Title: Vessel segmentation Author: Ján Dupej Department / Institute: Department of Software and Computer Science Education Supervisor of the master thesis: RNDr. Josef Pelikán, KSVI Abstract: In this thesis we researched some of the blood vessed segmentation and visualization techniques currently available for angiography on CT data. We then designed, implemented and tested a system that allows both semi-automatic and automatic vessel segmentation and visualization. For vessel segmantation and tracking we used a region-growing algorithm that we overhauled with several heuristics and combined with centerline detection. We then automated this algorithm by automatic seed generation. The visualization part is accomplished with an adaptation of the well-known straightened CPR method that we enhanced so that it visualizes the whole cross-section of the blood vessel, instead of just one line of it. Furthermore, we used the Bishop frame to maintain minimal twist of the curve-local coordinate system along the whole vessel. Keywords: vessel segmentation, medical data analysis, volume data

Real-time rendering of static volumetric data is generally known to be a memory and computationally intensive process. With the advance of graphic hardware, especially GPU, it is now possible to do this using desktop computers. However, with the evolution of real-time CT and MRI technologies, volumetric rendering is an even bigger challenge. The first one is how to reduce the data transmission between the main memory and the graphic memory. The second one is how to efficiently take advantage of the time redundancy which exists in the time-varying volumetric data. Most previous researches either focus on one problem or the other. In this thesis, we implemented a system which efficiently deals with both of the challenges. We proposed an optimized compression scheme that explores the time redundancy as well as space redundancy of time-varying volumetric data. The compressed data is then transmitted to graphic memory and directly rendered by GPU, so the data transfer between main memory and graphic memory is significantly reduced. With our implemented system, we successfully reduce more than half of the time of transferring the whole data directly. We also compare our proposed compression scheme with the one without exploiting time redundancy. The optimized compression scheme shows a reduce compression distortion over time. With usability, portability and extensibility in mind, the implemented system is also quite flexible.

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<p> In experimental microdosimetry one of the goals is to measure the absorbed dose in microscopic volumes of tissue. The traditional spherical tissue-equivalent proportional counter (TEPC) is the most common detector currently used for microdosimetry. A new microdosimetric detector based on a thick gas electron multiplier (THGEM) was developed. To investigate the feasibility of the THGEM type detector for microdosimetry, a prototype detector was designed and manufactured. The THGEM detector is robust, easy to manufacture and is cost effective. The THGEM foil is composed of a thin FR4-epoxy insulator coated with copper on both sides. The THGEM contains 32 holes each with a diameter of 0.35 mm and pitch of 0.64 mm. The sensitive volume of the detector is a right cylinder with a diameter of ~5 mm and height of ~5 mm and is located in the center of the detector. Systematic tests were conducted at the McMaster Accelerator Laboratory to investigate its overall performance. A neutron-gamma ray radiation field was generated using the 7Li(p,n) reaction. The detector was operated at low bias voltages initially to test the stability and then the relative multiplication gain was measured as a function of the operating high voltage. The detector performance was observed with different THGEM insulator thicknesses ranging from 0.12 mm to 1.48 mm. The multiplication gain was assessed and both neutron and gamma-ray radiation was detected by the THGEM detector. The spectra obtained with the THGEM detector were analyzed and compared to the data collected with the standard spherical TEPC. The investigations provided information about the THGEM detector operation for microdosimetry and the THGEM microdosimetric spectra observed are comparable to the standard TEPC data.</p> / Thesis / Doctor of Philosophy (PhD)

This thesis develops a strategy for polymerized volume data set construction. Given a volume data set defined over a regular three-dimensional grid, a polymerized volume data set (PVDS) can be defined as follows: edges between adjacent vertices of the grid are labeled 1 (active) or 0 (inactive) to indicate the likelihood that an edge is contained in (or spans the boundary of) a common underlying object, adding information not in the original volume data set. This edge labeling “polymerizes” adjacent voxels (those sharing a common active edge) into connected components, facilitating segmentation of embedded objects in the volume data set. Polymerization of the volume data set also aids real-time data compression, geometric modeling of the embedded objects, and their visualization. To construct a polymerized volume data set, an adjacency class within the grid system is selected. Edges belonging to this adjacency class are labeled as interior, exterior, or boundary edges using discriminant functions whose functional forms are derived for three local adjacency classes. The discriminant function parameter values are determined by supervised learning. Training sets are derived from an initial segmentation on a homogeneous sample of the volume data set, using an existing segmentation method. The strategy of constructing polymerized volume data sets is initially tested on synthetic data sets which resemble neuronal volume data obtained by three-dimensional microscopy. The strategy is then illustrated on volume data sets of mouse brain microstructure at a neuronal level of detail. Visualization and validation of the resulting PVDS is shown in both cases. Finally the procedures of polymerized volume data set construction are generalized to apply to any Bravais lattice over the regular 3D orthogonal grid. Further development of this latter topic is left to future work.

Este trabalho apresenta uma nova técnica de visualização que aproveita as vantagens do rendering volumétrico direto e do rendering de superfícies em um ambiente híbrido. O método faz uso de uma pré-visualização sobre o bordo do volume que viabiliza uma interação em tempo real com objetos volumétricos modelados por meio de malhas não estruturadas. Além disso, essa nova abordagem de visualização é paralelizável e pode se acelerada com placas gráficas comuns. / This work presents a new visualization technique that exploits the advantages of direct volume rendering and surface rendering in a hybrid environment. The method developed here makes use of a pre-visualization on the volume boundary to enable real time interaction with unstructured volumetric meshes. Furthermore, this new visualization approach can be implemented on existing parallel architectures and speed up by conventional graphical hardware.

Este trabalho apresenta uma nova técnica de visualização que aproveita as vantagens do rendering volumétrico direto e do rendering de superfícies em um ambiente híbrido. O método faz uso de uma pré-visualização sobre o bordo do volume que viabiliza uma interação em tempo real com objetos volumétricos modelados por meio de malhas não estruturadas. Além disso, essa nova abordagem de visualização é paralelizável e pode se acelerada com placas gráficas comuns. / This work presents a new visualization technique that exploits the advantages of direct volume rendering and surface rendering in a hybrid environment. The method developed here makes use of a pre-visualization on the volume boundary to enable real time interaction with unstructured volumetric meshes. Furthermore, this new visualization approach can be implemented on existing parallel architectures and speed up by conventional graphical hardware.

Smart infrastructure has become increasingly prevalent in recent decades due to the emergence of sophisticated and affordable sensing technologies. As sensors are deployed more widely and higher sampling rates are feasible, managing the massive scale of real-time data collected by these systems has become fundamental to providing relevant and timely information to decision-makers. To address this task, a novel open-source framework has been developed to manage and intuitively present high-volume data in near real-time. This design is centered around the goals of making data accessible, supporting decision-making, and providing flexibility to modify and reuse this framework in the future. In this work, the framework is tailored to vibration-based structural health monitoring, which can be used in near real-time to screen building condition. To promote timely intervention, distributed computing technologies are employed to accelerate the processing, storage, and visualization of data. Vibration data is processed in parallel using a publish-subscribe messaging queue and then inserted into a NoSQL database that stores heterogeneous data across several nodes. A REST-based web application allows interaction with this stored data via customizable visualization interfaces. To illustrate the utility of this framework design, it has been implemented to support a frequency domain monitoring dashboard for a 5-story classroom building instrumented with 224 accelerometers. A simulated scenario is presented to capture how the dashboard can aid decisions about occupant safety and structural maintenance. / Master of Science / Advances in technology have made it affordable and accessible to collect information about the world around us using sensors. When sensors are used to aid decision-making about structures, it is frequently referred to as Structural Health Monitoring (SHM). SHM can be used to monitor long-term structural health, inform maintenance decisions, and rapidly screen structural conditions following extreme events. Accelerometers can be used in SHM to capture vibration data that give insight into deflection patterns and natural frequencies in a structure. The challenge with vibration-based SHM and many other applications that leverage sensors is that the amount of data collected has the potential to grow to massive scales. To communicate relevant information to decision-makers, data must be processed quickly and presented intuitively. To facilitate this process, a novel open-source framework was developed for processing, storing, and visualizing high-volume data in near real-time. This framework combines multiple computers to extend the processing and storage capacity of our system. Data is processed in parallel and stored in a database that supports efficient data retrieval. A web application enables interaction with stored data via customizable visualization interfaces. To demonstrate the framework functionality, it was implemented in a 5-story classroom building instrumented with 224 accelerometers. A frequency-domain dashboard was developed for the building, and a simulated scenario was conducted to capture how the dashboard can aid decisions about occupant safety and structural maintenance.

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.

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.