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Abstract
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Automatic segmentation of bony structures
in CT angiography datasets is an essential pre-processing step necessary
for most visualization and analysis tasks. Since traditional density
and gradient operators fail in non-trivial cases (or at least requiere
extensive operator work), we propose a new method for segmentation of
CTA data based on a probabilistic atlas. Storing densities and marks of
previously manually segmented tissues to the atlas can constitute a statistical
information base for latter accurate segmentation. In order to eliminate
dimensional and anatomic variability of the atlas input datasets, these
have to be spatially normalized (registered) first by applying a non-rigid
transformation. After this trasnformation, densities and tissue masks
are statistically processed (e.g averaged) within the atlas. Records
in the atlas can be later evaluated for estimating the probability of
bone tissue in a voxel of an unsegmented dataset.
Keywords:CT Angiography, Knowledge Based Segmentation, Probabilistic Atlas, Thin-Plate Spline, Distance Fields, Histogram Classification
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BibTeX Entry
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@InProceedings{Straka03BSA,
author = "Matúš Straka and Alexandra La Cruz and
Leonid I. Dimitrov and Miloš Šrámek and Dominik Fleischmann and
Eduard Gröller",
title = "Bone Segmentation in CT-Angiography Data Using
a Probabilistic Atlas",
bookTitle = "Proceedings of Vision Modeling and Visualization",
year = "2003",
pages = "505--512"
}
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