O estudo das propriedades de transporte do meio poroso é um tema importante para muitas áreas como a Ciência do Solo, onde o conhecimento dos processos que envolvem o movimento da água é de fundamental importância para o manejo e a conservação do mesmo. Nas últimas décadas, as técnicas não-invasivas e o processamento de imagens têm ajudado muito na modelagem e visualização da estrutura do meio, e têm sido aplicadas no estudo da distribuição dos fluidos com diferentes abordagens. Nesse trabalho é apresentado um novo método para análise do movimento da água, baseado na descrição da estrutura tridimensional da frente de molhamento no processo de infiltração vertical na região não-saturada do solo, considerando que a frente de molhamento tem informação relevante sobre os primeiros estágios da infiltração na interface líquido-sólido. O método proposto para a descrição da estrutura tridimensional da frente de molhamento compõe-se de dois principais módulos. O primeiro é responsável pela segmentação de imagens tomográficas para a detecção da frente de molhamento e cujo resultado é crucial para a análise da superfície resultante. O segundo módulo efetua a determinação de descritores da superfície obtida baseada na computação da variabilidade morfológica e a identificação de zonas de máxima adsorção através da análise da curvatura. As imagens usadas nos experimentos foram obtidas usando um tomógrafo de campo específico para estudos de solos, permitindo o processamento sem alterar a estrutura do solo. Os resultados preliminares são encorajadores e indicam que a abordagem utilizada consegue descrever o movimento da água usando informação da frente de molhamento no espaço tridimensional e no tempo. / The study of the transport properties in porous media is an important issue for many areas such as soil science, where knowledge about processes that involve the movement of water in the soil has fundamental importance to soil management and soil conservation. In recent decades noninvasive techniques and image processing algorithms have been very helpful in modeling and visualization of the structure medium and have been applied to study of the distribution of fluid with different approaches. This work present a new method to analysis of the movement of water based on the description of the three-dimensional wetting front structure in vertical infiltration process in unsaturated soil, whereas the wetting front structure has relevant information in the earliest stages of infiltration in liquid-solid interface. The proposed method for the description of the three-dimensional wetting front structure is comprised of two main modules. The first module is responsible of the three-dimensional image segmentation for the wetting front detection and its result is a very crucial step to analysis of the surface obtained. The second module compute features of the surface obtained to analysis based on morphological variability and maximal adsorption zones identification through the curvature. The image used in the experimental test were obtained using a tomograph of field specific to soil study, allowing the processing without changing of the soil structure. Our preliminary results are encouraging and indicate that our approach can describe the movement of water using information from the wetting front in three-dimensional space and time.

Imagens da anatomia interna são essenciais para as práticas médicas. Estabelecer correlação entre elas, é um importante procedimento para diagnóstico e tratamento. Nessa dissertação, é proposta uma abordagem para correlacionar dados multidimensionais de mesma modalidade de aquisição baseando-se somente nas informações de intensidade de pixels e voxels. O trabalho foi dividido em duas fases de implementação. Na primeira, foi explorado o problema de similaridade entre imagens médicas usando a perspectiva de análise de qualidade de imagem. Isso levou ao desenvolvimento de uma técnica de dois passos que estabelece um equilíbrio entre a velocidade de processamento e precisão de duas abordagens conhecidas. Avaliou-se a qualidade e aplicabilidade do algoritmo e, na segunda fase, o método foi estendido para analisar similaridade e encontrar a localização de uma imagem arbitrária (2D) em um volume (3D). A solução minimiza o número virtualmente infinito de possíveis orientações transversais e usa otimizações para reduzir a carga de trabalho e entregar resultados precisos. Uma visualização tridimensional volumétrica funde o volume (3D) com a imagem (2D) estabelecendo uma correspondência entre os dados. Uma análise experimental demonstrou que, apesar da complexidade computacional do algoritmo, o uso de amostragem, tanto na imagem quanto no volume, permite alcançar um bom equilíbrio entre desempenho e precisão, mesmo quando realizada com conjuntos de dados de baixa intensidade de gradiente. / Images of the inner anatomy are essential for clinical practice. To establish a correlation between them is an important procedure for diagnosis and treatment. In this thesis, we propose an approach to correlate within-modality 2D and 3D data from ordinary acquisition protocols based solely on the pixel/voxel information. The work was divided into two development phases. First, we explored the similarity problem between medical images using the perspective of image quality assessment. It led to the development of a 2-step technique that settles the compromise between processing speed and precision of two known approaches. We evaluated the quality and applicability of the 2-step and, in the second phase, we extended the method to use similarity analysis to, given an arbitrary slice image (2D), find the location of this slice within the volume data (3D). The solution minimizes the virtually infinite number of possible cross section orientations and uses optimizations to reduce the computational workload and output accurate results. The matching is displayed in a volumetric three-dimensional visualization fusing the 3D with the 2D. An experimental analysis demonstrated that despite the computational complexity of the algorithm, the use of severe data sampling allows achieving a great compromise between performance and accuracy even when performed with low gradient intensity datasets.

Imagens da anatomia interna são essenciais para as práticas médicas. Estabelecer correlação entre elas, é um importante procedimento para diagnóstico e tratamento. Nessa dissertação, é proposta uma abordagem para correlacionar dados multidimensionais de mesma modalidade de aquisição baseando-se somente nas informações de intensidade de pixels e voxels. O trabalho foi dividido em duas fases de implementação. Na primeira, foi explorado o problema de similaridade entre imagens médicas usando a perspectiva de análise de qualidade de imagem. Isso levou ao desenvolvimento de uma técnica de dois passos que estabelece um equilíbrio entre a velocidade de processamento e precisão de duas abordagens conhecidas. Avaliou-se a qualidade e aplicabilidade do algoritmo e, na segunda fase, o método foi estendido para analisar similaridade e encontrar a localização de uma imagem arbitrária (2D) em um volume (3D). A solução minimiza o número virtualmente infinito de possíveis orientações transversais e usa otimizações para reduzir a carga de trabalho e entregar resultados precisos. Uma visualização tridimensional volumétrica funde o volume (3D) com a imagem (2D) estabelecendo uma correspondência entre os dados. Uma análise experimental demonstrou que, apesar da complexidade computacional do algoritmo, o uso de amostragem, tanto na imagem quanto no volume, permite alcançar um bom equilíbrio entre desempenho e precisão, mesmo quando realizada com conjuntos de dados de baixa intensidade de gradiente. / Images of the inner anatomy are essential for clinical practice. To establish a correlation between them is an important procedure for diagnosis and treatment. In this thesis, we propose an approach to correlate within-modality 2D and 3D data from ordinary acquisition protocols based solely on the pixel/voxel information. The work was divided into two development phases. First, we explored the similarity problem between medical images using the perspective of image quality assessment. It led to the development of a 2-step technique that settles the compromise between processing speed and precision of two known approaches. We evaluated the quality and applicability of the 2-step and, in the second phase, we extended the method to use similarity analysis to, given an arbitrary slice image (2D), find the location of this slice within the volume data (3D). The solution minimizes the virtually infinite number of possible cross section orientations and uses optimizations to reduce the computational workload and output accurate results. The matching is displayed in a volumetric three-dimensional visualization fusing the 3D with the 2D. An experimental analysis demonstrated that despite the computational complexity of the algorithm, the use of severe data sampling allows achieving a great compromise between performance and accuracy even when performed with low gradient intensity datasets.

O estudo das propriedades de transporte do meio poroso é um tema importante para muitas áreas como a Ciência do Solo, onde o conhecimento dos processos que envolvem o movimento da água é de fundamental importância para o manejo e a conservação do mesmo. Nas últimas décadas, as técnicas não-invasivas e o processamento de imagens têm ajudado muito na modelagem e visualização da estrutura do meio, e têm sido aplicadas no estudo da distribuição dos fluidos com diferentes abordagens. Nesse trabalho é apresentado um novo método para análise do movimento da água, baseado na descrição da estrutura tridimensional da frente de molhamento no processo de infiltração vertical na região não-saturada do solo, considerando que a frente de molhamento tem informação relevante sobre os primeiros estágios da infiltração na interface líquido-sólido. O método proposto para a descrição da estrutura tridimensional da frente de molhamento compõe-se de dois principais módulos. O primeiro é responsável pela segmentação de imagens tomográficas para a detecção da frente de molhamento e cujo resultado é crucial para a análise da superfície resultante. O segundo módulo efetua a determinação de descritores da superfície obtida baseada na computação da variabilidade morfológica e a identificação de zonas de máxima adsorção através da análise da curvatura. As imagens usadas nos experimentos foram obtidas usando um tomógrafo de campo específico para estudos de solos, permitindo o processamento sem alterar a estrutura do solo. Os resultados preliminares são encorajadores e indicam que a abordagem utilizada consegue descrever o movimento da água usando informação da frente de molhamento no espaço tridimensional e no tempo. / The study of the transport properties in porous media is an important issue for many areas such as soil science, where knowledge about processes that involve the movement of water in the soil has fundamental importance to soil management and soil conservation. In recent decades noninvasive techniques and image processing algorithms have been very helpful in modeling and visualization of the structure medium and have been applied to study of the distribution of fluid with different approaches. This work present a new method to analysis of the movement of water based on the description of the three-dimensional wetting front structure in vertical infiltration process in unsaturated soil, whereas the wetting front structure has relevant information in the earliest stages of infiltration in liquid-solid interface. The proposed method for the description of the three-dimensional wetting front structure is comprised of two main modules. The first module is responsible of the three-dimensional image segmentation for the wetting front detection and its result is a very crucial step to analysis of the surface obtained. The second module compute features of the surface obtained to analysis based on morphological variability and maximal adsorption zones identification through the curvature. The image used in the experimental test were obtained using a tomograph of field specific to soil study, allowing the processing without changing of the soil structure. Our preliminary results are encouraging and indicate that our approach can describe the movement of water using information from the wetting front in three-dimensional space and time.

La cryo-tomographie électronique (cryo-ET) est une technique d'imagerie capable de produire des vues 3D de spécimens biologiques. Cette technologie permet d’imager de larges portions de cellules vitrifiées à une résolution nanométrique. Elle permet de combiner plusieurs échelles de compréhension de la machinerie cellulaire, allant des interactions entre les groupes de protéines à leur structure atomique. La cryo-ET a donc le potentiel d'agir comme un lien entre l'imagerie cellulaire in vivo et les techniques atteignant la résolution atomique. Cependant, ces images sont corrompues par un niveau de bruit élevé et d'artefacts d'imagerie. Leur interprétabilité dépend fortement des méthodes de traitement d'image. Les méthodes computationelles existantes permettent actuellement d'identifier de larges macromolécules telles que les ribosomes, mais il est avéré que ces détections sont incomplètes. De plus, ces méthodes sont limitées lorsque les objets recherchés sont de très petite taille ou présentent une plus grande variabilité structurelle. L'objectif de cette thèse est de proposer de nouvelles méthodes d'analyse d'images, afin de permettre une identification plus robuste des macromolécules d'intérêt. Nous proposons deux méthodes computationelles pour atteindre cet objectif. La première vise à réduire le bruit et les artefacts d'imagerie, et fonctionne en ajoutant et en supprimant de façon itérative un bruit artificiel à l'image. Nous fournissons des preuves mathématiques et expérimentales de ce concept qui permet d'améliorer le signal dans les images de cryo-ET. La deuxième méthode s'appuie sur les progrès récents de l'apprentissage automatique et les méthodes convolutionelles pour améliorer la localisation des macromolécules. La méthode est basée sur un réseau neuronal convolutif et nous montrons comment l'adapter pour obtenir des taux de détection supérieur à l'état de l'art. / Cryo electron tomography (cryo-ET) is an imaging technique capable of producing 3D views of biological specimens. This technology enables to capture large field of views of vitrified cells at nanometer resolution. These features allow to combine several scales of understanding of the cellular machinery, from the interactions between groups of proteins to their atomic structure. Cryo-ET therefore has the potential to act as a link between in vivo cell imaging and atomic resolution techniques. However, cryo-ET images suffer from a high amount of noise and imaging artifacts, and the interpretability of these images heavily depends on computational image analysis methods. Existing methods allow to identify large macromolecules such as ribosomes, but there is evidence that the detections are incomplete. In addition, these methods are limited when searched objects are smaller and have more structural variability. The purpose of this thesis is to propose new image analysis methods, in order to enable a more robust identification of macromolecules of interest. We propose two computational methods to achieve this goal. The first aims at reducing the noise and imaging artifacts, and operates by iteratively adding and removing artificial noise to the image. We provide both mathematical and experimental evidence that this concept allows to enhance signal in cryo-ET images. The second method builds on recent advances in machine learning to improve macromolecule localization. The method is based on a convolutional neural network, and we show how it can be adapted to achieve better detection rates than the current state-of- the-art.

info:eu-repo/semantics/nonPublished

This thesis deals with the processing of stereo images. It described the principles of calibration and rectification of stereo images. The thesis described several methods for finding important points. SURF method is then implemented for practical solution. Finding correspondences is realized using the methods of image processing library OpenCV. Reconstructed spatial coordinates of points and write by the format specified VRML97. Finally, there is introduced evaluated accuracy of spatial data acquisition and comparison of computational cost generated programs.

Indiana University-Purdue University Indianapolis (IUPUI) / In this thesis, 3D image segmentation is targeted to a Xilinx Field Programmable Gate Array (FPGA), and verified with extensive simulation. Segmentation is performed using the Expectation-Maximization with Maximization of the Posterior Marginals (EM/MPM) Bayesian algorithm. This algorithm segments the 3D image using neighboring pixels based on a Markov Random Field (MRF) model. This iterative algorithm is designed, synthesized and simulated for the Xilinx FPGA, and greater than 100 times speed improvement over standard desktop computer hardware is achieved. Three new techniques were the key to achieving this speed: Pipelined computational cores, sixteen parallel data paths and a novel memory interface for maximizing the external memory bandwidth. Seven MPM segmentation iterations are matched to the external memory bandwidth required of a single source file read, and a single segmented file write, plus a small amount of latency.

No description available.

Our understanding of the complexity of the brain is limited by the data we can collect and analyze. Because of experimental limitations and a desire for greater detail, most investigations focus on just one aspect of the brain. For example, brain function can be studied at many levels of abstraction including, but not limited to, gene expression, protein interactions, anatomical regions, neuronal connectivity, synaptic plasticity, and the electrical activity of neurons. By focusing on each of these levels, neuroscience has built up a detailed picture of how the brain works, but each level is understood mostly in isolation from the others. It is likely that interaction between all these levels is just as important. Therefore, a key hypothesis is that functional units spanning multiple levels of biological organization exist in the brain. This project attempted to combine neuronal circuitry analysis with functional proteomics and anatomical regions of the brain to explore this hypothesis, and took an evolutionary view of the results obtained. During the process we had to solve a number of technical challenges as the tools to undertake this type of research did not exist. Two informatics challenges for this research were to develop ways to analyze neurobiological data, such as brain protein expression patterns, to extract useful information, and how to share and present this data in a way that is fast and easy for anyone to access. This project contributes towards a more wholistic understanding of the fruit fly brain in three ways. Firstly, a screen was conducted to record the expression of proteins in the brain of the fruit fly, Drosophila melanogaster. Protein expression patterns in the fruit fly brain were recorded from 535 protein trap lines using confocal microscopy. A total of 884 3D images were annotated and made available on an easy to use website database, BrainTrap, available at fruitfly.inf.ed.ac.uk/braintrap. The website allows 3D images of the protein expression to be viewed interactively in the web browser, and an ontology-based search tool allows users to search for protein expression patterns in specific areas of interest. Different expression patterns mapped to a common template can be viewed simultaneously in multiple colours. This data bridges the gap between anatomical and biomolecular levels of understanding. Secondly, protein trap expression patterns were used to investigate the properties of the fruit fly brain. Thousands of protein-protein interactions have been recorded by methods such as yeast two-hybrid, however many of these protein pairs do not express in the same regions of the fruit fly brain. Using 535 protein expression patterns it was possible to rule out 149 protein-protein interactions. Also, protein expression patterns registered against a common template brain were used to produce new anatomical breakdowns of the fruit fly brain. Clustering techniques were able to naturally segment brain regions based only on the protein expression data. This is just one example of how, by combining proteomics with anatomy, we were able to learn more about both levels of understanding. Results are analysed further in combination with networks such as genetic homology networks, and connectivity networks. We show how the wealth of biological and neuroscience data now available in public databases can be combined with the Brain- Trap data to reveal similarities between areas of the fruit fly and mammalian brain. The BrainTrap data also informs us on the process of evolution and we show that genes found in fruit fly, yeast and mouse are more likely to be generally expressed throughout the brain, whereas genes found only in fruit fly and mouse, but not yeast, are more likely to have a specific expression pattern in the fruit fly brain. Thus, by combining data from multiple sources we can gain further insight into the complexity of the brain. Neural connectivity data is also analyzed and a new technique for enhanced motifs is developed for the combined analysis of connectivity data with other information such as neuron type data and potentially protein expression data. Thirdly, I investigated techniques for imaging the protein trap lines at higher resolution using electron microscopy (EM) and developed new informatics techniques for the automated analysis of neural connectivity data collected from serial section transmission electron microscopy (ssTEM). Measurement of the connectivity between neurons requires high resolution imaging techniques, such as electron microscopy, and images produced by this method are currently annotated manually to produce very detailed maps of cell morphology and connectivity. This is an extremely time consuming process and the volume of tissue and number of neurons that can be reconstructed is severely limited by the annotation step. I developed a set of computer vision algorithms to improve the alignment between consecutive images, and to perform partial annotation automatically by detecting membrane, synapses and mitochondria present in the images. Accuracy of the automatic annotation was evaluated on a small dataset and 96% of membrane could be identified at the cost of 13% false positives. This research demonstrates that informatics technology can help us to automatically analyze biological images and bring together genetic, anatomical, and connectivity data in a meaningful way. This combination of multiple data sources reveals more detail about each individual level of understanding, and gives us a more wholistic view of the fruit fly brain.