Voxel-based Modeling with Multi-resolution Wavelet Transform for Layered Manufacturing

A voxel-based modeling system with multi-resolution for layered manufacturing is presented in this paper. When dealing with discretized data input, voxel-based modeling shows its clear advantages over the conventional geometric modeling methods. To increase the efficiency of voxel data due to its large storage space requirement, multi-resolution method with wavelet transform technique is implemented. Combining with iso-surface generation and lossless polygon reduction, this voxel-based modeling method can easily work with layered manufacturing. To demonstrate these concepts, components with different resolutions are built using Layered Manufacturing and presented in the paper.Mechanical Engineerin

Sparse Volumetric Deformation

Volume rendering is becoming increasingly popular as applications require realistic solid shape representations with seamless texture mapping and accurate filtering. However rendering sparse volumetric data is difficult because of the limited memory and processing capabilities of current hardware. To address these limitations, the volumetric information can be stored at progressive resolutions in the hierarchical branches of a tree structure, and sampled according to the region of interest. This means that only a partial region of the full dataset is processed, and therefore massive volumetric scenes can be rendered efficiently. The problem with this approach is that it currently only supports static scenes. This is because it is difficult to accurately deform massive amounts of volume elements and reconstruct the scene hierarchy in real-time. Another problem is that deformation operations distort the shape where more than one volume element tries to occupy the same location, and similarly gaps occur where deformation stretches the elements further than one discrete location. It is also challenging to efficiently support sophisticated deformations at hierarchical resolutions, such as character skinning or physically based animation. These types of deformation are expensive and require a control structure (for example a cage or skeleton) that maps to a set of features to accelerate the deformation process. The problems with this technique are that the varying volume hierarchy reflects different feature sizes, and manipulating the features at the original resolution is too expensive; therefore the control structure must also hierarchically capture features according to the varying volumetric resolution. This thesis investigates the area of deforming and rendering massive amounts of dynamic volumetric content. The proposed approach efficiently deforms hierarchical volume elements without introducing artifacts and supports both ray casting and rasterization renderers. This enables light transport to be modeled both accurately and efficiently with applications in the fields of real-time rendering and computer animation. Sophisticated volumetric deformation, including character animation, is also supported in real-time. This is achieved by automatically generating a control skeleton which is mapped to the varying feature resolution of the volume hierarchy. The output deformations are demonstrated in massive dynamic volumetric scenes

Efficient techniques for soft tissue modeling and simulation

Performing realistic deformation simulations in real time is a challenging problem in computer graphics. Among numerous proposed methods including Finite Element Modeling and ChainMail, we have implemented a mass spring system because of its acceptable accuracy and speed. Mass spring systems have, however, some drawbacks such as, the determination of simulation coefficients with their iterative nature. Given the correct parameters, mass spring systems can accurately simulate tissue deformations but choosing parameters that capture nonlinear deformation behavior is extremely difficult. Since most of the applications require a large number of elements i. e. points and springs in the modeling process it is extremely difficult to reach realtime performance with an iterative method. We have developed a new parameter identification method based on neural networks. The structure of the mass spring system is modified and neural networks are integrated into this structure. The input space consists of changes in spring lengths and velocities while a "teacher" signal is chosen as the total spring force, which is expressed in terms of positional changes and applied external forces. Neural networks are trained to learn nonlinear tissue characteristics represented by spring stiffness and damping in the mass spring algorithm. The learning algorithm is further enhanced by an adaptive learning rate, developed particularly for mass spring systems. In order to avoid the iterative approach in deformation simulations we have developed a new deformation algorithm. This algorithm defines the relationships between points and springs and specifies a set of rules on spring movements and deformations. These rules result in a deformation surface, which is called the search space. The deformation algorithm then finds the deformed points and springs in the search space with the help of the defined rules. The algorithm also sets rules on each element i. e. triangle or tetrahedron so that they do not pass through each other. The new algorithm is considerably faster than the original mass spring systems algorithm and provides an opportunity for various deformation applications. We have used mass spring systems and the developed method in the simulation of craniofacial surgery. For this purpose, a patient-specific head model was generated from MRI medical data by applying medical image processing tools such as, filtering, the segmentation and polygonal representation of such model is obtained using a surface generation algorithm. Prism volume elements are generated between the skin and bone surfaces so that different tissue layers are included to the head model. Both methods produce plausible results verified by surgeons

Modified mass-spring system for physically based deformation modeling

Mass-spring systems are considered the simplest and most intuitive of all deformable models. They are computationally efficient, and can handle large deformations with ease. But they suffer several intrinsic limitations. In this book a modified mass-spring system for physically based deformation modeling that addresses the limitations and solves them elegantly is presented. Several implementations in modeling breast mechanics, heart mechanics and for elastic images registration are presented

Modified mass-spring system for physically based deformation modeling

Mass-spring systems are considered the simplest and most intuitive of all deformable models. They are computationally efficient, and can handle large deformations with ease. But they suffer several intrinsic limitations. In this book a modified mass-spring system for physically based deformation modeling that addresses the limitations and solves them elegantly is presented. Several implementations in modeling breast mechanics, heart mechanics and for elastic images registration are presented

Développement d'un modèle numérique simplifié du tronc pour simuler l'effet d'une chirurgie de la scoliose sur l'apparence externe d'un patient

RÉSUMÉ La scoliose est une maladie du système musculo-squelettique caractérisée par une déformation tridimensionnelle complexe du tronc. Pour les cas les plus sévères, un traitement chirurgical est nécessaire. Celui-ci consiste à redresser la colonne vertébrale à l’aide de tiges métalliques ancrées aux vertèbres moyennant des vis et des crochets. La prédiction du résultat chirurgical s’avère un élément fondamental à toute planification opératoire. Actuellement, la méthode clinique pour élaborer une stratégie opératoire et estimer le résultat de la correction de la scoliose réside principalement dans l’analyse radiographique de la flexibilité du rachis jumelée à l’expérience du chirurgien. Afin d’assister davantage ce dernier lors de la planification, un simulateur biomécanique permettant d’identifier la configuration optimale des implants qui corrigera le mieux les déformations de la colonne est en cours de développement au Centre Hospitalier Universitaire Sainte-Justine à Montréal. Toutefois, ni ce simulateur ni l’analyse du chirurgien ne tient compte des tissus mous du tronc et ne fournit d’information sur l’apparence externe après l’intervention. Pour le chirurgien, le résultat de la chirurgie sur l’apparence externe s’avère hautement subjectif et son expérience demeure son seul atout. Tout ceci reste donc fort problématique, considérant que la principale raison pour prescrire une opération provient d’abord du mécontentement du patient vis-à-vis son apparence esthétique. Ne possédant aucune idée du niveau de correction esthétique qu’une chirurgie quelconque peut lui offrir, ce dernier est parfois déçu de l’imperfection de son apparence après l’intervention. L’objectif principal du projet consiste donc à définir une modélisation physique simplifiée des tissus déformables entre l’épiderme (surface de la peau) et les structures osseuses du tronc, afin de visualiser en 3D et d’évaluer l’effet d’une chirurgie de la scoliose sur l’apparence externe du patient. Cette étude s’attarde uniquement aux tissus mous car la modélisation biomécanique des structures osseuses fait l’objet d’un autre projet. Ainsi, pour prédire les résultats des corrections à l’externe, on utilise une configuration postopératoire déjà connue des structures osseuses.----------ABSTRACT Scoliosis is a musculoskeletal disorder characterized by a complex three-dimensional deformation of the trunk. For severe cases, surgical treatment is necessary. This procedure consists in rectifying the spine shape using metal rods anchored to the vertebrae by means of screws and hooks. The prediction of surgical outcome is a fundamental element of any preoperative evaluation. Currently, the clinical method to define a surgical strategy and estimate the result of curve correction relies primarily on radiographic analysis of spinal flexibility and on the surgeon's own experience. To further assist the clinician during surgical planning, a biomechanical simulator is currently being developed at Montreal’s Sainte-Justine University Hospital Center to identify the optimal configuration of the implants to best correct the spinal deformities. However, neither this simulator nor the spinal flexibility analysis consider the soft tissues of the trunk in order to provide information on the patient’s external appearance after the intervention. For the surgeon, the residual trunk asymmetry proves highly subjective and his experience remains his only asset. This is problematic considering that the main reason to prescribe an operation comes initially from the patient’s dissatisfaction towards their apparent deformity. Having no prior knowledge of the level of aesthetic improvement a surgery can offer him, the patient is sometimes disappointed by the imperfection of his appearance after the intervention. Therefore, the goal of this project is to define a simplified physical model of the deformable tissues between the skin surface (epidermis) and bone structures of the trunk in order to visualize in 3D and assess the effect of scoliosis surgery on the patient’s external appearance. This research focuses only on the soft tissues since biomechanical modeling of the bone structures is the subject of another ongoing project. Consequently, a known postoperative configuration of the bone structures serves as our basis to predict the external appearance after scoliosis surgery. To achieve our goal, we first propose a methodology to build a simplified system to model the different deformable structures of the trunk. Initially, 3D pre and postoperative reconstructions of the bone structures are obtained from standard radiographs while non-invasive 3D optical digitizers acquire the external surface of the trunk using white non-ionizing structure light. Following certain mesh preprocessing, we develop a generic method to generate three different tetrahedral layers starting from the external surface of the trunk to represent the skin, fat and muscles. From these new layers a generalized particle system based on elastic potential energy is defined. Forces preserving distance, area and volume constraints are calculated to describe the physical behavior of the various soft tissues. Finally, a rigid articulated model of the bone structures is created in order to transform the internal preoperative configuration to the postoperative state. By solving a set of dynamic equations, the displacements of this rigid model deform the simplified soft tissue layers of the trunk in order to predict the external appearance after scoliosis surgery. A suitable numerical integration scheme to compute the dynami

Real Time Rendering of Deformable and Semi-Transparent Objects by Volume Rendering

Volume rendering is one of the key technique to display data from diverse application fields like medicine, industrial quality control, and numerical simulations in an appropriate way. The current main limitations are still the inadequate rendering speed and the limited flexibility of the most efficient algorithms. In this dissertation, we developed three new algorithms for the acceleration of direct volume rendering and volume deformation. The first algorithm consists on a first step, on the reimplementation of the existing preintegration volume rendering approach, where the gray values between two sampling points change linearly, by considering the correct not simplified volume rendering integral, i.e, considering the attenuation factor as well as the shading function during the precompuation process. On a second step, we extended our algorithm to quadratic and higher order polynomial model. The preintegration speed for linear model is increased by a factor of 10. The second algorithm accelerates shear warp and ray casting process. While acceleration techniques like space leaping and early ray termination are efficient when rendering volumes with most of the voxels are mapped either opaque or transparent, encoding coherence appeared more efficient for rendering semi-transparent volumes. It's an approach for coding empty regions to a coherency encoding that can describe regions where the opacity changes linearly. We reimplemented this technique using a volume graphics library (VGL). We improved it by using the preintegration technique to evaluate opacity and shading inside the coherent region. We achieved a speedup of up to a factor of 3. The third algorithm is for volume deformation. The applied technique is the ray deformation where the volume deforming and the volume rendering are incorporated into a single process. This is implemented in our approach, by combining the Free Form Deformation (FFD) and inverse ray deformation. Unlike the previous implementation, our opacity and shading calculation are based on the preintegration technique which allows us to handle different lengths of the sampled intervals in the polyline segments which approximate the deformed ray

Efficient techniques for soft tissue modeling and simulation

Performing realistic deformation simulations in real time is a challenging problem in computer graphics. Among numerous proposed methods including Finite Element Modeling and ChainMail, we have implemented a mass spring system because of its acceptable accuracy and speed. Mass spring systems have, however, some drawbacks such as, the determination of simulation coefficients with their iterative nature. Given the correct parameters, mass spring systems can accurately simulate tissue deformations but choosing parameters that capture nonlinear deformation behavior is extremely difficult. Since most of the applications require a large number of elements i. e. points and springs in the modeling process it is extremely difficult to reach realtime performance with an iterative method. We have developed a new parameter identification method based on neural networks. The structure of the mass spring system is modified and neural networks are integrated into this structure. The input space consists of changes in spring lengths and velocities while a "teacher" signal is chosen as the total spring force, which is expressed in terms of positional changes and applied external forces. Neural networks are trained to learn nonlinear tissue characteristics represented by spring stiffness and damping in the mass spring algorithm. The learning algorithm is further enhanced by an adaptive learning rate, developed particularly for mass spring systems. In order to avoid the iterative approach in deformation simulations we have developed a new deformation algorithm. This algorithm defines the relationships between points and springs and specifies a set of rules on spring movements and deformations. These rules result in a deformation surface, which is called the search space. The deformation algorithm then finds the deformed points and springs in the search space with the help of the defined rules. The algorithm also sets rules on each element i. e. triangle or tetrahedron so that they do not pass through each other. The new algorithm is considerably faster than the original mass spring systems algorithm and provides an opportunity for various deformation applications. We have used mass spring systems and the developed method in the simulation of craniofacial surgery. For this purpose, a patient-specific head model was generated from MRI medical data by applying medical image processing tools such as, filtering, the segmentation and polygonal representation of such model is obtained using a surface generation algorithm. Prism volume elements are generated between the skin and bone surfaces so that different tissue layers are included to the head model. Both methods produce plausible results verified by surgeons.EThOS - Electronic Theses Online ServiceGBUnited Kingdo