Paper Info
Title
Surface curvature line clustering for polyp detection in CT colonography
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.
Year
DOI
Venue
2008
10.2312/VCBM/VCBM08/053-060
VCBM
Keywords
Field
DocType
surface partition,large-scale surface shape,curvature line,surface curvedness,ct colonography,clustering approach,aggregate surface shape information,colon wall,true polyp detection,automatic polyp detection,surface partition-based feature,surface area
Cluster (physics),Computer vision,Curvature,Similarity measure,Feature (computer vision),Homogeneous,Computer science,Artificial intelligence,Colon wall,Partition (number theory),Cluster analysis
Conference
Citations 
PageRank 
References 
1
0.38
15
Authors
6
Name
Order
Citations
PageRank
Lingxiao Zhao1261.94
Vincent Frans van Ravesteijn2705.94
Charl P. Botha318219.15
Roel Truyen421819.37
Frans M. Vos513318.49
Frits H. Post61389111.99