Multilayer representation for geological information systems

En esta tesis se propone el uso de la Representación de Terrenos Basada en Stacks (SBRT, de sus siglas en inglés) para datos geológicos volumétricos. Esta estructura de datos codifica estructuras geológicas representadas como stacks utilizando una compacta representación de datos. A continuación, hemos formalizado la SBRT con un esquema basado en la teoría de geo-átomos para proporcionar una definición precisa y determinar sus propiedades. Esta tesis también introduce una nueva estructura de datos llamada QuadStack, mejorando los resultados de compresión proporcionados por la SBRT al aprovechar la redundancia de información que a menudo se encuentra en los datos distribuidos por capas. También se han proporcionado métodos de visualización para estas representaciones basados en el conocido algoritmo de visualización raycasting. Al mantener los datos en todo momento en la memoria de la GPU de forma compacta, los métodos propuestos son lo suficientemente rápidos como para proporcionar velocidades de visualización interactivas.In this thesis we propose the use of the Stack-Based Representation of Terrains (SBRT) for volumetric geological data. This data structure encodes geological structures represented as stacks using a compact data representation. The SBRT is further formalized with a framework based on the geo-atom theory to provide a precise definition and determine its properties. Also, we introduce QuadStacks, a novel data structure that improves the compression results provided by the SBRT, by exploiting in its data arrangement the redundancy often found in layered dataset. This thesis also provides direct visualization methods for the SBR and QuadStacks based on the well-known raycasting algorithm. By keeping the whole dataset in the GPU in a compact way, the methods are fast enough to provide real-time frame rates.Tesis Univ. Jaén. Departamento de Informática. Leída el 19 de septiembre de 2019

Patient-specific anatomical illustration via model-guided texture synthesis

Medical illustrations can make powerful use of textures to attractively, effectively, and efficiently visualize the appearance of the surface or cut surface of anatomic structures. It can do this by implying the anatomic structure's physical composition and clarifying its identity and 3-D shape. Current visualization methods are only capable of conveying detailed information about the orientation, internal structure, and other local properties of the anatomical objects for a typical individual, not for a particular patient. Although one can derive the shape of the individual patient's object from CT or MRI, it is important to apply these illustrative techniques to those particular shapes. In this research patient-specific anatomical illustrations are created by model-guided texture synthesis (MGTS). Given 2D exemplar textures and model-based guidance information as input, MGTS uses exemplar-based texture synthesis techniques to create patient-specific surface and solid textures. It consists of three main components. The first component includes a novel texture metamorphosis approach for creating interpolated exemplar textures given two exemplar textures. This component uses an energy optimization scheme derived from optimal control principles that utilizes intensity and structure information in obtaining the transformation. The second component consists of creating the model-based guidance information, such as directions and layers, for that specific model. This component uses coordinates implied by discrete medial 3D anatomical models (m-reps). The last component accomplishes exemplar-based texture synthesis by textures whose characteristics are spatially variant on and inside the 3D models. It considers the exemplar textures from the first component and guidance information from the second component in synthesizing high-quality, high-resolution solid and surface textures. Patient-specific illustrations with a variety of textures for different anatomical models, such as muscles and bones, are shown to be useful for our clinician to comprehend the shape of the models under radiation dose and to distinguish the models from one another