Parallel Processing and Bio-inspired Computing for Biomedical Image Registration

Image Registration (IR) is an optimization problem computing optimal parameters of a geometric transform used to overlay one or more source images to a given model by maximizing a similarity measure. In this paper the use of bio-inspired optimization algorithms in image registration is analyzed. Results obtained by means of three different algorithms are compared: Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA) and Clonal Selection Algorithm (CSA). Depending on the images type, the registration may be: area based, which is slow but more precise, and features based, which is faster. In this paper a feature based approach based on the Scale Invariant Feature Transform (SIFT) is proposed. Finally, results obtained using sequential and parallel implementations on multi-core systems for area based and features based image registration are compared.