A reação álcali-agregado - RAA é uma patologia de ação lenta que tem sido observada em construções de concreto capaz de comprometer suas estruturas. Sabe-se que a reação álcali-agregado é um fenômeno bastante complexo em virtude da grande variedade de rochas na natureza que são empregadas como agregados no preparo do concreto, podendo cada mineral utilizado afetar de forma distinta a reação ocorrida. Em função dos tipos de estrutura, das suas condições de exposição e dos materiais empregados, a RAA não se comporta sempre da mesma forma, em virtude disto a pesquisa constante neste tema é necessária para o meio técnico e a sociedade. Pesquisas laboratoriais, empíricas e experimentais tem sido rotina em muitos dos estudos da RAA dada ainda à carência de certas definições mais precisas a respeito dos métodos de ensaio, mas também em função da necessidade do melhor conhecimento dos materiais de uso em concretos como os agregados, cimentos, adições, aditivos entre outros e do comportamento da estrutura. Embora técnicas de prevenção possam reduzir significativamente a incidência da RAA, muitas estruturas foram construídas antes que tais medidas fossem conhecidas, havendo no Brasil vários casos de estruturas afetadas, sendo custosos os reparos dessas estruturas. Em estudos recentes sobre o tamanho das partículas de álcali-agregado e sua distribuição foi concluído que o tamanho do agregado está relacionado com o potencial danoso da RAA. Existem ainda indícios de que o tamanho e a distribuição dos poros do concreto também sejam capazes de influenciar o potencial reativo do concreto. Neste trabalho desenvolvemos um Sistema de Visão Artificial (SVA) que, com o uso de técnicas de Processamento de Imagens, é capaz de identificar em imagens de concreto, agregado e poros que atendam em sua forma, às especificações do usuário, possibilitando o cálculo da porosidade e produzindo imagens segmentadas à partir das quais será possível extrair dados relativos à geometria desses elementos. Serão feitas duas abordagens para a obtenção das imagens, uma por Escâner Comercial, que possui vantagens relacionadas à facilidade de aquisição do equipamento, e outra por micro tomógrafo. Uma vez obtidas informações sobre as amostras de concreto, estas podem ser utilizadas para pesquisar a RAA, comparar estruturas de risco com estruturas antigas de forma a melhorar a previsão de risco de ocorrência, bem como serem aplicadas a outras no estudo de outras patologias do concreto menos comuns no nosso país, como o efeito gelo/degelo. / The alkali-aggregate reaction - RAA is a condition of slow action that has been observed in concrete constructions that could affect their structures. It is known that the alkali-aggregate reaction is a very complex phenomenon because of the great variety of rocks in nature that are used as aggregates for concrete, and each mineral used differently affects the reaction occurred. Depending on the type of structure, its exposure conditions and the materials used, this phenomenon does not always behaves the same way, because of this, constant research in this area is needed for the technical means and the society. Laboratory, empirical and experimental research has been routine in many of the RAA studies still given the lack of certain more precise definitions concerning the testing methods, but also because of the need for better understanding of the use of materials in concrete as aggregate, cement, additions, additives etc. and structure behavior. Prevention techniques could significantly reduce the incidence of RAA. Still, many structures were built before such measures were known, several cases of affected structures were discovered in Brazil, all with large spending on repairs of the affected structures. In recent studies on the particle size of the alkaliaggregate and its distribution was concluded that the aggregate size is related to the damaging potential of the RAA. There are also indications that the size and distribution of concrete pores are also capable of influencing the reactive potential of the concrete. In the present work we developed an Artificial Vision System ( VAS ) that uses image processing techniques to identify aggregate and pores in hardened concrete images, enabling the calculation of porosity and producing segmented images that can be used to investigate data about the geometry of these elements. Were made two approaches for obtaining the images, one by Scanner Commercial, which has related advantages will ease the acquisition of equipment, and other micro CT scanner. Once obtained information on the concrete samples, these can be used to search the AAR compared risk structures with old structures so as to enhance the occurrence of risk prediction, as well as be applied to other concrete in the study of other pathologies less common in our country, as ice effect / thaw.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-02-23T14:02:54Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tese-completa-Sidney_Lima_v21.pdf: 4757211 bytes, checksum: 205170db8b002cc2ab72255ab77628a3 (MD5) / Made available in DSpace on 2017-02-23T14:02:54Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tese-completa-Sidney_Lima_v21.pdf: 4757211 bytes, checksum: 205170db8b002cc2ab72255ab77628a3 (MD5) Previous issue date: 2016-02-25 / REUNI / O câncer de mama é a principal causa de morte de mulheres adultas por câncer no mundo. Do ponto de vista clínico, a mamografia é ainda a mais efetiva tecnologia de diagnóstico do câncer de mama, dada a grande difusão de uso e interpretações dessas imagens. De acordo com o estado da arte da classificação de lesões em mamogramas, as wavelets têm apresentado os melhores resultados do ponto de vista da taxa de classificação, quando utilizadas como etapa de préprocessamento que decompõe a imagem original em imagens de detalhes (verticais, horizontais e diagonais) e aproximações para, a partir dessas imagens componentes, serem extraídos atributos de textura. Neste trabalho, propõe-se a Decomposição baseada em Aproximações Morfológicas em regiões de interesse em mamogramas. O método proposto tem por base a decomposição inspirada em wavelets que emprega filtros não lineares passa-baixas e passa-altas, baseados em aberturas e fechamentos, que por sua vez são construídos a partir dos operadores morfológicos clássicos de erosão e dilatação. Neste trabalho, são propostas aproximações aritméticas para esses dois operadores morfológicos clássicos, substituindo os desvios condicionais, presentes na Morfologia Matemática, por operações aritméticas de somas, subtrações e multiplicações, computacionalmente mais rápidas. O trabalho proposto compara o tempo estimado de execução entre as aproximações aritméticas propostas e as operações morfológicas clássicas utilizando a notação Big-Oh e também faz uso de estimativas baseadas em arquitetura de hardware pipeline. Em todas as estimativas e cenários reais, as aproximações morfológicas propostas são mais rápidas do que a morfologia clássica. Além disso, por não empregar unidade de hardware em ambiente pipeline para tratamento de desvios condicionais, as aproximações morfológicas propostas se tornam uma solução mais barata, ocupa menos espaço, mais propícia à miniaturização, consome menos energia e reduz o número de codificações da UC (Unidade de Controle). Logo, as Aproximações Morfológicas criadas são superiores à morfologia clássica nos principais requisitos para o bom funcionamento do hardware. Quanto à classificação, a Decomposição baseada em Aproximações Morfológicas alcança um desempenho médio de 84,65% na distinção entre casos normais, benignos e malignos. Os classificadores empregados são redes neurais ELM e SVM, cujas classes são definidas de acordo com os critérios da American College of Radiology. Foram usadas 355 imagens de mama adiposa da base de dados IRMA, com 233 casos normais, 66 benignos e 56 malignos. Como método de tratamento da base de dados, foram estudados pesos ponderando a fronteira de decisão das redes neurais. / According to the World Health Organization, breast cancer is the main cause of death of women round the world. From the clinical point of view, mammography is still the most effective diagnostic technology, given the wide diffusion of the use and interpretation of these images. According the state-of-the-art lesions classification on mammograms, wavelets have produced the best results from the viewpoint of precision, when used as a preprocessing step that decomposes the original image into approximation and detail images (vertical, horizontal and diagonal) in order to, from these components images, extract shape or texture attributes. This work proposes the decomposition Morphological-based in regions of interest on mammograms. The proposed method is inspired on wavelets decomposition employing nonlinear low-pass and high-pass filters, based on openings and closings, which are constructed from classical morphological operators of erosion and dilation. In this work, we propose approaches of classical morphology, replacing the conditional branches, present in Mathematical Morphology, by arithmetic operations of addition, subtraction and multiplication, computationally faster. The proposed work compares the estimated run time of proposed arithmetic approximations and classical morphological operations using Big-Oh notation and also the thesis uses notation based on pipeline hardware architecture. In all real scenarios, our morphological operations are faster than classical morphology. Also, by not employing hardware unit in pipeline environment for treating conditional branches, the proposed morphology approximation becomes a cheaper solution, occupies less space, more propitious to miniaturization, consumes less power, and reduces the Control Unit coding number. Then, our approaches of classical morphology are superior to classical morphology in key requirements of hardware solution. Regarding the classification, the proposed decomposition reaches an average performance of 84.65% in distinguishing normal, benign, and malignant cases. Classifiers are neural networks ELM and SVM, classes are defined according American College of Radiology criteria. They are employed 355 adipose breast images with 233 normal cases, 66 benign and 56 malignant. As database processing method, weights were studied considering the decision boundary of neural networks.

L'objectif de cette thèse est l'élaboration et l'application de traitements d'images pour permettre une étude objective et fiable des sinus de Valsalva, importantes cavités de la base de l'aorte. Les méthodes proposées s'appliquent aux séquences ciné-IRM et aux examens de scanner sans qu'il n'y ait à modifier le paramétrage entre deux examens. Pour cela, nous avons d'abord étudié la morphologie de cette zone anatomique puis détaillé les différentes propriétés communes à toutes les images de sinus. Ceux-ci font en l'occurrence partie des principaux organes clairs et peu mobiles. Nous avons donc développé un algorithme qui détecte ces éléments et caractérise chacun d'entre eux par une trajectoire unique. Divers outils de morphologie mathématique ont été utilisés à cette occasion, tout comme pour l'extraction du contour des sinus dans chaque image. L'étape de segmentation repose elle sur la reconstruction géodésique, qui s'avère plus efficace et surtout plus robuste que l'usage de contours actifs usuels. L'intérieur des sinus forme un domaine simplement connexe et étoilé. Grâce à ce postulat, nous avons conçu une nouvelle reconstruction, nommée transformée en aurore, qui limite la propagation des intensités aux supports radiaux et présente les résultats dans un repère polaire pour une meilleure lecture des contours.Les points caractéristiques des sinus ont également été détectés, par étude de rayons et détermination de points dominants. Ces points fournissent les éléments nécessaires à une mesure automatique des sinus, mesure cohérente avec les mesures actuellement réalisées manuellement et les variations intra et inter-observateurs de celles-ci. D'autres outils sont enfin esquissés pour modéliser le contour par coniques, classer les images d'examens cinétiques en fonction du moment du cycle et suivre le mouvement des valves dans ces mêmes examens.L'ensemble de ces travaux ont amené à la réalisation d'un logiciel d'aide au diagnostic qui intègre nos méthodes et dont l'interface est également présentée dans le présent mémoire. / This Phd thesis deals with the design and the use of image processing tools in order to allow a reliable and objective study of the sinuses of Valsalva which are important cavities of the aortic root. The proposed methods can be applied on cine-MR sequences and CT examinations without any change in the settings between two examinations.Firstly, we studied the morphology of this anatomical area and its constant properties in all images of the dataset. Sinuses are one of the main bright organs with limited movements. Hence a new algorithm has been designed. It detects and characterizes each bright organ by a single trajectory. Various tools of mathematical morphology are used for this step, as for the extraction of the contour of the sinuses in each image.The segmentation step is based on the geodesic reconstruction, which is more effective and more robust than the usual active contours. The shape depicting the sinuses is simply connected and a star domain. With this assumption, a new reconstruction is proposed, called the Aurora transform. This transform limits the spread of intensities only on the radial lines and shows its results in a polar space for a better reading of edges.The relevant points of the sinuses are also detected by a study of radii and the determination of dominant points along edges. An automatic measurement of the sinuses is deduced from these points. The values are very close to the manual measures currently done according to the intra-and inter-observer variations.Some other tools are finally outlined. They includes the modeling of edges by conics, the image classification depending on the time of the cycle in sequences and the tracking of the aortic valves in these examinations.This work led to the devlopement of a diagnostic aid software based on our methods. Its interface is also presented herein.

Les tessellations, aussi appelées mosaïques, permettent de modéliser de nombreuses structures, comme des assemblages de cellules en biologie ou de grains en science des matériaux. La tessellation aléatoire la plus connue est le diagramme de Voronoï qui à partir d'un ensemble de points, appelés germes, partitionne le plan. L'approche innovante de cette thèse est d'utiliser des champs aléatoires gaussiens pour générer des germes et des distances aléatoires, qui vont permettre de simuler une grande variété de tessellations en termes de formes et de tailles des cellules.Pour connaître les propriétés des tessellations simulées à partir de champs aléatoires gaussiens, celles-ci vont être caractérisées et comparées à d'autres tessellations. Tout d'abord par une approche ponctuelle en étudiant les germes, dont leur distribution spatiale. Puis par une approche par région, en étudiant la géométrie et la morphométrie des cellules.L'endothélium cornéen humain est une monocouche de cellules formant un pavage hexagonal régulier à la naissance, et perdant de sa régularité ensuite. La qualité du greffon cornéen est donnée par certaines observations, comme la densité, l'homogénéité de la forme et des tailles des cellules endothéliales.L'évolution avec l'âge de cette mosaïque cornéenne va être caractérisée à partir d’une base d’images de l’endothélium. L'originalité est ensuite d'effectuer une estimation de l'âge d’un endothélium à partir des différentes mesures permettant de caractériser les tessellations, et enfin de mettre en place une méthode prometteuse afin de savoir si une cornée a une évolution normale. / Tessellations, also called mosaics, are used to model many structures, for example cellular arrangements in biology or grains in material science. The most known tessellation is the Voronoï diagram which partitions the space from a set of points, called germs. The innovative approach of this thesis is to use Gaussian random fields to generate germs and random distances. The use of random fields allows to simulate a great variety of tessellations in terms of cells forms and sizes.To study the properties of each type of tessellation, they are characterized: first, by studying the germs, including their spatial distribution, and then by analyzing the cells geometry and morphometry. These tessellations are also compared to other known tessellations.The human corneal endothelium is a mono-layer of cells forming a regular hexagonal mosaic at birth, and losing his regularity later. The corneal graft quality is given by some observations made on the endothelial mosaic (cells density, the homogeneity of cells sizes and shapes).A database of endothelium images allows to characterize the evolution with age of the corneal mosaic. The originality is to estimate the age of an endothelium based on the measures computed to characterize the tessellations, and finally to set up a promising method to evaluate if a corneal evolution is normal.

This master´s thesis deals with depiction aplication of camera like sensitive element for assisting system of car. It was proposed, how find a road, a lane and a obstacle on roadways. Only one camera was aplication for it. Solution is realized by methods, that are based on color and gradient of image. It applies simple methods and methods with mathematical model. Result is sum of method and its test and comparing. Realization of my program is present at resume of this thesis.

This master’s thesis is focused on automating the process of differential leukocyte count in peripherial blood using image processing. It deals with the design of the processing of digital images - from scanning and image preprocessing, segmentation nucleus and cytoplasm, feature selection and classifier, including testing on a set of images that were scanned in the context of this work. This work introduces used segmentation methods and classification procedures which separate nucleus and the cytoplasm of leukocytes. A statistical analysis is performed on the basis of these structures. Following adequate statistical parameters, a set of features has been chosen. This data then go through a classification process realized by three artificial neural networks. Overall were classified 5 types of leukocytes: neutropfiles, lymphocytes, monocytes, eosinophiles and basophiles. The sensitivity and specificity of the classification made for 4 out of 5 leukocyte types (neutropfiles, lymphocytes, monocytes, eosinophiles) is higher than 90 %. Sensitivity of classiffication basophiles was evaluated at 75 % and specificity at 67 %. The total ability of classification has been tested on 111 leukocytes and was approximately 91% successful. All algorithms were created in the MATLAB program.

Mathematical morphology stems from set theory and it makes use of properties of point sets. The first point set is an origin image and the second one (usually smaller) is a structuring element. Morphological image transformations are image to image transformations based on a few elementary set operators. Fundamental morphologic operations are dilation, erosion and hit or miss. Next operations described in this work are opening and closing. Originally morphological operators were used for binary images only, later they were generalized for grey tone and color ones. This work describes the basic morphological image processing methods including their practical usage in image filtering and segmentation.