In this thesis, the goal was to implement a method to do partial segmentation for general 3D volumetric datasets such as mummies, clay figurines or animal bodies. There are different approaches for segmenting volumes, such as automatic methods, semi-automatic methods and interactive methods. However, no automatic algorithm was found that could successfully segment any general 3D volume with high precision. Instead, the chosen approach are segmentation tools which allows the user to quickly and intuitively do partial segmentation from a 3D volume. The tools consist of a interactive 3D brush, a transfer function editor and a semi-automatic flood fill tool which performs region growing in 3D. User studies were carried out in order to evaluate the speed and effectiveness of the segmenting tools compared to the conventional method of segmenting using a stack of 2D images. Based on the user studies the results shows that the proposed method is faster compared to the old method as long as high precision is not required.

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / A anÃlise de imagens mÃdicas por meio de tÃcnicas de visÃo computacional tornou-se bastante promissora, principalmente pelo fato de aperfeiÃoar a acurÃcia diagnÃstica de diversas patologias. Por essas razÃo, a Pneumologia à considerada atualmente uma Ãrea de concentraÃÃo de projetos que envolvem mÃtodos de Processamento Digital de Imagens. A segmentaÃÃo de vasos sanguÃneos pulmonares à de bastante auxÃlio na detecÃÃo de cardiopatias pulmonares. Esse processo à realizado atravÃs da anÃlise dos resultados obtidos por exame de diagnÃstico por imagem, os quais se destacam as radiografias torÃcicas, tomografia computadorizada (TC) do tÃrax, ressonÃncia magnÃtica, cintilografia pulmonar e angiografia. A hipertensÃo pulmonar e o cÃncer sÃo exemplos de doenÃas que podem ser diagnosticadas com menor subjetividade ao realizar a segmentaÃÃo de vasos, visualizaÃÃo em trÃs dimensÃes e extraÃÃo de seus atributos. Devido a essa importÃncia, diversos algoritmos sÃo desenvolvidos com intuito de obter uma segmentaÃÃo Ãtima destas estruturas. Dentre estes, encontram-se os mÃtodos por contornos ativos, LÃgica Fuzzy, Crescimento de RegiÃes, Filtragem Multi-escalar 3D e algoritmo Expectation Maximization (EM). Nesta dissertaÃÃo, sÃo segmentados os vasos sanguÃneos pulmonares de imagens de tomografia computadorizada do tÃrax utilizando-se trÃs mÃtodos: uma combinaÃÃo de Crescimento de RegiÃes 3D controlado por uma funÃÃo de pertinÃncia gaussiana e limiarizaÃÃo; um mÃtodo hÃbrido de segmentaÃÃo por Conectividade Fuzzy e limiarizaÃÃo; por m, a segmentaÃÃo utilizando o classicador K-mÃdias. Os resultados obtidos pelas segmentaÃÃes sÃo analisados e comparados por meio de uma anÃlise dos coecientes de similaridade e sensibilidade. Os resultados da aplicaÃÃo dos trÃs mÃtodos sÃo caracterizados aceitÃveis e compatÃveis com os observados na literatura. / Medical image analysis using computer vision techniques has become quite promising because of its improvement on the diagnostic accuracy of various pathologies. For this reason, pulmonology became an area of high concentration of projects involving methods of Digital Image Processing. The blood vessels segmentation in the lung is an important aid in the detection of pulmonary heart diseases. This process is performed by analyzing the results obtained with known diagnostic imaging exams, like chest Xrays, computed tomography (CT) scan, magnetic resonance imaging, scintigraphy and angiography. Pulmonary hypertension and cancer are examples of diseases that can be diagnosed with less subjectivity if performing vessels segmentation, three-dimensional visualization and attribute extraction of these images. Thus, several algorithms are developed with the objective of obtaining an optimal segmentation of these structures. Among those algorithms are active contours, fuzzy logic, 3D Region Growing, 3D multi-scale ltering algorithm and Expectation Maximization (EM). In this study, the blood vessels were extracted from lung CT scans of the chest using three methods. The rst is a combination of 3D Region Growing controlled by a Gaussian membership function and thresholding, the second is a hybrid segmentation by thresholding and Fuzzy Connectedness. Finally,the third refers to segmentation using the K-means classier. The results and evaluation of applying these algorithms are presented.

The aim of this semestral thesis is to compare the possibilities of available micro-CT systems. Theoretic part of this thesis there deals with possibilities of staining soft tissues and embryos because of enhancement the contrast of micro-CT images. Here follows a description of sources X-ray and detectors of available micro-CT systems. In practice there was realized the staining of embryo in cooperation with Department of histology and embryology in Brno. Then followed a measuring on FSI in Brno, ČVUT in Prague and synchrotron Elettra in Italy. In semestral thesis are described of the thesis there are compared the micro-CT systems and results of measuring embryos by means of presented systems and results are compared.The best results were obtained on micro-CT in Brno, where X-ray tube and flat panel detector were used. This images were used for a segmentation of cartilage olfactory system by means of 3D region growing. From results they were created 3D models for comparison with a manually segmented model. A less accurate results were obtain with the semi-automatic segmentation but this method isn’t too time-consuming.