fMRI imaging is considered key on the understanding of the brain and the mind, for this reason has been the subject of tremendous research connecting different disciplines. The intrinsic complexity of this 4-D type of data processing and analysis has been approached with every single computational perspective, lately increasing the trend to include artificial intelligence. One step critical on the fMRI pipeline is image registration. A model of Deep Networks based on Fully Convolutional Neural Networks, spatial transformation neural networks with a self-learning strategy was proposed for the implementation of a Fully deformable model image registration algorithm. Publicly available fMRI datasets with images from real-life subjects were used for training, testing and validating the model. The model performance was measured in comparison with ANTs deformable registration method with good results suggesting that Deep Learning can be used successfully for the development of the field using the basic strategy of studying the brain using the brain-self strategies.

This thesis is concerned with model-based non-rigid registration of single-modality magnetic resonance images of compressed and uncompressed breast tissue in breast cancer diagnostic/interventional imaging. First, a volumetric registration algorithm is developed which solves the registration as a state estimation problem. Using a static deformation model. To reduce computations, the similarity measure is calculated at some specific points called control points. These control points can be from a low resolution image grid or any irregular image grid. Our numerical analysis has shown that control points placed in the area without much information; i.e with small or no changes in image intensity, yield negligible deformation. Therefore, the selection of the control points can significantly impact the accuracy and computation complexity of the registration algorithms. An extension of the speeded up robust features (SURF) to 3D is proposed for enhanced selection of the control points in deformable image registration. The impact of this new control point selection method on the performance of the registration algorithm is analyzed by comparing it to the case where regular grid control points are used. The results show that the number of control points could be reduced by a factor of ten with new selection methodology without sacrificing performance. Second image registration method is proposed in which, based on a segmented pre-operative image, a deformation model of the breast tissue is developed and discretized in the spatial domain using the method of finite elements. The compression of the preoperative image is modeled by applying smooth forces on the surface of the breast where compression plates are placed. Image registration is accomplished by formulating and solving an optimization problem. The cost function is a similarity measure between the deformed preoperative image and intra-operative image computed at some control point and the decision variables are the tissue interaction forces. / Thesis / Master of Applied Science (MASc)

Segmented analysis of subcortical structures within the nonhuman primate can potentially have a profound impact on studying the relationship between volumetric characteristics and alcohol dependencies. Image segmentations have been widely used in quantifying structural information. There are a variety of methods in which users can extract desired structures from a medical image; ranging from manual segmentations to fully-automated segmentations and 2-D to 3-D. The implications of this possibility can have tremendous applicability to medical research and diagnosis. The primary goal of my thesis is to investigate different implementation methodologies for segmenting subcortical structures such as the hippocampus and striatum and then apply that knowledge towards the development of an approach to segment these two structures from a group of alcohol-dependent Rhesus Macaque monkeys. Using the Level Set Deformable Model (LSDM) with a priori structural information, a series of T1-weighted MR images of Rhesus Macaque hippocampi and striatum were segmented in an effort to compare the structural hippocampal and striatal volumes between early and late stages of alcohol dependency. The results suggest that the volumes of both subcortical structures are affected negatively by alcoholism. Volume deficits of as much as 5% for the hippocampus and 8% for the caudate were found. / Master of Science

Simulação computacional de ambientes cirúrgicos têm sido amplamente utilizados, normalmente para treinamentos, ajudando no desenvolvimento de habilidades essenciais e minimizando erros em procedimentos cirúrgicos. Para estes ambientes, é essencial a obtenção de um comportamento mais realista, sendo importante o uso de técnicas com alta precisão, além de uma simulação em tempo real. A fim de melhor controlar este trade-off entre eficiência e eficácia, apresentamos um ambiente híbrido e adaptativo que combina um conjunto de métodos para alcançar uma boa precisão e desempenho na simulação. Nosso sistema mescla métodos físicos de deformação (Método de Elementos Finitos e Mass-Mola) com um método não-físico que aproxima o comportamento dos primeiros (Green Coordinates), sendo capaz de utilizar o método apropriado dependendo da situação. Para melhor simular um ambiente cirúrgico completo, foram implementadas ferramentas adicionais para interação, permitindo pegar e manipular, queimar, e sentir os objetos do cenário. Nosso sistema proporciona grande imersão ao usuário, consumindo menos recursos computacionais e aumentando as taxas de atualização da simulação. / Computational simulation of surgical environments have been widely used usually for trainings, improving essential skills and minimizing errors in surgical procedures. As these environments are always looking for a more realistic behavior, it is important to use high-precision techniques while ensuring a real-time simulation. In order to better manage this trade-off between efficiency and effectiveness, we present a hybrid and adaptive environment that combines a set of methods to achieve good accuracy and performance for a simulation. Our system merges physically deformation methods (Finite Elements Method and Mass Spring Damper) with a non-physical method that approximates the formers behavior (Green Coordinates), being able to use the appropriate method depending on the situation. To simulate an approximation of a complete surgical environment, we also implement interaction tools, such as picking, burning, and haptic feedback. Our system provides great immersion for the user, consuming less computational resources and increasing update rates.

Simulação computacional de ambientes cirúrgicos têm sido amplamente utilizados, normalmente para treinamentos, ajudando no desenvolvimento de habilidades essenciais e minimizando erros em procedimentos cirúrgicos. Para estes ambientes, é essencial a obtenção de um comportamento mais realista, sendo importante o uso de técnicas com alta precisão, além de uma simulação em tempo real. A fim de melhor controlar este trade-off entre eficiência e eficácia, apresentamos um ambiente híbrido e adaptativo que combina um conjunto de métodos para alcançar uma boa precisão e desempenho na simulação. Nosso sistema mescla métodos físicos de deformação (Método de Elementos Finitos e Mass-Mola) com um método não-físico que aproxima o comportamento dos primeiros (Green Coordinates), sendo capaz de utilizar o método apropriado dependendo da situação. Para melhor simular um ambiente cirúrgico completo, foram implementadas ferramentas adicionais para interação, permitindo pegar e manipular, queimar, e sentir os objetos do cenário. Nosso sistema proporciona grande imersão ao usuário, consumindo menos recursos computacionais e aumentando as taxas de atualização da simulação. / Computational simulation of surgical environments have been widely used usually for trainings, improving essential skills and minimizing errors in surgical procedures. As these environments are always looking for a more realistic behavior, it is important to use high-precision techniques while ensuring a real-time simulation. In order to better manage this trade-off between efficiency and effectiveness, we present a hybrid and adaptive environment that combines a set of methods to achieve good accuracy and performance for a simulation. Our system merges physically deformation methods (Finite Elements Method and Mass Spring Damper) with a non-physical method that approximates the formers behavior (Green Coordinates), being able to use the appropriate method depending on the situation. To simulate an approximation of a complete surgical environment, we also implement interaction tools, such as picking, burning, and haptic feedback. Our system provides great immersion for the user, consuming less computational resources and increasing update rates.

Dans cette thèse sont présentées deux méthodes visant à obtenir automatiquement une description tridimensionnelle des mouvements d'une main étant donnée une séquence vidéo monoculaire de cette main. En utilisant l'information fournie par la vidéo, l'objectif est de déterminer l'ensemble des paramètres cinématiques nécessaires à la description de la configuration spatiale des différentes parties de la main. Cet ensemble de paramètres est composé des angles de chaque articulation ainsi que de la position et de l'orientation globale du poignet. Ce problème est un problème difficile. La main a de nombreux degrés de liberté et les auto-occultations sont omniprésentes, ce qui rend difficile l'estimation de la configuration des parties partiellement ou totalement cachées. Dans cette thèse sont proposées deux nouvelles méthodes qui améliorent par certains aspects l'état de l'art pour ce problème. Ces deux méthodes sont basées sur un modèle de la main dont la configuration spatiale est ajustée pour que sa projection dans l'image corresponde au mieux à l'image de main observée. Ce processus est guidé par une fonction de coût qui définit une mesure quantitative de la qualité de l'alignement de la projection du modèle avec l'image observée. La procédure d'ajustement du modèle est réalisée grâce à un raffinement itératif de type descente de gradient quasi-newton qui vise à minimiser cette fonction de coût.Les deux méthodes proposées diffèrent principalement par le choix du modèle et de la fonction du coût. La première méthode repose sur un modèle de la main composé d'ellipsoïdes et d'une fonction coût utilisant un modèle de la distribution statistique de la couleur la main et du fond de l'image.La seconde méthode repose sur un modèle triangulé de la surface de la main qui est texturé est ombragé. La fonction de coût mesure directement, pixel par pixel, la différence entre l'image observée et l'image synthétique obtenue par projection du modèle de la main dans l'image. Lors du calcul du gradient de la fonction de coût, une attention particulière a été portée aux termes dûs aux changements de visibilité de la surface au voisinage des auto-occultations, termes qui ont été négligés dans les méthodes préexistantes.Ces deux méthodes ne fonctionnement malheureusement pas en temps réel, ce qui rend leur utilisation pour l'instant impossible dans un contexte d'interaction homme-machine. L'amélioration de la performance des ordinateur combinée avec une amélioration de ces méthodes pourrait éventuellement permettre d'obtenir un résultat en temps réel. / In this thesis we propose two methods that allow to recover automatically a full description of the 3d motion of a hand given a monocular video sequence of this hand. Using the information provided by the video, our aimto is to determine the full set of kinematic parameters that are required to describe the pose of the skeleton of the hand. This set of parameters is composed of the angles associate to each joint/articulation and the global position and orientation of the wrist. This problem is extremely challenging. The hand as many degrees of freedom and auto-occlusion are ubiquitous, which makes difficult the estimation of occluded or partially ocluded hand parts.In this thesis, we introduce two novel methods of increasing complexity that improve to certain extend the state-of-the-art for monocular hand tracking problem. Both are model-based methods and are based on a hand model that is fitted to the image. This process is guided by an objective function that defines some image-based measure of the hand projection given the model parameters. The fitting process is achieved through an iterative refinement technique that is based on gradient-descent and aims a minimizing the objective function. The two methos differ mainly by the choice of the hand model and of the cost function.The first method relies on a hand model made of ellipsoids and a simple discrepancy measure based on global color distributions of the hand and the background. The second method uses a triangulated surface model with texture and shading and exploits a robust distance between the synthetic and observed image as discrepancy measure.While computing the gradient of the discrepancy measure, a particular attention is given to terms related to the changes of visibility of the surface near self occlusion boundaries that are neglected in existing formulations. Our hand tracking method is not real-time, which makes interactive applications not yet possible. Increase of computation power of computers and improvement of our method might make real-time attainable.

Simulação computacional de ambientes cirúrgicos têm sido amplamente utilizados, normalmente para treinamentos, ajudando no desenvolvimento de habilidades essenciais e minimizando erros em procedimentos cirúrgicos. Para estes ambientes, é essencial a obtenção de um comportamento mais realista, sendo importante o uso de técnicas com alta precisão, além de uma simulação em tempo real. A fim de melhor controlar este trade-off entre eficiência e eficácia, apresentamos um ambiente híbrido e adaptativo que combina um conjunto de métodos para alcançar uma boa precisão e desempenho na simulação. Nosso sistema mescla métodos físicos de deformação (Método de Elementos Finitos e Mass-Mola) com um método não-físico que aproxima o comportamento dos primeiros (Green Coordinates), sendo capaz de utilizar o método apropriado dependendo da situação. Para melhor simular um ambiente cirúrgico completo, foram implementadas ferramentas adicionais para interação, permitindo pegar e manipular, queimar, e sentir os objetos do cenário. Nosso sistema proporciona grande imersão ao usuário, consumindo menos recursos computacionais e aumentando as taxas de atualização da simulação. / Computational simulation of surgical environments have been widely used usually for trainings, improving essential skills and minimizing errors in surgical procedures. As these environments are always looking for a more realistic behavior, it is important to use high-precision techniques while ensuring a real-time simulation. In order to better manage this trade-off between efficiency and effectiveness, we present a hybrid and adaptive environment that combines a set of methods to achieve good accuracy and performance for a simulation. Our system merges physically deformation methods (Finite Elements Method and Mass Spring Damper) with a non-physical method that approximates the formers behavior (Green Coordinates), being able to use the appropriate method depending on the situation. To simulate an approximation of a complete surgical environment, we also implement interaction tools, such as picking, burning, and haptic feedback. Our system provides great immersion for the user, consuming less computational resources and increasing update rates.

This thesis deals with design, implementation and analysis of the model for dynamic changes in the terrain. Present state of terrain deformation in OpenSceneGraph environment is described and available relevant software called TDS, which allows terrain adaptation to new inserted objects is presented. Special emphasis is placed on design of model for physically based terrain deformations that are caused by moving object or by bomb explosion. The results of simulation tests are presented and on the base of model analysis, the optimizations, which significantly improve final algorithm, are designed and realized.

Facial modelling and animation has been an active research subject in computer graphics since the 1970s. Due to extremely complex biomechanical structures of human faces and people’s visual familiarity with human faces, modelling and animating realistic human faces is still one of greatest challenges in computer graphics. Since we are so familiar with human faces and very sensitive to unnatural subtle changes in human faces, it usually requires a tremendous amount of artistry and manual work to create a convincing facial model and animation. There is a clear need of developing automatic techniques for facial modelling in order to reduce manual labouring. In order to obtain a realistic facial model of an individual, it is now common to make use of 3D scanners to capture range scans from the individual and then fit a template to the range scans. However, most existing template-fitting methods require manually selected landmarks to warp the template to the range scans. It would be tedious to select landmarks by hand over a large set of range scans. Another way to reduce repeated work is synthesis by reusing existing data. One example is expression cloning, which copies facial expression from one face to another instead of creating them from scratch. This aim of this study is to develop a fully automatic framework for template-based facial modelling, facial expression transferring and facial expression tracking from range scans. In this thesis, the author developed an extension of the iterative closest points (ICP) algorithm, which is able to match a template with range scans in different scales, and a deformable model, which can be used to recover the shapes of range scans and to establish correspondences between facial models. With the registration method and the deformable model, the author proposed a fully automatic approach to reconstructing facial models and textures from range scans without re-quiring any manual interventions. In order to reuse existing data for facial modelling, the author formulated and solved the problem of facial expression transferring in the framework of discrete differential geometry. The author also applied his methods to face tracking for 4D range scans. The results demonstrated the robustness of the registration method and the capabilities of the deformable model. A number of possible directions for future work were pointed out.

La formation aux gestes en obstétrique réalisée directement auprès du patient pose des problèmes éthiques et médico-légaux. C'est pourquoi une grande partie de cette formation repose sur l'observation in-situ. Cette approche ne permet pas de prendre en charge l'ensemble des dimensions nécessaires à la formation telles que l'extraction instrumentale, l'acquisition d'un niveau de dextérité suffisant ou encore la capacité de prise de décision face à une situation à risque.Les systèmes d'entrainement par simulation consituent une réponse à ce problème de formation. Toutefois, les simulateurs haptiques actuels ne permettent qu'une évaluation qualitative du geste obstétrique et ne fournissent pas d'informations quantitatives sur les efforts subis par les différents organes pelviens. Ces informations quantitatives ne sont accessibles que par des simulation biomécaniques de la descente fœtale. Par contre, pour pouvoir être utilisées conjointement avec un simulateur haptique dans un but d'apprentissage, ces simulations doivent être interactives.Dans ce manuscrit, nous proposons tout d'abord un état de l'art des différents modèles biomécaniques existants permettant de modéliser les organes pelviens et la descente fœtale. Après avoir identifié la tête foetale comme la structure la plus susceptible de subir des dommages importants lors de l'accouchement, nous présentons un modèle de la tête foetale basé sur des éléments finis de coque CST-DKT que nous étendons avec un formulation co-rotationelle et une contrainte de volume permettant de prendre en compte la matière intra-crânienne. Une implémentation GPU de ce modèle est proposée pour permettre des simulations interactives. Ce modèle est validé sur une simulation de la pression intra-utérine subie par la tête fœtale lors de la deuxième phase de l'accouchement. Enfin, nous proposons une ébauche de modèle complet pour la simulation interactive de la descente fœtale. / The training to obstetrical gestures performed directly on the patient raise ethical and medico-legal problems. That is why most of the formation is based on in-situ observations. This approach can not handle all the necessary dimensions to the formation such as instrumental extraction, a good level of dexterity or the capacity to take decision in high risk situations.Simulation based training systems can address this formation problems. Neverteless, today's haptic simulators don't provide quantitative informations about the efforts undergone by pelvic organs. This informations are available only through biomechanical simulations of the foetal descent. To be used in conjunction with an haptic simulator, such simulations must be interactive.In this manuscript, first we propose a state of the art of the existing biomechanical models allowing to model the pelvic organs. After having identified the foetal head as the structure that can potentially undergo important damages during childbirth, we present a model of the foetal head based on shell finite elements CST-DKT that we extend to a co-rotationnal formulation and a volume constraint allowing to take in count the intra-cranial matter. A GPU implementation of this model is also porposed to allow interactive simulations. This model is validated on a simulation of intra-uterine pressure undergone by the foetal head during the second phase of childbirth. Finally, we propose a draft of a complete model intended to simulate interactively the feotal descent.