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1
Prasai, Persis. "Multimodality image registration." Birmingham, Ala. : University of Alabama at Birmingham, 2006. http://www.mhsl.uab.edu/dt/2007m/prasai.pdf.
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Suri, Sahil. "Automatic image to image registration for multimodal remote sensing images." kostenfrei, 2010. https://mediatum2.ub.tum.de/node?id=967187.
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Wei, YaNing. "Image registration and matching." Thesis, University of Nottingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430757.
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Habboush, Isam H. (Isam Hussein). "Image registration and fusion." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/37009.
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Yanovsky, Igor. "Unbiased nonlinear image registration." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1619485511&sid=16&Fmt=2&clientId=1564&RQT=309&VName=PQD.
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Rohrer, Jonathan. "Accelerated nonrigid image registration." Berlin dissertation.de, 2009. http://d-nb.info/999883968/04.
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Bird, Joshua Campbell Cater. "Evaluation of Deformable Image Registration." Thesis, University of Canterbury. Physics, 2015. http://hdl.handle.net/10092/10577.
Testo completoAbstract (sommario):
Deformable image registration (DIR) is a type of registration that calculates a deformable vector field (DVF) between two image data sets and permits contour and dose propagation. However the calculation of a DVF is considered an ill-posed problem, as there is no exact solution to a deformation problem, therefore all DVFs calculated contain errors. As a result it is important to evaluate and assess the accuracy and limitations of any DIR algorithm intended for clinical use. The influence of image quality on the DIR algorithms performance was also evaluated.
The hybrid DIR algorithm in RayStation 4.0.1.4 was assessed using a number of evaluation methods and data. The evaluation methods were point of interest (POI) propagation, contour propagation and dose measurements. The data types used were phantom and patient data. A number of metrics were used for quantitative analysis and visual inspection was used for qualitative analysis.
The quantitative and qualitative results indicated that all DVFs calculated by the DIR algorithm contained errors which translated into errors in the propagated contours and propagated dose. The results showed that the errors were largest for small contour volumes (<20cm3) and for large anatomical volume changes between the image sets, which pushes the algorithms ability to deform, a significant decrease in accuracy was observed for anatomical volume changes of greater than 10%. When the propagated contours in the head and neck were used for planning the errors in the DVF were found to cause under dosing to the target tumour by up to 32% and over dosing to the organs at risk (OAR) by up to 12% which is clinically significant. The results also indicated that the image quality does not have a significant effect on the DIR algorithms calculations. Dose measurements indicated errors in the DVF calculations that could potentially be clinically significant. The results indicate that contour propagation and dose propagation must be used with caution if clinical use is intended. For clinical use contour propagation requires evaluation of every propagated contour by an expert user and dose propagation requires thorough evaluation of the DVF.
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Henson, Benjamin. "Image registration for sonar applications." Thesis, University of York, 2017. http://etheses.whiterose.ac.uk/19536/.
Testo completoAbstract (sommario):
This work develops techniques to estimate the motion of an underwater platform by processing data from an on-board sonar, such as a Forward Looking Sonar (FLS). Based on image registration, a universal algorithm has been developed and validated with in field datasets. The proposed algorithm gives a high quality registration to a fine (sub-pixel) precision using an adaptive filter and is suitable for both optical and acoustic images. The efficiency and quality of the result can be improved if an initial estimate of the motion is made. Therefore, a coarse (pixel-wide) registration algorithm is proposed, this is based on the assumption of local sparsity in the pixel motion between two images. Using a coarse and then fine registration, large displacements can be accommodated with a result that is to a sub-pixel precision. The registration process produces a displacement map (DM) between two images. From a sequence of DMs, an estimation of the sensor's motion is made. This is performed by a proposed fast searching and matching technique applied to a library of modelled DMs. Further, this technique exploits regularised splines to estimate the attitude and trajectory of the platform. To validate the results, a mosaic has been produced from three sets of in field data. Using a more detailed model of the acoustic propagation has the potential to improve the results further. As a step towards this a baseband underwater channel model has been developed. A physics simulator is used to characterise the channel at waymark points in a changing environment. A baseband equivalent representation of the time varying channel is then interpolated from these points. Processing in the baseband reduces the sample rate and hence reduces the run time for the model. A comparison to a more established channel model has been made to validate the results.
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Fitch, Alistair John. "Fast statistically robust image registration." Thesis, University of Surrey, 2003. http://epubs.surrey.ac.uk/844612/.
Testo completoAbstract (sommario):
Image registration is the automatic alignment of images. It is a fundamental task in computer vision. Image registration is challenging, in part, because of the wide range of applications with an equally wide range of content. Applications that require the automatic alignment of images include: super-resolution, face detection, video coding, medical imaging, mosaicking, post-production video effects, and satellite image registration. The wide and diverse range of applications have led to a wide and diverse range of image registration algorithms. An image registration algorithm is defined by its transformation, criterion, and search. The transformation is the model of image deformation required for alignment. The criterion is the definition of the best registration. The search describes how the best registration is to be found. This thesis presents two image registration methods; fast robust correlation and orientation correlation. The presented methods find translational transformations. Both define their criterion of the best registration using robust statistics. Fast robust correlation applies robust statistics to pixel intensity differences. Orientation correlation applies robust statistics to differences in orientation of intensity gradient. This gives orientation correlation the property of illumination invariance. Both use an exhaustive search to find the best registration. The novelty of fast robust correlation and orientation correlation is the combination of robust statistics, with an exhaustive search that can be computed quickly with fast Fourier transforms (FFTs). This is achieved by expressing a statistically robust registration surface with correlations. The correlations are computed quickly using FFTs. Computation with FFTs is shown to be particularly advantageous in registration of large images of similar size. Experimental comparisons demonstrate the advantages of the methods over standard correlation-based approaches. Advantage is shown in the experiments of: video coding, video frame registration, tolerance of rotation and zoom, registration of multimodal microscopy images, and face registration.
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FEI, Baowei. "Image Registration for the Prostate." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1224274091.
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Paquin, Dana. "Multiscale methods for image registration /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
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Pourmoshir, Shirin. "An Aerial Image Registration Tool." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-372091.
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GeoMemories is a project that aims to digitalize the historical aerial photograph archives of the Aerofototeca Nazionale in Rome. In order to use those photographs in GeoMemories, there is a need to have an application to help with producing better photographs. This study presents different steps of creating this aerial image registration application or what is called GeoImage. GeoImage is a desktop application which is intended to give researchers opportunities to work on aerial historical images easie and more practically. This study describes the reasons for choosing User-Centred Design or UCD as a methodology to help analysing unstructured users’ requirements. Moreover, it presents surveys as a chosen UCD method to help user to focus more on main requirements and they also increase the speed of gathering information from users. The result shows that survey is a key point for success of this project. Furthermore, this research analyses two available applications in the market with similar functionalities, ArcGIS and QGIS from all aspects of the User-Centred Design perspective. The outcome of this analysis helps extensively in the evaluation of the final version of the GeoImage application. Finally, this study can be complemented in the future by adding more functionalities based on the final GeoImage’s goal. Furthermore, all analysed requirements can be gathered in a web-based application for public audience.
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Song, Joo Hyun. "Methods for evaluating image registration." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5637.
Testo completoAbstract (sommario):
In the field of medical imaging, image registration methods are useful for many applications such as inter- and intra-subject morphological comparisons, creation of population atlases, delivery of precision therapies, etc. A user may want to know which is the most suitable registration algorithm that would work best for the intended application, but the vastness of medical image registration applications makes evaluation and comparison of image registration performance a non-trivial task. In general, evaluating image registration performance is not straightforward because in most image registration applications there is an absence of “Gold Standard” or ground truth correspondence map to compare against. It is therefore the primary goal of this thesis work to provide a means for recommending the most appropriate registration algorithm for a given task. One of the contributions of this thesis is to examine image registration algorithm performance at the component level. Another contribution of this thesis is to catalog the benefits and limitations of many of the most commonly used image registration evaluation approaches. One incremental contribution of this thesis was to demonstrate how existing evaluation methods can be applied in the midpoint coordinate system to evaluate some symmetric image registration algorithms such as the SyN registration algorithm. Finally, a major contribution of this thesis was to develop tools to evaluate and visualize 2D and 3D image registration shape collapse. This thesis demonstrates that many current diffeomorphic image registration algorithms suffer from the collapse problem, provides the first visualizations of the collapse problem in 3D for simple shapes and real human brain MR images, and provides the first experiments that demonstrate how adjusting image registration parameters can mitigate the collapse problem to some extent.
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Cabrera, Gil Blanca. "Deep Learning Based Deformable Image Registration of Pelvic Images." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279155.
Testo completoAbstract (sommario):
Deformable image registration is usually performed manually by clinicians,which is time-consuming and costly, or using optimization-based algorithms, which are not always optimal for registering images of different modalities. In this work, a deep learning-based method for MR-CT deformable image registration is presented. In the first place, a neural network is optimized to register CT pelvic image pairs. Later, the model is trained on MR-CT image pairs to register CT images to match its MR counterpart. To solve the unavailability of ground truth data problem, two approaches were used. For the CT-CT case, perfectly aligned image pairs were the starting point of our model, and random deformations were generated to create a ground truth deformation field. For the multi-modal case, synthetic CT images were generated from T2-weighted MR using a CycleGAN model, plus synthetic deformations were applied to the MR images to generate ground truth deformation fields. The synthetic deformations were created by combining a coarse and fine deformation grid, obtaining a field with deformations of different scales. Several models were trained on images of different resolutions. Their performance was benchmarked with an analytic algorithm used in an actual registration workflow. The CT-CT models were tested using image pairs created by applying synthetic deformation fields. The MR-CT models were tested using two types of test images. The first one contained synthetic CT images and MR ones deformed by synthetically generated deformation fields. The second test set contained real MR-CT image pairs. The test performance was measured using the Dice coefficient. The CT-CT models obtained Dice scores higherthan 0.82 even for the models trained on lower resolution images. Despite the fact that all MR-CT models experienced a drop in their performance, the biggest decrease came from the analytic method used as a reference, both for synthetic and real test data. This means that the deep learning models outperformed the state-of-the-art analytic benchmark method. Even though the obtained Dice scores would need further improvement to be used in a clinical setting, the results show great potential for using deep learning-based methods for multi- and mono-modal deformable image registration.
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Zhao, Bowen. "Tissue preserving deformable image registration for 4DCT pulmonary images." Thesis, University of Iowa, 2016. https://ir.uiowa.edu/etd/2172.
Testo completoAbstract (sommario):
This thesis mainly focuses on proposing a 4D (three spatial dimensions plus time) tissue-volume preserving non-rigid image registration algorithm for pulmonary 4D computed tomography (4DCT) data sets to provide relevant information for radiation therapy and to estimate pulmonary ventilation. The sum of squared tissue volume difference (SSTVD) similarity cost takes into account the CT intensity changes of spatially corresponding voxels, which is caused by variations of the fraction of tissue within voxels throughout the respiratory cycle. The proposed 4D SSTVD registration scheme considers the entire dynamic 4D data set simultaneously, using both spatial and temporal information. We employed a uniform 4D cubic B-spline parametrization of the transform and a temporally extended linear elasticity regularization of deformation field to ensure temporal smoothness and thus biological plausibility of estimated deformation. A multi-resolution multi-grid registration framework was used with a limited-memory Broyden Fletcher Goldfarb Shanno (LBFGS) optimizer for rapid convergence rate, robustness against local minima and limited memory consumption. The algorithm was prototyped in Matlab and then fully implemented in C++ in Elastix package based on the Insight Segmentation and Registration Toolkit (ITK). We conducted experiments on 2D+t synthetic images to demonstrate the effectiveness of the proposed method. The 4D SSTVD algorithm was also tested on clinical pulmonary 4DCT data sets in comparison with existing 3D pairwise SSTVD algorithm and 4D sum of squared difference (SSD) algorithm. The mean landmark error and mean landmark irregularity were calculated based on manually annotated landmarks on publicly available 4DCT data sets to evaluate the accuracy and temporal smoothness of the registration results. A 4D landmarking software tool was also designed and implemented in Java as an ImageJ plug-in to help facilitate the landmark labeling process in 4DCT data sets.
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Hamdan, Iyas. "Multimodal Image Registration in Image-Guided Prostate Brachytherapy." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0002/document.
Testo completoAbstract (sommario):
Le cancer de la prostate est le cancer le plus fréquent chez l'homme en France et aux pays occidentaux. Il est la troisième cause de décès liés au cancer, étant responsable d'environ 10% des morts. La curiethérapie, une technique de radiothérapie, est liée à une meilleure qualité de vie après le traitement, par rapport aux autres méthodes de traitement. La curiethérapie de la prostate consiste à insérer des sources radioactives dans la prostate afin de délivrer une dose d'irradiation localisée à la tumeur tout en protégeant les tissus sains environnants. L'imagerie multimodale est utilisée afin d'améliorer la précision du traitement. Les images Tomodensitométriques préopératoires, appelées Computed Tomography (CT), peuvent être utilisées pour calculer une distribution personnalisée et plus précise de dose. Pendant l'intervention, le chirurgien utilise un système de guidage temps-réel par l'Ultrason Transrectale, Transrectal Ultrasound (TRUS), pour placer correctement les sources radioactives dans leurs positions souhaitées. Par conséquent, si les positions des sources sont déterminées sur l'image CT, elles doivent être transférées à l'image US. Cependant, un recalage US/CT direct et robuste est difficilement envisageable parce que les tissus mous, telle que la prostate, offrent peu de contraste en CT et en US. En revanche, l'Imagerie par Résonance Magnétique (IRM) fournit un meilleur contraste et peut, potentiellement, améliorer le traitement en améliorant la visualisation. Donc, ces trois modalités (IRM, CT et US) doivent être correctement alignées. Pour compenser les déformations de la prostate, due au changement de taille et forme entre les différentes acquisitions, un recalage non-rigide est nécessaire. Une méthode de recalage entièrement automatique est nécessaire, afin de faciliter son intégration au bloc opératoire. Nous proposons dans un premier temps un recalage IRM/CT basé sur la maximisation de l'information mutuelle en combinaison avec un champ de déformation paramétré par B-Splines. Nous proposons de contraindre le recalage sur des volumes d'intérêt (VOIs) afin d'améliorer la robustesse et le temps de calcul. L'approche proposée a été validée sur des jeux de données cliniques. Une évaluation quantitative a montré que l'erreur de recalage est égale à 1.15±0.20 mm; qui répond à la précision clinique souhaitée. Ensuite, nous proposons un deuxième recalage US/IRM, où nous utilisons une approche multi-résolution pour éviter les minima locaux et améliorer le temps de calcul. Un critère de similarité, qui met en corrélation l'intensité de l'image US avec l'intensité ainsi que le gradient de l'image IRM, a été utilisé afin de trouver la transformation qui aligne les deux images. Cette méthode a été validée sur un fantôme de prostate dans un premier temps pour évaluer sa faisabilité. Ensuite, elle a été validée sur des jeux de données cliniques en utilisant des critères qualitatives et quantitatives. La distance Hausdorff a montré que l'erreur de recalage est égale à 1.44±0.06 mm. L'approche proposée dans ce travail permet d'aller vers un protocole de curiethérapie guidée par l'imagerie multimodale qui puisse améliorer la précision globale de cette procédure. Malgré ces résultats plutôt encourageants, les travaux futurs impliqueront une évaluation plus approfondie sur plus de jeux de données afin d'évaluer la fiabilité et l'efficacité de cette méthode avant de l'intégrer au bloc opératoire<br>Prostate cancer is the most common cancer in men in France and western countries. It is the third leading cause of death from cancer, being responsible for around 10% of deaths. Brachytherapy, a radiotherapy technique, is associated with a better health-related quality of life after the treatment, compared to other treatment techniques. Prostate brachytherapy involves the implantation of radioactive sources inside the prostate to deliver a localized radiation dose to the tumor while sparing the surrounding healthy tissues. Multi-modal imaging is used in order to improve the overall accuracy of the treatment. The pre-operative Computed Tomography (CT) images can be used to calculate a personalized and accurate dose distribution. During the intervention, the surgeon utilizes a real-time guiding system, Trasnrectal Ultrasound (TRUS), to accurately place the radioactive sources in their desired pre-planned positions. Therefore, if the positions of the sources were determined on CT, they need to be transferred to US. However, a robust and direct US/CT registration is hardly possible since they both provide low soft tissue contrast. Magnetic Resonance Imaging (MRI), on the other hand, has a superior contrast and can potentially improve the treatment planning and delivery by providing a better visualization. Thus, these three modalities (MRI, US and CT) need to be accurately registered. To compensate for prostate deformations, caused by changes in size and form between the different acquisitions, non-rigid registration is essential. Fully automatic registration methodology is necessary in order to facilitate its integration in a clinical workflow. At first, we propose a registration between pre-operative MR and CT images based on the maximization of the mutual information in combination with a deformation field parameterized by cubic B-Splines. We propose to constrain the registration to volumes of interest (VOIs) in order to improve the robustness and the computational efficiency. The proposed approach was validated on clinical patient datasets. Quantitative evaluation indicated that the overall registration error was of 1.15±0.20 mm; which satisfies the desired clinical accuracy. Then, we propose a second intra-operative US/MRI registration, where a multi-resolution approach is implemented to reduce the probability of local minima and improve the computational efficiency. A similarity measure, which correlates intensities of the US image with intensities and gradient magnitude of the MRI, is used to determine the transformation that aligns the two images. The proposed methodology was validated on a prostate phantom at first to assess its feasibility. Subsequently, the method was validated on clinical patient datasets and evaluated using qualitative and quantitative criteria, resulting in a registration error of 1.44±0.06 mm. The approach proposed in this work allows going towards a multimodal protocol for image-guided brachytherapy which can improve the overall accuracy of this procedure. Despite such encouraging results, future work will involve further evaluation on a larger number of datasets in order to assess the reliability and the efficiency of this methodology before integrating it in a clinical workflow
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Purwani, Sri. "Ensemble registration : combining groupwise registration and segmentation." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/ensemble-registration-combining-groupwise-registration-and-segmentation(5f3c06b4-4909-492b-bbc9-a0fecb77d216).html.
Testo completoAbstract (sommario):
Registration of a group of images generally only gives a pointwise, dense correspondence defined over the whole image plane or volume, without having any specific description of any common structure that exists in every image. Furthermore, identifying tissue classes and structures that are significant across the group is often required for analysis, as well as the correspondence. The overall aim is instead to perform registration, segmentation, and modelling simultaneously, so that the registration can assist the segmentation, and vice versa. However, structural information does play a role in conventional registration, in that if the registration is successful, it would be expected structures to be aligned to some extent. Hence, we perform initial experiments to investigate whether there is explicit structural information present in the shape of the registration objective function about the optimum. We perturbed one image locally with a diffeomorphism, and found interesting structure in the shape of the quality of fit function. Then, we proceed to add explicit structural information into registration framework, using various types of structural information derived from the original intensity images. For the case of MR brain images, we augment each intensity image with its own set of tissue fraction images, plus intensity gradient images, which form an image ensemble for each example. Then, we perform groupwise registration by using these ensembles of images. We apply the method to four different real-world datasets, for which ground-truth annotation is available. It is shown that the method can give a greater than 25% improvement on the three difficult datasets, when compared to using intensity-based registration alone. On the easier dataset, it improves upon intensity-based registration, and achieves results comparable with the previous method.
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Xu, Min. "Image registration and image fusion Algorithms and performance bounds /." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2009. http://wwwlib.umi.com/cr/syr/main.
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Chou, Yi-Yu. "Transitive and Symmetric Nonrigid Image Registration." Thesis, Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4966.
Testo completoAbstract (sommario):
The main topic of this thesis is nonrigid image registration for medical applications. We start with an overview and classification of existing registration techniques. We develop a general nonrigid image registration algorithm. It uses spline functions to describe the deformation and uses multi-scale strategy to search for the
optimal transformation. Then we present a new registration operator that is transitive and symmetric. We investigate the theoretical implication of these properties and apply this
operator to the registration of sequences of MR cardiac images.
In the second part of the thesis, two methods, one 2D and one 3D, for validation of nonrigid image registration algorithms are proposed and compared to a manual validation strategy. Both
methods provide pairs of deformed images as well as corresponding true displacement fields with known accuracy. Nonrigid registration algorithms can be run on the pairs of images and their outputs can be compared to the true displacement fields that were generated manually by five observers. While these phantom validation studies do not provide physically correct deformations,
they are certainly a useful way to test the algorithm's ability to recover various deformation patterns.
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Ye, Getian Information Technology & Electrical Engineering Australian Defence Force Academy UNSW. "Image registration and super-resolution mosaicing." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2005. http://handle.unsw.edu.au/1959.4/38653.
Testo completoAbstract (sommario):
This thesis presents new approaches to image registration and super-resolution mosaicing as well as their applications. Firstly, a feature-based image registration method is proposed for a multisensor surveillance system that consists of an optical camera and an infrared camera. By integrating a non-rigid object tracking technique into this method, a novel approach to simultaneous object tracking and multisensor image registration is proposed. Based on the registration and fusion of multisensor information, automatic face detection is greatly improved. Secondly, some extensions of a gradient-based image registration method, called inverse compositional algorithm, are proposed. These extensions include cumulative multi-image registration and the incorporation of illumination change and lens distortion correction. They are incorporated into the framework of the original algorithm in a consistent manner and efficiency can still be achieved for multi-image registration with illumination and lens distortion correction. Thirdly, new super-resolution mosaicing algorithms are proposed for multiple uncompressed and compressed images. Considering the process of image formation, observation models are introduced to describe the relationship between the superresolution mosaic image and the uncompressed and compressed low-resolution images. To improve the performance of super-resolution mosaicing, a wavelet-based image interpolation technique and an approach to adaptive determination of the regularization parameter are presented. For compressed images, a spatial-domain algorithm and a transform-domain algorithm are proposed. All the proposed superresolution mosaicing algorithms are robust against outliers. They can produce superresolution mosaics and reconstructed super-resolution images with improved subjective quality. Finally, new techniques for super-resolution sprite generation and super-resolution sprite coding are proposed. Considering both short-term and long-term motion influences, an object-based image registration method is proposed for handling long image sequences. In order to remove the influence of outliers, a robust technique for super-resolution sprite generation is presented. This technique produces sprite images and reconstructed super-resolution images with high visual quality. Moreover, it provides better reconstructed low-resolution images compared with low-resolution sprite generation techniques. Due to the advantages of the super-resolution sprite, a super-resolution sprite coding technique is also proposed. It achieves high coding efficiency especially at a low bit-rate and produces both decoded low-resolution and super-resolution images with improved subjective quality. Throughout this work, the performance of all the proposed algorithms is evaluated using both synthetic and real image sequences.
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Sotiras, Aristeidis. "Discrete Image Registration : a Hybrid Paradigm." Phd thesis, Ecole Centrale Paris, 2011. http://tel.archives-ouvertes.fr/tel-00677442.
Testo completoAbstract (sommario):
This thesis is devoted to dense deformable image registration/fusion using discrete methods. The main contribution of the thesis is a principled registration framework coupling iconic/geometric information through graph-based techniques. Such a formulation is derived from a pair-wise MRF view-point and solves both problems simultaneously while imposing consistency on their respective solutions. The proposed framework was used to cope with pair-wise image fusion (symmetric and asymmetric variants are proposed) as well as group-wise registration for population modeling. The main qualities of our framework lie in its computational efficiency and versatility. The discrete nature of the formulation renders the framework modular in terms of iconic similarity measures as well as landmark extraction and association techniques. Promising results using a standard benchmark database in optical flow estimation and 3D medical data demonstrate the potentials of our methods.
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Reinertsen, Ingerid R. "Robust registration of volumetric image data." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=29570.
Testo completoAbstract (sommario):
Head motion during fMRI experiments continues to be a significant problem for analysis, producing artifacts that can severely degrade image quality and make interpretation difficult and inaccurate. Such movement artifacts can lead to reduced statistical significance when detecting true activation or may lead to the creation of spurious activations. To address the problem of interscan motion, a retrospective image registration method has been developed. The registration technique is based on the use of non-linear deformation fields, the least-trimmed-squares robust estimator and Procrustes analysis.<br>The registration algorithm was validated using simulated anatomical MRI volumes and real fMRI datasets. The registration technique is robust in the presence of large amounts of noise; and the experiments show that the method gives accurate estimations of motion up to 5 mm translation in all three directions and 5 degrees rotation around the three axes. The correction procedure also yields increased statistical power in activated regions, and thereby facilitates the precise localization of true activated areas.
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Gu, Wei Q. "Automated tracer-independent MRIPET image registration." Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=27223.
Testo completoAbstract (sommario):
Existing MRI/PET registration methods are tracer-dependent and may not work well with the PET emission images obtained using tracers which only accumulate - significantly in specific regions, or with PET images of pathology which have abnormal tracer distribution.<br>This thesis describes an automated tracer-independent MRI/PET registration method, in which the registration of MR images to tracer-independent PET transmission images is performed to register the corresponding MRI and PET emission images. Two voxel-based registration methods--mutual information method (MI) and voxel intensity ratio (VR) method, were implemented for the registration of MRI/PET transmission images.<br>The validation of the two registration methods was first performed on simulated PET transmission images with real MR images. The quantitative evaluation of these registration results reveals that the MI method gives more accurate registration results (mean 3-D registration error less than 2 mm) than the VR method (mean 3-D registration error more than 2.5 mm); and the MI method is more robust against noise and data truncation than the VR method. Both methods are more sensitive to low spatial resolution than to the noise of the PET transmission images. The validation of the MI method on real MRI/PET images was also carried out, and the results show no obvious misregistration by visual inspection. Tracer-independent MRI/PET registration using the MI algorithm is shown to be a feasible and robust method to register the MRI to PET images, regardless of the tracer used in the PET studies.
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Xu, Dongjiang. "HYBRID AND HIERARCHICAL IMAGE REGISTRATION TECHNIQUES." Doctoral diss., University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3232.
Testo completoAbstract (sommario):
A large number of image registration techniques have been developed for various types of sensors and applications, with the aim to improve the accuracy, computational complexity, generality, and robustness. They can be broadly classified into two categories: intensity-based and feature-based methods. The primary drawback of the intensity-based approaches is that it may fail unless the two images are misaligned by a moderate difference in scale, rotation, and translation. In addition, intensity-based methods lack the robustness in the presence of non-spatial distortions due to different imaging conditions between images. In this dissertation, the image registration is formulated as a two-stage hybrid approach combining both an initial matching and a final matching in a coarse-to-fine manner. In the proposed hybrid framework, the initial matching algorithm is applied at the coarsest scale of images, where the approximate transformation parameters could be first estimated. Subsequently, the robust gradient-based estimation algorithm is incorporated into the proposed hybrid approach using a multi-resolution scheme. Several novel and effective initial matching algorithms have been proposed for the first stage. The variations of the intensity characteristics between images may be large and non-uniform because of non-spatial distortions. Therefore, in order to effectively incorporate the gradient-based robust estimation into our proposed framework, one of the fundamental questions should be addressed: what is a good image representation to work with using gradient-based robust estimation under non-spatial distortions. With the initial matching algorithms applied at the highest level of decomposition, the proposed hybrid approach exhibits superior range of convergence. The gradient-based algorithms in the second stage yield a robust solution that precisely registers images with sub-pixel accuracy. A hierarchical iterative searching further enhances the convergence range and rate. The simulation results demonstrated that the proposed techniques provide significant benefits to the performance of image registration.<br>Ph.D.<br>Department of Electrical and Computer Engineering<br>Engineering and Computer Science<br>Electrical Engineering
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Bejo, Siti. "Elastic image registration for landslides monitoring." Thesis, University of Surrey, 2006. http://epubs.surrey.ac.uk/804429/.
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Ettinger, Gil J. "Hierarchical three-dimensional medical image registration." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10448.
Testo completoAbstract (sommario):
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.<br>Includes bibliographical references (leaves 168-172).<br>by Gil J. Ettinger.<br>Ph.D.
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Zeledon, Lostalo Emilia Maria. "FMRI IMAGE REGISTRATION USING DEEP LEARNING." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2641.
Testo completoAbstract (sommario):
fMRI imaging is considered key on the understanding of the brain and the mind, for this reason has been the subject of tremendous research connecting different disciplines. The intrinsic complexity of this 4-D type of data processing and analysis has been approached with every single computational perspective, lately increasing the trend to include artificial intelligence. One step critical on the fMRI pipeline is image registration. A model of Deep Networks based on Fully Convolutional Neural Networks, spatial transformation neural networks with a self-learning strategy was proposed for the implementation of a Fully deformable model image registration algorithm. Publicly available fMRI datasets with images from real-life subjects were used for training, testing and validating the model. The model performance was measured in comparison with ANTs deformable registration method with good results suggesting that Deep Learning can be used successfully for the development of the field using the basic strategy of studying the brain using the brain-self strategies.
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Khare, Vinod. "Precise Image Registration and Occlusion Detection." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1308246730.
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Gong, Lixin. "Prostate ultrasound image segmentation and registration /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/5937.
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Castro, Pareja Carlos Raul. "Real-time 3D elastic image registration." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1086185793.
Testo completoAbstract (sommario):
Thesis (Ph. D.)--Ohio State University, 2004.<br>Title from first page of PDF file. Document formatted into pages; contains xv, 105 p. : ill. (some col.). Advisor: Jogikal Jagadeesh, Department of Electrical and Computer Engineering. Includes bibliographical references (p. 101-105).
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Zheng, Chaojie. "Deformable Image Registration with Uncertainty-Awareness." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20948.
Testo completoAbstract (sommario):
The popularization of information-sensing devices and rapid development of data storage and computing capability have launched the ongoing data explosion, and the value of such information assets has been widely acknowledged. Data often come in high volume, variety and velocity, and people are striving to mine useful information and new knowledge from the growing data sets for various domain applications. The fusion of heterogeneous types of data becomes increasingly essential for analysis and decision making in data incentive industry, especially in healthcare industry. Image registration, as a branch of data fusion technique, is the process of determining the geometric transformation that relates correspondences in images. As the image registration result is often used as the input of further analysis or process, it is crucial to establish the image correspondence through an accurate and confident registration method. However, due to the diverse characteristics of images, there are inherent uncertainty issues associated with various aspects of the registration process. The source of such uncertainty issues could be divided into two categories: input images and registration algorithms. This thesis provides three registration methods with the consideration of uncertainty issues the of registration processes. Our major contributions include: 1. A topology-guided deformable registration (TDR) framework to deal with the image correspondence uncertainty issue in the derivation of deformation direction. 2. A novel laminar flow (LF) model with an analogy to laminar flow regime from fluid dynamics to simulate the derivation of registration transformation. 3. A multi-view collaborative learning based image registration (MVCIR) framework to tackle the uncertainty of deciding reliable feature space for the correspondence inference.
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Kochan, Martin. "Enhancing registration for image-guided neurosurgery." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10045247/.
Testo completoAbstract (sommario):
Pharmacologically refractive temporal lobe epilepsy and malignant glioma brain tumours are examples of pathologies that are clinically managed through neurosurgical intervention. The aims of neurosurgery are, where possible, to perform a resection of the surgical target while minimising morbidity to critical structures in the vicinity of the resected brain area. Image-guidance technology aims to assist this task by displaying a model of brain anatomy to the surgical team, which may include an overlay of surgical planning information derived from preoperative scanning such as the segmented resection target and nearby critical brain structures. Accurate neuronavigation is hindered by brain shift, the complex and non-rigid deformation of the brain that arises during surgery, which invalidates assumed rigid geometric correspondence between the neuronavigation model and the true shifted positions of relevant brain areas. Imaging using an interventional MRI (iMRI) scanner in a next-generation operating room can serve as a reference for intraoperative updates of the neuronavigation. An established clinical image processing workflow for iMRI-based guidance involves the correction of relevant imaging artefacts and the estimation of deformation due to brain shift based on non-rigid registration. The present thesis introduces two refinements aimed at enhancing the accuracy and reliability of iMRI-based guidance. A method is presented for the correction of magnetic susceptibility artefacts, which affect diffusion and functional MRI datasets, based on simulating magnetic field variation in the head from structural iMRI scans. Next, a method is presented for estimating brain shift using discrete non-rigid registration and a novel local similarity measure equipped with an edge-preserving property which is shown to improve the accuracy of the estimated deformation in the vicinity of the resected area for a number of cases of surgery performed for the management of temporal lobe epilepsy and glioma.
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Björck, Olof. "Analyzing gyro data based image registration." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-397459.
Testo completoAbstract (sommario):
An analysis of gyro sensor data with regards to rotational image registration is conducted in this thesis. This is relevant for understanding how well images captured with a moving camera can be registered using only gyro sensor data as motion input. This is commonly the case for Electronic Image Stabilization (EIS) in handheld devices. The theory explaining how to register images based on gyro sensor data is presented, a qualitative analysis of gyro sensor data from three generic Android smartphones is conducted, and rotational image registration simulations using simulated noise as well as real gyro sensor data from the smartphones are presented. An accuracy metric for rotational image registration is presented that measures image registration accuracy in pixels (relevant for frame to frame image registration) or pixels per second (relevant for video EIS). This thesis shows that noise in gyro sensor data affects image registration accuracy to an extent that is noticeable in 1080x1920 resolution video displayed on larger monitors such as a computer monitor or when zooming digitally, but not to any significant extent displayed on a monitor the size of a regular smartphone display without zooming. Different screen resolutions and frame rates will affect the image registration accuracy and would be interesting to investigate in further work. Ways to improve the gyro sensor data would also be interesting to investigate.
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Jia, Zhen. "Image Registration and Image Completion: Similarity and Estimation Error Optimization." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406821875.
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Mitra, Jhimli. "Multimodal Image Registration applied to Magnetic Resonance and Ultrasound Prostatic Images." Phd thesis, Université de Bourgogne, 2012. http://tel.archives-ouvertes.fr/tel-00786032.
Testo completoAbstract (sommario):
This thesis investigates the employment of different deformable registration techniques to register pre-operative magnetic resonance and inter-operative ultrasound images during prostate biopsy. Accurate registration ensures appropriate biopsy sampling of malignant prostate tissues and reduces the rate of re-biopsies. Therefore, we provide comparisons and experimental results for some landmark- and intensity-based registration methods: thin-plate splines, free-form deformation with B-splines. The primary contribution of this thesis is a new spline-based diffeomorphic registration framework for multimodal images. In this framework we ensure diffeomorphism of the thin-plate spline-based transformation by incorporating a set of non-linear polynomial functions. In order to ensure clinically meaningful deformations we also introduce the approximating thin-plate splines so that the solution is obtained by a joint-minimization of the surface similarities of the segmented prostate regions and the thin-plate spline bending energy. The method to establish point correspondences for the thin-plate spline-based registration is a geometric method based on prostate shape symmetry but a further improvement is suggested by computing the Bhattacharyya metric on shape-context based representation of the segmented prostate contours. The proposed deformable framework is computationally expensive and is not well-suited for registration of inter-operative images during prostate biopsy. Therefore, we further investigate upon an off-line learning procedure to learn the deformation parameters of a thin-plate spline from a training set of pre-operative magnetic resonance and its corresponding inter-operative ultrasound images and build deformation models by applying spectral clustering on the deformation parameters. Linear estimations of these deformation models are then applied on a test set of inter-operative and pre-operative ultrasound and magnetic resonance images respectively. The problem of finding the pre-operative magnetic resonance image slice from a volume that matches the inter-operative ultrasound image has further motivated us to investigate on shape-based and image-based similarity measures and propose for slice-to-slice correspondence based on joint-maximization of the similarity measures.
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Yu, Hongliang. "Automatic Rigid and Deformable Medical Image Registration." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-050905-100341/.
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Chen, Hua-Mei. "Mutual information-based image registration with applications." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2002. http://wwwlib.umi.com/cr/syr/main.
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Yi, Zhao. "Nonrigid Image Registration Using Physically Based Models." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2952.
Testo completoAbstract (sommario):
It is well known that biological structures such as human brains, although may contain the same global structures, differ in shape, orientation, and fine structures across individuals and at different times. Such variabilities during registration are usually represented by nonrigid transformations. This research seeks to address this issue by developing physically based models in which transformations are constructed to obey certain physical laws. <br /><br /> In this thesis, a novel registration technique is presented based on the physical behavior of particles. Regarding the image as a particle system without mutual interaction, we simulate the registration process by a set of free particles moving toward the target positions under applied forces. The resulting partial differential equations are a nonlinear hyperbolic system whose solution describes the spatial transformation between the images to be registered. They can be numerically solved using finite difference methods. <br /><br /> This technique extends existing physically based models by completely excluding mutual interaction and highly localizing image deformations. We demonstrate its performance on a variety of images including two-dimensional and three-dimensional, synthetic and clinical data. Deformable images are achieved with sharper edges and clearer texture at less computational cost.
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Sivaramakrishna, Radhika. "Breast image registration using a textural transformation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq23666.pdf.
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Gu, Wei Q. "Automated tracer-independent MRI/PET image registration." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29596.pdf.
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Lorenzen, Peter Jonathan Joshi Sarang C. "Multi-modal image registration and atlas formation." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2006. http://dc.lib.unc.edu/u?/etd,336.
Testo completoAbstract (sommario):
Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2006.<br>Title from electronic title page (viewed Oct. 10, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science." Discipline: Computer Science; Department/School: Computer Science.
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Curran, Kathleen Mary. "Orientation matching for diffusion tensor image registration." Thesis, University College London (University of London), 2005. http://discovery.ucl.ac.uk/1444595/.
Testo completoAbstract (sommario):
This thesis develops a registration algorithm specifically for diffusion-tensor (DT) images. The proposed approach matches the tensor orientations to find the registration transformation. Early results show that local optimisation does not find the global minimum in registration of DT-MR brain images. Therefore, a global optimisation registration technique is also implemented. This thesis proposes several new similarity measures for DT registration and provides a comparison of them along with several others previously proposed in the literature. The thesis also proposes several new performance evaluation measures to assess registration quality and develops a performance evaluation framework that uses directional coherence and landmark separation. Experiments with direct optimisation demonstrate increased local minima in tensor registration objective functions over scalar registration. Using registration with global optimisation, this thesis compares the performance of scalar-derived similarity measures with those derived from the full tensor. Results suggest that similarity measures derived from the full tensor matrix do not find a more accurate registration than those based on the derived scalar indices. Affine and higher-order polynomial registration is not reliable enough to make a firm conclusion about whether diffusion tensor orientation matching improves the accuracy of registration over registration algorithms that ignore orientation. The main problem preventing a firm conclusion is that the local minima problem persists despite the use of global optimisation, causing poor registration of the regions of interest.
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Cahill, Nathan D. "Constructing and solving variational image registration problems." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:ed43a6f4-216f-45b5-88c5-2baaba1e684a.
Testo completoAbstract (sommario):
Nonrigid image registration has received much attention in the medical imaging and computer vision research communities, because it enables a wide variety of applications. Feature tracking, segmentation, classification, temporal image differencing, tumour growth estimation, and pharmacokinetic modeling are examples of the many tasks that are enhanced by the use of aligned imagery. Over the years, the medical imaging and computer vision communties have developed and refined image registration techniques in parallel, often based on similar assumptions or underlying paradigms. This thesis focuses on variational registration, which comprises a subset of nonrigid image registration. It is divided into chapters that are based on fundamental aspects of the variational registration problem: image dissimilarity measures, changing overlap regions, regularizers, and computational solution strategies. Key contributions include the development of local versions of standard dissimilarity measures, the handling of changing overlap regions in a manner that is insensitive to the amount of non-interesting background information, the combination of two standard taxonomies of regularizers, and the generalization of solution techniques based on Fourier methods and the Demons algorithm for use with many regularizers. To illustrate and validate the various contributions, two sets of example imagery are used: 3D CT, MR, and PET images of the brain as well as 3D CT images of lung cancer patients.
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White, Brandyn A. "Using FPGAs to perform embedded image registration." Honors in the Major Thesis, University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/1337.
Testo completoAbstract (sommario):
This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.<br>Bachelors<br>Engineering and Computer Science<br>Computer Engineering
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Martin, Stefan. "On information measures for medical image registration." Thesis, University of Strathclyde, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436843.
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Williams, Tamara S. (Tamara Shanese) 1976. "Multi-modal registration for image-guided therapy." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86626.
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Cuesta, Contreras Angel. "Geometric and photometric affine invariant image registration." Thesis, Heriot-Watt University, 2009. http://hdl.handle.net/10399/2228.
Testo completoAbstract (sommario):
This thesis aims to present a solution to the correspondence problem for the registration of wide-baseline images taken from uncalibrated cameras. We propose an affine invariant descriptor that combines the geometry and photometry of the scene to find correspondences between both views. The geometric affine invariant component of the descriptor is based on the affine arc-length metric, whereas the photometry is analysed by invariant colour moments. A graph structure represents the spatial distribution of the primitive features; i.e. nodes correspond to detected high-curvature points, whereas arcs represent connectivities by extracted contours. After matching, we refine the search for correspondences by using a maximum likelihood robust algorithm. We have evaluated the system over synthetic and real data. The method is endemic to propagation of errors introduced by approximations in the system.
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Dorgham, Osama. "High speed 2D/3D medical image registration." Thesis, University of East Anglia, 2010. https://ueaeprints.uea.ac.uk/32227/.
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Ivarsson, Magnus. "Evaluation of 3D MRI Image Registration Methods." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139075.
Testo completoAbstract (sommario):
Image registration is the process of geometrically deforming a template image into a reference image. This technique is important and widely used within thefield of medical IT. The purpose could be to detect image variations, pathologicaldevelopment or in the company AMRA’s case, to quantify fat tissue in variousparts of the human body.From an MRI (Magnetic Resonance Imaging) scan, a water and fat tissue image isobtained. Currently, AMRA is using the Morphon algorithm to register and segment the water image in order to quantify fat and muscle tissue. During the firstpart of this master thesis, two alternative registration methods were evaluated.The first algorithm was Free Form Deformation which is a non-linear parametricbased method. The second algorithm was a non-parametric optical flow basedmethod known as the Demon algorithm. During the second part of the thesis,the Demon algorithm was used to evaluate the effect of using the fat images forregistrations.
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Geng, Xiujuan. "Transitive inverse-consistent image registration and evaluation." Diss., University of Iowa, 2007. http://ir.uiowa.edu/etd/131.
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