Laidlaw, David H. and Fleischer, Kurt W. and Barr, Alan H. (1998) Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms. IEEE Transactions on Medical Imaging, 17 (1). pp. 74-86. ISSN 0278-0062. http://resolver.caltech.edu/CaltechAUTHORS:LAIieeetmi98
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The authors present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with magnetic resonance imaging (MRI) or computed tomography (CT). Because the authors allow for mixtures of materials and treat voxels as regions, their technique reduces errors that other classification techniques can create along boundaries between materials and is particularly useful for creating accurate geometric models and renderings from volume data. It also has the potential to make volume measurements more accurately and classifies noisy, low-resolution data well. There are two unusual aspects to the authors' approach. First, they assume that, due to partial-volume effects, or blurring, voxels can contain more than one material, e.g., both muscle and fat; the authors compute the relative proportion of each material in the voxels. Second, they incorporate information from neighboring voxels into the classification process by reconstructing a continuous function, ρ(x), from the samples and then looking at the distribution of values that ρ(x) takes on within the region of a voxel. This distribution of values is represented by a histogram taken over the region of the voxel; the mixture of materials that those values measure is identified within the voxel using a probabilistic Bayesian approach that matches the histogram by finding the mixture of materials within each voxel most likely to have created the histogram. The size of regions that the authors classify is chosen to match the sparing of the samples because the spacing is intrinsically related to the minimum feature size that the reconstructed continuous function can represent.
|Additional Information:||© Copyright 1998 IEEE. Reprinted with permission. Manuscript received December 27, 1996; revised November 19, 1997. This work was supported in part by grants from Apple, DEC, Hewlett Packard, and IBM. Additional support was provided by the National Science Foundation (NSF) under Grant ASC-89-20219, as part of the NSF/ARPA STC for Computer Graphics and Scientific Visualization; by the Department of Energy under Grant DE-FG03-92ER25134, as part of the Center for Research in Computational Biology; by the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Science Foundation as part of the Human Brain Project; by the National Science Foundation under Grant CCR-9619649; and by the Beckman Institute Foundation. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was W.E. Higgins. The authors would like to thank M. Avalos and D. Devault, who were instrumental in implementation. They would also like to thank B. Meier, D. Kirk, J. Snyder, B. Currin, and M. Montague for reviewing early drafts and making suggestions. The data was collected in collaboration with the Huntington Magnetic Resonance Center with the cooperation of B. Ross and J. Jimenez, and at the Caltech Biological Imaging Center jointly with P. Ghosh and R. Jacobs.|
|Subject Keywords:||Bayesian probability theory, discrete signal processing, feature detection, function theory, geometric modeling, image processing, magnetic resonance imaging microscopy, mixture modeling and estimation, multiscale analysis, multispectral classification, multivariate segmentation, partial volume, scale space, tissue classification, volume measurement|
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|Deposited On:||12 Mar 2008|
|Last Modified:||26 Dec 2012 09:52|
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