Fast Random Access to Wavelet Compressed Volumetric Data Using Hashing

We present a new approach to lossy storage of the coefficients of wavelet transformed data. While it is common to store the coefficients of largest magnitude (and let all other coefficients be zero), we allow a slightly different set of coefficients to be stored. This brings into play a recently proposed hashing technique that allows space efficient storage and very efficient retrieval of coefficients. Our approach is applied to compression of volumetric data sets. For the ``Visible Man'' volume we obtain up to 80% improvement in compression ratio over previously suggested schemes. Further, the time for accessing a random voxel is quite competitive

Incremental volume rendering using hierarchical compression

Includes bibliographical references.The research has been based on the thesis that efficient volume rendering of datasets, contained on the Internet, can be achieved on average personal workstations. We present a new algorithm here for efficient incremental rendering of volumetric datasets. The primary goal of this algorithm is to give average workstations the ability to efficiently render volume data received over relatively low bandwidth network links in such a way that rapid user feedback is maintained. Common limitations of workstation rendering of volume data include: large memory overheads, the requirement of expensive rendering hardware, and high speed processing ability. The rendering algorithm presented here overcomes these problems by making use of the efficient Shear-Warp Factorisation method which does not require specialised graphics hardware. However the original Shear-Warp algorithm suffers from a high memory overhead and does not provide for incremental rendering which is required should rapid user feedback be maintained. Our algorithm represents the volumetric data using a hierarchical data structure which provides for the incremental classification and rendering of volume data. This exploits the multiscale nature of the octree data structure. The algorithm reduces the memory footprint of the original Shear-Warp Factorisation algorithm by a factor of more than two, while maintaining good rendering performance. These factors make our octree algorithm more suitable for implementation on average desktop workstations for the purposes of interactive exploration of volume models over a network. This dissertation covers the theory and practice of developing the octree based Shear-Warp algorithms, and then presents the results of extensive empirical testing. The results, using typical volume datasets, demonstrate the ability of the algorithm to achieve high rendering rates for both incremental rendering and standard rendering while reducing the runtime memory requirements

Interactive volume visualization in a virtual environment.

by Yu-Hang Siu.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 74-80).Abstract also in Chinese.Abstract --- p.iiiAcknowledgements --- p.vChapter 1 --- Introduction --- p.1Chapter 1.1 --- Volume Visualization --- p.2Chapter 1.2 --- Virtual Environment --- p.11Chapter 1.3 --- Approach --- p.12Chapter 1.4 --- Thesis Overview --- p.13Chapter 2 --- Contour Extraction --- p.15Chapter 2.1 --- Concept of Intelligent Scissors --- p.16Chapter 2.2 --- Dijkstra's Algorithm --- p.18Chapter 2.3 --- Cost Function --- p.20Chapter 2.4 --- Summary --- p.23Chapter 3 --- Volume Cutting --- p.24Chapter 3.1 --- Basic idea of the algorithm --- p.25Chapter 3.2 --- Intelligent Scissors on Surface Mesh --- p.27Chapter 3.3 --- Internal Cutting Surface --- p.29Chapter 3.4 --- Summary --- p.34Chapter 4 --- Three-dimensional Intelligent Scissors --- p.35Chapter 4.1 --- 3D Graph Construction --- p.36Chapter 4.2 --- Cost Function --- p.40Chapter 4.3 --- Applications --- p.42Chapter 4.3.1 --- Surface Extraction --- p.42Chapter 4.3.2 --- Vessel Tracking --- p.47Chapter 4.4 --- Summary --- p.49Chapter 5 --- Implementations in a Virtual Environment --- p.52Chapter 5.1 --- Volume Cutting --- p.53Chapter 5.2 --- Surface Extraction --- p.56Chapter 5.3 --- Vessel Tracking --- p.59Chapter 5.4 --- Summary --- p.64Chapter 6 --- Conclusions --- p.68Chapter 6.1 --- Summary of Results --- p.68Chapter 6.2 --- Future Directions --- p.70Chapter A --- Performance of Dijkstra's Shortest Path Algorithm --- p.72Chapter B --- IsoRegion Construction --- p.7

Limited resource visualization with region-of-interest

Ph.DDOCTOR OF PHILOSOPH

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

Optimal caching of large multi-dimensional datasets

We propose a novel organization for multi-dimensional data based on the conceptof macro-voxels. This organization improves computer performance by enhancingspatial and temporal locality. Caching of macro-voxels not only reduces therequired storage space but also leads to an efficient organization of the dataset resulting in faster data access. We have developed a macro-voxel caching theory that predicts the optimal macro-voxel sizes required for minimum cache size and access time. The model also identifies a region of trade-off between time and storage, which can be exploited in making an efficient choice of macro-voxel size for this scheme. Based on the macro-voxel caching model, we have implemented a macro-voxel I/O layer in C, intended to be used as an interface between applications and datasets. It is capable of both scattered access, typical in online applications, and row/column access, typical in batched applications. We integrated this I/O layer in the ALIGN program (online application) which aligns images based on 3D distance maps; this improved access time by a factor of 3 when accessing local disks and a factor of 20 for remote disks. We also applied the macro-voxel caching scheme on SPEC.s Seismic (batched application) benchmark datasets which improved the read process by a factor of 8.Ph.D., Electrical and Computer Engineering -- Drexel University, 200

Fast Visualization by Shear-Warp using Spline Models for Data Reconstruction

This work concerns oneself with the rendering of huge three-dimensional data sets. The target thereby is the development of fast algorithms by also applying recent and accurate volume reconstruction models to obtain at most artifact-free data visualizations. In part I a comprehensive overview on the state of the art in volume rendering is given. Part II is devoted to the recently developed trivariate (linear,) quadratic and cubic spline models defined on symmetric tetrahedral partitions directly obtained by slicing volumetric partitions of a three-dimensional domain. This spline models define piecewise polynomials of total degree (one,) two and three with respect to a tetrahedron, i.e. the local splines have the lowest possible total degree and are adequate for efficient and accurate volume visualization. The following part III depicts in a step by step manner a fast software-based rendering algorithm, called shear-warp. This algorithm is prominent for its ability to generate projections of volume data at real time. It attains the high rendering speed by using elaborate data structures and extensive pre-computation, but at the expense of data redundancy and visual quality of the finally obtained rendering results. However, to circumvent these disadvantages a further development is specified, where new techniques and sophisticated data structures allow combining the fast shear-warp with the accurate ray-casting approach. This strategy and the new data structures not only grant a unification of the benefits of both methods, they even easily admit for adjustments to trade-off between rendering speed and precision. With this further development also the 3-fold data redundancy known from the original shear-warp approach is removed, allowing the rendering of even larger three-dimensional data sets more quickly. Additionally, real trivariate data reconstruction models, as discussed in part II, are applied together with the new ideas to onward the precision of the new volume rendering method, which also lead to a one order of magnitude faster algorithm compared to traditional approaches using similar reconstruction models. In part IV, a hierarchy-based rendering method is developed which utilizes a wavelet decomposition of the volume data, an octree structure to represent the sparse data set, the splines from part II and a new shear-warp visualization algorithm similar to that presented in part III. This thesis is concluded by the results centralized in part V