Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Tracking 3-D Pulmonary Tree Structures
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Registration and Analysis of Vascular Images
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
Isotropic Energies, Filters and Splines for Vector Field Regularization
Journal of Mathematical Imaging and Vision
Design of robust vascular tree matching: validation on liver
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Robust optical flow estimation based on a sparse motion trajectory set
IEEE Transactions on Image Processing
General framework for automatic detection of matching lesions in follow-up CT
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation
Computer Vision and Image Understanding
New CTA protocol and 2d-3d registration method for liver catheterization
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Tree matching applied to vascular system
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
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In this paper we propose a new two step method to register the liver from two acquisitions. This registration helps experts to make an intra-patient follow-up for hepatic tumors. Firstly, an original and efficient tree matching is applied on different segmentations of the vascular system of a single patient [1]. These vascular systems are segmented from CT-scan images acquired (every six months) during disease treatement, and then modeled as trees. Our method matches common bifurcations and vessels. Secondly, an estimation of liver deformation is computed from the results of the first step. This approach is validated on a large synthetic database containing cases with various deformation and segmentation problems. In each case, after the registration process, the liver recovery is very accurate (around 95%) and the mean localization error for 3D landmarks in liver is small (around 4mm).