Doctor of Philosophy

dissertationStochastic methods, dense free-form mapping, atlas construction, and total variation are examples of advanced image processing techniques which are robust but computationally demanding. These algorithms often require a large amount of computational power as well as massive memory bandwidth. These requirements used to be ful lled only by supercomputers. The development of heterogeneous parallel subsystems and computation-specialized devices such as Graphic Processing Units (GPUs) has brought the requisite power to commodity hardware, opening up opportunities for scientists to experiment and evaluate the in uence of these techniques on their research and practical applications. However, harnessing the processing power from modern hardware is challenging. The di fferences between multicore parallel processing systems and conventional models are signi ficant, often requiring algorithms and data structures to be redesigned signi ficantly for efficiency. It also demands in-depth knowledge about modern hardware architectures to optimize these implementations, sometimes on a per-architecture basis. The goal of this dissertation is to introduce a solution for this problem based on a 3D image processing framework, using high performance APIs at the core level to utilize parallel processing power of the GPUs. The design of the framework facilitates an efficient application development process, which does not require scientists to have extensive knowledge about GPU systems, and encourages them to harness this power to solve their computationally challenging problems. To present the development of this framework, four main problems are described, and the solutions are discussed and evaluated: (1) essential components of a general 3D image processing library: data structures and algorithms, as well as how to implement these building blocks on the GPU architecture for optimal performance; (2) an implementation of unbiased atlas construction algorithms|an illustration of how to solve a highly complex and computationally expensive algorithm using this framework; (3) an extension of the framework to account for geometry descriptors to solve registration challenges with large scale shape changes and high intensity-contrast di fferences; and (4) an out-of-core streaming model, which enables developers to implement multi-image processing techniques on commodity hardware

Visualisation of multi-dimensional medical images with application to brain electrical impedance tomography

Medical imaging plays an important role in modem medicine. With the increasing complexity and information presented by medical images, visualisation is vital for medical research and clinical applications to interpret the information presented in these images. The aim of this research is to investigate improvements to medical image visualisation, particularly for multi-dimensional medical image datasets. A recently developed medical imaging technique known as Electrical Impedance Tomography (EIT) is presented as a demonstration. To fulfil the aim, three main efforts are included in this work. First, a novel scheme for the processmg of brain EIT data with SPM (Statistical Parametric Mapping) to detect ROI (Regions of Interest) in the data is proposed based on a theoretical analysis. To evaluate the feasibility of this scheme, two types of experiments are carried out: one is implemented with simulated EIT data, and the other is performed with human brain EIT data under visual stimulation. The experimental results demonstrate that: SPM is able to localise the expected ROI in EIT data correctly; and it is reasonable to use the balloon hemodynamic change model to simulate the impedance change during brain function activity. Secondly, to deal with the absence of human morphology information in EIT visualisation, an innovative landmark-based registration scheme is developed to register brain EIT image with a standard anatomical brain atlas. Finally, a new task typology model is derived for task exploration in medical image visualisation, and a task-based system development methodology is proposed for the visualisation of multi-dimensional medical images. As a case study, a prototype visualisation system, named EIT5DVis, has been developed, following this methodology. to visualise five-dimensional brain EIT data. The EIT5DVis system is able to accept visualisation tasks through a graphical user interface; apply appropriate methods to analyse tasks, which include the ROI detection approach and registration scheme mentioned in the preceding paragraphs; and produce various visualisations

Segmentation of Unstructured Datasets

Datasets generated by computer simulations and experiments in Computational Fluid Dynamics tend to be extremely large and complex. It is difficult to visualize these datasets using standard techniques like Volume Rendering and Ray Casting. Object Segmentation provides a technique to extract and quantify regions of interest within these massive datasets. This thesis explores basic algorithms to extract coherent amorphous regions from two-dimensional and three-dimensional scalar unstructured grids. The techniques are applied to datasets from Computational Fluid Dynamics and from Finite Element Analysis

Towards Fully Dynamic Surface Illumination in Real-Time Rendering using Acceleration Data Structures

The improvements in GPU hardware, including hardware-accelerated ray tracing, and the push for fully dynamic realistic-looking video games, has been driving more research in the use of ray tracing in real-time applications. The work described in this thesis covers multiple aspects such as optimisations, adapting existing offline methods to real-time constraints, and adding effects which were hard to simulate without the new hardware, all working towards a fully dynamic surface illumination rendering in real-time.Our first main area of research concerns photon-based techniques, commonly used to render caustics. As many photons can be required for a good coverage of the scene, an efficient approach for detecting which ones contribute to a pixel is essential. We improve that process by adapting and extending an existing acceleration data structure; if performance is paramount, we present an approximation which trades off some quality for a 2–3× improvement in rendering time. The tracing of all the photons, and especially when long paths are needed, had become the highest cost. As most paths do not change from frame to frame, we introduce a validation procedure allowing the reuse of as many as possible, even in the presence of dynamic lights and objects. Previous algorithms for associating pixels and photons do not robustly handle specular materials, so we designed an approach leveraging ray tracing hardware to allow for caustics to be visible in mirrors or behind transparent objects.Our second research focus switches from a light-based perspective to a camera-based one, to improve the picking of light sources when shading: photon-based techniques are wonderful for caustics, but not as efficient for direct lighting estimations. When a scene has thousands of lights, only a handful can be evaluated at any given pixel due to time constraints. Current selection methods in video games are fast but at the cost of introducing bias. By adapting an acceleration data structure from offline rendering that stochastically chooses a light source based on its importance, we provide unbiased direct lighting evaluation at about 30 fps. To support dynamic scenes, we organise it in a two-level system making it possible to only update the parts containing moving lights, and in a more efficient way.We worked on top of the new ray tracing hardware to handle lighting situations that previously proved too challenging, and presented optimisations relevant for future algorithms in that space. These contributions will help in reducing some artistic constraints while designing new virtual scenes for real-time applications

Deep-learning the Latent Space of Light Transport

We suggest a method to directly deep‐learn light transport, i. e., the mapping from a 3D geometry‐illumination‐material configuration to a shaded 2D image. While many previous learning methods have employed 2D convolutional neural networks applied to images, we show for the first time that light transport can be learned directly in 3D. The benefit of 3D over 2D is, that the former can also correctly capture illumination effects related to occluded and/or semi‐transparent geometry. To learn 3D light transport, we represent the 3D scene as an unstructured 3D point cloud, which is later, during rendering, projected to the 2D output image. Thus, we suggest a two‐stage operator comprising a 3D network that first transforms the point cloud into a latent representation, which is later on projected to the 2D output image using a dedicated 3D‐2D network in a second step. We will show that our approach results in improved quality in terms of temporal coherence while retaining most of the computational efficiency of common 2D methods. As a consequence, the proposed two stage‐operator serves as a valuable extension to modern deferred shading approaches