Effect of Measure and Interpolation Methods for Intensity-Based Medical Image Registration

Abstract

The local extreme is main reason to hamper the optimization process and influence the registration accuracy in medical image registration algorithm. In general, the accuracy of image registration based on mutual information is afforded by interpolation methods. In this paper, we analyze the effect of the measure and interpolation methods for medical image registration and present a medical image registration algorithm using mutual strictly concave function measure and partial volume (PV) interpolation methods. The experiment results show that for images with low local correlation the algorithm has the ability to reduce the local extreme, the registration accuracy is improved, and the algorithm expended less time than mutual information based registration method with partial volume (PV) or generalized partial volume estimation (GPVE).