Non-rigid multi-modal medical image registration by combining L-BFGS-B with cat swarm optimization

Non-rigid multi-modal image registration plays an important role in medical image processing and analysis. Optimization is a key component of image registration. Mapped as a large-scale optimization problem, non-rigid image registration often requires global optimization methods because the functions defined by similarity metrics are generally non-convex and irregular. In this paper, a novel optimization method is proposed by combining the limited memory Broyden-Fletcher-Goldfarb-Shanno with boundaries (L-BFGS-B) with cat swarm optimization (CSO) for non-rigid multi-modal image registration using the normalized mutual information (NMI) measure and the free-form deformations (FFD) model. The proposed hybrid L-BFGS-B and CSO (HLCSO) method uses cooperative coevolving to tackle non-rigid image registration, and employs block grouping as the grouping strategy to capture the interdependency among variables. Moreover, to achieve faster convergence and higher accuracy of the final solution, the local optimization method L-BFGS-B and the roulette wheel method are introduced into the seeking mode and the tracing mode of the HLCSO, respectively. Extensive experiments on 3D CT, PET, T1, T2 and PD weighted MR images demonstrate that the proposed method outperforms the L-BFGS-B method and the CSO method in terms of registration accuracy, and it is provided with reasonable computational efficiency.

[1]  Karl Rohr,et al.  An extension of thin-plate splines for image registration with radial basis functions , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[2]  Nikos Komodakis,et al.  Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies , 2008, Comput. Vis. Image Underst..

[3]  E. Haber,et al.  Intensity Gradient Based Registration and Fusion of Multi-modal Images , 2007, Methods of Information in Medicine.

[4]  Jacek M. Zurada,et al.  An approach to multimodal biomedical image registration utilizing particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[5]  Frank Sauer,et al.  Learning Based Non-rigid Multi-modal Image Registration Using Kullback-Leibler Divergence , 2005, MICCAI.

[6]  Leo Grady,et al.  Spectral Log-Demons: Diffeomorphic Image Registration with Very Large Deformations , 2013, International Journal of Computer Vision.

[7]  Albert C. S. Chung,et al.  Non-rigid image registration by using graph-cuts with mutual information , 2010, 2010 IEEE International Conference on Image Processing.

[8]  Baba C. Vemuri,et al.  Non-Rigid Multi-Modal Image Registration Using Cross-Cumulative Residual Entropy , 2007, International Journal of Computer Vision.

[9]  Oscar Cordón,et al.  A comparative study of state-of-the-art evolutionary image registration methods for 3D modeling , 2011, Comput. Vis. Image Underst..

[10]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[11]  Albert C. S. Chung,et al.  Multi-modal non-rigid image registration based on similarity and dissimilarity with the prior joint intensity distributions , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[12]  Michaël Sdika,et al.  A Fast Nonrigid Image Registration With Constraints on the Jacobian Using Large Scale Constrained Optimization , 2008, IEEE Transactions on Medical Imaging.

[13]  Jorge Nocedal,et al.  Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization , 1997, TOMS.

[14]  Yun He,et al.  A generalized divergence measure for robust image registration , 2003, IEEE Trans. Signal Process..

[15]  Chin-Chen Chang,et al.  Optimizing least-significant-bit substitution using cat swarm optimization strategy , 2012, Inf. Sci..

[16]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[17]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[18]  Robert J. Maciunas,et al.  Registration of head volume images using implantable fiducial markers , 1997, IEEE Transactions on Medical Imaging.

[19]  Josien P. W. Pluim,et al.  Evaluation of Optimization Methods for Nonrigid Medical Image Registration Using Mutual Information and B-Splines , 2007, IEEE Transactions on Image Processing.

[20]  Daniel Rueckert,et al.  Diffeomorphic Registration Using B-Splines , 2006, MICCAI.

[21]  Xiaodong Li,et al.  Tackling high dimensional nonseparable optimization problems by cooperatively coevolving particle swarms , 2009, 2009 IEEE Congress on Evolutionary Computation.

[22]  Pei-wei Tsai,et al.  Enhanced parallel cat swarm optimization based on the Taguchi method , 2012, Expert Syst. Appl..

[23]  Ching-Wei Wang,et al.  Improved image alignment method in application to X-ray images and biological images , 2013, Bioinform..

[24]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[25]  Karl Rohr,et al.  Spline-Based Hybrid Image Registration using Landmark and Intensity Information based on Matrix-Valued Non-radial Basis Functions , 2013, International Journal of Computer Vision.

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

[27]  Ayman El-Baz,et al.  Non-rigid biomedical image registration using graph cuts with a novel data term , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[28]  Xia Liu,et al.  Topology preservation evaluation of compact-support radial basis functions for image registration , 2011, Pattern Recognit. Lett..

[29]  Xiaodong Li,et al.  Cooperatively Coevolving Particle Swarms for Large Scale Optimization , 2012, IEEE Transactions on Evolutionary Computation.

[30]  Philippe C. Cattin,et al.  Non-rigid registration of multi-modal images using both mutual information and cross-correlation , 2008, Medical Image Anal..

[31]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[32]  Fei Zhou,et al.  Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion , 2014, Inf. Sci..

[33]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[34]  Patrick Clarysse,et al.  A review of cardiac image registration methods , 2002, IEEE Transactions on Medical Imaging.

[35]  Nassir Navab,et al.  Dense image registration through MRFs and efficient linear programming , 2008, Medical Image Anal..

[36]  Oscar Cordón,et al.  An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification , 2009, Inf. Sci..

[37]  Ganapati Panda,et al.  Solving multiobjective problems using cat swarm optimization , 2012, Expert Syst. Appl..

[38]  Min Yao,et al.  Composite radiation dose representation using Fuzzy Set theory , 2012, Inf. Sci..

[39]  Shu-Chuan Chu,et al.  COMPUTATIONAL INTELLIGENCE BASED ON THE BEHAVIOR OF CATS , 2007 .

[40]  Antoine Simon,et al.  Multimodal Registration and Data Fusion for Cardiac Resynchronization Therapy Optimization , 2014, IEEE Transactions on Medical Imaging.

[41]  Christos Davatzikos,et al.  PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration , 2014, IEEE Transactions on Medical Imaging.

[42]  Nikos Paragios,et al.  Deformable Medical Image Registration: A Survey , 2013, IEEE Transactions on Medical Imaging.

[43]  Jorge Nocedal,et al.  Remark on “algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization” , 2011, TOMS.