Information
- Publication Type: Technical Report
- Workgroup(s)/Project(s): not specified
- Date: January 2004
- Number: TR-186-2-04-01
- Keywords: Histogram Classification, Distance Fields, Thin-Plate Spline, Probabilistic Atlas, Knowledge Based Segmentation, CT Angiography
Abstract
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 require 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 transformation, 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.Additional Files and Images
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No further information available.BibTeX
@techreport{Straka-2004-BSA, title = "Bone Segmentation in CT-Angiography Data Using a Probabilistic Atlas", author = "Mat\'{u}s Straka and Alexandra La Cruz and Leonid Dimitrov and Milo\v{s} \v{S}r\'{a}mek and Dominik Fleischmann and Eduard Gr\"{o}ller", year = "2004", abstract = "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 require 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 transformation, 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.", month = jan, number = "TR-186-2-04-01", address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria", institution = "Institute of Computer Graphics and Algorithms, Vienna University of Technology ", note = "human contact: technical-report@cg.tuwien.ac.at", keywords = "Histogram Classification, Distance Fields, Thin-Plate Spline, Probabilistic Atlas, Knowledge Based Segmentation, CT Angiography", URL = "https://www.cg.tuwien.ac.at/research/publications/2004/Straka-2004-BSA/", }