Document Type : Full Research Paper


1 M.Sc Student,Physic and Biomedical engineering Department, Tehran University of Medical Sciences

2 Associate Professor, Physic and Biomedical engineering Department, Tehran University of Medical Sciences

3 Associate Professor,Physic and Biomedical engineering Department, Tehran University of Medical Sciences

4 Ph.D Student, Physic and Biomedical engineering Department, Tehran University of Medical Sciences

5 Associate Professor, Radiology Department of Imam khomeini Hospital, Tehran University of Medical Sciences



Abstract: Image guided liver surgery based on intra-operative ultrasound images has received much attention in recent years. Using an efficient point-based registration method to improve both the accuracy and computational time for registration of pre-deformation CT liver images to post-deformation Ultrasound images is of great concern during surgical procedure. Although, Iterative Closest Point (ICP) algorithm is widely used in surface-based registration, its performance is strongly dependent on existence of noise and initial alignment. The registration technique based on the Unscented Kalman Filter (UKF) proposed recently can be a solution to overcome to noise and outliers on an incremental registration basis but it suffers from computational complexity. To overcome the limitations of ICP and UKF algorithms we proposed an incremental two-stage registration algorithm based on the combination of ICP and UKF algorithm to update the registration process based on arrival of intra-operative images. The two-stage algorithm is examined on phantom data sets. The results of phantom study confirm that the two-stage algorithm outperforms the accuracy of ICP and UKF by 23% and 13%, respectively and reduces the running time of UKF by 60%. 


Main Subjects

[1]     Lange, T., et al., 3D ultrasound-CT registration of the liver using combined landmark-intensity information. IJCARS, 2009. 4(1): p. 79-88
[2]     Penney, G.P., et al., Overview of an ultrasound to ct or mr registration system for use in thermal ablation ofliver metastases, in MIUA’01. 2001. p. 65—68
[3]     Paul Mullen, C.O., TECHNICAL INNOVATION: MR, Ultrasound Fusion, in A GE Healthcare MR publication. 2009.
[4]     Rasoulian, A., et al., Group-wise feature-based registration of CT and ultrasound images of spine. 2010: p. 76250R-76250R.
[5]     Zhijun, Z. Adaptive region intensity based rigid ultrasound and CT image registration. in IEEE Conference on CVPR. 2008.
[6]     Rasoulian, A., Group-wise CT to Ultrasound Registration of Lumbar Spine, in computer science faculty 2009, University of Munich
[7]     Sandhu, R., S. Dambreville, and A. Tannenbaum, Point Set Registration via Particle Filtering and Stochastic Dynamics. IEEE Trans Pattern Anal Mach Intell, 2010. 32(8): p. 1459-1473.
[8]     Moghari, M.H. and P. Abolmaesumi, Point-Based Rigid-Body Registration Using an Unscented Kalman Filter. IEEE Trans Med Imaging, 2007. 26(12): p. 1708-1728.
[9]     Mehdi, H.M., New Algorithms in Rigid-Body Registration and Estimation of Registration Accuracy, in Department of Electrical and Computer Engineering. 2008, Queen’s University: Kingston, Ontario, Canada.
[10]  Loizou, C.P. and C.S. Pattichis, Despeckle Filtering Algorithms and Software for Ultrasound Imaging. Synthesis Lectures on Algorithms and Software in Engineering, 2008. 1(1): p. 1-166.
[11]  Czerwinski, R.N., D.L. Jones, and W.D. O'Brien, Jr., Line and boundary detection in speckle images. Image Processing, IEEE Transactions on, 1998. 7(12): p. 1700-1714.
[12]  Lee D Fau - Nam, W.H., et al., Non-rigid registration between 3D ultrasound and CT images of the liver based on intensity and gradient information. Phys Med Biol, 2011. 56(1):117-37(1361-6560 (Electronic)).
[13]  Chan, T.F. and L.A. Vese, Active contours without edges. IEEE Trans Image Process 2001. 10(2): p. 266-277.
[14]  Hassouna, M.S. and A.A. Farag, MultiStencils Fast Marching Methods: A Highly Accurate Solution to the Eikonal Equation on Cartesian Domains. IEEE Trans. Pattern Anal. Mach. Intell., 2007. 29(9): p. 1563-1574.
[15]  Chen Sj Fau - Hellier, P., et al., An anthropomorphicpolyvinyl alcohol triple-modality brain phantom based on Colin27. Med Image Comput Comput Assist Interv, 2010. 13: p. 92-100.
[16]  Clements Lw Fau - Chapman, W.C., et al., Robust surface registration using salient anatomical features for image-guided liver surgery: algorithm and validation. 2008(0094-2405 (Print))