Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure (versión actualizada)

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.

Optimizing panchromatic image change detection based on change index multiband image analysis

This work proposes an optimization of a semi-supervised Change Detection methodology based on a combination of Change Indices (CI) derived from an image multitemporal data set. For this purpose, SPOT 5 Panchromatic images with 2.5 m spatial resolution have been used, from which three Change Indices have been calculated. Two of them are usually known indices; however the third one has been derived considering the Kullbak-Leibler divergence. Then, these three indices have been combined forming a multiband image that has been used in as input for a Support Vector Machine (SVM) classifier where four different discriminant functions have been tested in order to differentiate between change and no_change categories. The performance of the suggested procedure has been assessed applying different quality measures, reaching in each case highly satisfactory values. These results have demonstrated that the simultaneous combination of basic change indices with others more sophisticated like the Kullback-Leibler distance, and the application of non-parametric discriminant functions like those employees in the SVM method, allows solving efficiently a change detection problem.

Methodology of image analysis for study of the vertisols moisture content

The main problem to study vertical drainage from the moisture distribution, on a vertisol profile, is searching for suitable methods using these procedures. Our aim was to design a digital image processing methodology and its analysis to characterize the moisture content distribution of a vertisol profile. In this research, twelve soil pits were excavated on a ba re Mazic Pellic Vertisols ix of them in May 13/2011 and the rest in May 19 /2011 after a moderate rainfall event. Digital RGB images were taken from each vertisol pit using a Kodak? camera selecting a size of 1600x945 pixels. Each soil image was processed to homogenized brightness and then a spatial filter with several window sizes was applied to select the optimum one. The RGB image obtained were divided in each matrix color selecting the best thresholds for each one, maximum and minimum, to be applied and get a digital binary pattern. This one was analyzed by estimating two fractal scaling exponents box counting dimension D BC) and interface fractal dimension (D) In addition, three pre-fractal scaling coefficients were determinate at maximum resolution: total number of boxes intercepting the foreground pattern (A), fractal lacunarity (?1) and Shannon entropy S1). For all the images processed the spatial filter 9x9 was the optimum based on entropy, cluster and histogram criteria. Thresholds for each color were selected based on bimodal histograms.

Geometrical characterization of undisturbed soil samples using X-ray computed tomography image analysis. Effect of soil management on soil structure

El estudio de la estructura del suelo es de vital importancia en diferentes campos de la ciencia y la tecnología. La estructura del suelo controla procesos físicos y biológicos importantes en los sistemas suelo-planta-microorganismos. Estos procesos están dominados por la geometría de la estructura del suelo, y una caracterización cuantitativa de la heterogeneidad de la geometría del espacio poroso es beneficiosa para la predicción de propiedades físicas del suelo. La tecnología de la tomografía computerizada de rayos-X (CT) nos permite obtener imágenes digitales tridimensionales del interior de una muestra de suelo, proporcionando información de la geometría de los poros del suelo y permitiendo el estudio de los poros sin destruir las muestras. Las técnicas de la geometría fractal y de la morfología matemática se han propuesto como una poderosa herramienta para analizar y cuantificar características geométricas. Las dimensiones fractales del espacio poroso, de la interfaz poro-sólido y de la distribución de tamaños de poros son indicadores de la complejidad de la estructura del suelo. Los funcionales de Minkowski y las funciones morfológicas proporcionan medios para medir características geométricas fundamentales de los objetos geométricos tridimensionales. Esto es, volumen, superficie, curvatura media de la superficie y conectividad. Las características del suelo como la distribución de tamaños de poros, el volumen del espacio poroso o la superficie poro-solido pueden ser alteradas por diferentes practicas de manejo de suelo. En este trabajo analizamos imágenes tomográficas de muestras de suelo de dos zonas cercanas con practicas de manejo diferentes. Obtenemos un conjunto de medidas geométricas, para evaluar y cuantificar posibles diferencias que el laboreo pueda haber causado en el suelo. ABSTRACT The study of soil structure is of vital importance in different fields of science and technology. Soil structure controls important physical and biological processes in soil-plant-microbial systems. Those processes are dominated by the geometry of soil pore structure, and a quantitative characterization of the spatial heterogeneity of the pore space geometry is beneficial for prediction of soil physical properties. The technology of X-ray computed tomography (CT) allows us to obtain three-dimensional digital images of the inside of a soil sample providing information on soil pore geometry and enabling the study of the pores without disturbing the samples. Fractal geometry and mathematical morphological techniques have been proposed as powerful tools to analyze and quantify geometrical features. Fractal dimensions of pore space, pore-solid interface and pore size distribution are indicators of soil structure complexity. Minkowski functionals and morphological functions provide means to measure fundamental geometrical features of three-dimensional geometrical objects, that is, volume, boundary surface, mean boundary surface curvature, and connectivity. Soil features such as pore-size distribution, pore space volume or pore-solid surface can be altered by different soil management practices. In this work we analyze CT images of soil samples from two nearby areas with contrasting management practices. We performed a set of geometrical measures, some of them from mathematical morphology, to assess and quantify any possible difference that tillage may have caused on the soil.

Classifying image analysis techniques from their output

In this paper we discuss some main image processing techniques in order to propose a classification based upon the output these methods provide. Because despite a particular image analysis technique can be supervised or unsupervised, and can allow or not the existence of fuzzy information at some stage, each technique has been usually designed to focus on a specific objective, and their outputs are in fact different according to each objective. Thus, they are in fact different methods. But due to the essential relationship between them they are quite often confused. In particular, this paper pursues a clarification of the differences between image segmentation and edge detection, among other image processing techniques.

Core scan image analysis corrected for cracks, ODP Hole 154-926C

Drill cores are essential for the study of deep-sea sediments and on-land sites because often no suitable outcrop is available or accessible. These cores form the backbone of stratigraphical studies using and combining various dating techniques. Cyclostratigraphy is usually based on fast and inexpensive measurements of physical sediment properties. One indirect but highly valuable proxy for reconstructing the sediment composition and variability is sediment color. However, cracks and other disturbances in sediment cores may dramatically influence the quality of color data retrieved either directly from photospectrometry or derived from core image analysis. Here we present simple but powerful algorithms to extract color data from core images, and focus on routines to exclude cracks from these images. Results are discussed using the example of an ODP core from the Ceara Rise in the Central Atlantic. The crack correction approach presented highly improves the quality of color data and allows the easy incorporation of cracked cores into studies based on core images. This facilitates the quick and inexpensive generation of large color datasets directly from quantified core images, for cyclostratigraphy and other purposes.

Methods for automatic error analysis of numerical algorithms /

Vita.

Video image analysis of aggregates : a study report on evaluation of coarse aggregate shape and size properties using video imaging /

"Report no. FHWA-IL-UI-278"--Technical report documentation page.

Quantification of axonal loss in Alzheimer’s disease: an image analysis study

The density of axons in the optic nerve, olfactory tract and corpus callosum was quantified in non-demented elderly subjects and in Alzheimer’s disease (AD) using an image analysis system. In each fibre tract, there was significant reduction in the density of axons in AD compared with non-demented subjects, the greatest reductions being observed in the olfactory tract and corpus callosum. Axonal loss in the optic nerve and olfactory tract was mainly of axons with smaller myelinated cross-sectional areas. In the corpus callosum, a reduction in the number of ‘thin’ and ‘thick’ fibres was observed in AD, but there was a proportionally greater loss of the ‘thick’ fibres. The data suggest significant degeneration of white matter fibre tracts in AD involving the smaller axons in the two sensory nerves and both large and small axons in the corpus callosum. Loss of axons in AD could reflect an associated white matter disorder and/or be secondary to neuronal degeneration.

Use of colour image analysis for assessment of fire damaged concrete

The aim of this project was to carry out a fundamental study to assess the potential of colour image analysis for use in investigations of fire damaged concrete. This involved:(a) Quantification (rather than purely visual assessment) of colour change as an indicator of the thermal history of concrete.(b) Quantification of the nature and intensity of crack development as an indication of the thermal history of concrete, supporting and in addition to, colour change observations.(c) Further understanding of changes in the physical and chemical properties of aggregate and mortar matrix after heating.(d) An indication of the relationship between cracking and non-destructive methods of testing e.g. UPV or Schmidt hammer. Results showed that colour image analysis could be used to quantify the colour changes found when concrete is heated. Development of red colour coincided with significant reduction in compressive strength. Such measurements may be used to determine the thermal history of concrete by providing information regarding the temperature distribution that existed at the height of a fire. The actual colours observed depended on the types of cement and aggregate that were used to make the concrete. With some aggregates it may be more appropriate to only analyse the mortar matrix. Petrographic techniques may also be used to determine the nature and density of cracks developing at elevated temperatures and values of crack density correlate well with measurements of residual compressive strength. Small differences in crack density were observed with different cements and aggregates, although good correlations were always found with the residual compressive strength. Taken together these two techniques can provide further useful information for the evaluation of fire damaged concrete. This is especially so since petrographic analysis can also provide information on the quality of the original concrete such as cement content and water / cement ratio. Concretes made with blended cements tended to produce small differences in physical and chemical properties compared to those made with unblended cements. There is some evidence to suggest that a coarsening of pore structure in blended cements may lead to onset of cracking at lower temperatures. The use of DTA/TGA was of little use in assessing the thermal history of concrete made with blended cements. Corner spalling and sloughing off, as observed in columns, was effectively reproduced in tests on small scale specimens and the crack distributions measured. Relationships between compressive strength/cracking and non-destructive methods of testing are discussed and an outline procedure for site investigations of fire damaged concrete is described.