In Proceedings of Visual Computing in Biology and Medicine

Surface Curvature Line Clustering for Polyp Detection in CT Colonography

Lingxiao Zhao, V.F. van Ravesteijn, Charl P. Botha, R. Truyen, F.M. Vos, and Frits H. Post

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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.

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@inproceedings{bib:zhao:2008,
    author       = { Zhao, Lingxiao and van Ravesteijn, V.F. and Botha, Charl P. and Truyen, R. and Vos, F.M. and Post, Frits H. },    
    title        = { Surface Curvature Line Clustering for Polyp Detection in CT Colonography },
    booktitle    = { In Proceedings of Visual Computing in Biology and Medicine },
    year         = { 2008 },
    pages        = { 53--60 },
    doi          = { 10.2312/VCBM/VCBM08/053-060 },
    dblp         = { conf/vcbm/ZhaoRBTVP08 },
    url          = { https://publications.graphics.tudelft.nl/papers/619 },
}