Introduction to algorithms
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision
A Sequential 3D Thinning Algorithm and Its Medical Applications
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
A versatile computer-controlled assembly system
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
Breadth-first search and its application to image processing problems
IEEE Transactions on Image Processing
Visualization in Medicine: Theory, Algorithms, and Applications
Visualization in Medicine: Theory, Algorithms, and Applications
Artificial Intelligence in Medicine
Learning skeletons for shape and pose
Proceedings of the 2010 ACM SIGGRAPH symposium on Interactive 3D Graphics and Games
Speculation on the generality of the backward stepwise view of PCA
Proceedings of the international conference on Multimedia information retrieval
Segmentation of pulmonary vascular trees from thoracic 3D CT images
Journal of Biomedical Imaging
Liver registration for the follow-up of hepatic tumors
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Design of robust vascular tree matching: validation on liver
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Tree matching applied to vascular system
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Dimension reduction in principal component analysis for trees
Computational Statistics & Data Analysis
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Modern multislice X-ray CT scanners provide high-resolution volumetric image data containing a wealth of structural and functional information. The size of the volumes makes it more and more difficult for human observers to visually evaluate their contents. Similar to other areas of medical image analysis, highly automated extraction and quantitative assessment of volumetric data is increasingly important in pulmonary physiology, diagnosis, and treatment. We present a method for a fully automated segmentation of a human airway tree, its skeletonization, identification of airway branches and branchpoints, as well as a method for matching the airway trees, branches, and branchpoints for the same subject over time and across subjects. The validation of our method shows a high correlation between the automatically obtained results and reference data provided by human observers.