The first part of this work presents an accurate analysis of the most relevant 3D registration techniques, including initial pose estimation, pairwise registration and multiview registration strategies. A new classification has been proposed, based on both the applications and the approach of the methods that have been discussed.The main contribution of this thesis is the proposal of a new 3D multiview registration strategy. The proposed approach detects revisited regions obtaining cycles of views that are used to reduce the inaccuracies that may exist in the final model due to error propagation. The method takes advantage of both global and local information of the registration process, using graph theory techniques in order correlate multiple views and minimize the propagated error by registering the views in an optimal way. The proposed method has been tested using both synthetic and real data, in order to show and study its behavior and demonstrate its reliability. / La primera part d'aquest treball presenta una anàlisi acurada de les tècniques de registre 3D es rellevants, incloent tècniques d'estimació de la posició inicial, registre pairwise i registre entre múltiples vistes. S'ha proposat una nova classificació de les tècniques, depenent de les seves aplicacions i de l'estratègia utilitzada.La contribució mes important d'aquesta tesi és la proposta d'un nou mètode de registre 3D utilitzant múltiples vistes. El mètode proposat detecta regions ja visitades prèviament, obtenint cicles de vistes que s'utilitzen per tal de reduir els desalineaments en el model final deguts principalment a la propagació de l'error durant el procés de registre. Aquest mètode utilitza tant informació global com local, correlacionant les vistes mitjançant tècniques de grafs que permeten minimitzar l'error propagat i registrar les vistes de forma òptima. El mètode proposat ha estat provat utilitzant dades sintètiques i reals, per tal de mostrar i analitzar el seu comportament i demostrar la seva eficàcia.

This dissertation addresses the problem of scanning the geometry of real objects and building accurate computer models of those objects. We present a complete system which employs a structured light scanner to acquire 3D views of objects from multiple viewpoints. These multiple views, expressed in a sensor-oriented coordinate system, are then registered into a model-centred coordinate system, before being integrated into a single mesh describing the object's geometry. Line of sight constraints forbid any single view from capturing the entire surface of an object, so multiple scans must be performed. We have developed registration techniques which may register all of the views simultaneously, resulting in a globally optimal solution. Statistical error modeling of the sensor, and the use of these models in the registration process, forms a key part of the research. It is motiviated by the observation that all measurements are subject to some degree of random measurement error. The true values of these errors cannot be determined, however their statistical properties may be modeled. Our registration system utilises these error models to improve registration accuracy, and to allow the accuracy of the registration to be estimated. The resulting system is a flexible platform for 3D data capture and modeling. It may be used in conjunction with the structured light scanner, or 3D data acquired from any other source. We demonstrate this capability with models constructed from sources such as laser range finders and scanning touch probe systems. The contributions of this thesis are as follows: a novel stereo matching algorithm which permits the estimation of stereo disparity as well as the uncertainty in the disparity, development of a practical 3D vision sensor based on structured light techniques, two novel algorithms for performing simultaneous multiple view point set registration, while supporting individual point error models and estimating the uncertainty in the registration solution, a novel algorithm for efficiently solving the multiple view registration problem, and the implementation of a number of existing surface correspondence and reconstruction techniques, permitting the development of an integrated 3D vision system for capturing and modeling 3D objects.

In current practice, it is common in medical diagnosis or treatment monitoring for a patient to require multiple examinations using different imaging techniques. Magnetic resonance (MR) imaging and computed tomography (CT) are good at providing anatomical information. Three-dimensional functional information about tissues and organs is often obtained with radionuclide imaging modalities: positron emission tomography (PET) and single photon emission tomography (SPET). In nuclear medicine, such techniques must contend with poor spatial resolution, poor counting statistics of functional images and the lack of correspondence between the distribution of the radioactive tracer and anatomical boundaries. Information gained from anatomical and functional images is usually of a complementary nature. Since the patient cannot be relied on to assume exactly the same pose at different times and possibly in different scanners, spatial alignment of images is needed. In this thesis, a general framework for image registration is presented, in which the optimum alignment corresponds to a maximum of a similarity measure. Particular attention is drawn to entropy-based measures, and variance-based measures. These similarity measures include mutual information, normalized mutual information and correlation ratio which are the ones being considered in this study. In multimodality image registration between functional and anatomical images, these measures manifest superior performance compared to feature-based measures. A common characteristic of these measures is the use of the joint-intensity histogram, which is needed to estimate the joint probability and the marginal probability of the images. A novel similarity measure is proposed, the symmetric correlation ratio (SCR), which is a simple extension of the correlation ratio measure. Experiments were performed to study questions pertaining to the optimization of the registration process. For example, do these measures produce similar registration accuracy in the non-brain region as in the brain? Does the performance of SPET-CT registration depend on the choice of the reconstruction method (FBP or OSEM)? The joint-intensity based similarity measures were examined and compared using clinical data with real distortions and digital phantoms with synthetic distortions. In automatic SPET-MR rigid-body registration applied to clinical brain data, a global mean accuracy of 3.9 mm was measured using external fiducial markers. SCR performed better than mutual information when sparse sampling was used to speed up the registration process. Using the Zubal phantom of the thoracic-abdominal region, SPET projections for Methylenediphosponate (MDP) and Gallium-67 (67Ga) studies were simulated for 360 degree data, accounting for noise, attenuation and depth-dependent resolution. Projection data were reconstructed using conventional filtered back projection (FBP) and accelerated maximum likelihood reconstruction based on the use of ordered subsets (OSEM). The results of SPET-CT rigid-body registration of the thoracic-abdominal region revealed that registration accuracy was insensitive to image noise, irrespective of which reconstruction method was used. The registration accuracy, to some extent, depended on which algorithm (OSEM or FBP) was used for SPET reconstruction. It was found that, for roughly noise-equivalent images, OSEM-reconstructed SPET produced better registration than FBP-reconstructed SPET when attenuation compensation (AC) was included but this was less obvious for SPET without AC. The results suggest that OSEM is the preferable SPET reconstruction algorithm, producing more accurate rigidbody image registration when AC is used to remove artifacts due to non-uniform attenuation in the thoracic region. Registration performance deteriorated with decreasing planar projection count. The presence of the body boundary in the SPET image and matching fields of view were shown not to affect the registration performance substantially but pre-processing steps such as CT intensity windowing did improve registration accuracy. Non-rigid registration based on SCR was also investigated. The proposed algorithm for non-rigid registration is based on overlapping image blocks defined on a 3D grid pattern and a multi-level strategy. The transformation vector field, representing image deformation is found by translating each block so as to maximize the local similarity measure. The resulting sparsely sampled vector field is interpolated using a Gaussian function to ensure a locally smooth transformation. Comparisons were performed to test the effectiveness of SCR, MI and NMI in 3D intra- and inter-modality registration. The accuracy of the technique was evaluated on digital phantoms and on patient data. SCR demonstrated a better non-rigid registration than MI when sparse sampling was used for image block matching. For the high-resolution MR-MR image of brain region, the proposed algorithm was successful, placing 92% of image voxels within less than or equal to 2 voxels of the true position. Where one of the images had low resolution (e.g. in CT-SPET, MR-SPET registration), the accuracy and robustness deteriorated profoundly. In the current implementation, a 3D registration process takes about 10 minutes to complete on a stand alone Pentium IV PC with 1.7 GHz CPU and 256 Mbytes random access memory on board.

Object recognition is one of the most important problems in computer vision. Traditional object recognition techniques are usually performed on optical images that are 2D projections of the 3D world. Information about the depth of objects in the scene is not provided explicitly in these images and thus, it makes 2D object recognition techniques sensitive to changes in illumination and shadowing. As surface acquisition methods such as LADAR or range scanners are becoming more popular, there is an increasing interest in the use of three-dimensional geometric data in object recognition to overcome these limitations. However, the matching of 3D free-form surfaces is also a difficult problem due to the shape and topological complexity of 3D surfaces. In addition, the problem is further complicated by other issues such as variations in surface sampling resolution, occlusion, clutter and sensor noise. The huge amount of information required to describe a 3D surface is also another challenge that 3D surface matching techniques have to deal with. This thesis investigates the problems of 3D surface matching that include 3D surface registration and object recognition from range images. It focuses on developing a novel and efficient framework for aligning 3D surfaces in different coordinate systems and from this, recognizing 3D models from scenes with high levels of occlusion and clutter using multi-scale local features. The first part of the thesis presents two different schemes for extracting salient geometric features from 3D surfaces using surface curvature measures known as the curvedness and shape index. By deriving the scale-space representation of the input surface, surface positions with high local curvature or high local shape variations are selected as features at various degrees of scale. One advantage of the proposed approaches is their applicability to both 3D meshes with connectivity information and unstructured point clouds. In the second part of the thesis, an application of the multi-scale feature extraction framework to 3D surface registration and object recognition is proposed. A Delaunay tetrahedrization is performed on the features extracted from each input range image to obtain a set of triangles. Possible correspondences are found by matching all possible pairs of triangles between the scene and model surfaces. From these correspondences, possible transformations between the two surfaces can be hypothesized and tested. In order to increase the accuracy and efficiency of the algorithm, various surface geometric and rigidity constraints are applied to prune unlikely correspondences. By finding the match that aligns the largest number of features between the two surfaces, the best transformation can be estimated. In the case of surface registration, this transformation can be used to coarse-align two different views of the same object. In the case of 3D object recognition, it provides information about the possible pose (location and orientation) of the model in the scene surface. Experimental results on a variety of 3D models and real scenes are shown to verify the effectiveness and robustness of the approach. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1474505 / Thesis (M.App.Sc.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2009

Final Project (M.A.)--John F. Kennedy University, 2003. / "August 25, 2003"--T.p. Includes bibliographical references (p. 104-113).

Thesis (doctoral)--Universität Erlangen.

We formulate and interpret several multi-modal registration methods inthe context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptionsof each method yielding a better understanding of their relativestrengths and weaknesses. Additionally, we discuss a generativestatistical model from which we derive a novel analysis tool, the"auto-information function", as a means of assessing and exploiting thecommon spatial dependencies inherent in multi-modal imagery. Weanalytically derive useful properties of the "auto-information" aswell as verify them empirically on multi-modal imagery. Among theuseful aspects of the "auto-information function" is that it canbe computed from imaging modalities independently and it allows one todecompose the search space of registration problems.

Em radioterapia externa, uma nova técnica chamada terapia de radiação de intensidade modulada - IMRT - permite delinear a dose de radiação em imagens de 2 ou 3 dimensões, delimitando de forma bastante precisa e não necessariamente uniforme a região a ser irradiada. Assim, ao mesmo tempo que o tumor é irradiado, é possível evitar a irradiação aos tecidos vizinhos íntegros (sãos), limitando os efeitos secundários do tratamento. Para que a radioterapia externa tenha sucesso usando a técnica IMRT, é fundamental delinear previamente de forma precisa o tumor e os órgãos sãos que devem ser protegidos da radiação, garantindo assim a dose exata de radiação nos volumes alvos. O objetivo desta tese é fornecer ferramentas que sejam adequadas ao delineamento automático de estruturas de interesse e à radioterapia adaptativa para tumores da região da cabeça e do pescoço. Atualmente, a segmentação de estruturas de interesse, tais como os órgãos em risco e as regiões de propagação tumoral, é feita manualmente. Esta é uma tarefa que demanda bastante tempo de um especialista, além de ser tediosa. Além do mais, o planejamento em radioterapia é feito baseado na imagem adquirida na semana do pré-tratamento, onde é calculada a dose. Normalmente o tratamento ocorre em várias semanas, porém a dose estimada no início do tratamento é a mesma para todas as outras semanas do tratamento. Calcular a dose e mantê-la nas demais semanas é uma simplificação que não corresponde à realidade, já que ocorrem mudanças anatômicas no paciente ao longo do tratamento. Estas mudanças ocorrem devido ao encolhimento do tumor e ao possível emagrecimento do paciente, provocando alterações anatômicas locais e globais. As contribuições desta tese visam solucionar e avançar nestes problemas e são apresentadas em dois eixos. No primeiro eixo, é proposta uma metodologia para escolher uma anatomia que seja representativa da população, anatomia esta chamada de atlas. O registro do atlas na imagem do paciente permite que estruturas de interesse sejam segmentadas automaticamente, acelerando o processo de delineamento e tornando-o mais robusto. A segunda contribuição desta tese é voltada à radioterapia adaptativa. Para que a dose estimada na primeira semana seja adaptada às modificações anatômicas, é necessária a utilização de métodos de registro não-rígidos. Portanto, nesta etapa é feita uma avaliação e adaptação dos métodos de registros de forma que a região do tumor esteja bem alinhada. / Intensity Modulated Radiotherapy (IMRT) is a new technique enabling the delineation of the 3D radiation dose. It allows to delineate a radiation zone of almost any shape and to modulate the beam intensity inside the target. If IMRT enables to constrain the radiation plan in the beam delivery as well as in the protection of important functional areas (e.g. spinal cord), it also raises the issues of adequacy and accuracy of the selection and delineation of the target volumes. The purpose of this thesis is to provide tools to automatic delineation of the regions of interest and also to adaptive radiotherapy treatment for tumors located in the head and neck region. The delineation in the patient computed tomography image of the tumor volume and organs to be protected is currently performed by an expert who delineates slice by slice the contours of interest. This task is highly time-consuming and requires experts’ knowledge. Moreover, the planning process in radiotherapy typically involves the acquisition of a unique set of computed tomography images in treatment position on which target volumes (TVs) and normal structures are delineated, and which are used for dose calculation. Restricting the delineation of these regions of interest based solely on pre-treatment images is an oversimplification as it is only a snapshot of the patient´s anatomy at a given time. Shrinkage of the tumor and modification of the patient anatomy at large (e.g. due to weight loss) may indeed occur within the several weeks’ duration of a typical treatment. The main contributions of this thesis aim to advance in the solution to these issues and are presented in two axes. In the first one, it is proposed a methodology to choose an image with the most representative anatomy of a population; such image is called Atlas. The registration of the atlas into a new image of the patient allows to automatically segment the structures of interest, speeding up the delineation process and making it more robust. The second contribution of this thesis is focused on the adaptive radiotherapy. In order to adjust the estimated dose to the anatomical modifications, it is fundamental to have non-rigid registration algorithms. So, the evaluation and adaptation of non-rigid registration methods are required, addressing especially the alignment of the tumor’s region among different moments of the treatment.

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Em radioterapia externa, uma nova técnica chamada terapia de radiação de intensidade modulada - IMRT - permite delinear a dose de radiação em imagens de 2 ou 3 dimensões, delimitando de forma bastante precisa e não necessariamente uniforme a região a ser irradiada. Assim, ao mesmo tempo que o tumor é irradiado, é possível evitar a irradiação aos tecidos vizinhos íntegros (sãos), limitando os efeitos secundários do tratamento. Para que a radioterapia externa tenha sucesso usando a técnica IMRT, é fundamental delinear previamente de forma precisa o tumor e os órgãos sãos que devem ser protegidos da radiação, garantindo assim a dose exata de radiação nos volumes alvos. O objetivo desta tese é fornecer ferramentas que sejam adequadas ao delineamento automático de estruturas de interesse e à radioterapia adaptativa para tumores da região da cabeça e do pescoço. Atualmente, a segmentação de estruturas de interesse, tais como os órgãos em risco e as regiões de propagação tumoral, é feita manualmente. Esta é uma tarefa que demanda bastante tempo de um especialista, além de ser tediosa. Além do mais, o planejamento em radioterapia é feito baseado na imagem adquirida na semana do pré-tratamento, onde é calculada a dose. Normalmente o tratamento ocorre em várias semanas, porém a dose estimada no início do tratamento é a mesma para todas as outras semanas do tratamento. Calcular a dose e mantê-la nas demais semanas é uma simplificação que não corresponde à realidade, já que ocorrem mudanças anatômicas no paciente ao longo do tratamento. Estas mudanças ocorrem devido ao encolhimento do tumor e ao possível emagrecimento do paciente, provocando alterações anatômicas locais e globais. As contribuições desta tese visam solucionar e avançar nestes problemas e são apresentadas em dois eixos. No primeiro eixo, é proposta uma metodologia para escolher uma anatomia que seja representativa da população, anatomia esta chamada de atlas. O registro do atlas na imagem do paciente permite que estruturas de interesse sejam segmentadas automaticamente, acelerando o processo de delineamento e tornando-o mais robusto. A segunda contribuição desta tese é voltada à radioterapia adaptativa. Para que a dose estimada na primeira semana seja adaptada às modificações anatômicas, é necessária a utilização de métodos de registro não-rígidos. Portanto, nesta etapa é feita uma avaliação e adaptação dos métodos de registros de forma que a região do tumor esteja bem alinhada. / Intensity Modulated Radiotherapy (IMRT) is a new technique enabling the delineation of the 3D radiation dose. It allows to delineate a radiation zone of almost any shape and to modulate the beam intensity inside the target. If IMRT enables to constrain the radiation plan in the beam delivery as well as in the protection of important functional areas (e.g. spinal cord), it also raises the issues of adequacy and accuracy of the selection and delineation of the target volumes. The purpose of this thesis is to provide tools to automatic delineation of the regions of interest and also to adaptive radiotherapy treatment for tumors located in the head and neck region. The delineation in the patient computed tomography image of the tumor volume and organs to be protected is currently performed by an expert who delineates slice by slice the contours of interest. This task is highly time-consuming and requires experts’ knowledge. Moreover, the planning process in radiotherapy typically involves the acquisition of a unique set of computed tomography images in treatment position on which target volumes (TVs) and normal structures are delineated, and which are used for dose calculation. Restricting the delineation of these regions of interest based solely on pre-treatment images is an oversimplification as it is only a snapshot of the patient´s anatomy at a given time. Shrinkage of the tumor and modification of the patient anatomy at large (e.g. due to weight loss) may indeed occur within the several weeks’ duration of a typical treatment. The main contributions of this thesis aim to advance in the solution to these issues and are presented in two axes. In the first one, it is proposed a methodology to choose an image with the most representative anatomy of a population; such image is called Atlas. The registration of the atlas into a new image of the patient allows to automatically segment the structures of interest, speeding up the delineation process and making it more robust. The second contribution of this thesis is focused on the adaptive radiotherapy. In order to adjust the estimated dose to the anatomical modifications, it is fundamental to have non-rigid registration algorithms. So, the evaluation and adaptation of non-rigid registration methods are required, addressing especially the alignment of the tumor’s region among different moments of the treatment.