Large deformation minimum mean squared error template estimation for computational anatomy

Abstract

This paper presents a method for large deformation exemplar template estimation. This method generates a representative anatomical template from an arbitrary number of topologically similar images using large deformation minimum mean squared error image registration. The template that we generate is the image that requires the least amount of deformation energy to be transformed into every input image. We show that this method is also useful for image registration. In particular, it provides a means for inverse consistent image registration. This method is computationally practical; computation time grows linearly with the number of input images. Template estimation results are presented for a set of five 3D MR human brain images.