Parallel Swarm Intelligence Algorithm And Its Application On Image Registration

With rapid increasing of Digital Medical and Remote Diagnosis technology, Medical Image Processing has caused wide attention. Compared with common images, Medical image has the characteristics of fuzzy and not uniform. Several different modals image information is acquired by various imaging device. For providing more diagnosis information to the operation instructs and the doctor, this information should be integrated into one image in clinical and the Image Fusion technology is needed. Image Registration is the precondition and foundation of Image Fusion. The image can be fused efficiently after the image has been registered accurately.Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of agents interacting locally with their environment. SI algorithm is a kind of stochastic search method that can solve the specified problems by simulating the collective behaviors. The characteristic of SI algorithm is stochastic, parallel, robust and distributed. As a kind of optimization strategy in Image Registration, SI algorithms have got good effects on the image registration. However, SI algorithms have poor performance on the global convergence speed and real-time property as they need lots of iterations to complete the search. In addition, most medical images are three-dimensional and have a large amount of data to be processed. Therefore, high performance parallel optimization algorithm for medical image registration is needed.Firstly, based on the analysis of Genetic Algorithm (GA), Ant Colony Optimization (ACO) Algorithm,Particle Swarm Optimization (PSO) Algorithm and Quantum-behaved Particle Swarm Optimization (QPSO) Algorithm, a dynamic neighborhood topology model (DNTM) which is suited for parallel processing is designed by utilizing of the potential characteristic of parallel of SI algorithm. Three parallel computing models are designed based on the DNTM structure on the Drawning SMP Parallel Clustering Server and PC Cluster. The Parallel GA, Parallel PSO algorithm and Parallel QPSO algorithm are implemented by using MPI, OpenMP and MPI+OpenMP library. These three parallel algorithms are used to solve two kinds of nonlinear optimization problems. The results show that parallel QPSO algorithm has better performancesSecondly, the image registration based on maximization of mutual information algorithm and Powell algorithm is analysised. QPSO algorithm and parallelized QPSO algorithm are used as an optimization strategy in the image registration.Finally, two key steps in the process of image registration are improved by introducing a new similarity metric and a new hybrid searching strategy (by hybridizing NTPara-QPSO algorithm and Powell method). Based on the two improvements, the registration on the MR image and CT image are completed. Experimental results show that the hybrid algorithm has reached sub-pixels level and has better real time property.