Registration of bone structures in 3D ultrasound and CT data: Comparison of different optimization strategies

International Congress Series

Volumn 1281, Issue , 2005, Pages 242-247

  Winter, Susanne ^a   Brendel, Bernhard ^a   Igel, Christian ^a  

^a RUHR UNIVERSITY BOCHUM   (Germany)

Author keywords

Evolutionary optimization; Image registration; Spine; Ultrasound

Indexed keywords

EID: 33646458732     PISSN: 05315131     EISSN: None     Source Type: Book Series    
DOI: 10.1016/j.ics.2005.03.351     Document Type: Article

References (9)

1. Pluim J., and Fitzpatrick J. Image registration. IEEE Trans. Med. Imag. 8 22 (2003) 1341-1343
2. Brendel B., et al. Registration of 3D CT- and ultrasound-datasets of the spine using bone structures. Comput. Aided Surg. 7 (2002) 146-155
3. Maintz J., and Viergever M. A survey of medical image registration. Med. Image Anal. 2 1 (1998) 1-36
4. Rouet J.-M., Jacq J.-J., and Roux C. Genetic algorithms for a robust 3-D MR-CT registration. IEEE Trans. Inf. Technol. Biomed. 4 2 (2000) 126-136
5. Kagadis G., et al. A comparative study of surface- and volume-based techniques for the automatic registration between CT and SPECT brain images. Med. Phys. 29 2 (2002) 201-213
6. Riedmiller M. Advanced supervised learning in multi-layer perceptrons-from backpropagation to adaptive learning algorithms. Comput. Stand. Interfaces (1994) 265-278
7. Igel C., and Hüsken M. Empirical evaluation of the improved Rprop learning algorithm. Neurocomputing 50 C (2003) 105-123
8. Hansen N., and Ostermeier A. Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9 2 (2001) 159-195
9. Hansen N., and Kern S. Evaluating the CMA evolution strategy on multimodal test functions. In: Yao X., et al. (Ed). Parallel Problem Solving from Nature (PPSN VIII). LNCS vol. 3242 (2004), Springer 282-291