Extracting Curve Skeletons from Gray Value Images for Virtual Endoscopy

Abstract

The extraction of curve skeletons from tubular networks is a necessary prerequisite for virtual endoscopy applications. We present an approach for curve skeleton extraction directly from gray value images that supersedes the need to deal with segmentations and skeletonizations. The approach uses properties of the Gradient Vector Flow to derive a tube-likeliness measure and a medialness measure. Their combination allows the detection of tubular structures and an extraction of their medial curves that stays centered also in cases where the structures are not tubular such as junctions or severe stenoses. We present results on clinical datasets and compare them to curve skeletons derived with different skeletonization approaches from high quality segmentations. Our approach achieves a high centerline accuracy and is computationally efficient by making use of a GPU based implementation of the Gradient Vector Flow.