Robust Classification and Analysis of Anatomical Surfaces Using 3D Skeletons

Reniers, Dennie; Jalba, Andrei; Telea, Alexandru

Robust Classification and Analysis of Anatomical Surfaces Using 3D Skeletons

Files

061-068.pdf (563.73 KB)

Date

2008

Authors

Reniers, Dennie
Jalba, Andrei
Telea, Alexandru

Publisher

The Eurographics Association

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.

        @inproceedings{:10.2312/VCBM/VCBM08/061-068
,
booktitle = {Eurographics Workshop on Visual Computing for Biomedicine
},
editor = {Charl Botha and Gordon Kindlmann and Wiro Niessen and Bernhard Preim
},
title = {{Robust Classification and Analysis of Anatomical Surfaces Using 3D Skeletons
}},
author = {Reniers, Dennie and 
Jalba, Andrei and 
Telea, Alexandru
},
year = {2008
},
publisher = {The Eurographics Association
},
ISSN = {2070-5786
},
ISBN = {978-3-905674-13-2
},
DOI = {/10.2312/VCBM/VCBM08/061-068
}
}