Semi-Automatic Anatomical Tree Matching for Landmark-Based Elastic Registration of Liver Volumes

One promising approach to register liver volume acquisitions is based on the branching points of the vessel trees as anatomical landmarks inherently available in the liver. Automated tree matching algorithms were proposed to automatically find pair-wise correspondences between two vessel trees. However, to the best of our knowledge, none of the existing automatic methods are completely error free. After a review of current literature and methodologies on the topic, we propose an efficient interaction method that can be employed to support tree matching algorithms with important pre-selected correspondences or after an automatic matching to manually correct wrongly matched nodes. We used this method in combination with a promising automatic tree matching algorithm also presented in this work. The proposed method was evaluated by 4 participants and a CT dataset that we used to derive multiple artificial datasets.

[1]  Rangasami L. Kashyap,et al.  Building Skeleton Models via 3-D Medial Surface/Axis Thinning Algorithms , 1994, CVGIP Graph. Model. Image Process..

[2]  William E. Higgins,et al.  Globally optimal model-based matching of anatomical trees , 2006, SPIE Medical Imaging.

[3]  Bernhard Preim,et al.  Mathematical Methods in Medical Imaging: Analysis of Vascular Structures for Liver Surgery Planning , 2001 .

[4]  Steven Gold,et al.  A Graduated Assignment Algorithm for Graph Matching , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Zheng Lin,et al.  Unseeded Region Growing for 3D Image Segmentation , 2000, VIP.

[6]  M. Ilg,et al.  The application of Voronoi skeletons to perceptual grouping in line images , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[7]  Hans-Peter Meinzer,et al.  Statistical shape models for 3D medical image segmentation: A review , 2009, Medical Image Anal..

[8]  Maria Athelogou,et al.  Cognition Network Technology for a Fully Automated 3D Segmentation of Liver , 2007 .

[9]  Ross T. Whitaker,et al.  Variable-conductance, level-set curvature for image denoising , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[10]  Bernhard Preim,et al.  Analysis of vasculature for liver surgical planning , 2002, IEEE Transactions on Medical Imaging.

[11]  Milan Sonka,et al.  Quantitative Analysis of Intrathoracic Airway Trees: Methods and Validation , 2003, IPMI.

[12]  Francis K. H. Quek,et al.  Vessel extraction techniques and algorithms: a survey , 2003, Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings..

[13]  Heinz-Otto Peitgen,et al.  Hierarchical Matching of Anatomical Trees for Medical Image Registration , 2008, ICMB.

[14]  Bart M. ter Haar Romeny,et al.  A Novel 3D Multi-scale Lineness Filter for Vessel Detection , 2007, MICCAI.

[15]  Benoit M. Dawant,et al.  Automatic 3D segmentation of the liver from abdominal CT images: a level-set approach , 2001, SPIE Medical Imaging.

[16]  Thomas Lange,et al.  3D ultrasound-CT registration of the liver using combined landmark-intensity information , 2008, International Journal of Computer Assisted Radiology and Surgery.

[17]  Kin-Man Lam,et al.  Extraction of the Euclidean skeleton based on a connectivity criterion , 2003, Pattern Recognit..

[18]  Cristian Lorenz,et al.  A comprehensive shape model of the heart , 2006, Medical Image Anal..

[19]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[20]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Horst Bischof,et al.  A Novel Approach for Detection of Tubular Objects and Its Application to Medical Image Analysis , 2008, DAGM-Symposium.

[22]  Luc Soler,et al.  Tree Matching Applied to Vascular System , 2005, GbRPR.

[23]  Guido Gerig,et al.  Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images , 1998, Medical Image Anal..

[24]  Yufei Chen,et al.  Generation of a Graph Representation from Three-Dimensional Skeletons of the Liver Vasculature , 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

[25]  Atilla Peter Kiraly,et al.  A novel multipurpose tree and path matching algorithm with application to airway trees , 2006, SPIE Medical Imaging.

[26]  Horst Bischof,et al.  Liver Segmentation in CT Data: A Segmentation Refinement Approach , 2007 .

[27]  Kaleem Siddiqi,et al.  Matching Hierarchical Structures Using Association Graphs , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Wenyu Liu,et al.  Skeletonization using SSM of the Distance Transform , 2007, 2007 IEEE International Conference on Image Processing.

[29]  Akinobu Shimizu,et al.  Automatic Liver Segmentation Method based on Maximum A Posterior Probability Estimation and Level Set Method , 2007 .

[30]  Swarup Medasani,et al.  Graph matching by relaxation of fuzzy assignments , 2001, IEEE Trans. Fuzzy Syst..

[31]  Milan Sonka,et al.  Matching and anatomical labeling of human airway tree , 2005, IEEE Transactions on Medical Imaging.

[32]  Heinz-Otto Peitgen,et al.  Matching of Tree Structures for Registration of Medical Images , 2007, GbRPR.

[33]  Thomas Lange,et al.  Landmark-Based 3D Elastic Registration of Pre- and Postoperative Liver CT Data , 2009, Bildverarbeitung für die Medizin.

[34]  Martin Styner,et al.  Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.