The main aim of the reported work is to solve the registration problem for recognition purposes. We introduce two new evolutionary algorithms (EA) consisting of population-based search methods, followed by or combined with a local search scheme. We used a variant of the Firefly algorithm to conduct the population-based search, while the local exploration was implemented by the Two-Membered Evolutionary Strategy (2M-ES). Both algorithms use fitness function based on mutual information (MI) to direct the exploration toward an appropriate candidate solution. A good similarity measure is the one that enables us to predict well, and with the symmetric MI we tie similarity between two objects A and B directly to how well A predicts B, and vice versa. Since the search landscape of normalized mutual information proved more amenable for evolutionary computation algorithms than simple MI, we use normalized mutual information (NMI) defined as symmetric uncertainty. The proposed algorithms are tested against the well-known Principal Axes Transformation technique (PAT), a standard evolutionary strategy and a version of the Firefly algorithm developed to align images. The accuracy and the efficiency of the proposed algorithms are experimentally confirmed by our tests, both methods being excellently fitted to registering images.
Clinical validation of the normalized mutual information method for registration of CT and MR images in radiotherapy of brain tumors
