Many computer aided diagnosis (CAD) schemes have been developed for colon cancer detection using Virtual
Colonoscopy (VC). In earlier work, we developed an automatic polyp detection method integrating flow visualization
techniques, that forms part of the CAD functionality of an existing Virtual Colonoscopy pipeline. Curvature
streamlines were used to characterize polyp surface shape. Features derived from curvature streamlines correlated
highly with true polyp detections. During testing with a large number of patient data sets, we found
that the correlation between streamline features and true polyps could be affected by noise and our streamline
generation technique. The seeding and spacing constraints and CT noise could lead to streamline fragmentation,
which reduced the discriminating power of our streamline features.
In this paper, we present two major improvements of our curvature streamline generation. First, we adapted
our streamline seeding strategy to the local surface properties and made the streamline generation faster. It
generates a significantly smaller number of seeds but still results in a comparable and suitable streamline distribution.
Second, based on our observation that longer streamlines are better surface shape descriptors, we
improved our streamline tracing algorithm to produce longer streamlines. Our improved techniques are more
effcient and also guide the streamline geometry to correspond better to colonic surface shape. These two adaptations
support a robust and high correlation between our streamline features and true positive detections and
lead to better polyp detection results.