Elastic Registration of Brain Cine-MRI Sequences Using MLSDO Dynamic Optimization Algorithm

  • Julien Lepagnot
  • Amir Nakib
  • Hamouche Oulhadj
  • Patrick Siarry
Part of the Studies in Computational Intelligence book series (SCI, volume 433)


In this chapter, we propose to use a dynamic optimization algorithm to assess the deformations of the wall of the third cerebral ventricle in the case of a brain cine-MR imaging. In this method, an elastic registration process is applied to a 2D+t cine-MRI sequence of a region of interest (i.e. lamina terminalis). This registration process consists in optimizing an objective function that can be considered as dynamic. Thus, a dynamic optimization algorithm based on multiple local searches, called MLSDO, is used to accomplish this task. The obtained results are compared to those of several well-known static optimization algorithms. This comparison shows the efficiency of MLSDO, and the relevance of using a dynamic optimization algorithm to solve this kind of problems.


Local Search Dynamic Optimization Cerebral Ventricle Successive Image Dynamic Optimization Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Budoff, M.J., Ahmadi, N., Sarraf, G., Gao, Y., Chow, D., Flores, F., Mao, S.S.: Determination of left ventricular mass on cardiac computed tomographic angiography. Academic Radiology 16(6), 726–732 (2009)CrossRefGoogle Scholar
  2. 2.
    Chenoune, Y., Deléchelle, E., Petit, E., Goissen, T., Garot, J., Rahmouni, A.: Segmentation of cardiac cine-MR images and myocardial deformation assessment using level set methods. Computerized Medical Imaging and Graphics 29(8), 607–616 (2005)CrossRefGoogle Scholar
  3. 3.
    Clerc, M., et al.: The Particle Swarm Central,
  4. 4.
    Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159–195 (2001)CrossRefGoogle Scholar
  5. 5.
    Lepagnot, J., Nakib, A., Oulhadj, H., Siarry, P.: A multiple local search algorithm for continuous dynamic optimization (under submission)Google Scholar
  6. 6.
    Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)CrossRefGoogle Scholar
  7. 7.
    Nakib, A., Aiboud, F., Hodel, J., Siarry, P., Decq, P.: Third brain ventricle deformation analysis using fractional differentiation and evolution strategy in brain cine-MRI. In: Medical Imaging 2010: Image Processing, vol. 7623, pp. 76232I–76232I–10. SPIE, San Diego (2010)Google Scholar
  8. 8.
    Price, K., Storn, R., Lampinen, J.: Differential Evolution - A Practical Approach to Global Optimization. Springer (2005)Google Scholar
  9. 9.
    Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32(1), 71–86 (1999)CrossRefGoogle Scholar
  10. 10.
    Sundar, H., Litt, H., Shen, D.: Estimating myocardial motion by 4D image warping. Pattern Recognition 42(11), 2514–2526 (2009)CrossRefGoogle Scholar
  11. 11.
    Zitová, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21(11), 977–1000 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Julien Lepagnot
    • 1
  • Amir Nakib
    • 1
  • Hamouche Oulhadj
    • 1
  • Patrick Siarry
    • 1
  1. 1.LISSI, E.A. 3956Université Paris-Est Créteil (UPEC)CréteilFrance

Personalised recommendations