Visual Computing Tools for Studying Micro-scale Diffusion

In this dissertation, we present novel visual computing tools and techniques to facilitate the exploration, simulation, and visualization of micro-scale diffusion. Our research builds upon the latest advances in visualization, high-performance computing, medical imaging, and human perception. We validate our research using the driving applications of nano-assembly and diffusion kurtosis imaging (DKI). In both of these applications, diffusion plays a central role. In the former it mediates the process of transporting micron-sized particles through moving lasers, and in the latter it conveys brain micro-geometry. Nanocomponent-based devices, such as bio-sensors, electronic components, photonic devices, solar cells, and batteries, are expected to revolutionize health care, energy, communications, and the computing industry. However, in order to build such useful devices, nanoscale components need to be properly assembled together. We have developed a hybrid CPU/GPU-based computing tool to understand complex interactions between lasers, optical beads, and the suspension medium. We demonstrate how a high-performance visual computing tool can be used to accelerate an optical tweezers simulation to compute the force applied by a laser onto micro particles and study shadowing (refraction) behavior. This represents the first steps toward building a real-time nano-assembly planning system. A challenge in building such a system, however, is that optical tweezers systems typically lack stereo depth cues. We have developed a visual tool to provide an enhanced perception of a scene's 3D structure using the kinetic depth effect. The design of our tool has been informed by user studies of stereo perception using the kinetic-depth effect on monocular displays. Diffusion kurtosis imaging is gaining rapid adoption in the medical imaging community due to its ability to measure the non-Gaussian property of water diffusion in biological tissues. Compared with the traditional diffusion tensor imaging (DTI), DKI can provide additional details about the underlying microstructural characteristics of neural tissues. It has shown promising results in studies on changes in gray matter and mild traumatic brain injuries, where DTI is often found to be inadequate. However, the highly detailed spatio-angular fields in DKI datasets present a special challenge for visualization. Traditional techniques that use glyphs are often inadequate for expressing subtle changes in the DKI fields. In this dissertation, we outline a systematic way to manage, analyze, and visualize spatio-angular fields using spherical harmonics lighting functions to facilitate insights into the micro-structural properties of the brain

Global Contours

Figure (b) and the salient contours shown in Figure (c) are calculated by Vonikakis et al. [2006]. Figure (d) shows the image saliency computed by the method of Itti et al. [1998]. In this image, salient regions are marked by yellow circles. Image saliency tends to focus on the local changes. Figure (e) shows the Global Contours calculated by our method. The darker regions in the contour lines are considered more salient. As we perform global computation, our calculated contours tend to reflect the most dominant property of the image. We present a multi-scale approach that uses Laplacian eigenvectors to extract globally significant contours from an image. The input images are mapped into the Laplacian space by using Laplacian eigenvectors. This mapping causes globally significant pixels along the contours to expand in the Laplacian space. The measure of the expansion is used to compute the Global Contours. We apply our scheme to real color images and compare it with several other methods that compute image and color saliency. The contours calculated by our method reflect global properties of the image and are complementary to classic center-surround image saliency methods. We believe that hybrid image saliency algorithms that combine our method of Global Contours with center-surround image saliency algorithms will be able to better characterize the most important regions of images than those from just using contours calculated using bottom-up approaches. Laplacian eigenmaps, saliency, contours, global fea-Keywords: tur