Wavelet-Based Volume Rendering

Various biomedical technologies like CT, MRI and PET scanners provide detailed cross-sectional views of the human anatomy. The image information obtained from these scanning devices is typically represented as large data sets whose sizes vary from several hundred megabytes to about one hundred gigabytes. As these data sets cannot be stored on one\u27s local hard drive, SDSC provides a large data repository to store such data sets. These data sets need to be accessed by researchers around the world to collaborate in their research. But the size of these data sets make them difficult to be transmitted over the current network. This thesis presents a 3-D Haar wavelet algorithm which enables these data sets to be transformed into smaller hierarchical representations. These transformed data sets are transmitted over the network and reconstructed to a 3-D volume on the client\u27s side through progressive refinement of the images and 3-D texture mapping techniques

Texture-based 3-D Brain Imaging

Different modalities in biomedical imaging, like CT, MRI and PET scanners, provide detailed cross-sectional views of the human anatomy. The imagery obtained from these scanning devices are typically large-scale data sets whose sizes vary from several hundred megabytes to about one hundred gigabytes, making them impossible to be stored on a regular local hard drive. San Diego Supercomputer Center (SDSC) maintains a High-Performance Storage System (HPSS) where these large-scale data sets can be stored. Members of the National Partnership for Advanced Computational Infrastructure (NPACI) have implemented a Scalable Visualization Toolkit (Vistools), which is used to access the data sets stored on HPSS and also to develop different applications on top of the toolkit. 2-D cross-sectional images are extracted from the data sets stored on HPSS using Vistools, and these 2-D cross-sections are then transformed into smaller hierarchical representations using a wavelet transformation. This makes it easier to transmit them over the network and allows for progressive image refinement. The transmitted 2-D cross-sections are then transformed and reconstructed into a 3-D volume. The 3-D reconstruction has been implemented using texture-mapping functions of Java3D. In a typical application, the user might be interested in a certain section of the data set (region of interest). For example, when a physician wants to examine a tumor in the brain, he or she needs to visualize the section of the tumor in the brain at a high resolution. For this we need to extract sub-volumes of the data set. These sub-volumes are then transmitted and rendered at a higher resolution than the rest of the data set. 1

3-D Haar Wavelet Transformation and Texture-Based 3-D Reconstruction of Biomedical Data

Enhanced biomedical image scanning technology and growing network accessibility have created a need for faster and more efficient data exchange over the Internet and in closed networks. Researchers and biologists are conducting experiments on large biomedical data sets and want to share information with other scientists to progress in their research. But the information they are dealing with are large-scale data sets that occupy gigabytes of space, which makes the storing of these data sets onto one’s local hard drive very difficult. The size of these data sets also makes them difficult to transmit over currently existing network links. To overcome these difficulties, those data sets are stored in large-scale data repositories, from which they can be retrieved upon user’s request. The challenge is to make the data accessible within a reasonable amount of time at a reasonable quality without losing detail or image resolution. We describe a hierarchical storage scheme based on 3-D Haar wavelets, and a fast 3-D rendering algorithm based on 2-D texture mapping, which has been integrated with the Scalable Visualization Toolkits, an alpha project of the Nationa