A comparison between PML, infinite elements and an iterative BEM as mesh truncation methods for HP self-adaptive procedures in electromagnetics

Finite element hp-adaptivity is a technology that allows for very accurate numerical solutions. When applied to open region problems such as radar cross section prediction or antenna analysis, a mesh truncation method needs to be used. This paper compares the following mesh truncation methods in the context of hp-adaptive methods: Infinite Elements, Perfectly Matched Layers and an iterative boundary element based methodology. These methods have been selected because they are exact at the continuous level (a desirable feature required by the extreme accuracy delivered by the hp-adaptive strategy) and they are easy to integrate with the logic of hp-adaptivity. The comparison is mainly based on the number of degrees of freedom needed for each method to achieve a given level of accuracy. Computational times are also included. Two-dimensional examples are used, but the conclusions directly extrapolated to the three dimensional case.

Segmentation of RV in 4D Cardiac MR Volumes using region-merging graph cuts

Non-invasive quantitative assessment of the right ventricular anatomical and functional parameters is a challenging task. We present a semi-automatic approach for right ventricle (RV) segmentation from 4D MR images in two variants, which differ in the amount of user interaction. The method consists of three main phases: First, foreground and background markers are generated from the user input. Next, an over-segmented region image is obtained applying a watershed transform. Finally, these regions are merged using 4D graph-cuts with an intensity based boundary term. For the first variant the user outlines the inside of the RV wall in a few end-diastole slices, for the second two marker pixels serve as starting point for a statistical atlas application. Results were obtained by blind evaluation on 16 testing 4D MR volumes. They prove our method to be robust against markers location and place it favourably in the ranks of existing approaches.

Atlas-free Brain Tissue Segmentation Using a Single T1-weighted MRI Acquisition

Many studies investigating the aging brain or disease-induced brain alterations rely on accurate and reproducible brain tissue segmentation. Being a preliminary processing step prior to the segmentation, reliableskull-stripping the removal ofnon-brain tissue is also crucial for all later image assessment. Typically, segmentation algorithms rely on an atlas i.e. pre-segmented template data. Brain morphology, however, differs considerably depending on age, sex and race. In addition, diseased brains may deviate significantly from the atlas information typically gained from healthy volunteers. The imposed prior atlas information can thus lead to degradation of segmentation results. The recently introduced MP2RAGE sequence provides a bias-free T1 contrast with heavily reduced T2*- and PD-weighting compared to the standard MP-RAGE [1]. To this end, it acquires two image volumes at different inversion times in one acquisition, combining them to a uniform, i.e. homogenous image. In this work, we exploit the advantageous contrast properties of the MP2RAGE and combine it with a Dixon (i.e. fat-water separation) approach. The information gained by the additional fat image of the head considerably improves the skull-stripping outcome [2]. In conjunction with the pure T1 contrast of the MP2RAGE uniform image, we achieve robust skull-stripping and brain tissue segmentation without the use of an atlas

CFD modelling of the interaction between the Surface Boundary Layer and rotor wake. Comparison of results obtained with different turbulence models and mesh strategies

A simplified CFD wake model based on the actuator disk concept is used to simulate the wind turbine, represented by a disk upon which a distribution of forces, defined as axial momentum sources, are applied on the incoming non-uniform flow. The rotor is supposed to be uniformly loaded, with the exerted forces function of the incident wind speed, the thrust coefficient and the rotor diameter. The model is tested under different parameterizations of turbulence models and validated through experimental measurements downwind of a wind turbine in terms of wind speed deficit and turbulence intensity.

Mesh traversal and sorting for efficient memory usage in scientific codes

Applications that operate on meshes are very popular in High Performance Computing (HPC) environments. In the past, many techniques have been developed in order to optimize the memory accesses for these datasets. Different loop transformations and domain decompositions are com- monly used for structured meshes. However, unstructured grids are more challenging. The memory accesses, based on the mesh connectivity, do not map well to the usual lin- ear memory model. This work presents a method to improve the memory performance which is suitable for HPC codes that operate on meshes. We develop a method to adjust the sequence in which the data are used inside the algorithm, by means of traversing and sorting the mesh. This sorted mesh can be transferred sequentially to the lower memory levels and allows for minimum data transfer requirements. The method also reduces the lower memory requirements dra- matically: up to 63% of the L1 cache misses are removed in a traditional cache system. We have obtained speedups of up to 2.58 on memory operations as measured in a general- purpose CPU. An improvement is also observed with se- quential access memories, where we have observed reduc- tions of up to 99% in the required low-level memory size.

Microstructural segmentation of 3D images of alsphalt specimen using Matlab engine

En este proyecto se ha desarrollado un código de MATLAB para el procesamiento de imágenes tomográficas 3D, de muestras de asfalto de carreteras en Polonia. Estas imágenes en 3D han sido tomadas por un equipo de investigación de la Universidad Tecnológica de Lodz (LUT). El objetivo de este proyecto es crear una herramienta que se pueda utilizar para estudiar las diferentes muestras de asfalto 3D y pueda servir para estudiar las pruebas de estrés que experimentan las muestras en el laboratorio. Con el objetivo final de encontrar soluciones a la degradación sufrida en las carreteras de Polonia, debido a diferentes causas, como son las condiciones meteorológicas. La degradación de las carreteras es un tema que se ha investigado desde hace muchos años, debido a la fuerte degradación causada por diferentes factores como son climáticos, la falta de mantenimiento o el tráfico excesivo en algunos casos. Es en Polonia, donde estos tres factores hacen que la composición de muchas carreteras se degrade rápidamente, sobre todo debido a las condiciones meteorológicas sufridas a lo largo del año, con temperaturas que van desde 30° C en verano a -20° C en invierno. Esto hace que la composición de las carreteras sufra mucho y el asfalto se levante, lo que aumenta los costos de mantenimiento y los accidentes de carretera. Este proyecto parte de la base de investigación que se lleva a cabo en la LUT, tratando de mejorar el análisis de las muestras de asfalto, por lo que se realizarán las pruebas de estrés y encontrar soluciones para mejorar el asfalto en las carreteras polacas. Esto disminuiría notablemente el costo de mantenimiento. A pesar de no entrar en aspectos muy técnicos sobre el asfalto y su composición, se ha necesitado realizar un estudio profundo sobre todas sus características, para crear un código capaz de obtener los mejores resultados. Por estas razones, se ha desarrollado en Matlab, los algoritmos que permiten el estudio de los especímenes 3D de asfalto. Se ha utilizado este software, ya que Matlab es una poderosa herramienta matemática que permite operar con matrices para realización de operaciones rápidamente, permitiendo desarrollar un código específico para el tratamiento y procesamiento de imágenes en 3D. Gracias a esta herramienta, estos algoritmos realizan procesos tales como, la segmentación de la imagen 3D, pre y post procesamiento de la imagen, filtrado o todo tipo de análisis microestructural de las muestras de asfalto que se están estudiando. El código presentado para la segmentación de las muestras de asfalto 3D es menos complejo en su diseño y desarrollo, debido a las herramientas de procesamiento de imágenes que incluye Matlab, que facilitan significativamente la tarea de programación, así como el método de segmentación utilizado. Respecto al código, este ha sido diseñado teniendo en cuenta el objetivo de facilitar el trabajo de análisis y estudio de las imágenes en 3D de las muestras de asfalto. Por lo tanto, el principal objetivo es el de crear una herramienta para el estudio de este código, por ello fue desarrollado para que pueda ser integrado en un entorno visual, de manera que sea más fácil y simple su utilización. Ese es el motivo por el cual todos estos algoritmos y funciones, que ha sido desarrolladas, se integrarán en una herramienta visual que se ha desarrollado con el GUIDE de Matlab. Esta herramienta ha sido creada en colaboración con Jorge Vega, y fue desarrollada en su proyecto final de carrera, cuyo título es: Segmentación microestructural de Imágenes en 3D de la muestra de asfalto utilizando Matlab. En esta herramienta se ha utilizado todo las funciones programadas en este proyecto, y tiene el objetivo de desarrollar una herramienta que permita crear un entorno gráfico intuitivo y de fácil uso para el estudio de las muestras de 3D de asfalto. Este proyecto se ha dividido en 4 capítulos, en un primer lugar estará la introducción, donde se presentarán los aspectos más importante que se va a componer el proyecto. En el segundo capítulo se presentarán todos los datos técnicos que se han tenido que estudiar para desarrollar la herramienta, entre los que cabe los tres temas más importantes que se han estudiado en este proyecto: materiales asfálticos, los principios de la tomografías 3D y el procesamiento de imágenes. Esta será la base para el tercer capítulo, que expondrá la metodología utilizada en la elaboración del código, con la explicación del entorno de trabajo utilizado en Matlab y todas las funciones de procesamiento de imágenes utilizadas. Además, se muestra todo el código desarrollado, así como una descripción teórica de los métodos utilizados para el pre-procesamiento y segmentación de las imagenes en 3D. En el capítulo 4, se mostrarán los resultados obtenidos en el estudio de una de las muestras de asfalto, y, finalmente, el último capítulo se basa en las conclusiones sobre el desarrollo de este proyecto. En este proyecto se ha llevado han realizado todos los puntos que se establecieron como punto de partida en el anteproyecto para crear la herramienta, a pesar de que se ha dejado para futuros proyectos nuevas posibilidades de este codigo, como por ejemplo, la detección automática de las diferentes regiones de una muestra de asfalto debido a su composición. Como se muestra en este proyecto, las técnicas de procesamiento de imágenes se utilizan cada vez más en multitud áreas, como pueden ser industriales o médicas. En consecuencia, este tipo de proyecto tiene multitud de posibilidades, y pudiendo ser la base para muchas nuevas aplicaciones que se puedan desarrollar en un futuro. Por último, se concluye que este proyecto ha contribuido a fortalecer las habilidades de programación, ampliando el conocimiento de Matlab y de la teoría de procesamiento de imágenes. Del mismo modo, este trabajo proporciona una base para el desarrollo de un proyecto más amplio cuyo alcance será una herramienta que puedas ser utilizada por el equipo de investigación de la Universidad Tecnológica de Lodz y en futuros proyectos. ABSTRACT In this project has been developed one code in MATLAB to process X-ray tomographic 3D images of asphalt specimens. These images 3D has been taken by a research team of the Lodz University of Technology (LUT). The aim of this project is to create a tool that can be used to study differents asphalt specimen and can be used to study them after stress tests undergoing the samples. With the final goal to find solutions to the degradation suffered roads in Poland due to differents causes, like weather conditions. The degradation of the roads is an issue that has been investigated many years ago, due to strong degradation suffered caused by various factors such as climate, poor maintenance or excessive traffic in some cases. It is in Poland where these three factors make the composition of many roads degrade rapidly, especially due to the weather conditions suffered along the year, with temperatures ranging from 30 o C in summer to -20 ° C in winter. This causes the roads suffers a lot and asphalt rises shortly after putting, increasing maintenance costs and road accident. This project part of the base that research is taking place at the LUT, in order to better analyze the asphalt specimens, they are tested for stress and find solutions to improve the asphalt on Polish roads. This would decrease remarkable maintenance cost. Although this project will not go into the technical aspect as asphalt and composition, but it has been required a deep study about all of its features, to create a code able to obtain the best results. For these reasons, there have been developed in Matlab, algorithms that allow the study of 3D specimens of asphalt. Matlab is a powerful mathematical tool, which allows arrays operate fastly, allowing to develop specific code for the treatment and processing of 3D images. Thus, these algorithms perform processes such as the multidimensional matrix sgementation, pre and post processing with the same filtering algorithms or microstructural analysis of asphalt specimen which being studied. All these algorithms and function that has been developed to be integrated into a visual tool which it be developed with the GUIDE of Matlab. This tool has been created in the project of Jorge Vega which name is: Microstructural segmentation of 3D images of asphalt specimen using Matlab engine. In this tool it has been used all the functions programmed in this project, and it has the aim to develop an easy and intuitive graphical environment for the study of 3D samples of asphalt. This project has been divided into 4 chapters plus the introduction, the second chapter introduces the state-of-the-art of the three of the most important topics that have been studied in this project: asphalt materials, principle of X-ray tomography and image processing. This will be the base for the third chapter, which will outline the methodology used in developing the code, explaining the working environment of Matlab and all the functions of processing images used. In addition, it will be shown all the developed code created, as well as a theoretical description of the methods used for preprocessing and 3D image segmentation. In Chapter 4 is shown the results obtained from the study of one of the specimens of asphalt, and finally the last chapter draws the conclusions regarding the development of this project.

Brain tissue segmentation on Diffusion Weighted Magnetic Resonance data

Introduction Diffusion weighted Imaging (DWI) techniques are able to measure, in vivo and non-invasively, the diffusivity of water molecules inside the human brain. DWI has been applied on cerebral ischemia, brain maturation, epilepsy, multiple sclerosis, etc. [1]. Nowadays, there is a very high availability of these images. DWI allows the identification of brain tissues, so its accurate segmentation is a common initial step for the referred applications. Materials and Methods We present a validation study on automated segmentation of DWI based on the Gaussian mixture and hidden Markov random field models. This methodology is widely solved with iterative conditional modes algorithm, but some studies suggest [2] that graph-cuts (GC) algorithms improve the results when initialization is not close to the final solution. We implemented a segmentation tool integrating ITK with a GC algorithm [3], and a validation software using fuzzy overlap measures [4]. Results Segmentation accuracy of each tool is tested against a gold-standard segmentation obtained from a T1 MPRAGE magnetic resonance image of the same subject, registered to the DWI space. The proposed software shows meaningful improvements by using the GC energy minimization approach on DTI and DSI (Diffusion Spectrum Imaging) data. Conclusions The brain tissues segmentation on DWI is a fundamental step on many applications. Accuracy and robustness improvements are achieved with the proposed software, with high impact on the application’s final result.

Automatic segmentation of abdominal aortic aneurysm from medical images based on active shape models and texture models

A semi-automatic segmentation algorithm for abdominal aortic aneurysms (AAA), and based on Active Shape Models (ASM) and texture models, is presented in this work. The texture information is provided by a set of four 3D magnetic resonance (MR) images, composed of axial slices of the abdomen, where lumen, wall and intraluminal thrombus (ILT) are visible. Due to the reduced number of images in the MRI training set, an ASM and a custom texture model based on border intensity statistics are constructed. For the same reason the shape is characterized from 35-computed tomography angiography (CTA) images set so the shape variations are better represented. For the evaluation, leave-one-out experiments have been held over the four MRI set.

MBIS: Multivariate Bayesian Image Segmentation Tool

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multi-channel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

Higher-order regularization and morphological techniques for image segmentation

La segmentación de imágenes es un campo importante de la visión computacional y una de las áreas de investigación más activas, con aplicaciones en comprensión de imágenes, detección de objetos, reconocimiento facial, vigilancia de vídeo o procesamiento de imagen médica. La segmentación de imágenes es un problema difícil en general, pero especialmente en entornos científicos y biomédicos, donde las técnicas de adquisición imagen proporcionan imágenes ruidosas. Además, en muchos de estos casos se necesita una precisión casi perfecta. En esta tesis, revisamos y comparamos primero algunas de las técnicas ampliamente usadas para la segmentación de imágenes médicas. Estas técnicas usan clasificadores a nivel de pixel e introducen regularización sobre pares de píxeles que es normalmente insuficiente. Estudiamos las dificultades que presentan para capturar la información de alto nivel sobre los objetos a segmentar. Esta deficiencia da lugar a detecciones erróneas, bordes irregulares, configuraciones con topología errónea y formas inválidas. Para solucionar estos problemas, proponemos un nuevo método de regularización de alto nivel que aprende información topológica y de forma a partir de los datos de entrenamiento de una forma no paramétrica usando potenciales de orden superior. Los potenciales de orden superior se están popularizando en visión por computador, pero la representación exacta de un potencial de orden superior definido sobre muchas variables es computacionalmente inviable. Usamos una representación compacta de los potenciales basada en un conjunto finito de patrones aprendidos de los datos de entrenamiento que, a su vez, depende de las observaciones. Gracias a esta representación, los potenciales de orden superior pueden ser convertidos a potenciales de orden 2 con algunas variables auxiliares añadidas. Experimentos con imágenes reales y sintéticas confirman que nuestro modelo soluciona los errores de aproximaciones más débiles. Incluso con una regularización de alto nivel, una precisión exacta es inalcanzable, y se requeire de edición manual de los resultados de la segmentación automática. La edición manual es tediosa y pesada, y cualquier herramienta de ayuda es muy apreciada. Estas herramientas necesitan ser precisas, pero también lo suficientemente rápidas para ser usadas de forma interactiva. Los contornos activos son una buena solución: son buenos para detecciones precisas de fronteras y, en lugar de buscar una solución global, proporcionan un ajuste fino a resultados que ya existían previamente. Sin embargo, requieren una representación implícita que les permita trabajar con cambios topológicos del contorno, y esto da lugar a ecuaciones en derivadas parciales (EDP) que son costosas de resolver computacionalmente y pueden presentar problemas de estabilidad numérica. Presentamos una aproximación morfológica a la evolución de contornos basada en un nuevo operador morfológico de curvatura que es válido para superficies de cualquier dimensión. Aproximamos la solución numérica de la EDP de la evolución de contorno mediante la aplicación sucesiva de un conjunto de operadores morfológicos aplicados sobre una función de conjuntos de nivel. Estos operadores son muy rápidos, no sufren de problemas de estabilidad numérica y no degradan la función de los conjuntos de nivel, de modo que no hay necesidad de reinicializarlo. Además, su implementación es mucho más sencilla que la de las EDP, ya que no requieren usar sofisticados algoritmos numéricos. Desde un punto de vista teórico, profundizamos en las conexiones entre operadores morfológicos y diferenciales, e introducimos nuevos resultados en este área. Validamos nuestra aproximación proporcionando una implementación morfológica de los contornos geodésicos activos, los contornos activos sin bordes, y los turbopíxeles. En los experimentos realizados, las implementaciones morfológicas convergen a soluciones equivalentes a aquéllas logradas mediante soluciones numéricas tradicionales, pero con ganancias significativas en simplicidad, velocidad y estabilidad. ABSTRACT Image segmentation is an important field in computer vision and one of its most active research areas, with applications in image understanding, object detection, face recognition, video surveillance or medical image processing. Image segmentation is a challenging problem in general, but especially in the biological and medical image fields, where the imaging techniques usually produce cluttered and noisy images and near-perfect accuracy is required in many cases. In this thesis we first review and compare some standard techniques widely used for medical image segmentation. These techniques use pixel-wise classifiers and introduce weak pairwise regularization which is insufficient in many cases. We study their difficulties to capture high-level structural information about the objects to segment. This deficiency leads to many erroneous detections, ragged boundaries, incorrect topological configurations and wrong shapes. To deal with these problems, we propose a new regularization method that learns shape and topological information from training data in a nonparametric way using high-order potentials. High-order potentials are becoming increasingly popular in computer vision. However, the exact representation of a general higher order potential defined over many variables is computationally infeasible. We use a compact representation of the potentials based on a finite set of patterns learned fromtraining data that, in turn, depends on the observations. Thanks to this representation, high-order potentials can be converted into pairwise potentials with some added auxiliary variables and minimized with tree-reweighted message passing (TRW) and belief propagation (BP) techniques. Both synthetic and real experiments confirm that our model fixes the errors of weaker approaches. Even with high-level regularization, perfect accuracy is still unattainable, and human editing of the segmentation results is necessary. The manual edition is tedious and cumbersome, and tools that assist the user are greatly appreciated. These tools need to be precise, but also fast enough to be used in real-time. Active contours are a good solution: they are good for precise boundary detection and, instead of finding a global solution, they provide a fine tuning to previously existing results. However, they require an implicit representation to deal with topological changes of the contour, and this leads to PDEs that are computationally costly to solve and may present numerical stability issues. We present a morphological approach to contour evolution based on a new curvature morphological operator valid for surfaces of any dimension. We approximate the numerical solution of the contour evolution PDE by the successive application of a set of morphological operators defined on a binary level-set. These operators are very fast, do not suffer numerical stability issues, and do not degrade the level set function, so there is no need to reinitialize it. Moreover, their implementation is much easier than their PDE counterpart, since they do not require the use of sophisticated numerical algorithms. From a theoretical point of view, we delve into the connections between differential andmorphological operators, and introduce novel results in this area. We validate the approach providing amorphological implementation of the geodesic active contours, the active contours without borders, and turbopixels. In the experiments conducted, the morphological implementations converge to solutions equivalent to those achieved by traditional numerical solutions, but with significant gains in simplicity, speed, and stability.

Classical segmentation methods on novel MR imaging: a study of brain tissue segmentation of MP2RAGE vs MPRAGE

MP2RAGE has proven to be a bias-free MR acquisition with excellent contrast between grey and white matter. We investigated the ability of three state-of-the-art algorithms to automatically extract white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) from MPRAGE and MP2RAGE images: unified Segmentation (S) in SPM82 , its extension New Segment (NS), and an in-house Expectation-Maximization Markov Random Field tissue classification3 (EM-MRF) with Graph Cut (GC) optimization4 . Our goal is to quantify the differences between MPRAGE and MP2RAGE-based brain tissue probability maps.

Generación de modelos físicos personalizados y segmentación de la pared arterial y el trombo intraluminal en imágenes de aneurismas aórticos abdominales

Esta tesis doctoral está encuadrada dentro del marco general de la ingeniería biomédica aplicada al tratamiento de las enfermedades cardiovasculares, enfermedades que provocan alrededor de 1.9 millones (40%) de muertes al año en la Unión Europea. En este contexto surge el proyecto europeo SCATh-Smart Catheterization, cuyo objetivo principal es mejorar los procedimientos de cateterismo aórtico introduciendo nuevas tecnologías de planificación y navegación quirúrgica y minimizando el uso de fluoroscopía. En particular, esta tesis aborda el modelado y diagnóstico de aneurismas aórticos abdominales (AAA) y del trombo intraluminal (TIL), allí donde esté presente, así como la segmentación de estas estructuras en imágenes preoperatorias de RM. Los modelos físicos específicos del paciente, construidos a partir de imágenes médicas preoperatorias, tienen múltiples usos, que van desde la evaluación preoperatoria de estructuras anatómicas a la planificación quirúrgica para el guiado de catéteres. En el diagnóstico y tratamiento de AAA, los modelos físicos son útiles a la hora de evaluar diversas variables biomecánicas y fisiológicas de las estructuras vasculares. Existen múltiples técnicas que requieren de la generación de modelos físicos que representen la anatomía vascular. Una de las principales aplicaciones de los modelos físicos es el análisis de elementos finitos (FE). Las simulaciones de FE para AAA pueden ser específicas para el paciente y permiten modelar estados de estrés complejos, incluyendo los efectos provocados por el TIL. La aplicación de métodos numéricos de análisis tiene como requisito previo la generación de una malla computacional que representa la geometría de interés mediante un conjunto de elementos poliédricos, siendo los hexaédricos los que presentan mejores resultados. En las estructuras vasculares, generar mallas hexaédricas es un proceso especialmente exigente debido a la compleja anatomía 3D ramificada. La mayoría de los AAA se encuentran situados en la bifurcación de la arteria aorta en las arterias iliacas y es necesario modelar de manera fiel dicha bifurcación. En el caso de que la sangre se estanque en el aneurisma provocando un TIL, éste forma una estructura adyacente a la pared aórtica. De este modo, el contorno externo del TIL es el mismo que el contorno interno de la pared, por lo que las mallas resultantes deben reflejar esta particularidad, lo que se denomina como "mallas conformadas". El fin último de este trabajo es modelar las estructuras vasculares de modo que proporcionen nuevas herramientas para un mejor diagnóstico clínico, facilitando medidas de riesgo de rotura de la arteria, presión sistólica o diastólica, etc. Por tanto, el primer objetivo de esta tesis es diseñar un método novedoso y robusto para generar mallas hexaédricas tanto de la pared aórtica como del trombo. Para la identificación de estas estructuras se utilizan imágenes de resonancia magnética (RM). Deben mantenerse sus propiedades de adyacencia utilizando elementos de alta calidad, prestando especial atención al modelado de la bifurcación y a que sean adecuadas para el análisis de FE. El método tiene en cuenta la evolución de la línea central del vaso en el espacio tridimensional y genera la malla directamente a partir de las imágenes segmentadas, sin necesidad de reconstruir superficies triangulares. Con el fin de reducir la intervención del usuario en el proceso de generación de las mallas, es también objetivo de esta tesis desarrollar un método de segmentación semiautomática de las distintas estructuras de interés. Las principales contribuciones de esta tesis doctoral son: 1. El diseño, implementación y evaluación de un algoritmo de generación de mallas hexaédricas conformadas de la pared y el TIL a partir de los contornos segmentados en imágenes de RM. Se ha llevado a cabo una evaluación de calidad que determine su aplicabilidad a métodos de FE. Los resultados demuestran que el algoritmo desarrollado genera mallas conformadas de alta calidad incluso en la región de la bifurcación, que son adecuadas para su uso en métodos de análisis de FE. 2. El diseño, implementación y evaluación de un método de segmentación automático de las estructuras de interés. La luz arterial se segmenta de manera semiautomática utilizando un software disponible a partir de imágenes de RM con contraste. Los resultados de este proceso sirven de inicialización para la segmentación automática de las caras interna y externa de la pared aórtica utilizando métodos basado en modelos de textura y forma a partir de imágenes de RM sin contraste. Los resultados demuestran que el algoritmo desarrollado proporciona segmentaciones fieles de las distintas estructuras de interés. En conclusión, el trabajo realizado en esta tesis doctoral corrobora las hipótesis de investigación postuladas, y pretende servir como aportación para futuros avances en la generación de modelos físicos de geometrías biológicas. ABSTRACT The frame of this PhD Thesis is the biomedical engineering applied to the treatment of cardiovascular diseases, which cause around 1.9 million deaths per year in the European Union and suppose about 40% of deaths per year. In this context appears the European project SCATh-Smart Catheterization. The main objective of this project is creating a platform which improves the navigation of catheters in aortic catheterization minimizing the use of fluoroscopy. In the framework of this project, the specific field of this PhD Thesis is the diagnosis and modeling of abdominal aortic aneurysm (AAAs) and the intraluminal thrombus (ILT) whenever it is present. Patient-specific physical models built from preoperative imaging are becoming increasingly important in the area of minimally invasive surgery. These models can be employed for different purposes, such as the preoperatory evaluation of anatomic structures or the surgical planning for catheter guidance. In the specific case of AAA diagnosis and treatment, physical models are especially useful for evaluating pressures over vascular structures. There are multiple techniques that require the generation of physical models which represent the target anatomy. Finite element (FE) analysis is one the principal applications for physical models. FE simulations for AAA may be patient-specific and allow modeling biomechanical and physiological variables including those produced by ILT, and also the segmentation of those anatomical structures in preoperative MR images. Applying numeric methods requires the generation of a proper computational mesh. These meshes represent the patient anatomy using a set of polyhedral elements, with hexahedral elements providing better results. In the specific case of vascular structures, generating hexahedral meshes is a challenging task due to the complex 3D branching anatomy. Each patient’s aneurysm is unique, characterized by its location and shape, and must be accurately represented for subsequent analyses to be meaningful. Most AAAs are located in the region where the aorta bifurcates into the iliac arteries and it is necessary to model this bifurcation precisely and reliably. If blood stagnates in the aneurysm and forms an ILT, it exists as a conforming structure with the aortic wall, i.e. the ILT’s outer contour is the same as the wall’s inner contour. Therefore, resulting meshes must also be conforming. The main objective of this PhD Thesis is designing a novel and robust method for generating conforming hexahedral meshes for the aortic wall and the thrombus. These meshes are built using largely high-quality elements, especially at the bifurcation, that are suitable for FE analysis of tissue stresses. The method accounts for the evolution of the vessel’s centerline which may develop outside a single plane, and generates the mesh directly from segmented images without the requirement to reconstruct triangular surfaces. In order to reduce the user intervention in the mesh generation process is also a goal of this PhD. Thesis to develop a semiautomatic segmentation method for the structures of interest. The segmentation is performed from magnetic resonance image (MRI) sequences that have tuned to provide high contrast for the arterial tissue against the surrounding soft tissue, so that we determine the required information reliably. The main contributions of this PhD Thesis are: 1. The design, implementation and evaluation of an algorithm for generating hexahedral conforming meshes of the arterial wall and the ILT from the segmented contours. A quality inspection has been applied to the meshes in order to determine their suitability for FE methods. Results show that the developed algorithm generates high quality conforming hexahedral meshes even at the bifurcation region. Thus, these meshes are suitable for FE analysis. 2. The design, implementation and evaluation of a semiautomatic segmentation method for the structures of interest. The lumen is segmented in a semiautomatic way from contrast filled MRI using an available software. The results obtained from this process are used to initialize the automatic segmentation of the internal and external faces of the aortic wall. These segmentations are performed by methods based on texture and shape models from MRI with no contrast. The results show that the algorithm provides faithful segmentations of the structures of interest requiring minimal user intervention. In conclusion, the work undertaken in this PhD. Thesis verifies the investigation hypotheses. It intends to serve as basis for future physical model generation of proper biological anatomies used by numerical methods.

High-quality real-time temporal segmentation tool for video editing software

The increasing use of video editing software has resulted in a necessity for faster and more efficient editing tools. Here, we propose a lightweight high-quality video indexing tool that is suitable for video editing software.

Temporal segmentation tool for high-quality real time video editing software

The increasing use of video editing software requires faster and more efficient editing tools. As a first step, these tools perform a temporal segmentation in shots that allows a later building of indexes describing the video content. Here, we propose a novel real-time high-quality shot detection strategy, suitable for the last generation of video editing software requiring both low computational cost and high quality results. While abrupt transitions are detected through a very fast pixel-based analysis, gradual transitions are obtained from an efficient edge-based analysis. Both analyses are reinforced with a motion analysis that helps to detect and discard false detections. This motion analysis is carried out exclusively over a reduced set of candidate transitions, thus maintaining the computational requirements demanded by new applications to fulfill user needs.

Fractal analysis of laplacian pyramidal filters applied to segmentation of soil images

The laplacian pyramid is a well-known technique for image processing in which local operators of many scales, but identical shape, serve as the basis functions. The required properties to the pyramidal filter produce a family of filters, which is unipara metrical in the case of the classical problem, when the length of the filter is 5. We pay attention to gaussian and fractal behaviour of these basis functions (or filters), and we determine the gaussian and fractal ranges in the case of single parameter ?. These fractal filters loose less energy in every step of the laplacian pyramid, and we apply this property to get threshold values for segmenting soil images, and then evaluate their porosity. Also, we evaluate our results by comparing them with the Otsu algorithm threshold values, and conclude that our algorithm produce reliable test results.