Mr. Lingxiao Zhao Profile

Lingxiao Zhao

at Technische Univ Delft

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Proceedings Article | 12 March 2008 Paper

Efficient seeding and defragmentation of curvature streamlines for colonic polyp detection

Lingxiao Zhao, Charl Botha, Roel Truyen, Frans Vos, Frits Post

Proceedings Volume 6916, 69160E (2008) https://doi.org/10.1117/12.770763

KEYWORDS: Visualization, Virtual colonoscopy, Computer aided diagnosis and therapy, Colon, Technetium, Tellurium, Shape analysis, Computed tomography, Neck, Medicine

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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.

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