Image registration techniques for medical images

Abstract

In this research work, approaches for image registration are proposed. The image registration methods can be grouped into two classes. One is intensity based method which is based on gray values of the pair of images and the second one is based on image feature which is done by obtaining some features or landmarks in the images like points, lines or surfaces. Edges in the images can be detected very easily in the images. Thus, using these edges some features can be obtained by which we can accomplish feature based registration. But, feature based registration has some limitations as well as advantages. The proposed method employs feature based registration technique to obtain a coarsely registered image which can be given as input to intensity based registration technique to get a fine registration result. It helps to reduce the limitations of intensity based technique. i.e. it takes less time for registration. To achieve this task, the mutual information is selected as similarity parameter. Mutual information (MI) is used widely as a similarity measure for registration. In order to improve the robustness of this similarity measure, spatial information is combined with normalized mutual information(NMI). MI is multiplied with a gradient term to integrate spatial information to mutual information and this is taken as similarity measure. The registration function is less affected if sampling resolution is low. It contains correct global maxima which are sometimes not found in case of mutual information. For optimization purpose, Fast Convergence Particle Swarm Optimization technique (FCPSO) is used. In this optimization method, the diversity of position of single particle is balanced by adding a new variable, particle mean dimension (pmd) of all particles to the existing position and velocity equation. It reduces the convergence time by reducing the number of iterations for optimization.