13 resultados para object modeling from images

em Universidad Politécnica de Madrid


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La embriogénesis es el proceso mediante el cual una célula se convierte en un ser un vivo. A lo largo de diferentes etapas de desarrollo, la población de células va proliferando a la vez que el embrión va tomando forma y se configura. Esto es posible gracias a la acción de varios procesos genéticos, bioquímicos y mecánicos que interaccionan y se regulan entre ellos formando un sistema complejo que se organiza a diferentes escalas espaciales y temporales. Este proceso ocurre de manera robusta y reproducible, pero también con cierta variabilidad que permite la diversidad de individuos de una misma especie. La aparición de la microscopía de fluorescencia, posible gracias a proteínas fluorescentes que pueden ser adheridas a las cadenas de expresión de las células, y los avances en la física óptica de los microscopios han permitido observar este proceso de embriogénesis in-vivo y generar secuencias de imágenes tridimensionales de alta resolución espacio-temporal. Estas imágenes permiten el estudio de los procesos de desarrollo embrionario con técnicas de análisis de imagen y de datos, reconstruyendo dichos procesos para crear la representación de un embrión digital. Una de las más actuales problemáticas en este campo es entender los procesos mecánicos, de manera aislada y en interacción con otros factores como la expresión genética, para que el embrión se desarrolle. Debido a la complejidad de estos procesos, estos problemas se afrontan mediante diferentes técnicas y escalas específicas donde, a través de experimentos, pueden hacerse y confrontarse hipótesis, obteniendo conclusiones sobre el funcionamiento de los mecanismos estudiados. Esta tesis doctoral se ha enfocado sobre esta problemática intentando mejorar las metodologías del estado del arte y con un objetivo específico: estudiar patrones de deformación que emergen del movimiento organizado de las células durante diferentes estados del desarrollo del embrión, de manera global o en tejidos concretos. Estudios se han centrado en la mecánica en relación con procesos de señalización o interacciones a nivel celular o de tejido. En este trabajo, se propone un esquema para generalizar el estudio del movimiento y las interacciones mecánicas que se desprenden del mismo a diferentes escalas espaciales y temporales. Esto permitiría no sólo estudios locales, si no estudios sistemáticos de las escalas de interacción mecánica dentro de un embrión. Por tanto, el esquema propuesto obvia las causas de generación de movimiento (fuerzas) y se centra en la cuantificación de la cinemática (deformación y esfuerzos) a partir de imágenes de forma no invasiva. Hoy en día las dificultades experimentales y metodológicas y la complejidad de los sistemas biológicos impiden una descripción mecánica completa de manera sistemática. Sin embargo, patrones de deformación muestran el resultado de diferentes factores mecánicos en interacción con otros elementos dando lugar a una organización mecánica, necesaria para el desarrollo, que puede ser cuantificado a partir de la metodología propuesta en esta tesis. La metodología asume un medio continuo descrito de forma Lagrangiana (en función de las trayectorias de puntos materiales que se mueven en el sistema en lugar de puntos espaciales) de la dinámica del movimiento, estimado a partir de las imágenes mediante métodos de seguimiento de células o de técnicas de registro de imagen. Gracias a este esquema es posible describir la deformación instantánea y acumulada respecto a un estado inicial para cualquier dominio del embrión. La aplicación de esta metodología a imágenes 3D + t del pez zebra sirvió para desvelar estructuras mecánicas que tienden a estabilizarse a lo largo del tiempo en dicho embrión, y que se organizan a una escala semejante al del mapa de diferenciación celular y con indicios de correlación con patrones de expresión genética. También se aplicó la metodología al estudio del tejido amnioserosa de la Drosophila (mosca de la fruta) durante el cierre dorsal, obteniendo indicios de un acoplamiento entre escalas subcelulares, celulares y supracelulares, que genera patrones complejos en respuesta a la fuerza generada por los esqueletos de acto-myosina. En definitiva, esta tesis doctoral propone una estrategia novedosa de análisis de la dinámica celular multi-escala que permite cuantificar patrones de manera inmediata y que además ofrece una representación que reconstruye la evolución de los procesos como los ven las células, en lugar de como son observados desde el microscopio. Esta metodología por tanto permite nuevas formas de análisis y comparación de embriones y tejidos durante la embriogénesis a partir de imágenes in-vivo. ABSTRACT The embryogenesis is the process from which a single cell turns into a living organism. Through several stages of development, the cell population proliferates at the same time the embryo shapes and the organs develop gaining their functionality. This is possible through genetic, biochemical and mechanical factors that are involved in a complex interaction of processes organized in different levels and in different spatio-temporal scales. The embryogenesis, through this complexity, develops in a robust and reproducible way, but allowing variability that makes possible the diversity of living specimens. The advances in physics of microscopes and the appearance of fluorescent proteins that can be attached to expression chains, reporting about structural and functional elements of the cell, have enabled for the in-vivo observation of embryogenesis. The imaging process results in sequences of high spatio-temporal resolution 3D+time data of the embryogenesis as a digital representation of the embryos that can be further analyzed, provided new image processing and data analysis techniques are developed. One of the most relevant and challenging lines of research in the field is the quantification of the mechanical factors and processes involved in the shaping process of the embryo and their interactions with other embryogenesis factors such as genetics. Due to the complexity of the processes, studies have focused on specific problems and scales controlled in the experiments, posing and testing hypothesis to gain new biological insight. However, methodologies are often difficult to be exported to study other biological phenomena or specimens. This PhD Thesis is framed within this paradigm of research and tries to propose a systematic methodology to quantify the emergent deformation patterns from the motion estimated in in-vivo images of embryogenesis. Thanks to this strategy it would be possible to quantify not only local mechanisms, but to discover and characterize the scales of mechanical organization within the embryo. The framework focuses on the quantification of the motion kinematics (deformation and strains), neglecting the causes of the motion (forces), from images in a non-invasive way. Experimental and methodological challenges hamper the quantification of exerted forces and the mechanical properties of tissues. However, a descriptive framework of deformation patterns provides valuable insight about the organization and scales of the mechanical interactions, along the embryo development. Such a characterization would help to improve mechanical models and progressively understand the complexity of embryogenesis. This framework relies on a Lagrangian representation of the cell dynamics system based on the trajectories of points moving along the deformation. This approach of analysis enables the reconstruction of the mechanical patterning as experienced by the cells and tissues. Thus, we can build temporal profiles of deformation along stages of development, comprising both the instantaneous events and the cumulative deformation history. The application of this framework to 3D + time data of zebrafish embryogenesis allowed us to discover mechanical profiles that stabilized through time forming structures that organize in a scale comparable to the map of cell differentiation (fate map), and also suggesting correlation with genetic patterns. The framework was also applied to the analysis of the amnioserosa tissue in the drosophila’s dorsal closure, revealing that the oscillatory contraction triggered by the acto-myosin network organized complexly coupling different scales: local force generation foci, cellular morphology control mechanisms and tissue geometrical constraints. In summary, this PhD Thesis proposes a theoretical framework for the analysis of multi-scale cell dynamics that enables to quantify automatically mechanical patterns and also offers a new representation of the embryo dynamics as experienced by cells instead of how the microscope captures instantaneously the processes. Therefore, this framework enables for new strategies of quantitative analysis and comparison between embryos and tissues during embryogenesis from in-vivo images.

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Evaluating and measuring the pedagogical quality of Learning Objects is essential for achieving a successful web-based education. On one hand, teachers need some assurance of quality of the teaching resources before making them part of the curriculum. On the other hand, Learning Object Repositories need to include quality information into the ranking metrics used by the search engines in order to save users time when searching. For these reasons, several models such as LORI (Learning Object Review Instrument) have been proposed to evaluate Learning Object quality from a pedagogical perspective. However, no much effort has been put in defining and evaluating quality metrics based on those models. This paper proposes and evaluates a set of pedagogical quality metrics based on LORI. The work exposed in this paper shows that these metrics can be effectively and reliably used to provide quality-based sorting of search results. Besides, it strongly evidences that the evaluation of Learning Objects from a pedagogical perspective can notably enhance Learning Object search if suitable evaluations models and quality metrics are used. An evaluation of the LORI model is also described. Finally, all the presented metrics are compared and a discussion on their weaknesses and strengths is provided.

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Vision-based object detection from a moving platform becomes particularly challenging in the field of advanced driver assistance systems (ADAS). In this context, onboard vision-based vehicle verification strategies become critical, facing challenges derived from the variability of vehicles appearance, illumination, and vehicle speed. In this paper, an optimized HOG configuration for onboard vehicle verification is proposed which not only considers its spatial and orientation resolution, but descriptor processing strategies and classification. An in-depth analysis of the optimal settings for HOG for onboard vehicle verification is presented, in the context of SVM classification with different kernels. In contrast to many existing approaches, the evaluation is realized in a public and heterogeneous database of vehicle and non-vehicle images in different areas of the road, rendering excellent verification rates that outperform other similar approaches in the literature.

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La imagen fotográfica es un bloque espacio tiempo congelado, un fragmento referido al antes y el después de algo. Al contemplar una fotografía de un interior doméstico, descubrimos un entretejido sutil entre el habitante y su hábitat. Somos capaces de recaer en más detalles de los que el ojo humano puede apreciar en su visión cotidiana, siempre ligada al devenir espacio temporal. El acto de fotografiar el hogar, de congelar unidades habitadas infinitesimales, se revela como una manifestación radical del modo unipersonal de habitar de cada fotógrafo, profesional o aficionado, y por extensión, dado que hoy todos somos fotógrafos; de cada habitante. Por un lado, la fotografía se piensa aquí como herramienta, capaz de desvelar, de poner en el mundo, los elementos, percepciones y acontecimientos, que subyacen imbricados en la construcción del hogar. Por otro, la imagen se entiende como medio de expresión y de comunicación, como el lenguaje universal de nuestro tiempo, por todos conocido y utilizado. En este momento de interconexión máxima, entre redes, datos y capas de cognición, de velocidad y aceleración, esta tesis doctoral se plantea como una vuelta a la reflexión, a la contemplación del objeto imagen, desde la certeza de que para que ésta hable hay que darle tiempo. Así, la investigación hay que entenderla desde una base ontológica y fenomenológica; desde la experiencia del ser que habita un entorno concreto y determinado. Se enmarca en el actual entorno socio cultural de occidente, se busca desvelar el significado y modo de habitar del habitante común, poniendo de manifiesto aquello que acontece para que una casa cualquiera, de un habitante cualquiera, devenga hogar. Los primeros indicios sobre el tema surgirán del análisis y la reinterpretación hermenéutica de un atlas de imágenes del habitar: cuerpo de imágenes reunido a partir de series fotográficas de hogares, de habitantes anónimos, puestos a luz por la mirada de un grupo de artistas. Posteriormente, ponemos a prueba el conocimiento adquirido en el análisis anterior, mientras que expandimos la investigación hacia el sentir del habitante común, mediante la realización de tres experimentos participativos, o estudios de campo cualitativos. Los resultados, de ambos grupos de casos de estudio, se compilan, organizan y estructuran en una taxonomía del habitar. Esta taxonomía está compuesta por cuarenta y siete parámetros, que explicitan la naturaleza compleja del hogar del siglo XXI. Este hogar es entendido como un constructo personal de cada habitante, un proceso que acontece en el tiempo y en el espacio, y que entraña la propia construcción del habitante. Esta extensa taxonomía se organiza según tres ámbitos del ser humano, en el primero se expresan los factores relacionados con el modo de "estar físicamente" en el hogar, incluyendo: al propio habitante, la casa como espacio arquitectónico y como materialidad: objetos, muebles, iconos y símbolos que pueblan el hogar. En segundo lugar, se manifiestan los parámetros relacionados con el modo de “percibir”: por un lado, aquello que se deriva de lo que se ve, y por otro, lo que se deriva de aquello que no se ve, pero se siente. En tercer lugar, se explicitan los factores relativos al habitante que "crea/juega" su hogar, quién por un lado, es en el mundo actuando, y que por otro, siente el mundo construyéndolo mediante una serie de relaciones que establece con él. Finalmente, la investigación intenta revelar las sinergias, conexiones y relaciones, entre todos estos elementos extraídos del sentir del habitante común, y que fueron inducidos mediante el análisis y reinterpretación de los casos de estudio, poniendo de manifiesto un orden de cosas en el habitar occidental contemporáneo. ABSTRACT The photographic image is a frozen space time block, a fragment referred to a something before and after. When we stare at the photography of domestic interiors we discover a subtle interweaving between the inhabitant and her habitat. We are able to acknowledge infinite more details than the human eye, in its continuous quotidian vision always linked to the space time progression, appreciates. The act of photographing the home, of freezing infinitesimal inhabited units, reveals as a radical statement of the concept of inhabiting for each photographer, professional or amateur, and by extension, as we today all are photographers, for each inhabitant. On the one hand, photography is here conceived as a tool that is capable of revealing, "of placing in the world" the elements, perceptions and happenings that underlie imbricated in the construction of a home. On the other hand, image is thought as an expression and communication media, as the universal language of our time (as far as it is known and used by all of us). In this precise moment of maximum interconnection between networks, data and cognitive layers; of speed and acceleration, this PhD Dissertation is conceived as a return to reflection; to the contemplation of object image, from the certainty of its need of time for talking. Therefore, this research from an ontological and phenomenological base; from the experience of the self who inhabits a determined and concrete environment, that of the western countries at the present, pursues to unveil the meaning and way of inhabiting of a common dweller and manifest what conforms the transformation of any house, of any inhabitant into a home. The first clues will arise from the analysis and hermeneutical reinterpretation of the Atlas of inhabiting; an assembled body of images of anonymous inhabitants houses, brought into life through a group of artist´s glance. Afterwards, we will test the analysis´s acquired knowledge, while extending the investigation to the feel of the common inhabitant (and no longer the expert^ artist) through the execution of three participative experiments conceived as qualitative field works. The results of both case study groups, will be compiled, organized and structured in a taxonomy of the inhabiting. This taxonomy is composed by forty seven parameters that explicitly state the complex nature of the XXI century home, regarded as a personal construct of every single inhabitant, as a process that happens through time and space and that entails the construction of the inhabitant. This wide taxonomy is organized regarding three spheres of the human being, In first place, those elements related to the way of “physically being” at home are expressed, including: the inhabitant its self, the house as architectural space and as materiality: objects, furniture, icons and symbols that fill the home. In second place, parameters related to the way of “perceiving“ are manifested; on the one hand, those that derive from what we see; on the other hand, those that derive from what we do not see, but feel. In third place, those factors deriving from the inhabitant as a home "creator/player" who is acting in the world and feeling the world while constructing it through a myriad of relationships he establishes with it. Finally, the investigation tries to reveal the synergies, connections and relations between all these elements extracted from the feelings of the common inhabitant, induced through the analysis and reinterpretation of the case studies, and therefore exposing a state of things belonging to western world at present.

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Managing large medical image collections is an increasingly demanding important issue in many hospitals and other medical settings. A huge amount of this information is daily generated, which requires robust and agile systems. In this paper we present a distributed multi-agent system capable of managing very large medical image datasets. In this approach, agents extract low-level information from images and store them in a data structure implemented in a relational database. The data structure can also store semantic information related to images and particular regions. A distinctive aspect of our work is that a single image can be divided so that the resultant sub-images can be stored and managed separately by different agents to improve performance in data accessing and processing. The system also offers the possibility of applying some region-based operations and filters on images, facilitating image classification. These operations can be performed directly on data structures in the database.

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This paper aims to analyze the different adjustment methods commonly used to characterize indirect metrology circular features: least square circle, minimum zone circle, maximum inscribed circle and minimum circumscribed circle. The analysis was performed from images obtained by digital optical machines. The calculation algorithms, self-developed, have been implemented in Matlab® and take into consideration as study variables: the amplitude of angular sector of the circular feature, its nominal radio and the magnification used by the optical machine. Under different conditions, it was determined the radius and circularity error of different circular standards. The comparison of the results, obtained by the different methods of adjustments used, with certified values for the standards, has allowed us to determine the accuracy of each method and its scope.

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Real time Tritium concentrations in air in two chemical forms, HT and HTO, coming from an ITER-like fusion reactor as source were coupled the European Centre Medium Range Weather Forecast (ECMWF) numerical model with the Lagrangian Atmospheric-particle dispersion model FLEXPART. This tool was analyzed in nominal tritium discharge operational reference and selected incidental conditions affecting the Western Mediterranean Basin during 45 days during summer 2010 together with surface “wind observations” or weather data based in real hourly observations of wind direction and velocity providing a real approximation of the tritium behavior after the release to the atmosphere from a fusion reactor. From comparison with NORMTRI - a code using climatologically sequences as input - over the same area, the real time results have demonstrated an apparent overestimation of the corresponding climatologically sequence of Tritium concentrations in air outputs, at several distances from the reactor. For this purpose two development patterns were established. The first one was following a cyclonic circulation over the Mediterranean Sea and the second one was based on the plume delivered over the Interior of the Iberian Peninsula and Continental Europe by another stabilized circulation corresponding to a High Pressure System. One of the important remaining activities defined then, was the qualification tool. In order to validate the model of ECMWF/FLEXPART we have developed of a new complete data base of tritium concentrations for the months from November 2010 to March 2011 and defined a new set of four patterns of HT transport in air, in each case using real boundary conditions: stationary to the North, stationary to the South, fast and very fast displacement. Finally the differences corresponding to those four early patterns (each one in assessments 1 and 2) has been analyzed in terms of the tuning of safety related issues and taking into account the primary phase o- - f tritium modeling, from its discharge to the atmosphere to the deposition on the ground, will affect to the complete tritium environmental pathway altering the chronic dose by absorption, reemission and ingestion both from elemental tritium, HT and from the oxide of tritium, HTO

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This paper discusses the target localization problem in wireless visual sensor networks. Additive noises and measurement errors will affect the accuracy of target localization when the visual nodes are equipped with low-resolution cameras. In the goal of improving the accuracy of target localization without prior knowledge of the target, each node extracts multiple feature points from images to represent the target at the sensor node level. A statistical method is presented to match the most correlated feature point pair for merging the position information of different sensor nodes at the base station. Besides, in the case that more than one target exists in the field of interest, a scheme for locating multiple targets is provided. Simulation results show that, our proposed method has desirable performance in improving the accuracy of locating single target or multiple targets. Results also show that the proposed method has a better trade-off between camera node usage and localization accuracy.

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The application of thematic maps obtained through the classification of remote images needs the obtained products with an optimal accuracy. The registered images from the airplanes display a very satisfactory spatial resolution, but the classical methods of thematic classification not always give better results than when the registered data from satellite are used. In order to improve these results of classification, in this work, the LIDAR sensor data from first return (Light Detection And Ranging) registered simultaneously with the spectral sensor data from airborne are jointly used. The final results of the thematic classification of the scene object of study have been obtained, quantified and discussed with and without LIDAR data, after applying different methods: Maximum Likehood Classification, Support Vector Machine with four different functions kernel and Isodata clustering algorithm (ML, SVM-L, SVM-P, SVM-RBF, SVM-S, Isodata). The best results are obtained for SVM with Sigmoide kernel. These allow the correlation with others different physical parameters with great interest like Manning hydraulic coefficient, for their incorporation in a GIS and their application in hydraulic modeling.

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Once admitted the advantages of object-based classification compared to pixel-based classification; the need of simple and affordable methods to define and characterize objects to be classified, appears. This paper presents a new methodology for the identification and characterization of objects at different scales, through the integration of spectral information provided by the multispectral image, and textural information from the corresponding panchromatic image. In this way, it has defined a set of objects that yields a simplified representation of the information contained in the two source images. These objects can be characterized by different attributes that allow discriminating between different spectral&textural patterns. This methodology facilitates information processing, from a conceptual and computational point of view. Thus the vectors of attributes defined can be used directly as training pattern input for certain classifiers, as for example artificial neural networks. Growing Cell Structures have been used to classify the merged information.

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Along the recent years, several moving object detection strategies by non-parametric background-foreground modeling have been proposed. To combine both models and to obtain the probability of a pixel to belong to the foreground, these strategies make use of Bayesian classifiers. However, these classifiers do not allow to take advantage of additional prior information at different pixels. So, we propose a novel and efficient alternative Bayesian classifier that is suitable for this kind of strategies and that allows the use of whatever prior information. Additionally, we present an effective method to dynamically estimate prior probability from the result of a particle filter-based tracking strategy.

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Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

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Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.