
Proceedings Paper
A liver registration method for segmented multi-phase CT imagesFormat | Member Price | Non-Member Price |
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Paper Abstract
In order to build high quality geometric models for liver containing vascular system, multi-phase CT series used in a computer–aided diagnosis and surgical planning system aims at liver diseases have to be accurately registered. In this paper we model the segmented liver containing vascular system as a complex shape and propose a two-step registration method. Without any tree modeling for vessel this method can carry out a simultaneous registration for both liver tissue and vascular system inside. Firstly a rigid aligning using vessel as feature is applied on the complex shape model while genetic algorithm is used as the optimization method. Secondly we achieve the elastic shape registration by combine the incremental free form deformation (IFFD) with a modified iterative closest point (ICP) algorithm. Inspired by the concept of demons method, we propose to calculate a fastest diffusion vector (FDV) for each control point on the IFFD lattice to replace the points correspondence needed in ICP iterations. Under the iterative framework of the modified ICP, the optimal solution of control points’ displacement in every IFFD level can be obtained efficiently. The method has been quantitatively evaluated on clinical multi-phase CT series.
Paper Details
Date Published: 20 March 2015
PDF: 6 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941331 (20 March 2015); doi: 10.1117/12.2081886
Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)
PDF: 6 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941331 (20 March 2015); doi: 10.1117/12.2081886
Show Author Affiliations
Shuyue Shi, Huazhong Univ. of Science and Technology (China)
Rong Yuan, Huazhong Univ. of Science and Technology (China)
Rong Yuan, Huazhong Univ. of Science and Technology (China)
Zhi Sun, Huazhong Univ. of Science and Technology (China)
Qingguo Xie, Huazhong Univ. of Science and Technology (China)
Wuhan National Lab. for Optoelectronics (China)
Qingguo Xie, Huazhong Univ. of Science and Technology (China)
Wuhan National Lab. for Optoelectronics (China)
Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)
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