The methods presented in this study are aimed at the identification of subpopulations (clones) of genetically similar cells within tissue samples through measurement of loci-specific Fluorescence in-situ hybridization (FISH) spot signals for each nucleus and analyzing cell spatial distributions by way of Voronoi tessellation and Delaunay triangulation to robustly define cell neighbourhoods. The motivation for the system is to examine lung cancer patient for subpopulations of Non-Small Cell Lung Cancer (NSCLC) cells with biologically meaningful gene copy-number profiles: patterns of genetic alterations statistically associated with resistance to cis-platinum/vinorelbine doublet chemotherapy treatment. Current technologies for gene-copy number profiling rely on large amount of cellular material, which is not always available and suffers from limited sensitivity to only the most dominant clone in often heterogeneous samples. Thus, through the use of FISH, the detection of gene copy-numbers is possible in unprocessed tissues, allowing identification of specific tumour clones with biologically relevant patterns of genetic aberrations. The tissue-wide characterization of multiplexed loci-specific FISH signals, described herein, is achieved through a fully automated, multicolour fluorescence imaging microscope and object segmentation algorithms to identify cell nuclei and FISH spots within. Related tumour clones are identified through analysis of robustly defined cell neighbourhoods and cell-to-cell connections for regions of cells with homogenous and highly interconnected FISH spot signal characteristics. This study presents experiments which demonstrate the system’s ability to accurately quantify FISH spot signals in various tumour tissues and in up to 5 colours simultaneously or more through multiple rounds of FISH staining. Furthermore, the system’s FISH-based cell classification performance is evaluated at a sensitivity of 84% and specificity 81% and clonal identification algorithm results are determined to be comparable to clone delineation by a human-observer. Additionally, guidelines and procedures to perform anticipated, routine analysis experiments are established. / Science, Faculty of / Physics and Astronomy, Department of / Graduate

In computer graphics, when simulating fractured or cracked objects, most commonly physically based methods or hybrid solutions using physical and procedural methods are used to achieve the result. If only a static representation of a cracked surface, and not a simulation of an object cracking, is desired, these approaches of crack generation are not suitable. Non-physically based methods to generate the cracks or fractured objects has also been used where a fracture pattern is projected onto an object. In this thesis a novel method of generating 3D avalanche slabs directly from a 2D Voronoi pattern, in real-time, without using physically based methods or 3D procedural crack generation is presented. After the slabs are generated, they are placed on the surface of a terrain. To add a more realistic look, snow is added on the mountain around the avalanche slabs. Using this novel method of generating 3D avalanche slabs, separate 3D mesh objects are created directly from the Voronoi pattern, and no 3D model is needed initially to generate them from. The results show that this method can be used in real-time with limitations. With improvements suggested in this thesis, the presented method could be used to generate a large amount of slabs in a fairly large region, and software developers can benefit from using this method in their graphics renderer.

Random polytopes can be constructed in many different ways. In this thesis two certain kinds are considered - random polytopes as the convex hull of random points and as the intersection of finitely many random half spaces. Concerning these two models different issues are treated.

No description available.

Yes / Background and Objective Corneal endothelial cell abnormalities may be associated with a number of corneal and systemic diseases. Damage to the endothelial cells can significantly affect corneal transparency by altering hydration of the corneal stroma, which can lead to irreversible endothelial cell pathology requiring corneal transplantation. To date, quantitative analysis of endothelial cell abnormalities has been manually performed by ophthalmologists using time consuming and highly subjective semi-automatic tools, which require an operator interaction. We developed and applied a fully-automated and real-time system, termed the Corneal Endothelium Analysis System (CEAS) for the segmentation and computation of endothelial cells in images of the human cornea obtained by in vivo corneal confocal microscopy. Methods First, a Fast Fourier Transform (FFT) Band-pass filter is applied to reduce noise and enhance the image quality to make the cells more visible. Secondly, endothelial cell boundaries are detected using watershed transformations and Voronoi tessellations to accurately quantify the morphological parameters of the human corneal endothelial cells. The performance of the automated segmentation system was tested against manually traced ground-truth images based on a database consisting of 40 corneal confocal endothelial cell images in terms of segmentation accuracy and obtained clinical features. In addition, the robustness and efficiency of the proposed CEAS system were compared with manually obtained cell densities using a separate database of 40 images from controls (n = 11), obese subjects (n = 16) and patients with diabetes (n = 13). Results The Pearson correlation coefficient between automated and manual endothelial cell densities is 0.9 (p < 0.0001) and a Bland–Altman plot shows that 95% of the data are between the 2SD agreement lines. Conclusions We demonstrate the effectiveness and robustness of the CEAS system, and the possibility of utilizing it in a real world clinical setting to enable rapid diagnosis and for patient follow-up, with an execution time of only 6 seconds per image.

The past few years have seen advances in modelling of polycrystalline materi- als using parametric tessellation models from stochastic geometry. A promising class of tessellations, the Gibbs-type tessellation, allows the user to specify a great variety of properties through the energy function. This text focuses solely on tetrahedrizations, a three-dimensional tessellation composed of tetrahedra. The existing results for two-dimensional Delaunay triangulations are extended to the case of three-dimensional Laguerre tetrahedrization. We provide a proof of existence, a C++ implementation of the MCMC simulation and estimation of the models parameters through maximum pseudolikelihood. 1

Une tessellation de Voronoï est un moyen de diviser l'espace 3D en régions associées avec chaque élément d'un ensemble discret de points dans le but de caractériser leurs relations topologiques. Le processus associe à chacun de ces éléments un polyèdre, appelé cellule de Voronoï, défini par les intersections des plans de contact construits à mi chemin entre les points. Chaque cellule contient donc le voisinage le plus proche du point qui lui est associé et ses faces définissent les contacts avec ses plus proches voisins. Pour un ensemble donné de points, la décomposition en cellules de Voronoï est unique et absolue car il n'y a pas d'espace vide entre les cellules. De plus les caractéristiques des cellules telles que le nombre de face, le volume etc. sont des sources d'information utiles pour étudier l'organisation des points dans l'espace. Pour les structures protéiques deux échelles d'investigation peuvent être envisagées. Le niveau atomique qui est le plus représenté dans la littérature associe chaque cellule avec chaque atome ou groupe d'atomes présent dans la structure. Le second niveau associe chacune des cellules avec chaque résidu représenté par un point pouvant être un atome réel (le carbone alpha par exemple) ou un point virtuel comme le centre géométrique de la chaîne latérale. Le travail de thèse présenté ici décrit ces dernières tessellations tout d'abord d'un point de vue mathématique puis de manière plus concrète en l'appliquant aux structures protéiques et en étudiant les diverses propriétés des cellules. Deux applications concrètes sont ensuite présentées. La première est une étude statistique de la proximité des extrémités N terminale et C terminale des chaînes polypeptidiques, la seconde est une procédure d'attribution des structures secondaires régulières.

In remote sensing, segmentation is a procedure of partitioning the domain of a remotely sensed dataset into meaningful regions which correspond to different land use and land cover (LULC) classes or part of them. So far, the remotely sensed data segmentation is still one of the most challenging problems addressed by the remote sensing community, partly because of the availability of remotely sensed data from diverse sensors of various platforms with very high spatial resolution (VHSR). Thus, there is a strong motivation to propose a sophisticated data representation that can capture the significant amount of details presented in a VHSR dataset and to search for a more powerful scheme suitable for multiple remotely sensed data segmentations. This thesis focuses on the development of a segmentation framework for multiple VHSR remotely sensed data. The emphases are on VHSR data model and segmentation strategy. Starting with the domain partition of a given remotely sensed dataset, a hierarchical data model characterizing the structures hidden in the dataset locally, regionally and globally is built by three random fields: Markova random field (MRF), strict stationary random field (RF) and label field. After defining prior probability distributions which should capture and characterize general and scene-specific knowledge about model parameters and the contextual structure of accurate segmentations, the Bayesian based segmentation framework, which can lead to algorithmic implementation for multiple remotely sensed data, is developed by integrating both the data model and the prior knowledge. To verify the applicability and effectiveness of the proposed segmentation framework, the segmentation algorithms for different types of remotely sensed data are designed within the proposed segmentation framework. The first application relates to SAR intensity image processing, including segmentation and dark spot detection by marked point process. In the second application, the algorithms for LiDAR point cloud segmentation and building detection are developed. Finally, texture and colour texture segmentation problems are tackled within the segmentation framework. All applications demonstrate that the proposed data model provides efficient representations for hierarchical structures hidden in remotely sensed data and the developed segmentation framework leads to successful data processing algorithms for multiple data and task such as segmentation and object detection.

In remote sensing, segmentation is a procedure of partitioning the domain of a remotely sensed dataset into meaningful regions which correspond to different land use and land cover (LULC) classes or part of them. So far, the remotely sensed data segmentation is still one of the most challenging problems addressed by the remote sensing community, partly because of the availability of remotely sensed data from diverse sensors of various platforms with very high spatial resolution (VHSR). Thus, there is a strong motivation to propose a sophisticated data representation that can capture the significant amount of details presented in a VHSR dataset and to search for a more powerful scheme suitable for multiple remotely sensed data segmentations. This thesis focuses on the development of a segmentation framework for multiple VHSR remotely sensed data. The emphases are on VHSR data model and segmentation strategy. Starting with the domain partition of a given remotely sensed dataset, a hierarchical data model characterizing the structures hidden in the dataset locally, regionally and globally is built by three random fields: Markova random field (MRF), strict stationary random field (RF) and label field. After defining prior probability distributions which should capture and characterize general and scene-specific knowledge about model parameters and the contextual structure of accurate segmentations, the Bayesian based segmentation framework, which can lead to algorithmic implementation for multiple remotely sensed data, is developed by integrating both the data model and the prior knowledge. To verify the applicability and effectiveness of the proposed segmentation framework, the segmentation algorithms for different types of remotely sensed data are designed within the proposed segmentation framework. The first application relates to SAR intensity image processing, including segmentation and dark spot detection by marked point process. In the second application, the algorithms for LiDAR point cloud segmentation and building detection are developed. Finally, texture and colour texture segmentation problems are tackled within the segmentation framework. All applications demonstrate that the proposed data model provides efficient representations for hierarchical structures hidden in remotely sensed data and the developed segmentation framework leads to successful data processing algorithms for multiple data and task such as segmentation and object detection.

O mapeamento de texturas é uma técnica bastante importante para adicionar detalhamento a modelos geométricos. O mapeamento de texturas baseadas em imagens costuma ser a abordagem preferida, mas faz uso de imagens pré-computadas que são mais adequadas à representação de padrões estáticos. Por outro lado, texturas procedurais oferecem uma alternativa que depende de funções para descrever os padrões das texturas. Elas garantem mais flexibilidade na definição dos padrões em cenas dinâmicas, tendo ainda uma representação mais compacta e dando um maior controle da aparência da textura através do ajuste de parâmetros. Quando mapeadas por coordenadas 3D, as texturas procedurais não consideram a forma da superfície domodelo, e com coordenadas 2D torna-se necessária a definição dessas coordenadas de forma coerente, que, em modelos complexos ,não é uma tarefa simples. Neste trabalho nós introduzimos o leitor às texturas procedurais e ao mapeamento de texturas, então apresentamos GeoTextures, uma nova abordagem que faz uso de distâncias geodésicas definidas com base em múltiplos pontos de origem sobre a superfície do modelo. As distâncias geodésicas são passadas como parâmetros que permitem que a textura procedural se adeqüe ao relevo do modelo texturizado. Nós validamos a proposta ao usar alguns exemplos de texturas procedurais aplicadas em tempo real na texturização de superfícies complexas, mudando tanto a textura do modelo como a forma, através do uso de tesselagem em hardware. / Texture mapping is an important technique to add detail to geometric models. Imagebased texture mapping is the preferred approach but employs pre-computed images, which are better suited for static patterns. On the other hand, procedural-based texture mapping offers an alternative that rely on functions to describe texturing patterns. This allows more flexibility to define patterns in dynamic scenes, while also having a more compact representation and more control for parametric adjustments on the texture visual appearance. When mapped with 3D coordinates, the procedural textures do not consider the model surface, and with 2D mapping the coordinates must be defined in a coherent way, which for complex models is not an easy task. In this work we give a introduction to procedural texturing and texture mapping, and introduce GeoTextures, an original approach that uses geodesic distance defined from multiple sources at different locations over the surface of the model. The geodesic distance is passed as a parameter that allows the shape of the model to be considered in the definition of the procedural texture. We validate the proposal using procedural textures that are applied in real-time to complex surfaces, and show examples that change both the shading of the models, as well as their shape using hardware-based tessellation.