Surface Curvature Line Clustering for Polyp Detection in CT Colonography

Zhao, Lingxiao; Ravesteijn, Vincent F. van; Botha, Charl P.; Truyen, Roel; Vos, Frans M.; Post, Frits H.

Surface Curvature Line Clustering for Polyp Detection in CT Colonography

Files

053-060.pdf (298.81 KB)

Date

2008

Authors

Zhao, Lingxiao
Ravesteijn, Vincent F. van
Botha, Charl P.
Truyen, Roel
Vos, Frans M.
Post, Frits H.

Publisher

The Eurographics Association

Abstract

Automatic polyp detection is a helpful addition to laborious visual inspection in CT colonography. Traditional detection methods are based on calculating image features at discrete positions on the colon wall. However large-scale surface shapes are not captured. This paper presents a novel approach to aggregate surface shape information for automatic polyp detection. The iso-surface of the colon wall can be partitioned into geometrically homogeneous regions based on clustering of curvature lines, using a spectral clustering algorithm and a symmetric line similarity measure. Each partition corresponds with the surface area that is covered by a single cluster. For each of the clusters, a number of features are calculated, based on the volumetric shape index and the surface curvedness, to select the surface partition corresponding to the cap of a polyp. We have applied our clustering approach to nine annotated patient datasets. Results show that the surface partition-based features are highly correlated with true polyp detections and can thus be used to reduce the number of false-positive detections.

        @inproceedings{:10.2312/VCBM/VCBM08/053-060
,
booktitle = {Eurographics Workshop on Visual Computing for Biomedicine
},
editor = {Charl Botha and Gordon Kindlmann and Wiro Niessen and Bernhard Preim
},
title = {{Surface Curvature Line Clustering for Polyp Detection in CT Colonography
}},
author = {Zhao, Lingxiao and 
Ravesteijn, Vincent F. van and 
Botha, Charl P. and 
Truyen, Roel and 
Vos, Frans M. and 
Post, Frits H.
},
year = {2008
},
publisher = {The Eurographics Association
},
ISSN = {2070-5786
},
ISBN = {978-3-905674-13-2
},
DOI = {/10.2312/VCBM/VCBM08/053-060
}
}