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Classification of Material Mixtures in Volume Data for Visualization and Modeling

Laidlaw, David H. and Fleischer, Kurt W. and Barr, Alan H. (1994) Classification of Material Mixtures in Volume Data for Visualization and Modeling. Computer Science Technical Reports, California Institute of Technology , Pasadena, CA. (Unpublished)

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Material classification is a key stop in creating computer graphics models and images from volume data, We present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with Magnetic Resonance Imaging (NMI) or Computed Tomography (CT). The algorithm assumes that voxels can contain more than one material, e.g. both muscle and fat; we wish to compute the relative proportion of each material in the voxels. Other classification methods have utilized Gaussian probability density functions to model the distribution of values within a dataset. These Gaussian basis functions work well for voxels with unmixed materials, but do not work well where the materials are mixed together. We extend this approach by deriving non-Gaussian "mixture" basis functions. We treat a voxel as a volume, not as a single point. We use the distribution of values within each voxel-sized volume to identify materials within the voxel using a probabilistic approach. The technique reduces the classification artifacts that occur along boundaries between materials. The technique is useful for making higher quality geometric models and renderings from volume data, and has the potential to make more accurate volume measurements. It also classifies noisy, low-resolution data well.

Item Type:Report or Paper (Technical Report)
Additional Information:© California Institute of Technology Thanks to Barbara Meier, David Kirk, John Snyder, Bena Currin and Mark Montague for reviewing early drafts and making suggestions. Thanks also to Allen Corcoran, Constance Villani, Cindy Ball and Eric Winfree for production help, and Jose Jimenez for the latenight MR sessions. Our data was collected in collaboration with the Huntington Magnetic Resonance Center in Pasadena. This work was supported in part by grants from Apple, DEC, Hewlett Packard, and IBM. Additional support was provided by NSF (ASC-89-20219) as part of the NSF/ARPA STC for Computer Graphics and Scientific Visualization, by the DOE (DE-FG03-92ER25134) as part of the Center for Research in Computational Biology, by the National Institute on Drug Abuse and the National Institute of Mental Health as part of the Human Brain Project, and by the Beckman Institute Foundation. All opinions, findings, conclusions, or recommendations expressed in this document are those of the author(s) and do not necessarily reflect the views of the sponsoring agencies.
Group:Computer Science Technical Reports
Funding AgencyGrant Number
Advanced Research Projects Agency (ARPA)UNSPECIFIED
STC for Computer Graphics and Scientific VisualizationUNSPECIFIED
Department of Energy (DOE)DE-FG03-92ER25134
National Institute on Drug AbuseUNSPECIFIED
National Institute of Mental Health (NIMH)UNSPECIFIED
Arnold and Mabel Beckman FoundationUNSPECIFIED
Series Name:Computer Science Technical Reports
Record Number:CaltechCSTR:1994.cs-tr-94-07
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Usage Policy:You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.
ID Code:26786
Deposited By: Imported from CaltechCSTR
Deposited On:25 Apr 2001
Last Modified:03 Oct 2019 03:17

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