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Elastic Image Registration Using Attractive and Repulsive Particle Swarm Optimization

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Simulated Evolution and Learning (SEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4247))

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Abstract

Elastic image registration plays an important role in medical image registration. For elastic image registration based on landmarks of sub-images, optimization algorithm is applied to extract landmarks. But local maxima of similarity measure make optimization difficult to convergence to global maximum. The registration error will lead to location error of landmarks and lead to unexpected elastic transformation results. In this paper, an elastic image registration method using attractive and repulsive particle swarm optimization (ARPSO) is proposed. For each subimage, rigid registration is done using ARPSO. In attractive phase, particles converge to promise regions in the search space. In repulsive phase, particles are repelled each other along opposition directions and new particles are created, which might avoid premature greatly. Next, thin plate spline transformation is used for the elastic interpolation between landmarks. Experiments show that our method does well in the elastic image registration experiments.

Sponsored by: National Natural Science Funds(No. 60572101) and Guangdong Natura Science Funds (No. 31789).

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References

  1. Rohr, K.: Elastic Registration of Multimodal medical images: A Survey. Auszug aus: Kunstliche Intelligenz, Hef 3, 11–17 (2000)

    Google Scholar 

  2. Likar, B., Pernus, F.: A hierarchical approach to elastic registration based on mutual information. Image Vision and Computing 19, 33–44 (2001)

    Article  Google Scholar 

  3. Antoine Maintz, J.B., Meijering, E.H.W., Viegever, M.A.: General multimodal elastic registration based on mutual information. In: Hanson, K.M. (ed.) Proceedings of SPIE Medical Imaging 1998, vol. 3338, pp. 144–154 (1998)

    Google Scholar 

  4. Bookstein, F.L.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Analysis and Machine Intelligence 11(6), 567–585 (1989)

    Article  MATH  Google Scholar 

  5. Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43. IEEE Service Center, Piscataway (1995)

    Chapter  Google Scholar 

  6. Wachowiak, M.P., Smolíková, R., Zheng, Y., Zurada, J.M., Elmaghraby, A.S.: An Approach to Multimodal Biomedical Image Registration Utilizing Particle Swarm Optimization. IEEE Trans. Evolutionary Computation 8(3), 289–301 (2004)

    Article  Google Scholar 

  7. Butz, T., Thiran, J.P.: Feature-space mutual information for multi-modal signal processing with application to medical image registration. In: European Signal Processing Conference 2002, Toulouse, France, vol. 1, pp. 3–10 (2002)

    Google Scholar 

  8. Butz, T., Thiran, J.P.: Affine registration with feature space mutual information. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 549–556. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  9. Butz, T., Thiran, J.P.: From error probability to information theoretic (multi-modal) signal processing. Signal Processing 85, 875–902 (2005)

    Article  MATH  Google Scholar 

  10. Viola, P., Wells III, W.M.: Alignment by maximization of mutual information. In: Proceedings of the Fifth International Conference on Computer Vision, pp. 16–23. IEEE Computer Society Press, Los Alamitos (1995)

    Chapter  Google Scholar 

  11. Studholme, C., Hawkes, D.J., Hill, D.L.G.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32, 71–86 (1999)

    Article  Google Scholar 

  12. Riget, J., Vesterstroem, J.S.: A diversity-guided particle swarm optimizer - the ARPSO. Technical Report 2002-02, Department of Computer Science, University of Aarhus (2002)

    Google Scholar 

  13. Parsopoulos, K.E., Vrahatis, M.N.: Modification of the particle swarm optimizer for locating all the global minima. In: Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA 2001), pp. 324–327. Czech Republic, Prague (2001)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Intelligent Information Processing Laboratory, Shenzhen University, 518060, China

    Yang Xuan & Pei Jihong

  2. College of Information and Engineering, Shenzhen University, 518060, China

    Yang Xuan

Authors
  1. Yang Xuan
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  2. Pei Jihong
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Editor information

Editors and Affiliations

  1. Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung County Taiwan, ROC

    Tzai-Der Wang

  2. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  3. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taipei, Taiwan

    Shu-Heng Chen

  4. Department of Computer Science and Technology, University of Sci. & Tech. of China, 230026, Hefei, Anhui, P.R. China

    Xufa Wang

  5. School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, Australia

    Hussein Abbass

  6. The Univertity of Tokyo, Japan

    Hitoshi Iba

  7. Department of Computer, University of Science and Technology of China, 230027, Hefei, China

    Guo-Liang Chen

  8. University of Birmingham, Birmingham, UK

    Xin Yao

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© 2006 Springer-Verlag Berlin Heidelberg

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Xuan, Y., Jihong, P. (2006). Elastic Image Registration Using Attractive and Repulsive Particle Swarm Optimization. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_98

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  • DOI: https://doi.org/10.1007/11903697_98

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47331-2

  • Online ISBN: 978-3-540-47332-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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