A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching of tree structures for registration of medical images
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Tree matching applied to vascular system
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Graph matching by relaxation of fuzzy assignments
IEEE Transactions on Fuzzy Systems
Efficient globally optimal matching of anatomical trees of the liver
EG VCBM'10 Proceedings of the 2nd Eurographics conference on Visual Computing for Biology and Medicine
Improving diagnosis and intervention: a complete approach for registration of liver CT data
MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications
| Hi-index | 0.00 |
Many inexact automatic tree matching algorithms are available. However, they provide matches that are not completely error free. Another option is to use manually matched node-pairs, but this enormously slows down the process. Our contribution to the state of the art is to combine the advantages of both solutions. We enhance the automatic tree matching algorithm designed by Graham et al., so that it is possible to interact with it by previously selecting important matches or by subsequently fixing the provided wrong matches. We apply the tree matching algorithm to the anatomical vasculature of the liver. Furthermore, we developed several visualization features to make manual tree interaction as easy as possible. Both, the interactive and automatic part of the implemented component were evaluated. As a result, the speed of the automatic tree matching algorithm is increased. It takes 7.45s for trees up to 192 nodes and less than 1s if three input matches are provided. In addition to this, an in-depth evaluation of the robustness of the algorithm is presented. The results are remarkable. The average of wrong matches varies between 1.17 and 1.4 node-pairs in the worst cases. The rate of correct matches is high. The evaluation of the visualization features for interactive refinement of matches showed that the percentage of wrong matches found is increased from 56.25% to 78.43%. The mean time to find them is decreased from 227 to 122s.