Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-08-16T21:13:39Z No. of bitstreams: 1 HeberSousa.pdf: 2248069 bytes, checksum: e89eac1d4562ac1f2f53007d699f9c71 (MD5) / Made available in DSpace on 2017-08-16T21:13:39Z (GMT). No. of bitstreams: 1 HeberSousa.pdf: 2248069 bytes, checksum: e89eac1d4562ac1f2f53007d699f9c71 (MD5) Previous issue date: 2013-01-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Lung cancer is one of the most common of malignant tumors. It also has one of the highest rates of mortality among cancers. The reason for this is mainly linked to late diagnosis of the disease. For early detection of disease is very helpful to use medical images as support, the most important being, CT. With the acquisition of digital images is becoming more common to use computer systems for medical imaging. These systems assist in the clinical diagnosis, disease monitoring, and in some cases is used as a support for surgery. Because the search for new ways of human-computer interaction, natural interaction arises, which aims to provide a form of control with higher cognition. This control is usually performed using gestures. Interactions of gestures can be useful in controlling medical imaging systems and can ensure necessary sterility in operating rooms, because they are not required contacts manuals. Among the activities computer assisted important for the treatment of lung cancer, we have the segmentation of nodules. The segmentation of nodules can be performed automatically, semiautomatically or interactively. It is useful to speed up the diagnostic process, taking measurements, or observe the morphological appearance of the nodule. The objective of this study is to investigate the use of natural interaction interface for activities such as medical image visualization and segmentation of pulmonary nodules. The paper proposes the study of interaction techniques based on gestures to segment nodules in an interactive and semiautomatic. Finally, conducting experiments to evaluate the techniques proposed in the items ease of use, intuitiveness, accuracy and comfortability / O câncer de pulmão é um dos mais comuns dentre os tumores malignos. Ele também possui uma das taxas mais altas de mortalidade dentre os tipos de câncer. O motivo disso está ligado principalmente ao diagnóstico tardio da doença. Para a sua detecção precoce é muito útil a utilização de imagens médicas como apoio, sendo a mais importante, a tomografia computadorizada. Com a aquisição digital das imagens está cada vez mais comum a utilização de sistemas computacionais de visualização médica. Estes sistemas auxiliam no diagnóstico clínico, no acompanhamento de doenças, e em alguns casos é utilizado como apoio a cirurgias. Em virtude da busca por novos meios de interação humano-computador, surge a interação natural, que objetiva uma forma de controle mais próximo cognitivamente das ações realizadas, e geralmente é realizada através de gestos. Interações por gestos podem ser úteis no controle de sistemas de visualização médica e podem garantir a esterilização necessária em salas cirúrgicas, pois não são necessários contatos manuais. Dentre as atividades assistidas por computador importantes para o tratamento do câncer pulmonar, temos a segmentação de nódulos. A segmentação de nódulos pode ser realizada de forma automática, semiautomática ou interativamente. Elas são úteis para agilizar o processo de diagnóstico, realizar medições, ou observar o aspecto morfológico do nódulo. O objetivo do presente trabalho é investigar a utilização da interação natural como interface para atividades de visualização de imagens médicas e segmentação de nódulos pulmonares. Foi implementada uma série de ferramentas de segmentação, interativas e semiautomáticas, controladas a partir de gestos. Estes gestos foram desenvolvidos a partir de imagens capturadas por uma câmera especial chamada Kinect, que traduz a imagem em mapas de profundidade, podendo medir com precisão a distância de objetos na cena. Ao final do estudo, foi realizado experimentos para avaliar as técnicas propostas nos quesitos facilidade de uso, intuitividade, conforto e precisão.

Prostate segmentation is a required step in determining the volume of a prostate, which is very important in the diagnosis and the treatment of prostate cancer. In the past, radiologists manually segment the two-dimensional cross-sectional ultrasound images. Typically, it is necessary for them to outline at least a hundred of cross-sectional images in order to get an accurate estimate of the prostate's volume. This approach is very time-consuming. To be more efficient in accomplishing this task, an automated procedure has to be developed. However, because of the quality of the ultrasound image, it is very difficult to develop a computerized method for defining boundary of an object in an ultrasound image. The goal of this thesis is to find an automated segmentation algorithm for detecting the boundary of the prostate in ultrasound images. As the first step in this endeavour, a semi-automatic segmentation method is designed. This method is only semi-automatic because it requires the user to enter four initialization points, which are the data required in defining the initial contour. The discrete dynamic contour (DDC) algorithm is then used to automatically update the contour. The DDC model is made up of a set of connected vertices. When provided with an energy field that describes the features of the ultrasound image, the model automatically adjusts the vertices of the contour to attain a maximum energy. In the proposed algorithm, Mallat's dyadic wavelet transform is used to determine the energy field. Using the dyadic wavelet transform, approximate coefficients and detailed coefficients at different scales can be generated. In particular, the two sets of detailed coefficients represent the gradient of the smoothed ultrasound image. Since the gradient modulus is high at the locations where edge features appear, it is assigned to be the energy field used to drive the DDC model. The ultimate goal of this work is to develop a fully-automatic segmentation algorithm. Since only the initialization stage requires human supervision in the proposed semi-automatic initialization algorithm, the task of developing a fully-automatic segmentation algorithm is reduced to designing a fully-automatic initialization process. Such a process is introduced in this thesis. In this work, the contours defined by the semi-automatic and the fully-automatic segmentation algorithm are compared with the boundary outlined by an expert observer. Tested using 8 sample images, the mean absolute difference between the semi-automatically defined and the manually outlined boundary is less than 2. 5 pixels, and that between the fully-automatically defined and the manually outlined boundary is less than 4 pixels. Automated segmentation tools that achieve this level of accuracy would be very useful in assisting radiologists to accomplish the task of segmenting prostate boundary much more efficiently.

The exceptional properties of nanocrystals (NCs) are strongly influenced by many different characteristics, such as their size and shape, but also by characteristics on the atomic scale, such as their crystal structure, their surface structure, as well as by potential microstructure defects. While the size and shape of NCs are frequently determined in a statistical manner, atomic-scale characteristics are usually quantified only for a small number of individual NCs and thus with limited statistical relevance. Within this work, a characterization workflow was established that is capable of determining relevant NC characteristics simultaneously in a sufficiently detailed and statistically relevant manner. The workflow is based on transmission electron microscopy, networked by a correlative multiscale approach that combines atomic-scale information on NCs obtained from high-resolution imaging with statistical information on NCs obtained from low-resolution imaging, assisted by a semi-automatic segmentation routine. The approach is complemented by other characterization techniques, such as X-ray diffraction, UV-vis spectroscopy, dynamic light scattering, or alternating gradient magnetometry. The general applicability of the developed workflow is illustrated on several examples, i.e., on the classification of Au NCs with different structures, on the statistical determination of the facet configurations of Au nanorods, on the study of the hierarchical structure of multi-core iron oxide nanoflowers and its influence on their magnetic properties, and on the evaluation of the interplay between size, morphology, microstructure defects, and optoelectronic properties of CdSe NCs.:List of abbreviations and symbols 1 Introduction 1.1 Types of nanocrystals 1.2 Characterization of nanocrystals 1.3 Motivation and outline of this thesis 2 Materials and methods 2.1 Nanocrystal synthesis 2.1.1 Au nanocrystals 2.1.2 Au nanorods 2.1.3 Multi-core iron oxide nanoparticles 2.1.4 CdSe nanocrystals 2.2 Nanocrystal characterization 2.2.1 Transmission electron microscopy 2.2.2 X-ray diffraction 2.2.3 UV-vis spectroscopy 2.2.3.1 Au nanocrystals 2.2.3.2 Au nanorods 2.2.3.3 CdSe nanocrystals 2.2.4 Dynamic light scattering 2.2.5 Alternating gradient magnetometry 2.3 Methodical development 2.3.1 Correlative multiscale approach – Statistical information beyond size and shape 2.3.2 Semi-automatic segmentation routine 3 Classification of Au nanocrystals with comparable size but different morphology and defect structure 3.1 Introduction 3.1.1 Morphologies and structures of Au nanocrystals 3.1.2 Localized surface plasmon resonance of Au nanocrystals 3.1.3 Motivation and outline 3.2 Results 3.2.1 Microstructural characteristics of the Au nanocrystals 3.2.2 Insufficiency of two-dimensional size and shape for an unambiguous classification of the Au nanocrystals 3.2.3 Statistical classification of the Au nanocrystals 3.2.4 Advantage of a multidimensional characterization of the Au nanocrystals 3.2.5 Estimation of the density of planar defects in the Au nanoplates 3.3 Discussion 3.4 Conclusions 4 Statistical determination of the facet configurations of Au nanorods 4.1 Introduction 4.1.1 Growth mechanism and facet formation of Au nanorods 4.1.2 Localized surface plasmon resonance of Au nanorods 4.1.3 Catalytic activity of Au nanorods 4.1.4 Motivation and outline 4.2 Results 4.2.1 Statistical determination of the size and shape of the Au nanorods 4.2.2 Microstructural characteristics and facet configurations of the Au nanorods 4.2.3 Statistical determination of the facet configurations of the Au nanorods 4.3 Discussion 4.4 Conclusions 5 Influence of the hierarchical architecture of multi-core iron oxide nanoflowers on their magnetic properties 5.1 Introduction 5.1.1 Phase composition and phase distribution in iron oxide nanoparticles 5.1.2 Magnetic properties of iron oxide nanoparticles 5.1.3 Mono-core vs. multi-core iron oxide nanoparticles 5.1.4 Motivation and outline 5.2 Results 5.2.1 Phase composition, vacancy ordering, and antiphase boundaries 5.2.2 Arrangement and coherence of individual cores within the iron oxide nanoflowers 5.2.3 Statistical determination of particle, core, and shell size 5.2.4 Influence of the coherence of the cores on the magnetic properties 5.3 Discussion 5.4 Conclusions 6 Interplay between size, morphology, microstructure defects, and optoelectronic properties of CdSe nanocrystals 6.1 Introduction 6.1.1 Polymorphism in CdSe nanocrystals 6.1.2 Optoelectronic properties of CdSe nanocrystals 6.1.3 Nucleation, growth, and coarsening of CdSe nanocrystals 6.1.4 Motivation and outline 6.2 Results 6.2.1 Influence of the synthesis temperature on the optoelectronic properties of the CdSe nanocrystals 6.2.2 Microstructural characteristics of the CdSe nanocrystals 6.2.3 Statistical determination of size, shape, and amount of oriented attachment of the CdSe nanocrystals 6.3 Discussion 6.4 Conclusions 7 Summary and outlook References Publications