Successful content-based image registration relies on the accurate identification of corresponding features across images. Geometric and photometric transformations between images may hinder an algorithm's ability to precisely match features. In this work, we propose a novel region descriptor detection and matching algorithm for use with image registration. The detection process utilizes invariant feature points, as well as their spatial relationships and textural characteristics to create a connected graph whose structure represents an invariant region descriptor. With such a framework, feature matching can be accomplished by graph matching with a defined similarity metric. Subsequent image registration steps are outlined that employ the invariant region descriptors. The results provide strong evidence of the region descriptor's effectiveness in applications involving image registration. Several scenarios are presented including the registration of general objects, aerial photography, as well as scenes before and after a disaster.