Estimation of class membership functions for grey-level based image fusion
In this paper we propose a new unsupervised method for estimating class membership functions from statistical data. It combines in an original way information derived from the histogram as well as prior knowledge of the requirements that the functions must satisfy and that cannot be derived from the histogram. The method has been tested successfully on MR brain images, and applications to image fusion are illustrated.