RMI based non-rigid image registration using BF-QPSO optimization and P-spline

Registration is an image processing technique which aligns two or more images spatially and produces an informative image. Regional mutual information is an entropy based similarity measure that is invariant to the overlapped regions of the two images to be registered. But, it is difficult for images having deformation due to organ motion with a non-convex and irregular shape. In this paper, nonrigid image registration problem is addressed for multimodal medical images where P-spline interpolation method is adopted for modeling the transformation parameters. Intensity based registration requires optimization of similarity metric. As function of these parameters is complex, local optimization techniques frequently fail, which necessitates global optimization methods. Quantum-behaved particle swarm optimization (QPSO) requires fewer parameters to control and outperforms particle swarm optimization (PSO) in search ability. Bacterial Foraging Algorithm (BFA) is a non gradient global optimization technique where E. coli bacteria depends on random search direction during chemotaxis, which delays the rate of convergence. Here, a new algorithm is proposed hybridizing the notion of QPSO and BFA for tradeoff between local and global search. P-spline interpolated image registration is tested with 10 sets of MR images and some other medical images using RMI similarity index optimized by BF-QPSO. The proposed technique is compared with BFA, QPSO and hybrid BF-PSO algorithms.