Geometric model extraction from magnetic resonance volume data

Citation

Laidlaw, David H. (1995) Geometric model extraction from magnetic resonance volume data. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/1spw-fx17. https://resolver.caltech.edu/CaltechETD:etd-10152007-132141

Abstract

This thesis presents a computational framework and new algorithms for creating geometric models and images of physical objects. Our framework combines magnetic resonance imaging (MRI) research with image processing and volume visualization. One focus is feedback of requirements from later stages of the framework to earlier ones.

Within the framework we measure physical objects yielding vector-valued MRI volume datasets. We process these datasets to identify different materials, and from the classified data we create images and geometric models. New algorithms developed within the framework include a goal-based technique for choosing MRI collection protocols and parameters and a family of Bayesian tissue-classification methods.

The goal-based data-collection technique chooses MRI protocols and parameters subject to specific goals for the collected data. Our goals are to make identification of different tissues possible with data collected in the shortest possible time. Our method compares results across different collection protocols, and is fast enough to use for steering the data-collection process.

Our new tissue-classification methods operate on small regions within a volume dataset, not directly on the sample points. We term these regions voxels and assume that each can contain a mixture of materials. The results of the classification step are tailored to make extraction of surface boundaries between solid object parts more accurate.

Another new algorithm directly renders deformed volume data produced, for example, by simulating the movement of a flexible body. The computational framework for building geometric models allows computer graphics users to more easily create models with internal structure and with a high level of detail. Applications exist in a variety of fields including computer graphics modeling, biological modeling, anatomical studies, medical diagnosis, CAD/CAM, robotics, and computer animation. We demonstrate the utility of the computational framework with a set of computer graphics images and models created from data.

Thesis Files