Generalizing Demons Algorithm and Its Application

Abstract

The Demons algorithm is known as one of the classical methods for deformable image registration based on signal value difference and signal gradient, and its use is basically limited to for monomodal and scalar images. In this study, we show that the signal value difference and signal gradient used in the algorithm can be replaced with generalized distance between local features of two images and its gradient, such as color distances of color images. Through the experiments by using volume data sets of synthetic color images and diffusion tensor colormap images, we validate the proposed method and discussion its characteristics.