Biomedical Image Registration by Means of Bacterial Foraging Paradigm

Image registration (IR) is the process of geometric overlaying or alignment of two or more 2D/3D images of the same scene (unimodal registration), taken or not at different time slots, from different angles, and/or by different image acquisition systems (multimodal registration). Technically, image registration implies a complex optimization of different parameters, performed at local or/and global level. Local optimization methods often fail because functions of the involved metrics with respect to transformation parameters are generally nonconvex and irregular, and global methods are required, at least at the beginning of the procedure. This paper presents a new evolutionary and bio-inspired robust approach for IR, Bacterial Foraging Optimization Algorithm (BFOA), which is adapted for PET-CT multimodal and magnetic resonance image rigid registration. Results of optimizing the normalized mutual information and normalized cross correlation similarity metrics validated the efficacy and precision of the proposed method by using a freely available medical image database.