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Die vorliegende Arbeit befasst sich mit Algorithmen aus dem Bereich der Bildsegmentierung sowie der Datensynthese für das so genannte Hologrammdruck-Prinzip. Angelehnt an ein anatomisch motiviertes Forschungsprojekt werden aktive Konturen zur halbautomatischen Segmentierung digitalisierter histologischer Schnitte herangezogen. Die besondere Herausforderung liegt dabei in der Entwicklung von verschiedenen Ansätzen, die der Anpassung des Verfahrens für sehr große Bilder dienen, welche in diesem Kontext eine Größe von einigen hundert Megapixel erreichen können. Unter dem Aspekt der größtmöglichen Effizienz, jedoch mit der Beschränkung auf die Verwendung von Consumer-Hardware, werden Ideen vorgestellt, welche eine auf aktiven Konturen basierende Segmentierung bei derartigen Bildgrößen erstmals ermöglichen sowie zur Beschleunigung und Reduktion des Speicheraufwandes beitragen. Darüber hinaus wurde das Verfahren um ein intuitives Werkzeug erweitert, das eine interaktive lokale Korrektur der finalen Kontur gestattet und damit die Praxistauglichkeit der Methode maßgeblich erhöht. Der zweite Teil der Arbeit beschäftigt sich mit einem Druckprinzip für die Herstellung von Hologrammen, basierend auf virtuellen Abbildungsgegenständen. Der Hologrammdruck, der namentlich an die Arbeitsweise eines Tintenstrahldruckers erinnern soll, benötigt dazu spezielle diskrete Bilddaten, die als Elementarhologramme bezeichnet werden. Diese tragen die visuelle Information verschiedener Blickrichtungen durch einen festen geometrischen Ort auf der Hologrammebene. Ein vollständiges, aus vielen Elementarhologrammen zusammengesetztes Hologramm erzeugt dabei ein erhebliches Datenvolumen, das parameterabhängig schnell im Terabyte-Bereich liegen kann. Zwei unabhängige Algorithmen zur Erzeugung geeignet aufbereiteter Daten unter intensiver Ausnutzung von Standard-Graphikhardware werden präsentiert, hinsichtlich ihrer Berechnungs- sowie Speicherkomplexität verglichen und unter Berücksichtigung von Qualitätsaspekten bewertet.

Cerebral cortex, classified as gray matter, is the superficial layer of the cerebrum. In recent years, many studies have shown the abnormality of cortical thickness is possibly correlated to the disease or disorder in central nervous system, such as Alzheimer¡¦s disease and lissencephaly. Therefore, this purpose of this work is to implement the measurement of the cortical thickness. In general, two approaches, surface-based and voxel-based methods, have been proposed to measure the cortical thickness. In this thesis, a procedure of the voxel-based method using Laplace¡¦s equation was developed on the basis of a 2008 publication reported by Chloe Hutton et al to obtain voxel-based cortical thickness (VBCT) map. The result of our home-made program was further compared with those calculated by Hutton¡¦s program, whic h was generously provided by the author. The difference between two implementations was consisted of four main parts. First of all, different strategies of the tissue classification were used to define boundary condition of Laplace¡¦s equation. When grey matter, white matter, and cerebrospinal fluid were classified by maximizing the tissue probability, Hutton¡¦s program tends to search more voxels of cerebrospinal fluid in sulci by skeletonizing the non-parenchyma area. Second, the algorithm of layer growing also differs. The single layer obtained by the 26-neighborhood algorithm in our program would be obviously thicker than that provided by Hutton¡¦s program using 6-neighborhood. Third, compared with a fixed step size (usually 0.5 mm) porposed in the main reference to track cortical streamline, we designed a variable step size, reducing the underestimation of cortical thickness. The last but not the least, the connecting points of the cortical streamline usually are not grid points, thus requiring interpolation to estimate the stepping gradient. We adapted the linear interpolation for better accuracy when Hutton et al searched for the closest grid point for replacement to achieve faster computation.

Three-dimensional (3-D) medical imaging creates notable opportunities as input toward engineering analyses, whether for basic understanding of the normal function or patho-physiology of an organ, or for the simulation of virtual surgical procedures. These analyses most often require finite element (FE) models to be constructed from patient-specific 3-D medical images. However, creation of such models can be extremely labor-intensive; in addition, image processing and mesh generation are often operator-dependent, lack robustness and may be of suboptimal quality. Focusing on the human aorta, the goal of the present work is to create a fast and robust methodology for quadrilateral surface and hexahedral volume meshing from 3-D medical images with minimal user input. By making use of the segmentation capabilities of the 3-D gradient vector flow field combined with original ray-tracing and orientation control algorithms, we will demonstrate that it is possible to incrementally grow a structured quadrilateral surface mesh of the inner wall of the aorta. The process does not only require minimal input from the user, it is also robust and very fast compared to existing methods; it effectively combines segmentation and meshing into one single effort. After successfully testing the methodology and measuring the quality of the meshes produced by it from synthetic as well as real medical image datasets, we will make use of the surface mesh of the inner aortic wall to derive hexahedral meshes of the aortic wall thickness and of the fluid domain inside the aorta. We will finally outline a tentative approach to merge several structured meshes to process the main branches of the aorta.

Active contours have been proven to be a powerful semiautomatic image segmentation approach, that seems to cope with many applications and different image modalities. However, they exhibit inherent drawbacks, including the sensibility to contour initialization due to the limited capture range of image edges and problems with concave boundary regions. The Gradient Vector Flow replaces the traditional image force and provides an enlarged capture range as well as enhanced concavity extraction capabilities, but it involves an expensive computational effort and considerably increased memory requirements at the time of computation. In this paper, we present an enhancement of the active contour model to facilitate semiautomatic contour detection in huge images. We propose a tile-based image decomposition accompanying an image force computation scheme on demand in order to minimize both computational and memory requirements. We show an efficient implementation of this approach on the basis of general purpose GPU processing providing for continuous active contour deformation without a considerable delay.

Three-dimensional (3-D) medical imaging creates notable opportunities as input toward engineering analyses, whether for basic understanding of the normal function or patho-physiology of an organ, or for the simulation of virtual surgical procedures. These analyses most often require finite element (FE) models to be constructed from patient-specific 3-D medical images. However, creation of such models can be extremely labor-intensive; in addition, image processing and mesh generation are often operator-dependent, lack robustness and may be of suboptimal quality. Focusing on the human aorta, the goal of the present work is to create a fast and robust methodology for quadrilateral surface and hexahedral volume meshing from 3-D medical images with minimal user input. By making use of the segmentation capabilities of the 3-D gradient vector flow field combined with original ray-tracing and orientation control algorithms, we will demonstrate that it is possible to incrementally grow a structured quadrilateral surface mesh of the inner wall of the aorta. The process does not only require minimal input from the user, it is also robust and very fast compared to existing methods; it effectively combines segmentation and meshing into one single effort. After successfully testing the methodology and measuring the quality of the meshes produced by it from synthetic as well as real medical image datasets, we will make use of the surface mesh of the inner aortic wall to derive hexahedral meshes of the aortic wall thickness and of the fluid domain inside the aorta. We will finally outline a tentative approach to merge several structured meshes to process the main branches of the aorta.

Active contours have been proven to be a powerful semiautomatic image segmentation approach, that seems to cope with many applications and different image modalities. However, they exhibit inherent drawbacks, including the sensibility to contour initialization due to the limited capture range of image edges and problems with concave boundary regions. The Gradient Vector Flow replaces the traditional image force and provides an enlarged capture range as well as enhanced concavity extraction capabilities, but it involves an expensive computational effort and considerably increased memory requirements at the time of computation. In this paper, we present an enhancement of the active contour model to facilitate semiautomatic contour detection in huge images. We propose a tile-based image decomposition accompanying an image force computation scheme on demand in order to minimize both computational and memory requirements. We show an efficient implementation of this approach on the basis of general purpose GPU processing providing for continuous active contour deformation without a considerable delay.

Devido à variação na qualidade e ao ruído nas imagens médicas, a aplicação de técnicas tradicionais de segmentação é geralmente ineficiente. Nesse sentido, apresenta-se um novo algoritmo a partir de duas técnicas: o modelo de contornos deformáveis por fluxo do vetor gradiente (GVF deformable contours) e a técnica de espaço de escalas utilizando o processo de difusão. Assim, foi realizada uma revisão bibliográfica dos modelos que trabalham com os contornos deformáveis, os quais foram classificados em modelos paramétricos e geométricos. Entre os modelos paramétricos foi escolhido o modelo de contornos deformáveis por fluxo do vetor gradiente (GVF). Esta aproximação oferece precisão na representação de estruturas biológicas não observada em outros modelos. Desta forma, o algoritmo apresentado mapeia as bordas (edge map) e aperfeiçoa a condução da deformação utilizando uma técnica baseada em operações recursivas. Com este cálculo apoiado no comportamento de espaço de escalas, obtem-se a localização e correção de sub-regiões do edge map que perturbam a deformação. Por outro lado, é incorporada uma nova característica que permite ao algoritmo realizar atividades de classificação. O algoritmo consegue determinar a presença ou ausência de um objeto de interesse utilizando um valor mínimo de deformação. O algoritmo é validado através do tratamento de imagens sintéticas e médicas comparando os resultados com os obtidos no modelo tradicional de contornos deformáveis GVF. / Due to the variation of the quality and noise in medical images, the classic image segmentation techniques are usually ineffective. In this work, we present a new algorithm that is composed of two techniques: the gradient vector flow deformable contours (GVF) and the scale-space technique using a diffusion process. A bibliographical revision of the models that work with deformable contours was accomplished, they were classified in parametric and geometric models. Among the parametric models the gradient vector flow deformable contours (GVF) was chosen. This approach offers precision in the representation of biological structures where other models does not. Thus, the algorithm improves the edge map to guide the deformation using recursive operations. With this estimation based on the behavior of the scale-space techniques it is realized, the localization and correction of sub-areas of the edge map that disturb the deformation. On the other hand, it was incorporated a new characteristic that allows the algorithm to accomplish classification activities. That is, the algorithm determines the presence or absence of a target object using a minimal deformation area. Our method was validated on both, simulated images and medical images making a comparison with the traditional GVF deformable contours.

Le but de ces travaux est de proposer une contribution au recalage d'images TDM-TEP dans les régions thoraciques et abdominales. Notre méthodologie est fondée sur l'introduction de contraintes anatomiques au recalage non linéaire appliqué sur les intensités. Cette introduction est faite d'une manière explicite, en divisant la procédure en une phase d'initialisation recalant les structures segmentées dans les deux images, et une deuxième phase de recalage à niveaux de gris, raffinant l'étape précédente de l'algorithme. Les transformations sont modélisées dans les deux étapes à partir de Free Form Deformations (FFD). La segmentation est réalisée selon une procédure hiérarchique de reconnaissance de formes. La mesure fournie par le protocole d'évaluation que nous avons développé indique une erreur inférieure à 1cm pour les structures les plus significatives (poumons, foie, reins, coeur), sauf pour le stomach (erreur d'1.5cm).