CT-MRI automatic surface-based registration schemes combining global and local optimization techniques

  • Authors:

  • George K. Matsopoulos;Konstantinos K. Delibasis;Nicolaos A. Mouravliansky;Pantelis A. Asvestas;Konstantina S. Nikita;Vassilios E. Kouloulias;Nikolaos K. Uzunoglu

  • Affiliations:

  • (Correspd. E-mail: gmatso@esd.ece.ntua.gr) Institute of Communication and Computer Systems, Department of Electrical and Computer Engineering, National Technical University of Athens, Greece;Institute of Communication and Computer Systems, Department of Electrical and Computer Engineering, National Technical University of Athens, Greece;Institute of Communication and Computer Systems, Department of Electrical and Computer Engineering, National Technical University of Athens, Greece;Institute of Communication and Computer Systems, Department of Electrical and Computer Engineering, National Technical University of Athens, Greece;Institute of Communication and Computer Systems, Department of Electrical and Computer Engineering, National Technical University of Athens, Greece;Institute of Communication and Computer Systems, Department of Electrical and Computer Engineering, National Technical University of Athens, Greece;Institute of Communication and Computer Systems, Department of Electrical and Computer Engineering, National Technical University of Athens, Greece

  • Venue:
  • Technology and Health Care
  • Year:
  • 2003

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Abstract

Medical image registration is commonly required in order to combine the complementary information provided by different medical imaging modalities. In this paper, a new automatic registration scheme is proposed to register 3-D CT-MR head images and is currently tested on a clinical environment. The proposed scheme, after the preprocessing and the outer surface extraction of the data, is based on the use the rigid transformation method, in conjunction with a combination of global and local optimization techniques. Analytically, the paper exploits the optimization efficiency of three widely used optimization techniques, in obtaining the parameters of the rigid transformation model: the Downhill Simplex Method, the Genetic Algorithms and the Simulated Annealing. These optimization techniques are further combined by the sequential application of the Powell optimization method in order to refine the registration and increase its accuracy. A comparative study involving these optimization techniques in conjunction with the rigid transformation, and two other methods, the ICP and the manual methods, is also presented, for a sufficient number of clinical CT-MR brain images. Finally, quantitative and qualitative results are also presented to validate the performance of these automatic surface-based registration schemes, in terms of consistency and accuracy. Throughout of this study, the automatic registration scheme comprising of the rigid transformation in conjunction with the Simulated Annealing method sequentially combined with the Powell method has been performed superior regarding all the other compared registration schemes.