Tomographic reconstruction of label images using Gibbs priors.
Item
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Title
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Tomographic reconstruction of label images using Gibbs priors.
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Identifier
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AAI3187391
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identifier
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3187391
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Creator
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Liao, Hstau Y.
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Contributor
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Adviser: Gabor T. Herman
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Date
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2005
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Language
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English
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Publisher
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City University of New York.
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Subject
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Computer Science | Biophysics, Medical | Health Sciences, Radiology
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Abstract
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Our aim is to produce a tessellation of space into small voxels and, based on only a few tomographic projections of an object, assign to each voxel a label that indicates one of the components of interest constituting the object. Examples of application are in the areas of electron microscopy, industrial non-destructive testing, cardiac imaging, etc.;Current approaches first reconstruct the density distribution from the projections and then segment (label) this distribution. We instead postulate a low level prior knowledge regarding the underlying distribution of label images and then directly estimate the label image based on the prior and the projections. In particular we show, in the binary (i.e., two labels) case, that the marginal posterior mode estimator outperforms the widely-known maximum a posteriori probability estimator.;In terms of the label misclassification in the reconstructions, our direct labeling method was experimentally proved (in the binary case) to be superior to the current approaches, but performs less satisfactory under a detectability measure. We discuss possible improvements of our direct labeling methods.
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Type
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dissertation
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Source
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PQT Legacy CUNY.xlsx
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degree
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Ph.D.
