An artificial immune system for multimodality image alignment

Abstract

Alignment of multimodality images is the process that attempts to find the geometric transformation overlapping at best the common part of two images. The process requires the definition of a similarity measure and a search strategy. In the literature, several studies have shown the ability and effectiveness of entropy-based similarity measures to compare multimodality images. However, the employed search strategies are based on some optimization schemes which require a good initial guess. A combinatorial optimization method is critically needed to develop an effective search strategy. Artificial Immune Systems (AISs) have been proposed as a powerful addition to the canon of meta-heuristics. In this paper, we describe a framework which combines the use of an entropy-based measure with an AIS-based search strategy. We show how AISs have been tailored to explore efficiently the space of transformations. Experimental results are very encouraging and show the feasibility and effectiveness of the proposed approach. © Springer-Verlag Berlin Heidelberg 2003.