Surface Curvature Line Clustering for Polyp Detection in CT Colonography
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Date
2008
Journal Title
Journal ISSN
Volume Title
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.
Description
@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}
}