Material Classification of Magnetic Resonance Volume Data
Creators: name: Laidlaw, David H.
Publisher: name: California Institute of Technology
Year: 1992

description: A major unsolved problem in computer graphics is that of making high-quality models. Traditionally, models have consisted of interactively or algorithmically described collections of graphics primitives such as polygons. The process of constructing these models is painstaking and often misses features and behavior that we wish to model. Models extracted from volume data collected from real, physical objects have the potential to show features and behavior that are difficult to capture using these traditional modeling methods. We use vector-valued magnetic resonance volume data in this thesis. The process of extracting models from such data involves four main steps: collecting the sampled volume data; preprocessing it to reduce artifacts from the collection process; classifying materials within the data; and creating either a rigid geometric model that is static, or a flexible, dynamic model that can be simulated. In this thesis we focus on the the first three steps. We present guidelines and techniques for collecting and processing magnetic resonance data to meet the needs of the later steps. Our material classification and model extraction techniques work better when the data values for a given material are constant throughout the dataset, when data values for different materials are different, and when the dataset is free of aliasing artifacts and noise. We present a new material-classification method that operates on vector-valued volume data. The method produces a continuous probability function for each material over the volume of the dataset, and requires no expert interaction to teach it different material classes. It operates by fitting peaks in the histogram of a collected dataset using parameterized gaussian bumps, and by using Bayes' law to calculate material probabilities, with each gaussian bump representing one material. To illustrate the classification method, we apply it to real magnetic resonance data of a human head, a human hand, a banana, and a jade plant. From the classified data, we produce "computationally stained" slices that discriminate among materials better than do the original grey-scale versions. We also generate volume-rendered images of classified datasets clearly showing different anatomical features of various materials. Finally, we extract preliminary static and dynamic geometric models of different tissues.
description: © 1992 David Laidlaw, California Institute of Technology. 

Submitted May 29, 1992. 

I would like to thank Al Barr, my advisor, for the many new directions and fresh ideas that he suggested, for teaching me applied math, among other things, and for providing the resource; and creating the environment that made this work possible. I would also like to thank my fellow graduate students, Ronen Barze, Bena Currin, Kurt Fleischer, Jeff Goldsmith, Devendra Kalra, Dave Kirk, John Snyder, and Adam Woodbury, for lots of productive suggestions and help with this work, and for the sense of camaraderie that made working in the lab a lot more fun and stimulating. In particular, John and Adam helped extensively with model extract ion, Dave and Devendra started the process of collecting and working with MR data, and Ronen and Kurt, among others, provided me with lots of insight daring many long, involved conversations. I am also grateful to Matthew Avalos for implementing much of the volumen-rendering software and for producing many of the volume-rendered images in Chapter 5. I would also like to thank Donald Marks, Bruce Bell, Mike Meckler, Mark Montague, Pete Wenzel and Allen Corcoran for helping to keep the lab software and hardware running, and Dian De Sha and Sandra Reyna for keeping the lab functioning otherwise. Mark and Dian also made helpful comments on earlier versions of this work. The data used in this work was collected at the Huntington Magnetic Resonance Center in Pasadena. I am grateful to Dr. Brian Ross for his help in organizing the process, to Bassem for many instructive conversations, and to Jose Jiminez for the late-night collection sessions. Thanks also to John Allman and Russ Jacobs for help in learning about magnetic resonance, biology, and anatomy. This work was supported by grants from the Beckman Institute Foundation and the National Science Foundation Science and Technology Center for Computer Graphics and Visualization. Finally, I would like to thank my parents for their long-distance support and encouragement, and my wife, Barbara Meier, not only for her technical and moral support, but for her patience
and understanding.
description: Submitted - 92-21.pdf
Submitted - 92-21.ps