Abstract
We present a modification to the iterative closest point algorithm which improves the algorithm’s robustness and precision. At the start of each iteration, before point correspondence is calculated between the two feature sets, the algorithm randomly perturbs the point positions in one feature set. These perturbations allow the algorithm to move out of some local minima to find a minimum with a lower residual error. The size of this perturbation is reduced during the registration process. The algorithm has been tested using multiple starting positions to register three sets of data: a surface of a femur, a skull surface and a registration to hepatic vessels and a liver surface. Our results show that, if local minima are present, the stochastic ICP algorithm is more robust and is more precise than the standard ICP algorithm.
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Keywords
- Iterative Close Point
- Target Registration Error
- Iterative Close Point Algorithm
- Hepatic Vessel
- Target Dataset
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References
P.J. Besl and N.D. McKay. A method for registration of 3-D shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence, 14(2):239–256, 1992.
J. Feldmar, N. Ayache, and F. Betting. 3D-2D projective registration of free-form curves and surfaces. Comput. Vision Image Understanding, 65(3):403–424, 1997.
C.R. Maurer, Jr., G.B. Aboutanos, B.M. Dawant, Maciunas R.J., and J.M. Fitzpatrick. Registration of 3-D images using weighted geometrical features. IEEE Trans. Med. Imaging, 15(6):836–849, 1996.
T. Masuda and N. Yokoya. A robust method for registration and segmentation of multiple range images. Comput. Vision Image Understanding, 61(3):295–307, 1995.
J.P. Luck, W.A. Hoff, R.G. Underwood, and C.Q. Little. Registration of range data using a hybrid simulated annealing and iterative closest point algorithm. submitted to IEEE PAMI. available at http://egweb.mines.edu/whoff/publications/2000/pami2000.pdf
W. E. Lorensen and H. E. Cline. Marching cubes: A high resolution 3-D surface reconstruction algorithm. Computer Graphics, 21(4):163–169, 1987.
W. Schroeder, K. Martin, B. Lorensen, L. Avila, R. Avila, and C. Law. The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics. Prentice-Hall, 1997.
M.R. Fenlon, A.S. Jusczyzck, P.J. Edwards, and A.P. King. Acrylic resin dental stent for image guided surgery. J. of Prosthetic Dentistry, 83(4):482–485, 2000.
S. Kirkpatrick, C.D. Gelatt, Jr., and M.P. Vecchi. Optimization by simulated annealing. Science, 220(4598), 1983.
C. Studholme, D.L.G. Hill, and D.J. Hawkes. Automated 3D registration of MR and CT images of the head. Medical Image Analysis, 1(2):163–175, 1996.
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© 2001 Springer-Verlag Berlin Heidelberg
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Penney, G.P., Edwards, P.J., King, A.P., Blackall, J.M., Batchelor, P.G., Hawkes, D.J. (2001). A Stochastic Iterative Closest Point Algorithm (stochastICP). In: Niessen, W.J., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001. MICCAI 2001. Lecture Notes in Computer Science, vol 2208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45468-3_91
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DOI: https://doi.org/10.1007/3-540-45468-3_91
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