Robust Classification and Analysis of Anatomical Surfaces Using 3D Skeletons
| dc.contributor.author | Reniers, Dennie | en_US |
| dc.contributor.author | Jalba, Andrei | en_US |
| dc.contributor.author | Telea, Alexandru | en_US |
| dc.contributor.editor | Charl Botha and Gordon Kindlmann and Wiro Niessen and Bernhard Preim | en_US |
| dc.date.accessioned | 2014-01-29T17:02:09Z | |
| dc.date.available | 2014-01-29T17:02:09Z | |
| dc.date.issued | 2008 | en_US |
| dc.description.abstract | We present a method for computing a surface classifier that can be used to detect convex ridges on voxel sur- faces extracted from 3D scans. In contrast to classical approaches based on (discrete) curvature computations, which can be sensitive to various types of noise, we propose here a new method that detects convex ridges on such surfaces, based on the computation of the surface s 3D skeleton. We use a suitable robust, noise-resistant skeletonization algorithm to extract the full 3D skeleton of the given surface, and subsequently compute a surface classifier that separates convex ridges from quasi-flat regions, using the feature points of the simplified skeleton. We demonstrate our method on voxel surfaces extracted from actual anatomical scans, with a focus on cortical surfaces, and compare our results with curvature-based classifiers. As a second application of the 3D skeleton, we show how a partitioning of the brain skeleton can be used in a preprocessing step for the brain surface analysis. | en_US |
| dc.description.seriesinformation | Eurographics Workshop on Visual Computing for Biomedicine | en_US |
| dc.identifier.isbn | 978-3-905674-13-2 | en_US |
| dc.identifier.issn | 2070-5786 | en_US |
| dc.identifier.uri | https://doi.org/10.2312/VCBM/VCBM08/061-068 | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | Robust Classification and Analysis of Anatomical Surfaces Using 3D Skeletons | en_US |
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