Global ETD Search

21	Lokalizace skalpových EEG elektrod ve strukturálních MRI datech / Localization of EEG scalp electrodes in structural MRI data Koutek, Petr January 2016 (has links) The objective of this thesis is to design an algorithm used for localization of scalp electrodes in MRI structural data. The algorithm is based on fact that electrodes are visible on visualized head surface. The surface of a head is subdivided into smaller fragments, which are transformed from 3D space into 2D. The electrodes are then located in 2D space by use of registration techniques. The proposed algorithm is able to correctly locate up to 73% EEG electrodes, assuming that the subject has short hair. In case when a subject has long hair, the portion of correctly detected electrodes is 49%. The probability of false detection is 22% when the object is short-haired and 35% when long-haired. The algorithm should facilitate the process of EEG electrodes localization during examinations combining imaging modalities of type EEG and MRI.
22	A Narrow Band Level Set Method for Surface Extraction from Unstructured Point-based Volume Data Rosenthal, Paul, Molchanov, Vladimir, Linsen, Lars 24 June 2011 (has links) Level-set methods have become a valuable and well-established field of visualization over the last decades. Different implementations addressing different design goals and different data types exist. In particular, level sets can be used to extract isosurfaces from scalar volume data that fulfill certain smoothness criteria. Recently, such an approach has been generalized to operate on unstructured point-based volume data, where data points are not arranged on a regular grid nor are they connected in form of a mesh. Utilizing this new development, one can avoid an interpolation to a regular grid which inevitably introduces interpolation errors. However, the global processing of the level-set function can be slow when dealing with unstructured point-based volume data sets containing several million data points. We propose an improved level-set approach that performs the process of the level-set function locally. As for isosurface extraction we are only interested in the zero level set, values are only updated in regions close to the zero level set. In each iteration of the level-set process, the zero level set is extracted using direct isosurface extraction from unstructured point-based volume data and a narrow band around the zero level set is constructed. The band consists of two parts: an inner and an outer band. The inner band contains all data points within a small area around the zero level set. These points are updated when executing the level set step. The outer band encloses the inner band providing all those neighbors of the points of the inner band that are necessary to approximate gradients and mean curvature. Neighborhood information is obtained using an efficient kd-tree scheme, gradients and mean curvature are estimated using a four-dimensional least-squares fitting approach. Comparing ourselves to the global approach, we demonstrate that this local level-set approach for unstructured point-based volume data achieves a significant speed-up of one order of magnitude for data sets in the range of several million data points with equivalent quality and robustness. info:eu-repo/classification/ddc/004 ddc:004 info:eu-repo/classification/ddc/006 ddc:006 Daten; Extraktion Set
23	[pt] EXTRAÇÃO DE ISOSUPERFÍCIES DE DOMOS DE SAL EM VOLUMES BINÁRIOS MASSIVOS / [en] ISOSURFACE EXTRACTION OF MASSIVE SALT DOME BINARY VOLUME DATA SAMUEL BASTOS DE SOUZA JUNIOR 19 January 2021 (has links) [pt] Ao extrair isosuperfícies de dados volumétricos massivos, em geral a superfície de saída é densa, podendo demandar muita memória para seu processamento. Além disso, dependendo do método de extração utilizado, podese também obter um resultado contendo diversos problemas geométricos e topológicos. Neste estudo, experimentamos combinações de diferentes métodos de extração de isosuperfícies juntamente com estratégias out-of-core que permitem uso inteligente do recurso computacional para sintetizar aproximações poligonais dessas superfícies, preservando a topologia original segmentada. O método implementado foi testado em um volume sísmico real para extração da superfície de domo de sal. / [en] When extracting isosurfaces from massive volumetric datasets, in general, the output surface is dense, and may require a lot of memory for processing. In addition to this, depending on the extraction method used, the result can also include several geometric and topological problems. In this study, we experimented combinations of different isosurface extraction methods along out-of-core strategies to generate polygonal approximations to these surfaces, preserving the original topology segmented in the volumetric dataset. The implemented method was tested in a real seismic volume dataset for the salt dome extraction. [pt] EXTRACAO DE ISOSUPERFICIES [pt] CAMPOS DE DISTANCIA [pt] ESTRATEGIAS OUT-OF-CORE [pt] VOLUMES SISMICOS [en] ISOSURFACE EXTRACTION [en] DISTANCE FIELDS [en] OUT-OF-CORE STRATEGIES [en] SEISMIC VOLUMES
24	Topology simplification algorithm for the segmentation of medical scans / Algorithme de simplification topologique pour la segmentation d'images médicales volumétriques Jaume, Sylvain 23 February 2004 (has links) Magnetic Resonance Imaging, Computed Tomography, and other image modalities are routinely used to visualize a particular structure in the patient's body. The classification of the image region corresponding to this structure is called segmentation. For applications in Neuroscience, it is important for the segmentation of a brain scan to represent the boundary of the brain as a folded surface with no holes. However the segmentation of the brain generally exhibits many erroneous holes. Consequently we have developed an algorithm for automatically correcting holes in segmented medical scans while preserving the accuracy of the segmentation. Upon concepts of Discrete Topology, we remove the holes based on the smallest modification to the image. First we detect each hole with a front propagation and a Reeb graph. Then we search for a number of loops around the hole on the isosurface of the image. Finally we correct the hole in the image using the loop that minimizes the modification to the image. At each step we limit the size of the data in memory. With these contributions our algorithm removes every hole in the image with high accuracy and low complexity even for images too large to fit into the main memory. To help doctors and scientists to obtain segmentations without holes, we have made our software publicly available at http://www.OpenTopology.org. / Les images par Résonance Magnétique, la Tomographie par Rayons X et les autres modalités d'imagerie médicale sont utilisées quotidiennement pour visualiser une structure particulière dans le corps du patient. La classification de la région de l'image qui correspond à cette structure s'appelle la segmentation. Pour des applications en Neuroscience, il est important que la segmentation d'une image du cerveau représente la surface extérieure du cerveau comme une surface pliée sans trous. Cependant la segmentation du cerveau présente généralement de nombreux trous. Par conséquent, nous avons développé un algorithme pour corriger automatiquement les trous dans les images médicales segmentées tout en préservant la précision de la segmentation. Sur des concepts de Topologie Discrète, nous enlevons les trous en fonction de la plus petite modification apportée à l'image. D'abord nous détectons chaque trou avec un certain nombre de boucles autour du trou sur l'isosurface de l'image. Finalement nous corrigeons le trou dans l'image en utilisant la boucle qui minimise la modification de l'image. A chaque étape, nous limitons la taille des données en mémoire. Grâce à ces contributions notre algorithme enlève tous les trous dans l'image avec une grande précision et une faible complexité même pour des images trop grandes pour tenir dans la mémoire de l'ordinateur. Pour aider les médecins et les chercheurs à obtenir des segmentations sans trous, nous avons rendu notre logiciel disponible publiquement à http://www.OpenTopology.org. Medical image processing Segmentation Topologie Boucles non-séparatrices Connectivité Images volumétriques Images médicales Traitement d'images medicales Graphe de Reeb Reeb graph Trou Hole Isosurface Medical scans Connectivity Non-separating loops Volumetric images Topology Segmentation
25	Implementation and Analysis of Co-Located Virtual Reality for Scientific Data Visualization Jordan M McGraw (8803076) 07 May 2020 (has links) <div>Advancements in virtual reality (VR) technologies have led to overwhelming critique and acclaim in recent years. Academic researchers have already begun to take advantage of these immersive technologies across all manner of settings. Using immersive technologies, educators are able to more easily interpret complex information with students and colleagues. Despite the advantages these technologies bring, some drawbacks still remain. One particular drawback is the difficulty of engaging in immersive environments with others in a shared physical space (i.e., with a shared virtual environment). A common strategy for improving collaborative data exploration has been to use technological substitutions to make distant users feel they are collaborating in the same space. This research, however, is focused on how virtual reality can be used to build upon real-world interactions which take place in the same physical space (i.e., collaborative, co-located, multi-user virtual reality).</div><div><br></div><div>In this study we address two primary dimensions of collaborative data visualization and analysis as follows: [1] we detail the implementation of a novel co-located VR hardware and software system, [2] we conduct a formal user experience study of the novel system using the NASA Task Load Index (Hart, 1986) and introduce the Modified User Experience Inventory, a new user study inventory based upon the Unified User Experience Inventory, (Tcha-Tokey, Christmann, Loup-Escande, Richir, 2016) to empirically observe the dependent measures of Workload, Presence, Engagement, Consequence, and Immersion. A total of 77 participants volunteered to join a demonstration of this technology at Purdue University. In groups ranging from two to four, participants shared a co-located virtual environment built to visualize point cloud measurements of exploded supernovae. This study is not experimental but observational. We found there to be moderately high levels of user experience and moderate levels of workload demand in our results. We describe the implementation of the software platform and present user reactions to the technology that was created. These are described in detail within this manuscript.</div> Software Engineering Computer Software Computer Graphics Simulation and Modelling Virtual Reality and Related Simulation Mobile Technologies Networking and Communications Computer-Human Interaction Collaborative Virtual Reality Virtual Reality Collaborative Virtual Reality Networking Networked VR Mulitplayer Multiplayer VR Data Visualization Data Exploration Head Mounted Display Cyberspace Augmented Reality Emerging Technologies Consumer Devices Virtual Environment Virtual Aesthetics Collaborative VR Simulation Training Simulation and Training Colocated Colocated Virtual Environment Immersive Media Immersive Technology Collaborative Tools Educational Virtual Environment Point Cloud Representations Isosurface Representations Mesh Smoothng Supernova Supernovae Supernova Remnant Modeling Astronomy and Astrophysics Cassiopeia N132D E0102 Crab Nebula CAVE CAVE Theater Mobile VR Virtual Classrooms STEM Workload Workload Assessment Tool Presence Engagement Immersion Experience Consequence Simulator sickness Simulator Sickness Questionnaire NASA TLX NASA Task Load Index Unified User Experience Quesionnaire Modified User Experience Questionnaire Oculus Oculus Go Oculus Quest Novel Solutions Optitrack Motive Motion Capture Mocap User Experience Unity3D Game Engine Lidgren Room Scale VR Hall Scale VR Co-location Colocation Virtual Training Collaborative Virtual Environments Shared Virtual Environments Collaboration Whiteboard Mobile Virtual Reality VR HMD LAN UX CSCL UXQ TLX AR VE CVE 3D UI EVE

Page generated in 0.2777 seconds