IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Replicator equations, maximal cliques, and graph isomorphism
Neural Computation
Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable Parallel Algorithms for FPT Problems
Algorithmica
Dynamic local search for the maximum clique problem
Journal of Artificial Intelligence Research
A continuous-based approach for partial clique enumeration
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Hierarchical matching of anatomical trees for medical image registration
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
Tree matching applied to vascular system
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Complexity of computing distances between geometric trees
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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Many medical applications require a registration of different images of the same organ. In many cases, such a registration is accomplished by manual placement of landmarks in the images. In this paper, we propose a method which is able to find reasonable landmarks automatically. To achieve this, bifurcations of the vessel systems, which have been extracted from the images by a segmentation algorithm, are assigned by the so-called association graph method and the coordinates of these matched bifurcations can be used as landmarks for a non-rigid registration algorithm. Several constraints to be used in combination with the association graph method are proposed and evaluated on a ground truth consisting of anatomical trees from liver and lung. Furthermore, a method for preprocessing (tree pruning) as well as for postprocessing (clique augmentation) are proposed and evaluated on this ground truth. The proposed method achieves promising results for anatomical trees of liver and lung and for medical images obtained with different modalities and at different points in time.