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
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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 fourniesIn 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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Kubica, Roman. "Lícování ultrasonografických obrazových sekvencí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220029.
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The result of this thesis, focused on medical image registration, is an automatic image registration algorithm. It is constructed to be used on real ultrasonography images, created by perfusion imaging. In its introductory part, the thesis deals with registrations methods, next it describes types of optimization principles and single criteria functions served to determine correct image transformation. Based on the theoretical part, there are realized three optimization algorithms using three criterial functions, which served to registration of provided ultrasonography sequences. These algorithms are tested and results are passed on analysis, on its ground are judged its advantages and disadvantages.
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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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Song, Joo Hyun. "Methods for evaluating image registration." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5637.
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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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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.
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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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Mikuláš, Karol. "Registrace CT objemových obrazů mozku pomocí globální afinní 3D transformace." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219508.
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At present, the medical industry rapidly develops new imaging techniques and improves the imaging methods. Simultaneously also are developed new methods for processing data acquired by these methods. Especially in the past few years has become very used method of registration data, which leads to image transformations of the same scene so that the condition as possible. The work deals with the method of processing data that provides detailed information to individual structures, developments of individual structures over time, allows to simultaneously displayanatomical and physiological information or preprocessing data for further processing.
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Itta, Francesca. "Biomechanical modeling of parotid glands morphing in head & neck radiation therapy treatments." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11221/.
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Durante i trattamenti radioterapici dei pazienti oncologici testa-collo, le ghiandole parotidee (PGs) possono essere indebitamente irradiate a seguito di modificazioni volumetriche-spaziali inter/intra-frazione causate da fattori quali il dimagrimento, l’esposizione a radiazioni ionizzanti ed il morphing anatomico degli organi coinvolti nelle aree d’irraggiamento. Il presente lavoro svolto presso la struttura di Fisica Medica e di Radioterapia Oncologica dell’A.O.U di Modena, quale parte del progetto di ricerca del Ministero della Salute (MoH2010, GR-2010-2318757) “ Dose warping methods for IGRT and Adaptive RT: dose accumulation based on organ motion and anatomical variations of the patients during radiation therapy treatments ”, sviluppa un modello biomeccanico in grado di rappresentare il processo di deformazione delle PGs, considerandone la geometria, le proprietà elastiche e l'evoluzione durante il ciclo terapeutico. Il modello di deformazione d’organo è stato realizzato attraverso l’utilizzo di un software agli elementi finiti (FEM). Molteplici superfici mesh, rappresentanti la geometria e l’evoluzione delle parotidi durante le sedute di trattamento, sono state create a partire dai contorni dell’organo definiti dal medico radioterapista sull’immagine tomografica di pianificazione e generati automaticamente sulle immagini di setup e re-positioning giornaliere mediante algoritmi di registrazione rigida/deformabile. I constraints anatomici e il campo di forze del modello sono stati definiti sulla base di ipotesi semplificative considerando l’alterazione strutturale (perdita di cellule acinari) e le barriere anatomiche dovute a strutture circostanti. L’analisi delle mesh ha consentito di studiare la dinamica della deformazione e di individuare le regioni maggiormente soggette a cambiamento. Le previsioni di morphing prodotte dal modello proposto potrebbero essere integrate in un treatment planning system per metodiche di Adaptive Radiation Therapy.
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Pouzet, Mathieu. "Détection et segmentation robustes de cibles mobiles par analyse du mouvement résiduel, à l'aide d'une unique caméra, dans un contexte industriel. Une application à la vidéo-surveillance automatique par drone." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLV002.
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Nous proposons dans cette thèse une méthode robuste de détection d’objets mobiles depuis une caméra en mouvement montée sur un vecteur aérien de type drone ou hélicoptère. Nos contraintes industrielles sont particulièrement fortes : robustesse aux grands mouvements de la caméra, robustesse au flou de focus ou de bougé, et précision dans la détection et segmentation des objets mobiles. De même, notre solution doit être optimisée afin de ne pas être trop consommatrice en termes de puissance de calcul. Notre solution consiste en la compensation du mouvement global, résultant du mouvement de la caméra, puis en l’analyse du mouvement résiduel existant entre les images pour détecter et segmenter les cibles mobiles. Ce domaine a été particulièrement exploré dans la littérature, ce qui se traduit par une richesse des méthodes proposées fondamentalement différentes. Après en avoir étudié un certain nombre, nous nous sommes aperçus qu’elles avaient toutes un domaine d’applications restreint, malheureusement incompatible avec nos préoccupations industrielles. Pour pallier à ce problème, nous proposons une méthodologie consistant à analyser les résultats des méthodes de l’état de l’art de manière à en comprendre les avantages et inconvénients de chacune. Puis, des hybridations de ces méthodes sont alors mis en place. Ainsi, nous proposons trois étapes successives : la compensation du mouvement entre deux images successives, l’élaboration d’un arrière plan de la scène afin de pouvoir segmenter de manière correcte les objets mobiles dans l’image et le filtrage de ces détections par confrontation entre le mouvement estimé lors de la première étape et le mouvement résiduel estimé par un algorithme local. La première étape consiste en l’estimation du mouvement global entre deux images à l’aide d’une méthode hybride composée d’un algorithme de minimisation ESM et d’une méthode de mise en correspondance de points d’intérêt Harris. L’approche pyramidale proposée permet d’optimiser les temps de calcul et les estimateursrobustes (M-Estimateur pour l’ESM et RANSAC pour les points d’intérêt) permettent de répondre aux contraintes industrielles. La deuxième étape établit un arrière plan de la scène à l’aide d’une méthode couplant les résultats d’une différence d’images successives (après compensation) et d’une segmentation en régions. Cette méthode réalise une fusion entre les informations statiques et dynamiques de l’image. Cet arrière plan est ensuite comparé avec l’image courante afin de détecter les objets mobiles. Enfin, la dernière étape confronte les résultats de l’estimation de mouvement global avec le mouvement résiduel estimé par un flux optique local Lucas-Kanade afin de valider les détections obtenues lors de la seconde étape. Les expériences réalisées dans ce mémoire sur de nombreuses séquences de tests (simulées ou réelles) permettent de valider la solution retenue. Nous montrons également diverses applications possibles de notre méthode proposéeWe propose a robust method about moving target detection from a moving UAV-mounted or helicopter-mounted camera. The industrial solution has to be robust to large motion of the camera, focus and motion blur in the images, and need to be accurate in terms of the moving target detection and segmentation. It does not have to need a long computation time. The proposed solution to detect the moving targets consists in the global camera motion compensation, and the residual motion analysis, that exists between the successive images. This research domain has been widely explored in the literature, implying lots of different proposed methods. The study of these methods show us that they all have a different and limited application scope, incompatible with our industrial constraints. To deal with this problem, we propose a methodology consisting in the analysis of the state-of-the-art method results, to extract their strengths and weaknesses. Then we propose to hybrid them. Therefore, we propose three successive steps : the inter-frame motion compensation, thecreation of a background in order to correctly detect the moving targets in the image and then the filtering of these detections by a comparison between the estimated global motion of the first step and the residual motion estimated by a local algorithm. The first step consists in the estimation of the global motion between two successive images thanks to a hybrid method composed of a minimization algorithm (ESM) and a feature-based method (Harris matching). The pyramidal implementation allows to optimize the computation time and the robust estimators (M-Estimator for the ESM algorithm and RANSAC for the Harris matching) allow to deal with the industrial constraints. The second step createsa background image using a method coupling the results of an inter-frame difference (after the global motion compensation) and a region segmentation. This method merges the static and dynamic information existing in the images. This background is then compared with the current image to detect the moving targets. Finally, the last step compares the results of the global motion estimation with the residual motion estimated by a Lucas-Kanade optical flow in order to validate the obtained detections of the second step. This solution has been validated after an evaluation on a large number of simulated and real sequences of images. Additionally, we propose some possible applications of theproposed method
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Wilkie, Kathleen P. "Mutual Information Based Methods to Localize Image Registration." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/1121.
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Modern medicine has become reliant on medical imaging. Multiple modalities, e. g. magnetic resonance imaging (MRI), computed tomography (CT), etc. , are used to provide as much information about the patient as possible. The problem of geometrically aligning the resulting images is called image registration. Mutual information, an information theoretic similarity measure, allows for automated intermodal image registration algorithms. In applications such as cancer therapy, diagnosticians are more concerned with the alignment of images over a region of interest such as a cancerous lesion, than over an entire image set. Attempts to register only the regions of interest, defined manually by diagnosticians, fail due to inaccurate mutual information estimation over the region of overlap of these small regions.
This thesis examines the region of union as an alternative to the region of overlap. We demonstrate that the region of union improves the accuracy and reliability of mutual information estimation over small regions.
We also present two new mutual information based similarity measures which allow for localized image registration by combining local and global image information. The new similarity measures are based on convex combinations of the information contained in the regions of interest and the information contained in the global images.
Preliminary results indicate that the proposed similarity measures are capable of localizing image registration. Experiments using medical images from computer tomography and positron emission tomography demonstrate the initial success of these measures.
Finally, in other applications, auto-detection of regions of interest may prove useful and would allow for fully automated localized image registration. We examine methods to automatically detect potential regions of interest based on local activity level and present some encouraging results.
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Mellor, Matthew. "Phase methods for non-rigid medical image registration." Thesis, University of Oxford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409817.
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Bride, Jacques. "Direct methods for image registration : efficiency and robustness." Nice, 2003. http://www.theses.fr/2003NICE4016.
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Nous limitant au problème du recalage rigide d'images, nous nous sommes intéressés aux méthodes "directes" qui maximisent explicitement ressemblance photométrique en fonction des paramètres à estimer. Nous soulevons les problématiques de ces approches. La littérature emploie souvent un estimateur moindres-carrés ordinaire pour les résoudre mais cela induit un large biais dans la solution. Aussi, la stricte contrainte de préservation de l'intensité souvent assumée est un handicap pour prendre en compte des changements de conditions d'illumination. Nous proposons d'utiliser dans un estimateur moindres-carrés orthogonal une norme de Mahalanobis qui permet de tenir compte des propriétés hétérogènes du bruit de chaque mesure. Ceci supprime le biais dans la solution. De plus, nous généralisons la contrainte de préservation de l'intensité au profit de critères de ressemblance plus complexes, comme la corrélation, en prenant appui sur des études sur le recalage non-rigideAddressing the problem of global image registration, we have focused on methods called "direct" that explicitly maximize a photometric similarity measure with respect to the motion parameters to estimate. We put to the fore some problematics of this approach. The literature of direct methods often employ an ordinary least-square estimator to solve them but it introduces a large bias in the solution. Moreover, the strict brightness constancy constraint is often assumed but it is a handicap to handle illumination changes. We propose to use an orthogonal least-square estimator along with the Mahalanobis distance. It allows to take into consideration the heteroscedastic noise properties of the measurements and it removes the large bias in the solution. In addition we have removed the brightness constancy constraint to benefit from more complex similarity metrics such as the cross-correlation, using recent work in non-rigid registration as a basis
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Brooks, Rupert. "Efficient and reliable methods for direct parameterized image registration." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=22033.
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This thesis examines methods for efficient and reliable image registration in the context of computer vision and medical imaging. Direct, parameterized image registration approaches work by minimizing a difference measure between a fixed reference image, and the image warped to match it. The calculation of this difference measure is the most computationally intensive part of the process and for faster registration it either has to be calculated faster, or calculated fewer times. Both possibilities are addressed in detail. Efficiency and reliability are addressed in four ways (1) Methods are presented for generalizing the Gauss-Newton Hessian approximation to the non-least squares case, and for the optimal selection of scaling factors for the transformation parameters. Both of these enhance performance by enabling optimization algorithms to perform fewer evaluations of the difference measure. The performance of a wide range of optimization algorithms is analyzed both theoretically and experimentally, and guidelines are presented for optimizer selection based on the characteristics of the registration problem. (2) Using only a portion of the available pixels results in faster calculation but suffers from a potential loss of accuracy. An algorithm is presented which applies formal deliberation control methods to managing this tradeoff. By managing the amount of image data used at every evaluation of the cost function, the algorithm adapts to the nature of the images and the stage of the optimization. This adaptive approach allows greater efficiency without sacrificing reliability. (3) It is shown that the scale used to compute the derivative is a critical factor to consider when selecting subsets of pixels for registration, that has largely been ignored in previous work. Finally, (4) two existing efficient registration approaches, the inverse compositional, and efficient second order algorithms, rely on specialized optimizer update steps and specialized paCette thèse examine différentes méthodes efficaces et fiables de recalage dans les contextes de la vision numérique et de l'imagerie médicale. Les approches directes minimisent une mesure de différence entre une image de référence (fixe) et sa version déformée. Le calcul de cette mesure constitue la partie la plus intensive du processus. Pour un recalage rapide, la mesure doit être calculée plus rapidement, ou moins souvent. Les deux possibilités sont discutées en détail dans la thèse. Le rendement et la fiabilité sont abordés de quatre manières différentes. (1) Le nombre de calculs peut être diminué en généralisant l'approximation de Gauss-Newton de la matrice hessienne pour une fonction de coût autre que de type moindres carrés, ou par la sélection optimale d'un facteur d'échelle pour les paramètres de la transformation. Les performances d'une variété d'algorithmes d'optimisation sont analysées, et des lignes directrices sont proposées pour la sélection d'un optimiseur basée sur les caractéristiques du problème de recalage. (2) L'utilisation d'une portion des pixels résultent en un calcul plus rapide de la mesure de différence, mais peut engendrer une perte d'exactitude. Un algorithme basé sur le principe du contrôle de délibération est proposé afin de gérer ce compromis. La gestion de la quantité d'information utilisée à chaque évaluation de la fonction de coût permet une adaptation à la nature des images ainsi qu'au stade de l'optimisation. Cette approche permet un haut rendement sans sacrifier la fiabilité. (3) Le facteur d'échelle utilisé pour le calcul des dérivées a rarement été abordé. Il est démontré que ce facteur est critique pour la sélection de sous-ensembles de pixels pour le recalage. Finalement, (4) La méthode compositionnelle inverse et la méthode à haut rendement du second ordre sont basées sur des itérations de l'optimiseur ainsi que sur des paramétrisations spéc
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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.
Full textAbstract:
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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Gao, Yi. "Geometric statistically based methods for the segmentation and registration of medical imagery." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/39644.
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Medical image analysis aims at developing techniques to extract information from medical images. Among its many sub-fields, image registration and segmentation are two important topics. In this report, we present four pieces of work, addressing different problems as well as coupling them into a unified framework of shape based image segmentation. Specifically:
1. We link the image registration with the point set registration, and propose a globally optimal diffeomorphic registration technique for point set registration.
2. We propose an image segmentation technique which incorporates the robust statistics of the image and the multiple contour evolution. Therefore, the method is able to simultaneously extract multiple targets from the image.
3. By combining the image registration, statistical learning, and image segmentation, we perform a shape based method which not only utilizes the image information but also the shape knowledge.
4. A multi-scale shape representation based on the wavelet transformation is proposed. In particular, the shape is represented by wavelet coefficients in a hierarchical way in order to decompose the shape variance in multiple scales. Furthermore, the statistical shape learning and shape based segmentation is performed under such multi-scale shape representation framework.
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Kang, Xin, and 康欣. "Feature-based 2D-3D registration and 3D reconstruction from a limited number of images via statistical inference for image-guidedinterventions." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B48079625.
Full textAbstract:
Traditional open interventions have been progressively replaced with minimally invasive techniques.
Most notably, direct visual feedback is transitioned into indirect, image-based feedback,
leading to the wide use of image-guided interventions (IGIs). One essential process of all IGIs is to
align some 3D data with 2D images of patient through a procedure called 3D-2D registration during
interventions to provide better guidance and richer information. When the 3D data is unavailable, a
realistic 3D patient-speci_c model needs to be constructed from a few 2D images.
The dominating methods that use only image intensity have narrow convergence range and are
not robust to foreign objects presented in 2D images but not existed in 3D data. Feature-based
methods partly addressed these problems, but most of them heavily rely on a set of \best" paired
correspondences and requires clean image features. Moreover, the optimization procedures used in
both kinds of methods are not e_cient.
In this dissertation, two topics have been studied and novel algorithms proposed, namely, contour
extraction from X-ray images and feature-based rigid/deformable 3D-2D registration.
Inspired by biological and neuropsychological characteristics of primary visual cortex (V1), a
contour detector is proposed for simultaneously extracting edges and lines in images. The synergy
of V1 neurons is mimicked using phase congruency and tensor voting. Evaluations and comparisons
showed that the proposed method outperformed several commonly used methods and the results are
consistent with human perception. Moreover, the cumbersome \_ne-tuning" of parameter values is
not always necessary in the proposed method.
An extensible feature-based 3D-2D registration framework is proposed by rigorously formulating
the registration as a probability density estimation problem and solving it via a generalized expectation
maximization algorithm. It optimizes the transformation directly and treats correspondences
as nuisance parameters. This is signi_cantly di_erent from almost all feature-based method in the
literature that _rst single out a set of \best" correspondences and then estimate a transformation
associated with it. This property makes the proposed algorithm not rely on paired correspondences
and thus inherently robust to outliers. The framework can be adapted as a point-based method with
the major advantages of 1) independency on paired correspondences, 2) accurate registration using
a single image, and 3) robustness to the initialization and a large amount of outliers. Extended to
a contour-based method, it di_ers from other contour-based methods mainly in that 1) it does not
rely on correspondences and 2) it incorporates gradient information via a statistical model instead of
a weighting function. Tuning into model-based deformable registration and surface reconstruction,
our method solves the problem using the maximum penalized likelihood estimation. Unlike almost
all other methods that handle the registration and deformation separately and optimized them sequentially,
our method optimizes them simultaneously. The framework was evaluated in two example
clinical applications and a simulation study for point-based, contour-based and surface reconstruction,
respectively. Experiments showed its sub-degree and sub-millimeter registration accuracy and
superiority to the state-of-the-art methods.
It is expected that our algorithms, when thoroughly validated, can be used as valuable tools for
image-guided interventions.published_or_final_version
Orthopaedics and Traumatology
Doctoral
Doctor of Philosophy
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Schröter, Martin [Verfasser], and Uwe [Akademischer Betreuer] Helmke. "Newton Methods for Image Registration / Martin Schröter. Betreuer: Uwe Helmke." Würzburg : Universitätsbibliothek der Universität Würzburg, 2012. http://d-nb.info/1023837811/34.
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Mackay, Neilson. "A comparison of three methods of ultrasound to computed tomography registration." Thesis, Kingston, Ont. : [s.n.], 2009. http://hdl.handle.net/1974/1666.
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Santos, Ribeiro Andre Filipe. "Robust methods for medical image registration with application in clinical diagnosis." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/58011.
Full textAbstract:
Automated analysis of medical imaging data allows both researchers and clinicians to develop more accurate and faster diagnoses. Image registration plays an essential role in both longitudinal studies and group analysis, allowing the combination of different imaging modalities, and automatic parcellation of regions of interest. Despite its wide use, image registration is still challenging with many issues such as artefacts, scarcity of correspondences, multi-modality, and computational complexity. Additionally, due to the lack of highly annotated datasets, the validation of image registration, and specifically non-linear registration, is also problematic. In this thesis several of these issues are addressed by introducing: a framework to validate non-linear registration methods; a robust and fast algorithm for non-linear registration; and validating the proposed methods in a conventional analysis. Current techniques used for non-linear image registration validation are explored, and it is shown that techniques based on label overlap are both not theoretically valid while also having poor accuracy. This analysis further leads to the development of a multiscale metric to minimize these problems. Also, a method based on the Demons Framework is proposed to improve the convergence speed of non-linear registration algorithms, and further extended to be robust in the presence of intensity inhomogeneities and contrast variations. The proposed methods are validated in a synthetic simulation platform with a known ground truth, compared with a manually traced region-of-interest, and tested in a voxel-based morphometry analysis of real data. It is shown that the proposed methods outperform other leading registration methods in both the synthetic simulation study and the manually traced data, and present reliable results in the voxel-based morphometry analysis. Furthermore, the impact of different registration algorithms is explored through the voxel-based morphometry study, and shown to affect the final results and their interpretation.
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Kolesov, Ivan A. "Statistical methods for coupling expert knowledge and automatic image segmentation and registration." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/47739.
Full textAbstract:
The objective of the proposed research is to develop methods that couple an expert user's guidance with automatic image segmentation and registration algorithms. Often, complex processes such as fire, anatomical changes/variations in human bodies, or unpredictable human behavior produce the target images; in these cases, creating a model that precisely describes the process is not feasible. A common solution is to make simplifying assumptions when performing detection, segmentation, or registration tasks automatically. However, when these assumptions are not satisfied, the results are unsatisfactory. Hence, removing these, often times stringent, assumptions at the cost of minimal user input is considered an acceptable trade-off. Three milestones towards reaching this goal have been achieved. First, an interactive image segmentation approach was created in which the user is coupled in a closed-loop control system with a level set segmentation algorithm. The user's expert knowledge is combined with the speed of automatic segmentation. Second, a stochastic point set registration algorithm is presented. The point sets can be derived from simple user input (e.g. a thresholding operation), and time consuming correspondence labeling is not required. Furthermore, common smoothness assumptions on the non-rigid deformation field are removed. Third, a stochastic image registration algorithm is designed to capture large misalignments. For future research, several improvements of the registration are proposed, and an iterative, landmark based segmentation approach, which couples the segmentation and registration, is envisioned.
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Niazi, M. Khalid Khan. "Image Filtering Methods for Biomedical Applications." Doctoral thesis, Uppsala universitet, Centrum för bildanalys, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-158679.
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Filtering is a key step in digital image processing and analysis. It is mainly used for amplification or attenuation of some frequencies depending on the nature of the application. Filtering can either be performed in the spatial domain or in a transformed domain. The selection of the filtering method, filtering domain, and the filter parameters are often driven by the properties of the underlying image. This thesis presents three different kinds of biomedical image filtering applications, where the filter parameters are automatically determined from the underlying images. Filtering can be used for image enhancement. We present a robust image dependent filtering method for intensity inhomogeneity correction of biomedical images. In the presented filtering method, the filter parameters are automatically determined from the grey-weighted distance transform of the magnitude spectrum. An evaluation shows that the filter provides an accurate estimate of intensity inhomogeneity. Filtering can also be used for analysis. The thesis presents a filtering method for heart localization and robust signal detection from video recordings of rat embryos. It presents a strategy to decouple motion artifacts produced by the non-rigid embryonic boundary from the heart. The method also filters out noise and the trend term with the help of empirical mode decomposition. Again, all the filter parameters are determined automatically based on the underlying signal. Transforming the geometry of one image to fit that of another one, so called image registration, can be seen as a filtering operation of the image geometry. To assess the progression of eye disorder, registration between temporal images is often required to determine the movement and development of the blood vessels in the eye. We present a robust method for retinal image registration. The method is based on particle swarm optimization, where the swarm searches for optimal registration parameters based on the direction of its cognitive and social components. An evaluation of the proposed method shows that the method is less susceptible to becoming trapped in local minima than previous methods. With these thesis contributions, we have augmented the filter toolbox for image analysis with methods that adjust to the data at hand.
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Mastin, Dana Andrew. "Statistical methods for 2D-3D registration of optical and LIDAR images." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/55123.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 121-123).
Fusion of 3D laser radar (LIDAR) imagery and aerial optical imagery is an efficient method for constructing 3D virtual reality models. One difficult aspect of creating such models is registering the optical image with the LIDAR point cloud, which is a camera pose estimation problem. We propose a novel application of mutual information registration which exploits statistical dependencies in urban scenes, using variables such as LIDAR elevation, LIDAR probability of detection (pdet), and optical luminance. We employ the well known downhill simplex optimization to infer camera pose parameters. Utilization of OpenGL and graphics hardware in the optimization process yields registration times on the order of seconds. Using an initial registration comparable to GPS/INS accuracy, we demonstrate the utility of our algorithms with a collection of urban images. Our analysis begins with three basic methods for measuring mutual information. We demonstrate the utility of the mutual information measures with a series of probing experiments and registration tests. We improve the basic algorithms with a novel application of foliage detection, where the use of only non-foliage points improves registration reliability significantly. Finally, we show how the use of an existing registered optical image can be used in conjunction with foliage detection to achieve even more reliable registration.
by Dana Andrew Mastin.
S.M.
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Pszczolkowski, Parraguez Stefan. "Fast and robust methods for non-rigid registration of medical images." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/25579.
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The automated analysis of medical images plays an increasingly significant part in many clinical applications. Image registration is an important and widely used technique in this context. Examples of its use include, but are not limited to: longitudinal studies, atlas construction, statistical analysis of populations and automatic or semi-automatic parcellation of structures. Although image registration has been subject of active research since the 1990s, it is a challenging topic with many issues that remain to be solved. This thesis seeks to address some of the open challenges of image registration by proposing fast and robust methods based on the widely utilised and well established registration framework of B-spline Free-Form Deformations (FFD). In this work, a statistical method has been incorporated into the FFD model, in order to obtain a fast learning-based method that produces results that are in accordance with the underlying variability of the population under study. Several comparisons between different statistical analysis methods that can be used in this context are performed. Secondly, a method to improve the convergence of the B-Spline FFD method by learning a gradient projection using principal component analysis and linear regression is proposed. Furthermore, a robust similarity measure is proposed that enables the registration of images affected by intensity inhomogeneities and images with pathologies, e.g. lesions and/or tumours. All the methods presented in this thesis have been extensively evaluated using both synthetic data and large datasets of real clinical data, such as Magnetic Resonance (MR) images of the brain and heart.
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Golding, Dan. "A comparison of methods for the registration of tractographic fibre images." Master's thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/10536.
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Rivest-Hénault, David. "Differential geometry methods for biomedical image processing : from segmentation to 2D/3D registration." Mémoire, École de technologie supérieure, 2012. http://espace.etsmtl.ca/1068/1/RIVEST%2DH%C3%89NAULT_David.pdf.
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Cette thèse introduit une plate-forme d’imagerie biomédicale pour la visualisation avancée de
structures biologiques. Elle est constituée de deux principaux types d’opérations : 1) la segmentation de quelques structures d’intérêt dans des images 3D ; et 2) le recalage d’un modèle 3D de structures biologiques propres à un patient avec l’imagerie interventionnelle 2D. Le fait de segmenter une structure biologique en 3D résulte en un modèle discret qui soit simple à visualiser et à comprendre. Ce dernier peut également être analysé et mesuré de façon informatisée. En contrepartie, recaler un modèle 3D avec les images interventionnelles permet de situer celui-ci dans le monde physique et donc de le positionner par rapport aux autres objets de la salle d’opération. En combinant l’information d’un modèle 3D et celle des images interventionnelles 2D, la plate-forme proposée vise à améliorer le guidage chirurgical en réduisant les ambiguïtés inhérentes à l’interprétation des images 2D.
En ce qui a trait à la segmentation, deux problèmes spécifiques sont considérés : 1) les structures présentant des variations d’intensité de basse fréquence ; et 2) la détection de structures curvilinéaires fines. Premièrement, nous nous sommes concentrés sur la segmentation de structures larges présentant des variations d’intensité de basse fréquence. De telles structures sont importantes dans le contexte de l’imagerie médicale puisqu’elles se retrouvent fréquemment, notamment en imagerie par résonance magnétique (IRM). De même, en angiographie, il est possible qu’une diffusion non-uniforme de l’agent de contraste résulte en des structures présentant de telle variation d’intensité. Nous avons donc défini une méthode par surfaces de niveaux utilisant une modélisation localement linéaire des intensités de régions et appliqué cette méthode au problème de la segmentation de ces structures. Il est démontré que les caractéristiques propres à cette méthode permettent de prendre en compte de façon implicite la non-uniformité de l’intensité des structures dans les IRM. Ainsi, au meilleur de notre connaissance, cette méthode est la première utilisant les surfaces de niveaux à atteindre des résultats convaincants pour la segmentation des tissus cérébraux dans les IRM.
Le deuxième problème de segmentation considéré consiste en la détection et la segmentation des structures curvilinéaires fines dans les images tridimensionnelles. En imagerie médicale, la détection de ces structures est cruciale puisque ces dernières peuvent représenter des veines, des artères, des bronches ou d’autres classes importantes de tissus. Malencontreusement, la plupart des méthodes présentement disponibles produisent un signal fortement atténué à la rencontre de deux structures formant un carrefour. Cette singularité peut donc réduire la performance d’un traitement subséquent tel que : l’étude automatisée d’une image d’angiographie, le calcul de la trajectoire des vaisseaux, ou l’étiquetage automatique des pixels composant l’image. La présente thèse introduit un nouveau filtre de vaisseaux qui est robuste à la présence de carrefours en X ou en Y apparaissant le long des structures. En même temps, il reste conceptuellement simple, déterministe et il permet une représentation intuitive des directions principales de la structure de l’image.
Une fois qu’un modèle 3D discret a été créé, celui-ci peut être utilisé afin d’améliorer le guidage chirurgical. Aussi, une méthode de recalage non-rigide 2D/3D est proposée pour amener un modèle des artères coronaires en correspondance avec des images de fluoroscopie biplan. Le modèle ainsi recalé est superposé sur les images interventionnelles afin de favoriser un meilleur guidage chirurgical durant les interventions percutanées pour les opérations d’occlusions totales chroniques. De ce fait, l’incertitude inhérente aux images bidimensionnelles s’en trouve amoindri. Une méthode de recalage complètement non-rigide est définie et permet de réduire de façon localisée toute différence de forme. Cette nouvelle méthode, fondée sur le calcul variationnel, met en scène un processus d’appariement et de reconstruction simultanés afin de calculer la transformation. Grâce à un temps de calcul généralement sous les trois secondes, l’algorithme reste assez rapide pour être utilisé de façon interactive.
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Chumchob, Noppadol. "A study of effective variational models and efficient numerical methods for image registration." Thesis, University of Liverpool, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540045.
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Böhler, Tobias [Verfasser]. "Deformable Image Registration Methods for Clinical Applications of Magnetic Resonance Mammography / Tobias Böhler." München : Verlag Dr. Hut, 2012. http://d-nb.info/1021072729/34.
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Hellier, Pierre. "Image processing methods for 3D intraoperative ultrasound." Habilitation à diriger des recherches, Université Rennes 1, 2010. http://tel.archives-ouvertes.fr/tel-00536133.
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Ce document constitue une synth`ese de travaux de recherche en vue de l'obten- tion du diplˆome d'habilitation `a diriger les recherches. A la suite ce cette in- troduction r ́edig ́ee en franc ̧ais, le reste de ce document sera en anglais. Je suis actuellement charg ́e de recherches INRIA au centre de Rennes Bretagne Atlantique. J'ai rejoint en Septembre 2001 l' ́equipe Vista dirig ́ee par Patrick Bouthemy, puis l' ́equipe Visages dirig ́ee par Christian Barillot en Janvier 2004. Depuis Janvier 2010, je travaille dans l' ́equipe-projet Serpico dirig ́ee par Charles Kervrann dont l'objet est l'imagerie et la mod ́elisation de la dynamique intra- cellulaire. Parmi mes activit ́es pass ́ees, ce document va se concentrer uniquement sur les activit ́es portant sur la neurochirurgie guid ́ee par l'image. En parti- culier, les travaux effectu ́es sur le recalage non-rigide ne seront pas pr ́esent ́es ici. Concernant le recalage, ces travaux ont commenc ́e pendant ma th`ese avec le d ́eveloppement d'une m ́ethode de recalage 3D bas ́e sur le flot optique [72], l'incorporation de contraintes locales dans ce processus de recalage [74] et la validation de m ́ethodes de recalage inter-sujets [71]. J'ai poursuivi ces travaux apr`es mon recrutement avec Anne Cuzol et Etienne M ́emin sur la mod ́elisation fluide du recalage [44], avec Nicolas Courty sur l'acc ́el ́eration temps-r ́eel de m ́ethode de recalage [42], et sur l' ́evaluation des m ́ethodes de recalage dans deux contextes : celui de l'implantation d' ́electrodes profondes [29] et le re- calage inter-sujets [92]. L'utilisation de syst`emes dits de neuronavigation est maintenant courante dans les services de neurochirurgie. Les b ́en ́efices, attendus ou report ́es dans la litt ́erature, sont une r ́eduction de la mortalit ́e et de la morbidit ́e, une am ́elio- ration de la pr ́ecision, une r ́eduction de la dur ́ee d'intervention, des couˆts d'hospitalisation. Tous ces b ́en ́efices ne sont pas `a l'heure actuelle d ́emontr ́es `a ma connaissance, mais cette question d ́epasse largement le cadre de ce doc- ument. Ces syst`emes de neuronavigation permettent l'utilisation du planning chirurgical pendant l'intervention, dans la mesure ou` le patient est mis en cor- respondance g ́eom ́etrique avec les images pr ́eop ́eratoires `a partir desquelles est pr ́epar ́ee l'intervention. Ces informations multimodales sont maintenant couramment utilis ́ees, com- prenant des informations anatomiques, vasculaires, fonctionnelles. La fusion de ces informations permet de pr ́eparer le geste chirurgical : ou` est la cible, quelle est la voie d'abord, quelles zones ́eviter. Ces informations peuvent main- tenant ˆetre utilis ́ees en salle d'op ́eration et visualis ́ees dans les oculaires du mi- croscope chirurgical grˆace au syst`eme de neuronavigation. Malheureusement, cela suppose qu'il existe une transformation rigide entre le patient et les im- ages pr ́eop ́eratoires. Alors que cela peut ˆetre consid ́er ́e comme exact avant l'intervention, cette hypoth`ese tombe rapidement sous l'effet de la d ́eformation des tissus mous. Ces d ́eformations, qui doivent ˆetre consid ́er ́ees comme un ph ́enom`ene spatio-temporel, interviennent sous l'effet de plusieurs facteurs, dont la gravit ́e, la perte de liquide c ́ephalo-rachidien, l'administration de pro- duits anesth ́esiants ou diur ́etiques, etc. Ces d ́eformations sont tr`es difficiles `a mod ́eliser et pr ́edire. De plus, il s'agit d'un ph ́enom`ene spatio-temporel, dont l'amplitude peut varier consid ́era- blement en fonction de plusieurs facteurs. Pour corriger ces d ́eformations, l'imagerie intra-op ́eratoire apparait comme la seule piste possible.
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Kannala, J. (Juho). "Models and methods for geometric computer vision." Doctoral thesis, University of Oulu, 2010. http://urn.fi/urn:isbn:9789514261510.
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Abstract
Automatic three-dimensional scene reconstruction from multiple images is a central problem in geometric computer vision. This thesis considers topics that are related to this problem area. New models and methods are presented for various tasks in such specific domains as camera calibration, image-based modeling and image matching. In particular, the main themes of the thesis are geometric camera calibration and quasi-dense image matching. In addition, a topic related to the estimation of two-view geometric relations is studied, namely, the computation of a planar homography from corresponding conics. Further, as an example of a reconstruction system, a structure-from-motion approach is presented for modeling sewer pipes from video sequences.
In geometric camera calibration, the thesis concentrates on central cameras. A generic camera model and a plane-based camera calibration method are presented. The experiments with various real cameras show that the proposed calibration approach is applicable for conventional perspective cameras as well as for many omnidirectional cameras, such as fish-eye lens cameras. In addition, a method is presented for the self-calibration of radially symmetric central cameras from two-view point correspondences.
In image matching, the thesis proposes a method for obtaining quasi-dense pixel matches between two wide baseline images. The method extends the match propagation algorithm to the wide baseline setting by using an affine model for the local geometric transformations between the images. Further, two adaptive propagation strategies are presented, where local texture properties are used for adjusting the local transformation estimates during the propagation. These extensions make the quasi-dense approach applicable for both rigid and non-rigid wide baseline matching.
In this thesis, quasi-dense matching is additionally applied for piecewise image registration problems which are encountered in specific object recognition and motion segmentation. The proposed object recognition approach is based on grouping the quasi-dense matches between the model and test images into geometrically consistent groups, which are supposed to represent individual objects, whereafter the number and quality of grouped matches are used as recognition criteria. Finally, the proposed approach for dense two-view motion segmentation is built on a layer-based segmentation framework which utilizes grouped quasi-dense matches for initializing the motion layers, and is applicable under wide baseline conditions.
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Lingg, Andrew James. "Statistical Methods for Image Change Detection with Uncertainty." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1357249370.
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Sandhu, Romeil Singh. "Statistical methods for 2D image segmentation and 3D pose estimation." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37245.
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The field of computer vision focuses on the goal of developing techniques to exploit and extract information from underlying data that may represent images or other multidimensional data. In particular, two well-studied problems in computer vision are the fundamental tasks of 2D image segmentation and 3D pose estimation from a 2D scene.
In this thesis, we first introduce two novel methodologies that attempt to independently solve 2D image segmentation and 3D pose estimation separately. Then, by leveraging the advantages of certain techniques from each problem, we couple both tasks in a variational and non-rigid manner through a single energy functional. Thus, the three theoretical components and contributions of this thesis are as follows:
Firstly, a new distribution metric for 2D image segmentation is introduced. This is employed within the geometric active contour (GAC) framework.
Secondly, a novel particle filtering approach is proposed for the problem of estimating the pose of two point sets that differ by a rigid body transformation.
Thirdly, the two techniques of image segmentation and pose estimation are coupled in a single energy functional for a class of 3D rigid objects.
After laying the groundwork and presenting these contributions, we then turn to their applicability to real world problems such as visual tracking. In particular, we present an example where we develop a novel tracking scheme for 3-D Laser RADAR imagery. However, we should mention that the proposed contributions are solutions for general imaging problems and therefore can be applied to medical imaging problems such as extracting the prostate from MRI imagery
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Waldkirch, Barbara [Verfasser], and Lothar R. [Akademischer Betreuer] Schad. "Methods for three-dimensional Registration of Multimodal Abdominal Image Data / Barbara Ingeborg Waldkirch ; Betreuer: Lothar R. Schad." Heidelberg : Universitätsbibliothek Heidelberg, 2020. http://d-nb.info/121816803X/34.
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Waldkirch, Barbara Ingeborg [Verfasser], and Lothar R. [Akademischer Betreuer] Schad. "Methods for three-dimensional Registration of Multimodal Abdominal Image Data / Barbara Ingeborg Waldkirch ; Betreuer: Lothar R. Schad." Heidelberg : Universitätsbibliothek Heidelberg, 2020. http://d-nb.info/121816803X/34.
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Ellingwood, Nathan David. "Methods for improving performance of particle tracking and image registration in computational lung modeling using multi-core CPUs And GPUs." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/2205.
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Graphics Processing Units (GPUs) have grown in popularity beyond the original video game enthusiast audience. They have been embraced by the high-performance computing community due to their high computational throughput, low cost, low energy demands, wide availability, and ability to dramatically improve application performance. In addition, as hybrid computing continues into mainstream applications, the use of GPUs will continue to grow. However, due to architectural difference between the CPU and GPU, adapting CPU-based scientific computing applications to fully exploit the potential speedup that GPUs offer is a non-trivial task. Algorithms must be designed with the architecture benefits and limitations in mind in order to unlock the full performance gains afforded by the use GPU. In this work, we develop fast GPU methods to improve the performance of two important components in computational lung modeling - image registration and particle tracking. We first propose a novel method for multi-level mass-preserving deformable image registration. The strength of this method is that it allows for flexibility of choice for the similarity criteria to be used by the registration method, making possible the implementation of simple and complex similarity measures on the GPU with excellent performance results. The method is tested using three similarity criteria for registering two CT lung datasets - the commonly used sum of squared intensity differences (SSD), the sum of squared tissue value differences (SSTVD), and a symmetric version of SSTVD currently being developed by our research group. The GPU method is validated against a previously validated single-threaded CPU counterpart using six healthy human subjects, and demonstrated strong agreement of results. Separately, three GPU methods were developed for tracking particle trajectories and deposition efficiencies in the human airway tree, including a multiple-GPU method. Though parallelization was straightforward, the complex geometry of the lungs and use of an unstructured mesh provided challenges that were addressed by the GPU methods. The results of the GPU methods were tested for various numbers of particles and compared to a previously validated single-threaded CPU version and demonstrated dramatic speedup over the single-threaded CPU version and 12-threaded CPU versions.
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Paudel, Danda Pani. "Local and global methods for registering 2D image sets and 3D point clouds." Thesis, Dijon, 2015. http://www.theses.fr/2015DIJOS077/document.
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Pas de résuméIn this thesis, we study the problem of registering 2D image sets and 3D point clouds under threedifferent acquisition set-ups. The first set-up assumes that the image sets are captured using 2Dcameras that are fully calibrated and coupled, or rigidly attached, with a 3D sensor. In this context,the point cloud from the 3D sensor is registered directly to the asynchronously acquired 2D images.In the second set-up, the 2D cameras are internally calibrated but uncoupled from the 3D sensor,allowing them to move independently with respect to each other. The registration for this set-up isperformed using a Structure-from-Motion reconstruction emanating from images and planar patchesrepresenting the point cloud. The proposed registration method is globally optimal and robust tooutliers. It is based on the theory Sum-of-Squares polynomials and a Branch-and-Bound algorithm.The third set-up consists of uncoupled and uncalibrated 2D cameras. The image sets from thesecameras are registered to the point cloud in a globally optimal manner using a Branch-and-Prunealgorithm. Our method is based on a Linear Matrix Inequality framework that establishes directrelationships between 2D image measurements and 3D scene voxels
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Wang, Chengjia. "Development of registration methods for cardiovascular anatomy and function using advanced 3T MRI, 320-slice CT and PET imaging." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/22918.
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Different medical imaging modalities provide complementary anatomical and functional information. One increasingly important use of such information is in the clinical management of cardiovascular disease. Multi-modality data is helping improve diagnosis accuracy, and individualize treatment. The Clinical Research Imaging Centre at the University of Edinburgh, has been involved in a number of cardiovascular clinical trials using longitudinal computed tomography (CT) and multi-parametric magnetic resonance (MR) imaging. The critical image processing technique that combines the information from all these different datasets is known as image registration, which is the topic of this thesis. Image registration, especially multi-modality and multi-parametric registration, remains a challenging field in medical image analysis. The new registration methods described in this work were all developed in response to genuine challenges in on-going clinical studies. These methods have been evaluated using data from these studies. In order to gain an insight into the building blocks of image registration methods, the thesis begins with a comprehensive literature review of state-of-the-art algorithms. This is followed by a description of the first registration method I developed to help track inflammation in aortic abdominal aneurysms. It registers multi-modality and multi-parametric images, with new contrast agents. The registration framework uses a semi-automatically generated region of interest around the aorta. The aorta is aligned based on a combination of the centres of the regions of interest and intensity matching. The method achieved sub-voxel accuracy. The second clinical study involved cardiac data. The first framework failed to register many of these datasets, because the cardiac data suffers from a common artefact of magnetic resonance images, namely intensity inhomogeneity. Thus I developed a new preprocessing technique that is able to correct the artefacts in the functional data using data from the anatomical scans. The registration framework, with this preprocessing step and new particle swarm optimizer, achieved significantly improved registration results on the cardiac data, and was validated quantitatively using neuro images from a clinical study of neonates. Although on average the new framework achieved accurate results, when processing data corrupted by severe artefacts and noise, premature convergence of the optimizer is still a common problem. To overcome this, I invented a new optimization method, that achieves more robust convergence by encoding prior knowledge of registration. The registration results from this new registration-oriented optimizer are more accurate than other general-purpose particle swarm optimization methods commonly applied to registration problems. In summary, this thesis describes a series of novel developments to an image registration framework, aimed to improve accuracy, robustness and speed. The resulting registration framework was applied to, and validated by, different types of images taken from several ongoing clinical trials. In the future, this framework could be extended to include more diverse transformation models, aided by new machine learning techniques. It may also be applied to the registration of other types and modalities of imaging data.
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Farina, Sofia. "Barycentric Subspace Analysis on the Sphere and Image Manifolds." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15797/.
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In this dissertation we present a generalization of Principal Component Analysis (PCA) to Riemannian manifolds called Barycentric Subspace Analysis and show some applications. The notion of barycentric subspaces has been first introduced first by X. Pennec. Since they lead to hierarchy of properly embedded linear subspaces of increasing dimension, they define a generalization of PCA on manifolds called Barycentric Subspace Analysis (BSA). We present a detailed study of the method on the sphere since it can be considered as the finite dimensional projection of a set of probability densities that have many practical applications. We also show an application of the barycentric subspace method for the study of cardiac motion in the problem of image registration, following the work of M.M. Rohé.
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Zemčíková, Petra. "Přesné lícování obrazu kalibračních vzorků pro korekci geometrické distorze." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316841.
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Cílem předkládané diplomové práce je pomocí lícování obrazů přesně popsat distorzní pole pro následné odstranění geometrické distorze. Snímky zkreslené geometrickou distorzí pochází z prozařovacího elektronového mikroskopu. První část práce se zabývá zejména teorií spojenou s elektronovou mikroskopií, vznikem geometrické distorze a samotnou obrazovou registrací s důrazem na intenzitní flexibilní metody lícování. Ve druhé části je pak představena vytvořená metoda pro modelování geometrické distorze a lícování obrazů postižených slabou geometrickou distorzí. Vyvinutá metoda je následně otestována na testovacích i reálných datech a srovnána s existujícími popsanými metodami pro obrazovou registraci (například open-source softwarem Elastix).
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Marciano, Abraham. "Méthodes d'Analyse et de Recalage d'images radiographiques de fret et de Véhicules." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLED040/document.
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La société contemporaine fait face à un niveau de menace sans précédent depuis la seconde guerre mondiale. La lutte contre le trafic illicite mobilise aussi l’ensemble desorganes de police, visant à endiguer le financement du crime organisé. Dans cet effort, les autorités s’engagent à employer des moyens de plus en plus modernes, afin notamment d’automatiser les processus d’inspection. L’objectif de cette étude est de développer des outils de vision par ordinateur afin d’assister les officiers de douanes dans la détection d’armes et de narcotiques. Letravail présenté examine l’emploi de techniques avancées de classification et de recalage d’images pour l’identification d’irrégularités dans des acquisitions radiographiques de fret. Plutôt que de recourir à la reconnaissance par apprentissage, nos méthodes revêtent un intérêt particulier lorsque les objets ciblés présentent des caractéristiques visuelles variées. De plus, elles augmentent notablement la détectabilité d’éléments cachés dans des zones denses, là où même les algorithmes de reconnaissance n’identifieraient pas d’anomalie. Nos travaux détaillent l’état de l’art des méthodes de classification et de recalage, explorant aussi diverses pistes de résolution. Les algorithmes sont testés sur d’importantes bases de données pour apprécier visuellement et numériquement leurs performancesOur societies, faced with an unprecedented level of security threat since WWII, must provide fast and adaptable solutions to cope with a new kind of menace. Illicit trade also, oftencorrelated with criminal actions, is viewed as a defining stake by governments and agencies. Enforcement authorities are thus very demandingin terms of technological features, asthey explicitly aim at automating inspection processes. The main objective of our research is to develop assisting tools to detect weapons and narcotics for lawenforcement officers. In the present work, we intend to employ and customize both advanced classification and image registration techniques for irregularity detection in X-ray cargo screening scans. Rather than employing machine-learning recognition techniques, our methods prove to be very efficient while targeting a very diverse type of threats from which no specific features can be extracted. Moreover, the proposed techniques significantly enhance the detection capabilities for law-enforcement officers, particularly in dense regions where both humans or trained learning models would probably fail. Our work reviews state-of-the art methods in terms of classification and image registration. Various numerical solutions are also explored. The proposed algorithms are tested on a very large number ofimages, showing their necessity and performances both visually and numerically
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Jia, Yue Verfasser], Timon [Akademischer Betreuer] Rabczuk, Klaus [Gutachter] [Gürlebeck, and Alessandro [Gutachter] Reali. "Methods based on B-splines for model representation, numerical analysis and image registration / Yue Jia ; Gutachter: Klaus Gürlebeck, Alessandro Reali ; Betreuer: Timon Rabczuk." Weimar : Institut für Strukturmechanik, 2015. http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20151210-24849.
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Jia, Yue [Verfasser], Timon [Akademischer Betreuer] Rabczuk, Klaus [Gutachter] Gürlebeck, and Alessandro [Gutachter] Reali. "Methods based on B-splines for model representation, numerical analysis and image registration / Yue Jia ; Gutachter: Klaus Gürlebeck, Alessandro Reali ; Betreuer: Timon Rabczuk." Weimar : Institut für Strukturmechanik, 2015. http://d-nb.info/1116366770/34.
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Gutiérrez, Becker Benjamín [Verfasser], Nassir [Akademischer Betreuer] Navab, Nassir [Gutachter] Navab, Martin [Gutachter] Reuter, and Diana [Gutachter] Mateus. "Machine Learning Methods for Computer Assisted Diagnosis and Medical Image Registration / Benjamín Gutiérrez Becker ; Gutachter: Nassir Navab, Martin Reuter, Diana Mateus ; Betreuer: Nassir Navab." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/1204562237/34.
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