Relevant bibliographies by topics / Image registration method

Academic literature on the topic 'Image registration method'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Image registration method"

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Bingjian, Wang, Lu Quan, Li Yapeng, Li Fan, Bai Liping, Lu Gang, and Lai Rui. "Image registration method for multimodal images." Applied Optics 50, no. 13 (April 25, 2011): 1861. http://dx.doi.org/10.1364/ao.50.001861.

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Xu, Hong Kui, Ming Yan Jiang, and Ming Qiang Yang. "An Image Registration Method Combing Feature Constraint with Multilevel Strategy." Applied Mechanics and Materials 58-60 (June 2011): 286–91. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.286.

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A novel method combing feature constraint with multilevel strategy to improve simultaneously the registration accuracy and speed is proposed for non-parametric image registrations. To images between which the local difference is large, integrating feature constraint constructed with local structure information of images into objective function of image registration improves the registration accuracy. When applying feature constraint under multilevel strategy, parameter searching is prevented from entrapped into local extremum by using the optimization result on coarser levels as the starting points on finer levels; meanwhile traditional optimization methods without demanding intelligent optimization algorithms which consume more time can find the accurate registration parameter on finer levels, so registration speed is improved. Experimental results indicate that this method can finish fast and accurate registration for images between which there exists large local difference.
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Mezura-Montes, Efrén, Héctor-Gabriel Acosta-Mesa, Darío-del-Sinaí Ramírez-Garcés, Nicandro Cruz-Ramírez, and Rodolfo Hernández-Jiménez. "An Image Registration Method for Colposcopic Images." Computational and Mathematical Methods in Medicine 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/285962.

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A nonrigid body image registration method for spatiotemporal alignment of image sequences obtained from colposcopy examinations to detect precancerous lesions of the cervix is proposed in this paper. The approach is based on time series calculation for those pixels in the first image of the 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.
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Yang, Lu Jing, Wei Hao, and Chong Lun Li. "A Modified Phase Correlation Method for Image Registration." Applied Mechanics and Materials 48-49 (February 2011): 48–51. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.48.

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Image registration is a very fundamental and important part in many multi-sensor image based applications. Phase correlation-based image registration method is widely concerned for its small computation amount, strong anti-interference property. However, it can only solve the image registration problem with translational motion. Hence, we proposed a modified phase correlation registration method in the paper. We analyzed the principle of registration, gave the flow chart, and applied the method to the SAR image registration problems with scaling, rotation and translation transformation. Simulation results show that the method can accurately estimate the translation parameters, zoom scale and rotation angle of registrating image relative to the reference image.
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Fan, Shu Kai S., Yu Chiang Chuang, and Jia Rong Wu. "A New Cross-Correlation Based Image Registration Method." Applied Mechanics and Materials 58-60 (June 2011): 1979–84. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1979.

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Image registration is a fundamental task for combining two or more images taken from different viewpoints, different times, or different sensors. It is a process of determining the point by point correspondence between two images from the same scene. The proposed image registration method uses the area-based approach to process image registration and the objective is to find the maximum similarity through the cross-correlation measure. Most cross-correlation methods are developed based on image intensities for the direct matching purpose. However, it is extremely sensitive to the intensity changes. To counteract illumination effect, the proposed method replaces the intensity with the gradient information, and this concept comes originally from the Hough transform that points having the same parameters and should be on the same line. These two parameters are combined as the similarity between images for image registration. The experimental results obtained by means of several test images illustrate the effectiveness of the proposed image registration method.
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Liang, Bo, Xi Chen, Lan Yu, Song Feng, Yangfan Guo, Wenda Cao, Wei Dai, Yunfei Yang, and Ding Yuan. "High-precision Multichannel Solar Image Registration Using Image Intensity." Astrophysical Journal Supplement Series 261, no. 2 (July 20, 2022): 10. http://dx.doi.org/10.3847/1538-4365/ac7232.

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Abstract Solar images observed in different channels with different instruments are crucial to the study of solar activity. However, the images have different fields of view, causing them to be misaligned. It is essential to accurately register the images for studying solar activity from multiple perspectives. Image registration is described as an optimizing problem from an image to be registered to a reference image. In this paper, we proposed a novel coarse-to-fine solar image registration method to register the multichannel solar images. In the coarse registration step, we used the regular step gradient descent algorithm as an optimizer to maximize the normalized cross correlation metric. The fine registration step uses the Powell–Brent algorithms as an optimizer and brings the Mattes mutual information similarity metric to the minimum. We selected five pairs of images with different resolutions, rotation angles, and shifts to compare and evaluate our results to those obtained by scale-invariant feature transform and phase correlation. The images are observed by the 1.6 m Goode Solar Telescope at Big Bear Solar Observatory and the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Furthermore, we used the mutual information and registration time criteria to quantify the registration results. The results prove that the proposed method not only reaches better registration precision but also has better robustness. Meanwhile, we want to highlight that the method can also work well for the time-series solar image registration.
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Zheng, Qian, Qiang Wang, Xiaojuan Ba, Shan Liu, Jiaofen Nan, and Shizheng Zhang. "A Medical Image Registration Method Based on Progressive Images." Computational and Mathematical Methods in Medicine 2021 (July 27, 2021): 1–9. http://dx.doi.org/10.1155/2021/4504306.

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Background. Medical image registration is an essential task for medical image analysis in various applications. In this work, we develop a coarse-to-fine medical image registration method based on progressive images and SURF algorithm (PI-SURF) for higher registration accuracy. Methods. As a first step, the reference image and the floating image are fused to generate multiple progressive images. Thereafter, the floating image and progressive image are registered to get the coarse registration result based on the SURF algorithm. For further improvement, the coarse registration result and the reference image are registered to perform fine image registration. The appropriate progressive image has been investigated by experiments. The mutual information (MI), normal mutual information (NMI), normalized correlation coefficient (NCC), and mean square difference (MSD) similarity metrics are used to demonstrate the potential of the PI-SURF method. Results. For the unimodal and multimodal registration, the PI-SURF method achieves the best results compared with the mutual information method, Demons method, Demons+B-spline method, and SURF method. The MI, NMI, and NCC of PI-SURF are improved by 15.5%, 1.31%, and 7.3%, respectively, while MSD decreased by 13.2% for the multimodal registration compared with the optimal result of the state-of-the-art methods. Conclusions. The extensive experiments show that the proposed PI-SURF method achieves higher quality of registration.
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Nobnop, Wannapha, Imjai Chitapanarux, Somsak Wanwilairat, Ekkasit Tharavichitkul, Vicharn Lorvidhaya, and Patumrat Sripan. "Effect of Deformation Methods on the Accuracy of Deformable Image Registration From Kilovoltage CT to Tomotherapy Megavoltage CT." Technology in Cancer Research & Treatment 18 (January 1, 2019): 153303381882118. http://dx.doi.org/10.1177/1533033818821186.

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Introduction: The registration accuracy of megavoltage computed tomography images is limited by low image contrast when compared to that of kilovoltage computed tomography images. Such issues may degrade the deformable image registration accuracy. This study evaluates the deformable image registration from kilovoltage to megavoltage images when using different deformation methods and assessing nasopharyngeal carcinoma patient images. Methods: The kilovoltage and the megavoltage images from the first day and the 20th fractions of the treatment day of 12 patients with nasopharyngeal carcinoma were used to evaluate the deformable image registration application. The deformable image registration image procedures were classified into 3 groups, including kilovoltage to kilovoltage, megavoltage to megavoltage, and kilovoltage to megavoltage. Three deformable image registration methods were employed using the deformable image registration and adaptive radiotherapy software. The validation was compared by volume-based, intensity-based, and deformation field analyses. Results: The use of different deformation methods greatly affected the deformable image registration accuracy from kilovoltage to megavoltage. The asymmetric transformation with the demon method was significantly better than other methods and illustrated satisfactory value for adaptive applications. The deformable image registration accuracy from kilovoltage to megavoltage showed no significant difference from the kilovoltage to kilovoltage images when using the appropriate method of registration. Conclusions: The choice of deformation method should be considered when applying the deformable image registration from kilovoltage to megavoltage images. The deformable image registration accuracy from kilovoltage to megavoltage revealed a good agreement in terms of intensity-based, volume-based, and deformation field analyses and showed clinically useful methods for nasopharyngeal carcinoma adaptive radiotherapy in tomotherapy applications.
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Watcharawipha, Anirut, Nipon Theera-Umpon, and Sansanee Auephanwiriyakul. "Space Independent Image Registration Using Curve-Based Method with Combination of Multiple Deformable Vector Fields." Symmetry 11, no. 10 (September 28, 2019): 1210. http://dx.doi.org/10.3390/sym11101210.

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This paper proposes a novel curve-based or edge-based image registration technique that utilizes the curve transformation function and Gaussian function. It enables deformable image registration between images in different spaces, e.g., different color spaces or different medical image modalities. In particular, piecewise polynomial fitting is used to fit a curve and convert it to the global cubic B-spline control points. The transformation between the curves in the reference and source images are performed by using these control points. The image area is segmented with respect to the reference curve for the moving pixels. The Gaussian function, which is symmetric about the coordinates of the points of the reference curve, was used to improve the continuity in the intra- and inter-segmented areas. The overall result on curve transformation by means of the Hausdroff distance was 5.820 ± 1.127 pixels on average on several 512 × 512 synthetic images. The proposed method was compared with an ImageJ plugin, namely bUnwarpJ, and a software suite for deformable image registration and adaptive radiotherapy research, namely DIRART, to evaluate the image registration performance. The experimental result shows that the proposed method yielded better image registration performance than its counterparts. On average, the proposed method could reduce the root mean square error from 2970.66 before registration to 1677.94 after registration and can increase the normalized cross-correlation coefficient from 91.87% before registration to 97.40% after registration.
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Zhou, Wu, and Yaoqin Xie. "Interactive Multigrid Refinement for Deformable Image Registration." BioMed Research International 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/532936.

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

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Al-Hasan, Muhannad A. T. "Medical image registration using a graph theoretic method." Thesis, University of East Anglia, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432431.

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Hömke, Lars. "A multigrid method for elastic image registration with additional structural constraints." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=983423725.

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Hemily, Julie. "Towards liver shear wave vibro-elastography : method repeatability and image registration technique." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/63130.

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Liver fibrosis is a largely prevalent concern in Canada and world-wide, due to high rates of Hepatitis, fatty liver disease, alcoholism, as well as several other possible causes. It is currently diagnosed and staged by performing biopsies or by tissue elasticity measurements referred to as elastography. Elastography methods are a relatively new means of measuring the mechanical properties of soft tissue non-invasively by measuring and processing the propagation of shear waves through the body. The Robotics and Control Laboratory at the University of British Columbia has developed an elastography technique, Vibro-elastography, that can quantitatively measure soft tissue stiffness in real time. It has previously been applied to prostate and breast pathologies. It is now being developed and optimized for liver applications. To validate Vibro-elastography as a new diagnostic tool, a comparison study should be performed on a clinical population. This work sets out to lay out the prerequisites needed to implement a full clinical study. It starts out with a repeatability study using a tissue phantom to ensure repeatable results and compare our results to manufacturer stiffness values. In this work, we compare the precision of several different implementations of Vibro-elastography including the placement of the excitation source, data acquisition techniques and single versus multi-frequency excitation. Most of the implementations resulted in good, repeatable results, regardless of exciter placement. The quality of wave propagation deteriorated with depth as expected, but elasticity results remained repeatable even at deeper regions of interest. The parameters are selected and designed for the use on the liver. Finally, a registration pipeline and initial case trial has been presented as a suggested means of comparing the elastography data obtained using Vibro-elastography and any elastography measures that can be obtained from a magnetic resonance system. Using manual fiducial vessel markers and applying an Iterative Closest Point registration process results in a quick alignment of the ultrasound and MRI volumes with registration error less than 20 mm.
Applied Science, Faculty of
Graduate
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ATTA-FOSU, THOMAS. "Fourier Based Method for Simultaneous Segmentation and Nonlinear Registration." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1492439037011351.

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Sampath, Rahul Srinivasan. "A parallel geometric multigrid method for finite elements on octree meshes applied to elastic image registration." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29702.

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Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2009.
Committee Chair: Vuduc, Richard; Committee Member: Biros, George; Committee Member: Davatzikos, Christos; Committee Member: Tannenbaum, Allen; Committee Member: Zhou, Hao Min. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Cramphorn, E. A. "The viability of image registration as a method for the quantification of displacement in penetrating impact experiments." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/21942/.

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Experimental characterisation of tissue deformations associated with penetrating impact of fragments from explosive devices is challenging. Whereas experiments involving ballistic gelatine tissue simulants enable direct visualisation of deformation patterns, quantification of these deformations remains difficult. This thesis investigates the use of image registration for this purpose. Image registration methods optimise alignment of corresponding structures in image pairs, and in the process estimate the deformation fields that best achieve this. In the current context, it is hypothesised that registration of consecutive images from videos of gelatine penetration events can enable the corresponding gelatine deformation fields to be estimated. Three main activities were undertaken towards validation of this hypothesis: the proposed registration approach was tested on a series of synthetic images emulating the types of deformations expected in penetration events; the approach was then tested on images derived from a carefully controlled indentation experiment, in which a block of gelatine was deformed quasi-statically with a rigid indenter while the resulting deformation was filmed; and finally it was tested on video footage from projectile penetration experiments, in which metal projectiles were fired into blocks of gelatine and filmed with a high speed video camera. A series of complementary studies was also undertaken in support of these experiments. Firstly, to better understand the parameters of real penetration scenarios, the fragment generation and flight behaviour of a typical explosive device were analysed. Secondly, to improve understanding of the material behaviour of the test gelatine, mechanical characterisation tests were undertaken, and a visco-hyperelastic constitutive model was proposed. The individual registration operations themselves appeared to perform well, in the sense that initially disparate consecutive image pairs were brought into good alignment. However, composition of the corresponding transformation fields, necessary for tracking accumulated deformations over the course of a video sequence, was found to yield artefacts and unphysical deformation estimates in some cases. These were judged to result both from deficiencies in the methods themselves, and flaws in the experimental arrangements. Therefore, while the proposed registration approach appears to show promise, further work is needed to establish its validity conclusively. The thesis closes with a discussion of possible approaches to the latter.
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Rehman, Tauseef ur. "Efficient numerical method for solution of L² optimal mass transport problem." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33891.

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In this thesis, a novel and efficient numerical method is presented for the computation of the L² optimal mass transport mapping in two and three dimensions. The method uses a direct variational approach. A new projection to the constraint technique has been formulated that can yield a good starting point for the method as well as a second order accurate discretization to the problem. The numerical experiments demonstrate that the algorithm yields accurate results in a relatively small number of iterations that are mesh independent. In the first part of the thesis, the theory and implementation details of the proposed method are presented. These include the reformulation of the Monge-Kantorovich problem using a variational approach and then using a consistent discretization in conjunction with the "discretize-then-optimize" approach to solve the resulting discrete system of differential equations. Advanced numerical methods such as multigrid and adaptive mesh refinement have been employed to solve the linear systems in practical time for even 3D applications. In the second part, the methods efficacy is shown via application to various image processing tasks. These include image registration and morphing. Application of (OMT) to registration is presented in the context of medical imaging and in particular image guided therapy where registration is used to align multiple data sets with each other and with the patient. It is shown that an elastic warping methodology based on the notion of mass transport is quite natural for several medical imaging applications where density can be a key measure of similarity between different data sets e.g. proton density based imagery provided by MR. An application is also presented of the two dimensional optimal mass transport algorithm to compute diffeomorphic correspondence maps between curves for geometric interpolation in an active contour based visual tracking application.
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Debroux, Noémie. "Mathematical modelling of image processing problems : theoretical studies and applications to joint registration and segmentation." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMIR02/document.

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Dans cette thèse, nous nous proposons d'étudier et de traiter conjointement plusieurs problèmes phares en traitement d'images incluant le recalage d'images qui vise à apparier deux images via une transformation, la segmentation d'images dont le but est de délimiter les contours des objets présents au sein d'une image, et la décomposition d'images intimement liée au débruitage, partitionnant une image en une version plus régulière de celle-ci et sa partie complémentaire oscillante appelée texture, par des approches variationnelles locales et non locales. Les relations étroites existant entre ces différents problèmes motivent l'introduction de modèles conjoints dans lesquels chaque tâche aide les autres, surmontant ainsi certaines difficultés inhérentes au problème isolé. Le premier modèle proposé aborde la problématique de recalage d'images guidé par des résultats intermédiaires de segmentation préservant la topologie, dans un cadre variationnel. Un second modèle de segmentation et de recalage conjoint est introduit, étudié théoriquement et numériquement puis mis à l'épreuve à travers plusieurs simulations numériques. Le dernier modèle présenté tente de répondre à un besoin précis du CEREMA (Centre d'Études et d'Expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement) à savoir la détection automatique de fissures sur des images d'enrobés bitumineux. De part la complexité des images à traiter, une méthode conjointe de décomposition et de segmentation de structures fines est mise en place, puis justifiée théoriquement et numériquement, et enfin validée sur les images fournies
In this thesis, we study and jointly address several important image processing problems including registration that aims at aligning images through a deformation, image segmentation whose goal consists in finding the edges delineating the objects inside an image, and image decomposition closely related to image denoising, and attempting to partition an image into a smoother version of it named cartoon and its complementary oscillatory part called texture, with both local and nonlocal variational approaches. The first proposed model addresses the topology-preserving segmentation-guided registration problem in a variational framework. A second joint segmentation and registration model is introduced, theoretically and numerically studied, then tested on various numerical simulations. The last model presented in this work tries to answer a more specific need expressed by the CEREMA (Centre of analysis and expertise on risks, environment, mobility and planning), namely automatic crack recovery detection on bituminous surface images. Due to the image complexity, a joint fine structure decomposition and segmentation model is proposed to deal with this problem. It is then theoretically and numerically justified and validated on the provided images
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Pernicová, Lenka. "Optimalizační metoda TRUST pro registraci medicínských obrazů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219506.

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The aim of the thesis is optimization for a medical images registration. The basis is to acquaint with the images registration and to peruse component global optimization methods, especially an optimization method TRUST. After theoretic knowledge it is possible to proceed to a suggestion of an optimization method based on the TRUST method and to realize in the program setting MATLAB. Created algorithms has been tested on test data and compared with other optimization methods as Simulated annealing.
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Chmelík, Jiří. "Afinní lícování nativních a postkontrastních CT snímků mozku." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-219946.

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This thesis is dealing with problem of brain images registration aquired by computed tomography. At the beginning is explanation of image geometrical transformation methods, notably affine transformation. Following part of text the is dealing with interpolation methods issues, calculation of similarity criterion and subsequent optimalization. All issues are handled especially for three-dimensional data. Second part of this work is practical sample of MatLab® program enviroment for registration of acquired frames by affine tranformation. In this program is algorithm for removement of stair-step artefact, under head pillow and patient’s desk, too. As an optimalization algorithm is used control random search (CRS) methode. Due to medical images type, which are distributed in DICOM format, is included process for their load and save at this work.
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Books on the topic "Image registration method"

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Schmidt-Richberg, Alexander. Registration Methods for Pulmonary Image Analysis. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-01662-3.

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service), SpringerLink (Online, ed. Image Registration: Principles, Tools and Methods. London: Springer London, 2012.

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Registration methods for pulmonary image analysis: Integration of morphological and physiological knowledge. Wiesbaden: Springer Vieweg, 2014.

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Fischer, Bernd. Biomedical Image Registration: 4th International Workshop, WBIR 2010, Lübeck, Germany, July 11-13, 2010. Proceedings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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Modersitzki, Jan. Numerical Methods for Image Registration. Oxford University Press, Incorporated, 2004.

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Image Registration Principles Tools And Methods. Springer, 2012.

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Goshtasby, A. Ardeshir. Image Registration: Principles, Tools and Methods. Springer London, Limited, 2014.

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Medical Image Registration (Biomedical Engineering). CRC, 2001.

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Modersitzki, Jan. Numerical Methods for Image Registration (Numerical Mathematics and Scientific Computation). Oxford University Press, USA, 2004.

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An, Jung-Ha. Various methods in shape analysis and image segmentation and registration. 2005.

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Book chapters on the topic "Image registration method"

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Freire, Luis, and Mark Jenkinson. "A Gradient-Informed Robust Motion Correction Method for FMRI." In Biomedical Image Registration, 377–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39701-4_40.

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Joshi, Anand A., David W. Shattuck, and Richard M. Leahy. "A Method for Automated Cortical Surface Registration and Labeling." In Biomedical Image Registration, 180–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31340-0_19.

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Zheng, Guoyan. "A Novel 3D/2D Correspondence Building Method for Anatomy-Based Registration." In Biomedical Image Registration, 75–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11784012_10.

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Compte, Benoît, Adrien Bartoli, and Daniel Pizarro. "Constant Flow Sampling: A Method to Automatically Select the Regularization Parameter in Image Registration." In Biomedical Image Registration, 110–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31340-0_12.

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Breman, Hester, Joost Mulders, Levin Fritz, Judith Peters, John Pyles, Judith Eck, Matteo Bastiani, Alard Roebroeck, John Ashburner, and Rainer Goebel. "An Image Registration-Based Method for EPI Distortion Correction Based on Opposite Phase Encoding (COPE)." In Biomedical Image Registration, 122–30. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50120-4_12.

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Camara, Oscar, Gaspar Delso, and Isabelle Bloch. "Free Form Deformations Guided by Gradient Vector Flow: A Surface Registration Method in Thoracic and Abdominal PET-CT Applications." In Biomedical Image Registration, 224–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39701-4_24.

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Sun, Pan, Weiling Hu, Jiquan Liu, Bin Wang, Fei Ma, Huilong Duan, and Jianmin Si. "An Iterative Method for Gastroscopic Image Registration." In Lecture Notes in Computer Science, 562–70. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21978-3_49.

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Yang, Chunlan, Tianzi Jiang, Jianzhe Wang, and Lian Zheng. "A Neighborhood Incorporated Method in Image Registration." In Lecture Notes in Computer Science, 244–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11812715_31.

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Zhou, Hong, and Benjamin Ray Seyfarth. "A Pattern Search Method for Image Registration." In Lecture Notes in Computer Science, 664–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428831_82.

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Kannala, Juho, Esa Rahtu, Janne Heikkilä, and Mikko Salo. "A New Method for Affine Registration of Images and Point Sets." In Image Analysis, 224–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_25.

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Conference papers on the topic "Image registration method"

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du Bois d'Aische, A., M. de Craene, B. Macq, and S. K. Warfield. "An articulated registration method." In 2005 International Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1529677.

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Jiang, Jing-Dai, and Guo-Shiang Lin. "An Image Registration Method for Engineering Images." In 2016 International Computer Symposium (ICS). IEEE, 2016. http://dx.doi.org/10.1109/ics.2016.0092.

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Tang, Chao, Xiaohui Xie, and Ruxu Du. "Improved Image Registration Technique Using Demons and B-Spline." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-65830.

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Non-rigid image registration is an important and challenge work in image processing. The demons algorithm is one of the most effective non-rigid image registration methods. However, it is only suitable for images with small deformation. In recent years, many improving techniques are proposed. The free form deformation method based on B-spline function is widely employed in non-rigid image registration and is good at dealing with large deformation image registration. However, the performance of the demons algorithm is better than that of the B-spline method in dealing with small deformation registration. Therefore, in this paper, we propose to combine the demons algorithm and the B-spline method. The new method consists of two steps: First, it applies the B-spline method to deal with the large deformation. Then, it uses the demons algorithm to treat the small deformation. The testing results show that the new method is effective in dealing images with both small and large deformations. Comparing to the demons algorithm as well as the B-spline method, the new method has the smallest registration error and hence, is the best.
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Chen, Zekang, Jia Wei, and Rui Li. "Unsupervised Multi-Modal Medical Image Registration via Discriminator-Free Image-to-Image Translation." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/117.

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In clinical practice, well-aligned multi-modal images, such as Magnetic Resonance (MR) and Computed Tomography (CT), together can provide complementary information for image-guided therapies. Multi-modal image registration is essential for the accurate alignment of these multi-modal images. However, it remains a very challenging task due to complicated and unknown spatial correspondence between different modalities. In this paper, we propose a novel translation-based unsupervised deformable image registration approach to convert the multi-modal registration problem to a mono-modal one. Specifically, our approach incorporates a discriminator-free translation network to facilitate the training of the registration network and a patchwise contrastive loss to encourage the translation network to preserve object shapes. Furthermore, we propose to replace an adversarial loss, that is widely used in previous multi-modal image registration methods, with a pixel loss in order to integrate the output of translation into the target modality. This leads to an unsupervised method requiring no ground-truth deformation or pairs of aligned images for training. We evaluate four variants of our approach on the public Learn2Reg 2021 datasets. The experimental results demonstrate that the proposed architecture achieves state-of-the-art performance. Our code is available at https://github.com/heyblackC/DFMIR.
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Guarneri, I., M. Guarnera, G. Lupica, and S. Casale. "Image registration method for consumer devices." In 2005 Digest of Technical Papers. International Conference on Consumer Electronics, 2005. ICCE. IEEE, 2005. http://dx.doi.org/10.1109/icce.2005.1429806.

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Li, Jinping, Xiaoli Chang, Yingjie Xia, and Yanbin Han. "Assembly method for multimodal image registration." In 2012 IEEE International Conference on Granular Computing (GrC-2012). IEEE, 2012. http://dx.doi.org/10.1109/grc.2012.6468655.

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Chen, Yu, Guy Courbebaisse, and Dongxiang Lu. "Fast Image Registration by LB Method." In 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2018. http://dx.doi.org/10.1109/cisp-bmei.2018.8633107.

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Wang, Di, Jinyuan Liu, Xin Fan, and Risheng Liu. "Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/487.

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Recent learning-based image fusion methods have marked numerous progress in pre-registered multi-modality data, but suffered serious ghosts dealing with misaligned multi-modality data, due to the spatial deformation and the difficulty narrowing cross-modality discrepancy. To overcome the obstacles, in this paper, we present a robust cross-modality generation-registration paradigm for unsupervised misaligned infrared and visible image fusion (IVIF). Specifically, we propose a Cross-modality Perceptual Style Transfer Network (CPSTN) to generate a pseudo infrared image taking a visible image as input. Benefiting from the favorable geometry preservation ability of the CPSTN, the generated pseudo infrared image embraces a sharp structure, which is more conducive to transforming cross-modality image alignment into mono-modality registration coupled with the structure-sensitive of the infrared image. In this case, we introduce a Multi-level Refinement Registration Network (MRRN) to predict the displacement vector field between distorted and pseudo infrared images and reconstruct registered infrared image under the mono-modality setting. Moreover, to better fuse the registered infrared images and visible images, we present a feature Interaction Fusion Module (IFM) to adaptively select more meaningful features for fusion in the Dual-path Interaction Fusion Network (DIFN). Extensive experimental results suggest that the proposed method performs superior capability on misaligned cross-modality image fusion.
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Tanaka, Chika, Tohru Kamiya, and Takatoshi Aoki. "Image Registration Method from LDCT Image Using FFD Algorithm." In 2020 20th International Conference on Control, Automation and Systems (ICCAS). IEEE, 2020. http://dx.doi.org/10.23919/iccas50221.2020.9268267.

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Yetik, Imam Samil. "A novel non-rigid registration method." In 2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI). IEEE, 2010. http://dx.doi.org/10.1109/ssiai.2010.5483923.

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Reports on the topic "Image registration method"

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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.
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Reed, R. A. Comparison of Subpixel Phase Correlation Methods for Image Registration. Fort Belvoir, VA: Defense Technical Information Center, April 2010. http://dx.doi.org/10.21236/ada519383.

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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