Model-based detection of tubular structures in 3D images
Computer Vision and Image Understanding
A Sequential 3D Thinning Algorithm and Its Medical Applications
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
A Novel Approach for Detection of Tubular Objects and Its Application to Medical Image Analysis
Proceedings of the 30th DAGM symposium on Pattern Recognition
Differential fly-throughs (DFT): a general framework for computing flight paths
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
3D vector flow guided segmentation of airway wall in MSCT
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
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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.