with accurate motion error compensation. Furthermore, during data processing, we verify the flat-earth-removing capacity of the BP algorithm that calculates and compensates the master and slave antenna phase centers individually.

  14. A Modified Frequency Domain Imaging Method for One-stationary Bistatic SAR

    Directory of Open Access Journals (Sweden)

    Jin Ri-chu

    2014-04-01

    Full Text Available Bistatic Synthetic Aperture Radar (BiSAR in one-stationary mode has many advantages over the traditional monostatic SAR. Its echo, however, shows serious space variance in both range and azimuth directions due to its complex imaging geometry, making it hard to be processed by the frequency methods used in the monostatic SAR. To solve that problem, a method based on blocks and interpolation has been proposed by Wang Yu et al.. With this method, points can be well focused except for those located on the edge of each block. In this paper, a modified method is put forward, which adopts new block-dividing strategy and new mapping relationship in the interpolation. With the proposed method, points on the edge can also be well focused, making the quality of the final image greatly improved.

  15. An estimation method for InSAR interferometric phase combined with image auto-coregistration

    Institute of Scientific and Technical Information of China (English)

    LI Hai; LI Zhenfang; LIAO Guisheng; BAO Zheng

    2006-01-01

    In this paper we propose a method to estimate the InSAR interferometric phase of the steep terrain based on the terrain model of local plane by using the joint subspace projection technique proposed in our previous paper. The method takes advantage of the coherence information of neighboring pixel pairs to auto-coregister the SAR images and employs the projection of the joint signal subspace onto the corresponding joint noise subspace to estimate the terrain interferometric phase. The method can auto-coregister the SAR images and reduce the interferometric phase noise simultaneously. Theoretical analysis and computer simulation results show that the method can provide accurate estimate of the interferometric phase (interferogram) of very steep terrain even if the coregistration error reaches one pixel. The effectiveness of the method is verified via simulated data and real data.

  16. Coupling Regular Tessellation with Rjmcmc Algorithm to Segment SAR Image with Unknown Number of Classes

    Science.gov (United States)

    Wang, Y.; Li, Y.; Zhao, Q. H.

    2016-06-01

    This paper presents a Synthetic Aperture Radar (SAR) image segmentation approach with unknown number of classes, which is based on regular tessellation and Reversible Jump Markov Chain Monte Carlo (RJMCMC') algorithm. First of all, an image domain is portioned into a set of blocks by regular tessellation. The image is modeled on the assumption that intensities of its pixels in each homogeneous region satisfy an identical and independent Gamma distribution. By Bayesian paradigm, the posterior distribution is obtained to build the region-based image segmentation model. Then, a RJMCMC algorithm is designed to simulate from the segmentation model to determine the number of homogeneous regions and segment the image. In order to further improve the segmentation accuracy, a refined operation is performed. To illustrate the feasibility and effectiveness of the proposed approach, two real SAR image is tested.

  17. Deformable image and dose registration evaluation using two commercial programs

    Directory of Open Access Journals (Sweden)

    Rachel Tuohy

    2014-03-01

    Full Text Available Purpose: To evaluate the daily dose delivered to the patients using daily imaging.Methods: Thirty (n = 30 patients that were previously treated in our clinic (10 prostate, 10 SBRT lung and 10 abdomen were used in this study. The patients’ plans were optimized and calculated using the Pinnacle treatment planning system. The daily CBCT scans were retrieved and imported into the Velocity and RayStation software along with the corresponding planning CTs, structure sets and 3D dose distributions. In addition, the critical structures were contoured on each CBCT by the prescribing physician and were included in the evaluation of the daily delivered dose. After registering each CBCT scan to the planning CT using deformable registration, the dose volume histograms (DVH for the organs at risk (OAR and the respective planning target volumes (PTV were calculated in Velocity and Raystation.Results: For the prostate patients, we observed daily volume changes for the bladder, rectum and sigmoid. The DVH analysis for those patients showed variation in the sparing of the critical structures while PTV coverage showed no significant changes. Similar results were observed for patients with abdominal targets. In contrast, in SBRT lung patients, the DVH for the critical structures and the PTV were comparable to those from the initial treatment plan. By using daily CBCT dose reconstruction, we proved PTV coverage for prostate and abdominal targets is adequate. However, there is significant dosimetric change for the OAR. These changes were random with no apparent trending. For lung SBRT patients, the delivered daily dose for both PTV and OAR is comparable to the planned dose with no significant differences.Conclusion: Daily tracking of the delivered dose is feasible. The doses can be evaluated only if the OARs have been segmented taken into account any daily anatomical changes and not by deformation of the structures along.-------------------Cite this article as

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

    Directory of Open Access Journals (Sweden)

    Xiaogang Du

    2016-01-01

    Full Text Available 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.

  19. MUTUAL INFORMATION BASED 3D NON-RIGID REGISTRATION OF CT/MR ABDOMEN IMAGES

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    A mutual information based 3D non-rigid registration approach was proposed for the registration of deformable CT/MR body abdomen images. The Parzen Windows Density Estimation (PWDE) method is adopted to calculate the mutual information between the two modals of CT and MRI abdomen images. By maximizing MI between the CT and MR volume images, the overlapping part of them reaches the biggest, which means that the two body images of CT and MR matches best to each other. Visible Human Project (VHP) Male abdomen CT and MRI Data are used as experimental data sets. The experimental results indicate that this approach of non-rigid 3D registration of CT/MR body abdominal images can be achieved effectively and automatically, without any prior processing procedures such as segmentation and feature extraction, but has a main drawback of very long computation time. Key words: medical image registration; multi-modality; mutual information; non-rigid; Parzen window density estimation

  20. Matching suitable feature construction for SAR images based on evolutionary synthesis strategy

    Institute of Scientific and Technical Information of China (English)

    Bu Yanlong; Tang Geshi; Liu Hongfu; Pan Liang

    2013-01-01

    In the paper, a set of algorithms to construct synthetic aperture radar (SAR) matching suitable features are firstly proposed based on the evolutionary synthesis strategy. During the pro-cess, on the one hand, the indexes of primary matching suitable features (PMSFs) are designed based on the characteristics of image texture, SAR imaging and SAR matching algorithm, which is a process involving expertise;on the other hand, by designing a synthesized operation expression tree based on PMSFs, a much more flexible expression form of synthesized features is built, which greatly expands the construction space. Then, the genetic algorithm-based optimized searching process is employed to search the synthesized matching suitable feature (SMSF) with the highest efficiency, largely improving the optimized searching efficiency. In addition, the experimental results of the airborne synthetic aperture radar ortho-images of C-band and P-band show that the SMSFs gained via the algorithms can reflect the matching suitability of SAR images accurately and the matching probabilities of selected matching suitable areas of ortho-images could reach 99 ± 0.5%.

  1. Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN.

    Science.gov (United States)

    Guo, Hao; Wu, Danni; An, Jubai

    2017-08-09

    Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features.

  2. Implicit reference-based group-wise image registration and its application to structural and functional MRI.

    Science.gov (United States)

    Geng, Xiujuan; Christensen, Gary E; Gu, Hong; Ross, Thomas J; Yang, Yihong

    2009-10-01

    In this study, an implicit reference group-wise (IRG) registration with a small deformation, linear elastic model was used to jointly estimate correspondences between a set of MRI images. The performance of pair-wise and group-wise registration algorithms was evaluated for spatial normalization of structural and functional MRI data. Traditional spatial normalization is accomplished by group-to-reference (G2R) registration in which a group of images are registered pair-wise to a reference image. G2R registration is limited due to bias associated with selecting a reference image. In contrast, implicit reference group-wise (IRG) registration estimates correspondences between a group of images by jointly registering the images to an implicit reference corresponding to the group average. The implicit reference is estimated during IRG registration eliminating the bias associated with selecting a specific reference image. Registration performance was evaluated using segmented T1-weighted magnetic resonance images from the Nonrigid Image Registration Evaluation Project (NIREP), DTI and fMRI images. Implicit reference pair-wise (IRP) registration-a special case of IRG registration for two images-is shown to produce better relative overlap than IRG for pair-wise registration using the same small deformation, linear elastic registration model. However, IRP-G2R registration is shown to have significant transitivity error, i.e., significant inconsistencies between correspondences defined by different pair-wise transformations. In contrast, IRG registration produces consistent correspondence between images in a group at the cost of slightly reduced pair-wise RO accuracy compared to IRP-G2R. IRG spatial normalization of the fractional anisotropy (FA) maps of DTI is shown to have smaller FA variance compared with G2R methods using the same elastic registration model. Analyses of fMRI data sets with sensorimotor and visual tasks show that IRG registration, on average, increases the

  3. Terrain topographic inversion using single-pass polarimetric SAR image data

    Institute of Scientific and Technical Information of China (English)

    JIN Yaqiu; LUO Lin

    2004-01-01

    The shift of polarization orientation angle ψat the maximum of co-polarized or cross-polarized back-scattering signature can be used to estimate the surface slopes. It has been utilized to generate the digital elevation mapping (DEM) and terrain topography using two-pass fully polarimetric SAR or interferometric SAR (INSAR) image data. This paper presents an approach to DEM inversion by using a single pass of polarimetric SAR data. The ψ shift is derived, by using the Mueller matrix solution, as a function of three Stokes parameters, /vs, Ihs, Us, which are measured by the SAR polarimetry. Using the Euler angles transformation, the orientation angle ψ is related to both the range and azimuth angles of the tilted surface and radar viewing geometry, as has been discussed by many authors. When only a single-pass SAR data is available, the adaptive thresholding method and image morphological thinning algorithm for linear textures are proposed to first determine the azimuth angle. Then, making use of full multi-grid algorithm, both the range and azimuth angles are utilized to solve the Poisson equation of DEM to produce the terrain topography.

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

    OpenAIRE

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

    1997-01-01

    The majority of the available rigid registration measures are based on a 2-dimensional histogram of corresponding grey-values in the registered images. This paper shows that these features are similar to a family of texture measures based on grey level co-occurrence matrices (GLCM). Features from the GLCM literature are compared to the current range of measures using images from the visible human data set. The voxel-based rigid registration of cryosection and CT images have not been reported ...

  5. Time Series Analysis OF SAR Image Fractal Maps: The Somma-Vesuvio Volcanic Complex Case Study

    Science.gov (United States)

    Pepe, Antonio; De Luca, Claudio; Di Martino, Gerardo; Iodice, Antonio; Manzo, Mariarosaria; Pepe, Susi; Riccio, Daniele; Ruello, Giuseppe; Sansosti, Eugenio; Zinno, Ivana

    2016-04-01

    The fractal dimension is a significant geophysical parameter describing natural surfaces representing the distribution of the roughness over different spatial scale; in case of volcanic structures, it has been related to the specific nature of materials and to the effects of active geodynamic processes. In this work, we present the analysis of the temporal behavior of the fractal dimension estimates generated from multi-pass SAR images relevant to the Somma-Vesuvio volcanic complex (South Italy). To this aim, we consider a Cosmo-SkyMed data-set of 42 stripmap images acquired from ascending orbits between October 2009 and December 2012. Starting from these images, we generate a three-dimensional stack composed by the corresponding fractal maps (ordered according to the acquisition dates), after a proper co-registration. The time-series of the pixel-by-pixel estimated fractal dimension values show that, over invariant natural areas, the fractal dimension values do not reveal significant changes; on the contrary, over urban areas, it correctly assumes values outside the natural surfaces fractality range and show strong fluctuations. As a final result of our analysis, we generate a fractal map that includes only the areas where the fractal dimension is considered reliable and stable (i.e., whose standard deviation computed over the time series is reasonably small). The so-obtained fractal dimension map is then used to identify areas that are homogeneous from a fractal viewpoint. Indeed, the analysis of this map reveals the presence of two distinctive landscape units corresponding to the Mt. Vesuvio and Gran Cono. The comparison with the (simplified) geological map clearly shows the presence in these two areas of volcanic products of different age. The presented fractal dimension map analysis demonstrates the ability to get a figure about the evolution degree of the monitored volcanic edifice and can be profitably extended in the future to other volcanic systems with

  6. Exploiting image registration for automated resonance assignment in NMR.

    Science.gov (United States)

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

    2015-06-01

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

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

    Science.gov (United States)

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

    2009-01-01

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

  8. Stride for Developing a New Image Registration Technique using Mutual Information and Optimization Technique

    Directory of Open Access Journals (Sweden)

    Sumitha

    2014-01-01

    Full Text Available Registration is a fundamental task in image processing used to match two or more pictures taken, for example, at different times, from different sensors, or from different viewpoints. Specific examples of systems where image registration is a significant component include matching a target with a real-time image of a scene for target recognition, monitoring global land usage using satellite images, matching stereo images to recover shape for autonomous navigation, and aligning images from different medical modalities for diagnosis. Methods are classified according to the different aspects of mutual information based registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, grey value interpolation, optimization, adaptations to the mutual information measure and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects This paper overviews the theoretical aspects of an image registration problem. The purpose of this paper is to present a survey of image registration techniques

  9. ICA Based Speckle Filtering for Target Extraction in SAR Images Using Adaptive Space Separation

    Institute of Scientific and Technical Information of China (English)

    LI Yu-tong; ZHOU Yue; YANG Lei

    2008-01-01

    A novel approach based on independent component analysis (ICA) for speckle filtering and target extraction of synthetic aperture radar (SAR) images is proposed using adaptive space separation with weighted information entropy (WIE) incorporated. First the basis and the independent components are respectively obtained by ICA technique, and WIE of the image is computed; then based on the threshold computed from function T-WIE (threshold versus weighted-information-entropy), independent components are adaptively separated and the bases are classified accordingly. Thus, the image space is separated into two subspaces: "clean" and "noise". Then, a proposed nonlinear operator ABO is applied on each component of the 'clean' subspace for further optimization. Finally, recovery image is obtained reconstructing this subspace and target is easily extracted with binarisation. Note that here T-WIE is an interpolated function based on several representative target SAR images using proposed space separation algorithm.

  10. A NEW UNSUPERVISED CLASSIFICATION ALGORITHM FOR POLARIMETRIC SAR IMAGES BASED ON FUZZY SET THEORY

    Institute of Scientific and Technical Information of China (English)

    Fu Yusheng; Xie Yan; Pi Yiming; Hou Yinming

    2006-01-01

    In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combination of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means (FCM) clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classification accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by experiments using SIR-C/X-SAR (Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar) data.

  11. SAR image despeckling based on edge detection and nonsubsampled second generation bandelets

    Institute of Scientific and Technical Information of China (English)

    Zhang Wenge; Liu Fang; Jiao Licheng; Gao Xinbo

    2009-01-01

    To preserve the sharp features and details of the synthetic aperture radar (SAR) image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform (NSBT) domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges are detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Ganssian scale mixture (BLS-GSM).

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

    Directory of Open Access Journals (Sweden)

    Yachna Sharma

    2011-01-01

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

  13. SAR image classification with non-stationary multinomial logistic mixture of amplitude and texture densities

    NARCIS (Netherlands)

    Kayabol, K.; Voisin, A.; Zerubia, J.

    2011-01-01

    We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nakagami density to model the class amplitudes. To model the textures of the classes, we exploit a non-Gaussian Markov Random Fiel

  14. Random Forests as a tool for estimating uncertainty at pixel-level in SAR image classification

    DEFF Research Database (Denmark)

    Loosvelt, Lien; Peters, Jan; Skriver, Henning

    2012-01-01

    , we introduce Random Forests for the probabilistic mapping of vegetation from high-dimensional remote sensing data and present a comprehensive methodology to assess and analyze classification uncertainty based on the local probabilities of class membership. We apply this method to SAR image data...... be easily assessed when using the Random Forests algorithm....

  15. A Novel Speckle Filter for SAR Images Based on Informationtheoretic Heterogeneity Measurements

    Institute of Scientific and Technical Information of China (English)

    Chen Jie; Zhu Jing; Li Chunsheng; Zhou Yinqing

    2009-01-01

    Most adaptive speckle filters are based on the local coefficient of variation, which serves to measure the heterogeneity of synthetic aperture radar (SAR) images. However, the sensitivity of the measurements to speckle and noise of SAR images would greatly deteriorate the speckle reduction. This article, based upon the information theory, presents a novel parameter for the heterogeneity measurement as a general index to quantitate the SAR image heterogeneity. Further, as a new speckle reduction algorithm based on the aforesaid quantitative heterogeneity measurements, it puts forward a heterogeneity-based speckle reduction filter (HBSRF), which uses the information-theoretic heterogeneity measurements as a criterion to classify the SAR images as belonging to homogeneous or heterogeneous regions. Then the finite iteration procedure and edge detection algorithms are adopted to strike the best balance between speckle reduction and edge preservation. The results from the computer simulation have demonstrated that the proposed effective method is superior to the conventional speckle filters based on the local coefficient of variation both in textural preservation and speckle reduction.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Silviu Ioan Bejinariu

    2014-07-01

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

  18. Correcting image placement errors using registration control (RegC®) technology in the photomask periphery

    Science.gov (United States)

    Cohen, Avi; Lange, Falk; Ben-Zvi, Guy; Graitzer, Erez; Vladimir, Dmitriev

    2012-11-01

    The ITRS roadmap specifies wafer overlay control as one of the major tasks for the sub 40 nm nodes in addition to CD control and defect control. Wafer overlay is strongly dependent on mask image placement error (registration errors or Reg errors)1. The specifications for registration or mask placement accuracy are significantly tighter in some of the double patterning techniques (DPT). This puts a heavy challenge on mask manufacturers (mask shops) to comply with advanced node registration specifications. The conventional methods of feeding back the systematic registration error to the E-beam writer and re-writing the mask are becoming difficult, expensive and not sufficient for the advanced nodes especially for double pattering technologies. Six production masks were measured on a standard registration metrology tool and the registration errors were calculated and plotted. Specially developed algorithm along with the RegC Wizard (dedicated software) was used to compute a correction lateral strain field that would minimize the registration errors. This strain field was then implemented in the photomask bulk material using an ultra short pulse laser based system. Finally the post process registration error maps were measured and the resulting residual registration error field with and without scale and orthogonal errors removal was calculated. In this paper we present a robust process flow in the mask shop which leads up to 32% registration 3sigma improvement, bringing some out-of-spec masks into spec, utilizing the RegC® process in the photomask periphery while leaving the exposure field optically unaffected.

  19. An information theoretic approach for non-rigid image registration using voxel class probabilities.

    Science.gov (United States)

    D'Agostino, Emiliano; Maes, Frederik; Vandermeulen, Dirk; Suetens, Paul

    2006-06-01

    We propose two information theoretic similarity measures that allow to incorporate tissue class information in non-rigid image registration. The first measure assumes that tissue class probabilities have been assigned to each of the images to be registered by prior segmentation of both of them. One image is then non-rigidly deformed to match the other such that the fuzzy overlap of corresponding voxel object labels becomes similar to the ideal case whereby the tissue probability maps of both images are identical. Image similarity is assessed during registration by the divergence between the ideal and actual joint class probability distributions of both images. A second registration measure is proposed that applies in case a segmentation is available for only one of the images, for instance an atlas image that is to be matched to a study image to guide the segmentation thereof. Intensities in one image are matched to the fuzzy class labels in the other image by minimizing the conditional entropy of the intensities in the first image given the class labels in the second image. We derive analytic expressions for the gradient of each measure with respect to individual voxel displacements to derive a force field that drives the registration process, which is regularized by a viscous fluid model. The performance of the class-based measures is evaluated in the context of non-rigid inter-subject registration and atlas-based segmentation of MR brain images and compared with maximization of mutual information using only intensity information. Our results demonstrate that incorporation of class information in the registration measure significantly improves the overlap between corresponding tissue classes after non-rigid matching. The methods proposed here open new perspectives for integrating segmentation and registration in a single process, whereby the output of one is used to guide the other.

  20. SAR and Infrared Image Fusion in Complex Contourlet Domain Based on Joint Sparse Representation

    Directory of Open Access Journals (Sweden)

    Wu Yiquan

    2017-08-01

    Full Text Available To investigate the problems of the large grayscale difference between infrared and Synthetic Aperture Radar (SAR images and their fusion image not being fit for human visual perception, we propose a fusion method for SAR and infrared images in the complex contourlet domain based on joint sparse representation. First, we perform complex contourlet decomposition of the infrared and SAR images. Then, we employ the KSingular Value Decomposition (K-SVD method to obtain an over-complete dictionary of the low-frequency components of the two source images. Using a joint sparse representation model, we then generate a joint dictionary. We obtain the sparse representation coefficients of the low-frequency components of the source images in the joint dictionary by the Orthogonal Matching Pursuit (OMP method and select them using the selection maximization strategy. We then reconstruct these components to obtain the fused low-frequency components and fuse the high-frequency components using two criteria——the coefficient of visual sensitivity and the degree of energy matching. Finally, we obtain the fusion image by the inverse complex contourlet transform. Compared with the three classical fusion methods and recently presented fusion methods, e.g., that based on the Non-Subsampled Contourlet Transform (NSCT and another based on sparse representation, the method we propose in this paper can effectively highlight the salient features of the two source images and inherit their information to the greatest extent.

  1. 14-bit pipeline-SAR ADC for image sensor readout circuits

    Science.gov (United States)

    Wang, Gengyun; Peng, Can; Liu, Tianzhao; Ma, Cheng; Ding, Ning; Chang, Yuchun

    2015-03-01

    A two stage 14bit pipeline-SAR analog-to-digital converter includes a 5.5bit zero-crossing MDAC and a 9bit asynchronous SAR ADC for image sensor readout circuits built in 0.18um CMOS process is described with low power dissipation as well as small chip area. In this design, we employ comparators instead of high gain and high bandwidth amplifier, which consumes as low as 20mW of power to achieve the sampling rate of 40MSps and 14bit resolution.

  2. A novel autofocus algorithm based on maximum total variation criteria for SAR images

    Institute of Scientific and Technical Information of China (English)

    MA Lun; LIAO Guisheng

    2007-01-01

    A novel autofocus algorithm for synthetic aperture radar (SAR)based on total variation is presented in this Paper.The method,which starts with a complex phase-degraded SAR image,after the phase errors model is introduced into the range-compressed phase-history domain,carries out phase errors correction by changing the focus till the total variation of the azimuth profile is maximized.Compared with the minimum entropy autofocus algorithm,the autofocus algorithm has less computational complexity and is easier to implement.The simulation and the processing results of the measured data show the validity of the proposed method.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

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

    2012-12-01

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

  5. A Practical Approach Based on Analytic Deformable Algorithm for Scenic Image Registration.

    Directory of Open Access Journals (Sweden)

    Wei-Yen Hsu

    Full Text Available Image registration is to produce an entire scene by aligning all the acquired image sequences. A registration algorithm is necessary to tolerance as much as possible for intensity and geometric variation among images. However, captured image views of real scene usually produce unexpected distortions. They are generally derived from the optic characteristics of image sensors or caused by the specific scenes and objects.An analytic registration algorithm considering the deformation is proposed for scenic image applications in this study. After extracting important features by the wavelet-based edge correlation method, an analytic registration approach is then proposed to achieve deformable and accurate matching of point sets. Finally, the registration accuracy is further refined to obtain subpixel precision by a feature-based Levenberg-Marquardt (FLM method. It converges evidently faster than most other methods because of its feature-based characteristic.We validate the performance of proposed method by testing with synthetic and real image sequences acquired by a hand-held digital still camera (DSC and in comparison with an optical flow-based motion technique in terms of the squared sum of intensity differences (SSD and correlation coefficient (CC. The results indicate that the proposed method is satisfactory in the registration accuracy and quality of DSC images.

  6. Oil spill detection from SAR image using SVM based classification

    Directory of Open Access Journals (Sweden)

    A. A. Matkan

    2013-09-01

    Full Text Available In this paper, the potential of fully polarimetric L-band SAR data for detecting sea oil spills is investigated using polarimetric decompositions and texture analysis based on SVM classifier. First, power and magnitude measurements of HH and VV polarization modes and, Pauli, Freeman and Krogager decompositions are computed and applied in SVM classifier. Texture analysis is used for identification using SVM method. The texture features i.e. Mean, Variance, Contrast and Dissimilarity from them are then extracted. Experiments are conducted on full polarimetric SAR data acquired from PALSAR sensor of ALOS satellite on August 25, 2006. An accuracy assessment indicated overall accuracy of 78.92% and 96.46% for the power measurement of the VV polarization and the Krogager decomposition respectively in first step. But by use of texture analysis the results are improved to 96.44% and 96.65% quality for mean of power and magnitude measurements of HH and VV polarizations and the Krogager decomposition. Results show that the Krogager polarimetric decomposition method has the satisfying result for detection of sea oil spill on the sea surface and the texture analysis presents the good results.

  7. Orthogonal moments for determining correspondence between vessel bifurcations for retinal image registration.

    Science.gov (United States)

    Patankar, Sanika S; Kulkarni, Jayant V

    2015-05-01

    Retinal image registration is a necessary step in diagnosis and monitoring of Diabetes Retinopathy (DR), which is one of the leading causes of blindness. Long term diabetes affects the retinal blood vessels and capillaries eventually causing blindness. This progressive damage to retina and subsequent blindness can be prevented by periodic retinal screening. The extent of damage caused by DR can be assessed by comparing retinal images captured during periodic retinal screenings. During image acquisition at the time of periodic screenings translation, rotation and scale (TRS) are introduced in the retinal images. Therefore retinal image registration is an essential step in automated system for screening, diagnosis, treatment and evaluation of DR. This paper presents an algorithm for registration of retinal images using orthogonal moment invariants as features for determining the correspondence between the dominant points (vessel bifurcations) in the reference and test retinal images. As orthogonal moments are invariant to TRS; moment invariants features around a vessel bifurcation are unaltered due to TRS and can be used to determine the correspondence between reference and test retinal images. The vessel bifurcation points are located in segmented, thinned (mono pixel vessel width) retinal images and labeled in corresponding grayscale retinal images. The correspondence between vessel bifurcations in reference and test retinal image is established based on moment invariants features. Further the TRS in test retinal image with respect to reference retinal image is estimated using similarity transformation. The test retinal image is aligned with reference retinal image using the estimated registration parameters. The accuracy of registration is evaluated in terms of mean error and standard deviation of the labeled vessel bifurcation points in the aligned images. The experimentation is carried out on DRIVE database, STARE database, VARIA database and database provided

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

    Directory of Open Access Journals (Sweden)

    Fang-Ju Jao

    2014-12-01

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

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

    Directory of Open Access Journals (Sweden)

    Andrew Zisserman

    2007-01-01

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

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

    Science.gov (United States)

    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.

  11. Prostate: registration of digital histopathologic images to in vivo MR images acquired by using endorectal receive coil.

    Science.gov (United States)

    Ward, Aaron D; Crukley, Cathie; McKenzie, Charles A; Montreuil, Jacques; Gibson, Eli; Romagnoli, Cesare; Gomez, Jose A; Moussa, Madeleine; Chin, Joseph; Bauman, Glenn; Fenster, Aaron

    2012-06-01

    To develop and evaluate a technique for the registration of in vivo prostate magnetic resonance (MR) images to digital histopathologic images by using image-guided specimen slicing based on strand-shaped fiducial markers relating specimen imaging to histopathologic examination. The study was approved by the institutional review board (the University of Western Ontario Health Sciences Research Ethics Board, London, Ontario, Canada), and written informed consent was obtained from all patients. This work proposed and evaluated a technique utilizing developed fiducial markers and real-time three-dimensional visualization in support of image guidance for ex vivo prostate specimen slicing parallel to the MR imaging planes prior to digitization, simplifying the registration process. Means, standard deviations, root-mean-square errors, and 95% confidence intervals are reported for all evaluated measurements. The slicing error was within the 2.2 mm thickness of the diagnostic-quality MR imaging sections, with a tissue block thickness standard deviation of 0.2 mm. Rigid registration provided negligible postregistration overlap of the smallest clinically important tumors (0.2 cm(3)) at histologic examination and MR imaging, whereas the tested nonrigid registration method yielded a mean target registration error of 1.1 mm and provided useful coregistration of such tumors. This method for the registration of prostate digital histopathologic images to in vivo MR images acquired by using an endorectal receive coil was sufficiently accurate for coregistering the smallest clinically important lesions with 95% confidence.

  12. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart

    Directory of Open Access Journals (Sweden)

    Hua Wen-qiang

    2015-02-01

    Full Text Available In this study, we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR images, aiming at handling the issue that the number of train set is small. First, considering the scattering characters of PolSAR data, this method extracts multiple scattering features using target decomposition approach. Then, a semi-supervised learning model is established based on a co-training framework and Support Vector Machine (SVM. Both labeled and unlabeled data are utilized in this model to obtain high classification accuracy. Third, a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study, it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small.

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

    Science.gov (United States)

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

    2016-03-01

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

  14. PSI Deformation Map Retrieval by Means of Temporal Sublook Coherence on Reduced Sets of SAR Images

    Directory of Open Access Journals (Sweden)

    Rubén Iglesias

    2015-01-01

    Full Text Available Prior to the application of any persistent scatterer interferometry (PSI technique for the monitoring of terrain displacement phenomena, an adequate pixel selection must be carried out in order to prevent the inclusion of noisy pixels in the processing. The rationale is to detect the so-called persistent scatterers, which are characterized by preserving their phase quality along the multi-temporal set of synthetic aperture radar (SAR images available. Two criteria are mainly available for the estimation of pixels’ phase quality, i.e., the coherence stability and the amplitude dispersion or permanent scatterers (PS approach. The coherence stability method allows an accurate estimation of the phase statistics, even when a reduced number of SAR acquisitions is available. Unfortunately, it requires the multi-looking of data during the coherence estimation, leading to a spatial resolution loss in the final results. In contrast, the PS approach works at full-resolution, but it demands a larger number of SAR images to be reliable, typically more than 20. There is hence a clear limitation when a full-resolution PSI processing is to be carried out and the number of acquisitions available is small. In this context, a novel pixel selection method based on exploiting the spectral properties of point-like scatterers, referred to as temporal sublook coherence (TSC, has been recently proposed. This paper seeks to demonstrate the advantages of employing PSI techniques by means of TSC on both orbital and ground-based SAR (GB-SAR data when the number of images available is small (10 images in the work presented. The displacement maps retrieved through the proposed technique are compared, in terms of pixel density and phase quality, with traditional criteria. Two X-band datasets composed of 10 sliding spotlight TerraSAR-X images and 10 GB-SAR images, respectively, over the landslide of El Forn de Canillo (Andorran Pyrenees, are employed for this study. For both

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

    Science.gov (United States)

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

    2016-10-21

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

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

    Science.gov (United States)

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

    2016-10-01

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

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

    Science.gov (United States)

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

    2016-03-01

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

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

    Science.gov (United States)

    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.

  19. AUTOMATIC SHIP DETECTION IN SINGLE-POL SAR IMAGES USING TEXTURE FEATURES IN ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    E. Khesali

    2015-12-01

    Full Text Available This paper presents a novel method for detecting ships from high-resolution synthetic aperture radar (SAR images. This method categorizes ship targets from single-pol SAR images using texture features in artificial neural networks. As such, the method tries to overcome the lack of an operational solution that is able to reliably detect ships with one SAR channel. The method has the following three main stages: 1 feature extraction; 2 feature selection; and 3 ship detection. The first part extracts different texture features from SAR image. These textures include occurrence and co occurrence measures with different window sizes. Then, best features are selected. Finally, the artificial neural network is used to extract ship pixels from sea ones. In post processing stage some morphological filters are used to improve the result. The effectiveness of the proposed method is verified using Sentinel-1 data in VV polarization. Experimental results indicate that the proposed algorithm can be implemented with time-saving, high precision ship extraction, feature analysis, and detection. The results also show that using texture features the algorithm properly discriminates speckle noise from ships.

  20. IMAGING AND MTI PROCESSING BASED ON DUAL-FREQUENCIES DUAL-APERTURES SPACEBORNE SAR

    Institute of Scientific and Technical Information of China (English)

    Yin Jianfeng; Li Daojing; Wu Yirong

    2009-01-01

    Based on dual-frequencies dual-apertures spaceborne SAR (Synthetic Aperture Radar), a new SAR system with four receiving channels and two operation modes is presented in this paper. SAR imaging and Moving Target Indication (MTI) are studied in this system. High resolution imaging with wide swath is implemented by the Mode I, and MTI is completed by the Mode II. High azimuth resolution is achieved by the Displaced Phase Center (DPC) multibeam technique. And the Coherent Accumulation (CA) method, which combines dual channels data of different carrier frequency, is used to enhance the range resolution. For the data of different carrier frequency, the two aperture interferometric processing is executed to implement clutter cancellation, respectively. And the couple of clutter suppressed data are employed to implement Dual Carrier Frequency Conjugate Processing (DCFCP), then both slow and fast moving targets detection can be completed, followed by moving target imaging. The simulation results show the validity of the signal processing method of this new SAR system.

  1. An efficient two-objective automatic SAR image segmentation framework using artificial immune system

    Science.gov (United States)

    Yang, Dongdong; Niu, Ruican; Fei, Rong; Jiang, Qiaoyong; Li, Hongye; Cao, Zijian

    2015-12-01

    Here, an efficient multi-objective automatic segmentation framework (MASF) is formulated and applied to synthetic aperture radar (SAR) image unsupervised classification. In the framework, three important issues are presented: 1) two reasonable image preprocessing techniques, including spatial filtering and watershed operator, are discussed at the initial stage of the framework; 2)then, an efficient immune multi-objective optimization algorithm with uniform clone, adaptive selection by online nondominated solutions, and dynamic deletion in diversity maintenance is proposed; 3 two very simple, but very efficient conflicting clustering validity indices are incorporated into the framework and simultaneously optimized. Two simulated SAR data and two complicated real images are used to quantitatively validate its effectiveness. In addition, four other state-of-the-art image segmentation methods are employed for comparison.

  2. SAR IMAGING SIMULATION OF HORIZONTAL FULLY TWO-DIMENSIONAL INTERNAL WAVES

    Institute of Scientific and Technical Information of China (English)

    SHEN Hui; HE Yi-Jun

    2006-01-01

    Based on the research of Lynett and Liu, a new horizontal fully two-dimensional internal wave propagation model with rotation effect was deduced, which can be used to simulate the characteristics of internal waves in a horizontal fully two-dimensional plane. By combining the imaging mechanism of Synthetic Aperture Radar(SAR), a simulation procedure was fatherly acquired, which can simulate the propagation characteristics of oceanic internal waves into SAR images. In order to evaluate the validity of the proposed simulation procedure, case studies are performed in South China Sea and results from simulation procedure are analyzed in detail. A very good consistency was found between the simulation results and satellite images. The proposed simulation procedure will be a possible foundation for the quantitative interpretation of internal waves from fully two-dimensional satellite images.

  3. Discernibility of Burial Mounds in High-Resolution X-Band SAR Images for Archaeological Prospections in the Altai Mountains

    Directory of Open Access Journals (Sweden)

    Timo Balz

    2016-09-01

    Full Text Available The Altai Mountains are a heritage-rich archaeological landscape with monuments in almost every valley. Modern nation state borders dissect the region and limit archaeological landscape analysis to intra-national areas of interest. Remote sensing can help to overcome these limitations. Due to its high precision, Synthetic Aperture Radar (SAR data can be a very useful tool for supporting archaeological prospections, but compared to optical imagery, the detectability of sites of archaeological interest is limited. We analyzed the limitations of SAR using TerraSAR-X images in different modes. Based on ground truth, the discernibility of burial mounds was analyzed in different SAR acquisition modes. We show that very-high-resolution TerraSAR-X staring spotlight images are very well suited for the task, with >75% of the larger mounds being discernible, while in images with a lower spatial resolution only a few large sites can be detected, at rates below 50%.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-11-15

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-11-01

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

  6. Probability Density Components Analysis: A New Approach to Treatment and Classification of SAR Images

    Directory of Open Access Journals (Sweden)

    Osmar Abílio de Carvalho Júnior

    2014-04-01

    Full Text Available Speckle noise (salt and pepper is inherent to synthetic aperture radar (SAR, which causes a usual noise-like granular aspect and complicates the image classification. In SAR image analysis, the spatial information might be a particular benefit for denoising and mapping classes characterized by a statistical distribution of the pixel intensities from a complex and heterogeneous spectral response. This paper proposes the Probability Density Components Analysis (PDCA, a new alternative that combines filtering and frequency histogram to improve the classification procedure for the single-channel synthetic aperture radar (SAR images. This method was tested on L-band SAR data from the Advanced Land Observation System (ALOS Phased-Array Synthetic-Aperture Radar (PALSAR sensor. The study area is localized in the Brazilian Amazon rainforest, northern Rondônia State (municipality of Candeias do Jamari, containing forest and land use patterns. The proposed algorithm uses a moving window over the image, estimating the probability density curve in different image components. Therefore, a single input image generates an output with multi-components. Initially the multi-components should be treated by noise-reduction methods, such as maximum noise fraction (MNF or noise-adjusted principal components (NAPCs. Both methods enable reducing noise as well as the ordering of multi-component data in terms of the image quality. In this paper, the NAPC applied to multi-components provided large reductions in the noise levels, and the color composites considering the first NAPC enhance the classification of different surface features. In the spectral classification, the Spectral Correlation Mapper and Minimum Distance were used. The results obtained presented as similar to the visual interpretation of optical images from TM-Landsat and Google Maps.

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

    Science.gov (United States)

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

    2011-08-01

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

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

    Science.gov (United States)

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

    2017-02-01

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

  9. Automatic multi-resolution image registration based on genetic algorithm and Hausdorff distance

    Institute of Scientific and Technical Information of China (English)

    Famao Ye; Lin Su; Shukai Li

    2006-01-01

    @@ Image registration is a crucial step in all image analysis tasks in which the final information is gained from the combination of various data sources, and it is difficult to automatically register due to the complexity of image. An approach based on genetic algorithm and Hausdorff distance to automatic image registration is presented. We use a multi-resolution edge tracker to find out the fine-quality edges and utilize the Hausdorff distance between the input image and the reference image as similarity measure. We use wavelet decomposition and genetic algorithm, which combine local search methods with global ones balancing exploration and exploitation, to speed up the search of the best transformation parameters.Experimental results show that the proposed approach is a promising method for registration of image.

  10. Hybrid bronchoscope tracking using a magnetic tracking sensor and image registration.

    Science.gov (United States)

    Mori, Kensaku; Deguchi, Daisuke; Akiyama, Kenta; Kitasaka, Takayuki; Maurer, Calvin R; Suenaga, Yasuhito; Takabatake, Hirotsugu; Mori, Masaki; Natori, Hiroshi

    2005-01-01

    In this paper, we propose a hybrid method for tracking a bronchoscope that uses a combination of magnetic sensor tracking and image registration. The position of a magnetic sensor placed in the working channel of the bronchoscope is provided by a magnetic tracking system. Because of respiratory motion, the magnetic sensor provides only the approximate position and orientation of the bronchoscope in the coordinate system of a CT image acquired before the examination. The sensor position and orientation is used as the starting point for an intensity-based registration between real bronchoscopic video images and virtual bronchoscopic images generated from the CT image. The output transformation of the image registration process is the position and orientation of the bronchoscope in the CT image. We tested the proposed method using a bronchial phantom model. Virtual breathing motion was generated to simulate respiratory motion. The proposed hybrid method successfully tracked the bronchoscope at a rate of approximately 1 Hz.

  11. Redundant Discrete Wavelet Transform Based Super-Resolution Using Sub-Pixel Image Registration

    Science.gov (United States)

    2003-03-01

    AFIT/GE/ENG/03-18 REDUNDANT DISCRETE WAVELET TRANSFORM BASED SUPER-RESOLUTION USING SUB-PIXEL IMAGE REGISTRATION THESIS Daniel L. Ward Second...position of the United States Air Force, Department of Defense, or the United States Government. AFIT/GE/ENG/03-18 REDUNDANT DISCRETE WAVELET TRANSFORM BASED...O3-18 REDUNDANT DISCRETE WAVELET TRANSFORM BASED SUPER-RESOLUTION USING SUB-PIXEL IMAGE REGISTRATION THESIS Daniel Lee Ward, B.S.E.E. Second

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

    Energy Technology Data Exchange (ETDEWEB)

    Presles, Benoît, E-mail: benoit.presles@creatis.insa-lyon.fr; Rit, Simon; Sarrut, David [Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, Lyon F-69621, France and Léon Bérard Cancer Center, Université de Lyon, Lyon F-69373 (France); Fargier-Voiron, Marie; Liebgott, Hervé [Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, Lyon F-69621 (France); Biston, Marie-Claude; Munoz, Alexandre; Pommier, Pascal [Léon Bérard Cancer Center, Université de Lyon, Lyon F-69373 (France); Lynch, Rod [The Andrew Love Cancer Centre, University Hospital Geelong, Geelong 3220 (Australia)

    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 computed from the dilated reference positioning volume drawn on the reference image and use mutual information as a similarity measure. The considered geometric transformations are translations and have been optimized by using the adaptive stochastic gradient descent algorithm. Validation has been carried out using manual registration by three operators of the same set of image pairs as the algorithms. Sixty-two treatment US images of seven patients irradiated after a prostatectomy have been registered to their corresponding reference US image. The reference registration has been defined as the average of the manual registration values. Registration error has been calculated by subtracting the reference registration from the algorithm result. For each session, the method has been considered a failure if the registration error was above both the interoperator variability of the session and a global threshold of 3.0 mm. Results: All proposed registration algorithms have no systematic bias. Method B leads to the best results with mean errors of −0.6, 0.7, and −0.2 mm in left–right (LR), superior–inferior (SI), and anterior–posterior (AP) directions, respectively. With this method, the standard deviations of the mean error are of 1.7, 2.4, and 2.6 mm in LR, SI, and AP directions, respectively

  13. Kernel Bundle Diffeomorphic Image Registration Using Stationary Velocity Fields and Wendland Basis Functions.

    Science.gov (United States)

    Pai, Akshay; Sommer, Stefan; Sorensen, Lauge; Darkner, Sune; Sporring, Jon; Nielsen, Mads

    2016-06-01

    In this paper, we propose a multi-scale, multi-kernel shape, compactly supported kernel bundle framework for stationary velocity field-based image registration (Wendland kernel bundle stationary velocity field, wKB-SVF). We exploit the possibility of directly choosing kernels to construct a reproducing kernel Hilbert space (RKHS) instead of imposing it from a differential operator. The proposed framework allows us to minimize computational cost without sacrificing the theoretical foundations of SVF-based diffeomorphic registration. In order to recover deformations occurring at different scales, we use compactly supported Wendland kernels at multiple scales and orders to parameterize the velocity fields, and the framework allows simultaneous optimization over all scales. The performance of wKB-SVF is extensively compared to the 14 non-rigid registration algorithms presented in a recent comparison paper. On both MGH10 and CUMC12 datasets, the accuracy of wKB-SVF is improved when compared to other registration algorithms. In a disease-specific application for intra-subject registration, atrophy scores estimated using the proposed registration scheme separates the diagnostic groups of Alzheimer's and normal controls better than the state-of-the-art segmentation technique. Experimental results show that wKB-SVF is a robust, flexible registration framework that allows theoretically well-founded and computationally efficient multi-scale representation of deformations and is equally well-suited for both inter- and intra-subject image registration.

  14. EXTRACTION OF COASTLINES WITH FUZZY APPROACH USING SENTINEL-1 SAR IMAGE

    Directory of Open Access Journals (Sweden)

    N. Demir

    2016-06-01

    Full Text Available Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Then the image is terrain-corrected using SRTM digital surface model. Classification of SAR image is a challenging task since SAR and optical sensors have very different properties. Even between different bands of the SAR sensors, the images look very different. So, the classification of SAR image is difficult with the traditional unsupervised methods. In this study, a fuzzy approach has been applied to distinguish the coastal pixels than the land surface pixels. The standard deviation and the mean, median values are calculated to use as parameters in fuzzy approach. The Mean-standard-deviation (MS Large membership function is used because the large amounts of land and ocean pixels dominate the SAR image with large mean and standard deviation values. The pixel values are multiplied with 1000 to easify the calculations. The mean is calculated as 23 and the standard deviation is calculated as 12 for the whole image. The multiplier parameters are selected as a: 0.58, b: 0.05 to maximize the land surface membership. The result is evaluated using airborne LIDAR data, only for the areas where LIDAR dataset is available and secondly manually digitized coastline. The laser points which are below 0,5 m are classified as the ocean points. The 3D alpha-shapes algorithm is used to detect the coastline points from LIDAR data. Minimum distances are calculated between the

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

    Science.gov (United States)

    Yang, Xuan; Pei, Jihong; Shi, Jingli

    2014-01-01

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

  16. Monitoring water level using Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) images

    Science.gov (United States)

    Stavroulaki, Eleni; Alexakis, Dimitrios D.; Tsanis, Ioannis K.

    2017-04-01

    Interferometric Synthetic Aperture Radar (SAR) methodology can successfully detect phase variations related to water level changes and produce corresponding water level maps. Two lakes located in Western Crete, Greece, namely Lake Kournas and Lake Agia were used as case studies to study water level change with means of SAR interferometry. The change of the water surface in the lake is examined over a period of two years, 2015-2016 using Sentinel 1 IW mode images and in situ water level data. Initially, all the SAR images were preprocessed in terms of atmospheric and radiometric corrections. Various interferograms were developed to study the multi-temporal regime of water level in both lakes. Optical satellite sensor data (Landsat 8) were used to study the vegetation regime and how this affect the interferogram processing. The results denoted the fact that the combination of SAR backscattering intensity and unwrapped phase water level data can provide additional insight into hydrological state. It is also shown that integrated analysis of the backscattering mechanism and interferometric characteristics can considerably enhance the reliability of the water-level retrieval scheme and optimize the capture of hydrological patterns spatial distribution. Keywords: Sentinel-1, interferogram, water level, Backscattering

  17. Extracting hurricane eye morphology from spaceborne SAR images using morphological analysis

    Science.gov (United States)

    Lee, Isabella K.; Shamsoddini, Ali; Li, Xiaofeng; Trinder, John C.; Li, Zeyu

    2016-07-01

    Hurricanes are among the most destructive global natural disasters. Thus recognizing and extracting their morphology is important for understanding their dynamics. Conventional optical sensors, due to cloud cover associated with hurricanes, cannot reveal the intense air-sea interaction occurring at the sea surface. In contrast, the unique capabilities of spaceborne synthetic aperture radar (SAR) data for cloud penetration, and its backscattering signal characteristics enable the extraction of the sea surface roughness. Therefore, SAR images enable the measurement of the size and shape of hurricane eyes, which reveal their evolution and strength. In this study, using six SAR hurricane images, we have developed a mathematical morphology method for automatically extracting the hurricane eyes from C-band SAR data. Skeleton pruning based on discrete skeleton evolution (DSE) was used to ensure global and local preservation of the hurricane eye shape. This distance weighted algorithm applied in a hierarchical structure for extraction of the edges of the hurricane eyes, can effectively avoid segmentation errors by reducing redundant skeletons attributed to speckle noise along the edges of the hurricane eye. As a consequence, the skeleton pruning has been accomplished without deficiencies in the key hurricane eye skeletons. A morphology-based analyses of the subsequent reconstructions of the hurricane eyes shows a high degree of agreement with the hurricane eye areas derived from reference data based on NOAA manual work.