A Novel Feature Expansion Algorithm for Nonrigid Ct/Mri Brain Image Registration

Abstract

Image registration is one of the challenging tasks in medical image analysis. While coming to non rigid image registration there are mainly two issues to consider. They are i) intensity similarity and ii) gray level transformation. The issue with intensity similarity is it is not necessarily equivalent to anatomical similarity when the anatomical correspondences between source and target images are established. Another issue is choosing an appropriate registration algorithm. It should be robust against monotonic gray-level transformation when aligning anatomical structures in the presence of bias fields. Here new feature- intensity based registration method developed for nonrigid brain image registration to overcome the above stated issues named as Anatomical Region Descriptor (ARD). This method is developed on image feature, it encodes geometric properties of anatomical structures and pixel wise interaction details. It is efficient and theoretically monotonic gray level transformation invariant. This method is integrated with intensity based registration algorithm named as residual complexity for Registration purpose. This proposed method is compared with three other non rigid image registration algorithms. Experimental results of the proposed method show that it achieves the highest accuracy rate among the compared methods.