Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Accurate anisotropic fast marching for diffusion-based geodesic tractography
Journal of Biomedical Imaging - Recent Advances in Neuroimaging Methodology
Robust Segmentation and Anatomical Labeling of the Airway Tree from Thoracic CT Scans
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Fast Object Segmentation by Growing Minimal Paths from a Single Point on 2D or 3D Images
Journal of Mathematical Imaging and Vision
Boundary-specific cost functions for quantitative airway analysis
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Characterizing vascular connectivity from micro CT images
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Optimal graph based segmentation using flow lines with application to airway wall segmentation
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
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This paper proposes a method to extract the airway tree from CT images by continually extending the tree with locally optimal paths. This is in contrast to commonly used region growing based approaches that only search the space of the immediate neighbors. The result is a much more robust method for tree extraction that can overcome local occlusions. The cost function for obtaining the optimal paths takes into account of an airway probability map as well as measures of airway shape and orientation derived from multi-scale Hessian eigen analysis on the airway probability. Significant improvements were achieved compared to a region growing based method, with up to 36% longer trees at a slight increase of false positive rate.