Estudio y desarrollo de un preproceso basado en la difusión no lineal para la segmentación en imágenes

En esta Tesis Doctoral se aborda la utilización de filtros de difusión no lineal para obtener imágenes constantes a trozos como paso previo al proceso de segmentación. En una primera parte se propone un formulación intrínseca para la ecuación de difusión no lineal que proporcione las condiciones de diseño necesarias sobre los filtros de difusión. A partir del marco teórico propuesto, se proporciona una nueva familia de difusividades; éstas son obtenidas a partir de técnicas de difusión no lineal relacionadas con los procesos de difusión regresivos. El objetivo es descomponer la imagen en regiones cerradas que sean homogéneas en sus niveles de grises sin contornos difusos. Asimismo, se prueba que la función de difusividad propuesta satisface las condiciones de un correcto planteamiento semi-discreto. Esto muestra que mediante el esquema semi-implícito habitualmente utilizado, realmente se hace un proceso de difusión no lineal directa, en lugar de difusión inversa, conectando con proceso de preservación de bordes. Bajo estas condiciones establecidas, se plantea un criterio de parada para el proceso de difusión, para obtener imágenes constantes a trozos con un bajo coste computacional. Una vez aplicado todo el proceso al caso unidimensional, se extienden los resultados teóricos, al caso de imágenes en 2D y 3D. Para el caso en 3D, se detalla el esquema numérico para el problema evolutivo no lineal, con condiciones de contorno Neumann homogéneas. Finalmente, se prueba el filtro propuesto para imágenes reales en 2D y 3D y se ilustran los resultados de la difusividad propuesta como método para obtener imágenes constantes a trozos. En el caso de imágenes 3D, se aborda la problemática del proceso previo a la segmentación del hígado, mediante imágenes reales provenientes de Tomografías Axiales Computarizadas (TAC). En ese caso, se obtienen resultados sobre la estimación de los parámetros de la función de difusividad propuesta. This Ph.D. Thesis deals with the case of using nonlinear diffusion filters to obtain piecewise constant images as a previous process for segmentation techniques. I have first shown an intrinsic formulation for the nonlinear diffusion equation to provide some design conditions on the diffusion filters. According to this theoretical framework, I have proposed a new family of diffusivities; they are obtained from nonlinear diffusion techniques and are related with backward diffusion. Their goal is to split the image in closed contours with a homogenized grey intensity inside and with no blurred edges. It has also proved that the proposed filters satisfy the well-posedness semi-discrete and full discrete scale-space requirements. This shows that by using semi-implicit schemes, a forward nonlinear diffusion equation is solved, instead of a backward nonlinear diffusion equation, connecting with an edgepreserving process. Under the conditions established for the diffusivity and using a stopping criterion I for the diffusion time, I have obtained piecewise constant images with a low computational effort. The whole process in the one-dimensional case is extended to the case where 2D and 3D theoretical results are applied to real images. For 3D, develops in detail the numerical scheme for nonlinear evolutionary problem with homogeneous Neumann boundary conditions. Finally, I have tested the proposed filter with real images for 2D and 3D and I have illustrated the effects of the proposed diffusivity function as a method to get piecewise constant images. For 3D I have developed a preprocess for liver segmentation with real images from CT (Computerized Tomography). In this case, I have obtained results on the estimation of the parameters of the given diffusivity function.

Pulmonary Artery-Vein Segmentation in Real and Synthetic CT Images = Segmentación Arteria-Vena Pulmonares en Imágenes de CT Reales y Sintéticas

La tomografía axial computerizada (TAC) es la modalidad de imagen médica preferente para el estudio de enfermedades pulmonares y el análisis de su vasculatura. La segmentación general de vasos en pulmón ha sido abordada en profundidad a lo largo de los últimos años por la comunidad científica que trabaja en el campo de procesamiento de imagen; sin embargo, la diferenciación entre irrigaciones arterial y venosa es aún un problema abierto. De hecho, la separación automática de arterias y venas está considerado como uno de los grandes retos futuros del procesamiento de imágenes biomédicas. La segmentación arteria-vena (AV) permitiría el estudio de ambas irrigaciones por separado, lo cual tendría importantes consecuencias en diferentes escenarios médicos y múltiples enfermedades pulmonares o estados patológicos. Características como la densidad, geometría, topología y tamaño de los vasos sanguíneos podrían ser analizados en enfermedades que conllevan remodelación de la vasculatura pulmonar, haciendo incluso posible el descubrimiento de nuevos biomarcadores específicos que aún hoy en dípermanecen ocultos. Esta diferenciación entre arterias y venas también podría ayudar a la mejora y el desarrollo de métodos de procesamiento de las distintas estructuras pulmonares. Sin embargo, el estudio del efecto de las enfermedades en los árboles arterial y venoso ha sido inviable hasta ahora a pesar de su indudable utilidad. La extrema complejidad de los árboles vasculares del pulmón hace inabordable una separación manual de ambas estructuras en un tiempo realista, fomentando aún más la necesidad de diseñar herramientas automáticas o semiautomáticas para tal objetivo. Pero la ausencia de casos correctamente segmentados y etiquetados conlleva múltiples limitaciones en el desarrollo de sistemas de separación AV, en los cuales son necesarias imágenes de referencia tanto para entrenar como para validar los algoritmos. Por ello, el diseño de imágenes sintéticas de TAC pulmonar podría superar estas dificultades ofreciendo la posibilidad de acceso a una base de datos de casos pseudoreales bajo un entorno restringido y controlado donde cada parte de la imagen (incluyendo arterias y venas) está unívocamente diferenciada. En esta Tesis Doctoral abordamos ambos problemas, los cuales están fuertemente interrelacionados. Primero se describe el diseño de una estrategia para generar, automáticamente, fantomas computacionales de TAC de pulmón en humanos. Partiendo de conocimientos a priori, tanto biológicos como de características de imagen de CT, acerca de la topología y relación entre las distintas estructuras pulmonares, el sistema desarrollado es capaz de generar vías aéreas, arterias y venas pulmonares sintéticas usando métodos de crecimiento iterativo, que posteriormente se unen para formar un pulmón simulado con características realistas. Estos casos sintéticos, junto a imágenes reales de TAC sin contraste, han sido usados en el desarrollo de un método completamente automático de segmentación/separación AV. La estrategia comprende una primera extracción genérica de vasos pulmonares usando partículas espacio-escala, y una posterior clasificación AV de tales partículas mediante el uso de Graph-Cuts (GC) basados en la similitud con arteria o vena (obtenida con algoritmos de aprendizaje automático) y la inclusión de información de conectividad entre partículas. La validación de los fantomas pulmonares se ha llevado a cabo mediante inspección visual y medidas cuantitativas relacionadas con las distribuciones de intensidad, dispersión de estructuras y relación entre arterias y vías aéreas, los cuales muestran una buena correspondencia entre los pulmones reales y los generados sintéticamente. La evaluación del algoritmo de segmentación AV está basada en distintas estrategias de comprobación de la exactitud en la clasificación de vasos, las cuales revelan una adecuada diferenciación entre arterias y venas tanto en los casos reales como en los sintéticos, abriendo así un amplio abanico de posibilidades en el estudio clínico de enfermedades cardiopulmonares y en el desarrollo de metodologías y nuevos algoritmos para el análisis de imágenes pulmonares. ABSTRACT Computed tomography (CT) is the reference image modality for the study of lung diseases and pulmonary vasculature. Lung vessel segmentation has been widely explored by the biomedical image processing community, however, differentiation of arterial from venous irrigations is still an open problem. Indeed, automatic separation of arterial and venous trees has been considered during last years as one of the main future challenges in the field. Artery-Vein (AV) segmentation would be useful in different medical scenarios and multiple pulmonary diseases or pathological states, allowing the study of arterial and venous irrigations separately. Features such as density, geometry, topology and size of vessels could be analyzed in diseases that imply vasculature remodeling, making even possible the discovery of new specific biomarkers that remain hidden nowadays. Differentiation between arteries and veins could also enhance or improve methods processing pulmonary structures. Nevertheless, AV segmentation has been unfeasible until now in clinical routine despite its objective usefulness. The huge complexity of pulmonary vascular trees makes a manual segmentation of both structures unfeasible in realistic time, encouraging the design of automatic or semiautomatic tools to perform the task. However, this lack of proper labeled cases seriously limits in the development of AV segmentation systems, where reference standards are necessary in both algorithm training and validation stages. For that reason, the design of synthetic CT images of the lung could overcome these difficulties by providing a database of pseudorealistic cases in a constrained and controlled scenario where each part of the image (including arteries and veins) is differentiated unequivocally. In this Ph.D. Thesis we address both interrelated problems. First, the design of a complete framework to automatically generate computational CT phantoms of the human lung is described. Starting from biological and imagebased knowledge about the topology and relationships between structures, the system is able to generate synthetic pulmonary arteries, veins, and airways using iterative growth methods that can be merged into a final simulated lung with realistic features. These synthetic cases, together with labeled real CT datasets, have been used as reference for the development of a fully automatic pulmonary AV segmentation/separation method. The approach comprises a vessel extraction stage using scale-space particles and their posterior artery-vein classification using Graph-Cuts (GC) based on arterial/venous similarity scores obtained with a Machine Learning (ML) pre-classification step and particle connectivity information. Validation of pulmonary phantoms from visual examination and quantitative measurements of intensity distributions, dispersion of structures and relationships between pulmonary air and blood flow systems, show good correspondence between real and synthetic lungs. The evaluation of the Artery-Vein (AV) segmentation algorithm, based on different strategies to assess the accuracy of vessel particles classification, reveal accurate differentiation between arteries and vein in both real and synthetic cases that open a huge range of possibilities in the clinical study of cardiopulmonary diseases and the development of methodological approaches for the analysis of pulmonary images.

Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images

The image by Computed Tomography is a non-invasive alternative for observing soil structures, mainly pore space. The pore space correspond in soil data to empty or free space in the sense that no material is present there but only fluids, the fluid transport depend of pore spaces in soil, for this reason is important identify the regions that correspond to pore zones. In this paper we present a methodology in order to detect pore space and solid soil based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. In order to find pixels groups with a similar gray level intensity, or more or less homogeneous groups, a novel image sub-segmentation based on a Possibilistic Fuzzy c-Means (PFCM) clustering algorithm was used. The Artificial Neural Networks (ANNs) are very efficient for demanding large scale and generic pattern recognition applications for this reason finally a classifier based on artificial neural network is applied in order to classify soil images in two classes, pore space and solid soil respectively.

Plasma kinetics issues in an ESA study for a plasma laboratory in space

A study supported by the European Space Agency (ESA), in the context of its General Studies Programme, performed an investigation of the possible use of space for studies in pure and applied plasma physics, in areas not traditionally covered by ‘space plasma physics’. A set of experiments have been identified that can potentially provide access to new phenomena and to allow advances in several fields of plasma science. These experiments concern phenomena on a spatial scale (101–104 m) intermediate between what is achievable on the ground and the usual solar system plasma observations. Detailed feasibility studies have been performed for three experiments: active magnetic experiments, largescale discharges and long tether–plasma interactions. The perspectives opened by these experiments are discussed for magnetic reconnection, instabilities, MHD turbulence, atomic excited states kinetics, weakly ionized plasmas,plasma diagnostics, artificial auroras and atmospheric studies. The discussion is also supported by results of numerical simulations and estimates.

Space-time analysis of the urban-rural grafient in the metropolitan area of Madrid and ist surroundings -Guadalajara and Toledo.

According to UN provisions in the period from 2007 to 2050 world population will grow up to 9200 million people. In fact, for the first time in history, in the year 2008 world urban population became higher than rural population. The increase of urban areas and their transport infrastructures has influenced agricultural land use due to their irreversible change, especially when they remain as periurban vacant land, losing their character and identity. In the Europe of the nineties, the traditional urban-rural gradient, characterized by a neat contact between both land types, has become so complex that it has change to a gradient in which it is difficult to separate urban and rural land uses. [Antrop 2004]. A literature review has been made on methodologies used for the urban-rural gradient analysis. One of these methodologies was selected that integrates ecological characterization based on the use of spatial metrics and geographical characterization based on spatial components. Cartographical sources used were Corine Land Cover at 1: 100000 scale and the Spanish Land Use Information System at 1:25000 scale. Urban-rural gradient paradigm is an analysis methodology, coming from landscape ecology, which enables to investigate how urbanization provokes changes in ecological patterns and processes into landscape. [Hahs and McDonnell 2006].The present research adapt this methodology to study the urban-rural gradient in the outskirts of Madrid, Toledo and Guadalajara. Both scales (1:25000 and 1:100000) were simultaneously used to reach the next objectives: 1) Analysis of landscape pattern dynamics in relation to distance to the town centre and major infrastructures. 2) Analysis of landscape pattern dynamics in the fringe of protected areas. The paper presents a new approach to the urban-rural relationship which allows better planning and management of urban áreas.

The ESA "Plasma Laboratory in Space" study

The European Space Agency has initiated, in the context of its General Studies Programme, a study of the possible use of space for studies in pure and applied plasma physics, in areas not traditionally covered by “space plasma physics”. A team of experts has been set-up to review a broad range of area including industrial plasma physics and pure plasma physics, astrophysical and solar-terrestrial areas. A set of experiments have been identified that can potentially provide access to new phenomena and to allow advances in several fields of plasma science. These experiments concern phenomena on spatial scale (102 to104 m) intermediate between what is achievable on ground experiment and usual solar system plasma observations.

COST Action Urban Agriculture Europe Training School «Urban agriculture inside the city:alternatives for vacant lots and public space»

The third Training School of the Action took place in Vitoria-Gasteiz (Basque country, Spain) from 24th to 26th September 2014. Vitoria-Gateiz has experimented an important urban outgrowth in the last decade, mainly through the planning and development of two new neighborhoods, Zabalgana and Salburúa, situated at the eastern and western border of the city, by the Greenbelt. These new development are well-equipped and designed according to sustainability principles. Nevertheless, among the main problems they present is their over-dimensioned public space, which creates some areas lacking enough density and mix of uses. On the other hand it is very expensive for the municipality to maintain these public space with the high Vitorian urban standards for public space. The proposed solution for this problem is a strategy of "re-densification" through the insertion of new uses The debate has arisen about which are the most adequate uses to insert in order to get an increasing of urban vitality, specially considering that housing has reached its peak and that Vitoria-Gasteiz is well served with social and sport amenities. The main goal of the TS was to offer an opportunity for the reflection about how urban agriculture might be an optimal alternative for the re-qualifying of this over-dimensioned public space in the new neighbourhoods, especially considering it synergic potential as a tool for production, leisure and landscaping, including the possibility of energy crops within the limits of urban space. Continuity with rural and natural surrounding area through alternatives for urban fringe at the small scale is a relevant issue to be considered as well within the reflection. Taking Zabalgana neighbourhood as a practical field for experiment, the Training School is conceived as a practical and intensive design charrette to be held during a whole day after two days of local knowledge-deepening through field visits and presentations.

Outraged spatialities: the production of public scale in the #spanishrevolution

The spatial processes deployed by the 15-M movement in Spain include elements of social change that exceed the limits of conventional politics. Located at a liminal level, these processes operate in the often unnoticed realm of the micro-politics of urban everyday life and the regimes of place that regulate it, providing new criteria for understanding sociospatial and urban phenomena. This article shows how public space, its representations and the spatialities associated with them have served as a support for, have determined and, ultimately, have been reshaped and transformed by the Spanish “indignados” (outraged), in particular in the city and the metropolitan area of Madrid. Drawing on a series of theoretical approaches to the articulation of recent revolts, the deployment of a prefigurative politics and the occupation of public space, I will give an experience-based account of the spatial constitution and effects of these connections in and around Madrid’s Puerta del Sol. As a whole, the indignados’ occupations and actions provide urban theory with conceptual and practical tools to imagine alternative forms of collective commitment in the production of spaces of hope for social progress and generalized self-management.