[pt] entendimento do macro comportamento dos materiais, este trabalho apresenta soluções inovadoras para a análise de imagens 3D obtidas por microtomografia computadorizada de raios-X (microCT). O material estudado conhecido pelo termo em inglês “strain-hardening cement-based composites” ou pela abreviação SHCC é um compósito cimentício reforçado com fibras que atinge deformações significativas através da formação de múltiplas fissuras, estabelecendo um material cimentício com característica pseudo-dúctil. O primeiro desafio deste trabalho foi reconhecer e quantificar as fases constituintes nas imagens 3D de SHCC obtidas por microCT. Materiais com estruturas complexas podem apresentar imagens em que as fases internas não podem ser distinguidas pela técnica de limiarização clássica, exigindo o uso de outra técnica como a segmentação por Deep Learning (DL). Portanto, este trabalho utilizou DL como solução para esta tarefa. Desta forma, as características de cada fases puderam ser correlacionadas ao comportamento mecânico macro do material em ensaios de microCT in-situ. Outro método moderno de análise de imagens 3D utilizado foi a correlação digital de volume (em inglês, digital volume correlation - DVC). O DVC é uma técnica que estima o campo de deformação sobre todo o volume da amostra, correlacionando as imagens 3D nos estados descarregado e carregado. Assim, as imagens obtidas nos ensaios de tração e compressão in-situ puderam ter seus deslocamentos internos medidos e deformações calculadas. Em síntese, este trabalho trouxe avanços ao campo do processamento digital e análise de imagens 3D, aplicadas a materiais cimentícios, mas que também podem se adaptar à análise de diversos materiais. / [en] Considering the importance of micro and mesoscale analyses to understand the macro behavior of materials, this work brings innovative solutions for analyzing 3D images obtained by X-ray micro-computed tomography (microCT). The studied material was the strain-hardening cement-based composites (SHCC), a fiber reinforced cementitious composite that achieves significant deformations through multiple cracks formation, resulting in a cementitious material with pseudo ductile features. The first challenge of this work was to recognize and quantify the constituent phases in the 3D images of SHCC obtained by microCT. Materials with complex structures may present images in which the internal phases cannot be distinguished by the classical thresholding technique, requiring the use of another technique such as segmentation by Deep Learning (DL). Therefore, this work used DL as a solution for this task. Then, the features of each phase could be correlated to the macro mechanical behavior of the material in in-situ microCT tests. Another modern method for analyzing 3D images used was the digital volume correlation (DVC). DVC is a technique that estimates full-field strain in 3D over the entire volume of the specimen by correlating imaging volumes of the specimen in unloaded and loaded states. Thus, the images obtained from tensile and compression in-situ tests could have their internal displacements measured and strain calculated. In summary, this work brought advances to the 3D image processing and analysis field, applied to cementitious materials, but which could also adapt for the analysis of various materials.

The dissertation deals with the image processing in digital holographic microscopy and X-ray computed tomography. The focus of the work lies in the proposal of data processing techniques to meet the needs of the biological experiments. Transmitted light holographic microscopy is particularly used for quantitative phase imaging of transparent microscopic objects such as living cells. The phase images are affected by the phase aberrations that make the analysis particularly difficult. Here, we present a novel algorithm for dynamical processing of living cells phase images in a time-lapse sequence. The algorithm compensates for the deformation of a phase image using weighted least squares surface fitting. Moreover, it identifies and segments the individual cells in the phase image. This property of the algorithm is important for real-time cell quantitative phase imaging and instantaneous control of the course of the experiment. The efficiency of the propounded algorithm is demonstrated on images of rat fibrosarcoma cells using an off-axis holographic microscope. High resolution X-ray computed tomography is increasingly used technique for the study of the small rodent bones micro-structure. In this part of the work, the trabecular and cortical bone morphology is assessed in the distal half of rat femur. We developed new method for mapping the cortical position and dimensions from a central longitudinal axis with one degree angular resolution. This method was used to examine differences between experimental groups. The bone position in tomographic slices is aligned before the mapping using the propound standardization procedure. The activity of remodelling process of the long bone is studied on the system of cortical canals.