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.