Hybrid optimization for ultrasound and multimodal image registration

Optimization of similarity metrics is an important component of many biomedical image registration applications that greatly affects registration outcome. Selection of an optimization approach is dependent on the modalities being registered, the characteristics of the similarity function, and available computational resources. This paper addresses optimization approaches for 2D and 3D rigid body intra- and multimodal ultrasound registration. Stochastic and direct techniques are compared for mutual information and correlation ratio functions. A new direct/stochastic hybrid approach based on the tabu search is also proposed. Visualization and experimental results suggest the usefulness of such an approach. Results also show that the tabu hybrid technique compares favorably with traditional techniques, based on accuracy and number of function evaluations.

[1]  Linet Özdamar,et al.  Experiments with new stochastic global optimization search techniques , 2000, Comput. Oper. Res..

[2]  Nicholas Ayache,et al.  The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration , 1998, MICCAI.

[3]  Christian Roux,et al.  Genetic algorithms for a robust 3-D MR-CT registration , 2000, IEEE Transactions on Information Technology in Biomedicine.

[4]  Max A. Viergever,et al.  Image Registration by Maximization of Combined Mututal Information and Gradient Information , 2000, MICCAI.

[5]  P. Siarry,et al.  FITTING OF TABU SEARCH TO OPTIMIZE FUNCTIONS OF CONTINUOUS VARIABLES , 1997 .

[6]  Colin Studholme,et al.  An overlap invariant entropy measure of 3D medical image alignment , 1999, Pattern Recognit..

[7]  Sébastien Ourselin,et al.  Generalized Correlation Ratio for Rigid Registration of 3D Ultrasound with MR Images , 2000, MICCAI.

[8]  Patrick Siarry,et al.  Enhanced simulated annealing for globally minimizing functions of many-continuous variables , 1997, TOMS.

[9]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[10]  G. Marchal,et al.  Multi-modal volume registration by maximization of mutual information , 1997 .

[11]  Roberto Battiti,et al.  The continuous reactive tabu search: Blending combinatorial optimization and stochastic search for global optimization , 1996, Ann. Oper. Res..

[12]  J. Arendt.  Paper presented at the 10th Nordic-Baltic Conference on Biomedical Imaging: Field: A Program for Simulating Ultrasound Systems , 1996 .

[13]  Patrick Siarry,et al.  Tabu Search applied to global optimization , 2000, Eur. J. Oper. Res..

[14]  Max A. Viergever,et al.  Image registration by maximization of combined mutual information and gradient information , 2000, IEEE Transactions on Medical Imaging.

[15]  Max A. Viergever,et al.  A survey of medical image registration , 1998, Medical Image Anal..

[16]  Konstantina S. Nikita,et al.  Automatic retinal image registration scheme using global optimization techniques , 1999, IEEE Transactions on Information Technology in Biomedicine.

[17]  Viergever,et al.  An Overview of Medical Image Registration Methods , 1998 .

[18]  Bruce E. Rosen,et al.  Genetic Algorithms and Very Fast Simulated Reannealing: A comparison , 1992 .

[19]  William H. Press,et al.  Numerical recipes in C , 2002 .

[20]  Robyn A. Owens,et al.  Registration of stereo and temporal images of the retina , 1999, IEEE Transactions on Medical Imaging.